The Pennsylvania State University
The Graduate School
College of Agricultural Sciences
TELECOMMUNICATIONS MARKET DISEQUILIBRIUM
IN SOUTHEAST ASIA
A Thesis in
Agricultural, Environmental and Regional Economics
by
Montira Mahinchai
!2012 Montira Mahinchai
Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Science
August 2012
The thesis of Montira Mahinchai was reviewed and approved* by the following:
Spiro E. Stefanou Professor of Agricultural Economics Thesis Advisor
Edward C. Jaenicke Associate Professor of Agricultural Economics
Alessandro Bonanno Assistant Professor of Agricultural Economics
Ann R. Tickamyer Head of the Department of Agricultural Economics and Rural Sociology
*Signatures are on file in the Graduate School
ii
ABSTRACT
There has been a tremendous growth worldwide in a telecommunications sector during the past decade. Mobile telephone service serves as a perfect example for this trend – the global mobile-cellular subscriptions rose from 962 millions in 2000 to 5,373 millions in 2010 (ITU, 2012). Identifying which market factors influence this remarkable increase in subscriptions and how a mobile telephone market changes over time is crucial for understanding how this growth opportunity can benefit mobile telephone consumers, producers, and the society as a whole.
The demand for and supply of mobile telephone services across five developing countries in Southeast Asia are analyzed. This study contributes to the existing literature on telecommunications by using a disequilibrium approach which acknowledges the inherent lumpy capacity building aspect of the infrastructure investment along with the rapidly growing demand for mobile phone services. Two specifications of this approach, the directional method and the two-step method, are employed. The first method utilizes a change in price to separate the observations into the demand and supply equations, then estimates the mobile demand and supply via a simple linear ordinary least squares regression. Alternatively, the second method uses a probit maximum likelihood estimation in the first stage to obtain the selectivity correction factors. The demand and supply equations, along with the correction factors, are then estimated via OLS in the second stage.
The findings suggest that a one-year lagged mobile subscription charges, a share of population in urban areas, a literacy rate, and an age-dependency ratio have significant
iii impacts on mobile telephone subscriptions. However, an income effect reflected by GDP per capita has no effect. As for a supply for mobile telephone subscriptions, in addition to the one-year lagged mobile subscription charges, total number of staff, investment in and revenue generated from mobile phone services also affect the supply significantly.
The demand and supply curves generated from the parameters estimated indicate the existence of market disequilibrium, which results in deadweight loss for the society.
The magnitude of deadweight loss generated vary across countries and time periods, with the mobile telephone market is in excess demand or excess supply. From 2003 onward, the mobile phone markets in Southeast Asia are under excess supply with a growing magnitude, causing mobile users the gainers from this social welfare loss. The Malaysian market is the nearest to the equilibrium levels. The demands for mobile phones in
Philippines and Vietnam commence later and lag behind than those of their neighboring countries. These findings imply that policy makers should concentrate on both encouraging mobile phone service consumption, especially for the smartphones, and facilitating market activities along with incentivizing network expansions among the producers.
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TABLE OF CONTENTS
LIST OF TABLES ...... vi LIST OF FIGURES ...... viii ACKNOWLEDGEMENTS ...... ix Chapter 1 INTRODUCTION ...... 1 Chapter 2 LITERATURE REVIEW ...... 8 2.1 The Determinants of ICT Adoption ...... 8 2.1.1 Diffusion Studies ...... 8 2.1.2 Industry Structure – Relationship between Different Types of ICT .... 10 2.2 Impacts of ICT ...... 11 2.2.1 ICT Development and Economic Growth ...... 12 2.2.2 ICT Development and Productivity Growth ...... 13 2.3 Disequilibrium Approach ...... 14 2.4 Concluding Comments ...... 15 Chapter 3 METHODOLOGY ...... 16 3.1 Introduction ...... 16 3.2 Theoretical Model ...... 16 3.3 Descriptions of the Data Set ...... 20 3.4 Estimation Procedure ...... 22 3.5 Estimation Results and Discussion ...... 25 3.5.1 Demand Analysis ...... 25 3.5.2 Supply Analysis ...... 28 3.6 Concluding Comments ...... 29 Chapter 4 MARKET DEVELOPMENT DISCUSSION ...... 37 4.1 Introduction ...... 37 4.2 Overall Story ...... 37 4.3 Country Cases ...... 39 4.3.1 Indonesia ...... 39 4.3.2 Thailand ...... 40 4.3.3 Malaysia ...... 43 4.3.4 Philippines ...... 44 4.3.5 Vietnam ...... 46 4.4 Concluding Comments ...... 47
v
Chapter 5 CONCLUSIONS ...... 59 5.1 Overview ...... 59 5.2 Policy Implications ...... 61 5.3 Limitations ...... 62 5.4 Suggestions for Future Research ...... 63 Bibliography ...... 65
vi
LIST OF TABLES
Table Page
Table 3.1 Explanatory Variable Descriptions ...... 30"
Table 3.2 Summary Statistics of Variables ...... 31"
Table 3.3 Comparison of Mobile-Cellular Subscriptions per 100 Inhabitants ...... 32"
Table 3.4 Demand Equation Estimates ...... 33"
Table 3.5 Supply Equation Estimates ...... 34"
Table 3.6 Price Elasticities of Demand ...... 35"
vii
LIST OF FIGURES
Figure Page
1.1 Mobile Cellular Subscriptions per 100 Inhabitants, 2001-2011 ...... 6
1.2 Comparison of Investment in Basic ICT Infrastructure, 2002-2005 ...... 7
3.1 Comparison between Ex Ante and Observed Demand and Supply Curves ...... 36
4.1 Mobile Telephone Market in Indonesia ...... 49
4.2 Mobile Telephone Market in Thailand ...... 51
4.3 Mobile Telephone Market in Malaysia ...... 53
4.4 Mobile Telephone Market in Philippines ...... 55
4.5 Mobile Telephone Market in Vietnam ...... 57
viii
ACKNOWLEDGEMENTS
I would like to extend my sincere gratitude to my advisor and mentor, Professor
Spiro Stefanou, for his invaluable guidance, encouragement, and inspirations throughout my research. Without him, I could not have possibly finished this thesis, gained priceless knowledge, hands-on experiences, and positive perspectives in both personal and academic aspects of life. I would also like to thank the Royal Thai government for providing funding throughout my seven years of education in the United States. Finally,
I am grateful to my dear parents, Gowit and Rossarin Mahinchai, my sister, Nisachon
Mahinchai, and friends, whose never-ending love and support enabled me to complete this thesis and my graduate program.
ix
Chapter 1
INTRODUCTION
1.1 Justification
Telecommunications is one of the fastest growing sectors in the global economy.
The emergence of mobile telephone services provides phenomenal contribution to the telecommunications growth worldwide, with mobile-cellular subscriptions per 100 inhabitants increasing at a rate of 19 percent per year over the last ten years (see figure
1.1). For developing countries, telecommunications, which is one component of ICT
(information and communications technology)1, plays an essential role in building a knowledge-based society. Mobile penetration rates in the developing world, mainly driven by the Asia and Pacific region, reached 68 percent at the end of 2010 (ITU, 2010).
Southeast Asia region is an excellent case study for mobile telephone markets due to its immense population of more than 600 million people and vibrant economy.
The region’s economic growth was 9.3 percent in 2010 and the nominal GDP rose to 1.1 trillion US dollars in 2011 (Chin, 2011). The emergence of mobile phone markets in
Southeast Asia is also remarkable – a number of subscriptions increased from 2.2 million in 2000 to 57.5 million in 2010. Within these ten years, subscriptions per 100 inhabitants skyrocketed from 15 to 94 (ITU, 2012).
In spite of this prominent market expansion of mobile telephones, some economists and policy makers are concerned about socio-economic impacts of the rapid
1 The term ICT usually refers to the convergence of audio-visual technology of telephone networks and computer networks.
1 growth of mobile telephone communications. One issue raised by this growth is how the demand and supply for mobile telephones evolve over time and whether or not the market is in equilibrium. Cellular phone networks involve an extensive infrastructural requirement, which may not be able to catch up with the rising demand for this technology and suggests that the telecommunications market is in disequilibrium.2
Analyzing and estimating the disequilibrium condition of the mobile telephone market is important to the society, especially to the producers and regulators. Mobile developers, producers, and telecommunications investors should be fully informed of the real underlying state of the market to provide the most desirable products and forecast how the demand changes in the future. In addition, policy makers can utilize the accurate information of mobile telephone markets to tailor it to putting ICT to its most effective use and in support of enhancing public welfare.
1.2 Background
Modern mobile telephone technology originated with the Bell’s System Mobile
Telephone Service in 1946 in the US. This first commercial cellular phone is referred to as a mobile radio telephone or zero generation system. These phones usually came in a bulky suitcase style or were installed in vehicle trunks (Messmer, 2008). In 1979, the
Japanese national monopoly telecommunications operator, Nippon Telegraph and
Telephone (NTT), launched the world’s first generation (1G) mobile telephone network, which was operated by analog radio signals, in the Tokyo metropolitan area. Two years later, this 1G technology emerged in Denmark, Finland, Norway and Sweden by the
2 According to Ang (2000), only 5 percent of the total ICT expenditures in Asian and Pacific countries are contributed to infrastructure; other 95 percent go to ICT content, software, and application technologies.
2
Nordic Mobile Telephone (NMT) system, followed by the first launch in Chicago, USA, in 1983 (Ahonen, 2009).
Due to the inconvenient size, short-lived batteries, and high costs of the 1G mobile telephones, the second generation (2G) cellular phone technology was introduced on the Global System for Mobile Communications (GSM) standard developed by the
Finnish company, Radiolinja, in 1991 (Freewimaxinfo.com). Not only did the 2G network operate on the more efficient digital radio spectrum, but it also supported data services such as SMS text messaging.
As the 2G mobile phones became more widespread and successful around the world, consumers began to demand even more advanced technology they could utilize in their daily lives such as the Internet access. This growing demand gave rise to the third generation (3G) cellular phone systems, which were first launched by NTT DoCoMo in
Tokyo on October 1st, 2001 (Bandara, 2009). The capability of 3G systems include superior-quality voice communication, high-speed (broadband) data services, short range wireless communications (e.g., Bluetooth), and multimedia supports such as video calling, video conferencing and mobile television.
To catch up with the overwhelming growth of bandwidth-intensive features of the
3G technology, the fourth generation (4G) mobile telephone networks employing the
Worldwide Interoperability for Microwave Access (WiMax) and Long Term Evolution
(LTE) standards, were developed and first introduced by TeliaSonera in Scandinavia in
2009. The 4G mobile phones provide ultra-broadband Internet access, whose speed is up to 10 times faster than the existing 3G technologies, and support high-definition and 3D mobile television (Techblog, 2010).
3
It was not until the mid 1980s or the early 1990s that the first generation of mobile telephones were introduced in Southeast Asia. Older technology such as the 2G system was adopted relatively fast after its launch in the developed world. However, the
3G networks emerged in the region’s telecommunications market fairly late in 2008. One of the reasons for the late adoption and slow penetration of mobile telephones in
Southeast Asian countries is the geographic obstacle when building mobile phone signal towers, especially in Malaysia, Indonesia, and Philippines, which are extended archipelago. In those countries, the cellular phone infrastructure development is mainly concentrated in the metropolitan areas, and contributes to the market disequilibrium.3
Investment in the fundamental ICT infrastructure is another factor causing disequilibrium in the mobile phone market. The value of ICT infrastructure investment of the ASEAN group,4 plus India, during 2002-2005 was nearly as low as that of China alone, and much lower than Japan (see figure 1.2). The financing needs for
ASEAN+India during 2006-2015 are estimated to be 4.9 billion US dollars, which account for 15 percent of all the Asian Pacific countries (UNESCAP, 2006, table IV.3).
3 This is the case of the ICT infrastructure development in Malaysia in 2007; over 3,000 villages did not have connection to the national communication infrastructure (Kakroo, 2007). 4 ASEAN or the Association of South East Asian Nations was established in 1967. It consists of 10 member countries: Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.
4
1.3 Objectives
The main goals of this study are to:
1) develop an empirical model suitable for a market in disequilibrium;
2) empirically estimate the proposed disequilibrium model using the data of ICT, in particular mobile telephones, from the markets in five Southeast Asian countries, which include Indonesia, Malaysia, Philippines, Thailand, and Vietnam during 1990-2004; and,
3) provide implications for mobile telephone market development and corresponding policy implications.
1.4 Structure of the Study
The literature of relevant ICT papers are categorized by topics and reviewed in
Chapter 2. Then, Chapter 3 explains the general theoretical model of disequilibrium markets and its modification, the descriptions of the data set, and the empirical model.
Basic estimation results for the demand and supply equations are also provided in this chapter. Chapter 4 further analyzes the mobile telephone market development in each country by providing the overview of the market and the discussion of estimated results from a social welfare perspective. Concluding remarks, policy implications, limitations of the study, and suggestions for future research are discussed in Chapter 5.
5
Figure 1.1 Mobile Cellular Subscriptions per 100 Inhabitants, 2001-2011
Source: World Telecommunications/ICT Indicators Database. ITU. 30 May 2012. Web. 9 Jun 2012.
Note: The value for 2011 is estimated. The developed/developing country classifications are based on the UN M49, see: http://www.itu.int/ITU-D/ict/definitions/regions/ index.html
6
Figure 1.2 Comparison of Investment in Basic ICT Infrastructure, 2002-2005
Source: “Information and Communication Technology Infrastructure.” 62nd Commission Theme Study: Enhancing Regional Cooperation in Infrastructure Development Including that Related to Disaster Management. Chapter IV. United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). 20 Mar 2006. Web. 7 Jun 2012. < http://www.unescap.org/pdd/publications/themestudy 2006/10_ch4.pdf>.
Note: In each country or country group, the bars from left to right demonstrate the value of ICT investment for 2002-2005, respectively.
7
Chapter 2
LITERATURE REVIEW
There have been extensive studies on ICT especially on fixed-line and mobile telephones. Some attempts to develop the theory of ICT demand model include Perl
(1978) and Taylor (1994, 2002). The relevant empirical literature can be organized into two different groups. The first group focuses on the determinants of ICT adoption, using
ICT as a dependent variable. Alternatively, ICT enters models analyzed in the second group of literature as an independent variable in order to measure the impacts of ICT.
When analyzing the demand and supply for ICT, almost all of the ICT studies assume that the market is in equilibrium; none employ a disequilibrium approach. Thus, the last section of this chapter reviews empirical papers applying the concept of disequilibrium to various economic problems.
2.1 The Determinants of ICT Adoption
2.1.1 Diffusion Studies
The early discussion of ICT centers around the behavior of demand for ICT services, in particular for fixed-line telephone services, using individual- or survey-type data of consumer behavior in the United States. These studies were mostly provided by the Bell Labs or AT&T’s forecasting division. For example, Train et al. (1987) analyze how an individual’s calling pattern affects households’ choices among different local
8 service options. Similarly, Train et al. (1989) study households’ demand for local telephone services when individuals face various tariff options. At a time where long distance services subsidized local services, Hartman and Naqvi (1994) analyze determinants of long distance calls on carrier choices in the U.S. whereas Tardiff (1995) examines the same thing using individual data from Japan.
The introduction of mobile telephone services led to the rapid change in telecommunications industry, and a large portion of more recent literature has been devoted to mobile telephone diffusion. With fixed-line telephone penetration is often considered exogenous, this category of studies is concerned mostly with the speed and pattern of mobile penetration. Hausman (1999, 2000, 2002) study demand for mobile telephone services among major cities of the U.S. for the period 1988-1993 and find a price elasticity of approximately -0.5. Another within-country study of demand for mobile telephone services finds price elasticities in the Austrian market for mobile telecommunications ranging from -0.47 to -1.1 using firm specific tariff data in the period between 1998 and 2003 (Dewenter and Haucap, 2008).
Other studies on mobile telephone demand are cross-country analyses, including some incorporating a large number of countries as they compare the results from developed with those from developing countries. Gruber (2001) and Gruber and
Verboven (2001) analyze countries in EU during 1984 and 1997 finding that multiple operators, high fixed-line penetration rate, and long wait time promote mobile diffusion.
An analysis of data from 32 countries during 1991 and 1999 finds that incorporating the time of entry to digital mobile phones is important (Koski and Kretschmer, 2002). Moreover, both between and within standards competition increase mobile diffusion and lower users’ cost.
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2.1.2 Industry Structure – Relationship between Different Types of ICT
The industry structure empirical studies focus on issues exploring diverse viewpoints regarding the relationship between different types of ICT, particularly between mobile services and landline services. One of the key concepts analyzed by the following studies is fixed-to-mobile substitution (FMS). The relevant studies can be organized into single-country and multi-country studies.
Generally, the cross-elasticities estimates from single-country research agree on positive values in both directions implying substitutability between mobile and fixed-line telephones. Rodini et. al (2003) and Rodini (2009) employ U.S. household surveys and bill-harvesting data covering the years 1999-2001 and conclude that there exists mild substitutability between mobile telephones and fixed wirelines. Their cross-elasticities estimates range between 0.13 and 0.26. Ward and Woroch (2004) confirm the previous result but with stronger substitution effect. Strong substitution between mobile services and wireline long distance services is also suggested by the work of Ingraham and Sidak
(2004).
Contrary to the results from single-country studies, cross-country research on
FMS show relatively mixed outcomes of both complementarity and substitutability of fixed-line and mobile telephones. The first study in this subcategory was carried out by
Ahn and Lee (1999) who employ the International Telecommunications Union (ITU) data from 64 countries in 1997 and find complementary relationship between fixed-line and mobile telephone services. Heimeshoff (2008) also finds cross-elasticity of 0.94 from the data of 30 OECD countries in the period of 1990-2003. On the other hand, competing
10 studies find a substantial substitution effect between the two telephone services (Madden and Coble-Neal, 2004; Waverman, Meschi, and Fuss, 2005)
Other studies such as Garbacz and Thompson (2005, 2007), who investigate both developing countries and developed countries between 1996-2003, discover rather less conclusive results: 1) FMS is more evident over time in developing countries than developed countries; and 2) telecommunications markets are not symmetric – fixed-line telephones are substitutes in the mobile market but mobile telephones are not substitutes in the fixed-line market.
Apart from mobile and wireline telephones, another fast growing form of ICT influencing telephone demand is the Internet. Some research addresses the relationship between fixed-line telephones and the Internet. For example, Garbacz and Thompson
(2003) suggest a positive effect of the Internet penetration on the mainline telephone demand. There is also a positive relationship between the Internet and demand for business fixed-line telephones according to Garbacz and Thompson (2005).
2.2 Impacts of ICT
Contrary to the industry structure focus of ICT literature, which is based on what economic and demographic factors influence the demand for ICT, another stream topic focuses on the impacts that the development of ICT has on economic growth and productivity growth. There is a large pool of studies trying to answer this question from a various aspects.
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2.2.1 ICT Development and Economic Growth
Empirical studies on this topic include Roller and Waverman (2001) who study a panel data of 21 OECD countries and 14 developing countries from 1970-1990. The authors analyze the impact of investment in telecommunications infrastructure on GDP and conclude that the impact is greater in OECD countries and in the countries where fixed telephone lines exceed a “critical mass” of 40 lines per 100 inhabitants.
Chakraborty and Nandi (2003) also confirm this non-linear impact of ICT as they analyze a relationship between teledensity (number of active landline telephones per 100 inhabitants) and GDP in 12 developing countries in Asia in both the short and long run.
The authors suggest that there exists causality running from teledensity to GDP only in the group of countries with a low degree of privatization. The causality is bidirectional in other countries with higher degree of privatization.
A more recent study is Venturini (2009) who analyzes the ICT markets in the U.S. and 15 EU countries over the period 1980-2004. The author estimates impact of ICT capital on GDP growth within production function framework using panel cointegration analysis finding that ICT significantly spurs GDP growth in the long run.
In summary, previous studies indicate either unidirectional or bidirectional relationship between ICT development and economic growth. The results also vary across different groups of countries and their stages of telecommunications development implying that the contribution of ICT to economic growth is dependent on the level of income and telecommunications development.
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2.2.2 ICT Development and Productivity Growth
Since the privatization and liberalization of telecommunications sector of a several developed countries in the early 1980s, researchers have been trying to assess the reform outcomes. Some studies confirmed that privatization improves total factor productivity (TFP) and efficiency of the sector (Madden and Savage, 2001; Ros, 1999).
With the emergence of mobile technology in the 1990s, research focuses on estimating how mobile telephones increase TFP and efficiency of ICT. For example, Jha and
Majumdar (1999) analyze panel data of 23 OECD countries over 1980-1995 by using stochastic production frontier estimation method and suggest that ICT development in a form of mobile technology diffusion has a positive and significant effect on the productivity of ICT sector.
The implication of this positive relationship leads to another theme in the literature – “leapfrogging”. The term refers to how developing countries and transition economies can foster their mobile and wireless system to bypass investments in mainline telephones and jump into the information age. Gruber (2001) and Gruber and Verboven
(2001) claim that leapfrogging process has already occurred in central and eastern
European countries. A number of studies (Lam and Shiu, 2008; Dai, 2000, 2003; Mu and
Lee, 2005) also discover the similar phenomenon in China, whose government decides to set up a nationwide fiber-optic cable network instead of undergoing an expensive reconstruction of the analogue copper wire network.
To conclude, empirical evidence confirms that ICT development can improve telecommunications productivity and efficiency through innovation, technological changes, market reforms, and catch-up. Less developed countries have latecomers’ benefit of employing the most advanced technologies when building the mobile and wireless network.
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2.3 Disequilibrium Approach
So far, studies in the structure of ICT sector infer results from the models that assume this market is in equilibrium. However, this might not be the case for ICT markets due to its nature – the provision of telecommunications services requires large infrastructure of the network implying quantity constraint and later on the capacity that might exceed the market demand, in which prices cannot move sufficiently to clear the market. Early studies on disequilibrium model tend to be theoretical and focus on the econometric foundation and specifications, though they provide fairly simple applications using data from the U.S. housing sector (Fair and Jaffee, 1972; Fair and Kelejian, 1974;
Maddala and Nelson, 1974).
Empirical studies using the disequilibrium approach applies to diverse topics in economics including: i) general macro disequilibrium – unemployment, real wages, and labor supply markets (Rosen and Quandt, 1978; Ashenfelter, 1980); ii) housing markets
(Lee and Trost, 1978; Dynarski, 1985); iii) commodity markets (Brännlund, 1991; Arano and Blair, 2008); iv) financial sector – credit and loan markets (Laffont and Garcia, 1977;
Bauwens and Lubrano, 2007; Hurlin and Kierzenkowski, 2007); and v) effectiveness of centrally planned economies (Charemza and Quandt, 1982; Portes and Winter, 1980).
A field of study that is the most relevant to ICT market is an energy commodity market due to the industry structure and high fixed cost. Arano and Blair (2008) analyze the effect of regulation on the US natural gas industry over a period of 1977-2000 under a disequilibrium framework. They find that actual prices converge to estimated equilibrium prices overtime resulting in decreased deadweight losses.
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2.4 Concluding Comments
This chapter provides a scope of empirical ICT literature, including both the determinants of ICT adoption or diffusion and the impacts of ICT on economic and productivity growth. Although none of the studies has ever applied a disequilibrium concept to their ICT demand and supply analyses, the disequilibrium approach has been utilized in a several interesting fields in economics. The next chapter explains the methodology of this study: the theoretical model and the empirical setup, including the regression estimates of the demand and supply equations.
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Chapter 3
METHODOLOGY
3.1 Introduction
In this chapter, a general theoretical model of markets in disequilibrium with different modifications is presented, followed by a brief overview of the International
Telecommunications Union data set used in this study. Lastly, the model specifications and the estimation procedures of the mobile telephone services in Southeast Asian countries of interest are discussed.
3.2 Theoretical Model
A general model of disequilibrium markets is initially formulated by Fair and
Jaffee (1972). The model consists of standard demand and supply equations, with each equation is a function of explanatory variables, a price variable, and an error term as follows:
!! ! !!!! ! !!!!! ! !!! (t = 1, 2, …, T), (3.1)
!! ! !!!! ! !!!!! ! !!! (t = 1, 2, …, T), (3.2) where Dt denotes the quantity demanded during period t, St denotes the quantity supplied during period t, and Pt denotes the price of the good during period t. !!!!and !!! denote vectors of exogenous variables determining demand and supply, respectively. The error components are !!! and !!!. Unlike an equilibrium model, !! is not assumed to adjust in
16 each period so as to equate Dt and St. Hence, the quantity transacted in the market in period t cannot exceed the minimum of the quantity demanded or supplied; that is,
!! ! !"#!!!! !!!! (3.3)
Furthermore, Fair and Jaffee (1972) add an additional assumption regarding price- setting behavior. Since price cannot fully adjust to equate demand and supply, the change in price (!!! ! !! ! !!!!! is assumed to be indicative of the existence of excess demand or supply in the market. This leads to the formulation of price adjustment.
!!!! ! !!!!! ! ! as !!!!!!!! ! !!! ! ! (3.4)
Equations (3.1) - (3.4) characterize one specific class of a disequilibrium model known as the “directional model.” In this model, one can assume whether the quantity transacted is equivalent to quantity demanded or quantity supplied by observing a direction of price change. Equation (3.4) implies that direction of the price movement is indicative of the existence of excess demand or excess supply in the market, due to the failure of price adjustment.
However, this directional model has the limitation that !!! is now an endogenous variable. Having only one structural equation provided in equations (3.1) - (3.4) is not enough to solve these simultaneous equations with two jointly dependent variables, !! and !!!. As a result, Fair and Kelejian (1974) modify a structure of the directional model slightly by using a one-period lag of the price variable instead. Hence, the demand and supply equations become:
!! ! !!!!!! ! !!!!! ! !!! (3.5)
!! ! !!!!!! ! !!!!! ! !!! (3.6)
17
Moreover, combining equations (3.3) and (3.4) yields:
!!!!!!!!!!!!!!!!!"#!!!!! ! !!!!! ! ! !!! ! !! ! !!!!!!"#! !!! ! !!!! ! ! (3.7) !!!!!!!!!!!!!!!!!"#!!!!! ! !!!!! ! !
These conditions can also be graphically illustrated. In figure 3.1, ex ante demand and supply curves intersect at the market-clearing price, P*. When an observed price is less than P*, there is excess demand. Hence, only supply schedule will be observed.
Equation (3.4) implies that price will be rising. Conversely, price will be falling when there is excess supply, and the observed quantity equals to demand schedule.
Based on the equations (3.5) - (3.7), the estimation of demand and supply equations of markets in disequilibrium allows one to analyze price movement over time to separate observations into corresponding states of the market. Although this modified directional method can be estimated easily by separating the observations into demand and supply samples and applying OLS methods, the parameter estimates will not be consistent even if the sample separation is correct. This is due to the fact that the error terms, !!!!!!"!!!!, in the demand and supply equations will have nonzero mean; hence, they will not be independent of the price and other explanatory variables (Fair and Jaffee
1972, p. 503-4).
To correct this problem, Lee (1976) and Maddala (1983) suggest using two-stage estimation methods for endogenous switching regression models, which are built upon
Heckman’s (1979) pioneer study of sample selection methods. These methods apply to various topics such as the union-nonunion-wage model of Lee (1978) and the housing- demand model of Trost (1977). In the case of disequilibrium market model, the two regimes are demand and supply schedules, respectively:
18
Regime 1: !! ! !!!! ! !!!!! ! !!! iff !!!! ! !!!!! ! !!! ! !!!! ! !!!!! ! !!! (3.8)
Regime 2: !! ! !!!! ! !!!!! ! !!! iff !!!! ! !!!!! ! !!! ! ! !!!! ! !!!!! ! !!! (3.9)
! and the criterion function, is: ! !! ! !!, where the two terms are defined as:
! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! !! ! !!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!!!! ! ! ! ! !! !! !! ! ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! !
! and ! ! !"#!!!! ! !!!!. The vector !! is a set of exogenous variables!!!!, !!!, and at least one additional exogenous variable served as an instrument. The two-stage model with endogenous switching assumes the error terms !! are correlated with !!! and !!!.
Define a dummy variable,
! !! ! !!!"!! !! ! !!!!!! ! !!!!!!!!!"#! (3.12)
!! ! !!!"#$%&'($!!! !! ! !! (3.13) and consider the direction model in which sample separation is known as:
!!!!"#!!!! ! !!!!! ! ! !! ! (3.14) !!!!"#!!!! ! !!!!! ! !
Then, the observations !! are readily available by observing price variations. The first stage of the two-stage estimation methods is utilizing a probit ML method, with !! as a dependent variable, to obtain an estimate of !. This process is the same as constructing the selectivity correction factors, the inverse Mills ratios, or the expected values of the residuals !!! and !!! in the equations (3.8) and (3.9). Note that the conditional
! ! distribution of !!! , given !! , is normal with mean !!!!! and variance !! ! !!!
(assuming that Var(!! ) = 1). Hence, the conditional means of !!! and !!! are the following:
! ! !!! !!! ! !!! !!"#$%& ! ! !!!!! !!! ! ! !! ! !!!! ! (3.15) !!! !!!
19
Similarly,
! ! !!! !!! ! !!! !!"##$% ! ! !!!!! !!! ! ! !! ! !!! ! (3.16) !!!!! !!! where !!! ! !"#!!!!! !!!, !!! ! !"#!!!!! !!!, ! is the probability density function, and
! is the cumulative distribution function of a standard normal function. For simplicity,
! ! ! ! let !!! ! !!! !!! ! ! !! and !!! ! !!! !!! !! ! ! ! !! !. As a result, the regime 1 and 2 in equations (3.8) and (3.9) become:
!! ! !!!! ! !!!!! ! !!!!!! ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!"#!!! ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!
!! ! !!!! ! !!!!! ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"#!!! ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!"! where !!! and !!! are the new residuals, with zero conditional expectations:
!!! ! !!! ! !!!!!! , and !!! ! !!! ! !!!!!! . By obtaining the selectivity correction factors from a probit ML of the total samples in the first stage, adding them to the demand and supply equations, and estimating equations (3.17) and (3.18) by OLS in the second stage, one can acquire consistent estimates of !!! !!! !!! !!! !!!, and !!!.
3.3 Descriptions of the Data Set
This study analyzes the data set mainly from the International
Telecommunications Union (ITU) and some additional demographic indicators from the
World Development Indicators (WDI) of the World Bank. The specific publication of the
ITU data set is World Telecommunications/ICT Indicators Database 2011 (15th Edition), which covers time series data from years 1960, 1965, 1970, and annually from 1975-2010 over 200 countries. However, only the data from 1990-2004 were included in this study due to the data availability, especially the mobile telephone service data. The data are
20 obtained from annual surveys sent to regulatory authority in charge of ICT and telecommunications sector of each country.
The ITU data set contains a number of variables which are divided into different categories as follows: demography; fixed telephone network; mobile cellular network; the
Internet; quality of telephone services; telephone traffic; staff; tariffs; telephone service revenue; investment in telephone services; community access to telecommunications indicator; and ICT access and usage (individual- and household-level). Since some variables, for example literacy rate, tend not to change much over time, the data for those variables are not available every year. To deal with missing values, I employ an exponential extrapolation method using an exponential function.
The ITU data of the five Southeast Asian countries, Thailand, Malaysia,
Philippines, Indonesia, and Vietnam are presented in table 3.2. Overall, the average total number of mobile telephone subscriptions of the first four countries far exceed that of
Vietnam. During 2001-2004, the global mobile-cellular subscriptions per 100 inhabitants
(hereafter, mobile density) is 20.9, compared to 57.2 and 12.3 of developed and developing countries, respectively. The mobile density during this period for Malaysia is
42.3, close to that of developed countries. Thailand and Philippines’s mobile density are almost identical – 25.6 and 25.3. However, Indonesia and Vietnam fall behind, with the mobile density of 7.6 and 3.3, respectively (ITU, 2012).
When looking at average mobile density up until 2010, this trend of mobile density continues – Malaysia still has the highest, followed by Thailand and Philippines; however, Vietnam one surpasses that of Indonesia (see table 3.3). The superior numbers of mobile density in Malaysia, Thailand, and Philippines seem to correspond to the
21 variables affecting demand for mobile telephones such as the countries’ GDP per capita and the share of households that own a computer. Moreover, some mobile telephone supply driving variables such as total investment in and revenue from cellular phone industry are also highest in Malaysia (see table 3.2). In the following section, the demand and supply equations are specified and estimated by different approaches of the disequilibrium model.
3.4 Estimation Procedure
This section explains how the disequilibrium model of mobile telephone services can be investigated empirically based on the two different approaches: directional method and two-step estimation. I first specify the base model of demand and supply equations for mobile telephone markets. The demand for mobile telephone subscriptions5 during
! year t for country i, (!!!!), where t = 1, 2, …, 15 (for year 1999-2004) and i = 1, 2, …, 5
(for Indonesia, Malaysia, Philippines, Thailand, and Viet Nam, respectively), is assumed to be a linear function of price, income, demographic factors, country dummies, and time
! trend. Similarly, the supply equation for mobile telephone subscriptions, !!!!, is assumed to be linear function of price, staff, revenue, investment, country dummies, and time trend.
Hence, the demand and supply equations for mobile telephones are as follows:
! !!!! ! ! !! ! !!!!!! ! !!!"#!!! ! !!!"#!!! ! !!!"#$!!! ! !!!"#!!! ! !!!"#$!!! !
! !!!"#!!! ! !!!"#$! ! !!"!"#"$! ! !!!!!!"! ! !!"!!!"! ! !!"!! ! !!!! (3.19)
5 Mobile cellular telephone subscriptions (in thousand) refer to the number of subscriptions to a public mobile-telephone services that provide access to the public switched telephone network using cellular voice-communication technology.
22
! !!!! ! ! !! ! !!!!!! ! !!!"#!!! ! !!!"#!!! ! !!!"#!!! ! !!!"#$! ! !!!!"!#! !
! !!!!!"! ! !!!!!"! ! !!!! ! !!!! (3.20)
Descriptions of the explanatory variables are presented in table 3.1. Their summary statistics is shown in table 3.2.
To estimate equations (3.19) and (3.20) using the directional method suggested by
Fair and Jaffee (1972), I first separate the observations of quantity transacted, !!!!, into demand or supply schedules using price variation, !!! ! !! ! !!!!. For example, for any country i, if its mobile monthly subscription charge increases from year t-1 to year t
(!!! ! !!, it is assumed that the market is in excess demand in that year; hence, the quantity (mobile subscription) transacted in year t, will be included in the supply equation.
Conversely, if !!! ! !, the quantity for that year will be counted as quantity demanded.
Note that if the price in any year is unchanged from that of the previous year, (!!! ! !!, that specific year is in temporary equilibrium and will be included in both demand and supply sample periods. To correct for an endogeneity problem of the price variable as suggested by Fair and Kelejian (1974), the variable Pt in equations (3.19) and (3.20) can be replaced by their one-year lags, Pt-1. After all observations are properly separated, the demand and supply schedules can be estimated using OLS.
Since the directional method yields inconsistent parameter estimates, the two- stage estimation or sample selection is estimated. In the first stage, the same sample separation method using !!! gives an indicator Ii, which is equal to 1 if an observation
6 falls into demand schedule, and equal to 0 otherwise. I regress all indicators Ii on the exogenous variables !"#!!!! !"#!!!! !"#$!!!! !"#!!!! !"#$!!!! !"#!!!! !"#!!!! !"#!!!! !"#!!! ,
6 According to equation (3.14), if there is no change in price (!!! ! !) for any observation in a given year, that observation falls into the supply schedule. Hence, an indicator I is 0 in this case.
23 and total capacity of public switching exchange variable7 using a probit maximum likelihood method over all observations. The estimates from this probit ML serve as the inverse Mills ratio or the selectivity correction factors, specified by !!!!, are added to the equations (3.19) and (3.20). Hence, the demand and supply equations in the second stage become:
! !!!! ! ! !! ! !!!!!!!! ! !!!"#!!! ! !!!"#!!! ! !!!"#$!!! ! !!!"#!!! ! !!!"#$!!! ! !!!"#!!! !
! !!!"#$! ! !!"!"#"$! ! !!!!!!"! ! !!"!!!"! ! !!"!! ! !!!!! ! !!!! (3.21)
! !!!! ! ! !! ! !!!!!!!! ! !!!"#!!! ! !!!"#!!! ! !!!"#!!! ! !!!"#$! ! !!!!"!#! !
! !!!!!"! ! !!!!!"! ! !!!! ! !!!!! ! !!!! (3.22)
These new demand and supply equations (3.21) and (3.22) with the corrected error terms yield consistent estimates via OLS. The econometric software LIMDEP is utilized for the estimations of this two-stage sample selectivity.8 Nevertheless, there is one potential problem with these estimation procedures arising from heteroskedasticity of
! ! the new error terms, !!!! and !!!!. This problem can be solved either by White’s (1980) correction methods or by estimating equations (3.21) and (3.22) using weighted least squares instead of ordinary least squares (Maddala 1983, p. 225). Yet, the LIMDEP software provides some estimation methods that can correct this problem automatically
(Shehata, 1991).
In summary, this section presents a theoretical and empirical model of a disequilibrium market using the mobile telephone data of five Southeast Asian countries
7 This variable measures “the maximum number of fixed-telephone lines that can be connected. This number includes fixed-telephone lines already connected and fixed lines available for future connection, including those used for technical operation of the exchange (test numbers). The measure is the actual capacity of the system, rather than the theoretical potential when the system is upgraded or if compression technology is employed” (ITU, Telecommunications/ICT Handbook 2011). 8 The Heckman selection (two-step) command in STATA also yields identical results.
24 from the ITU database over the period of 1999-2004. Two different approaches are used to estimate the parameters of the mobile telephone demand and supply equations: the directional and the two-stage method. The next section addresses the estimation results from the directional and two-step methods.
3.5 Estimation Results and Discussion
The parameter estimates from demand and supply equations are presented in table
3.4 and table 3.5, respectively. Results for models (1) and (2) are from the directional method; and those for models (3) and (4) are from the two-step sample selection method.
Overall, the parameter estimates from all four models are significant. The high values of
R-Square and Chi-Square imply that the models have relatively high explanatory power.
3.5.1 Demand Analysis
The results in table 3.4 indicate how each variable affects the demand for mobile telephone subscriptions. The first and probably most important is a price effect. The coefficient estimates of the lagged mobile monthly subscription charge variable (Pt-1) in all model specifications are statistically significant and have negative signs as expected.
The magnitudes of the estimates imply the mobile price elasticities of demand as shown in table 3.6. The average price elasticities across all five countries from model specifications (1) – (4) range from -3.73 to -4.25 indicating that the demand for mobile telephone is elastic. However, comparing among countries, only the price elasticity from
Philippines suggests that the demand is inelastic.
A coefficient estimate of GDP per capita reflects an income effect on mobile telephone demand. The parameter estimates of GDP variable show both positive and
25 negative signs but none of them are significant implying that the income effect is negligible. This corresponds with the values of income elasticity of mobile telephone demand ranging from -0.164 to 0.125, calculated at the average GDP per capita and mobile telephone subscriptions of each country over fifteen years.9
This zero income effect could result from the fact that this analysis is at an aggregated level; hence, it could be difficult to analyze income effect on mobile subscriptions without taking into account all other goods. In addition, since GDP is one of the main indicators of a country’s macroeconomic conditions, strongly significant country dummies could reflect the country-level differences instead. This rationale also holds for the employment ratio variable, of which the parameter coefficients are insignificant in every model specification.
The proportion of urban population variable (Urb) has negative and statistically significant coefficients across all models. This result is unexpected because people who live in metropolitan areas are generally regarded as better informed of new technologies.
Yet, the more populated an area is (assuming a majority of the inhabitants own mobile telephones), the more likely mobile telephone services are negatively affected by network externalities. The absence of fixed-line telephones in some rural areas can also be another underlying reason for this negative coefficient, since it leads to a greater need for mobile telephone services.
Some previous studies found a complex impact of the share of the population living in urban areas on mobile telephone service as well. For example, Garbacz and
Thompson (2007) found a negative density effect of this variable in their second model of
9 The range of income elasticities are average across countries and across four model specifications, calculated in a similar way as in table 3.6.
26 mobile telephone using an instrumented price variable. The proportion of urban population also reduces the share of population that are potential adopters of mobile telephone and digital phones, in particular (Liikanen, Stoneman, and Toivanen, 2004).
Another similar result is from Rouvinen (2006) who finds that the total number of residents in the largest city negatively affects the diffusion of digital mobile telephone across developing countries.
The percentage of households who own a computer (Comp) has negative impact on mobile subscriptions in the first two models but that effect becomes insignificant in the two-step model. As for the literacy rate variable (Lit), the negative and significant parameter coefficients in all models might seem unexpected. However, this can result from a low explanatory power of this variable since literacy rates tend to be invariant over time.10 In addition, as mobile telephone technology becomes more easily accessible, literacy could be a less important factor. According to some recent surveys, 59% of mobile telephone users who report that they have no educational attainment or they only finish primary school indicate that they use mobile telephones at least once a week
(Audiencescapes.org, 2012).
The parameter coefficients of the variable AgeD, the age-dependency ratio, in all model specifications are positive and statistically significant. Since the dependent population comprises those who are younger than 15 or older than 64 years old, this result might seem surprising if one assumes that this group of population are less likely to adopt new technologies. However, this assumption may be questionable because the phenomenal spread and higher competition in the mobile telephone market can lead to a
10 Because of this nature of the literacy rates, the data reported in the World Bank WDI is relatively scarce. Many data points used in the present analysis are extrapolated.
27 general decrease in mobile prices, making it easier for household members to own mobile telephones. In fact, Liikanen, Stoneman, and Toivanen (2004) also report a positive and highly significant impact of age dependency ratio on prospective mobile phone adopters.
The estimates for country dummies are all significant and positive, with the Thailand dummy having the largest magnitude, in general. The exception is the insignificant coefficient estimate of Philippines dummy, which is why it is removed in models (2) and (4).
Overall, the results after dropping the Philippines dummy do not alter much; but a noteworthy change is an increase in significance level of the price variable in model (4) from model (3).11 Lastly, the positive and significant coefficient estimates of time trend variable (t) in all four models imply that the demand for mobile telephone subscriptions in Southeast
Asian countries are growing over time at a rate of approximately 5.7 million subscriptions each year. This number is close to the increase in the actual mobile subscriptions particularly in a period of 2002-2004.
3.5.2 Supply Analysis
The coefficient estimates of factors influencing the supply for mobile telephones are reported in table 3.5. The lagged mobile subscription charge variable (Pt-1) has significant and expected positive signs in every specification. Its magnitude is smaller than that of demand, with the price elasticities of supply of mobile telephone services are 0.81, 1.18, 0.70, and 1.03 for models (1) – (4), respectively.12 This implies that the supply for mobile telephones alternate between inelastic and elastic from one model specification to another.
The mobile staff (Stf) and revenue (Rev) variables both have strong positive impacts on the supply of mobile telephones as expected. These results are reasonable since more
11 Yet, the R-square and Chi-square of model (2) and (4) values decrease a little bit from those of model (1) and (3), respectively. 12 The price elasticities of supply are calculated at the mean price and quantity across time and countries.
28 human capital and higher financial returns should encourage the producers to provide more supply of mobile telephone services to the market.
However, the parameter estimates of mobile telephone investment variable (Inv) have negative signs and are significant in model (2) and model (4). An underlying explanation for these negative impacts is that there could be an adjustment cost associated with each investment plan resulting in a lag between the time of investment and its outcomes.
Unlike the results from demand equation, the magnitudes of the coefficient estimates for the country dummies tend to vary with model specifications. When dropping the dummies for Malaysia, which are negative, the overall results improve with coefficients for
Inv and Thailand dummies becoming strongly significant.
Contrary to the demand case, the time trend coefficient estimates are negative and statistically significant. The same reasoning as why the supply of mobile is inelastic in some cases might apply – mobile telephone supply does not grow much from one year to another due to the constraint in infrastructure. Moreover, there may exist other constraints in providing mobile services such as market competition and regulations. The parameter estimates for the inverse Mills ratio, !, are significant implying necessity of correcting for the error terms in the two-step estimation to obtain consistent coefficient estimates.
3.6 Concluding Comments
This section discusses general results from the empirical model of mobile telephone markets using the directional and two-step methods. I will elaborate on the implications of these results on welfare loss from the existence of disequilibrium in each country’s case in Chapter 4.
29
Table 3.1 Explanatory Variable Descriptions Variable Definition Unit Source
P Monthly mobile telephone subscription charge $US ITU GDP GDP per capita $US ITU Urb Proportion of population living in urban areas % ITU Comp Proportion of households with a computer % ITU Lit Literacy rate, proportion of working-age populationa % World Bank AgeD Age dependency ratiob, proportion of working-age populationa % World Bank Emp Employment ratio, proportion of working-age populationa % World Bank Stf Total number of full-time mobile telephone service employeesc 100 ITU Rev Total revenue of mobile telephone servicesc 1,000,000 ITU Inv Annual investment in mobile telephone servicesc 1,000,000 ITU Indo, Malay, Dummy variables for observations from Indonesia, Malaysia, the N/A N/A Phil, Thai Philippines, and Thailand, respectively. t Time trend variable N/A N/A L Selectivity correction factors for demand and supply functions N/A N/A a Working-age populations are those of ages 15 and older (15-64 for Age dependency raio). b Dependent population includes people who are younger than 15 or older than 64 years old. c The variables Stf, Rev, and Inv are constructed from total staff, revenue, and investment in telecommunications sector.
30
Table 3.2 Summary Statistics of Variables Variable Total Indonesia Malaysia Philippines Thailand Vietnam Mob. Subscriptions Mean 4,335.42 5,060.53 3,889.27 6,480.56 5,415.69 831.06 SD 7,407.31 8,776.62 4,587.75 10,006.32 8,349.68 1,408.02 Min 0.03 18.10 86.62 21.36 63.22 0.03 Max 32,935.88 30,336.61 14,611 32,935.88 27,378.66 4,960.00 !!!! Mean 15.27 12.33 19.97 5.95 16.17 21.90 SD 7.89 6.35 3.67 3.38 3.92 8.64 Min 2.93 6.33 15.29 2.93 10.88 7.03 Max 31.01 23.14 23.96 13.58 20.07 31.01 GDP Mean 1,613.65 845.06 3,786.41 973.71 2,140.87 322.21 SD 1,307.35 202.94 748.59 143.67 422.88 136.45 Min 98 459.23 2,417.77 719.01 1,495.36 98.03 Max 4,875 1,143.50 4,874.84 1,169.65 3,019.47 557.82 Urb Mean 38.98 38.02 54.92 53.45 26.55 21.97 SD 14.32 5.29 7.64 3.31 2.81 2.44 Min 19.5 30.90 43 48.05 22.20 19.46 Max 64 46.81 64.18 58.60 31.10 25.93 Comp Mean 3.92 1.29 9.86 3.42 3.69 1.34 SD 5.61 0.93 9.74 1.74 3.61 1.41 Min 0.2 0.28 0.76 1.36 0.43 0.17 Max 28 3 28.20 6.60 11.70 5.00 Lit Mean 90.70 92.46 86.55 92.99 91.89 89.62 SD 4.16 7.18 2.75 0.34 1 1.07 Min 81.5 81.52 81.60 92.59 90.29 87.86 Max 105 104.59 90.34 93.57 93.33 91.31 AgeD Mean 61.73 58.66 62.73 73.91 47.56 65.80 SD 10.05 5.13 4 3.42 3.23 8.17 Min 43.9 51.85 57.88 68.85 43.90 51.70 Max 79 67.28 68.63 79.50 53.18 75.46 Emp Mean 65.97 62.63 60.21 59.78 73.68 73.52 SD 6.49 0.89 0.33 0.86 2.20 1.65 Min 58.5 61.30 59.70 58.50 70.90 71.10 Max 78 63.70 60.90 61.30 77.90 75.50 Stf Mean 95.44 97.85 88.83 85.60 86.62 118.30 SD 84.20 99.88 54.91 71.84 61.71 121.29 Min 0.2 6.85 14.54 5.83 8.56 0.24 Max 345 277.15 189.79 211.79 215.38 345.44 Rev Mean 741.86 601.72 1,228 618.71 1,105.60 155.27 SD 858.13 768.60 969.60 653.36 1,051.47 167.54 Min 16.8 16.81 199.92 42.41 149.56 27.43 Max 3630 2,485.32 3,236.84 2,064.67 3,630.04 591.14 Inv Mean 313.62 307.06 448.72 426.47 347.34 38.50 SD 321.28 404.53 306.58 316.07 266.49 38.31 Min 0.016 9.59 15.36 12.52 17.45 0.02 Max 1167 1,167.37 989.63 914.40 919.98 101.33 Capacity Mean 5,035.20 6,388.56 6,153.30 4,178.25 5,693.21 2,762.69 SD 3,095.24 3,414.93 2,331.63 2,826.24 2,643.56 2,912.01 Min 153.64 1,422.92 2,505.53 712 1,684.96 153.64 Max 12,112.11 12,112.11 8,997 6,918.73 8,625.55 9,863.85
31
Table 3.3 Comparison of Mobile-Cellular Subscriptions per 100 Inhabitants
2001-2004 2001-2010 Global 20.9 41.6 Developed Countries 57.2 84.2 Developing Countries 12.3 31.9
Malaysia 42.3 69.5 Thailand 25.6 53.4 Philippines 25.3 46.8 Indonesia 7.6 31.0 Vietnam 3.3 43.4
Source: World Telecommunications/ICT Indicators Database. ITU. 30 May 2012. Web. 10 Jun 2012.
Note: The developed/developing country classifications are based on the UN M49, see: http://www.itu.int/ITU-D/ict/definitions/regions/index.html
32
Table 3.4 Demand Equation Estimates Variables Directional Methoda Two-Stage Method (Sample Selection)a (1) (2) (3) (4) Lagged Subs. -571.596*** -589.197*** -516.714** -581.403*** Charge (197.860) (195.145) (216.226) (205.441) GDP per Capita -0.070 0.301 -0.395 0.069 (1.467) (1.378) (1.498) (1.417) % Urban -631.426*** -559.916** -589.097*** -566.866** (226.160) (205.283) (220.380) (223.895) % HH Comps -405.289** -395.678** -859.514 -639.404 (170.809) (169.128) (735.239) (701.180) Literacy Rate -1606.772*** -1535.784*** -1562.707*** -1470.253*** (320.966) (305.585) (298.535) (286.806) Age Depend. 2509.389*** 2663.563*** 2470.528*** 2732.354*** (450.649) (402.141) (510.890) (414.630) Employment 947.241 273.022 963.236 261.162 (947.411) (386.854) (910.060) (392.837) Indonesia 46139.852*** 38325.753*** 45437.498*** 38474.092*** (11732.844) (6078.374) (10402.460) (6556.394) Malaysia 43955.590** 31924.093*** 45695.337*** 33806.931*** (17062.272) (7271.011) (15717.302) (7264.238) Philippines 12846.365 13880.683 (16454.023) (16329.251) Thailand 53341.143*** 54623.499*** 54022.229*** 56626.602*** (7443.567) (7207.408) (7655.746) (7158.572) Time Trend 5656.942*** 5576.163*** 5834.519*** 5725.518*** (572.600) (559.088) (545.084) (547.700) Lambda -605.963 -1235.534 (2122.819) (2022.882) Constant -110015* -77574.316 -115243.8** -87124.169* (62667.545) (46571.572) (56844.917) (46818.943) Observations 39 39 75b 75b R-Square, (Chi2)c 0.913 0.911 318.79 295.53 Dfres 26 27 12 11 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 a Dependent variable = Mobile subscriptions b In the first stage, selected samples of demand are 37, and those for supply are 38. c Chi-square for Wald test of model (3) and model (4) are reported.
33
Table 3.5 Supply Equation Estimates Variables Directional Methoda Two-Stage Method (Sample Selection)a (1) (2) (3) (4) Lagged Subscription 233.326*** 340.561*** 200.693* 296.798*** Charge (75.930) (74.916) (114.480) (110.154) Staff (k) 43.754*** 53.353*** 34.132** 42.396*** (8.054) (8.265) (14.286) (13.610) Revenue (m) 7.383*** 6.927*** 8.898*** 8.560*** (0.472) (0.500) (1.090) (1.104) Investment -2.109 -3.945*** -3.160 -4.887** (1.358) (1.355) (2.331) (2.277) Indonesia 4120.051*** 6804.620*** 4107.858** 6554.548*** (1332.823) (1104.884) (2013.575) (1614.164) Malaysia -3210.507*** -2940.439* (1069.662) (1718.144) Philippines 5691.290*** 9239.091*** 6219.685** 9480.691*** (1837.899) (1573.641) (2792.813) (2333.430) Thailand 577.406 2916.746*** 1211.647 3376.355*** (1002.644) (705.247) (1646.934) (1157.706) Time Trend -516.255*** -478.435*** -633.619*** -605.085** (146.799) (163.538) (232.219) (244.150) Lambda 2585.641** 2716.582** (1104.999) (1167.931) Constant -5835.958** -9935.092*** -6294.213* -10054.273*** (2243.560) (1990.294) (3304.589) (2851.741) Observations 41 41 75b 75b R-Square, (Chi2)c 0.969 0.960 283.83 254.20 Dfres 31 32 9 8 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 a Dependent variable = Mobile subscription b In the first stage, selected samples of demand are 37, and those for supply are 38. c Chi-square for Wald test of model (3) and model (4) are reported.
34
Table 3.6 Price Elasticities of Demand
Model Average across (1) (2) (3) (4) Country Models
Indonesia -1.40 -1.44 -1.26 -1.42 -1.38 Malaysia -2.87 -2.96 -2.59 -2.92 -2.83 Philippines -0.55 -0.56 -0.49 -0.56 -0.54 Thailand -1.66 -1.71 -1.50 -1.69 -1.64 Vietnam -14.16 -14.59 -12.80 -14.40 -13.99 Average across -4.13 -4.25 -3.73 -4.20 Countries
Note: Prices and mobile subscriptions for each country are average values over 15 years.
35
Figure 3.1 Comparison between Ex Ante and Observed Demand and Supply Curves
Source: Maddala (1983), p.293.
36
Chapter 4
MARKET DEVELOPMENT DISCUSSION
4.1 Introduction
This chapter analyzes the regression results estimated in Chapter 3 from a welfare perspective. The analysis is centered around implications on each country’s mobile telephone market development in terms of (i) what appears to cause disequilibrium,
(ii) to what extent the existence of disequilibrium creates deadweight loss, and
(iii) a projection of how the market tends to grow in the future. Implications from both estimation procedures, the directional method and two-step method, are compared.
4.2 Overall Story
To visualize mobile telephone market development in each country, the annual demand and supply curves are graphed based on the parameter estimates from the model specifications (2) and (4) of the demand and supply equations presented in table 3.4 and table 3.5 and on the mean of exogenous variables, which are evaluated over the relevant group of observations.13 Even though this study analyzes the 15-year longitudinal dataset, attention is focused from 2000 onward as the market development of mobile telephone services become distinctive.
13 For the directional method, only observations that fall into a demand (supply) schedule are calculated for the mean of explanatory variables in the demand (supply) equation. In case of two-step method, all observations are included. 37 Figures 4.1 - 4.5 illustrate the demand and supply curves of mobile telephone in
Indonesia, Thailand, Malaysia, Philippines, and Vietnam, respectively, based on the results generated from both estimation methods. Note that the shorter-dashed lines represent the demand curves and the longer-dashed lines represent the supply curves. The square dots represent the observed quantity and prices in that particular year. Since the observed prices are usually low, its scale is presented on the secondary Y-axis to the right of the graph. The vertical lines attached to the square dots illustrate a reference for how the level of quantities transacted in a given year are compared to the demand and supply relationship from the estimation, and whether there is an excess demand or excess supply in that particular year.
In general, the mobile telephone markets over the period of 2000 - 2004 are not in equilibrium, except for Indonesia during 2002 - 2003, Thailand in 2003, and Malaysia during 2001 - 2003. The equilibrium levels of price and quantity vary across these five years, corresponding to the growing market of mobile telephones. In all the countries, the changes in those equilibrium points appear to be driven by the demand rather than the supply, which is nearly constant over time. The estimated results from the directional method and two-step method yield relatively similar demand curves, but the estimated supply curve from the two-step method is marginally higher than that from the other method. The next section focuses on individual cases of the countries. It starts with the overview of mobile telephone market in each country, followed by a discussion of the estimated results from social welfare perspective.
38 4.3 Country Cases
4.3.1 Indonesia
Being the fourth most populated country in the world, with the population of almost 240 million people, there is considerable opportunity for mobile telephone market expansion in Indonesia. In 1998, the Indonesian mobile telephone service was nearly non-existent – mobile telephone users were only accounted for 0.5 percent of the population and the usage was concentrated in Jakarta (Guerin, 2012).
However, this has changed considerably, especially after the Indonesian government deregulated the telecommunications market in 2000. The number of mobile telephone subscriptions in Indonesia increased by approximately 70 percent each year during the period of five year (2000 - 2004) and continued to grow (ITU, 2011). The competitive Indonesian mobile-phone market is dominated by a few firms. The largest one is Telkomsel which dominates with 50 percent market share. The firm is 65 percent state-owned and, interestingly, 35 percent owned by a Singaporean telecommunications company. The other two major companies in the Indonesian mobile phone market are
Indosat (24 percent market share with half of its ownership being public) and XL (15 percent market share) (Digital Media across Asia, 2010).
Based on the estimated results in this study as presented in table 3.4, table 3.5 and figure 4.1, the Indonesian mobile-phone market experiences excess demand during 2000-
2002 and excess supply during 2003 - 2004. The observed price and quantity in the market seems to be closest to the equilibrium levels in 2002 and 2003 as illustrated by the graph from the directional method and two-step method, respectively.
39 When there is either excess demand or excess supply in the market, the triangle consisting of the gap between the observed quantities of mobile and the equilibrium quantities and between the demand and supply curves is considered deadweight loss
(DWL). If there is underproduction or excess demand of mobile telephones (the observed quantity is less than the efficient quantity), consumer surplus and producer surplus shrink.
Total surplus is decreased by the DWL. Likewise, the quantity level when the market is in excess supply or overproduction exceeds the efficient quantity, creating positive DWL that reduces social surpluses.
The two empirical methods estimated the DWL in Indonesia during 2000 - 2004 to be $124 million and $148 million on average,14 with the smaller number belonging to the directional method. As a result of a swift change in DWL from excess supply in 2003 to 2004, the directional method and two-step method estimate a 5-year average annual growth rate of the DWL to be 603 percent and 2,174 percent, respectively. The
Indonesian mobile users burden less DWL than the producers by $40.6 million per year
(2000 - 2004) on average.
4.3.2 Thailand
The mobile telephone market in Thailand has seen spectacular growth over the past decade. During 2000-2004, the mobile subscriptions in Thailand grew by approximately 80.5 percent annually (ITU, 2011). The mobile-phone penetration rate rose from 5 percent of the 63 million population in 2000 to around 30 percent in 2003
(Leek and Chansawatkit, 2006).
14 In this case, the measurement of the deadweight loss is million of USD * Lines. 40 The Thai telecommunications market has been dominated exclusively by the two state-owned enterprises: the Telephone Organization of Thailand (TOT) and the
Communications Authority of Thailand (CAT), which are the major providers of domestic telephone service and the international gateway, respectively. The two monopolies maintained the ownership and operation of the public telecommunications infrastructure and network until the early 1990s when they could not keep up with the surge in the demand. Since 1992, TOT and CAT started granting over 30 telecom build- transfer-operate (BTO)15 concessions to private companies to develop the network for mobile, fixed-line and other communication services (Nikomborirak and Cheevasitti- yanon, 2008). The major private mobile operators, which are all concessionaires, include
Advanced Info Service (AIS), Total Access Communications (DTAC), and True
Corporation (True Move).
However, the statutory state monopolies was terminated in 2001 due to the enforcement of the Telecommunications Act, which also established a governmental regulatory body, the National Telecommunications Commission (NTC), to be in charge of all telecommunications issues including license issuing. Even though the privatization of TOT and CAT is believed to create a more competitive environment of the mobile- phone market, there have been some on-going problems regarding the inconsistency in the regulatory rules established by the NTC, the delay in the committee selection and the conversion process. Despite these problems, the mobile telephone industry in Thailand has been proliferating, especially after the late 1990s. After the third mobile phone
15 Build-transfer-operate refers to a type of telecommunications contract by which concessionary companies are allowed to build the network and operate it until the concession expires. However, the ownership of the network facilities must be transferred to the public owner at the time that construction is completed.
41 operator, True Move, entered the market in 2000, the total number of mobile telephone subscriptions soared rapidly and exceeded those of the fixed-line phone in 2001
(Nikomborirak and Cheevasittiyanon, 2008, figure 1).
The estimated results from the empirical model and figure 4.2 provide a picture of the Thai mobile-phone market, which is relatively similar to that of the Indonesian case.
Clearly the market is in excess demand but its level diminishes during 2000-2002. Then, the market switches to excess supply in 2003 and 2004. Yet, the result from the two-step method demonstrates that the observed quantity is almost at the equilibrium level in 2003.
The demand curve shifts upward over time much more quickly relative to the supply curve.
The two methods show similar pattern of the disequilibrium generated deadweight loss (DWL). The triangles become smaller from 2000-2002 and increase from 2003 to
2004 again when there is excess supply. The value of deadweight loss over 2000 - 2004 is calculated to be $117 million and $211 million, on average, from the directional method and the two-step method, respectively. The decrease in consumer surplus that resulted from the DWL is lower than the drop in producer surplus by $49.8 million on average. Clearly, mobile telephone consumers are the winners of the market over this period. The average year-to-year rate of change of the DWL from the two methods differ considerably – 41 percent for the directional method and 1,854 percent for the two-step method.
42 4.3.3 Malaysia
Since the introduction of the first mobile cellular system in Malaysia during the mid 1980s, the mobile telephone networks in Malaysia experience explosive growth. The average annual growth rate of the mobile subscribers during the 1990s was approximately
50 percent, but dropped down slightly to 30 percent during 2000 - 2004 (ITU, 2011). The main mobile telephone companies in Malaysia comprise Maxis Communications Berhad
(Maxis), Celcom Malaysia Berhad (Celcom), DiGi.Com Berhad (DiGi), Telekom
Malaysia Berhad, and Mobikom. The first three companies are the oligopolies in the market with subscriber market shares, measured in 2005, of 41 percent, 35 percent, and
24 percent, respectively. By the end of September 2007, there were 22.1 million mobile subscriptions out of 27.3 million population, a penetration rate of 80.8 per 100 inhabitants
(Skmm.gov.my, 2012).
As for the Malaysian mobile-phone market regulatory intervention, the government agent called the Malaysian Communications and Multimedia Commission
(MCMC) enacted the Communications and Multimedia Act in 1998, which provides a fundamental regime for complicated issues of market entry, service convergence, and product offerings. However, in terms of mobile telephone service pricing, only wholesale rates for the carriage of voice communications, determined under Mandatory Access
Pricing, are regulated; the mobile retails rates are not. As a result, there are competitive pricing schemes especially on prepaid starter kits and prepaid services, and a downward price pressure in the postpaid services offered by DiGi and Maxis (Skmm.gov.my, 2012).
In contrast to the cases of Indonesia and Thailand, the outcomes from the two empirical methods are reasonably different. The graphs from the directional method
43 illustrate that the Malaysian mobile-phone market has been in disequilibrium driven by excess supply throughout the period of 2000 - 2004. However, the two-step method suggests the market has been considerably close to the equilibrium levels – there is small level of excess demand during the first 4 years and the market slightly switches to excess supply in the last year (see figure 4.3).
Since the degree of disequilibrium in the Malaysian mobile telephone market is fairly low compared to the first two country cases, the deadweight loss calculated for the directional method and two-step method are $28 million and $3.6 million, respectively.
The magnitude of DWL produced by the two-step method is much smaller than that of the directional method. The difference between the DWL burdened by consumers and producers, which is $4.4 million on average, provides contrast to the results from those of the Indonesian and Thai mobile-phone services. On average, the DWL increases annually by 50 percent and 1,212 percent in the directional method and two-step method, respectively. Similar to the first two country cases, mobile telephone users in Malaysia are less negatively affected by the market disequilibrium than the service providers.
4.3.4 Philippines
Mobile telephone market in Philippines emerged in 1991, then the total number of mobile subscriptions rose approximately 67 percent per year during 1999 - 2004 (ITU,
2011). This tremendous growth is fueled by pioneering billing packages and short messaging service (SMS) as a substitute to conventional voice calls. By 2009, the SMS volume being sent daily in Philippines is approximately 1.8 billion text messages, outweighing that of voice calls by a factor of 10 to 1 (Evans, 2011). The revenues generated from text messaging services are accounted for roughly 40 percent of the total
44 mobile revenues (Zita, 2004). Both the SMS usage and the proportion of non-voice revenues in the Philippines mobile market are considered the highest in the world.
Major operators in the Philippines mobile telephone landscape are the Philippines
Long Distance Telephone Company (PLDT), the biggest and oldest company founded in
1928; Smart Communications Inc., the PLDT subsidiary; Globe Telecom; and
DigitalTelecommunications Inc. (Digitel). Similar to Thailand, an important regulatory authority in Philippines is the National Telecommunications Commission (NTC), established in 1979 and mandated by the Telecommunications Policy Act of 1995; however, the telecommunications market has been privately held since the beginning.
Based on the outstanding usage of SMS and the high level of prepaid users (including lower-income segments of the population), the mobile penetration rate in
Philippines has skyrocketed to 80 percent in early 2009, with 73 million subscribers compared to only 6 million users in 2000 (Russell, 2012). Additional growth in this mobile market depends on the pricing strategies and the technology development of the mobile providers.
Regarding the estimated demand and supply curves for the Philippines mobile- phone market as shown in figure 4.4, the directional method illustrates that the demand for mobile telephone service essentially starts in 2002 with excess supply continuing throughout the three-year period. Although the two-step method demonstrates a greater level of demand, there also exists the same condition of excess supply in the market over the five years of interest. The levels of deadweight loss and their growth rates measured from the results of the directional method (only the years 2002 - 2004 are taken into account) and the two-step method are $898 million compared to $570 million on average,
45 and 63 percent compared to 140 percent, respectively. The big magnitude of the DWL implies market inefficiency. The mobile telephone users in Philippines are the gainers in this market over these three years due to the $379.5 million difference in DWL on average.
4.3.5 Vietnam
The telecommunications sector in Vietnam is among the Southeast Asian fastest growing telecommunications markets. The telecommunications industry in Vietnam commenced at the end of the 1980s. After the Government of Vietnam (GNV) implemented the open-door policies in 1988, the number of mobile telephone subscribers in Vietnam rose from 200,000 to 640,000 in 2000, and grew further at annual rate of 59 percent on average (Export.gov, 2011; ITU, 2011). At present, there are eight leading mobile-phone service providers in Vietnam; namely Viettel, MobiFone, VinaPhone, S-
Fone, EVN Telecom, Vietnamobile, GTel mobile, and Indochina Telecom. The first three companies comprise nearly 90 percent of the market share.
The mobile telephone market in Vietnam is strictly controlled by the General Post
Bureau, which carries out license issuing and license fee collecting processes. The telecommunications business license is generally effective for 3-5 years (Ecdc.net.cn).
Foreign investment is allowed under the business cooperation contract (BCC). A several foreign telecommunications companies from Britain, Australia, France, Japan, Korea, and
Sweden have been entering the Vietnamese mobile-phone markets through the BCC mode to develop a communication network system in Hanoi and other business centers in
Vietnam (Ecdc.net.cn).
46 Figure 4.5 provides an illustration of the estimated mobile telephone demand and supply relationship in the Vietnamese landscape. Similar to the Philippines case, the demand is not present until 2002 in the directional method results. Nonetheless, unlike the mobile-phone service in Philippines and all other three Southeast Asian countries, the
Vietnamese market does not even reach an equilibrium condition (the demand and supply curves are not intersected), except for the graphs of 2003 and 2004 of the two-step method. This could result from the low development and the strict regulation of the
Vietnamese mobile cellular market. The DWL from the directional method estimates
(during 2002 - 2004) is $52 million with the average annual rate of change of 13 percent; whereas the two-step method estimates contribute to $18 million of DWL with the average annual rate of change of 31 percent. The decrease in consumer surplus is $20.6 million less than that in the producer surplus.
4.4 Concluding Comments
This chapter presents a country-by-country analysis of the disequilibrium conditions in the Southeast Asian mobile-phone market. The brief overviews of mobile cellular service development in each country are provided. Then, I discuss in details about the estimated results, along with their diagrams of the relevant demand and supply curves during the period of 2000 - 2004.
In short, except for the results for Indonesia in 2000 and Malaysia in 2000 - 2003, the directional and two-step methods yield comparable results in terms of whether the market is in excess demand or excess supply in that particular year. The mobile telephone market situations in Indonesia and Thailand are similar – they both start of with excess
47 demand then switch to excess supply in 2003. The Malaysian market seems the closest to equilibrium levels, resulting in the smallest deadweight loss generated. The demand in
Philippines and Vietnam develop rather later than the rest of the countries; hence, the markets are in excess supply throughout these 5 years period.
In terms of the magnitude of DWL, the overall annual average of DWL is
$1,084.8 million. In all five countries, the underproduction and/or the overproduction of mobile telephone services result in a decrease in consumer surplus that is smaller than that of producer surplus by $99 million on average. The discrepancy is largest in the
Philippine market and smallest in the Malaysian market. Clearly, over 2000 - 2004, mobile telephone users are the gainers from this market disequilibrium. The next chapter provides the summary of findings, the discussion of limitations, suggestions for future research, along with the policy implications of this study.
48 Figure 4.1 Mobile Telephone Market in Indonesia
Directional Method Two-step Method
Supply Indonesia, 2000 Indonesia, 2000 Demand
Observed 120 7.75 P&Q 7.7 100 120 7.8 7.65 80 7.6 100 7.6 7.55 7.4 60
80 Price 7.5 7.2 40 7.45 60 Price Price 7 7.4 40 20 6.8 7.35 20 0 7.3 6.6 0
0 6.4 5000 10000 15000 20000 25000 30000 35000 0 Quantity
5000 10000 15000 20000 25000 30000 35000
Quantity
Indonesia, 2001 Indonesia, 2001 120 6.4 120 6.4 100 6.35 100 6.3 6.3 80 6.2 80 6.25 60 6.2 60 6.1 Price Price Price Price 40 6.15 40 6 6.1 20 5.9 20 6.05 0 6 0 5.8 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 !"#$%&'( Quantity
49 Directional Method Two-Step Method
Indonesia, 2002 Indonesia, 2002
120 7 120 7 100 100 6.9 6.9 80 80 60 6.8 60 6.8 Price Price
Price Price 40 40 6.7 6.7 20 20 0 6.6 0 6.6 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Indonesia, 2003 Indonesia, 2003
120 7.7 120 7.7 100 7.6 100 7.6 80 7.5 80 7.5 60 7.4 60 7.4 Price Price Price 40 7.3 40 7.3 20 7.2 20 7.2 0 7.1 0 7.1 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Indonesia, 2004 Indonesia, 2004
120 7.3 140 7.3 120 100 7.2 7.2 80 100 7.1 80 7.1 60 Price Price Price Price 7 60 7 40 40 6.9 20 6.9 20 0 6.8 0 6.8 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
50 Figure 4.2 Mobile Telephone Market in Thailand
Directional Method Two-Step Method
Supply Thailand, 2000 Demand Thailand, 2000 140 12.6 Observed 12.5 P&Q 120 100 12.4 140 12.6 12.3 80 120 12.5 12.2 Price Price 60 100 12.4 12.1 12.3 40 80 12 12.2 20 Price Price 60 11.9 12.1 40 0 11.8 12 0
20 11.9 5000 10000 15000 20000 25000 30000 35000 0 11.8 0 Quantity 5000 10000 15000 20000 25000 30000 35000 Quantity
Thailand, 2001 Thailand, 2001
140 11.3 140 11.3 120 11.2 120 11.2 100 11.1 100 11.1 80 11 80 11 Price Price Price Price 60 10.9 60 10.9 40 10.8 40 10.8 20 10.7 20 10.7 0 10.6 0 10.6 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
51 Directional Method Two-Step Method
Thailand, 2002 Thailand, 2002
140 11.7 140 11.7 120 11.6 120 11.6 100 11.5 100 11.5 80 11.4 80 11.4
Price Price 60 11.3 Price 60 11.3 40 11.2 40 11.2 20 11.1 20 11.1 0 11 0 11 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Thailand, 2003 Thailand, 2003
140 12.1 140 12.1 120 12 120 12 100 11.9 100 11.9 80 11.8 80 11.8
Price Price 60 11.7 Price 60 11.7 40 11.6 40 11.6 20 11.5 20 11.5 0 11.4 0 11.4 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Thailand, 2004 Thailand, 2004
140 12.5 160 12.5 120 12.4 140 12.4 100 12.3 120 12.3 12.2 80 100 12.2 12.1 80 12.1 Price Price
60 Price 12 60 12 40 11.9 40 11.9 20 11.8 20 11.8 0 11.7
0 0 11.7 0 5000 10000 15000 20000 25000 30000 35000 5000 10000 15000 20000 25000 30000 35000 Quantity Quantity
52 Figure 4.3 Mobile Telephone Market in Malaysia
Directional Method Two-Step Method
Supply Malaysia, 2000 Malaysia, 2000 Demand 140 15.9 Observed 15.8 120 P&Q 15.7 140 15.9 100 15.6 15.8 120 80 15.5 15.7 15.4
100 15.6 Price 60 15.3 80 15.5 15.4 40 15.2
Price Price 60 15.1 15.3 20 40 15.2 15 15.1 0 14.9 20 15 0 5000
0 14.9 10000 15000 20000 25000 30000 35000 0 Quantity
5000 10000 15000 20000 25000 30000 35000 Quantity
Malaysia, 2001 Malaysia, 2001
140 15.9 140 16 15.8 120 120 15.8 15.7 100 15.6 100 15.6 80 15.5 80 15.4 15.4 Price Price
Price Price 60 15.2 60 15.3 40 15.2 40 15 15.1 20 20 14.8 15 0 14.6
0 14.9 0 0 5000 10000 15000 20000 25000 30000 35000 5000 10000 15000 20000 25000 30000 35000 Quantity Quantity
53 Directional Method Two-Step Method
Malaysia, 2002 Malaysia, 2002
140 16 140 16 120 120 15.8 15.8 100 100 15.6 15.6 80 80 15.4 15.4 Price Price
Price Price 60 60 15.2 40 15.2 40 20 15 20 15 0 14.8 0 14.8 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Malaysia, 2003 Malaysia, 2003
140 16 140 16 120 15.8 120 15.8 100 100 15.6 15.6 80 80 15.4 15.4 Price Price Price Price 60 60 15.2 40 15.2 40 20 15 20 15 0 14.8 0 14.8 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Malaysia, 2004 Malaysia, 2004 140 16 140 16 120 15.8 120 15.8 100 100 15.6 15.6 80 80 15.4
15.4 Price 60 Price Price 60 15.2 40 15.2 40 20 15 20 15 0 14.8 0 14.8 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
54 Figure 4.4 Mobile Telephone Market in Philippines
Directional Method Two-Step Method
Supply Philippines, 2000 Philippines, 2000 Demand Observed 120 13.7 P&Q 13.6 100 120 13.7 13.5 13.6 80 13.4 100 13.5 13.3 60
80 13.4 Price 13.2 13.3 40 13.1 60 Price Price 13.2 13 20 40 13.1 12.9 13 20 0 12.8 12.9 0
0 12.8 5000 10000 15000 20000 25000 30000 35000 0 Quantity
5000 10000 15000 20000 25000 30000 35000 Quantity
Philippines, 2001 Philippines, 2001
120 11.8 120 11.9 11.7 11.8 100 100 11.7 11.6 80 80 11.6 11.5 11.5 60 60 Price Price 11.4 Price 11.4 40 11.3 40 11.3 11.2 20 11.2 20 11.1 0 11.1 0 11 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
55 Directional Method Two-Step Method
Philippines, 2002 Philippines, 2002
120 10.3 120 10.3 100 10.2 100 10.2 80 10.1 80 10.1 10 10 60 60
Price Price 9.9 Price 9.9 40 9.8 40 9.8 20 9.7 20 9.7 0 9.6 0 9.6 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Philippines, 2003 Philippines, 2003
120 8.9 120 8.9 100 8.8 100 8.8 80 8.7 80 8.7 60 8.6 60 8.6 Price Price Price Price 40 8.5 40 8.5 20 8.4 20 8.4 0 8.3 0 8.3 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Philippines, 2004 Philippines, 2004
120 7.7 120 7.7 100 100 7.6 7.6 80 80 7.5 7.5 60 60 Price Price Price Price 7.4 7.4 40 40 20 7.3 20 7.3 0 7.2 0 7.2 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
56 Figure 4.5 Mobile Telephone Market in Vietnam
Directional Method Two-Step Method
Supply Vietnam ,2000 Vietnam, 2000 Demand 160 12.9 Observed 140 12.8 140 P&Q 12.9 120 12.7 12.8 120 100 12.6 12.7 100 80 12.5 12.6 Price 80 60 12.4 12.5 Price Price 60 40 12.3 12.4 20 12.2 40 12.3 0 12.1
20 12.2 0
0 12.1 5000 10000 15000 20000 25000 30000 35000 0 Quantity 5000 10000 15000 20000 25000 30000 35000 Quantity
Vietnam, 2001 Vietnam, 2001
140 9.3 160 9.3 140 120 9.2 9.2 100 120 9.1 100 9.1 80 9 80 9 Price Price Price Price 60 60 8.9 8.9 40 40 20 8.8 20 8.8 0 8.7 0 8.7 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
57 Directional Method Two-Step Method
Vietnam, 2002 Vietnam, 2002
140 9.9 160 9.9 120 9.8 140 9.8 100 120 9.7 9.7 80 100 9.6 80 9.6 Price Price
60 Price 9.5 60 9.5 40 40 20 9.4 20 9.4 0 9.3 0 9.3 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Vietnam, 2003 Vietnam, 2003
140 7.1 160 7.1 120 140 7 120 7 100 100 80 6.9 6.9 80 Price Price Price Price 60 6.8 60 6.8 40 40 6.7 6.7 20 20 0 6.6 0 6.6 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
Vietnam, 2004 Vietnam, 2004
140 5.1 160 5.1 120 140 5.05 100 5 120 5 100 80 4.95 4.9 80 Price Price Price Price 60 60 4.9 40 4.8 40 4.85 20 20 4.8 0 4.7 0 4.75 0 0 5000 5000 10000 15000 20000 25000 30000 35000 10000 15000 20000 25000 30000 35000 Quantity Quantity
58 Chapter 5
CONCLUSIONS
5.1 Overview
Southeast Asia region consists of a large population and emerging economies such as Thailand, Malaysia, Indonesia, Philippines, and Vietnam, whose governments are trying to build a knowledge-based economy and improve the country’s economic status.
Telecommunications, as one component of information and communication technologies
(ICT), plays an important role in accomplishing those ambitious goals. However, challenges lying in developing country-wide telecommunications networks are an extensive infrastructure, which requires considerable investment capital, and implementing regulations that jointly promote competitive market environments and enhance social welfare.
Mobile telephone market is a classic case study for a disequilibrium analysis of telecommunications problems due to its needs for network infrastructure and a rapidly increasing demand for introductions of new products and technologies. The markets are usually in excess demand in early phases of cellular signal tower construction. Then, after the network building is completed, the markets are in excess supply because of a high cost of services impeding demand to catch up.
A disequilibrium modeling has been an interesting topic in econometrics since the
1970s, but its applications are rare. This study contributes to an existing pool of empirical studies of markets in disequilibrium, in addition to those addressing macroeconomic
59 issues, monetary markets, and command and control economies. There are also a number of papers focusing on telecommunications markets, especially mobile telephones, in terms of the technology diffusion, impacts of telecommunications on economic growth and productivity growth. Yet, their assumption of equilibrium may give inaccurate results of mobile telephone demand and supply analysis, given a nature of the market.
With the sizeable base of consumers who become more aware of new cellular phone technologies, the market in Southeast Asia will continue to have unique expansion opportunities. Thus, analyzing the telecommunications problems using the disequilibrium approach is appropriate for promoting the country’s economic growth and competitive advantages. I investigate the demand and supply of mobile telephone subscriptions in the five Southeast Asian countries using the ITU panel data from 1990 - 2004. Disequilibrium models with two different techniques, the directional method and the two-step method, are employed.
The findings of this study are as follows. In terms of what influences the demand and supply for mobile telephone subscriptions in Southeast Asia, only the age- dependency ratio, country dummies, and time trend variables have positive and significant effects. The lagged mobile subscription charge, share of urban population, and literacy rate negatively and significantly affect the demand. Interestingly, there is no income effect when it comes to consumers’ demand for mobile phones, as indicated by an insignificant coefficient estimate of GDP per capita. Regarding the supply, the lagged subscription charge, number of staff, average annual revenue, and country dummies have significant and positive effects on mobile subscriptions. The coefficient estimates of mobile investment and time trend variables are negative and significant.
60 By graphing the demand and supply curves from the parameter estimates, the results suggest evident existence of disequilibrium in the markets with different degrees of deadweight loss. The findings from five country cases can be categorized into three groups. The first group consists of Indonesia and Thailand, where both disequilibrium methods indicate that the markets are in excess demand then they change to excess supply in 2003. Malaysia is in the second group, whose results differ across the two methods – the market has small and constant magnitude of excess supply in the directional method, but the result from two-step method implies that the Malaysian mobile phone market almost stays at the equilibrium levels. The last group includes
Philippines and Vietnam. In these two countries, the directional method results imply that their mobile telephone markets are not present until 2002. Both methods indicate that the
Philippines and Vietnam cellular phone markets are in excess supply throughout all five years.
5.2 Policy Implications
Based on this study’s findings, roughly after 2002, cellular phone markets in all five countries face excess supply. This indicates that regulators should focus on promoting mobile telephone demand growth, especially the demand for recent technologies such as mobile telephones supporting the 3G and 4G innovations, known as
“smartphones”. One potential strategy is to encourage more market competition, which will eventually lead to a decrease in service prices. Having clear regulatory plans is also favorable for attracting foreign direct investment, which fosters the long-run growth of mobile telephone markets.
61 The deadweight loss results suggest that over 2000 - 2004, the mobile telephone market in Southeast Asia is moving toward excess supply or an overproduction of cellular phone service capacity. The difference in the decreases in consumer surplus and producer surplus implies that producers are more negatively affected by the market disequilibrium condition than consumers. Thus, in addition to promoting the mobile demand growth, the policy implication from this pattern of DWL is that the telecommunications regulators should also provide support to mobile phone service providers. This can be accomplished by facilitating the commercial market activities such as infrastructure investments, consolidation, a process of obtaining concessions, and coming up with progressive institutional framework.
5.3 Limitations
The findings from this study can be improved if better data with longer time series and more disaggregated cross-sectional variables were obtained. More recent data can add values to the study of later generations of mobile telephones (3G and 4G
“smartphones”), which are becoming more popular among consumers. Additional variables that can advance the analysis include those with information regarding an average price of mobile devices and mobile usage (in addition to access data). Moreover, macroeconomic variables such as a country’s stock of capital, human capital, telecommunications infrastructure, and mobile telephone capacity would be useful to measure national economic activities, particularly in telecommunications.
The mobile subscriptions, a dependent variable of this study, can generate more insightful results if it was disaggregated into different types – prepaid and postpaid
62 services. In Southeast Asian countries, a majority of mobile users utilize prepaid subscriber identification module (SIM) cards, which can be transferred from one device to another. Compared to a postpaid mobile phone service, a prepaid one with a SIM card is cheaper and requires less paper work. 16 As a result, total number of mobile subscriptions may give incorrect number of users or devices, as household members can share the same device or one user may own more than one SIM card to take advantage of different calling plans, leaving some cards temporarily inactive.
Finally, the price variable is another limitation of this study as the monthly subscription charge may not represent a precise cost of services, especially for the prepaid mobile phones. Some papers create an index for price from a combination of different types of price data or from an average revenue per subscription. However, even though the ITU database provides a number of different mobile telephone price variables, the observations for Southeast Asia are scarce.17
5.4 Suggestions for Future Research
Information and communications technologies (ICT) involve not only mobile telephones, but also fixed-line telephones and the Internet. With an advanced technology nowadays, these three services are interconnected – in addition to interchangeable calls between mobile and fixed-line telephones, more mobile phones have access to the
Internet, and global online calls to fixed-line and mobile telephones are enabled via a
Voice over Internet Protocol (VoIP) service. Thus, incorporating these services into a
16 This includes only the access process. Usage fees charging system is complicated and a postpaid service can sometimes be less expensive. 17 I also attempted to use an average revenue per subscription as an index for price, but this does not improve the results much.
63 disequilibrium telecommunications analysis can be a potentially useful extension of this study.
An alternative track of future studies can focus on effectiveness and welfare effects of knowledge-based society initiatives and universal services of ICT in developing countries. For example, in Southeast Asia, the governments are forging ahead with ambitious projects such as e-commerce, e-health, e-education, and e-government
(Zita, 2004). Further studies should examine whether these program foster ICT adoption, narrow down the digital divide, and elevate social welfare.
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