ICT for DevelopmentICT4D

Dr. Christoph Stork

1

researchICTafrica

Research network of universities and think tanks in 20 African countries

Year Research titleNo African 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

2007Household e-Access & e-Usage Survey

(with focus poverty and demand elasticities)17

2

TOC

Access & Prices(policy and regulation makes a difference)

SME e-Access & Usage(why ICT matters)

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

0%

20%

40%

60%

80%

100%Fixed Line Phones

Mobiles

Fax

Post Box

Computer

Internet Connection

InformalSemi formalFormal

8

61%

26%

83%

31%

41%

52%

71%

98% 95%

99%

99%

95%

Fixed Line Phones

Mobiles

Fax

Post Box

Computer

Internet Connection

Don't have it

Have it

ICT perceptions: ICTs are important or very important!

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.

ICT Possession

Index

Informal 1606 1275.4

1327.61 2 0Semi-formal 1234 2112.36

Formal 1126 2852.24Total 3966

ICT Usage Index

Informal 1606 1361.15

1034.54 2 0Semi-formal 1234 2069.24

Formal 1126 2777.19Total 3966

ICT Usage Intensity

Index

Informal 1606 1989.19

0.64 2 0.726Semi-formal 1234 1962.71

Formal 1126 1998.17Total 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 personal and household goods

0.736

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 personal service activities

0.905

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Informal business operate on a higher profit margin

Ranks Formality N Mean Rank

Chi- Square

df Asymp. Sig.

Profit margin:

after tax profits

divided by turnover

Informal 1590 2081.59

26.051 2 0.000

Semi-formal

1230 1913

Formal 1120 1875.94

Total 3940

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Informal businesses are more profitable

Ranks Formality N Mean Rank

Chi- Square

df Asymp. Sig.

Profitability: after tax

profit divided by total fixed

assets

Informal 1504 2020.61

70.846 2 0.000

Semi-formal

1139 1761.75

Formal 1048 1686.98

Total 3691

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Formal businesses have higher labour productivity

Ranks Formality NMean Rank

Chi- Square df

Asymp. Sig.

Labour productivity: Value added (Sales minus direct costs, rent, water,

electricity etc.:) divided by full-time employees including owners that manage the

business

Informal 1571 1546.48

479.988 2 0.000

Semi-formal 1223 1968.59

Formal 1114 2514.43

Total 3908

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Formal businesses re-invest more

Ranks Formality NMean Rank

Chi- Square df

Asymp. Sig.

Re-investment rate: Invest

ment divided by fixed assets

Informal 1559 1834.37

44.438 2 0.000Semi-formal 1217 1908.94

Formal 1100 2118.79

Total 3876

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Turnover or Sales Model

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

€

F1FA

= β1 + β2F2FA

+ β3F3FA

+ β4F4FA

+ β5F5FA

+ β6F6FA

+ ε

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ICT expenditure contributes significantly to higher sales

Robust regression of turnover function  Formal Semi-formal InformalN 1048 1139 1504

R Square 0.7775 0.9199 0.9481F 74.39 208.58 193.52

Sig. For equation 0 0 0

Mean Variance

InflationFactor (VIF) 1.5 1.82 1.19

Unstandardized Coefficients for ICT Usage Expenditure 3.93 2.77 51.28

Sig. of ICT Usage Expenditure 0.000 0.000 0.000

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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

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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 Coefficients

t Sig. VIF

(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

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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 Coefficients

t Sig. VIF

(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

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No doubt!

ICTs help SMEs to become more profitable

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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%

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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

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ICT Access & Prices

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2006 Fixed-line Subscribers per 100 inhabitants

0.240.380.41

0.650.780.820.840.930.971.02

1.401.48

2.236.84

7.098.75

9.93

RwandaMozambique

TanzaniaCameroon

ZambiaNigeriaKenya

Burkina FasoEthiopia

BeninIvory Coast

GhanaSenegalNamibiaUganda

BotswanaSouth Africa

Source: ResearchICTafrica.net, (population based on IMF data)

Access to fixed-line phones

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2006 Mobile Subscribers per 100 inhabitants

1.152.94

5.176.73

8.229.30

11.3112.59

14.4914.5514.97

16.8819.05

26.8627.51

57.5468.15

EthiopiaRwanda

Burkina FasoUganda

Ivory CoastZambia

BeninGhana

SenegalTanzania

MozambiqueNigeriaKenya

NamibiaCameroonBotswana

South Africa

Source: ResearchICTafrica.net, (population based on IMF data)

Access to mobile phones

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OECD Usage BasketsMinutes or units Low User Medium User High UserCell2Cell own Network Peak 6.91 15.60 39.48Cell2Cell own Network Off Peak 3.60 7.49 12.50Cell2Cell own Network Off Off Peak 3.17 7.49 17.11Cell2Cell other Network Peak 4.32 10.08 27.72Cell2Cell other Network Off Peak 2.25 4.84 8.78Cell2Cell other Network OffOffPeak 1.98 4.84 12.01Cell2Fixed Peak 3.17 6.83 16.80Cell2Fixed Off Peak 1.65 3.28 5.32Cell2Fixed Off Off Peak 1.45 3.28 7.28SMS Peak 16.16 25.33 33.60SMS Off Peak 8.42 12.16 10.64SMS Off Off Peak 7.41 12.16 14.56

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2.25.6

5.86.5

7.07.37.37.47.6

7.98.6

9.69.7

10.210.6

10.912.5

EthiopiaRwanda

TanzaniaGhana

BotswanaUgandaSenegal

BeninMozambique

ZambiaCameroon

NamibiaBurkina FasoCôte d'Ivoire

KenyaSouth Africa

Nigeria

2006 Low OECD User Basket - cost in US$ using nominal end of 2006 exhange rates

2006 Mobile Nominal Usage Costs

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11.613.3

14.316.3

17.918.418.418.4

20.020.5

27.227.5

29.230.530.8

32.436.2

ZambiaEthiopiaTanzania

BeninCôte d'Ivoire

SenegalCameroonBotswana

NigeriaKenya

South AfricaNamibia

Burkina FasoRwanda

GhanaMozambique

Uganda

2006 Low OECD User Basket - cost in US$ using implied PPP conversion rates

2006 Mobile PPP Usage Costs

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Cost of a local 1 minute call (peak rate)- cost in US$ using end of 2006 nominal exchange rates

0.000.04

0.050.05

0.050.050.05

0.060.07

0.070.10

0.100.10

0.110.12

0.120.15

EthiopiaBenin

ZambiaBotswana

NigeriaGhana

NamibiaSenegalRwanda

South AfricaUganda

CameroonTanzania

KenyaMozambiqueCôte d'IvoireBurkina Faso

2006 Fixed-line Nominal Usage Costs

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0.020.07

0.080.09

0.130.150.16

0.190.210.210.21

0.250.26

0.390.46

0.480.49

EthiopiaZambiaNigeria

BeninBotswana

SenegalNamibia

South AfricaCôte d'Ivoire

CameroonKenyaGhana

TanzaniaRwanda

Burkina FasoUganda

Mozambique

Fixed-Line: Cost of a local 1 minute call (peak rate)cost in US$ using implied PPP conversion rates

2006 Fixed-line PPP Usage Costs

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Cost of a local 3 minute call to US (peak rate)- cost in US$ using end of 2006 nominal exchange rates

0.350.52

0.900.90

1.011.02

1.151.451.54

1.812.142.20

2.413.00

3.423.90

4.77

NigeriaSouth Africa

Côte d'IvoireSenegal

GhanaMozambique

BotswanaBenin

UgandaCameroon

TanzaniaNamibia

KenyaRwandaEthiopia

Burkina FasoZambia

2006 Fixed-line Nominal Usage Costs

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0.61.31.6

2.33.03.2

3.84.34.74.7

5.36.3

7.07.7

11.716.5

20.7

NigeriaSouth Africa

Côte d'IvoireSenegal

BotswanaBenin

CameroonMozambique

KenyaGhana

TanzaniaNamibiaZambiaUganda

Burkina FasoRwandaEthiopia

Fixed-line: Cost of a 3 minute call to the US (peak rate)cost in US$ using implied PPP conversion rates

2006 Fixed-line PPP Usage Costs

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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

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Affect of Competition in

Namibia

35

Nominal PricesNominal cost of OECD Usage Baskets in N$

83

174

296

70

147

250

48

101

210

51

106

228

Low User Medium User High User

Cheapest MTC September 2005

Cheapest MTC October 2007

Cheapest Switch October 2007

Cheapeast Cell One October 2007

36

Real PricesReal cost of OECD Usage Baskets in N$ (September

2005 prices)

83

174

296

62

130

221

43

89

186

45

94

202

Low User Medium User High User

Cheapest MTC September 2005

Cheapest MTC October 2007

Cheapest Switch October 2007

Cheapeast Cell One October 2007

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Price Change MTCMTC price change compared to September 2005

85% 85% 84%

75% 75% 75%

Low User Medium User High User

nominal prices real prices (in Sep 2005 prices)

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Price Change of Cheapest overallOverall price change (cheapest available in

Namibia) compared to September 2005

58% 58%

71%

52% 51%

63%

Low User Medium User High User

nominal prices real prices (in Sep 2005 prices)

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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

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