frontier news - bangladesh mobile market

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Frontier Economics Ltd, London.

Taxation and Mobile Telecommunicationsin BangladeshPOLICY PROPOSALS FOR MAXIMISING TAX REVENUE AND PROMOTINGTELE-DENSITY GROWTH - A REPORT PREPARED FOR THE GSMA

August 2006


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i Frontier Economics | August 2006 | Confidential Draft

Contents rpt-RL-taxes&mobiles-Bangladesh-FINAL-V2-STC-270406.doc

Taxation and Mobile Telecommunicationsin Bangladesh

Table of Contents

Executive summary.......................................................................................11 Introduction .........................................................................................32 The development of the mobile market in Bangladesh ......................5

2.1 Development of the mobile market in Bangladesh .................. ..............52.2 The tax regime for mobile services in Bangladesh................. .............. 11

3 Tax simulation, analysis and results.................................................. 133.1 Methodological framework ..................................................................... 133.2 A description of the data and estimated mobile elasticities...... .......... 143.3 Tax simulation modelling ................. ................... .................. ................. . 21

4 Conclusion..........................................................................................33 Annexe Statistical modelling and regression results ...............................37

Statistical modelling techniques ........................................................................ 37Regression results.... .................. .................. .................. ................... .................. . 39

The relationship between the price of new handsets and the proportion ofhandset sales in taking place in the formal market .............................. 45


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ii Frontier Economics | August 2006 | Confidential Draft

Tables & figures

Taxation and Mobile Telecommunicationsin Bangladesh

List of Figures

Figure 1: The estimated effect on tax revenues and subscriber numbers fromremoval of SIM tax .................. ................... .................. .................. .................. ......2

Figure 2: The effect on tax revenues and subscriber numbers from removal ofthe handset import tax............................................................................................3

Figure 3: Comparative growth in subscriber base following removal of mobile-specific taxes .................. .................. .................... .................. .................. ................4

Figure 4: Percentage change in tax revenues following removal of mobile-specifictaxes...........................................................................................................................5

Figure 5: Number of mobile subscribers in Bangladesh ............... .................. ...........6Figure 6: Percentage of mobile subscribers that are on pre-paid contracts.............6Figure 7: The relationship between GDP per capita and mobile penetration

amongst Lower Income countries, 2005 .................. ................... .................. ....8Figure 8: The relationship between growth in real GDP per capita and growth in

penetration ................. .................... ................. ................... ................... .................. .9Figure 9: The relationship between growth in mobile coverage (% of the

population) and mobile penetration.....................................................................9Figure 10: Changes in mobile penetration over time comparing Bangladesh

with other South Asian countries ................. ................... .................. ................ 10Figure 11: Tax burden in the cost of mobile services................ .................. ............ 12Figure 12: Projected consumer tax revenues in 2006 base case................. ......... 21Figure 13: Comparative growth in subscriber base following removal of mobile-

specific taxes ......................................................................................................... 31Figure 14: Percentage change in tax revenues following removal of mobile-

specific taxes ......................................................................................................... 32Figure 15: The estimated effect on tax revenues and subscriber numbers fromremoval of SIM tax .............................................................................................. 34Figure 16: The estimated effect on tax revenues and subscriber numbers from

removal of the handset import tax .................................................................... 35Figure 17: Estimating the relationship between the log of informal handset sales

(black market and second had) and the log of the legitimate (first-handretail) handset price (as a proportion of GDP per capita). ..................... ....... 46


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iii Frontier Economics | August 2006 | Confidential Draft

Tables & figures


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iv Frontier Economics | August 2006 | Confidential Draft

Tables & figures

List of TablesTable 1: Estimated market elasticities ................ ................... ................... ................. . 16Table 2: Estimated mobile elasticities at the operator level ................ .................. .. 17Table 3: A comparison of the estimated elasticities for Bangladesh with

elasticities reported elsewhere ............................................................................ 19Table 4: Outline of the tax simulation scenarios .................. .................. .................. 22Table 5: Tax simulation scenario 1 reducing VAT on mobile services from 15%

to 10%....... ................. ................... ................... .................. ................... ................. 24Table 6: Tax simulation scenario 2 reduce import tax on handsets from Taka

300 to zero...................... .................. .................... .................. .................. ............. 26Table 7: Sensitivity analysis of tax simulation scenario 2 ................ .................. ...... 27Table 8: Tax simulation scenario 3 removal of SIM tax (Taka 900) holding all

other taxes unchanged...................... .................. .................. .................. ............. 28Table 9: Tax simulation scenario 3 sensitivity analysis around the removal of

SIM tax.................. .................. .................. .................. ................... .................. ...... 30Table A10: Estimated mobile elasticities ................ .................. .................... ............. 39Table A11: GrameenPhone regression results post-paid............ ................... ...... 40Table A12: GrameenPhone regression results pre-paid.................... .................. . 41Table A13: Aktel regression results pre- and post-paid together ............... ....... 42Table A14: Banglalink regression results pre- and post-paid together............... 43Table A15: Estimated market elasticities full regression results ................ ......... 45


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1 Frontier Economics | August 2006 | Confidential Draft

Executive summary

Executive summary

The purpose of imposing taxes on any economic transaction is so that thegovernment can collect tax revenues, which are then used to finance the various

functions of government. The analysis presented in this report shows that amore streamlined tax regime for mobile customers in Bangladesh, in whichmobile specific taxes are removed, could ultimately result in higher tax revenuesfor the government.

The existing tax regime for mobiles in Bangladesh, and in particular the mobile-specific taxes levied on customers, are estimated to constrain penetration growthand mobile usage in the mobile sector. This is because mobile customers inBangladesh are sensitive to the prices that are charged for mobile services. Ifcertain taxes levied on mobile users were removed, such as the SIM tax or theimport tax on mobile handsets, then prices for consumers would be expected tofall. Consumers will respond to this fall in prices by consuming more bothincreasing mobile penetration growth and also overall mobile usage.

With respect to the removal of mobile-specific taxes, there are three importantfactors to be taken into account.

1. Removal of the Taka 900 SIM card tax for new connections

| We find that the introduction of the Taka 900 SIM tax for new connectionsin mid-2005 could be expected to have a significant impact on thedevelopment of the Bangladeshi mobile market. We estimate that by 2009there could be approximatelytwo million fewer subscribers compared withthe counter factual of no SIM tax. This translates to a 9% difference in theestimated subscriber base by 2009.

| We also find that the removal of the tax could actually boost tax revenues.

We estimate that if the SIM tax was removed, tax revenues could actually be2.5% higher by 2009 and in certain cases up to 4.4% higher in 2009,compared with the situation with the SIM tax in-place. The net increase intax revenues is driven by the higher tax revenue from higher growth rate innew connections and higher usage due to the associated fall in mobileconnection costs. This is estimated to counter-balance the fall in taxrevenues from the removal of the SIM tax. Figure 1 illustrates the potentialestimated impact of removing the SIM tax on tax revenues and subscribergrowth in 2009.


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Executive summary

Removing SIM tax may boost subscriber numbers & tax revenues

0

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35

Tax revenues (2006) Tax revenues (2009-base

case)

Tax revenues (2009-remove

handset tax)

Projectedtaxrevenues

fromm

obile(BDTbillions)

Tax revenue from new SIMs Tax revenues new handsets

Tax revenues from usage Tax revenues from subscription

12.5m subscribers in

2006


2009, keep SIM tax - tax

revenues of BDT 30.3 bn


2009, removing tax - tax


Taxes from usage:

BDT 12bn (62%)

Taxes from usage:

BDT 22bn (74%)

Taxes from usage:

BDT 26bn (82%)

Figure 1: The estimated effect on tax revenues and subscriber numbers from removal ofSIM tax

Source: Frontier Economics

2. Removal of the Taka 300 import tax on new mobile handsets

| The Bangladeshi government currently taxes all new handsets that areimported to the country at a flat rate of Taka 300. We find that the removalof this tax could also increase subscriber numbers, compared with thecounterfactual of the import tax in place. We estimate that the subscriberbase could be up to 1.8% to 2.9% larger by 2009, approximately 400thousand to 600 thousand additional mobile subscribers, were this fixedimport tax to be removed. This is because one of the key requirements forbecoming a mobile phone subscriber is to actually buy a mobile phone.

Therefore, if the import tax is removed, then this pushes down the price ofbuying a mobile phone and therefore the overall cost of becoming a mobilesubscriber.

| The relatively high import tax prompts many consumers to buy mobilephones in either the grey or second hand markets. If the handset tax was

removed, we project that not only will more handsets be sold, but a greaterproportion of these will be sold through legitimate means. In the medium-term, by 2009, we estimate that the fall in taxes following the removal of thefixed import tax could be almost offset by higher taxes from moresubscribers (variable taxes and the SIM tax), higher overall usage and morehandset sales in the legitimate market (which are taxed according to generaltaxation, i.e. VAT at 15%). Figure 2 illustrates the impact of removing thehandset tax on tax revenues and subscriber growth in 2009.


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Executive summary

Removing handst tax may boost subscriber numbers & tax revenues in the longer-term

0

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35

Tax revenues (2006) Tax revenues (2009-base case) Tax revenues (2009-remove

handset tax)


fromm

obile

(BDTbillions)




2006

22.4m subscribers in 2009,

keep handset tax - tax



removing tax - tax


Taxes from usage:

BDT 12bn (62%)

Taxes from usage:

BDT 23bn (78%)

Taxes from usage:

BDT 22bn (74%)

Figure 2: The effect on tax revenues and subscriber numbers from removal of thehandset import tax


3. Removal of the Taka 300 import tax on new mobile handsets and theTaka 900 SIM card tax

Removing both mobile specific taxes could result in significantly lower

connection and usage costs for mobile customers. Figure 3 shows the differencein the projected subscriber base under the scenario where the mobile specifictaxes remain in place, and the scenario where both taxes are removed1.

The figure shows that even by 2009 the subscriber base is higher under thescenario with no mobile-specific taxes, by almost 11%.

1 In the main report we explain that the tax simulation model created for the analysis of tax changes isnot a forecasting model. Therefore, the projections presented in the figure should not be viewedas Frontier Economics forecast of subscriber growth going forward. The figure only serves toillustrate the potential drag effect that mobile-specific taxes can have on the growth of mobilepenetration in Bangladesh.


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Executive summary

Projected growth in mobile subscriber base

under different tax scenarios

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2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Mobilesubscribers

(millions)

0%

2%

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6%

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12%

%d

ifferencebetween

subscriberbasewithtaxes

inplaceandfollowingthe

removaloftaxes

Base case (taxes remain in place) Simulation (no mobile specific taxes)

Difference (%, right axis)

Figure 3: Comparative growth in subscriber base following removal of mobile-specifictaxes


The impact of removing both mobile specific taxes on tax revenues is shown inFigure 4. The figure shows that, according to the simulation, in the initial periodfollowing the removal of both taxes overall tax revenue falls by as much as 18%.Revenues recover quite quickly after this point driven by the higher variable

taxes on increased penetration growth and higher overall levels of usage suchthat by 2009, the net effect on tax revenues is estimated to be zero. From 2009onwards, we see that tax revenues are estimated to be higher, following theremoval of the mobile-specific taxes.


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Executive summary

Percentage change in tax revenues from mobile

following removal of mobile-specific taxes

-15%

-10%

-5%

0%

5%

10%

15%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Percentagechangeintaxrevenues

comparedwiththebasecase

(i.e.mobilespecifictaxesinplace

)

Initially, tax

revenues fall

following removal

of taxes

However, in later years

increased subscription

and usage have a

counterbalencing positive

effect on tax revenues

Figure 4: Percentage change in tax revenues following removal of mobile-specific taxes


The focus of the analysis in this report is on the effect of changes in the taxregime on subscription, usage and tax revenues in the mobile sector. It has alsobeen recognised that changes in mobile penetration growth can potentially havestrong positive knock-effects in the economy more generally. Box 1, below,summarises the key results from this literature.


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Executive summary

BOX 1 - Impact of a streamlined tax policy for mobiles on the economymore widely

This report focuses on the impact of changing the tax regime in the mobilesector.

Research carried out by economists at the London Business School (LBS*),shows that telecommunications infrastructure, and the way in which this isspread across the population is a significant driver of economic growth.Empirical evidence presented in this research indicates that a 10% increase inthe penetration rate of mobile phones could boost GDP per capita growthrates by around 0.59 per cent per year.

Referring back to Figure 3, we project that the removal of the mobile-specifictaxes will boost the average mobile penetration rate by more than 10% from2008 onwards. The potential impact of this additional subscriber growth on

annual GDP per capita growth (using the LBS results) would thereforeexpected to be significant.

While it is true that increased penetration growth and a more streamlined taxregime could have significant benefits for the economy more widely, theseissues are not addressed directly in this report.

(*)Fuss, M., M. Meschi and L. Waverman, The Impact of Telecoms on Economic Growth in Developing Countries,Vodafone Policy Paper Series, No. 3, March 2005.


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Introduction

1 Introduction

This report presents an economic assessment of the effect of the taxation regimefor mobile telecoms in Bangladesh on the development of the mobile market

going forward. The research has been commissioned by the GSM Association,and has been carried out with the cooperation of the three major mobileoperators in Bangladesh: GrameenPhone, Aktel and Banglalink. Together, thesethree mobile operators account for approximately 95% of the mobile market inBangladesh (Q3 2005).

The overarching objective of the study is to establish the extent to which there isscope to optimise the current tax regime facing mobile phone customers inBangladesh. There are two stages to the analysis:

| Estimate the sensitivity of the demand for mobile services to the pricecharged for these services - the demand sensitivity measure we estimate iscalled the (price) elasticity of demand.

| Simulate the effect of tax changes on demand - using the elasticityestimates, we simulate the effect of a change in the taxes facing mobileconsumers on the demand for mobile services. We present estimates of theeffects on consumer demand (subscriber numbers and minutes of usage) as

well as estimates of the effect on overall tax revenues from mobile.

In the analysis section below, we look at the demand sensitivity for threedifferent aspects of the overall demand for mobile services:

| The elasticity of demand for mobile minutes i.e. how sensitive is theaverage mobile users minutes of usage to the price per minute that they pay.

| The elasticity of demand for mobile handsets i.e. how responsive is the

average customers demand for mobile handsets to changes in the handsetprice.

| The overall elasticity of demand for mobile services i.e. what is therelationship between mobile subscriber growth and the average cost ofmobile services, i.e. the cost of new connections, subscription and usage.

The elasticities themselves are estimated using the statistical technique ofregression analysis. The price sensitivity equations use time series data onsubscriber numbers, minutes of usage and revenues for each operator, allowingus to relate total mobile subscribers and mobile minutes of usage to the averageprice for each service over time.

In simulating the effect of tax changes on the demand for mobile services, we

consider two types of taxes that consumers face:| General consumption taxes: This is VAT, which is levied at a rate of 15%.

VAT is levied on all usage charges, i.e. the price per mobile minute eachcustomer pays. VAT is also levied on sales of handsets, monthly subscriptioncharges and upfront mobile connection charges.


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Introduction

| Mobile specific taxes: there are two mobile specific taxes levied on mobileusers in Bangladesh: a fixed handset-specific tax of Taka 300; and a tax on allnew SIM cards of Taka 900.

The remainder of this report is structured as followed:

| Section 2 presents some background on the development of the mobilemarket in Bangladesh in recent years. This section describes the evolution ofmobile penetration over time, and shows how growth in mobile penetrationin Bangladesh has tended to follow changes in GDP per capita over time.

This section also outlines the existing tax regime for mobiles in Bangladesh.

| Section 3 presents results from the analysis. We begin with an outline of themethodological framework and the data used in the analysis. We thenpresent the estimated elasticities of demand. Finally we present the resultsfrom the tax simulation analysis.

| Section 4 presents our conclusions and recommendations.

Before proceeding to section 2, we note the following. The operators role hasbeen to provide Frontier Economics (Frontier) with the necessary data to carryout the analysis. This includes data on minutes of usage, number of subscribers,tariff information and revenues. All of the analysis presented in this report, andthe conclusions that are derived from it, are based on work carried out byFrontier Economics.

The focus of this research is on the relationship between the taxes consumerspay and the likely development of the mobile market in Bangladesh goingforward. We also recognise however, that there are other taxes levied inBangladesh which could potentially impact on the prices customers pay andhence the demand for mobile services. For example, import taxes on equipmentused for building mobile infrastructure, are also likely to affect the prices

customers pay. However, for the purposes of the current research, weconcentrate on the role of consumer taxes in the development of the Bangladeshmobile market. The GSMA may wish to carry out research at a later stage intothe effect of other taxes on the development of the Bangladeshi mobile market.


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The development of the mobile market in Bangladesh

2 The development of the mobile market inBangladesh

In the first part of this section we provide a brief background on the current stateof the mobile market in Bangladesh. This includes details on current marketshares, as well as summary information on the characteristics of mobile users andhow these compare with mobile users in other countries.

In the second part to this section we briefly outline the existing tax regime formobile users in Bangladesh. We also compare the tax burden for mobile users inBangladesh measured as a proportion of the average annual cost of mobileservices with the tax burden facing other mobile users in South Asiancountries.

2.1 DEVELOPMENT OF THE MOBILE MARKET IN

BANGLADESHIn 1996, the Bangladesh government issued three mobile licences to compete

with the incumbent D-AMPS (a forerunner to GSM technology) operator PacificBangladesh Telecom Limited (PBTL, which has since been re-branded asCityCell).

At the end of 2005, there were approximately 8.1 million mobile subscribers inBangladesh, representing a mobile penetration rate of around 6%. By February2006, the number of subscribers was reported to have grown to 10.1 million, apenetration rate of 7.1%2. Figure 5 shows the growth in subscriber numbersover the 2001 to 2005 period.

In September 2005, the market share of each of the four Bangladeshi operators,

in terms of number of subscribers, was as follows: GrameenPhone, 59%; Aktel,29%; Banglalink, 8.4% and CityCell 4.4%3.

Figure 6 shows that the vast majority of mobile phone subscribers in Bangladesh around 93% - are on pre-paid contracts. This is similar to the situation in mostother South Asian countries, where pre-paid customers are the dominant type ofsubscriber.

2 A 20 February 2006 press release by Telegeography(http://www.telegeography.com/cu/article.php?article_id=11281), stated that the total number ofmobile subscribers in Bangladesh had risen to 10.1 million. This data is provisional.

3 Sourced from the GlobalComms database at http://www.telegeography.com.


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Mobilesubscribers(millio

ns)

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Mobile

penetration 7%

Figure 5: Number of mobile subscribers in Bangladesh

Source: Mobile operators and the GlobalComms data base atwww.telegeography.com

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Figure 6: Percentage of mobile subscribers that are on pre-paid contracts

Source: Bangladeshi mobile operators


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Due to the presence of the GrameenPhone Village Phoneprogramme where asingle mobile phone can have several different users - the headline mobilepenetration rate for Bangladesh may underestimate the true number of mobileusers. In 2004 GrameenPhone stated that of its 4.2 million subscribers, around110 thousand (or 2.6%) were part of the village phone programme. Since then,as GrameenPhones subscriber base has grown to five million, it is estimated thataround 250 thousand of these subscribers are part of the village phoneprogramme. This can have important implications for the analysis that follows,as it would imply that all other factors the same, average tax revenues from usageof mobile services would be expected to be relatively high in Bangladesh.

2.1.1 Comparing mobile subscriber growth in Bangladesh withother countries

The main driver of differences in mobile penetration between countries isdifferences in levels of GDP per capita. This is a standard result in the literaturethat looks at the determinants of mobile penetration4.

Figure 7 shows that, in terms of GDP per capita, the level of mobile penetrationin Bangladesh is below the level we might expect, given the average relationshipin the sample. This is illustrated by the fact that Bangladesh is below itsexpected penetration given its level of GDP per capita, plotted in the figure.

4 See for example, the 2005 report by the GSMA and Frontier (among others) Tax and the DigitalDivide. The paper by R. Lydon and M. Williams on Communications Networks and Foreign DirectInvestment in Developing Countries (published in: Communications Strategies: Vol. 58 2nd Quarter 2005,pages 43 61) also contains a detailed discussion on the determinants of growth in mobilepenetration.


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0%

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0 100 200 300 400 500 600 700 800 900 1,000

GDP per capita ($US, 2004)

Mobilepenetration(2005

)

Pakistan

India

Bangladesh

Figure 7: The relationship between GDP per capita and mobile penetration amongstLower Income countries, 2005

Source: Frontier Economics. Penetration data is for September 2005 and is taken from the GlobalComms database (http://www.telegeography.com/). Data on GDP per capita is from the World Bank World DevelopmentIndicators (WDI, 2006) database.

Figure 8 shows that the recent growth in mobile penetration in Bangladeshclosely follows growth in GDP per capita. Whilst they are both on differentscales, the figure shows that changes in the growth of GDP per capita are closelycorrelated with changes in mobile penetration.

Figure 9 shows that between 2001 and 2004, changes in mobile coverage werealso positively correlated with changes in mobile penetration.


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scribers

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7.0%

Annual%growthinGDPpercapita(real)Annual percentage

change in GDP per

capita (real, right scale)

Annual % growth in

number of subscribers

(left scale)

Figure 8: The relationship between growth in real GDP per capita and growth inpenetration

Source: Frontier Economics. Data on growth in subscriber numbers is from the operators. Data on annualgrowth (%) in real GDP per capita is taken from the World Bank and the Bangladesh Ministry of Finance

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fthethepopulation)

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(% of the population)

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% of the population

Figure 9: The relationship between growth in mobile coverage (% of the population) andmobile penetration

Source: Mobile penetration data provided by the operators


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Finally in this section, we compare the growth in the level of mobile penetrationin Bangladesh with that in other South Asian countries, as well as the group ofLow Income countries5. The figure suggests that growth mobile penetration inBangladesh has tended to lag that in other South Asian and low incomecountries. As shown in Figure 5, however, mobile penetration is estimated tohave grown to 7.1% in the first quarter of 2006, which would move Bangladeshjust above the low income group of countries.

0%

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1997 1998 1999 2000 2001 2002 2003 2004 2005

Mobilepenetrationovertime

Sri Lanka, 15.5%

Pakistan, 10.9%

Low-income country

group, 6.8%

India, 5.8%

Bangladesh 6.1%

(2005)

Figure 10: Changes in mobile penetration over time comparing Bangladesh with otherSouth Asian countries

Source: Frontier Economics. Data on mobile penetration for the 1997 2002 period is taken from the ITUdatabase. Data for the period 2003 2005 is from the Telegeography GlobalComms data base(http://www.telegeography.com/), and is for December of each year.

5 Low-income economies are those in which 2004 GNI per capita is $825 or less. (World Bank WorldDevelopment Indicators, 2006).


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2.2 THE TAX REGIME FOR MOBILE SERVICES INBANGLADESH

This section begins with an outline of the tax regime facing mobile subscribers inBangladesh. We follow this with a comparison of the taxes levied on mobileservices in Bangladesh with the taxes levied on mobile services in other South

Asian countries.

2.2.1 Background on the tax regime for mobile users inBangladesh

There are two main types of taxes levied on mobile users in Bangladesh:

General consumption taxes VAT at 15%, which is levied on usage(the price per minute paid), subscription costs, new handsets andconnection costs; and

Mobile specific taxesthere are two fixed taxes: a Taka 900 SIM cardtax and a Taka 300 handset import tax.

The VAT rate at 15% has remained stable since the mid-1990s. The mainchanges in the taxation of mobile services have been to the mobile-specific taxes.

The SIM tax was introduced at the June 2005 budget. Prior to this, connectioncharges, which included the cost of a new SIM card, were taxed at the standard

variable rate of 15%.

The fixed tax on handsets at the import stage has existed for several years,prompting many potential buyers to purchase their handsets in the grey orinformal market. In recent years, the tax has gone through numerous changes, assummarised below6:

2002/03 The import tax on mobile handsets was set at a flat rate ofTaka 4,000;

2003/04 The import tax on each mobile handsets was equal to Taka2,500 if the handset price was below Taka 10,000. For handsets in excessof Taka 10,000 the import tax was equal to Taka 3,000.

2004/05 The fixed import tax on mobile handsets was set at a flat rateof Taka 1,500;

2005/06 The import tax on mobile handsets was reduced toTaka 300. At the same time, the government introduced the Taka 900SIM tax for new connections.

6 The information on the import taxation regime for mobile handsets has been provided by the threeBangladeshi mobile operators who have supplied data as part of this study.


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2.2.2 Comparison with taxes in other countries

The GSMA, in the 2005 report Tax and the Digital Divide, estimated the averageshare of taxes in the overall cost of mobile ownership for a sample of 50developing countries.. Figure 11 illustrates the results from the GSMA report.

The figure shows the average share of tax in the cost of mobile services7.According these calculations, the share of taxes in the cost of ownership forBangladeshi mobile consumers is estimated to be considerably higher than thatobserved in the comparator countries shown. The GSMA calculations includedthe Taka 900 SIM tax as part of the estimated cost of consuming mobile services.

0

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Bangladesh Pakistan Sri Lanka India

Taxburdenincostofmobileservices(%)

Figure 11: Tax burden in the cost of mobile services

Source: Tax and the Digital Divide, GSMA 2005

Based on discussions with the operators, we estimate the average price of newhandsets, inclusive of all taxes, to be in the range of Taka 4,000 to Taka 5,000(equivalent to US$57 US$72). Therefore in the tax simulation modelling, weassume a starting value for the average handset price inclusive of all taxes of Taka4,000. We also test the sensitivity of the simulation results to this assumption.

7 Mobile service costs includes all costs a customer incurs on an annual basis, with the exception ofhandsets costs. That is, the measure takes account of average usage patterns, average cost perminute and additional costs associated with subscription and connection (spread over a three yearperiod).


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Tax simulation, analysis and results

3 Tax simulation, analysis and results

This section presents the results from the tax simulation. We begin by outliningthe methodological framework for the analysis. We then describe the data we

have used in the analysis and present the estimated elasticities of demand formobile usage. Finally, we present the results of the tax simulation.

3.1 METHODOLOGICAL FRAMEWORK

The aim of this study is to analyse the implications of a change in taxes on thedemand for mobile services in Bangladesh. The methodological frameworkcomprises three steps:

| Step 1: for a given change in taxes, we assume that the tax change is passedon to consumers. For example, if the Taka 900 SIM tax were removed, weassume that the cost of becoming a mobile subscriber (new connections) alsofalls by Taka 900. Therefore, if the average cost of connection in Bangladesh

is Taka 2,500, then removing the SIM tax reduces the connection cost by36% (900/2,500).

| Step 2: for a given change in prices due to a change in taxes, estimate theeffect on the demand for mobile services. We model the demand sensitivitiesacross three areas:

the change in the take-up rate, or penetration/subscription rate, as a resultof the tax change;

the change in average minutes of usage per customer as a result of the taxchange;

the change in the demand for handsets as a result of the tax change.

| Step 3: Having estimated the change in demand due to a change in prices,we then estimate the change in total tax revenues as a result - that is, takingaccount of the fact that taxes have changed, but mobile demand has alsochanged.

There are two main channels through which taxes can affect the mobile marketand tax revenues: by affecting overall usage; or, by affecting handset demand.For example, a reduction in the tax rate, through either a reduction in VAT ormobile specific taxes, could increase subscription growth, handset demand andusage This would lead to higher growth of the mobile market than wouldotherwise be the case, and hence a (second round) increase in tax revenues.

The methodological framework requires three elasticities. The next sectiondescribes how each of these elasticities is estimated, including a brief descriptionof the data that has been provided by each of the operators to estimate theelasticities.


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3.2 A DESCRIPTION OF THE DATA AND ESTIMATEDMOBILE ELASTICITIES

We first describe the data and statistical techniques we have used in the analysis,and then summarise the results from the elasticity analysis, which is covered in

more detail in the Annexe.

3.2.1 Data and statistical techniques used in the analysis

Three of the mobile operators in Bangladesh have provided us with data onrevenues, subscribers and usage. The following data was provided by each of theoperators:

Subscriber numbers quarterly/monthly growth in subscriber numbersback to 2000, where applicable.

Minutes of usage data quarterly/monthly MOU data, and in somecases split by pre-pay and post-pay;

Revenue data quarterly revenue data, split by pre-/post-pay andrevenues from connection/subscription and usage (for some operators).

In the statistical model, we estimate the following relationship for the number of

mobile subscribers at time t,t

S :

t

m

t

s

ttXDPcPbAS +++= [1],

where A is a constant term,s

tP is the cost of subscription,

m

tP is the price per

minute of usage andt

X is a vector of other factors that affect subscription, such

as coverage, GDP per capita and other demographic factors. The relationshipdescribed by equation [1] is estimated using the statistical technique of regression

analysis. The results from the regression analysis provide us with estimates of theown- and cross-price elasticities, that is parameters b and c in equation [1].

Annexe 1 provides a more detailed description of the statistical estimationtechniques. The Annexe also lists the full regression results with details of the

variables used and the number of observations.

As well as estimating the model given by equation [1], we estimated the followingrelationship for the average mobile minutes of usage per subscriber at time t,

tM :

t

s

t

m

ttXPPM +++= [2],

where, as in equation [1], and are the own- and cross-price elasticities

respectively.

Ideally, we would also want to estimate two further equations: one that describesthe relationship between mobile subscription and the price of (new) handsets;and another that describes the relationship between new handset sales and theprice of new handsets. However, in the absences of a long time series of data on


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handset sales and handset prices we have been unable to estimate these twofurther relationships. We discuss the implications of this for the analysis below.

3.2.2 Estimated elasticity of demand for mobile services

The full set of regression results for the elasticity estimates are outlined inAnnexe 1. In this section we summarise the results. We begin with a summaryof the subscription and usage elasticities, followed by a summary of the elasticityof demand for handsets.

Elasticity of demand for new connections and elasticity of demand forusage at the market level

The tax simulation model, which considers tax revenues for the entire market,requires marketelasticities. Given that GrameenPhone has a significant marketshare, then we might expect GrameenPhones elasticities to more closely reflectthe market elasticities. However, we have also attempted to come up with ourown estimates of the market elasticities. In order to do this, we estimate two

regressions at the market level, which are variants on the regressions shown inequations [1] and [2]:

All variables are measured as natural logarithms; therefore the coefficients band dcan be interpreted as elasticities. We estimate each regression for the market as a

whole - that is, combining revenue, subscriber and usage data for all operators(but one). We are unable to separate the revenue data for each operator intorevenues from connection and usage. Hence we are unable to calculate separateown-price and cross-price market elasticities8.

The estimated elasticities are reported in Table 1.

8 Technically, the coefficients band dare constrained estimates of the subscription and usage priceelasticities given in equations [1] and [2]. That is, constraining the coefficient on the subscriptionprice and the per minute usage price to be equal.

Quarterly change in subscribers=A +b * (quarterly change in total revenue per subscriber) [3]

Quarterly change average minutes per user=C +d * (quarterly change in average revenue per minute) [4]


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Dependent variable Independent variable and estimated elasticity

Quarterly change insubscribers

Quarterly change in total revenue per subscriber (*),

-0.30

Quarterly change in averageminutes per user

Quarterly change in average revenue per minute(**),

-0.66

Table 1: Estimated market elasticities


Notes: (*) Quarterly change in total revenue per subscriber is defined as total revenues from mobile operations for alloperators divided by the total number of subscribers. (**) Quarterly change in average revenue per minute is definedas total revenues from mobile operations for all operators divided by total minutes of usage across all operators.

The results in Table 1 should be interpreted as follows:

for a 10% decrease in the average cost (or average mobile revenue) per

subscriber, penetration growth increases by 3%;

for a 10% decrease in the average cost (or average mobile revenue) persubscriber, average minutes of usage per subscriber increases by 6.6%

Ideally, we would require cross-elasticity estimates also, that is estimates of all ofthe parameters in equations [1] and [2]. However, it is not possible to do this atthe market level, as we do not have market based estimates of revenues fromsubscription/connection and revenues from usage. Some of the operators have,however, provided us with this data, and it is therefore feasible to estimate thecross-elasticities at the operator level. The next section presents these results

Elasticity of demand for connection and elasticity of demand for usage at

the operator levelWe also estimated demand elasticities for each of the mobile operators, subject toavailability of the required data. The connection and usage elasticities at theoperator level are shown in Table 2.

The elasticities in Table 2 are not market elasticities, as they only show the pricesensitivities at the operator level. These elasticities are likely to overstate themarket elasticity as the price sensitivities implicitly include switching betweenoperators9.

9 The technical Annexe reports the regression diagnostics for each of the operator elasticityestimates. Note that for reasons of data confidentiality, we do not report the regressioncoefficients for each operator separately. The tables in the annexe do, however, show the samplesize used in the estimation, as well as the usual regression diagnostics, such as the goodness-of-fitand the statistical significance (t-statistics) of the estimated parameters.


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Elasticity parameters used in taxsimulation

Prices

Quantities Cost of connection

(average revenue fromconnection per user)

Mobile usage

(average usagerevenue per minute/per

user)

Number of mobile subscribers -0.46 -0.18

Mobile minutes of usage -0.22 -0.73

Table 2: Estimated mobile elasticities at the operator level

Source: Frontier Economics.

The demand elasticities shown in Table 2 should be interpreted as follows:

A fall in the cost of connection has two effects. First, there is the directeffect on demand (own-price elasticity), that is, a 10% fall in theconnection price leads to a 4.6% increase in the number of newsubscribers/connections above the base case. Second, there is theindirect effect on demand (cross-price elasticity), that is, a 10% fall inthe cost leads to a 2.2% increase in the average MOU per subscriber10.

For mobile usage costs, there are also direct and indirect effects. Thedirect effect implies that a 10% fall in the average cost leads to a 7.3%increase in average MOU per user. The indirect effect implies that a10% fall in the average cost per MOU also leads to a 1.8% increase in theaverage number of new connections above the base case.

Although the elasticities in Table 2 are not directly comparable with thosereported in Table 1 because the variable definitions are different we wouldexpect the elasticity of demand with respect to the price of mobile usage (-0.18for subscription and -0.73 for MOU) to be fairly similar. This is because the vastmajority of total revenues are revenues from usage at least for GrameenPhoneand Banglalink, the operators who have provided the revenue data at such adisaggregated level. We find that this is indeed the case, with the estimateelasticity on average revenue per minute of usage coming out at -0.66.

For the tax simulation model we propose to use the market based elasticity ofdemand of -0.66 for MOU. For the elasticity of subscription with respect to theprice of usage, we propose to use average operator elasticity reported in Table 2,

that is -0.18; and similarly for the elasticity of usage with respect to the price ofconnection/subscription (-0.22). These two elasticities, while based on the

10 This could be due to the externality that affects existing subscribers when new subscribers join themobile network. That is, as more subscribers join the network, there are more individuals to calland hence minutes of usage of subscribers increases as a result.


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operator elasticities, are the only cross-elasticities we have been able to estimatewith the data provided.

For the elasticity of demand for new connections with respect to the price ofconnection we have the estimate of -0.46 from the operator elasticities.

However, for the reasons we have outlined above, this is likely to be biasedupwards (in absolute value), i.e. because it includes switching between operators.

The other estimate we have is -0.30 from the market-based elasticities. However,this is actually a weighted average of the market elasticity of demand for newconnections with respect to the price of connection andwith respect to the priceof usage. In the absence of any information as to which elasticity is likely to bemore or less biased (or more or less robust), we propose to use the mid-point ofthe two estimates in the tax simulation model, -0.3811.

Comparison of results with elasticities reported elsewhere

Overall, we find that the estimated elasticities for Bangladesh are broadly in linewith elasticities reported elsewhere. We have compared the results for

Bangladesh with elasticities reported in two other studies (Table 3):

The GSMA/Frontier report Tax and the Digital Divide (GSMA, 2005);and

The review carried out on behalf of the New Zealand CommerceCommission into the price elasticity of demand for mobiletelecommunications services, (NZCC, 2003)12.

Of the two comparator studies, the previous work carried out by Frontier for theGSMA study Tax and the Digital Divide provides probably the most relevantcomparator as the sample of 50 developing countries is more similar (in terms ofeconomic development) to Bangladesh. The estimated elasticities are similar in

value to those for Bangladesh, with the elasticity of demand for usage withrespect to the price of usage (U-U) being almost identical, -0.73 compared with-0.76 (at the top end of the range).

11 Note, in the tax simulation modelling, we also test the sensitivity of the model-projections toassumptions regarding the elasticity of the demand for new connections with respect to the overallcost of connection.

12 Review of the price elasticity of demand for fixed line and mobile telecommunications services,New Zealand Commerce Commission. See also New Zealand Commerce Commission, Schedule 3investigation into regulation of mobile termination, October 2004.


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Estimatedelasticit ies acrossthe Bangladeshi

operators,weighted average

by market share

Estimates inTax and the

Digital Dividestudy

Estimates from theNZCC study

(range)

Date of study => 2006 2005 2003

Quantity Price

Subscription Connection -0.46

Subscription Usage -0.18

-0.30

Usage Connection -0.22 N/A*

Usage Usage -0.73

-0.66

-0.76, -0.54

Table 3: A comparison of the estimated elasticities for Bangladesh with elasticities reportedelsewhere

Source: Frontier Economics (*) The Tax and the Digital Divide study did not include estimates of the elasticity ofdemand for usage (U) with respect to the price of subscription (S)

Price elasticity of demand for handsets

Another key component in the overall cost of mobile ownership is the price of ahandset. The handset cost accounts for a considerable proportion of the overall

cost of becoming a mobile subscriber, and, if handset prices are relatively high,then this represents a potential barrier to mobile penetration growth. Up to themiddle of 2005 the tax on mobile handsets imported to Bangladesh was

Taka 1,500, equivalent to around 13% of annual GDP per capita.

As a direct result of the high import taxes on handsets, a large proportion ofhandsets were entering Bangladesh and being sold through informal channels. In2005, it was estimated that between 40% and 70% of all handsets sold were soldin the informal market13. In a bid to turn around the flow of grey imports, thegovernment reduced the tax on imported handsets to Taka 300 in mid-2005

while at the same time raising the tax on new SIM cards to Taka 1,200, which hassince been reduced to Taka 900.

In order to facilitate the tax simulation modelling in the following section werequire an estimate of the relationship between subscriber numbers and the priceof handsets assuming that one the requirements for becoming a mobile phonesubscriber in the first place is actually owning a mobile phone. While one of theoperators has provided us with several months of data on the price of new

13 Bangladesh Mobile Telecommunications BIS Shrapnel report 2005.

-0.50-0.06 to

-0.54

-0.09 to

-0.80


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handsets and the number of new handset sales, this time series is not sufficientlylong enough for us to back out a subscription-handset price elasticity. In the taxsimulation model, therefore, we use the estimate of the subscription elasticity ofdemand with respect to the price of subscription as our estimate of the elasticity,i.e. -0.38.

The relationship between the price of new handsets and the number of handsets sold in the formal market

The final piece of information we need for the tax simulation model is anestimate of the relationship between the proportion of handset sales taking placesin the formal (or legitimate) market (i.e. handsets that are not sold in either theinformal market or the second-hand market).

Data on relative prices and the sales of handsets in the formal and informalmarket in Bangladesh is difficult to come by. We therefore rely on evidencefrom the GSMA report Tax and the Digital Divide, which used cross-section datafor 25 developing countries to analyse the relationship between the market share

of grey (i.e. informal market and second hand handset sales) and the averageretail price of legitimate handsets (as a percentage of GDP per capita in eachcountry).

The analysis in the Tax and the Digital Dividestudy, which is describe in moredetail in the Annexe, finds that for 10% decrease in the price of new legitimatehandsets, holding the price of handsets in the informal market constant, the shareof new handset sales in total handset sales increases by 4.7%.

To conclude this section, there are two channels through which anX% reductionin the handset price as a direct result of a change in the handset tax can affect taxrevenues:

a first-round reduction in tax revenues as a result of the reduction in thetax;

an increase in tax revenues from a second round increase the growth inmobile subscribers above the base level (byX%*elasticity of demand ofsubscription). In the tax simulation model, we use an estimate of theelasticity of demand of -0.38; and

an increase in tax revenues from handset sales, because of an increase inthe proportion of handsets sold through legitimate marketing channelsrelative to the base case (byX%*0.47)


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3.3 TAX SIMULATION MODELLING

The previous section outlined a number of different channels through which areduction in taxes, and the subsequent reduction in each of the component prices

of mobile services, can impact on tax revenues: The initial first round effect is to reduce total tax revenues. For example,

if the Bangladeshi government were to remove the Taka 900 SIM tax weestimate that tax revenues for mobile could fall by up to 20%, asillustrated in Figure 12, for the 2006 tax year.

However, there are counter-balancing effects which can also act toincrease tax revenue in the short- to medium term. This is because areduction in taxes ultimately reduces the prices that customers have topay. If prices fall, then demand for mobile services could also rise leading to higher penetration, usage and handset sales. The aim of thissection is to try and estimate the size of these potentially counterbalancing

effects.Figure 12 shows the projected consumer tax revenues from mobile for 2006.Note that the projected revenues, in the order of Taka 20 billion, depend verymuch on the modelling assumptions we have made in the tax simulation model,in particular around:

subscriber growth in 2006;

average usage patterns across subscribers in 2006;

the number of new handsets bought in 2006 on which taxes are paid;

the number of new connections on which taxes, including the SIM tax,

are paid (in the base case, this is always assumed to be 100% of newconnections).

Tax revenues from

usage

62%

Tax revenues from

subscription

3%Tax revenue from

new SIMs

20%

Tax revenues new

handsets

15%

Projected total tax

revenues in 2006 = Taka

20,026 million

Figure 12: Projectedconsumer tax revenuesin 2006 base case



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3.3.1 Outline of the tax simulation scenarios

We run four tax simulation scenarios to illustrate the impact of a tax change onthe development of the mobile market and tax revenues collected from mobileservices. The three tax simulation scenarios are outlined in Table 4. We reportthe results from each simulation in each of the following sections.

Variable taxes(VAT)

Fixed SIM card tax(Taka)

Fixed handsetimport tax (Taka)

Base case 15% 900 300

Tax simulationscenarios

(1) Cut in VAT 10% 900 300

(2) Removal of SIMcard tax

15% 0 300

(3) Removal ofhandset import tax

15% 900 0

(4) Removal ofboththe SIM card taxand the handset tax

15% 0 0

Table 4: Outline of the tax simulation scenarios


3.3.2 Establishing a base-case scenario

The change in tax revenues, subscriber numbers, minutes of usage and handsetsales for each tax simulation must be measured relative to a base case scenario.

We compare the output from the tax simulation with this base case for the years2007 and 2009. We therefore need to make assumptions about the base casegoing forward to 2009. The base case assumptions for each potential tax-revenue stream are as follows:

Annual growth in total mobile subscriber numbers:The base case forgrowth in subscriber numbers is taken from the GSMAs own forecastsfor market growth presented as part of the 2005 study Tax and the Digital

Divide. The base case for growth in subscriber numbers over the 2007 2009 period is an average annual growth rate of 22%.

Pre-paid versus post-paid subscribers:Not all mobile charges apply topre-pay and post pay subscribers. We therefore need to make anassumption about the pre-paid/post-paid market split in the base case.

We use the pre-paid subscriber market share at the end of 2005, equal to93%, as our estimate going forward. This split remains constant undereach of the three scenarios.


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Usage:Data on MOU provided by the three operators shows that theaverage minutes of usage (average MOU) per subscriber has changedlittle in recent years. We therefore use the average annual MOU in 2005for all subscribers with each of the three operators as our base case MOU

assumption. This works out at 2,189 minutes per year in 2005. Handset prices: In accordance with our discussions with the operators,

we use a value of Taka 4,000 ($59) as our estimate of the average price ofhandsets. Therefore, if the Taka 300 handset import tax were to beremoved, the average price of a handset would fall by 7.5% (i.e. Taka300/4,000)

Handset sales: Based on data on handset sales provided by one of theoperators for 2005, we estimate the total number of new handset sales ataround 1.85 million in 2005. Given that the market grew by 4.63 millionsubscribers in 2005, this implies approximately 40% (1.85/4.63) of allhandset sales (assuming all new subscribers also acquire a new handset)

were first-hand, or legitimate handset sales. Other estimates of thenumber of legitimate handset sales range from 60%14 to 30%15. In theabsence of more robust information on the number of new mobilesubscribers that actually purchase a new mobile phone through thelegitimate market - and therefore pay taxes we propose to use anestimate of 50% in the base case. If the market share of legitimatehandset sales in total handset sales were to rise about 50% (assuming totalhandset sales are constant or increasing), then tax revenues from handsetsales would also increase.

Handset upgrades for existing customers:The typical assumption inmodelling the cost of mobile usage is to assume a mobile phone lifespanof 3 years, i.e. a third of all mobile users upgrade their mobiles every year.

For Bangladesh, we have been slightly more conservative and assumed anupgrade rate of 4 years. We also assume that of those existing subscribers

who upgrade their mobile phones, only 50% do so in the legitimatemarket.

Cost of connection:One of the scenarios we run below is a reduction inthe SIM card tax from Taka 900 to zero. In order to simulate the impacton subscription and usage, we need to translate this change to aproportionate change in the connection price, and then multiply thisproportionate price change by the relevant elasticity. Our base-caseestimate of the costs associated with connecting for the first time is Taka2,500. This figure is derived from two sources. First the Bangladesh

government reportedly valued the cost of a new mobile connection in2005, including all the relevant taxes, at Taka 2,17216. Second, Frontiers

14 http://www.bangladeshinfo.com/business/features06.php

15 Tax and the Digital Divide, GSMA 2005.

16 http://www.bangladeshinfo.com/business/features06.php


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own discussions with mobile operators indicate an average cost ofconnection of around Taka 3,000

3.3.3 Scenario 1: cut in VAT from 15% to 10%

Table 5 presents the tax simulation results from the first scenario, a reduction inVAT on mobile services from 15% to 10%. The VAT reduction affects the priceof all services. That is, a reduction in VAT will lead to:

a decrease in connection, subscription and usage costs

a decrease in handset costs

We find that the VAT reduction has a significant impact on tax revenues, whichfall by more than a quarter relative to the base case. Given that VAT is levied onalmost all of the cost components for mobile services, i.e. connection,subscription, handsets, usage, etc, it is therefore unsurprising that the fall in VATleads to a significant drop in overall tax revenues.

The five percentage-point decrease in VAT from 15% to 10% represents a 33.3%fall in revenue from VAT (5%/15%). However, the fall in tax revenues ispartially offset by higher tax revenues resulting from the increase in customernumbers (SIM tax, VAT @ 10%, new handset sales) and usage (VAT @ 10%)resulting from the fall in prices. By 2009, customer numbers are 1.8% highercompared with the base case scenario.

Subscribers2007

(millions)

Subscribers2009

(millions)

Total consumertax revenuesfrom mobile

2007

(Taka, millions)


2009

(Taka, millions)

Base case 15.1 22.4 21,140 30,279

Tax simulation 15.3 22.8 15,290 21,721

% difference 1.3% 1.8% -27.7% -28.3%

Table 5: Tax simulation scenario 1 reducing VAT on mobile services from 15% to 10%



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3.3.4 Scenario 2: removal of Taka 300 import tax on mobilehandsets

The removal of the handset import tax has the effect of reducing the average

handset price, relative to the base case assumption of Taka 4,000, by 7.5%. Theimmediate impact of this tax decrease is to increase handset sales, increasegrowth in subscriber numbers and usage, and also increase the proportion ofhandsets bought and sold first-hand in the legitimate market.

Note that due to a lack of appropriate data, we are unable to estimate thesensitivity of the demand for new connections to the price of new handsets.

Therefore, in the tax simulation we use the elasticity of connection with respectto the cost of connection17.

The effect on growth in subscriber numbers is significant, increasing the numberof subscribers by 1.8% in 2009, compared with the base case. In terms of taxrevenues, the main fiscal benefits are:

increased VAT on connection and subscription from subscriber boostrelative to the base case and also increased revenues from the SIM tax;

increased VAT on sales of additional new phones relative to the basecase.

We project that tax revenues would be approximately 2% lower in 2009,compared with the base case. However, tax revenues will slowly recover, as salesof handsets, growth in subscriber numbers, and increases in usage all increasesteadily over time, compared with the base case.

17 The implicit assumption we are making is that a reduction in the handset price is equivalent to adecrease in the overall cost of becoming a mobile subscriber, and that the rate of amortisation ofboth of these connection costs is the roughly the same.


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Subscribers2007

(millions)

Subscribers2009

(millions)


2007

(Taka, millions)


2009

(Taka, millions)

Base case 15.1 22.4 21,140 30,279

Tax simulation 15.3 22.8 20,569 29,676

% difference frombase case 1.3% 1.8% -2.7% -2.0%

Table 6: Tax simulation scenario 2 reduce import tax on handsets from Taka 300 tozero


The handset tax simulation requires information on the average price ofhandsets. As we have argued above, this is one of the areas of analysis where theempirical evidence for Bangladesh is limited, and we have therefore relied on anumber of assumptions to obtain the results in the simulation. It is therefore

worth checking the sensitivity of the results to certain key assumptions.Specifically, we alter the parameters of the simulation in two ways:

increase the elasticity of subscription with respect to the handset pricefrom -0.38 to -0.60. Recall from section 3.2 that the data was notamenable to estimating a subscription-handset price elasticity, and wetherefore relied on the subscription own-price elasticity of demand;

increase the number of handsets sold in the grey market in the base case

from 50% to 70%. Clearly, if fewer handsets are sold in the legitimatemarket, then taxes from such sales will account for a smaller proportionof total tax revenues therefore, removing these taxes will have a smallerimpact on the total tax take.

Table 7 shows the results of the sensitivity analysis. The results show that thesimulated tax revenues in later years are sensitive to assumptions about the keyparameters. Under both sensitivity scenarios, the fall in tax revenues followingthe removal of the handset import tax (the first round effect) is almostcompletely offset by the increasein tax revenues from higher subscription growth,usage and legitimate handset sales.


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Subscribers2007

(millions)

Subscribers2009

(millions)


2007

(Taka, millions)


2009

(Taka, millions)

Sensitivity 1: assume subscription-handset price elasticity of -0.60

Base case 15.1 22.4 21,140 30,279

Tax simulation, 15.4 23.0 20,864 30,235


Sensitivity 2: assume subscription-handset price elasticity of -0.38, and share oflegitimate handset sales in total handset sales of 30% in the base case

Base case 15.1 22.4 20,102 28,882

Tax simulation, 15.3 22.8 19,923 28,806


Table 7: Sensitivity analysis of tax simulation scenario 2



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3.3.5 Scenario 3: removal of Taka 900 SIM tax for newconnections

The removal of the Taka 900 SIM tax, has the effect of reducing the cost of

becoming a mobile subscriber by 36%. That is Taka 900/Taka 2,500 (theestimated total cost of connecting to a mobile network for the first time). Thesubsequent effect on subscription and tax revenues is large and significant (Table8):

growth in new subscribers increases by almost 9% relative to the basecase scenario by 2009, equivalent to almost 2 million more subscribersrelative to the base case (i.e. 24.3 million minus 22.4 million).

in accordance with the elasticities we have estimated above, these newsubscribers also have higher MOU relative to the base case, because theoverall cost of mobile usage has fallen.

The additional subscribers and the higher minutes of usage - both relative to the

base case result in higher tax revenues for the government. By 2009, taxrevenues are 2.5% higher, a difference which continues to grow significantly overtime.

Subscribers2007

(millions)

Subscribers2009

(millions)


2007

(Taka, millions)


2009

(Taka, millions)

Base case 15.1 22.4 21,140 30,279

Tax simulation 16.1 24.3 20,451 31,032

% difference 6.3% 8.7% -3.3% 2.5%

Table 8: Tax simulation scenario 3 removal of SIM tax (Taka 900) holding all othertaxes unchanged


As with all of the tax simulation scenarios, the output of the simulation isdependent on certain key assumptions. In the case of the SIM tax simulation, the

key assumption is what proportion of current connection costs are currentlyaccounted for by the SIM tax. As we outlined in setting out the base case,precise estimates of the average cost of connection are not available forBangladesh. However, based on discussions we have had with the operators,combined with the available evidence on average connection costs, we proposeda range of Taka 2,172 Taka 3,000 for the average connection cost.


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The simulation shown in Table 8 assumes a connection cost (inclusive of theSIM tax) of Taka 2,500, approximately the mid-point of the proposed range.However, given the level of uncertainty around the current value of the averageconnection charge, it is useful to illustrate the sensitivity of the simulation resultsto assumptions around the value of the connection charge. Table 9 compares thebase case tax revenues and subscriber growth projections with two taxsimulations, each of which makes different assumptions about the value ofconnection charges.

The first sensitivity test assumes the connection charge is Taka 2,172, the lowerend of the proposed range. In this case, the removal of the SIM tax reduces theoverall connection charge by 41% (i.e. Taka 900/2,172). The second sensitivitytest, sets the connection charge to Taka 3,000, meaning that the removal of theSIM tax lowers the overall costs of connection by 30% (i.e. Taka 900/3,000).

The results from the sensitivity analysis show that the effect of the removal ofthe SIM tax on subscriber growth is quite robust to assumptions around the

value of the average cost of connection. For example, in the scenario where the

SIM tax is removed and the average cost of connection is Taka 2,172, we projectthat by 2009 the subscriber base could be 10% higher, compared with the basecase. The same figure for the scenario where the connection cost is Taka 3,000 is7.2%.

The change in tax revenues from the removal of the SIM tax is slightly moresensitive to assumptions about the average cost of connection however itshould be noted that this delays the counterbalancing tax revenue gains, ratherthan remove them completely. For example, in 2009 tax revenues are 4.4%higher than the base case, assuming an average connection cost of Taka 2,172,and 0.5% higher, assuming an average connection cost of Taka 3,000. In thelatter case by 2011, tax revenues are 3.0% higher.


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Subscribers2007

(millions)

Subscribers2009

(millions)


2007

(Taka, millions)


2009

(Taka, millions)

Base case 15.1 22.4 21,140 30,279

Tax simulation(connectioncost of Taka2,172) 16.2 24.6 20,748 31,611

% difference 7.3% 10.0% -1.9% 4.4%

Tax simulation(connectioncost of Taka

3,000) 15.9 24.0 20,148 30,437

% difference 5.3% 7.2% -4.7% 0.5%

Table 9: Tax simulation scenario 3 sensitivity analysis around the removal of SIM tax


3.3.6 Scenario 4: removal of both the Taka 900 SIM tax and theTaka 300 handset

Removing both mobile specific taxes could result in significantly lower

connection a costs for mobile customers. Figure 3 shows the difference in theprojected subscriber base under the scenario where the mobile specific taxesremain in place, and the scenario where both taxes are removed18.

The figure shows that by 2009 the subscriber base is estimated to be almosthigher under the scenario with no mobile-specific taxes.

18 In the main report we explain that the tax simulation model created for the analysis of tax changes isnot a forecasting model. Therefore, the projections presented in the figure should not be viewedas Frontier Economics forecast of subscriber growth going forward. The figure only serves toillustrate the potential drag effect that mobile-specific taxes can have on the growth of mobilepenetration in Bangladesh.


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Projected growth in mobile subscriber base

under different tax scenarios

0

5

10

15

20

25

30

35

40

45

50

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Mobilesubscribers

(millions)

0%

2%

4%

6%

8%

10%

12%

%d

ifferencebetween

subscriberbasewithtaxes

inplaceandfollowingthe

removaloftaxes

Base case (taxes remain in place) Simulation (no mobile specific taxes)

Difference (%, right axis)

Figure 13: Comparative growth in subscriber base following removal of mobile-specifictaxes


The impact of removing both mobile specific taxes on tax revenues is shown inFigure 4. The figure shows that, according to the simulation, in the initial periodfollowing the removal of both taxes overall tax revenue falls by as much as 18%.Revenues recover quite quickly after this point driven by the higher variable

taxes on increased penetration growth and higher overall levels of usage suchthat by 2009, the net effect on tax revenues is estimated to be zero. From 2009onwards, we see that tax revenues are estimated to be higher, following theremoval of the mobile-specific taxes.

It is important to point out that in simulating the impact of removing both of themobile-specific taxes considered here, we have assumed that the average cost of anew handset is Taka 4,000, and the average cost of a new connection is Taka2,500. These are the central case scenarios set out scenarios 2 and 3 above.


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Percentage change in tax revenues from mobile

following removal of mobile-specific taxes

-15%

-10%

-5%

0%

5%

10%

15%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Percentagechangeintaxrevenues

comparedwiththebasecase

(i.e.mobilespecifictaxesinplace

)

Initially, tax

revenues fall

following removal

of taxes

However, in later years

increased subscription

and usage have a

counterbalencing positive

effect on tax revenues

Figure 14: Percentage change in tax revenues following removal of mobile-specific taxes



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Conclusion

4 Conclusion

In concluding the research presented in this report it is important to re-iterateonce more that the projections presented in this report are the output from a tax

simulation model, and are subject to the assumptions that have gone into theconstruction of that model. The projections presented here do not representeconomic forecasts, and should not be viewed as such.

The counter-balancing tax revenue effects that we obtain under the taxsimulation scenarios that remove the mobile-specific taxes are based on plausibleassumptions that we have backed up with the available empirical evidence, bothfrom the operators and on the market more generally. If these assumptions wereto be changed, as we have illustrated in the report, then this could impactsignificantly on the output from the tax simulations.

The main findings are as follows:

Taka 900 SIM card tax for new connections

| We find that the introduction of the Taka 900 SIM tax for new connectionsin mid-2005 could be expected to have a significant impact on thedevelopment of the Bangladeshi mobile market. We estimate that by 2009there could be approximatelytwo million fewer subscribers compared withthe counter factual of no SIM tax. This translates to a 9% difference in theestimated subscriber base by 2009.

| We also find that the removal of the tax could actually boost tax revenues.We estimate that if the SIM tax was removed, tax revenues would actually be2.5% higher by 2009, and in certain cases up to 4.4% higher in 2009,compared with the situation with the SIM tax in-place. The net increase intax revenues is driven by the higher tax revenue from higher growth rate in

new connections and higher usage patterns due to the fall in mobileconnection costs associated. This counter-balances the fall in tax revenuesfrom the removal of the SIM tax. Figure 15 illustrates the impact ofremoving the SIM tax on tax revenues and subscriber growth in 2009.


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Conclusion

Removing SIM tax may boost subscriber numbers & tax revenues

0

5

10

15

20

25

30

35

Tax revenues (2006) Tax revenues (2009-base

case)

Tax revenues (2009-remove

handset tax)


fromm

obile(BDTbillions)




2006


2009, keep SIM tax - tax



2009, removing tax - tax


Taxes from usage:

BDT 12bn (62%)

Taxes from usage:

BDT 22bn (74%)

Taxes from usage:

BDT 26bn (82%)

Figure 15: The estimated effect on tax revenues and subscriber numbers from removalof SIM tax


Taka 300 import tax on new mobile handsets

| The Bangladeshi government currently taxes all new handsets that areimported to the country at a flat rate of Taka 300. We find that the removal

of this tax could also significantly boost subscriber numbers, compared withthe counterfactual of the import tax in place. We estimate that the subscriberbase could be up to 1.8% to 2.9% larger by 2009, approximately 400thousand to 600 thousand additional mobile subscribers, were this fixedimport tax to be removed. This is because one of the key requirements forbecoming a mobile phone subscriber is to actually buy a mobile phone.

Therefore, if the import tax is removed, then this pushes down the price ofbuying a mobile phone and therefore the overall cost of becoming a mobilesubscriber.

| The relatively high import tax prompts many consumers to buy mobilephones in either the grey or second hand markets. If the handset tax wasremoved, we project that not only will more handsets be sold, but a greater

proportion of these will be sold through legitimate means. In the medium-term, by 2009, we estimate that the fall in taxes following the removal of thefixed import tax is could be almost completely offset by higher taxes frommore subscribers (variable taxes and the SIM tax), higher overall usage andmore handset sales in the legitimate market (which are taxed according togeneral taxation, i.e. VAT at 15%). Figure 16 illustrates the impact ofremoving the handset tax on tax revenues and subscriber growth in 2009.


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Conclusion

Removing handst tax may boost subscriber numbers & tax revenues in the longer-term

0

5

10

15

20

25

30

35

Tax revenues (2006) Tax revenues (2009-base case) Tax revenues (2009-remove

handset tax)


fromm

obile

(BDTbillions)




2006


keep handset tax - tax



removing tax - tax


Taxes from usage:

BDT 12bn (62%)

Taxes from usage:

BDT 23bn (78%)

Taxes from usage:

BDT 22bn (74%)

Figure 16: The estimated effect on tax revenues and subscriber numbers from removalof the handset import tax


Removal of SIM tax and handset import tax

We have also considered the potential impact on subscription growth and taxrevenues from the removal ofbothmobile specific taxes considered in this report

the SIM tax and the handset import tax. The main findings from the taxsimulation are as follows:

removing the two major mobile specific taxes facing consumers mayprovide a strong boost for growth in the mobile subscriber base inBangladesh. We estimate that with the space of approximately two- tothree-years, the number of mobile subscribers in Bangladesh could beover 11% higher without the mobile-specific taxes, compared with thecounterfactual of those taxes remaining in place.

as we might expect, the immediate effect of removing the mobile specifictaxes is to reduce the tax take from mobile by approximately 18%.However, the counter-balancing effect from added subscriber growth,

and the associated overall higher usage, quickly acts to offset thisdecrease. From 2010 onwards, tax revenues are projected to be abovethelevel they would be with the taxes in place.


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Annexe Statistical modelling and regression results

Annexe Statistical modelling andregression results

STATISTICAL MODELLING TECHNIQUES

The following data was provided by each of the operators:

Subscriber numbers quarterly/monthly growth in subscriber numbersback to 2000, where applicable.

Minutes of usage data quarterly/monthly MOU data, and in somecases split by pre-pay and post-pay;

Revenue data quarterly revenue data, split by pre-/post-pay andrevenues from connection/subscription and usage

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