Ict For Development 2

ICT for Development ICT4D Dr. Christoph Stork 1

researchICTafrica Research network of universities and think tanks in 20 African countries No African Year Research title countries 2003 ICT Sector Performance Review 7 2004 Household e-Access & e-Usage Survey 11 2005 SME e-Access & e-Usage Survey 14 2006 ICT Sector Performance Review 17 Household e-Access & e-Usage Survey 2007 17 (with focus poverty and demand elasticities) 2

TOC SME e-Access & Usage (why ICT matters) Access & Prices (policy and regulation makes a difference) Impact of Competition in Namibia (how ICTs can help) 3

Small and Medium Enterprise e-Access & Usage 4

5

BACKGROUND • Aims: • Looking at the impact of ICTs, • Identifying obstacles and • Providing guidance for policy recommendations • SME sector is the sector in which most of the world’s poor are working and it contributes signiﬁcantly to economic growth and employment • No random sampling: qualitative interviews, 3967 SMEs across 14 countries, 280 each • Intensive training of enumerators for them to understand every single participating business 6

Distinguishing by formality • Form of ownership? • Is your business registered with the Receiver of Revenues? (pay taxes?) • Is your business registered for VAT? • How many of your employees have a written employment contract? • Does your business strictly separate business from personal ﬁnances? • Does your business keep ﬁnancial records? 7

Access to ICTs by formality Fixed Line Phones Informal 100% Semi formal Formal 80% 60% Internet Connection Mobiles 40% 20% 0% Computer Fax Post Box 8

ICT perceptions: ICTs are important or very important! Fixed Line Phones Don't have it 99% Have it 61% Internet Connection Mobiles 95% 99% 41% 71% 31% 52% 26% Computer Fax 98% 95% 83% Post Box 9

The more formal a SME is the more ICTs it has and uses. Usage intensity is the same (access/usage) Formality N Mean Rank Chi-Square df Asymp. Sig. Informal 1606 1275.4 ICT Semi-formal 1234 2112.36 Possession 1327.61 2 0 Formal 1126 2852.24 Index Total 3966 Informal 1606 1361.15 Semi-formal 1234 2069.24 ICT Usage 1034.54 2 0 Index Formal 1126 2777.19 Total 3966 Informal 1606 1989.19 ICT Usage Semi-formal 1234 1962.71 Intensity 0.64 2 0.726 Formal 1126 1998.17 Index Total 3966 10

Turnover and ICT expenditure = significantly and positively correlated across sector! Correlation coefficients that are significant at the 0.01 level D: Manufacturing 0.483 F: Construction 0.808 G: Wholesale and retail trade; repair of motor vehicles, motorcycles and 0.736 personal and household goods H: Hotels and restaurants 0.219 I: Transport, storage and communications 0.99 J & K: Financial intermediation & real estate, renting and business activities 0.544 M & N & O: Education, health, social work, other community, social and 0.905 personal service activities 11

Informal business operate on a higher profit margin Mean Chi- Asym Ranks Formality N df Rank Square p. Sig. Informal 1590 2081.59 Profit Semi- 1230 1913 margin: formal after tax 26.051 2 0.000 profits Formal 1120 1875.94 divided by turnover Total 3940 12

Informal businesses are more profitable Mean Chi- Asym Ranks Formality N df Rank Square p. Sig. Informal 1504 2020.61 Profitability: Semi- after tax 1139 1761.75 formal profit 70.846 2 0.000 divided by total fixed Formal 1048 1686.98 assets Total 3691 13

Formal businesses have higher labour productivity Mean Chi- Asymp Ranks Formality N df Rank Square . Sig. Labour Informal 1571 1546.48 productivity: Value added Semi- (Sales minus 1223 1968.59 direct costs, formal rent, water, 479.988 2 0.000 electricity etc.:) divided by full- Formal 1114 2514.43 time employees including owners that manage the Total 3908 business 14

Formal businesses re-invest more Mean Chi- Asymp. Ranks Formality N df Rank Square Sig. Informal 1559 1834.37 Re- investment Semi-formal 1217 1908.94 rate: Invest 44.438 2 0.000 ment divided Formal 1100 2118.79 by ﬁxed assets Total 3876 15

Turnover or Sales Model F1 F2 F3 F4 F5 F6 = β1 + β 2 + β3 + β4 + β5 + β6 +ε FA FA FA FA FA FA F1= Turnover F2= AVERAGE water, electricity, cost F3= AVERAGE cost for your premises in terms of rent, land taxes mortgage payments F4= AVERAGE business expenditure on telephone calls, fax, postage, Internet F5= AVERAGE Wage Bill F6= AVERAGE Direct Cost (raw materials and other intermediary inputs or goods bought for resale) FA=Total value of ﬁxed assets 16

ICT expenditure contributes significantly to higher sales Formal Semi-formal Informal Robust regression of turnover function N 1048 1139 1504 R Square 0.7775 0.9199 0.9481 F 74.39 208.58 193.52 Sig. For equation 0 0 0 Mean Variance InflationFactor (VIF) 1.5 1.82 1.19 Unstandardized Coefficients 3.93 2.77 51.28 for ICT Usage Expenditure Sig. of ICT Usage Expenditure 0.000 0.000 0.000 17

Labour Productivity • V= Value Added • W= AVERAGE Wage Bill • ICTU= ICT Usage Index • ICTP = ICT Possession Index • EA=Full-time employees + owners that manage the business • W/EA is hence the average wage and V/EA labour productivity 18

Access to ICTs is linked to higher labour productivity N R Square F Sig. Mean VIF 3908 0.5695 32.21 0 1.01 Unstandardized t Sig. VIF Coefﬁcients (Constant) -21836.49 -2.32 0.021 Average Wage 5.641971 7.75 0 1.01 ICT Possession Index 12284.54 2.6 0.009 1.01 19

Usage of ICTs is linked to higher labour productivity N R Square F Sig. VIF 3908 0.5701 30.69 0.0000 1.02 Unstandardized t Sig. VIF Coefﬁcients (Constant) -25785.1 -1.73 0.083 Average Wage 5.64 7.81 0 1.02 ICT Usage Index 7659.58 2.24 0.025 1.02 20

No doubt! ICTs help SMEs to become more proﬁtable 21

Main Obstacle to ICT adoption remains high cost informal semi formal formal average Network problems / unreliable infrastructure 11.3% 11.7% 10.5% 11.2% Lack of ﬁnancial resources 10.6% 4.5% 7.3% 8.0% Lack of awareness & knowledge of ICTs 10.3% 8.4% 10.5% 9.7% High cost, too expensive 55.6% 60.8% 58.8% 57.9% Lack of skills & ICT illiteracy 2.8% 7.4% 6.9% 5.1% No need 9.5% 7.2% 6.1% 8.0% 22

Conclusion Part 2 • Mobile phones are the most used tools in supporting the running of SMEs • Designing mobile ﬁnancial applications to integrate informal SMEs into the formal economy (for example formal ﬁnancial services) are promising avenues • The main constraint to ICT usage remains high investments and us age costs • Hence, effective regulations and policies that enable a competitive ICT environment will facilitate economic growth, employment and social inclusion - in particular for the poor 23

ICT Access & Prices 24

Access to ﬁxed-line phones 2006 Fixed-line Subscribers per 100 inhabitants 9.93 South Africa 8.75 Botswana 7.09 Uganda 6.84 Namibia 2.23 Senegal 1.48 Ghana 1.40 Ivory Coast 1.02 Benin 0.97 Ethiopia 0.93 Burkina Faso 0.84 Kenya 0.82 Nigeria 0.78 Zambia 0.65 Cameroon 0.41 Tanzania 0.38 Mozambique 0.24 Rwanda Source: ResearchICTafrica.net, (population based on IMF data) 25

Access to mobile phones 2006 Mobile Subscribers per 100 inhabitants 68.15 South Africa 57.54 Botswana 27.51 Cameroon 26.86 Namibia 19.05 Kenya 16.88 Nigeria 14.97 Mozambique 14.55 Tanzania 14.49 Senegal 12.59 Ghana 11.31 Benin 9.30 Zambia 8.22 Ivory Coast 6.73 Uganda 5.17 Burkina Faso 2.94 Rwanda 1.15 Ethiopia Source: ResearchICTafrica.net, (population based on IMF data) 26

OECD Usage Baskets Minutes or units Low User Medium User High User Cell2Cell own Network Peak 6.91 15.60 39.48 Cell2Cell own Network Off Peak 3.60 7.49 12.50 Cell2Cell own Network Off Off Peak 3.17 7.49 17.11 Cell2Cell other Network Peak 4.32 10.08 27.72 Cell2Cell other Network Off Peak 2.25 4.84 8.78 Cell2Cell other Network OffOffPeak 1.98 4.84 12.01 Cell2Fixed Peak 3.17 6.83 16.80 Cell2Fixed Off Peak 1.65 3.28 5.32 Cell2Fixed Off Off Peak 1.45 3.28 7.28 SMS Peak 16.16 25.33 33.60 SMS Off Peak 8.42 12.16 10.64 SMS Off Off Peak 7.41 12.16 14.56 27

2006 Mobile Nominal Usage Costs 2006 Low OECD User Basket - cost in US$ using nominal end of 2006 exhange rates 12.5 Nigeria 10.9 South Africa 10.6 Kenya 10.2 Côte d'Ivoire 9.7 Burkina Faso 9.6 Namibia 8.6 Cameroon 7.9 Zambia 7.6 ozambique 7.4 Benin 7.3 Senegal 7.3 Uganda 7.0 Botswana 6.5 Ghana 5.8 Tanzania 5.6 Rwanda 2.2 Ethiopia 28

2006 Mobile PPP Usage Costs 2006 Low OECD User Basket - cost in US$ using implied PPP conversion rates 36.2 Uganda 32.4 Mozambique 30.8 Ghana 30.5 Rwanda 29.2 Burkina Faso 27.5 Namibia 27.2 South Africa 20.5 Kenya 20.0 Nigeria 18.4 Botswana 18.4 Cameroon 18.4 Senegal 17.9 Côte d'Ivoire 16.3 Benin 14.3 Tanzania 13.3 Ethiopia 11.6 Zambia 29

2006 Fixed-line Nominal Usage Costs Cost of a local 1 minute call (peak rate)- cost in US$ using end of 2006 nominal exchange rates 0.15 Burkina Faso 0.12 Côte d'Ivoire 0.12 Mozambique 0.11 Kenya 0.10 Tanzania 0.10 Cameroon 0.10 Uganda 0.07 South Africa 0.07 Rwanda 0.06 Senegal 0.05 Namibia 0.05 Ghana 0.05 Nigeria 0.05 Botswana 0.05 Zambia 0.04 Benin 0.00 Ethiopia 30

2006 Fixed-line PPP Usage Costs Fixed-Line: Cost of a local 1 minute call (peak rate) cost in US$ using implied PPP conversion rates 0.49 Mozambique 0.48 Uganda 0.46 Burkina Faso 0.39 Rwanda 0.26 Tanzania 0.25 Ghana 0.21 Kenya 0.21 Cameroon 0.21 Côte d'Ivoire 0.19 South Africa 0.16 Namibia 0.15 Senegal 0.13 Botswana 0.09 Benin 0.08 Nigeria 0.07 Zambia 0.02 Ethiopia 31

2006 Fixed-line Nominal Usage Costs Cost of a local 3 minute call to US (peak rate)- cost in US$ using end of 2006 nominal exchange rates 4.77 Zambia 3.90 Burkina Faso 3.42 Ethiopia 3.00 Rwanda 2.41 Kenya 2.20 Namibia 2.14 Tanzania 1.81 Cameroon 1.54 Uganda 1.45 Benin 1.15 Botswana 1.02 Mozambique 1.01 Ghana 0.90 Senegal 0.90 Côte d'Ivoire 0.52 South Africa 0.35 Nigeria 32

2006 Fixed-line PPP Usage Costs Fixed-line: Cost of a 3 minute call to the US (peak rate) cost in US$ using implied PPP conversion rates 20.7 Ethiopia 16.5 Rwanda 11.7 Burkina Faso 7.7 Uganda 7.0 Zambia 6.3 Namibia 5.3 Tanzania 4.7 Ghana 4.7 Kenya 4.3 Mozambique 3.8 Cameroon 3.2 Benin 3.0 Botswana 2.3 Senegal 1.6 Côte d'Ivoire 1.3 South Africa 0.6 Nigeria 33

Conclusion Access • Access and usage varies considerably across Africa • Usage costs vary equally • Link between Tele-density and price is not straight forward: GDP per capita, competition in the sector, market structure, policies, regulation are all important factors 34

Affect of Competition in Namibia 35

Nominal Prices Nominal cost of OECD Usage Baskets in N$ Cheapest MTC September 2005 Cheapest MTC October 2007 296 Cheapest Switch October 2007 Cheapeast Cell One October 2007 250 228 210 174 147 106 101 83 70 51 48 Low User Medium User High User 36

Real Prices Real cost of OECD Usage Baskets in N$ (September 2005 prices) Cheapest MTC September 2005 296 Cheapest MTC October 2007 Cheapest Switch October 2007 Cheapeast Cell One October 2007 221 202 186 174 130 94 89 83 62 45 43 Low User Medium User High User 37

Price Change MTC MTC price change compared to September 2005 nominal prices real prices (in Sep 2005 prices) 85% 85% 84% 75% 75% 75% Low User Medium User High User 38

Price Change of Cheapest overall Overall price change (cheapest available in Namibia) compared to September 2005 nominal prices real prices (in Sep 2005 prices) 71% 63% 58% 58% 52% 51% Low User Medium User High User 39

Conclusion • Mobile telephony provides Africa with the additional economic growth that was experienced by OECD countries in the 80s by the deployment of ﬁxed line telephony. • Lower prices will increase access and usage and amplify this effect. • A competitive ICT sector is the only recipe for low prices and high service delivery. • Policy and regulatory environment are very important factors for establishing a competitive ICT sector 40

Ict For Development 2

0 comments

Notes on slide 1

Favorites, Groups & Events