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1st International Workshop on System Dynamics, Nairobi, Kenya, 2015

Title: A System Dynamics Regulatory Tool for the Telecom Sector

Discipline: Information Systems, Decision Support Systems (Telecommunication Policy)

J. WALUBENGO, T. Waema, D. Williamsjwalubengo@mmu.ac.ke,

Jan 2015

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Chapter 1: Introduction

Chapter 1:-Intro Summary

Background

Problem Definition

Research Aim+Objectives

Research Questions/Dynamic Hypothesis

Scope of Study

Significance of Study

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1.1 Background(a)

Cost of Telco (Internet Access) Prices remains high despite the landing of the submarine cables, liberalization.

Internet Price “NOT Affordable” based on the fact that monthly internet rates i.e. cost of 1Mbs of Internet Access per month is 44% of Average incomes (ITU, 2014)

It is 2.5%, 2.8%, 6.0%, 17.4%, 32% for Egypt,, S.A,Bostwana, Nigeria & UG. respectively (ITU, 2014)

1.1 Background(b)Fixed Broadband Prices, %GNI

ITU, 2014

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1.2 Problem Definition

The eventual Price of Telco/Internet Access is determined by various parameters including but not limited to, Suppliers, Market Size, Domestic Infrastructure, Regulation etc (Dutta & Roy 2004)

Whereas factors affecting Telco/Internet Pricing are known, less is known about how this price holistically impacts the rest of the Telco sector over time.

Problem Area: IS/Decision Support

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1.3 Research Aim+Objectives(a)

Aim: To develop a Telco Regulatory tool to manage the Telco/Internet Sector.

Objectives

1. To identify, describe and estimate the relationships between the factors influencing Telco/Internet Price in Kenya (Causal Loop Diag)

2. To identify the dominant/key feedback loops in the above relationships in order to determine critical points for policy and strategic intervention with respect to Telco/Internet Prices.

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1.3 Research Aim+Objectives(b)

3. To provide a time-based Model describing the dynamic interplay between the various factors that Influence Internet/Telco Pricing in Kenya (SFD).

4. To Simulate and Test the Proposed Model (Conceptional Test, Data Test and Validity Test as by (Martis 2006),

5. To automate the Proposed Model in order to provide a "What-If-Analysis" tool for Stakeholders (Policy Shapers, Operators, Regulators, Consumers)

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1.4 Research Q/Hypothesis (a) RQ: What are the Structures behind the

Dynamic Behaviour of the Internet Price Variables in Kenya? What are the dominant loops and time-delays?

Proposed Dynamic Hypothesis for testing & validation is based on the following:

High prices depresses Demand

High prices (Revenue) attracts more Suppliers/Operators

Increased Supply (Competition) drives down prices

Reduced prices increase uptake but increase Congestion

Congestion drives away Consumers/Customers

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1.4 Research Q/Hypothesis(b)

Demand 1a

Demand 1b

Supply 1a

Supply 1bPrice Axis

Price 0

Qty 0 Quantity Axis

Pe1(equilib) Equilibrium Point1

Qty1 (equilibrium)

Economic Theory of Price-Supply

Demand Curves Informed by:

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1.5 Scope

Regulatory role ensures sustainable equilibrium between Competition, Usage and Investment in Telco sector

Limited to Variables of Regulatory Interest

Key Regulatory parameters/indicators Include Price(Affordability), Revenues, Competition, Network Capacity, Quality of Service(QoS), Market Penetration.

Key stakeholder Includes:-Operators, Regulators, Users, Govt

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1.6 Significance

Expected Model would provide regulators with new insights on the dynamics of Telco/internet market with Pricing as the key driver.

Model would provide a cheaper mechanism for stakeholders to experiment (Forrester 1991) by subjecting different parameters and policies to the model and viewing the impact in a “lab” environment

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Chapter 2: Literature Review

Chapter 2:-Summary

Background

Exisiting Models Reviewed

Dutta (2001),

Jain(2003),

Dutta(2004)),

Walrand(2008),

Rouskas(2009),

ITU (2014)

2.0 Background

• Most economies moved from Monopolistic Telco environments to Liberalized environments

• Regulators have to manage different stakeholders often with diverse and conflicting interest.

• Operators want to maximize Profits(Operator Surplus), Users want to maximize Utility(User Surplus)

• Regulator must sustain both interests by maximizing the Social Surplus(Operator+User).

• Literature shows Price is the key denominator for each stakeholder

• Several pricing methods (LRIC, Ramsey, etc) exist but not focused on holistic impact of pricing in the Telco sector

2.1 Holistic View Models(Dutta, 2001)

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2.2: Holistic View Models (Jain 2003)

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2.3 Holistic View (Dutta 2004)• Dutta & Roy (2004) described how Internet Access

Price affects the Internet Diffusion as shown below

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2.4: Holistic View Models(Walrand 2008)

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2.5 Holistic View Models(Rouskas, 2009)

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2.5 Holistic View Models (ITU, 2014)

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Chapter 3: Research Methodology

Chapter 3:-RM

The System Dynamic Approach

What is System Dynamics(SD)

What are SD benefits

What are SD components

What are SD key archetypes and behaviour

What archetype suits Internet Price behaviour

Where has SD been applied in Internet Studies

3.1 System Dynamics ApproachVogstad (2004) describes & uses the following:

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3.2 System Dynamics Research Design

1. Using Existing Literature, identify the factors affecting Internet Pricing in general to built an Initial Model based on the System Dynamics Approach.2. Propose Model Structures based on observed behaviour or patterns of Key Variables (Ref Mode). Create and enhance Causal Loop Diagrams (CLD) from data collected.3. Simulate the Model by deriving a Stock and Flow that mathematically defines the relationships.4. Test and Validate the model (Martis 2006)5. Policy Design: Model the dynamic impacts of various possible Interventions (What if Analysis) on the Internet Price as desired by different Stakeholders (e.g. Policy,Regulatory, Competition, Usage etc)

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3.3

Dyn

am

ic S

ynth

esis

Resea

rch

Meth

odo

logy(W

illia

ms,

20

02

)

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3.4 What is System Dynamics

System dynamics is a powerful methodology and computer simulation modeling technique for framing, understanding, and discussing complex issues and problems.

It applies to dynamic problems arising in complex social, managerial, economic, or ecological systems -- literally any dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality. (System Dynamics Society)

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3.4 What are SD Components- Vogstad (2004)

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3.4 Characteristics of Dynamic Systems(1) Cause and effect are separated by a lot more time and

distance than you think. Time delays greatly complicate the effort to firmly connect cause with effect - particularly when there are multiple causes and multiple effects.

The more you try to improve the system, the stronger the resistance to change. This is the result of "balancing loops" - forces that counteract changes.

There are unintended consequences and indirect effects. Sometimes the most obvious solution makes a problem worse.

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3.4 Characteristics of Dynamic Systems(2)

Things can get worse before they get better.

Reinforcing Loops amplify small changes into big changes.

SD stresses that there is no single right answer/model. There are often trade-offs that require choices. (Sterman 2002)

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3.5 What are the Key SD Archetypes/Behaviour

Exponential Growth/Decline Structure

Goal-Seeking Structure

Oscillating Structure

S-Shaped Growth

-According to Kirkwood 2010, SD Methods – A quick Introduction

3.6 Archetype:-Exp Growth/Decline

Positive (Reinforcing Loop) – Kirkwood C. (2010)

3.7 Archetype:-Goal-Seeking

Negative (Balancing Loop) – Kirkwood C. (2010)

3.8 Archetype:-Oscillating

Negative Loop with Delay (Osc. Loop) – Kirkwood C. (2010)

3.9 Archetype:-S-Shaped GrowthCombined +ve & –ve Loops – Kirkwood C. (2010)

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3.10 What Archetype suits the Internet Price behavior?

Since the Internet Prices are dropping, they imply a Goal-Seeking behaviour and that is what will be used to create the initial draft model for Internet Price dynamics

Internet Price Pattern could also signify the reverse S-Shaped behaviour that can also be explored further.

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4.0 Proposed Model

Trends in Internet Pricing(Reference Mode)

Dynamic Hypothesis

Key Loops

Stock Flow Diag(SFD):Customer, Finance & Network View – Dutta (2001)

Regulatory view – Rouskas et al(2009)

SD Regulatory view –Author (2014)

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4.1 Trends in Internet Prices

• Reference Mode (ITU, 2014)

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4. 2 Dynamic Hypothesis

Current PriceX

Price Change

+

-

Ideal Price

Gap

Reducing Rate

-

+

++Competition

(Supply)

TotalNo

Customers(Demand)

Market Size

Network

Perf(Congestion)

+

+

-

-

Revenues +

Network Capacity+

-

-

-

+

+

Regulation

-

+

Price & Competition, Revenues,

Capacity, Network Performance

B1

B2

B3

B4

B5

Expansion Delay

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4.3a Dynamic Hypothesis-Key Loops

Loop Number 1 (Balancing Loop1, B1): Current PriceX -> Price Change

An increase in the Current Price for telecommunication service, potentially provides an opportunity to change the price in equal measure. A high price is awaiting reduction as soon as sufficient competition builds up.

Loop Number 2 (Balancing Loop2, B2): Current PriceX->Gap->Reducing Rate -> Price Change

The Ideal Price from a regulatory point of view is one that is affordable (%of GNI) to the consumers. The difference between the Current Price and the Ideal Price creates a Gap. The size of the Gap dictates the rate of reduction required to bring the Current Price towards the Ideal Price.

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4.3b Dynamic Hypothesis-Key LoopsLoop Number 3 (Balancing Loop3, B3): Current PriceX-> Competition(Supply)-> Reducing Rate ->Price Change

The balancing loop3, B3 describe the supply side of the telecommunication sector. An increase in the telecommunication prices attracts more competitors into the market, thus increasing suppliers and competition. According to the classic supply curve, Competition puts pressure on the prices, pushing them down towards the Ideal Price.

Loop Number 4 (Balancing Loop4, B4): Current PriceX-> TotalNoCustomers(Demand) ->Reducing Rate -> Price Change

The balancing loop4, B4, describes the demand side of the telecommunication sector. An increase in the telecommunication prices depresses demand leading to reducing number of users or customers. However, since the high price attracts more players(competition), the current price is eventually moved downwards towards the ideal price.

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4.3c Dynamic Hypothesis-Key Loops

Loop Number 5 (Balancing Loop5, B5): Current PriceX ->Revenues -> Network Capacity -> Network Perf(Congestion) -> TotalNoCustomers(Demand) ->Reducing Rate -> Price Change

The balancing loop5, B5, describes the Network Performance (Congestion) side of the telecommunication sector. Specifically, the regulator is concerned about the quality of service(QoS) the operators are providing in the market. Essentially, higher prices fetch better revenues, enabling operators to improve capacity and QoS of their networks. Better Network performance leads to a higher number of customers (demand) which eventually overloads or congests the network.

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4.4 High-Level Customer, Revenue, Capacity-Dutta, 2001

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4.4 SFD, Customer View (a)

Customer View

TotCustDeparting

Customers

CurrentQualRep

QualityRep

Perception

Delay PercievedQual

DelayTime1<NetworkPerf>

ServiceQuality

Quality Effect on

NewCustomersQuality Effect on

DepartingCustomers

Initial

NoCustomers

Normal Attrition

Normal

Subscription

<PriceX>

Market SizeNewCustomers

Max Price

Effect on

MarketSize

Internet Market

Penetration

Initial Market Size

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4.4 SFD, Revenue View(b)Financial View

Cummulative

EarningsRevenues Expenses

Reinv$

ReinvRate

OpExpense

OpIncome

<TotCust>

<NetworkCapacity>

CapExp Network

Expension

<Initial Network

Capacity>

<PriceX>

<CostX>

<Projected No

Customers>

Minimum No of

Customers

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4.4 SFD-Network Cap View(c) Network View

Network

CapacityCapExpansion

ImpDelay

AuthorizedCap InstalledCapNetUtil

AvgTraffic

NetworkPerf

<Reinv$>

<TotCust>

Initial Performance

Effect on Network

Performance

Effect on Authorized

Capacity

Initial Cost of

Capacity

DelayTime2

Initial Network

Capacity

Projected No

Customers

Projected

AveTraffic

Contention Ratio

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4.4 SFD-Regulator View (d)-Author(2014)

5.0 Simulated Results/Discussion

• Policy Test: Regulator wishes to appreciate long term impact of Low, Moderate and High Telco/Internet prices.

• Selected Market Scenario: The telecommunication market is assumed to be moderately competitive and without any investing in network capacity expansion.

• Regulatory Indicators: The performance of selected regulatory indicators of (Market Penetration, Network Performance (QoS) and Operational Income for the Provider) are observed over time for simulated

Internet price levels (Lower, Base and Higher).

5.1 Impact of Low Prices on Market

With Lower prices, the long-term (steady state) values increases

to (30%) for market penetration, while QoS (Network Performance) drops to 1% and Operating income rises to 90.

Selected Variables

0.4

1

100

0.2

0.5

50

0

0

0

0 12 24 36 48 60 72 84 96 108 120

Time (Month)

Internet Market Penetration : Lower Price_InitCust

NetworkPerf : Lower Price_InitCust

OpIncome : Lower Price_InitCust

5.2 Impact of Moderate Prices on Market

With Base prices, the long-term (steady state) values drops to

(20%) for market penetration, while QoS (Network Performance) increases to 4% and Operating income rises further to 100

Selected Variables

0.4

1

200

0.2

0.5

100

0

0

0

0 12 24 36 48 60 72 84 96 108 120

Time (Month)

Internet Market Penetration : Base Price_InitCust

NetworkPerf : Base Price_InitCust

OpIncome : Base Price_InitCust

5.3 Impact of High Prices on Market

With Higher prices, the long-term (steady state) values drops to (5%) for market penetration, while QoS (Network Performance) increases to 100% and Operating income drops slightly to 70.

Selected Variables

0.2

2

80

0.14

1

60

0.08

0

40

0 12 24 36 48 60 72 84 96 108 120

Time (Month)

Internet Market Penetration : Higher Price_InitCust

NetworkPerf : Higher Price_InitCust

OpIncome : Higher Price_InitCust

5.4a Impact on Market –Summary View

The steady-state values represents the dynamic system in equilibrium. These values are often arrived at after the system has ran for a reliable period of time(10yrs) and therefore a good basis for decision making. The steady state values for the telecommunication system without capacity expansion is shown above.

Internet PriceLower Base Higher

Market Penetration 30 20 5

Network Perfomance(QoS) 10 40 100Operational Income 90 100 70

5.4b Impact on Market –Summary View

Both pictures show that for a moderate competitive environment, the operational income does not change significantly across the various price ranges. The telecommunication providers are therefore better of providing the service at a higher price given that this gives them similar income while avoiding poor network performance (congestion) that will arise from offering lower prices.

Market Penetration Network Perfomance(QoS) Operational Income

0

20

40

60

80

100

120

Regulatory Performance Measures

Steady State View

Lower

Base

Higher

Regulatory Indicator

%

5.5 Conclusion

Based on “steady-state” (long-term) simulated values for the selected regulatory indicators (Internet Penetration, Network Performance (QoS), & Operational Income), it is demonstrated that lowering prices for internet services has a significant impact on the uptake meaning that Internet services have a high price elasticity of demand. It is however demonstrated that the Operational Income for the Telecom provider is not significantly different across the different price ranges (Low, Base & High).

This means that the Telecoms provider would rather maintain higher prices since they fetch similar incomes compared to lower prices but without the network congestion that comes with lower prices.

These are however preliminary results since the proposed model is yet to be validated by the operators and regulators.

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5.6 Future Work

A potential area of improvement would be to use the Bass Diffusion model to fine tune the internet market adoption/penetration aspect of the proposed model. Potential policies that can be tested in future include whether or not sharing telecommunications infrastructure will lead to a better market outcome.

Either way, the paper demonstrates the way in which the regulators can improve their policy decisions by subjecting them through the model and seeing the futuristic impacts within a virtual, simulated environment.

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6b. ReferencesMayoka, Kituyi, Moya Musa, Agnes S. Rwashana, and Angnes Rwashana. 2012. “A System

Dynamics Pricing Model for Stabilizing Prices for Telecommunication Products and Services in Uganda.” http://www.crownjournal.org/Geoffrey%20et%20al.pdf.

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7. AppendixSystem Dynamics Equations for the Model

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Q&A

End