THE DIFFUSION OF MOBILE PHONES IN INDIA. - Dr. Sanjay K. Singh Department of Humanities and Social Sciences Indian Institute of Technology Kanpur INDIA. Growth in telephone subscriber base in India.
Dr. Sanjay K. Singh
Department of Humanities and Social Sciences
Indian Institute of Technology Kanpur
Mobile is becoming the dominant means for accessing communications primarily because deploying mobile network is not only more cost-efficient but also mobile provides greater flexibility and convenience to its subscribers than landline telephone.
Growth in mobile-density has been phenomenal during the last 5 years or so. Mobile-density in the country has increased more than 23-fold from 0.35 in 2000-01 to 8.12 in 2005-06.
There has been 25-fold increase in mobile subscriber base in a span of just five years from 2000-01 to 2005-06. During the same period, mobile-density has increased more than 23-fold from 0.35 in 2000-01 to 8.12 in 2005-06.
An effective management of mobile services requires an understanding of the factors that underlie the evolution of the market. Factors such as market potential and timing and speed of adoption are of great importance for telecom operators for capacity planning. Understanding the evolution of mobile phone market and its likely future trend is equally important for policy makers.
The main objective of this study is to analyze the diffusion of mobile phones in India to inform the larger discussion of managing the communication services as well as to assist analysts concerned about assessing the impact of public policies in the evolution of telecom sector.
Estimation of the future trend and analysis of the pattern and rate of adoption of mobile phones in India.
Spread of a successful innovation over time typically follows a sigmoid or S-shaped curve. During an early phase of diffusion only a few members of the social system adopt the innovation whereas, over time, due to network consumption externality and dissemination of information, many people opt for innovation as the diffusion process unfold. Finally, during the maturing phase, the rate of diffusion goes down when diffusion curve approaches a saturation level.
Therefore, it is hypothesized that the growth in mobile-density over time follows a sigmoid curve. Among various functional forms that can describe sigmoid curves (the logistic, Gompertz, logarithmic logistic, log reciprocal, simple modified exponential, etc.), the first two are the most widely used ones. Therefore, it is decided to use these two functions to model and forecast the development of mobile-density in India.
The logistic model can be written as: and rate of adoption of mobile phones in India.
where Mdt is mobile-density (no. of mobile phones per 100 inhabitants), (time)t is value assigned to time at period t, is the saturation level and t is an error term.
All the parameters: , and are positive.
Mdt ranges from a lower asymptote of 0 to the upper bound as time ranges from - to +. Maximum growth rate (= /4) occurs when Mdt = /2 (i.e., at half of the saturation level). Thus, the logistic curve is rotationally symmetric about its inflection point (the point at which maximum rate of diffusion takes place).
Similarly, the Gompertz model can be written as: and rate of adoption of mobile phones in India.
where all the variables and parameters have their previous meaning and t is an error term.
Again, all the parameters: , and are positive. In this case, maximum growth rate (= /e) occurs when Mdt = /e (i.e., at 37% of the saturation level).
These two models are estimated using non-linear least square method once by assuming no restriction on the saturation level and then by imposing restrictions on the same. This is because there is no guarantee that the final estimate of the saturation level, , will be close to the global optimum (Heij C. et al., 2004).
The shape of logistic and Gompertz curves and rate of adoption of mobile phones in India.
The saturation level of mobile-density for a country is likely to depend on whether it is an early adopter or a late adopter of telephones. Early adopters (developed countries) are expected to have lesser reliance on mobile phones (due to high switching cost) whereas late adopters (developing countries) are expected to have lesser reliance on main line telephones (due to high infrastructure cost).
Teledensity and Percentage Share of Mobile in Selected Developed Countries
Analysis reveals that the saturation level of mobile share in developed countries could be anywhere between 50% and 70% whereas the same would be between 80% and 90% for the developing countries. Assuming that the saturation level of teledensity could be anywhere between 120 and 150 telephones per 100 inhabitants, the saturation level of mobile-density in developing countries is likely to be between 100 and 120 mobile phones per 100 inhabitants.
Teledensity and Percentage Share of Mobile in Selected Developing Countries
Model estimation in developed countries could be anywhere between 50% and 70% whereas the same would be between 80% and 90% for the developing countries. Assuming that the saturation level of teledensity could be anywhere between 120 and 150 telephones per 100 inhabitants, the saturation level of mobile-density in developing countries is likely to be between 100 and 120 mobile phones per 100 inhabitants.
Since India is a late adopter of telephones, its saturation level of mobile-density is likely to be between 100 and 120 mobile phones per 100 inhabitants.
However, both logistic and Gompertz models are estimated for six different saturation levels (70, 80, 90, 100, 110 and 120 mobile phones per 100 inhabitants) along with without imposing any restriction on the same. The mean absolute percentage error (MAPE) for the last three observations is used to find out the most appropriate model and the saturation level.
Annual data of mobile-density from 1995-96 to 2005-06 is used for the estimation of the models.
Data on mobile subscriber base and mobile-density is taken from Telecom Regulatory Authority of India (TRAI) publications (www.trai.gov.in) and telecom sector database from www.infraline.com.
Estimation results (with t-statistic in parentheses ): According to both R2 and MAPE, the Gompertz models fit the data better than the logistic ones. As expected, final estimate of the saturation level in the no restriction model does not seem to be globally optimal. It seems that the Gompertz model with the saturation level of 120 mobile phones per 100 inhabitants is the best model to depict the diffusion of mobile phones in India.
Assumptions and Projections of Mobile-density in India According to both R
Further analysis will primarily be based on the estimated Gompertz
model at saturation level of 120 mobile phones per 100 inhabitants:
Rate of growth of mobile-density According to both R
The analysis reveals that the inflection point (the maximum growth
rate point) of the curve will occur between 2011-12 and 2012-13
(when mobile-density will be around 45). During the year 2015-16,
there will be 71 mobile phones for 100 people in the country. Analysis
show that the no. of mobile phones will exceed the no. of people in
the country by 2022-23.
Future Mobile Subscriber Base in India According to both R
It is projected that almost 350 million new mobile subscribers will
be added between 2005-06 and 2010-11 and more than 450 million
will be added between 2010-11 and 2015-16.
Note: Future population of India is taken from the United Nations Population Division publication.
Average Revenue per Mobile User per Month in India
Estimates of Mobile Operators’ Revenue government
Rapid increase in mobile subscriber base and mobile spending
will have equally important implications for the government revenue
particularly in the form of regulatory charges (license fee including universal service obligation and spectrum charges) and service tax.
Estimates of the Government’s Revenue government
Presently, on an average, annual direct regulatory charges faced by the operators in India is around 13% [far more than that in Pakistan (4.5%), Sri Lanka (0.3%), Malaysia (6.5%), and South Africa (5%)]. If we include the education cess of 2% (of 12%), service tax burden on the sector would be 12.24% from the financial year 2006-07 onwards.
Concluding Remarks government
In this study, the growth of the mobile phone and mobile-density in India has been analyzed using S-shaped growth curve models.
The result shows that the Gompertz model adequately describes the path of mobile phone diffusion in India.
The analysis shows that the high growth phase of the diffusion of mobile phones will continue till 2012-13.
It is estimated that there will be 71 mobile phones per 100 inhabitants in India at the end of year 2015-16. The number of mobile phones will exceed the number of people in the country by 2022-23.
Total mobile phone demand is projected to increase from 90 million in 2005-06 to 433 million in 2010-11 and nearly 900 million in 2015-16.
Concluding Remarks government ….
Rapid growth in mobile subscriber base in the India will have important implications for revenues collected by the operators and the government.
Revenue collected by the mobile operators is projected to increase from Rs. 405 billion (1.3% of GDP) in 2005-06 to Rs. 1559 billion (2.7% of GDP) in 2010-11 and Rs. 3236 billion (3.1% of GDP) in 2015-16.
The government’s revenue from regulatory charges and service tax will increase substantially due to rapid increase in operators’ revenue.
Revenue from regulatory charges is expected to increase from Rs. 53 billion in 2005-06 to Rs. 156 billion in 2010-11 and Rs. 324 billion in 2015-16. Revenue from service tax is projected to increase from Rs. 41 billion in 2005-06 to Rs. 187 billion in 2010-11 and Rs. 388 billion in 2015-16.
Concluding Remarks government ….
It is quite likely that the rapid expansion of mobile services will provide economic, logistic and strategic challenges to the operators.
As operators expand coverage into urban, semi-urban, and rural areas, they will be confronted with the daunting tasks of developing a countrywide infrastructure and improving and maintaining the quality of service.
Mobile operators should be ready with contingency plans to deploy and operate infrastructure including customer care, billing, applications, etc., faster than that they might have initially planned.
Infrastructure providers, handset suppliers, and vendors should also be geared up to respond to such plans.