Modelling and Forecasting the Diffusion of Telephones in India

1. Modelling and Forecasting the Diffusion of Telephones in India - Dr. Sanjay K. Singh Department of Humanities and Social Sciences Indian Institute of Technology Kanpur INDIA

2. Growth in telephone subscriber base in India No. of telephones in 1950-51: 0.17 million (all landlines) No. of telephones in 1995-96: 11.9 million (0.03 million mobiles) No. of telephones in 2000-01: 36.3 million (3.58 million mobiles) No. of telephones in 2005-06: 140 million (90 million mobiles)

3. Teledensity in India from 1995-96 to 2005-06 Teledensity in the country has increased almost 10-fold in a span of 10 years from 1.28 in 1995-96 to 12.60 in 2005-06.

4. Growth in Mobile Subscriber Base in India From 2000-01 to 2005-06, mobile subscriber base in India increased at the rate of 90% per year whereas corresponding growth rate for the landline telephone was less than 9% per year. This is because deploying mobile network is not only more cost-efficient than deploying copper landline but also mobile provides greater flexibility and convenience to its subscribers than landline telephone.

5. An effective management of telephone 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 telephone market and its likely future trend is equally important for policy makers. The main objective of this study is to model and forecast the diffusion of telephones 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.

6. Estimation of the future trend and analysis of the pattern and rate of adoption of telephones in India. There are typically two ways to estimate the future teledensity and telephone demand. First: Independent projections of mobile and landline telephone demand. Projection for each means of access may be based on a different method, and the total demand becomes simply an aggregate of the independent estimates for mobile and landline demand. Second: Based on projection of total telephone demand (the aggregate of mobile and landline demand) in a first step, and the related percentage share of mobile and landline computed afterward. This approach is a better one for developing long-term scenarios since it takes into account for competition between mobiles and landlines. This paper follows the second approach.

7. Estimation …... Demand for telephone depends on various socio-economic factors such as income and income distribution, price and quality of communication services, age distribution and household composition, etc. At national level, the relationship between teledensity and factors influencing the same can be written as, (1) where Td is teledensity (number of telephones per 100 inhabitants) and X is a vector of variables determining the same. Since time series data for many of these variables are not readily available, it is important to find out the key determinants of teledensity for which time series data are available.

8. Estimation …... Lee (1994), Greenstein and Spiller (1995), Madden and Savage (1998), Mbarika et al. (2002), and many others have shown that there is a close relationship between income and demand for telecommunication services. Relationship between teledensity and per capita GDP in India Therefore, per capita GDP can be used as the main explanatory variable ….. Assuming that the time captures the effect of omitted variables, (1) can be written as, (2)

9. Estimation …... Teledensity increases slowly at initial stage when only a few members of the social system opt for telephones whereas, over time, due to network externality, dissemination of information, increase in income, etc., it increases rapidly as many people opt for the services. Finally, during the maturing phase, its growth rate slows down as it converges to a certain maximum. Therefore, if we plot teledensity against the GDP per capita or time, it is expected that it will look like some sort of S-shaped curve. Among various functional forms that can describe S-shaped curves (the logistic, Gompertz, simple modified exponential, logarithmic logistic, log reciprocal, etc.), the first three are the most widely used ones. Therefore, it is decided to use these three functions to model and forecast the development of teledensity in India.

10. Logistic Model The logistic model can be written as: where  is the saturation level. Parameters  and  and are positive. Equation (3) can be transformed in a linear form as follows: (3) (4) This can further be simplified by making a reasonable assumption … (5) where 1, 2, 3, 4, and 5 are parameters to be estimated using OLS and  is a disturbance term with zero mean and constant variance.

11. Gompertz Model The Gompertz model can be written as: where  is the saturation level. Parameters  and  and are positive. Equation (6) can be transformed in a linear form as follows: (6) (7) This can further be simplified by making a reasonable assumption … (8) where 1, 2, 3, 4, and 5 are parameters to be estimated using OLS and  is a disturbance term with zero mean and constant variance.

12. Simple Modified Exponential Model The simple modified exponential model can be written as: where  is the saturation level. Parameters  and  and are positive. Equation (9) can be transformed in a linear form as follows: (9) (10) This can further be simplified by making a reasonable assumption … (11) where 1, 2, 3, 4, and 5 are parameters to be estimated using OLS and  is a disturbance term with zero mean and constant variance.

13. These models can easily be estimated by OLS method provided we know the saturation level, . If we analyze the teledensity in rich countries, we find that the saturation level in India could be anywhere between 100 and 160. Since India relies heavily on mobile phones, we may expect higher value for its saturation level. Teledensity in selected rich countries

14. Model estimation These models - logistic, Gompertz, and simple modified exponential - are estimated for seven different saturation levels (100, 110, 120, 130, 140, 150, and 160 telephones per 100 inhabitants) not only to illustrate the different possible paths of teledensity but also to find out the most appropriate saturation level. These models are compared by using the Mean Absolute Percentage Error (MAPE), F-test, and the Durbin-Watson (DW) statistic. Data of teledensity and per capita GDP (Rs. in thousand at constant 1993-94 prices) from 1950-51 to 2005-06 are used for the estimation of the models. Since we are interested in long-term forecast, it is decided to use five-yearly data rather than annual data from 1950-51 to 2005-06. Therefore, the variable time, t, is taken as 1 for 1950-51, 2 for 1955-56, 3 for 1960-61,……, and 12 for 2005-06.

15. According to the R2 values, models fit the data very well. Estimated parameters have the expected signs and most are highly significant. The residuals of all the models are well behaved (DW  2). MAPE is the lowest (0.61 to 0.68) for the Gompertz models. According to both R2 and MAPE, Gompertz models fit the data better. For Gompertz models, according to F-test, the null hypotheses (4=0, 5=0, and 4=5=0) are rejected for all the selected saturation levels.

16. Projection of teledensity and telephone demand in India Since among the Gompertz models, the one that is associated with 160 telephones per 100 inhabitants saturation level has the lowest MAPE, further analysis will primarily be based on this model. Between the year 1995-96 and 2005-06, GDP/cap in India increased at the rate of 4.46% per year. Assuming that GDP/cap will increase at the same rate up to the year 2015-16, teledensity has been projected.

17. The analysis reveals that the inflection point (the maximum growth rate point) of the curve will occur between 2011-12 and 2012-13. During the year 2015-16, there will be 108 telephones for 100 people in the country. Projected growth in teledensity and total telephone demand in India Note: Population of India is assumed to be 1189 million in 2010-11 and 1266 million in 2015-16. It is projected that there will be more than 500 million telephone subscribers in the country by 2010-11. Telephone demand is likely to increase from 140 million in 2005-06 to 1361 million in 2015-16.

18. Projection of share of mobile and main line telephone Mobile is becoming the dominant means for accessing communication. Reasons: flexibility and convenience to users, cost effective (only Rs. 6000 in added infrastructure vs. Rs. 24000 for a new landline connection), and low switching cost. Percentage share of mobile and per capita GDP in India from 1995-96 to 2005-06

19. Projection of share of mobile ….. As GDP/cap increases, it is expected that the percentage share of mobile phone will go up. Maximum value (saturation level) of mobile share for a country depends on whether it is early adopter or late adopter of telephones. Countries which are late adopters of telephones will have greater reliance on mobile phones (due to low switching cost). Teledensity and Percentage Share of Mobile in Selected Developed Countries

20. Projection of share of mobile ….. 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. Therefore, although mobile share in India will increase as GDP/cap increases, it would converge to a certain maximum (say 85%). Teledensity and Percentage Share of Mobile in Selected Developing Countries

21. Projection of share of mobile ….. Assuming that 85% would be the maximum share of mobile phone in total telephone in the country, the relationship between percentage share of mobile phones and per capita GDP is estimated to be: Share of mobile and main line telephone in India from 1995-96 to 2015-16

22. Projection of share of mobile ….. Mobile subscriber base in the country is expected to reach 436 million in 2010-11 and more than 1150 million in 2015-16. Number of main line telephone subscribers in the country is projected to increase from around 50 million in 2005-06 to 96 million in 2010-11 and 207 million in 2015-16. Projection of mobile and mail line telephone demand in India

23. Estimates of revenues collected by operators and the government • Operators’ revenue depends on ARPU and no. of subscribers • Assuming that the ARPU will stabilize at around Rs. 300 per month • for mobile phones and Rs. 400 per month for landlines by the year 2010-11 (due to inflationary and income effect), telecom operators’ revenues during the year 2010-11 and 2015-16 have been estimated. Average Revenue per User per Month in India

24. Estimates of Telecom Operators’ Revenue Telecom operators’ revenue in India as a percentage of its GDP

25. Estimates of the Government’s Revenue Telecom sector in India pays direct regulatory charges in the form of license fee (including universal service obligation levy) and spectrum charges. License fee varies from 5% to 10% whereas spectrum charges vary from 2% to 6% of the revenue. On an average, annual direct regulatory cost faced by the sector is around 13%. The sector paid nearly Rs. 100 billion to the government as regulatory charges during the year 2005-06. Even if we assume a reduction in regulatory charges from 13% to say 10% in forthcoming years, contribution of the telecom sector to the government’s revenue will be more than Rs. 200 billion in 2010-11 and Rs. 500 billion in 2015-16. Telecom sector is already the largest contributor of service tax in India (30% of the total service tax). Service tax (12% + 2% of 12% as education cess) revenue from telecom sector is projected to increase from around Rs. 75 billion in 2005-06 to around Rs. 250 billion in 2010-11 and Rs. 630 billion in 2015-16.

26. Concluding Remarks In this study, we analyzed the diffusion of telephones in India and also examined its implications on telecom operators’ and the government’s revenues. The analysis shows that the high growth phase of the diffusion of telephones in India will continue till 2012-13. It is estimated that there will be 108 telephones per 100 inhabitants in India at the end of year 2015-16. It is projected that there will be more than 500 million telephone subscribers in the country by 2010-11. Telephone demand is likely to increase from 140 million in 2005-06 to 1361 million in 2015-16. Rapid growth in telephone subscriber base in the India will have important implications for revenues collected by the operators and the government.

27. Concluding Remarks …. Revenue collected by the telecom operators is projected to increase from Rs. 763 billion (2.4% of GDP) in 2005-06 to Rs. 2031 billion (3.5% of GDP) in 2010-11 and Rs. 5149 billion (5.0% of GDP) in 2015-16. Most of the revenue increases in India’s telecom sector will accrue to mobile operators. Revenue from mobile services is estimated to increase from 1.3% of GDP in 2005-06 to 4.0% of GDP in 2015-16. This, in turn, will lead to continuing deployment of mobile infrastructure and, with that, an increase in the revenues of the infrastructure providers. The government’s revenue from regulatory charges and service tax will increase substantially due to rapid increase in operators’ revenue. The government’s revenue from regulatory charges is expected to increase from Rs. 100 billion in 2005-06 to Rs. 500 billion in 2015-16.

28. Concluding Remarks …. The government’s revenue from service tax is projected to increase from Rs. 75 billion in 2005-06 to Rs. 630 billion in 2015-16. It is quite likely that the rapid expansion of telephone 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. 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.

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