Bibliographic record
Abstract
Telecommunications in India has been one of the success stories of economic reforms that increased GDP growth to 9% from the earlier “Hindu rate of growth” of 3%. The number of telephone connections per one hundred people, or teledensity, has increased from a low of 0.81 in 1994 to 64.34 at the end of 2010. By most standards this is a creditable achievement. The growth in telecommunication services has also been spectacular clocking some of the fastest growth rates in the world. A closer look at the sector though reveals a less rosy picture. It has become fashionable to compare China and India across most parameters of economic growth and well being. Here, as with other parameters, India does not compare favourably. In 2009 India’s mobile teledensity was 43.48 while that of China’s was 55.973. India also compares unfavourably with other Asian neighbours. It could of course be argued that given her late start and current robust growth rates she will catch up, sooner rather than later. Yet, there are other misgivings. One is the sorry state of rural teledensity and another is the lack of broadband penetration. Further, the telecommunications sector seems to get embroiled in political problems from time to time. Currently, the Controller and Auditor General of India (CAG) has suggested that in the last round of allocation of 2G spectrum using administrative procedures rather than an auction has led to substantial loss of revenue to the government. One estimate has pegged it at 176645 crores (1.76 trillion Rupees). Regardless of the truth of the charge this event has caused some turmoil in the telecommunications sector leading to the resignation of the telecommunications minister and calls for cancellation of licenses. Clearly events of this kind should affect the health of this sector. However, nothing much seems to have happened. It is as if the key drivers of this sector are so strong that minor hiccups don’t make a dent. The other possibility is that if such events had been avoided then telecommunications growth would have been even higher and conditions healthier. In this paper I aim to take a closer look at the cellular mobile segment from the standpoint of competition and policy. The usual assumption is that the cellular mobile segment enjoys strong competition. It would be useful to try and take a closer look at that assumption. It is also intriguing that the Telecommunications Regulatory Authority of India (TRAI) while not interfering on a regular basis does, at times regulate tariffs on the basis of insufficient competition. The rationale behind the TRAI’s decision seems to be based on casual observation of behaviour and not on robust analysis. The fact that now India has a 3 Competition Commission that is up and running makes the situation even more interesting. Anti-competitive behaviour is also the preserve of the Competition Commission of India (CCI). How the two agencies will coordinate their decisions in the future will be interesting to observe. Other actors like the Department of Telecommunications (DOT), the parent ministry, the incumbent state operators, BSNL and MTNL, also keep the plot ticking over.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".