Sub Saharan Africa Research Paper

Journal of Economic Perspectives—Volume 24, Number 3—Summer 2010—Pages 207–232

Mobile Phones and
Economic Development in Africa
Jenny C. Aker and Isaac M. Mbiti

S

ub-Saharan Africa has some of the lowest levels of infrastructure investment in the world. Merely 29 percent of roads are paved, barely a quarter of the population has access to electricity, and there are fewer than three landlines available per 100 people (ITU, 2009; World Bank, 2009a). Yet access to and use of mobile telephony in sub-Saharan Africa has increased dramatically over the past decade. There are ten times as many mobile phones as landlines in sub-Saharan
Africa (ITU, 2009), and 60 percent of the population has mobile phone coverage.
Mobile phone subscriptions increased by 49
percent annually between 2002 and
2007, as compared with 17 percent per year in Europe (ITU, 2008).
Mobile telephony has brought new possibilities to the continent. Across urban– rural and rich–poor divides, mobile phones connect individuals to individuals, information, markets, and services. In Mali, residents of Timbuktu can call relatives living in the capital city of Bamako—or relatives in France. In Ghana, farmers in Tamale are able to send a text message to learn corn and tomato prices in Accra, over 400 kilometers away. In Niger, day laborers are able to call acquaintances in Benin to find out about job opportunities without making the US$40 trip. In
Malawi, those affected by HIV and AIDS can receive text messages daily, reminding them to take their medicines on schedule. Citizens in countries as diverse as Kenya,
Nigeria, and Mozambique are able to report violent confrontations via text message to a centralized server that is viewable, in real time, by the entire world.
Jenny C. Aker is Assistant Professor of Development Economics, Department of Economics and Fletcher School, Tufts University, Medford, Massachusetts. She is also a Non-Resident
Fellow, Center for Global Development, Washington, D.C. Isaac M. Mbiti is Assistant
Professor of Economics, Southern Methodist University, Dallas, Texas. Their e-mail addresses are 〈 [e-mail]〉 and 〈[e-mail]〉.
[e-mail]〉
[e-mail]〉
■

doi=10.1257/jep.24.3.207

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These effects can be particularly dramatic in rural Africa, where in many places mobile phones have represented the first modern telecommunications infrastructure of any kind. Mobile phones have greatly reduced communication costs, thereby allowing individuals and firms to send and to obtain information quickly and cheaply on a variety of economic, social, and political topics. An emerging body of research shows that the reduction in communication costs associated with mobile phones has tangible economic benefits, improving agricultural and labor market efficiency and producer and consumer welfare in specific circumstances and countries (Jensen, 2007; Aker, 2008; Aker, 2010; Klonner and Nolen, 2008).
As telecommunication markets mature, mobile phones in Africa are evolving from simple communication tools into service delivery platforms. This has shifted the development paradigm surrounding mobile phones from one that simply reduces communication and coordination costs to one that could transform lives through innovative applications and services.
The rapid adoption of mobile phones has generated a great deal of speculation and optimism regarding its effect on economic development in Africa.
Policymakers, newspapers, and mobile phone companies have all touted the poverty-eradicating potential of mobile phones (Corbett, 2008). At the Connect
Africa Summit in 2007, Paul Kagame, President of Rwanda, said: “In 10 short years, what was once an object of luxury and privilege, the mobile phone, has become a basic necessity in Africa.” An article in The Economist (2008) similarly reported:
“A device that was a yuppie toy not so long ago has now become a potent force for economic development in the world’s poorest countries.” Do such sentiments and slogans reflect the reality of the consequences of the mobile phone for economic development in Africa?
This paper first examines the evolution of mobile phone coverage and adoption in sub-Saharan Africa over the past decade. We then explore the main channels through which mobile phones can effect economic outcomes and appraise current evidence of its potential to improve economic development. We conclude with directions for future research and outline the necessary conditions for mobile phones to promote broader economic development in Africa.

Mobile Phone Coverage in Africa: What a Difference a Decade Makes
Mobile phone coverage in Africa has grown at staggering rates over the past decade. In 1999, only 10 percent of the African population had mobile phone coverage, primarily in North Africa (Algeria, Egypt, Libya, Morocco, and Tunisia) and South Africa (GSMA data for 2009).1 By 2008, 60 percent of the population
(477 million people) could get a signal, and an area of 11.2 million square kilometers had mobile phone coverage—equivalent to the United States and Argentina

1
For data on mobile phone coverage and adoption, we will be using data from the GSM Association, an association that represents the interests of the worldwide mobile communications
industry.

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combined.2 By 2012, most villages in Africa will have coverage, with only a handful of countries—Guinea Bissau, Ethiopia, Mali, and Somalia—relatively unconnected
(GSMA data for 2008).
There have been huge disparities in the geographic rollout of this coverage, prompting concerns over an intra-African digital divide (ITU, 2008). In 1999, most African countries had no mobile phone coverage, and only Egypt, Morocco,
Senegal, and South Africa had coverage rates of over 40 percent. By 2008, however, over 65 percent of the African population had access to mobile phone service, with
93 percent covered in North Africa and 60 percent in sub-Saharan Africa. Overall, the expansion of mobile phone coverage has been the lowest in Ethiopia, Somalia, and the landlocked countries of Central and West Africa.
While the telecommunications industry in the United States, Canada, and
Europe invested in landlines before moving to mobile phone networks, the mobile phone has effectively leapfrogged the landline in Africa. After all, landlines require that wires be installed on every road and into every community, with smaller lines into every household. A full landline network can be prohibitively expensive, especially in countries with poor roads, vast distances, and low population densities.
Mobile phone coverage in sub-Saharan Africa, by contrast, is primarily provided via a network of specialized base stations, which can provide service to a 5–10 kilometer radius. (The right axis of Figure 1 shows the expansion of cell phone coverage in sub-Saharan Africa.) Due to unreliable electricity supplies across Africa, the base stations are primarily powered by diesel generators.
What Factors Determine the Spread of Mobile Phone Coverage?
The growth of mobile phone coverage across Africa has shown a strong positive correlation with population density, but other factors matter as well. Using a spatially disaggregated dataset of mobile phone coverage and geographic characteristics, Buys, Dasgupta, Thomas, and Wheeler (2009) find that the probability of having a mobile phone tower in a particular location is strongly and positively associated with potential demand factors, such as population density and per capita income, as well as the competitiveness of the mobile phone sector within the country. They also find that factors associated with higher costs—namely, higher elevation, steeper slopes, and distance from a main road and major urban centers—are negatively associated with mobile phone coverage. Empirical evidence suggests that these factors partially explain the rollout of mobile phone service within countries as well.3
Adopting Mobile Phones on Less than a Dollar per Day
The rapid adoption of mobile phones in some of the poorest countries in the world has far exceeded expectations. In 1999, for example, the Kenyan-based
2

For comparison, there were approximately 8.2 million fi xed telephone lines in Africa in 1998, covering 1.4 percent of the population. Between 1998 and 2008, a mere 2.4 million additional landlines were installed (ITU, 2009).
3
Detailed regression results and maps showing the spatial rollout of mobile phone coverage in Mozambique and Niger over time are available by request from the authors.

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Figure 1
Number of Cell Phone Subscribers and Cell Phone Coverage in Sub-Saharan
Africa, 2000–2008
450

70%

400

376

60%

350
50%
300

279

40%

250
200

200

30%

150

135

Percent of population

Number of subscribers (millions)

% of population with cell phone coverage (right axis)

20%
100
50

Total number of subscribers (left axis)
52
37
25
16

82

10%

0%

0
2000

2001

2002

2003

2004

2005

2006

2007

2008

Sources: Mobile phone subscription data are provided by Wireless Intelligence. The percentage of the population with mobile phone coverage is provided by GSMA.
Notes: “Mobile phone subscribers” are active SIM cards rather than individual subscribers. One individual could have multiple SIM cards for different cell phone service providers, thereby potentially inflating the total number of individual users within a particular country. “Cell phone coverage” refers to having cell phone service in one’s area (being able to get a signal.)

service provider Safaricom projected that the mobile phone market in Kenya would reach three million subscribers by 2020. Safaricom, alone, currently has over
14 million subscribers (Safaricom, 2009).
Mobile phone subscriptions on the continent have risen from 16 million in
2000 to 376 million in 2008 as shown on the left axis of Figure 1. This is one-third of sub-Saharan Africa’s population. However, these figures potentially overestimate the actual number of mobile phone users, because many individuals own several handsets or have multiple subscriber identity module (SIM) cards. At the same time, there could potentially be more than 376 million mobile phone users, as sharing mobile phones is a common practice in Africa.
The increase in mobile phone subscriptions is all the more surprising considering the prevalence of poverty in sub-Saharan Africa and the price of mobile phone handsets and services. Approximately 300 million Africans are classified as poor
(living on less than US$1 per day), with 120 million classified as “ultra-poor” (living on less than US$0.50 per day) (Ahmed, Hill, Smith, Wiesmann, Frankenberger,

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211

Figure 2
Number of Mobile Phone Subscribers as a Percentage of the Population, 2008
(countries arranged by Human Development Index, from low to high)

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%

Niger
Sierra Leone
CAR
Mali
Burkina Faso
DRC
Chad
Burundi
Guinea-Bissau
Mozambique
Ethiopia
Guinea
Liberia
Gambia
Rwanda
Senegal
Eritrea
Zambia
Cote d’Ivoire
Benin
Malawi
Togo
Nigeria
Uganda
Lesotho
Djibouti
Mauritania
Cameroon
Ghana
Tanzania
Zimbabwe
Sudan
Kenya
Madagascar
Angola
Swaziland
Congo
STP
South Africa
Namibia
Botswana
Cape Verde
E. Guinea
Gabon
Mauritius

0%

Low Human Development Index

High Human Development Index

Source: Data on the number of mobile phone subscribers by country provided by Wireless Intelligence.
Notes: The graph reflects the percentage of mobile phone subscribers as a function of the total population in the country (2008). Countries are sorted by ranking on the UN’s Human Development
Index, from a high of 74 (Mauritius) to 182 (Niger). CAR is the Central African Republic, DRC is the
Democratic Republic of Congo, and STP is Sao Tome and Principe.

Gulati, Quabili, and Yohannes, 2007). The price of the cheapest mobile phone in Kenya, for example, costs half the average monthly income, whereas the price of the cheapest mobile phone in Niger is equivalent to 12.5 kilograms of millet, enough to feed a household of five for five days.
Figure 2 shows the number of subscribers as a percentage of the population, by country. The countries are sorted in ascending order by their ranking on the UN’s Human Development Index (HDI) (which combines measures of income, health, and education), from 182nd (Niger) to 81st (Mauritius) (UNDP
2009). Even in those countries with a HDI ranking lower than 160th (denoted by the dotted line in Figure 2), where the GDP per capita is less than US$761 (in purchasing power parity), an average of 23 percent of the population has mobile phone subscriptions.

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Who are the Mobile Phone Adopters in Africa?
Coinciding with the growth in mobile phone coverage and adoption in developing countries over the past decade, a rich body of literature has emerged examining the determinants of mobile phone adoption. However, as Donner’s (2008) survey shows, very little of this research has been conducted by economists, and economic studies of the subject have often focused on diffusion rather than individual adoption
(Baliamoune-Lutz, 2003; Kshetri and Chung, 2002). The relative lack of economics literature on mobile phone adoption appears to be related to two factors: First, mobile phone adoption data are often limited or inaccurate, as they report subscriptions rather than individual handset or subscriber identity module (SIM) ownership, which can result in serious measurement error. Second, finding credible estimation strategies to address the omitted variable bias, particularly when estimating the effect of neighbors and peers, is a significant challenge (Manski, 1993).
Despite these constraints, we use data from East Africa to gain further insights into individual- and firm-level mobile phone adoption. Using firm-level data from the World Bank Enterprise Surveys for Kenya, Tanzania, and Uganda, we find that a large percentage of firms had already adopted mobile phones in 2003, ranging from 83 to 93 percent across these countries. This high level of adoption appears to be correlated with the poor quality of landline services. For example, Kenyan firms reported an average of 36 days of interrupted landline service per year, with interruptions lasting an average of 37 hours. This was also the case in Tanzania and Uganda. Many firms also faced challenges in even obtaining landline service.
On average, Kenyan firms had to wait 100 days to obtain landline service, with a majority of firms paying bribes to facilitate this connection. (The average bribe was reported to be worth US$117, compared with a GDP per capita of US$780). Thus, explicit and implicit landline costs could have provided powerful incentives for firms to adopt mobile phones.
While Kenyan firms rapidly adopted mobile phones, the individual adoption rate has been significantly lower. Using data from the FinAccess surveys, we examine some basic patterns of individual mobile phone adoption in Kenya. Between 2006 and 2009, the percentage of the Kenyan population with mobile phone coverage remained relatively static, but the number of subscriptions tripled—reaching
17 million by 2009 (GSMA data for 2009). The adoption of mobile phone handsets increased by 74 percent during this period, from 27 percent in 2006 to 47 percent in 2009, as shown in Table 1. One-third of Kenyans shared their mobile phones with friends or relatives, supporting qualitative evidence of free riding and the use of mobile phones as a common property resource in sub-Saharan Africa. At the same time, such patterns could also reflect cost-sharing, especially among poorer rural households for whom the cost of handsets and services is still prohibitively expensive. For these reasons, reported data on mobile phone subscriptions could significantly underestimate the number of mobile phone users; in fact, while only
47 percent of individuals owned a phone, 80 percent reported having access to a mobile phone through direct ownership or sharing.
Looking at Table 2, we see that the first mobile phone adopters were primarily male, educated, young, wealthy, and urban populations; this is because the initial costs

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Table 1
Summary Statistics of Mobile Phone
Adoption and Use in Kenya
(portion of population)
2006 Mean
Has mobile phone
Shares mobile phone
Has mobile phone access
Has landline
Has multiple SIM cards
Transfers airtime
Sends text messages
Buys ringtones
Uses phone to surf Web
Pays bills by phone
M-Pesa user
M-Pesa recipient
M-Pesa sender
Age 25 to 39
Age 40 to 54
Age over 55
Male
Married
Completed primary school
Completed secondary school
Completed college
Has bank account
Urban

0.272
0.266
0.537
0.028
–
0.208
0.292
0.079
–
–
–
–
–
0.393
0.213
0.145
0.440
0.610
0.314
0.161
0.089
0.165
0.319

Number of observations

4,418

2009 Mean
0.467
0.334
0.801
0.021
0.081
0.350
0.411
0.090
0.050
0.036
0.383
0.339
0.291
0.375
0.224
0.183
0.413
0.602
0.315
0.165
0.085
0.244
0.285
6,598

Sources: Data from FinAcess 2006 and 2009 Surveys in Kenya.
Notes: “–” implies that the service was not available in
2006, and therefore there were no adopters. A SIM card is a subscriber identity module card. M-Pesa is Kenya’s mobile money service.

of handsets and services were relatively high. But secondary adopters span the demographic spectrum—young and old, rich and poor, urban, and rural. By 2009, mobile phone ownership included more poor, elderly, and rural individuals, in part facilitated by the introduction of lower-priced handsets and lower-denomination airtime cards.

Consequences of Mobile Phones for Economic Development in Africa
We identify five potential mechanisms through which mobile phones can provide economic benefits to consumers and producers in sub-Saharan Africa. First, mobile phones can improve access to and use of information, thereby reducing search costs, improving coordination among agents, and increasing market efficiency. Second, this

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Journal of Economic Perspectives

Table 2
Adoption and Use of Mobile Phones and M-Pesa

Own mobile phone

Use M-Pesa

Receive money with M-Pesa

Send money with M-Pesa

2006
Wealth
Not poor
Poor
Gender
Female
Male
Residence
Rural
Urban
Education
Less than primary
At least primary school
Age
Under 55
Over 55
Financial access
No bank account
Bank account
Sample size

2009

2009

2009

2009

42.0%
7.0%

64.6%
21.6%

52.4%
18.7%

46.0%
17.1%

43.2%
9.4%

23.0%
32.4%

41.6%
53.9%

35.5%
42.5%

32.1%
36.6%

24.7%
35.3%

16.8%
49.2%

35.9%
73.7%

28.8%
62.2%

25.9%
54.1%

18.8%
54.8%

8.9%
41.1%

22.8%
65.0%

16.4%
55.2%

14.1%
49.2%

9.4%
44.3%

29.3%
14.5%

50.9%
27.7%

42.4%
20.2%

37.4%
18.4%

32.9%
11.9%

18.2%
72.7%

33.9%
86.3%

27.5%
71.9%

23.9%
65.2%

17.8%
64.0%

4,418

6,598

6,598

6,598

6,598

Source: Data are from FinAcess 2006 and 2009 Surveys in Kenya.
Notes: M-Pesa is Kenya’s mobile money service. “Poor” is defined as individuals in the bottom two wealth quintiles of an asset index.

increased communication should improve firms’ productive efficiency by allowing them to better manage their supply chains. Third, mobile phones create new jobs to address demand for mobile-related services, thereby providing income-generating opportunities in rural and urban areas. Fourth, mobile phones can facilitate communication among social networks in response to shocks, thereby reducing households’ exposure to risk. Finally, mobile phone-based applications and development projects—sometimes known as “m-development”—have the potential to facilitate the delivery of financial, agricultural, health, and educational services. We address this final mechanism in a separate section.
In all of these cases, the evidence on mobile phones in Africa is quite recent, and so the available studies necessarily focus on specific sectors, countries, and examples. In this section, we present existing evidence. Later in the paper, we offer some thoughts about the research agenda that will unfold as mobile phones continue to spread and their medium- and long-term effects become more apparent.
How Mobile Phones Can Reduce Search Costs and Improve Markets
Examples of imperfect and asymmetric information abound in markets in subSaharan Africa. As a result, households and firms use numerous avenues to search for information in a variety of areas: input prices, output prices, jobs, potential

Mobile Phones and Economic Development in Africa

215

buyers and sellers, natural disasters, new technologies, politics, and the status of friends and family members. Traditional search mechanisms include personal travel, radio, and, to a much lesser extent, landlines, letters, newspapers, and television. Of these, personal travel has often been the most common mechanism used—primarily due to limited access to other alternatives. In Niger, for example,
89 percent of grain traders surveyed preferred obtaining price information by visiting weekly grain markets, rather than listening to the weekly radio program
(Aker, 2008). However, personal travel requires transport and opportunity costs, which can be relatively high with a combination of long distances and poor roads.
The rollout of mobile phones in sub-Saharan Africa over the past decade has introduced a new search technology that offers several advantages. First, mobile phones greatly reduce search costs. While mobile phones require an initial fixed cost, the variable costs associated with their use are significantly lower than equivalent travel and other opportunity costs. In Niger, for example, an average trip to a market located
65 kilometers away can take 2–4 hours roundtrip, as compared to a two-minute call.
Using a local daily wage of 500 CFA francs (US$1) per agricultural laborer in Niger, mobile phones reduce search costs by 50 percent as compared with personal travel.
Mobile phones can also allow people to obtain information immediately and on a regular basis, rather than waiting for weekly radio broadcasts, newspapers, or letters.
Furthermore, rather than being passive recipients of information, mobile phones allow individuals and firms to take an active role in the search process, enabling them to ask questions and corroborate information with multiple sources.
Finally, mobile phones are more accessible than other alternatives in terms of cost, geographic coverage, and ease of use. While radios can be used across all segments of the population (over 55 percent of sub-Saharan African households listen to the radio weekly), they generally provide a limited range of information.4 Newspapers are primarily concentrated in urban areas, are expensive (the cost of private newspapers in Mozambique average US$1), and are inaccessible to illiterate populations. Less than 19 percent of individuals in sub-Saharan Africa read a newspaper at least once per week, with a much smaller share in rural areas.
As previously discussed, landline coverage has been limited, with less than one landline subscriber per 1,000 people in 2008 (ITU, 2009). Access to other search mechanisms, such as fax machines, e-mail, and Internet, is similarly low, primarily due to their dependence upon landline infrastructure. On average, less than
4.2 percent of the African population has access to Internet (ITU, 2009).5
Search theory predicts that lowering search costs for output prices will change the reservation prices of market agents and increase the number of markets over

4
Summary statistics for radio and newspaper usage are computed from the Demographic and Health
Surveys (DHS) data for 20 sub-Saharan countries.
5
There are a number of challenges to the development of the fi xed broadband in Africa and hence Internet usage. Installation of broadband Internet access via Asymmetric Digital Subscriber
Lines (ADSL) is constrained by the limited number of fi xed telephone lines on the continent. 3G mobile cellular networks may hold greater potential for many countries in the region. For example,
98 percent of Kenya’s 1.7 million Internet subscribers access the Internet using the mobile phone network (CCK, 2009).

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Journal of Economic Perspectives

which consumers and producers search (Baye, Morgan, and Scholten, 2007; Reinganum, 1979; Stahl, 1989; Aker, 2008). The market equilibrium results of these models can be ambiguous, depending upon different assumptions with respect to consumers’ demand and the fixed or sequential nature of search and firm cost heterogeneity (Baye, Morgan, and Scholten, 2007). Nevertheless, in general, the sequential search models of Reinganum (1979), Stahl (1989), and Aker (2008) predict that a reduction in search costs will decrease the variance of equilibrium prices, thereby improving market efficiency.6 While improvements in information will result in net welfare gains under standard assumptions, how these gains are distributed among consumers, producers, and firms is theoretically ambiguous.
For example, lower search costs could improve traders’ welfare in the short term as they take advantage of spatial arbitrage opportunities, but reduce some of their welfare in the longer term as markets approach the law of one price. Similarly, in markets where traders have local monopoly pricing power, increased access to information could improve consumer welfare by disrupting this monopoly power, but reduce traders’ welfare.
Figure 3 provides the intuition for effects of mobile phone coverage (and hence lower search costs) on price dispersion and welfare under the assumption of perfectly inelastic supply. Prior to the introduction of mobile phones, search costs are prohibitively high and traders (or farmers) do not engage in arbitrage between high ( ) and low ( ) production areas. Once mobile phones are intro(H)
(L)
duced, traders are able to learn about prices in each region and begin trading. In the low-production region, consumers gain A + B while traders/producers lose A and gain C. This is a net gain of B + C, a transfer of A from traders/producers to
.
, consumers. In the high-production region, consumers lose D + E, while traders/
,
producers gain D and lose F, representing a net loss of E + F and a transfer of D
,
from consumers to traders/producers. The sum of consumer and producer surplus rises with the reduction in search costs—suggesting that the overall net change is positive—but the distributional effects are ambiguous. In a market with a highly perishable commodity, such as fish or vegetables, lower search costs would also coincide with less wastage, which is Pareto-improving as well.
Although the evidence on Africa is quite recent, an emerging body of literature assesses the role of information technology on market efficiency in developing countries, primarily in agricultural markets (Abraham, 2007; Jensen, 2007; Aker,
2008; Aker, 2010; Muto and Yamano, 2009; Goyal, forthcoming). These studies primarily focus on the relationship between mobile phone coverage and specific outcomes, such as price dispersion across markets (Overå, 2006; Jensen, 2007;
Aker, 2010), market agents’ behavior (Aker, 2008; Muto and Yamano, 2009), and producer and consumer welfare (Jensen, 2007; Aker, 2008).
A central concern in estimating the effect of mobile phones on market efficiency is omitted variables bias, because it can be difficult to attribute changes in

6

Reinganum (1979) develops a model of sequential search and firm cost heterogeneity, whereas
MacMinn (1980) develops a model of fi xed sample search and firm cost heterogeneity. MacMinn shows that a reduction in search costs can increase price dispersion.

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217

Figure 3
Mobile Phones, Search Costs, Arbitrage, and Welfare
Low Production Zone
Price

High Production Zone
Price

SupplyH

SupplyL

P(Q L)

B

A

Demand

P(Q L+X)

P(Q H−X )
Demand
C

P(Q H )

Q L Q L+X

Quantity

With Arbitrage
Consumers: A + B
Traders/Producers: C – A
Net change: B + C
Transfer: A (to consumers)

E

D
F

Q H−X Q H

Quantity

With Arbitrage
Consumers: – D – E
Traders/Producers: D – F
Net change: – E – F
Transfer: D (to traders/producers)

the outcomes of interest to mobile phone coverage rather than to unobservable factors. In addition, estimates can be subject to reverse causality as changes in the dependent variable could influence the geographic location or speed of mobile phone coverage. To address these concerns, existing economic studies typically rely upon panel data and the quasi-experimental nature of the rollout of mobile phone service to identify the effect of mobile phones on development outcomes.
Jensen (2007) and Aker (2008, 2010) both exploit the staggered introduction of mobile phone coverage to estimate the impact of mobile phones on agricultural markets in developing countries. Examining the effect of mobile phones on the fisheries sector in Kerala, India, Jensen finds that the expansion of mobile phone coverage leads to a significant reduction in the dispersion of fish prices across markets as well as a decline in waste. He shows that this leads to important welfare improvements for both fishermen and consumers; fishermen’s profits increased by
8 percent, consumer prices declined by 4 percent, and consumer surplus increased by 6 percent. With improved access to information via mobile phones, fishermen are better able to take advantage of spatial arbitrage opportunities, thereby improving allocative efficiency.
Examining the impact of mobile phones on grain markets in Niger, Aker
(2010) finds that the introduction of mobile phones reduces dispersion of grain prices across markets by 10 percent. The effect is stronger for those market pairs with higher transport costs, namely, those that are farther apart and linked by poor-quality roads. The effect is also stronger over time, suggesting that there are networks effects similar to those found by Brown and Goolsbee (2002). While the effect is smaller in magnitude as compared to Jensen (2007), it is perhaps more

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Journal of Economic Perspectives

surprising, because grains are a storable commodity. The primary mechanism through which mobile phones improve market efficiency is a change in traders’
(middlemen) marketing behavior: grain traders operating in mobile phone markets search over a greater number of markets, sell in more markets and have more market contacts as compared with their nonmobile phone counterparts
(Aker, 2008). Aker (2008) also finds that the introduction of mobile phones is associated with increased trader and consumer welfare. The introduction of mobile phones led to a reduction in the intra-annual coefficient of variation, thereby subjecting consumers to less intra-annual price risk. Mobile phones also increased traders’ welfare, primarily by increasing their sales prices, as they were able to take advantage of spatial arbitrage opportunities. The net effect of these changes was an increase in average daily profits, equivalent to a 29 percent increase per year.
However, the effects of mobile phones upon farmers’ welfare were not measured.
Muto and Yamano (2009) similarly estimate the impact of mobile phones on agricultural markets in Uganda, focusing on farmers’ market participation rather than market efficiency. Using a panel dataset on farm households between 2003 and 2005, they find that mobile phone coverage is associated with a 10 percent increase in farmers’ probability of market participation for bananas, although not maize, thereby suggesting that mobile phones are more useful for perishable crops.7 This effect was greater for farmers located in communities farther away from district centers. While the authors do not empirically explore the specific mechanisms behind their results, they suggest that improved access to price information reduced marketing costs and increased farm-gate prices, increasing productive efficiency.
Mobile phones should similarly reduce search costs in labor markets. Labor market search theory predicts that a reduction in search costs should increase workers’ reservation wage, increase the job arrival rate, and reduce unemployment.
Mobile phones should therefore decrease the equilibrium dispersion of wage offers and could potentially increase equilibrium wages and productivity if they raise the reservation match quality of both employers and workers (Autor, 2001).
To date, there is limited empirical research assessing the linkages between mobile phones and labor market outcomes in Africa. As one example, Klonner and Nolen (2008) assess the effect of mobile phone coverage on rural labor market outcomes in South Africa.
How Mobile Phones Can Improve Coordination among Firms
Information technology has the potential to increase productivity growth in Africa, especially of small-scale firms. In the literature from industrialized countries, Litan and Rivlin (2001) found that the Internet improved management
7

Muto and Yamano (2009) estimate the effect of both household-level mobile phone adoption and village-level mobile phone coverage on household participation. To correct for the endogeneity of the adoption variable, the authors use village-level mobile phone coverage and household time-invariant characteristics as instruments. One of the household-level instruments used is farm assets, which could be strongly correlated with mobile phone adoption and market participation, the dependent variable. Thus, the validity of the instrument is of some concern for household-level results.

Mobile Phones and Economic Development in Africa

219

efficiency of U.S. firms. By improving communication between firms and their suppliers, mobile phones can enable firms to manage their supply chains more effectively, streamline their production processes, and engage in new activities
(Hardy, 1980; Roller and Waverman, 2001). This would reduce stock-outs and interruptions in production, which are of particular concern for small-scale firms in rural areas with limited supply options. While there are no empirical studies of the impact of mobile phones on supply chain management in Africa, qualitative research in South Africa and Egypt suggests that mobile phones were associated with increased profits, significant time savings, and improved communication with suppliers for small-scale firms (Samuel, Shah, and Handingham, 2005).
How Mobile Phones Can Generate Additional Employment
One of the most direct economic impacts of mobile phones in Africa is through job creation. With an increase in the number of mobile phone operators and greater mobile phone coverage, labor demand within these sectors has increased.
For example, formal sector employment in the private transport and communications sector in Kenya rose by 130 percent between 2003 and 2007 (CCK, 2008), suggesting that mobile phones have contributed to job creation.
The mobile phone sector has also spawned a wide variety of business and entrepreneurship opportunities in the informal sector. While we would expect job creation in any new growth sector, many of these employment opportunities are directly related to the specific business strategies of mobile phone companies in
Africa. For example, because most Africans use prepaid phones (or “pay as you go”), mobile phone companies had to create extensive phone credit distribution networks in partnership with the formal and informal sector.8 Thus, small shops that have traditionally sold dietary staples and soap now sell mobile phone credit (airtime), particularly in small denominations. Young men and women are often found selling airtime cards in the streets. Numerous small-scale (and often informal) firms have also opened shops to sell, repair, and charge mobile phone handsets, either using car batteries or small generators. In the early years of mobile phone usage, entrepreneurial individuals started businesses to rent mobile phones, especially in rural areas. While Klonner and Nolen (2008) suggest that mobile phone coverage has been successful in generating employment opportunities, to date, there have not been studies examining the impact of mobile phones on both formal and informal job creation.
How Mobile Phones Can Reduce Risk
Sub-Saharan Africa is an inherently risky environment. Covariate shocks, such as natural disasters, conflicts, and epidemics, routinely affect households.
Kinship ties play both important social and economic functions in African society, specifically in creating informal insurance networks, increasing access to credit

8

Mohammed Ibrahim, the Sudanese businessman who established Celtel, a pan-African mobile group now owned by Zain, stated: “Mobile phones could not work in Africa without prepaid because it’s a cash society” (The Economist, 2009).

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and savings, and reducing risk (Grimard, 1997; De Weerdt and Dercon, 2006). At a basic level, mobile phones improve communications among members of a social network both within a country and across international boundaries. The reduction in communication costs can increase the speed of information flows within the network, thereby allowing them to respond better to shocks. Mobile phones also allow households to obtain information about potential shocks, allowing them to use such information to make planting and harvesting decisions, which can have important effects on yields (Rosenzweig and Binswanger, 1993). Finally, improved communications among members of a social network can also affect social learning, which can in turn influence the rate of technology adoption, especially of cash crops (Bandiera and Rasul, 2006; Conley and Udry, 2010). Existing economic evidence on the impacts of mobile phones and social networks is limited, but this has been extensively discussed in the field of sociology (de Bruijn, Nyamnjoh, and
Brinkman, 2009).

How Mobile Phones Can Provide Services and Innovative
Development Projects
The potential for using mobile phones as a tool for economic development has not gone unnoticed by African governments, donors, mobile phone companies, and nongovernmental organizations. An emerging trend is the development of mobile phone-based services and products that go beyond basic voice calls and text messaging. While these services have often focused on entertainment applications
(“apps”) in wealthier countries, these applications are providing opportunities for disseminating agricultural price information, monitoring health care, and transferring money in poorer countries. Some mobile-based services are being provided entirely by the telecommunications sector, some entirely by the public sector, and some through partnerships between the two.
We do not provide a comprehensive examination of mobile phone services and development projects in Africa, but rather explore some current initiatives in key thematic areas. Many of the innovations in these contexts are quite new, and so available information focuses on emerging research in specific countries. As mobile phone networks evolve to third-generation (3G) and fourth-generation (4G) systems and more advanced yet inexpensive phones become available, the scope, sophistication, and impact of mobile application and services will continually expand.
Banking the Unbanked? Mobile Money
Since 2005, mobile financial applications (known as “m-money” or “m-banking”) have emerged in a variety of developing countries. The systems usually involve a set of applications that facilitate a variety of financial transactions via mobile phone, including transmitting airtime, paying bills, and transferring money between individuals. There are also currently a few m-money systems in developing countries that allow international money transfers. Different institutional and business models provide these services; some are offered entirely by banks, others entirely by

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telecommunications providers and still others involve a partnership between a bank and a mobile phone service provider (Porteous, 2006). Most m-money systems allow the user to store value in an account accessible by the handset, convert cash in and out of the stored value account, and transfer value between users by using a set of text messages, menu commands, and personal identification numbers (PINs). A “pseudo account” can be established by purchasing “electronic money” (e-money) from an agent, usually a third party or someone who works for the mobile phone operator or bank. The user can then send e-money to another recipient with a phone, who then withdraws the e-money from their local transfer agent. Fees are generally charged for each transaction.
M-money applications have emerged in Asia, Latin America, and Africa.9 The
Kenyan mobile money service, M-Pesa, has probably received the most attention.
Introduced in 2007, M-Pesa (“M” for mobile, “Pesa” for “money” in Swahili) is a mobile phone application that facilitates a variety of financial transactions for its users, such as purchasing airtime, transferring money, and paying bills. As of
September 2009, M-Pesa had 8 million subscribers and a network of 13,000 agents, with almost 40 percent of Kenyans ever having used the service to send and receive money (as shown earlier in Table 2). Since its inception, the cumulative value of the money transferred via M-Pesa was over US$3.7 billion—almost 10 percent of
Kenya’s annual GDP (Safaricom, 2009).
Although M-Pesa has been touted as “banking the unbanked,” on average,
M-Pesa users are wealthier, better educated, urban, and “already banked” (again, see Table 2). Moreover, the data suggest that most of the transfers are occurring within urban areas. M-Pesa and other m-money systems have recently transitioned from a pure money transfer system into a payment platform that allows nongovernmental organizations, schools, hospitals, and firms to send and receive payments.
The rapid uptake of M-Pesa and similar m-money services is not surprising when one considers the level of financial development in Kenya and in sub-Saharan
Africa. Less than 30 percent of the population in East and Southern African has a formal bank account, ranging from 9 percent in Tanzania to 63 percent in South
Africa (FinMark Trust, 2008). In 2006, Kenya had only 450 bank branches and
600 automatic teller machines, or less than two bank branches per 100,000 people
(Vaughan, 2007). In the absence of formal financial systems, Kenyans primarily sent money by one of three mechanisms: via Western Union or post office, via intermediaries (such as bus drivers), or via friends or relatives. Wire transfers via Western
Union are secure but often prohibitively expensive, and are not always available in remote rural areas. Sending money via transport services or friends and relatives is more accessible, but carries a high risk of theft. By contrast, the cost of sending
1,000 Kenya Shillings (US$15) from Nairobi to the western provinces via M-Pesa in
2008 was two-fi fths the post office rate and one-fi fth the cost of sending it via bus
(Morawczynski, 2009).

9

A full list of countries with mobile money applications is provided at 〈http://www.wireless intelligence.com/mobile-money/〉. 222

Journal of Economic Perspectives

A variety of qualitative studies provide some insights into the characteristics, patterns, and potential impacts of M-Pesa usage. For example, Morawczynski and
Pickens (2009) find that users often keep a balance on their M-Pesa accounts, thereby using the system as a rudimentary bank account. M-Pesa users also send smaller but more frequent remittances compared with users of other transfer services or methods, suggesting that the system might allow informal insurance networks to function more effectively. Jack and Suri (2009) suggest that the inconspicuous nature of M-Pesa transfers could allow individuals to increase their personal savings, because friends and relatives would be less likely to know about the timing or amount of transfers. Wilson, Harper, and Griffith (forthcoming) find that members of informal savings groups in Nairobi are using M-Pesa to deposit individual savings into their group account.
What are the consequences of m-money systems for economic development?
A large body of theoretical and empirical literature suggests that the expansion of banking and financial systems can have significant impacts on economic growth and poverty in developing countries (Burgess and Pande, 2005; Levine, 2005). Yet many m-money systems in developing countries are not technically banking from either a financial or legal perspective: they do not provide interest on savings or facilitate access to credit from formal financial institutions, nor do they insure the value stored in the mobile account. While m-money systems have effectively expanded the breadth and reach of money transfer systems for the rural and urban poor—as well as provided a gateway to formal financial services—questions remain regarding the nature and extent of m-money’s effect on the welfare of poor users in developing countries.
Other Mobile Phone Development Projects (m-Development)10
In response to increases in mobile phone coverage and adoption in Africa, mobile phone-based development projects have proliferated in a variety of sectors, including agriculture, health, education, emergency response, and governance.
The objective, target group, and use of mobile phones in each project differ significantly, but the underlying belief is that mobile phones can offer a useful platform for providing information and services.
Health practitioners have often been at the forefront of using mobile phones as a development tool in Africa, with a variety of mobile health (m-health) projects on the continent. These projects range in variety and scope, from monitoring measles outbreaks in the Zambia, to supporting diagnosis and treatment by health workers in Mozambique, to sending health education messages in Benin, Malawi, and Uganda. In Kenya, Malawi, and South Africa, mobile phones are being used to send several reminders a day to HIV-positive patients about their anti-retroviral therapy schedule, as well as allow community health workers to send information about HIV patients’ status. Mobile phones are also extending the reach of medical workers and medical services. In the Democratic Republic of Congo, mothers can
10
These projects are more commonly referred to as “Information and Communication Technology for Development,” or ICT4D.

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call a hotline to ask questions about their child’s health status. Mobile phones have been used in the collection, measurement, and monitoring of health data, such as monitoring and tracking epidemics. For example, low-cost medical imaging systems have used mobile phone technology to transmit data and images to a central processor (Granot, Ivorra, and Rubinsky, 2008).
Mobile phones are facilitating access to agricultural market information, in many cases replacing the message boards and radio programs of traditional market information systems. In the francophone countries in West Africa, for example, consumer prices for staple grains are broadcast weekly via radio for the largest markets in the country. Yet in many cases, farmers live tens of kilometers from the nearest large market and the data is up to six days old. Farmers in countries as diverse as Niger, Senegal, and Ghana can now type in a code, send a text message, and receive the price of a variety of goods immediately. Mobile phones are also extending the reach of agricultural extension services; in Kenya,
Uganda, and India, farmers can call or text hotlines to ask for technical agricultural advice.
Mobile phones have also been used as a regular part of election campaigns around the world, from the United States to Thailand and Spain. Yet they have served as a powerful tool to assist with election monitoring on the continent, often overcoming logistical challenges of organizing volunteers and verifying results. Prior to 2005, so-called “parallel vote tabulation” systems—an electoral observation methodology that uses a representative sample of polling stations to monitor and verify election results independently—received information from trained observers via phone calls, radio, or messengers on motorbikes. In countries with limited infrastructure and communications systems, this verification process could take days, even weeks. During the elections in Ghana, 1,000 locally trained observers in a parallel vote tabulation system were able to transmit voting results via text message to a central system, resulting in almost instantaneous independent verification of the election. It has been argued that the absence of parallel vote tabulation and the timely and credible independent information it provides contributed to the recent post-election violence in Kenya.
Mobile phones have been used in other ways to foster good governance, mainly via voter education and citizen-based monitoring often called “crowdsourcing.”
Crowdsourcing—the idea of outsourcing a task to a large community or group of people—allows regular citizens to report election abnormalities and violent confrontations via text message or calls to a centralized server. In Kenya, such citizen-based monitoring was mapped via a software called “Ushahidi” (“testimony” in Swahili) to allow Kenyans to report post-election unrest via voice, text message, and Internet and to map it, in real time, to the entire world.11 However, such systems

11
More recently, Ushahidi has been used for search, rescue, and recovery operations following the
January 2010 earthquake in Haiti. Individuals in Haiti sent text messages to report the locations of survivors, which were then mapped and used by search and rescue teams. Additional information is available at 〈http://haiti.ushahidi.com/alerts〉.

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depend primarily upon verification by other users, which raises possible questions about their accuracy.
Simple and affordable mobile phones are being used as a means to promote literacy for adults in Africa (Aker, 2009). Despite the fact that text messages are one-seventh the price of voice calls in Niger, the use of text messages has been relatively limited, in part due to high illiteracy rates. In addition to the normal literacy curriculum, adult learners in Niger are taught where to find letters and numbers on a mobile phone and how to send and receive text messages. Within one cycle of classes, students are able to send text messages in local languages to their friends and family, thereby allowing them to practice their newly acquired literacy skills. In a country without local language newspapers and village-level libraries, text messaging makes literacy functional. Preliminary results suggest that the mobile phone-based literacy students have higher test scores than students in normal literacy classes, and these results are maintained six months after the end of classes (Aker, Ksoll, and Lybberty
(2010). Similar mobile literacy projects are starting in Senegal, and others in India are using smart phones and mobile games for children.

Mobile Phones, Economic Development, and Future Research
Existing empirical evidence on the effect of mobile phone coverage and services suggests that the mobile phone can potentially serve as a tool for economic development in Africa. But this evidence, while certainly encouraging, remains limited. First, while economic studies have focused on the effects of mobile phones for particular countries and markets, there is little evidence showing that this has translated into macroeconomic gains. Second, while the proliferation of mobilebased services and projects has the potential to promote economic development, there is a tendency for development agencies and donors to “ jump on the information technology bandwagon” without properly assessing its effects. Finally, communications technology cannot replace investments in public goods such as education, power, roads, and water—especially when access to mobile phones and services still remains out-of-reach for the poor.
Mobile Phones and Economic Growth: Are the Estimates Credible?
Existing micro- and macro-level evidence suggests that mobile phones can improve consumer and producer welfare in developing countries (Jensen 2007,
Aker 2008, Klonner and Nolen, 2008). Yet can mobile phones serve as an engine for economic growth? The effect of mobile phones on changes in GDP and growth, especially in sub-Saharan Africa, is still relatively unexamined. Roller and Waverman (2001) assessed the impact of telecommunications infrastructure on economic development in 21 OECD countries and found that a 10 percent increase in the telecommunications penetration rate increased economic growth by 1.5 percent. Waverman, Meschi, and Fuss (2005) conducted a similar analysis in developing countries, finding that a 10 percent increase in mobile penetration levels was associated with a 0.6 percent increase in growth rates.

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But while these studies provide some evidence of the positive relationship between mobile phones and economic growth, they are plagued by endogeneity problems. Mobile phone penetration rates are subject to significant measurement error, leading to potential bias in the coefficient estimates. Perhaps more importantly, finding credible exogenous instruments for mobile phone penetration in the context of a cross-country growth regression is a challenge. Waverman,
Meschi, and Fuss (2005) attempt to address these concerns by using lagged landline penetration as an instrument for current mobile penetration. Yet the same
(unobservable) factors that caused certain countries to have high lagged landline penetration could also drive current mobile adoption and growth. This raises questions about the validity of the instrument and hence the direction of causality. If we want to identify the magnitude of the impact of mobile phones on GDP growth in Africa, more research addressing these endogeneity concerns is required. In addition, to the extent that mobile phone adoption is associated with increases in consumer surplus—as the current micro-level evidence seems to suggest—changes in measured GDP will not capture the true welfare gains of this technology.
A Research Agenda for Mobile Phones and Economic Development
To ascertain whether and how mobile phones can be an effective poverty reduction tool in Africa, we need to identify our current knowledge, unanswered questions, and potential areas of future research. Of the five potential mechanisms through which mobile phones could impact economic development, current research primarily focuses on the short- to medium-term effect of mobile phones on search costs, market agents’ behavior, and price dispersion. This work is useful, but it is a subset of what could and should be known. Here, we identify what we see as the primary areas of future research in the short, medium, and long term.12
Relative to the spread of some other technologies that have been introduced into sub-Saharan Africa—improved seeds, solar cook stoves, and agricultural techniques—mobile phone adoption has occurred at a staggering rate on the continent.
Yet few empirical economic studies examine mobile phone adoption. This could be due to a variety of factors, including unreliable or nonexistent data on individuallevel adoption (leading to measurement error), the multiple economic and social benefits of mobile phones (making it difficult to assess the relevant benefits versus costs), and the effect of neighbors and peers on individual-level adoption. All of these factors make it difficult to develop an estimation strategy to circumvent omitted variables bias and the “reflection problem” (as explained in Manski, 1993).
Nevertheless, identifying the determinants of the level and rate of mobile phone adoption in Africa can provide important insights into future demand for mobilebased services and products, as well as demand for nonmobile technologies.
While mobile phones can facilitate communication among members of a social network, they also have the potential to change fundamentally the way these

12

In each of the areas discussed, there will be short- and long-term impacts. For example, while mobile phone technology improves market efficiency in the medium-term, firms and farmers may respond to this improved efficiency by adopting different production processes.

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networks function. Both Olken (2009) and Jensen and Oster (2009) have found that new technologies—in these cases, radio and television—have had positive and negative impacts on social relationships and individuals’ behavior in developing countries. Aker, Klein, and O’Connell (2010) find that the introduction of mobile phones reduces the magnitude of a “border effect” across different ethnic regions in Niger.13 Will mobile phones change the nature of these relationships in Africa, and if so, how? Mobile phone technology can strengthen some social networks by allowing individuals to communicate more frequently, and broaden other networks as traders and firms conduct business in new markets. At the same time, mobile phone technology could potentially weaken local social networks as individuals become able to reach beyond their inner circle to access credit and services. Understanding the effects of mobile phones on these networks—and hence households economic and social outcomes—will be of primary importance.
The impact of m-money systems on microeconomic and macroeconomic outcomes is a rich area of research, especially as these systems expand their geographic coverage and range of services. While m-money systems have the potential to create a new class of currency as they grow in magnitude, the largest potential impact of these systems is in the area of international money transfers. The World Bank estimates that officially recorded remittance flows to developing countries reached US$338 billion in 2008, with US$21 billion in transfers to sub-Saharan Africa (World Bank, 2009b).
M-money transfer systems could change the duration, frequency, and magnitude of these remittances, thereby affecting households’ business opportunities, educational investments, and income. The introduction of m-money transfers in sub-Saharan
Africa provides a unique opportunity to measure the impact of this service on migration patterns, remittance flows, and welfare outcomes.
Finally, mobile phone-based development projects are often based upon the assumption that mobile phones can improve communication, coordination, and service delivery. Yet the use of mobile phone technology in these contexts may not always be Pareto-improving. Some nongovernmental organizations have begun using mobile phones as a mechanism for distributing cash transfers. While this approach could be more efficient, it is not without risks: it could potentially target the wrong populations (if the individuals one would wish to target do not have mobile phones) or increase beneficiaries’ risk (if they must travel to find an agent to withdraw the cash). This suggests that the mobile-based approach might have higher costs and lower benefits than the “low-tech” approach. Thus, rigorous impact evaluations of m-development projects, in some cases using randomized evaluations, are needed to determine whether, how, and under what conditions mobile-based solutions are superior to their traditional counterparts.
There are two primary challenges to addressing this research agenda: data and identification. To measure the determinants of mobile phone adoption as well as its impacts on social networks, access to financial services, and remittances, reliable and accurate data at the individual-, household-, and village-level are needed. Yet
13

A border effect measures whether or not international (or other) borders affect the degree of integration or price dispersion for the same good across two countries (or regions).

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obtaining access to mobile phone coverage and usage data, even at aggregate levels, is notoriously difficult and often bound by strict rules of nondisclosure and privacy concerns. In such cases, researchers will need to partner with mobile phone service providers and local institutions to collect such data, especially in Africa.14 Furthermore, in the absence of quasi-experiments or randomized rollout of mobile phone coverage and services, credibly establishing causality is difficult, especially for economywide services such as M-Pesa. Nevertheless, as nongovernmental organizations, mobile phone service providers, and donors pilot new interventions, there are opportunities for researchers to partner with such organizations to conduct evaluations of these projects using experimental or nonexperimental techniques.
Mobile Phones and Information Technology Markets
If mobile phones are to be a transformative development tool, what policies can increase their usage? As mobile phones can have positive spillovers on nonusers, universal adoption is not required to generate significant benefits. But even with rapid increases in adoption over the past decade, adoption rates are still quite low in many parts of sub-Saharan Africa, ranging from 2 percent (in Ethiopia) to
98 percent (in Botswana) (as shown earlier in Figure 2). This is partly due to the cost: the technology is still financially out of reach for about half of the continent’s population. In Niger, the cost of a one-minute call off-network is US$0.38 per minute, representing 40 percent of a household’s daily income. (Consumers have adapted to this situation in creative ways; individuals will “beep” or “flash” friends and family members to let them know that they want to be called.)
The telecommunications regulatory environment can play a key role in fostering increased mobile phone adoption. In 1997, over 75 percent of countries in sub-Saharan Africa had no mobile phone network, while all of the existing networks were monopolies. By 2009, a mobile phone network existed in every country, with 49 percent of markets fully liberalized, 24 percent partially deregulated, and 26 percent as monopolies (Figure 4). Certain countries have maintained monopolistic structures (such as the Central African Republic, Chad, and
Ethiopia), whereas others have tried to reestablish monopolies (including Benin,
Sierra Leone, and Zimbabwe).
There is a strong correlation between mobile phone coverage, the types of services offered, the price of such services, and the telecommunications market structure for a particular country. In markets with limited competition, we would expect profit-maximizing firms to offer more limited services at higher prices.
GSMA (2006), an association that represents the interests of the worldwide mobile communications industry, found that, on average, prices decreased and services increased following market liberalization; average call prices fell by a minimum of 31 percent with partial liberalization and by up to 90 percent following full liberalization. Liberalization was also associated with an increase in international traffic volumes and improved call quality. Under monopoly conditions in Central
14

The MIT Reality Mining Project, for example, has successfully partnered with mobile phone companies and collected mobile phone usage data from users in Rwanda and Kenya.

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Figure 4
Evolution of Cell Phone Market Structure in Africa, 1995–2009
100%
90%
80%

Monopoly
Partially deregulated
Fully liberalized

70%
60%
50%
40%
30%
20%
10%
0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Notes: Data are provided by the GSMA for various years. “Monopolies” refer to those countries whereby all international mobile traffic was through an incumbent. “Partially deregulated” countries are those where markets are deregulated (but no licenses were issued) or operators must send their mobile traffic through another fi xed operator. “Fully liberalized” refers to markets where market operators are granted their own international gateway licenses.

Africa, for example, calls between Kinshasa and Brazzaville (a distance of three kilometers), were routed via London or New York. After the market was liberalized in 2006, prices between the countries plummeted and call volumes spiked (GSMA,
2006). Overall, these patterns suggest that more competitive telecommunications environments can be beneficial for poor consumers.15
Yet even if mobile phones can enhance access to resources and information, they cannot replace investments in public goods such as roads, power, and water. In fact, they are less effective without them. Without roads, a trader might be able obtain better price information, but still be unable to transport goods to
15

A particular government might not want to support liberalized telecommunications markets as they could improve access to information and threaten their authority. This could be the case in certain countries in sub-Saharan Africa: 27 percent of non-democracies had a monopolistic telecommunications structure, as compared with 15 percent of democracies. (Many of the monopolies in democratic countries are island countries, where multiple carriers might not be appropriate). Yet even democracies struggle with mobile communication: in 2007, the Kenyan government briefly considered shutting down text messaging as it was being used to incite violence (Goldstein and Rotich, 2008). Democracies
(as identified by Freedom House 2008 data) also had higher mobile coverage rates as compared to nondemocracies: 43 percent mobile phone coverage, as compared with 31 percent for nondemocracies.

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the market. Without power, a firm could receive more customer orders via mobile phone, but would still have work hours limited by the available sunlight. In Nigeria, due to continuing electricity problems, one mobile phone company had to deploy its own power supply using generators in its 3,600 base stations—burning 450 liters of diesel every second to keep its mobile towers operational. For economic development to occur, complementarity between mobile phones and these other forms of capital is needed.

Conclusion
Mobile phone usage in sub-Saharan Africa has grown significantly over the past decade and now covers 60 percent of the population. Empirical evidence shows that mobile phones have the potential to benefit consumer and producer welfare, and perhaps broader economic development.
As the prices of both handsets and airtime continue to fall, the mobile phone will complete its transformation from an elite status symbol to a necessity for adults at nearly all income levels. Indeed, mobile operators are continuing to innovate in their push to reach more subscribers. The price of handsets has also fallen and new solar-powered phones have recently been introduced into the market. The challenge is now to ensure complementary access to public goods and the development of appropriate policies to evaluate and propagate the benefits of mobile phones throughout the continent.
■ We are grateful to the GSM Association, Wireless Intelligence, and the Financial Sector
Deeping Trust of Kenya (FSD Kenya) for providing data. We thank the editors David Autor,
Chad Jones, and Timothy Taylor for extensive comments on earlier drafts. We also thank Delia
Furtado, Heinrich Hock, Michael Klein, Daniel Millimet, Emily Oster, Andrei Shleifer, David
Weil, and seminar participants at the Center for Global Development, Université d’Auvergne, the NBER Africa Project conference, and Stanford University for helpful comments and suggestions. The authors acknowledge financial support from the NBER Africa Project.

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