The Transformative Impact of the Mobile Phone and Electronic Transactions in India: Lessons, Policy Issues and Challenges
Bibliographic record
Abstract
In emerging economies like India, the mobile phone is often the only available device for accessing the Internet and large variety of associated services. With a teledensity, hugely mobile, of nearly 89% of some 1.2bn population as on December 31, 2016, India has become the second largest mobile phone market in the world. The urban teledensity was 170% whereas the rural was 53%. Further, although licenses have been allocated for broadband services such as 3G, 4G/LTE, their deployment is limited, often to urban areas. Additionally, despite the increasing adoption of smartphones, nearly 60% of mobile users have a feature phone in 2016, such numbers being larger in rural areas. The teledensity, extent of deployments and smartphone adoption gaps show the magnitude of the urban-rural digital divide in India. On another dimension, through its very ambitious Digital India project, the Government of India is trying to promote electronic transactions and payments through a variety of means, to turn India into a “less cash” economy. An aspect that needed to be considered in this initiative was the absence of a nationally recognized identity. In order to respond to this situation, a massive program to provide unique identities, called Aadhar, resulted in the creation of a database of 1.1bn. For digital disbursements and transactions, Aadhar database can be used for real time authentication of identity, thus accelerating adoption of digital transactions. The success of the Digital India program rests on the quality of the underlying infrastructure policies which, besides access devices, include the fiber backbone and the wireless deployments including the mobile and public wi-fi in rural areas, and the policies and processes of enabling institutions involved in Digital India. This paper will focus on the impacts of the widespread penetration and use of mobile networks, smartphones and other intelligent mobile devices in India. It will discuss issues such as: • How does the availability and quality of telecom services influence the design of other enabling and supporting infrastructure and institutions involved in digital transactions, especially for rural areas? • In addition to efficiently allocating and managing the use of the spectrum, what other roles can governments play in enabling the continued growth of mobile services in the context of the digital divide? • Under the Digital India project, what initiatives are governments taking in moving India from a “cash driven” to a “less cash” economy, where electronic transactions become the norm? Policy makers in developing countries like India traditionally deal with conventional issues related to facilitating the growth of mobile communications (e.g. spectrum availability and re-allocation; infrastructure deployment etc.). Moving forward, they will need to formulate policies and regulations, where required, to address new challenges related to the large-scale migration of mobile users from 2G to 3G/4G broadband networks and the use of Internet-based transactional services. How can “Demand Pull” policies complement “Supply Push” initiatives, especially for maintaining the integrity of transactional services against cyber-attacks and cyber-fraud? Methodology: Based on secondary data and in-depth interviews with officials in UIDAI, banking sector, beneficiaries and state governments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".