Measuring ICT Sector Performance

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Dr. Christoph Stork

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Measuring ICT Sector Performance

  1. 1. Measuring ICT Sector Performance Dr. Christoph Stork
  2. 2. Research ICT Africa Year Project African Countries 2003 2004 2005 2006 2007 2009 ICT Sector Performance Review 7 Household e-Access & e-Usage Survey 10 SME e-Access & e-Usage Survey 15 ICT Sector Performance Review 17 Household e-Access & e-Usage Survey 17 ICT Sector Performance Review 18
  3. 3. ICT Sector Performance
  4. 4. Tools Supply Side: Annual reports from Operators OECD Price Baskets National Accounts Demand Side User Surveys (Household, SMEs, Institutions) Telecom Regulatory Environment Survey (TRE)
  5. 5. Impact of regulatory interventions Demand Side Supply Side Access UsageUsage Affordability Operator Performance Operator Performance Operator Performance Operator Performance Economy Ownership (rural urban, gender, income) Subscribers Expenditure (total, share of disposable income) Average revenue per user (ARPU) No of SMS send and minutes called Minutes of Use (MOU) Price elasticity WTP OECD Price baskets ARPU/MOU Investment EBITDA Margin Return on Equity Revenue Business Survey GDP contribution and employment National accounts GDP contribution and employment
  6. 6. Tool: Surveys Untapped demand: WTP of non-users Income elasticity of demand of users Multiple SIM card ownership Internet adoption: with focus on mobile internet Mobile money transfer adoption and m-banking Employment generation and GDP contribution of SMEs and informal operators ICT access function of public institution
  7. 7. Nationally Representative Surveys
  8. 8. 2007/2008 Current market in US$ million Monthly untapped market in US $ million Untapped market as percentage of current market Nigeria* South Africa Namibia Mozambique Botswana Kenya Zambia* Senegal Ghana Côte d'Ivoire Cameroon Tanzania Benin Burkina Faso Ethiopia 686.54 65.25 9.5% 320.49 36.27 11.3% 7.14 1.35 19% 30.47 6.7 22% 6.67 1.47 22.1% 112.11 25.69 22.9% 25.96 8.2 31.6% 27.54 11.33 41.2% 78.23 38.4 49.1% 63.13 31.44 49.8% 21.29 13.14 61.7% 30.79 21.42 69.6% 11.38 8.26 72.6% 10.77 13.71 127.3% 5.29 25.68 485.7% Willingness and ability to spend on communication of none-users exceeds current market in some countries AFRICANS are price sensitive and will talk more if prices are lowered Mobile Expenditure and WTP
  9. 9. Namibia’s digital divide at a household level (RIA 2004 & 2007 surveys) ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Households with working... NamibiaNamibiaNamibia UrbanUrbanUrban RuralRuralRural Urban Rural Difference Urban Rural Difference Urban Rural Difference Households with working... 2004 2007 2004 2007 2004 2007 2004 2007 Electricity 34% 47% 78% 81% 6% 24% 72% 57% Fixed-line 13% 17% 31% 32% 2% 8% 29% 24% Radio 77% 73% 88% 82% 70% 66% 18% 16% TV 31% 38% 71% 72% 5% 15% 66% 57% Computer 5% 11% 11% 23% 0.4% 4% 11% 19% Internet 2% 3% 4% 7% 0.0% 1% 4% 6%
  10. 10. Tool: OECD Mobile Basket Methodology OECD Basket Methodology Comparing Countries Comparing Operators Comparing Products
  11. 11. Comparing Countries Comparing cheapest product available from dominant operators cheapest operator most expensive operator Comparing the difference between cheapest in country - cheapest from dominant operators cheapest in country - cheapest from most expensive operator
  12. 12. Ghana Tanzania Kenya Nigeria Ethiopia* Rwanda Benin Botswana Tunisia Namibia Senegal Uganda Zambia Côte d’Ivoire Mozambique South Africa Cameroon Burkina Faso 11.04 8.59 7.64 7.45 7 6.57 6.33 6.12 5.06 5.06 5.04 4.92 3.74 3.74 3.63 3.35 2.93 2.29 Ghana Tanzania Kenya Nigeria Ethiopia* Rwanda Benin Botswana Tunisia Namibia Senegal Uganda Zambia Côte d’Ivoire Mozambique South Africa Cameroon Burkina Faso 12.54 9.3 7.64 7.45 8.15 6.6 6.95 6.12 8.96 5.06 5.04 7.5 6.87 3.74 7.76 5.93 7.26 3.04 Cheapest operator Dominant operator Ghana Tanzania Kenya Nigeria Ethiopia* Rwanda Benin Botswana Tunisia Namibia Senegal Uganda Zambia Côte d’Ivoire Mozambique South Africa Cameroon Burkina Faso 12.54 9.3 10.36 8.32 9.54 8.18 7.04 7.52 8.96 5.36 6.66 8.81 6.87 3.74 7.76 5.93 7.26 3.15 Most expensive operator Cheapest Prepaid product in country in US$ Low OECD user basket
  13. 13. Example: Towards Cost based Mobile Termination Rates Namibia, Kenya, South Africa
  14. 14. Cost based Mobile Termination rates Economics: Increased Competition Lower Retail Prices More Investment Better Sector Performance Dominant Operators Argue: Higher retail prices Lower Profits Less investment
  15. 15. Cheapest product available of incumbent (MTC) in Namibia Low User Medium User High User 106 3636 146 5050 179 119 79 296 174 83 N$/ZAR Sep 2005 Dec 2008 May 2010 May 2010 (2005 prices)
  16. 16. Performance of incumbent mobile operator in Namibia: MTC 2005 2006 2007 2008 2009 Subscribers 403,743 555,501 743,509 1,008,658 1,283,530 EBITDA Margin 61% 60.2% 52.2% 50.9% 53.8% After tax Profit million N$ 292.9 337.2 339.6 356.2 387.5 Dividend paid in million N$ 110 80 245 221 370 Base Stations 250 (2004) 763 Investments announced into 4G LTE and WACS (N$400 million)
  17. 17. Regulation of Market Entry Scarce Resources Interconnection & facilities leasing Tariff Regulation Regulation of Anti-Competitive Practices Universal Service Obligation (USO) QoS Regulation Average -2 -1 0 1 2 -0.5 -0.3 -0.6 -0.6 -0.7 -0.2 -0.3 -0.2 -1.2 -1.1 -1.7 -1.4 -1.1 -0.8 -0.9 2006 2009 Telecommunication Regulatory Environment (TRE)
  18. 18. Example Kenya August 2010: Most innovate Interconnection ruling in Africa: Cost based termination rates (pure LRIC) Off-net=On-Net prices for Dominate operator Fair commercial agreement on SMS and money transfer interconnection or else...
  19. 19. Safaricom Zain Orange 52.6 67 11.3 54.2 66.8 9.9 55.2 66.6 8.9 OECD Low OECD Medium OECD High Immediate price drop in %
  20. 20. Lowercross-net tariffshitVodacom Operator'sreuenttetakesRB00mknock THABISOMOCHIKO InformationTechnologyEditor THEvoluntaryreductionin inter- connectlonrates to 89c from R1,25in MarchhaswipedR800m offVodacom'srevenuefor the si,x monthsto September. Theterminationrates- which operatorspaytocarryeachother's peak in March 2012,a process calleda glidepath. Chief financial officer Rob ShutersaidVodacomwillcontinue looking for ways to offset the expectedreductionin earnings. Vodacom'sservicesrevenue rose4,4o/oto R26,09bnwhiletotal revenuerose 5,17oto R29,5bn. However,a 41,1o/ogrowthin data Q5ltt / Z"'io (Svt ?Il]?"Tyfryg,!9.aaco1CEO.P|eterUysspeaksatVoi-a.ini'r-t.ioq'rrrtersinMidra afthoughtheterminationrateisaffectingearnings,retrenchmentsarenotontheia;;; ;l;i al ;;if=B ;+ Example South Africa
  21. 21. Vodacom Interim Results Vodacom South Africa September 2009 September 2010 Subscribers in million ARPU ZAR MOU APRU/MOU Operating Profit Rm Revenue Rm 28.2 23.87 125 162 78 105 1.6 1.54 6,841 7,170 24,371 25,697
  22. 22. Conclusion Assessing sector performance requires a set of tools Combination of demand side and supply side data is crucial to: Measure regulatory impact Monitor policy objectives Protect consumer interests Provide policy advice

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