Tsukuba Economics Working Papers
No. 2022-001

Is Better Access to Mobile Networks Associated with Increased Mobile Money
Adoption? Evidence from the Micro-data of Six Developing Countries
byHisahiro Naito
Shinnosuke Yamamoto

January 2022

UNIVERSITY OF TSUKUBA
Program in Economic and Public Policy
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANIs Better Access to Mobile Networks Associated with
Increased Mobile Money Adoption? Evidence from
the Micro-data of Six Developing Countries
Hisahiro Naito ∗†Graduate School of Humanities and Social Sciences
University of Tsukuba
Shinnosuke Yamamoto ∗‡College of International Studies
University of Tsukuba
December 17, 2021∗

The authors appreciate the comments from Yuko Nakano and ZengFei Yu.
Email: naito@dpipe.tsukuba.ac.jp. Address: Tennodai 1-1-1 University of Tsukuba, Tsukuba
City, Ibaraki Prefecture Japan‡
Email: shinno6132@gmail.com Address: Tennodai 1-1-1, Tsukuba City, Ibaraki Prefecture, Japan†Abstract
In several developing countries in Sub-Saharan Africa, accessibility to digital financial
services is increasing because of the development of mobile money services. People
previously excluded from the financial system have started to have access to financial
services such as receiving and sending remittances, saving, and borrowing. This study
examines the effect of network accessibility on the use of mobile money in six developing
countries (Bangladesh, Kenya, Nigeria, Pakistan, Tanzania, and Uganda) using GPS
information on each household and mobile phone network coverage maps. We find thatamong these six countries, network accessibility is associated with the use of mobile
money in a robust way only in Pakistan and Tanzania. In those two countries, when ahousehold location becomes 10 km closer to the center of the area with multiple mobile
networks, the probability of using mobile money increases by 10 percent. In the othercountries, we did not find a robust relationship between the use of mobile money andnetwork accessibility. This suggests that increasing network accessibility may not be
an efficient method for increasing mobile money adoption in certain countries. The
fact that mobile money use rates differ between Tanzania and Pakistan also suggeststhat the effect of mobile networks is unrelated to the overall level of mobile money
adoption.

1

Introduction

Mobile phone networks are rapidly becoming an important infrastructure in developing
countries. For example, in Asia and Africa, the mobile phone use rate is more than 90
percent (Figure A1). However, the percentage of people with bank accounts remains
low. For example, the proportion of adults in developing countries that have a bankaccount is 63 percent compared with 94 percent in developed countries. Interestingly,
among the approximately 1.7 billion “unbanked” people, about one billion have mobile
phones (Demirguc-Kunt, Klapper, Singer, Ansar and Hess, 2018).
Economics research emphasizes the importance of financial inclusion: the conceptthat individuals and entities can access convenient and affordable financial services
that meet their needs for transactions, payments, savings, credit, and insurance provided through responsible and sustainable means. The literature argues that financial
inclusion is expected to promote economic growth (Aggarwal and Klapper, 2013) and
improve quality of life in developing regions (Demirguc-Kunt et al., 2018).
Financial inclusion in some African countries has recently started to change despite
their low rate of bank account ownership due to the growth in mobile money services,which enable individuals to send, receive, and save using a mobile phone (Hughes
and Lonie, 2007; GSM Association, 2020). For example, in 2017, only 55, 19, and 17
percent of adults had a bank account in Kenya, Tanzania, and Zimbabwe, respectively,
whereas 58, 32, and 32 percent already had mobile money accounts. The growth inmobile money services has been dizzying, too. In Tanzania, mobile money was officiallyintroduced in 2008. In 2009, the user rate was just 1.1 percent; however, this rose to 32
percent in 2013 and 55.8 percent in 2017 (World Bank, 2014). Globally, the number ofmobile money agents tripled between 2014 and 2019, reaching seven times the numberof ATMs and 20 times the number of bank branches (GSM Association, 2020).
Yet, this high proliferation of mobile money is not universal in developing countries.
According to the Global Financial Inclusion database of the World Bank, the mobile
money penetration rate in 2017 was below 10 percent in more than half of the 77 countries surveyed (Figure A2). India, which has the largest population in the South Asian1

region, had a use rate of about 2 percent, while Nigeria, with the largest population in
Africa, had a use rate of 5.6 percent.
One natural question that arises is what prevents the adoption of mobile money
and resulting financial inclusion in developing countries. Several studies have examined
this issue (Evans and Pirchio, 2014; Mothobi and Grzybowski, 2017; Lashitew, vanTulder and Liasse, 2019; Asongu, Biekpe and Cassimon, 2020). One of the issues
that has attracted the attention of researchers and policymakers is the role of mobile
networks. Broadband coverage such as 3G and 4G, which are common communication
technologies in developed countries, are limited to urban and densely populated areas,
while 2G (GSM), which is slower than 3G and 4G, is commonly used in rural areas(Perlman and Wechsler, 2019). Although 2G has wider coverage than other faster
networks, it does not cover all areas far from cities and national highways or areas not
yet served by electricity. For example, in the survey of Nigerian households conductedby Tonuchi (2020), 65 percent of the 300 offline respondents and 83 percent of the
200 online respondents cited the network as the biggest barrier to using mobile money
services. Thus, network accessibility may have an important effect on the use of mobile
money. Lashitew, van Tulder and Liasse (2019) show that network coverage does nothave a statistically or economically significant effect on mobile money adoption, while
Asongu, Biekpe and Cassimon (2020) show that it has a statistically and economicallysignificant effect.1
Thus, given the importance of this issue and lack of evidence using micro-data andinformation on individual-level network accessibility, we empirically examine the effectof accessibility to mobile networks on the use of mobile money using micro-data as wellas GPS information on household locations and network coverage map data, whichhave recently become available. Examining such issues is important for several reasons. First, from the perspective of the government, such information is critical. Past
research shows that the use of mobile money has many positive effects on the economy
such as smoothing consumption, increasing the receipt of remittances, improving the1

Perlman and Wechsler (2019) discuss how different mobile network providers use different typesof networks in developing countries.

2

welfare system, and increasing human capital. (Jack and Suri, 2014; Muralidharan
et al., 2016; Munyegera and Matsumoto, 2016; Aker et al., 2016; Abiona and Koppensteiner, 2020). On the other hand, the mobile phone market is often dominatedby several companies due to the high initial cost of the network infrastructure. Thus,
how to regulate this industry is an important issue. If the expansion of mobile phone
networks is likely to enhance the use of mobile money, then the government mightneed to formulate a policy that encourages mobile phone operators to make additional
investments in mobile phone networks.
Second, from the perspective of mobile money operators, it is critical to knowwhether expanding mobile networks is likely to affect the use of mobile money. Since
the cost of expanding mobile phone networks is substantial, such information is critical
for the investment decisions of mobile phone operators.
Finally, several satellite-based Internet constellations are planned by large companies such as Amazon (Project Kuiper) and Space X (Starlink). A satellite-basedInternet constellation is thousands of mass-produced small satellites in low earth orbit,
which allow people to access the Internet everywhere at any place on Earth at any timethrough satellites. If the expansion of mobile phone networks has changed financial
inclusion in developing countries, the spread of Internet networks could affect financial
inclusion in those countries in the future. 2 To predict the degree to which this newtechnology will affect financial inclusion, it is important to know how mobile phone
networks have affected financial inclusion in the past.
One might think that the results of this study would be obvious. Since people need
to be connected to mobile networks to use mobile money, readers might speculate that
expanding networks always increases its adoption. However, such an argument canbe misleading for several reasons. First, people can use mobile money even when no
mobile network is available at home. People living outside network coverage areas often
use mobile phones when they visit urban areas for work or to buy groceries. Thus,
living outside a network-covered area does not imply that people cannot use mobile
2

For example, Asian Development Bank (2021) discusses the potential impact of those constellations for developing countries in Asia and the Pacific region.

3

money. Second, if the decision not to use mobile money is driven by other reasons
such as low service quality and a culture of relying on cash transactions, increasing
accessibility to mobile networks would be unlikely to increase the use of mobile money.
Indeed, in many developing countries, the rate of mobile phone use is more than 90
percent but that of mobile money use is low.
One strand of the literature examines the cause of adopting mobile money in developing countries. Batista and Vicente (2020) conduct a field experiment that introduces
mobile money to residents and examine the factors behind its adoption. Evans and Pirchio (2014) show that heavy regulation on mobile money operators is the key barrier tothe growth of mobile money adoption. Murendo, Wollni, De Brauw and Mugabi (2018)
analyze the effect of social networks on mobile money adoption in Uganda. Lashitew,
van Tulder and Liasse (2019) use macro-data to identify that ATM penetration andbank concentration have a negative effect on mobile money adoption, while mobile
network connectivity does not have a significant effect. Moreover, Asongu, Biekpe andCassimon (2020) show that ATM penetration, mobile subscription rates, GDP growth,mobile phone connectivity, and the rate of urbanization all have a positive effect on
mobile money adoption, again using macro data. Afawubo, Couchoro, Agbaglah and
Gbandi (2020) show that factors related to education and sex and being clients ofa bank or microfinance institution are key determinants of mobile money adoption.
Akinyemi and Mushunje (2020) find that young, educated, and rural dwellers tend touse mobile money in Africa. Tonuchi (2020) identifies infrastructure as a key challenge
to the expansion of digital financial services in Nigeria. Mothobi and Grzybowski (2017)
examine whether the infrastructure—proxied by the level of night light—influences mobile money adoption using microeconomic data. They find that the adoption of mobile
phones is higher in areas with a better physical infrastructure.3
In addition, another strand of the literature examines the effect of the use of mobile money on economic outcomes. Aker et al. (2016) and Muralidharan et al. (2016)3

The literature is expanding rapidly (Hasbi and Dubus, 2020; N’dri and Kakinaka, 2020; Akinyemi
and Mushunje, 2020; Kabengele and Hahn, 2021). For example, Kabengele and Hahn (2021) analyze
the institutional factors behind mobile money adoption and Akinyemi and Mushunje (2020) examine
the determinants of adopting mobile money technology in rural areas of Africa.

4

analyze the role of secure payments in Niger and India, respectively. Blumenstock
et al. (2015) conduct a randomized experiment to test the effectiveness of using mobile
money to pay salaries. Dupas and Robinson (2013) analyze the role of mobile money
as a secure way to deposit daily cash in microenterprises in Kenya. Jack and Suri
(2014) empirically demonstrate that, in Kenya, a household that uses mobile money
does not decrease consumption when faced with a negative income shock. Munyegera
and Matsumoto (2016) show that, in Uganda, a mobile money user receives remittances
more frequently and has higher real per capita consumption than a non-user. Blumenstock et al. (2016) and Riley (2018) analyze whether mobile money is useful to smoothconsumption for households that experience negative shocks. Suri and Jack (2016) analyze the long-run effect of the use of mobile money and find that 2 percent of Kenyan
households have moved out of poverty since its availability in the country because ofincreases in saving and financial resilience. Abiona and Koppensteiner (2020) analyze
how the use of mobile money affects education expenditure in Tanzania. Riley (2020)
finds, using field experiments, that disbursing loans through a mobile money accountto female business borrowers has a more significant effect on profit than disbursing
loans in cash.
We contribute to this literature in two ways. First, we examine the effect of accessibility to mobile networks on the use of mobile money using GPS data on household
location and micro-data that include household characteristics. Previous studies thatexamine the relationship between network accessibility and mobile money adoption
rely on macro-data.4 By contrast, using GPS information and micro-data, we candirectly measure accessibility to mobile networks and control for various confounding
factors. To the best of our knowledge, our study is the first to combine mobile network
maps and household GPS information to examine the effect of accessibility to mobile
networks on the use of mobile money. In addition, we can quantify the extent to whichpeople live in areas with multiple mobile network connections. We find that about
70 percent of sample households live in an area with multiple mobile networks. This4

One exception is the study by Mothobi and Grzybowski (2017). However, their key explanatory
variable is night light level as opposed to network accessibility.

5

information can be useful for considering a mobile money policy. Second, we use various geographically referenced data such as satellite night light data and the population
density map constructed by CIESIN (2015) to control for confounding factors such as
income level and population density and thus provide a causal interpretation of theestimates.
The remainder of our paper is organized as follows. In Section 2, we discuss ourdataset. In Section 3, we describe the estimating equation. In Section 4, we present
the estimation results. In Section 5, we discuss our results and conclude.
2
2.1

DatasetHousehold Datasets

This study uses household survey data published by Financial Inclusion Insights, a
research program funded by the Bill & Melinda Gates Foundation. Financial Inclusion
Insights provides data on household living conditions and finance in Africa and Asia
for use in consumer demand-side research and policy interventions (Financial Inclusion
Insights Program, 2017).
Among the datasets available from Financial Inclusion Insights, we select datasets
based on the following inclusion criteria: GPS information on household location isavailable, mobile network information is available, and mobile money is introduced inthis country. The countries that satisfy these criteria are Bangladesh, Kenya, Nigeria, Pakistan, Tanzania, and Uganda, and we choose the datasets of those countries in2017. Figure A3 shows the bank account and mobile phone ownership rates in those six
countries. Each dataset is an interview-based survey of randomly selected households
aged 15 years and above and includes questions on personal and household characteristics such as the age, sex, occupation, and education level of respondents as wellas questions on financial inclusion such as access to and usage of bank accounts andmobile money. In addition, GPS information and the distances to banks and ATMs areavailable. In our study, the GPS information in this micro-level dataset is combinedwith the map data described below as spatial information. We convert this information
6

into numerical information and use it as the variables.
2.2

Map Data on Network Coverage

In addition to the Financial Inclusion Insights data, we use map data on network
coverage published by the GSM Association. The GSM Association asks the mobile
operators in each country to provide network coverage maps and publishes the network
coverage map of each operator in each country to the public.5
The key variable to quantify network accessibility is the shortest distance to theborder of the area with multiple mobile networks. This distance takes a negative value
if a household is located within the area with multiple mobile networks and takes apositive value otherwise. Thus, for a household in the center of area with multiple
mobile networks, this value is a large negative number. For a household on the borderof the area with multiple mobile networks, this value is zero. For a household outside
the area with multiple mobile networks, the value is high and positive. When the
distance to the area with multiple mobile networks is a high negative value, it implies
that a household is living in an area covered by multiple mobile networks. Thus, whena member of the household visits a nearby place for shopping or work, this person’s
location is still covered by multiple mobile networks. On the other hand, when thenumber is positive and high, it implies that this person is not covered by multiple
mobile networks regardless of the direction in which they are moving.
One of the major motivations for using mobile money is to send and receive money(Jack and Suri, 2014; Munyegera and Matsumoto, 2016). When a person needs to sendor receive money, this person naturally wants to use the same brand of mobile money
service as her or his friends and family use. If multiple mobile networks are available,
the probability that the same brand of mobile money service as the friends and family
use is available in this area becomes high. In such a case, this person has a strong
motivation to use mobile money. On the other hand, when only one mobile network
is available, the brand of mobile money service available in this area could differ from
5

Available at https://www.gsma.com/coverage/ (2020/11/05 accessed).

7

that used by their friends and family. In such a case, the person has less incentive to
adopt mobile money.
To calculate the shortest distance to the border of the area with multiple mobile
networks, we first list mobile network providers whose market shares are in the top
three in their respective countries. Then, we select mobile network providers whosemarket share is at least 80 percent. Table A1 shows the market share of mobile phonecompanies in each country.
We select Banglalink and Robi Axiata in Bangladesh; Safaricom and Airtel in
Kenya; MTN, Airtel, and Glo in Nigeria; Jazz and Zong in Pakistan; Airtel and Tigo
in Tanzania; and MTN and Airtel in Uganda.6 Then, for each country, we identify
the intersection of the network areas covered by the selected network providers. Once
we identify such an area for each country, for each household location, we measure theshortest distance to the boundary of the area covered by multiple mobile networks.
When a household is inside (outside) this area, we code it with a negative (positive)
value. We use this distance variable as the proxy for the network accessibility of eachhousehold. As an example, Figure A4 shows the intersection of the network coverage
map of three mobile network providers in Uganda. The yellow area is the area inwhich multiple mobile networks are available and the blue points are the locations ofhouseholds.
2.3

Map Data on Population Density and Night Light Intensity

We add population density and night light intensity around each household as control
variables to more accurately estimate the impact of network accessibility on households’
adoption of mobile money while controlling for the effect of other covariates. Population
density is used as a proxy of the urbanicity of the area and night light intensity is used
as a proxy of income level following a recent study measuring income at the sub-regional
6

Bangladesh’s Grameenphone has the largest market share in the country (52 percent), but is
excluded from the analysis because it covers the entire country and using its network-covered area
does not offer additional information. Telenor in Pakistan, which has the second largest market sharein the country (21.9 percent), is also excluded since no adequate coverage map is available.

8level (Henderson et al., 2012).7 Because these datasets are not included in the Financial
Inclusion Insights data, we create those variables by overlaying the GPS informationof each household onto the population density and night light maps.
For population density, we use the map data published by the Center for International Earth Science Information Network (CIESIN) at Columbia University (CIESIN,2015). These map data are available in a raster format. To each 30-second square(about 1 km near the equator) cell, the map assigns a value from 0 to 455,159 to estimate population density based on census and population registries. This population
dataset has been updated every five years since 2000. We use data on 2005, before
the deployment of mobile money services, to avoid the endogenous effect of network
coverage on population movement.
The National Oceanic and Atmospheric Administration at the National GeophysicalData Center processes and archives night light data acquired by the U.S. Air ForceDefense Meteorological Satellite Program Operational Linescan System from 1992 to2013 (NOAA, 2015). These satellite data are available in raster format. Each 30second square cell is assigned a value from 0 to 63 depending on night light intensity.
As with the mapping data for population density, we use the dataset on 2005 to avoid
the endogeneity from the mobile network to income and night light. Figure A6 shows
the night light map of 2005 used in our analysis.
3

Estimation Strategy

To analyze the impact of network accessibility on the use of mobile money, we estimate
the following linear probability model using ordinary least squares estimation:
Mobile Moneyi = β0 + β1 Distancei + γXi + εi
7(1)Several economists have recently used night light level to approximate income when information
on income at the sub-regional level is unavailable. For example, see Alesina, Michalopoulos andPapaioannou (2016), Storeygard (2016) and Pfeifer et al. (2018).

9where Mobile Money is a dummy variable equal to one if a household used a mobile
money service in the past 90 days. i is an index of each household. The explanatory
variable Distancei is the shortest distance (in kilometers) from the household’s location
to the boundary of the area covered by multiple mobile networks, which is negative(positive) if the household is inside (outside) an area with multiple mobile networks.
Xi is a set of control variables.
We estimate the above equation separately for the six countries since the estimatedcoefficients of the explanatory variables are unlikely to be the same across these countries.
There are several concerns when using ordinary least squares to estimate equation(1). The first is omitted variable bias because the distance to the mobile network
could be correlated with other covariates such as education, income, and urban/rural
location. Since those variables are also directly correlated with the use of mobile
money, the estimated coefficient would be biased and would not measure the effect ofthe distance to the mobile network.
The second concern is the measurement error problem. The data provided bymobile network operators may not be sufficiently accurate since this is a theoreticalvalue calculated from the location of the mobile network tower and strength of its radio
waves.
The third concern is endogeneity. The mobile network operator may build a mobile
network tower in an area in which demand for using mobile money is high. In such acase, our estimated coefficient of the distance to the mobile network would be upward
biased. This implies that when the estimated coefficient is significant, we need tocarefully interpret the estimated coefficient of the key explanatory variable, namely,
the distance to mobile network areas.
To address the first issue, we include a large set of control variables in Xi in equation(1): age, sex, marital status, status in the household, employment status, educationlevel (14 categories), residential location (urban or rural), distance to the nearest bankbranch, and distance to an ATM. To control for the average income of the area and10

population density, we include in Xi population density and average night light intensity within a radius of 10 km from each household’s location. To control for the
potential effect of the mobile network on population density and economic activity, wemeasure population density and average night light intensity in 2005.
To address the second issue, we first identify the areas covered by multiple mobile
networks and assume that the mobile connection is stable and strong in those areas. To
measure network accessibility, the areas covered by the multiple mobile networks suffer
less measurement error than those covered by a single company’s mobile network.
To address the third issue, we conduct the so-called coefficient stability test originally proposed by Altonji, Elder and Taber (2005) and later refined by Oster (2019).
This method gradually increases the number of control variables that could be correlated with the error term and examines how the estimated coefficient of the key
explanatory variable changes. If the change in the estimated coefficient of the key
explanatory is relatively small, this test concludes that the chance of the estimatedcoefficient being biased due to endogeneity is low.8
4

Results

4.1

Description of the Data

Table 1 shows the summary statistics of the dataset of the Financial Inclusion Insights
and map data. We examine a sample of 18,340 households from the six countries.
The dependent variable is a dummy variable for the use of mobile money, coded 1
if the household has used any mobile money service within the past 90 days and 0
otherwise. The main explanatory variable is the shortest distance from the household
location to the boundary of the areas covered by multiple mobile networks. Thisdistance is positive (negative) if the household is outside (inside) the area covered by
8

The coefficient stability test proposed by Altonji, Elder and Taber (2005) and Oster (2019) has
become influential in empirical research. For example, Oster (2019) already had more than 1600
citations in Google Scholar by January 2021. Their technique is now widely used in papers publishedin major economics journals. For recent applications, see Mian and Sufi (2014) and Michalopoulos
and Papaioannou (2016).

11

Table 1. Summary Statistics of Selected Variables in Pooled Data Set
(1)
mean

(2)
sd

(3)
min

(4)
max

Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)

0.228

0.420

0

1

Distance to nearest network border (km)(Control Variables)-3.09215.28-44.61

166.9

Age of respondant

33.86
0.440
0.751
0.416
0.624
0.341
0.503
6.261
1.731

7.962
0.496
0.433
0.493
0.484
0.474
0.500
1.636
0.897

22
0
0
0
0
0
0
-2.461
0.693

50
1
1
1
1
1
1
10.83
4.143

0.714
0.230
0.702

0.452
0.421
0.457

0
0
0

1
1
1

VARIABLES(Main Dependent Variable)

Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0)
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0)
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummyN

18,340multiple mobile networks. Since the mean value of the explanatory variable is -3.081,
households live 3.081 km inside the boundary of the network area covered by multiple
mobile networks on average. The mean value of the network coverage dummy is 0.714,
indicating that 71.4 percent of households live in an area in which multiple mobile
networks are available.
The control variables are the respondent’s age, sex, marital status, and educationlevel (14 categories). We also include as control variables a male-headed dummy,
currently working dummy, urban dummy, and farmer dummy as well as the distanceto a bank (five categories), distance to an ATM (five categories), and the natural logs
of population density and night light intensity at the household location. Population
density and night light intensity are measured within a radius of 10 km from the12household location using the population density map and night light map from 2005.9
To save space in Table 1, we pool the descriptive statistics of the six countries. Tables
A2–A7 in the Appendices shows the descriptive statistics for each country. Figure

0.05

Fraction
.1

.15

.2

Figure 1: Histogram of the Distance from the Border of the Area with Multiple
Mobile Networks

-50

0

50
100
Distance to nearest network border

150

Notes: The above histogram uses the pooled observations of the six countries. The
horizontal axis is the shortest distance from the border of the area with multiple mobile
networks. The unit is km.

1 shows the histogram of our key explanatory variable, the distance to the area withmultiple mobile networks. This variable takes a negative value if the household location
is in the network area and a positive value if it is outside. The histogram confirms thatabout 70 percent of households live in an area with multiple mobile networks.
To check the differences in the characteristics between countries, Table 2 shows themean values of the variables related to mobile money for each country. The averageuse rate of the mobile money dummy varies among the six countries. The highest rateis Kenya’s 78 percent and the second highest rate is Tanzania’s 45.2 percent. The
countries with a low use rate of mobile money are Nigeria and Pakistan, whose rates
are 2.1 percent and 2.8 percent, respectively. The overall average use rate is 22.8
percent. Thus, there is substantial variation in the mobile money use rate among thesix countries. In contrast, the differences in mobile network coverage and mobile phone9

We do not include a bank account ownership dummy or a mobile phone ownership dummy becauseof the possible endogeneity of these variables.

13

ownership between countries are small and do not deviate significantly from the overall
average.
Table 2. Mean of Mobile Money Related Variables by Country
Multiple

MobileBanknearestNetworks

Mobile Phone

money useaccount

network

coverage

Ownership

dummy
0.155
0.780
0.02110.0282
0.452
0.446
0.228

dummy
0.236
0.364
0.379
0.130
0.122
0.127
0.23

border (km)-10.16-2.6681.201-1.937-0.270-3.135-3.092

dummy
0.791
0.800
0.594
0.690
0.709
0.769
0.714

dummy
0.724
0.841
0.697
0.653
0.692
0.644
0.702

Country
Bangladesh
Kenya
Nigeria
Pakistan
Tanzania
UgandaAll

4.2

Distance to

N
3,995
2,034
3,843
4,538
2,029
1,901
18,340Results Using the Bangladesh Dataset

Figures 2(a) and 2(b) show the relationship between the distance from the border of
the area with multiple mobile networks and the average mobile money use rate in
Bangladesh. The size of each circle shows the sample size at each distance. In Figure1(a), we do not control for any of the covariates; however, in Figure 2(b), we control
for all the control variables: the respondent’s age, sex, marital status, and educationlevel (14 categories); a male-headed dummy, currently working dummy, urban dummy,
and farmer dummy; distance to a bank (five categories) and distance to an ATM (fivecategories); and the natural logs of population density and night light intensity at thehousehold location.
When we do not control for any of the control variables, there is a clear negative
relationship between the distance from the border of the area with multiple mobile
networks and the average mobile money use rate. However, once we control for thecontrol variables, Figure 2(b) shows no clear relationship between those two variables.
Table 3 shows the regression results. In column (1) of Table 3, we do not include
any of the control variables. In columns (1) to (4), we increase the number of control
variables. When there are no other control variables, column (1) shows that if a
14

Figure 2: Relationship between the Mobile Money Usage Rate and Distance to the
Network Area in Bangladesh
Mobile Money Use Rate and Distance to Network in Bangladesh

0.1

Mobile Money Use Rate
.2 .3 .4 .5 .6

Mobile Money Use Rate-.2 -.1 0 .1 .2 .3 .4 .5 .6 .7.7.8

Mobile Money Use Rate and Distance to Network in Bangladesh

-40

-30
-20
-10
0
10
20
Distance to the Nearest Border of Network Coverage(km)(mean) mmoney

30

-25

Mean of Mobile Money Use Rate

-15
-5
5
15
25
Distance to the Nearest Border of Network Coverage(km)(mean) resY(a) Before Control

35

Residuals of Mobile Money Use Rate(b) After Control

Notes: In Figure 2(a), for each bin, the average mobile money use rate is calculated fora bin size of 0.5 km. Then, the scatterplot of the average mobile money use rate andfitted line are displayed with the average use rate of mobile money on the vertical axis
and the distance variable on the horizontal axis. The size of the circle is the sample
size in each bin. On the right of Figure 2(b), the horizontal axis is the residual of theregression regressing the distance on all the covariates. The vertical axis is the residualof the regression regressing the mobile money use dummy on all the covariates. The sizeof the bin is 0.5 km. the covariates are the respondent’s age, sex, marital status, andeducation level (14 categories); a male-headed dummy, currently working dummy, urban
dummy, and farmer dummy; distance to a bank (five categories) and distance to an ATM(five categories); and the natural logs of population density and night light intensity at
the household location.

household is 10 km closer to the area with multiple mobile networks, the probabilityof using mobile money increases by 1.81 percentage points. However, column (4)shows that such a relationship between the two variables disappears once we control
for several confounding factors, especially population density and average night lightintensity, which are proxies of income and urbanicity. Column (4) shows that theestimated coefficient of the distance from the area with multiple mobile networks to
the use of mobile money is low, positive (in the opposite direction), and not significant.
Thus, we cannot confirm that better accessibility to mobile networks increases the useof mobile money.

15

Table 3. The Effect of Distance to the Area with Multiple Mobile Networks in Bangladesh
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM
(1)

(2)

(3)

(4)

‐0.00191***‐0.000903*‐0.00103**0.000270(0.000499)(0.000478)(0.000487)(0.000702)

R‐squared

0.005

0.100

0.103

0.105

N

3,995

3,995

3,995

3,995

Yes

Yes

Yes

Yes

Yes

Distance

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Demographic characteristics are therespondentʹs age, sex, marital status, and education level (14 categories) as well as a male‐headed dummy, currently working dummy, urban dummy, and farmer dummy, . Both the
distance to a bank and the distance to an ATM are categorical variables (five categories). ***p<0.01, ** p<0.05, * p<0.1.

4.3

Results Using the Kenya Dataset

Figures 3(a) and 3(b) show the relationship between the distance from the border of
the area with multiple mobile networks and the average use rate of mobile money in
Kenya. In Figure 3(a), we do not control for any of the control variables, whereas
we include all the control variables used in Bangladesh’s figure in Figure 3(b). When
we do not include any of the control variables, there is a clear negative relationshipbetween the distance from the border of the area with multiple mobile networks andthe average use rate of mobile money. However, once we control for the effect of thecontrol variables, Figure 3 (b) shows no clear relationship between the two variables.
Table 4 shows the regression results. When we do not control for any of the control
variables, column (1) shows that if a household is 10 km closer to the area with multiple
mobile networks, the probability of using mobile money increases by 4.35 percentage
points. However, column (2) shows that the statistically significant relationship dis16

Figure 3: Relationship between the Mobile Money Usage Rate and Distance to the
Network Area in Kenya
Mobile Money Use Rate and Distance to Network in Kenya

0

.1

-.4

Mobile Money Use Rate
-.2
0
.2
.4

Mobile Money Use Rate
.2 .3 .4 .5 .6 .7 .8.9.6

Mobile Money Use Rate and Distance to Network in Kenya

-40

-20
0
20
40
60
80
100
120
Distance to the Nearest Border of Network Coverage(km)(mean) mmoney

140

-40

Mean of Mobile Money Use Rate

-20
0
20
40
60
80
100
120
Distance to the Nearest Border of Network Coverage(km)(mean) resY(a) Before Control

Residuals of Mobile Money Use Rate(b) After Control

Note: Notes of Figure 2 apply.
Table 4. The Effect of Distance to the Area with Multiple Mobile Networks in Kenya
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM
(1)

(2)

(3)

(4)

-0.00435***-0.00111*-0.00107-6.19e-05(0.000650)(0.000659)(0.000676)(0.000735)

R-squared

0.035

0.169

0.175

0.180

N

2,034

2,034

2,034

2,034

Yes

Yes

Yes

Yes

Yes

Distance

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

appears once we control for the demographic factors. Columns (3) and (4) show thatwhen we control for the distances to a bank and an ATM, population density, and night
light intensity, the estimated coefficient becomes low and not statistically significant.
This shows that when a household becomes 10 km closer to the area with multiple mobile networks, the probability of using mobile money increases by -6.19e-04 percentage
17

points. Given that the average use rate of mobile money in Kenya is 78 percent, theeffect is practically zero.
4.4

Results Using the Nigeria Dataset

Figure 4: Relationship between the Mobile Money Usage Rate and Distance to the
Network Area in Nigeria

Mobile Money Use Rate
-.1
0
.1
-.2

0

Mobile Money Use Rate
.1
.2

.2

Mobile Money Use Rate and Distance to Network in Nigeria.3

Mobile Money Use Rate and Distance to Network in Nigeria

-20

-10
0
10
20
30
40
Distance to the Nearest Border of Network Coverage(km)(mean) mmoney

50

-15

Mean of Mobile Money Use Rate

-5
5
15
25
35
45
Distance to the Nearest Border of Network Coverage(km)(mean) resY(a) Before Control

Residuals of Mobile Money Use Rate(b) After Control

Note: Notes of Figure 2 apply.

Table 5. The Effect of Distance to the Area with Multiple Mobile Networks in Nigeria
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM

Distance

(1)

(2)

(3)

(4)

‐0.000753***

7.12e‐05

0.000275

0.000184(0.000250)(0.000269)(0.000280)(0.000299)

R‐squared

0.001

0.073

0.080

0.080

N

3,843

3,843

3,843

3,843

Yes

Yes

Yes

Yes

Yes

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05,* p<0.1.

18

Nigeria has the lowest mobile money use rate (2.1 percent; Table 2). Figures 4(a)and 4(b) show the relationship between the distance from the border of the area withmultiple mobile networks and the average use rate of mobile money in Nigeria. In
Figure 4(a), we do not control for any of the control variables, whereas we include all
the control variables used in Figure 1 in Figure 4(b). When we do not include any of
the control variables, there is a negative relationship between the distance from theborder of the area with multiple mobile networks and the average use rate of mobile
money. However, once we control for the effect of the control variables, Figure 4(b)shows no clear relationship between the two variables.
Table 5 shows the regression results. When we do not include any of the control
variables, column (1) shows that if a household is 10 km closer to the area with multiple
mobile networks, the probability of using mobile money increases by 0.75 percentage
points. However, column (2) shows that the statistically significant relationship disappears and the estimated coefficient becomes virtually zero once we control for thedemographic factors. Columns (3) and (4) show that when we control for the distances
to a bank and an ATM, population density, and night light intensity, the estimatedcoefficient becomes low, positive (having the opposite sign) and not statistically significant. Thus, we cannot confirm that increasing accessibility to mobile networks
increases the use of mobile money in Nigeria.
4.5

Results Using the Pakistan Dataset

Pakistan has a low mobile money use rate, almost as low as that of Nigeria (2.8 percent;
Table 2). Figures 5(a) and 5(b) show the relationship between the distance from theborder of the area with multiple mobile networks and the average use rate of mobile
money in Pakistan. In Figure 5(a), we do not control for any of the control variables,
whereas we include all the control variables used in the previous figures in Figure 5(b).
When we do not control for any of the control variables, there is no clear relationshipbetween the distance from the border of the area with multiple mobile networks andthe average use rate of mobile money. However, once we control for the effect of the19control variables, Figure 5(b) shows a solid relationship between the two variables.
Table 6 shows the regression results. When there are no control variables, column(1) shows that the effect of increasing network accessibility is practically zero. However, once we control for the demographic variables, there is a statistically significant
relationship between the distance to the area with multiple networks and the use ofmobile money. Column (2) of Table 6 shows that if a household is 10 km closer to thearea with multiple mobile networks, the probability of using mobile money increases
by 0.33 percentage points. Column (4) shows that if a household is 10 km closer to thearea with multiple mobile networks, the probability of using mobile money increases
by 0.4 percentage points. Although this 0.4 percentage point effect looks small, theaverage mobile money use rate in Pakistan is just 2.8 percent. Thus, if a household
location becomes 10 km closer to the area with multiple mobile networks, the probability of using mobile money increases by (0.4/2.8) × 100 = 14.3 percent, which is not
small economically.
Figure 5: Relationship between the Mobile Money Usage Rate and Distance to the
Network Area in Pakistan
Mobile Money Use Rate and Distance to Network in Pakistan

0.1

Mobile Money Use Rate
.2
.3
.4
.5
.6

Mobile Money Use Rate-.2 -.1 0 .1 .2 .3 .4 .5 .6 .7 .8.7

Mobile Money Use Rate and Distance to Network in Pakistan

-40

-20

0
20
40
60
80
100
120
140
Distance to the Nearest Border of Network Coverage(km)(mean) mmoney-40

Mean of Mobile Money Use Rate

-20
0
20
40
60
80
100
120
Distance to the Nearest Border of Network Coverage(km)(mean) resY(a) Before Control

Residuals of Mobile Money Use Rate(b) After Control

Note: Notes of Figure 2 apply.

4.6

Results Using the Tanzania Dataset

The mobile money use rate in the Tanzania dataset is 45.2 percent, making it one of
the six countries with relatively high use along with Uganda. Figures 6(a) and 6(b)20Table 6. The Effect of Distance to the Area with Multiple Mobile Networks in Pakistan
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM

Distance

(1)

(2)

(3)

(4)

‐7.56e‐05‐0.000334***‐0.000372***‐0.000406***(8.61e‐05)(9.19e‐05)(9.55e‐05)(0.000119)

R‐squared

0.000

0.053

0.056

0.061

N

4,538

4,538

4,538

4,538

Yes

Yes

Yes

Yes

Yes

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

show the relationship between the distance from the border of the area with multiple
mobile networks and the average use rate of mobile money in Tanzania. In Figure6(a), we do not include any of the control variables, whereas we include all the control
variables used in the previous figures in Figure 6(b). When we do not control for anyof the covariates, there is a clear negative relationship between the distance from theborder of the area with multiple mobile networks and the average use rate of mobile
money. Further, even when we control for the effect of the control variables, Figure6(b) shows a clear relationship between the two variables.
Table 7 shows the regression results. When we do not include any of the control
variables, column (1) shows that if a household is 10 km closer to the area with multiple
mobile networks, the probability of using mobile money increases by 7.6 percentage
points, statistically significant at the 1 percent level. Column (4) shows that evenwhen we include all the control variables and when a household’s location becomes 10km closer to the area with multiple mobile networks, the probability of using mobile
money increases by 4.16 percentage points, statistically significant at the 1 percent

21

Figure 6: Relationship between the Mobile Money Usage Rate and Distance to the
Network Area in Tanzania
Mobile Money Use Rate and Distance to Network in Tanzania

0

-.4

.1

Mobile Money Use Rate
-.2
0
.2
.4

Mobile Money Use Rate
.2 .3 .4 .5 .6 .7

.8

.6

.9

Mobile Money Use Rate and Distance to Network in Tanzania

-20

0
20
40
60
Distance to the Nearest Border of Network Coverage(km)(mean) mmoney

80

-20

Mean of Mobile Money Use Rate

0
20
40
60
Distance to the Nearest Border of Network Coverage(km)(mean) resY(a) Before Control

80

Residuals of Mobile Money Use Rate(b) After Control

Note: Notes of Figure 2 apply.

level. Since the average use rate of mobile money in Tanzania is 45.2 percent, this
implies that if a household location becomes 10 km closer, the probability of using
mobile money rises by (4.16/45.2) × 100 = 9.2 percent.
Table 7. The Effect of Distance to the Area with Multiple Mobile Networks in Tanzania
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM

Distance

(1)

(2)

(3)

(4)

‐0.00759***‐0.00477***‐0.00463***‐0.00416***(0.000833)(0.000803)(0.000797)(0.000803)

R‐squared

0.037

0.188

0.201

0.206

N

2,029

2,029

2,029

2,029

Yes

Yes

Yes

Yes

Yes

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

22

4.7

Results Using the Uganda Dataset

The mobile money use rate in Uganda is 44.6 percent, similar to the rate in Tanzania.
Figures 7(a) and 7(b) show the relationship between the distance from the border of
the area in which multiple mobile networks are available and the average use rate ofmobile money. In Figure 7(a), we do not include any of the control variables, whereas
we include all the control variables used in the previous figures in Figure 7(b). When
we do not include any of the control variables, there is a clear negative relationshipbetween the distance from the border of the area with multiple mobile networks andthe average use rate of mobile money. Further, even if we control for the effect of thecontrol variables, Figure 7(b) shows a clear relationship between the two variables.
Figure 7: Relationship between the Mobile Money Usage Rate and Distance to the
Network Area in Uganda
Mobile Money Use Rate and Distance to Network in Uganda

-.4

0

.1

Mobile Money Use Rate
-.2
0
.2
.4

Mobile Money Use Rate
.2 .3 .4 .5 .6 .7

.8

.6

.9

Mobile Money Use Rate and Distance to Network in Uganda

-20

0
20
Distance to the Nearest Border of Network Coverage(km)(mean) mmoney

40

-20

Mean of Mobile Money Use Rate

020
Distance to the Nearest Border of Network Coverage(km)(mean) resY(a) Before Control

40

Residuals of Mobile Money Use Rate(b) After Control

Note: Notes of Figure 2 apply.

Table 8 shows the regression results. When we do not include any of the control
variables, column (1) shows that if a household is 10 km closer to the area with multiple
mobile networks, the probability of using mobile money increases by 11.1 percentage
points, statistically significant at the 1 percent level. Column (4) shows that when
we control for the effect of all the control variables and when a household’s location
becomes 10 km closer to the area with multiple mobile networks, the probability ofusing mobile money increases by 3.95 percentage points, statistically significant at the
10 percent level. Since the average use rate of mobile money in Uganda is 44.6 percent,23

Table 8. The Effect of Distance to the Area with Multiple Mobile Networks in Uganda
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM
(1)

(2)

(3)

(4)

‐0.0151***‐0.00634***‐0.00514***‐0.00395*(0.00213)(0.00181)(0.00186)(0.00209)

R‐squared

0.030

0.217

0.228

0.229

N

1,901

1,901

1,901

1,901

Yes

Yes

Yes

Yes

Yes

Distance

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

this implies that if a household location becomes 10 km closer, the probability of using
mobile money increases by 8.8 percent.
4.8

Robustness Checks

In the previous subsection, we examined the relationship between the distance fromthe area with multiple mobile networks and the use of mobile money. The analysis
showed that in Uganda, Tanzania, and Pakistan, there are statistically and economically significant relationships between the distance from the area with multiple mobile
networks and the use of mobile money. In this subsection, we examine the robustnessof those results.
Using the Probit model and Restricting the Sample to within 20 km of the Border
Table 9 shows the results of the robustness check when using a different model anddifferent samples. The rows in Table 9 show the estimation results using the datasetsof the different countries. To compare the estimation results of the Probit model withthose of the linear probability model, we show column (4) of the estimation results of24

the linear probability model in column (1) of Table 9. Column (2) of Table 9 shows theestimation results using the Probit model. To estimate the Probit model, we include
all the control variables used in the linear probability model. Column (2) of Table 9
presents the marginal effect of the distance from the border of the area with multiple
mobile networks, showing that the estimated coefficients of the Probit model are similar
to those of the linear probability model.
Column (3) of Table 9 shows the estimation results when we restrict the sample
to households who live within 20 km of the border of the area with multiple mobile
networks regardless of whether they live inside or outside the network area. The idea
is that if we include a remote household, it might differ substantially from households
that live close to the border of the network. In such a case, the estimated coefficient
might represent not only the distance effect but also the household heterogeneity effect.
To prevent such a possibility, we restrict the sample to households that live within 20
km of the border of the network area regardless of whether they live inside or outside
the network area.
The estimation results in column (3) show that the estimated coefficients change
little even if we restrict the sample to households that live within 20 km of the border ofthe network area in this way. In Pakistan’s case, the statistical significance disappears,
although the estimated coefficient is similar. This is likely due to the smaller sample
size and resulting higher standard error. Thus, we conclude that the results in theprevious subsection are robust to using a Probit model estimation and restricting thesample to households that live within 20 km of the area with multiple mobile networks.
Oster’s Coefficient Stability Test
Table 10 presents the results of the coefficient stability test. The different rows show
the estimation results using the datasets of the different countries. Column (1) showsthe estimated coefficient of the distance from the area with multiple mobile networks,
its standard error, and its R-squared value when no control variables are included in theestimating equation. The standard errors are in parentheses and the R-squared values
are in square brackets. Column (2) shows the estimated coefficient of this distance25

Table 9. Comparison of the Estimated Coefficient of Distance in the Different Specifications
Mobile Money Use Dummy

Dependent Variable
(1)

(2)

(3)

Estimation Model

LPM

ProbitLPM

SampleAll

All

Within 20km fromthe border

0.000270

0.0001300.000896(0.000702)(0.000881)(0.000937)[3995, 0.105][3955, 0.118][3214, 0.088]

Country
Bangladesh

Kenya

Nigeria

Pakistan

Tanzania

Uganda-6.19e-05

7.51e-05-0.000310(0.000735)(0.000580)(0.00234)[2034, 0.180][2034, 0.178][1881, 0.167]

0.000184

0.000258

0.000498(0.000299)(0.000325)(0.000408)[3843, 0.080][3843, 0.277][3734, 0.080 ]-0.000406***-0.000453***-0.000306(0.000119)(0.000159)(0.000393)[4538, 0.061][4538, 0.237][ 3920, 0.061]-0.00416***-0.00447***-0.00736***(0.000803)(0.000948)(0.00254)[2029, 0.206][2029, 0.164][1915, 0.204]-0.00395*-0.00400*-0.00561**(0.00209)(0.00210)(0.00264)[1901, 0.229][1901, 0.187][1877, 0.229]

Notes: Each cell shows the estimated coefficient of the distance from the border of the network
area, its standard error, the sample size, and the R-squared value. In all the regressions, thedependent variable is the mobile money use dummy and the regression equation includes all thecontrol variables used in column (4) in the previous tables. In the Sample row, "all" implies that
there is no restriction on the data. Within 20km from the border means that the sample isrestricted to households within 20 km of the border of the network area. Standard errors areshown in parentheses and the sample size and R-squared value are shown in square brackets. ***p<0.01, ** p<0.05, * p<0.1.

variable, its standard error, and its R-squared value when all the control variables areused.
Column (3) shows the R-max, which is a hypothetical R-squared when all the26Table 10: Coefficient Robustness to Unobservable Factors

Bangladesh

Kenya

Nigeria

Pakistan

Tanzania

Uganda

(1)

(2)

(3)

(4)

(5)

Baseline EffectControlled Effect

R‐max

δ for

Identified Set(Std. Err), [R2]

(Std Err),[R2]

b =0

of b

‐0.00191***

0.00027

0.136

‐0.17

[0.00027, 0.00326 ](0.000499),[0.005 ](0.000702),[0.105]
0.234

0.025

[‐6.19e‐05, 0.00284 ]‐0.00435***‐6.19E‐05(0.000650),[0.035](0.000735),[0.180]‐0.000753***0.000184(0.000250),[0.001](0.000299),[0.080]‐7.56e‐05‐0.000406***(8.61e‐05), [0.000](0.000119),[0.061]‐0.00759***‐0.00416***(0.000833), [0.037](0.000803),[0.206]‐0.0151***‐0.00395*(0.00213), [0.030](0.00209),[0.229]

0.105 ‐0.47117 [0.000184, 0.00066 ]

0.079

‐4.47

[‐0.00062 , ‐0.000406]

0.268

2.32

[‐0.00416, ‐0.00263]

0.298

0.67

[‐0.00395, 0.00230 ]Notes: The table shows the coefficient robustness to unobservable factors based on Oster (2019).
The rows show the estimation results using each countryʹs dataset. Column (1) shows theestimated coefficient of the distance from the border of the area with multiple mobile networks,
its standard error, and the R‐squared value in the baseline model. Standard errors are inparentheses and the R‐squared value is in square brackets. Column (2) shows the estimatedcoefficient of the same variable, its standard error, and the R‐squared value when all the control
variables are used. Column (3) shows the R‐max value, the maximum R‐squared value when all
unobservable factors are hypothetically included in the control variables. Oster (2019) argues that
1.3 multiplied by the R‐squared value when all observable control variables are used isappropriate. Thus, we follow Osterʹs recommendation. Column (4) shows the degree to which
unobservable factors need to be important to zero out the estimated coefficient of the distance.
Column (5) shows the potential region of the estimated coefficient of the distance when
unobservable factors are correlated with the distance as observable control variables.

observable and unobservable control variables are included in the estimating equation.10
10Since our dependent variable is a dummy variable, it is reasonable that the R-max does not
become 1 even if all the unobservable control variables are included.

27

Oster argues that it is reasonable to assume that the R-max is equal to 1.3 multipliedby the R-squared value of the estimation when all observable control variables areincluded. Following Oster’s recommendation, we calculate the R-max as the R-squared
value of column (2) multiplied by 1.3. Column (4) includes Altonji et al. (2005) andOster (2019)’s δ, which show the degree to which unobservable factors need to affectthe estimated coefficient of the distance variable compared with the degree to whichobservable control variables affect the estimated coefficient of the distance variable tozero out the latter. For example, if δ is 2, this implies that unobservable factors need
to affect the estimated coefficient of the distance variable twice as much as the degree
to which observable factors affect it to make the estimated coefficient zero. Altonji,
Elder and Taber (2005) and Oster (2019) argue that if δ is above one, then it is unlikelythat unobservable factors make the estimated coefficient of the distance variable zero.
Moreover, if δ is negative, this implies that as more observable control variables areadded, the estimated coefficient becomes further away from zero and more significant.
As a result, to make the estimated coefficient zero, unobservable factors must affect theestimated coefficient in the opposite direction to observable control variables. Sincesuch a case is unlikely, when δ is above one or negative, we can conclude that it isunlikely that unobservable factors will zero out the estimated coefficient of the distancevariable. Column (5) shows the robust region of the true value of the coefficient of the
distance variable assuming that unobservable factors affect the estimated coefficientto the same degree as the observable control variables. If this robust region is within
the negative (positive) region, it implies that the estimated coefficient in column (2) isrobust (not robust) to unobservable factors.
Investigating columns (4) and (5) of Table 10, we can conclude that the estimation
results using the Pakistan and Tanzania datasets are robust to potential unobservablefactors. Regarding Uganda, however, we cannot reject the possibility that unobservablefactors might make the estimated coefficient of the distance variable zero.
Analysis Using Different Network Maps
In the regression analysis using the Kenyan dataset, we obtain the network area by28

taking the intersection of the network areas of Safaricom (90.4 percent market share)
and Airtel (21.2 percent). However, since the network area of Airtel is much smaller
than that of Safaricom, one might argue that such a method is unsuitable since 90
percent of people in Kenya use Safaricom. Thus, as a robustness check, we use only
the network area of Safaricom. We measure the shortest distance to the network areaof Safaricom and use it as the distance to the network area in the regression analysis.
Table 11 shows the regression results using the distance to the network of Safaricomas the key explanatory variable. We see that the results are similar to those when the
distance variable is created using the intersection of the network areas of Safaricomand Airtel (see Table 4). In column (4), where all the control variables are included,
the results do not reach significance and the sign is the opposite to that in Table 4.
This result confirms that there is no significant relationship between the distance tothe network area and the use of mobile money in Kenya, where the mobile money use
rate is above 70 percent.
Table 11. Robustness Check for Kenya (Only Safaricom)
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM
(1)

(2)

(3)

(4)

‐0.00354***‐0.000928*‐0.000928*

6.58e‐06(0.000553)(0.000563)(0.000563)(0.000618)

R‐squared

0.025

0.175

0.175

0.180

N

2,034

2,034

2,034

2,034

Yes

Yes

Yes

Yes

Yes

Distance

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, **p<0.05, * p<0.1.

Similarly, in Nigeria, MTN (64.1 percent market share), Airtel (31.7 percent), andGlo (22.8 percent) are the major mobile network operators. One might think that the29

inclusion of Glo, which has a small market share, reduces the network area since we
use the intersection of these three mobile network operators. To check the robustnessof this result, we remove the network map of Glo. Thus, the network area is theintersection of only two companies, MTN and Airtel. Then, we measure the shortest
distance to the area with multiple mobile networks and rerun the regression. Table 12shows that there is no significant relationship between the two variables, confirming
the results in Table 5.
Table 12. Robustness Check for Nigeria (Only Two Mobile Operators)
Dependent Variable

Mobile Money Use Dummy

Estimation Model

LPM
(1)

(2)

(3)

(4)

8.28e‐06

0.000187

0.000227*

0.000189(0.000111)(0.000117)(0.000120)(0.000128)

Distance
R‐squared

0.000

0.030

0.080

0.080

N

3,843

3,843

3,843

3,843

Yes

Yes

Yes

Yes

Yes

Control Variables
Demographic Characteristics
Distances to Bank & ATM
Population Density

Yes

Night Light Luminosity

Yes

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, **p<0.05, * p<0.1.

5Discussion and Conclusion

In this study, we examine the effect of network accessibility on the use of mobile money
using GPS data on household location and the network coverage maps of mobile network providers. To quantify network accessibility, we measure the shortest distancefrom each household location to the border of the areas in which several mobile networks are available and use it as an index of network accessibility. To control for
confounding factors, we use various individual and household characteristics as well asnight light intensity and population density at the household location. To avoid the30

reverse causality running from the use of mobile money to night light intensity andpopulation density, we use the night light and population density maps for 2005 (i.e.,
before the introduction of mobile money). We find that 70 percent of households ineach country live in areas with multiple mobile networks.
The estimation results found in this study indicate that the effect of network accessibility on the use of mobile money is not universal in developing countries. Morespecifically, among the six countries (Bangladesh, Kenya, Nigeria, Pakistan, Tanzania,
and Uganda), there is a negative relationship between the distance to the area withmultiple mobile networks and the use of mobile money in five countries. However,once we control for demographic factors, distances to a bank and an ATM, population
density, and night light intensity, we find a negative relationship between those twovariables only in Pakistan, Tanzania, and Uganda. Furthermore, for Uganda, our coefficient stability test indicates that we cannot reject the possibility that unobservablefactors generate this negative relationship between the distance to the network area
and the use of mobile money. Thus, we find a robust relationship between the distanceto the network area and use of mobile money only in Pakistan and Tanzania.
In our six countries, the use rates of mobile money differ substantially. The highest
rate is 78 percent in Kenya and the lowest rate is 2.1 percent in Nigeria. Among thetwo countries in which the effect of network accessibility on the adoption of mobile
money is robust (Tanzania and Pakistan), the mobile money use rate in Tanzania is 45
percent, while that in Pakistan is 3 percent. Thus, the overall mobile money adoption
rate does not seem to affect how mobile networks impact on the use of mobile money.
This suggests that the impediment of the use of mobile money is not likely to comefrom the poor infrastructure but other reasons such as the reliability of mobile network
providers, inconvenience, or culture.
Our estimation results explain why studies using macro-data on network coverage,
which treat one country’s data as one observation, sometimes provide different resultson how network accessibility affects the use of mobile money. For example, Lashitew,
van Tulder and Liasse (2019) show that network coverage does not affect the use of31

mobile money using macro-data, while Asongu, Biekpe and Cassimon (2020) showthat network connectivity has a positive effect. Our results indicate that the effectof network accessibility can differ by country. Thus, the assumption of cross-country
studies that in principle assume that the effect of network accessibility is the sameacross countries is unlikely to hold.
Quantitatively, our results show that in Tanzania and Pakistan, if a household’s
location becomes 10 km closer to the area with multiple mobile networks or moves
10 km inside such an area, the probability of using mobile money increases by 10
percent. Although the overall mobile money use rates in Pakistan and Tanzania differ,these effects are similar in both countries, which may suggest that similar economic
mechanisms are working regarding mobile network and mobile money adoption in thesetwo countries.
However, our analysis had two limitations. First, the lack of panel data prevented
us from examining the dynamic aspect of the effect of the network on mobile money
adoption. The second limitation of our study was the number of countries used in our
analysis due to the lack of detailed network coverage maps in other countries. Owing
to this limitation, we restricted our analysis to six countries. This prevented us fromconducting a more extensive analysis, as discussed below.
Our results may have important policy implications for the expansion of mobile
networks. Recent economics research shows that mobile money offers various benefits
to the economy. For instance, it enhances consumption smoothing in the presenceof shocks (Jack and Suri, 2014; Blumenstock et al., 2016; Riley, 2018), encourages
the receipt of remittances (Munyegera and Matsumoto, 2016), economizes the welfare
system (Aker et al., 2016), and increases human capital accumulation (Abiona andKoppensteiner, 2020). Given such benefits, one might be tempted to suggest that the
government should encourage the expansion of mobile networks despite this approachneeding substantial initial investment, given that mobile network coverage is insufficientin the rural areas of many developing countries. In addition, as we discussed in the
Introduction, several companies are planning satellite-based Internet constellations,32

which can increase accessibility to the Internet. However, our results may indicatethat increasing network accessibility does not always raise the use of mobile money indeveloping countries.
An important future research question is which country, a government should increase network coverage to have the largest effect on financial inclusion and economic
activities. This information would be useful for obtaining the largest benefit from thegiven investment. When the number of countries in the analysis increases through theavailability of network coverage maps, such an analysis will be feasible.
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Appendices0

Mobile Phone Subscription Rate
20
40
60
80
100

120

Figure A1: Mobile Phone Subscriptions Globally (Percentage of the Population)

2003

2008

Year

East Asia & PacificSouth Asia

2013

2018

Middle East & North Africa
Sub-Saharan Africa

Note: Based on the authors’ calculation from the World Development Indicators.

38

Figure A2: The Spread of Mobile Money Globally

3

2

6

12
41

13

Less than10%
More than 10
More than 40%

More than 20%
More than 30%
More than 50%

Note: Based on the authors’ calculation from the Global Findex Report. The
number of sample countries is 77. This graph shows that of the 77 sample
countries, the mobile money use rate is less than 10 percent in 41 and lessthan 20 percent in 54.

39

100

Figure A3: Bank Account and Mobile Phone Ownership Rates in the Six Countries(Percentage of the Population)
94.5

80

85.3
75.9

73.1

69.5

40

60

60.6

35.9

36.4

0

20

23.3

Bangladesh

Kenya

Nigeria
BankAccount

13.4

12.7

12.0

Pakistan

Tanzania

UgandaMobile Phone

Note: Based on the authors’ calculation from the Findex Survey and World
Development Indicators. The numbers of bank accounts and mobile phones
are the percentage of the population above age 15.

40

Figure A4: Mobile Network Coverage Map and Household Locations in Uganda

Note: Constructed by the authors. Overlaying the maps of the three mobile
networks, we select the area of their intersection and then overlay household
locations (blue points).

41

Figure A5: Population Density Map

Source: CIESIN Gridded Population of the World, v4UN WPP-Adjusted
Population Density, v4.11 (2005).

Figure A6: Night Light Map

Source: NOAA

42

Table A1. Mobile Phone Operators and Market Shares
Country

Bangladesh

Kenya

Nigeria

Pakistan

Tanzania

UgandaMobile Phone
Rank

Operator

Share

(1)

Grameenphone

52.0%

(2)

Banglalink

22.0%

(3)

Robi Axiata

20.6%

(1)

Safaricom

90.4%

(2)

Airtel21.1%(3)

Orange

8.3%

(1)

MTN

64.1%

(2)

Airtel

31.7%

(3)

Glo

22.8%

(1)

Jazz

32.8%

(2)

Telenor

21.9%

(3)

Zong

12.3%

(1)

Vodacom

48.4%

(2)

Airtel

40.1%

(3)

Tigo

35.0%

(1)

MTN

58.4%

(2)

Airtel39.9%(3)Africell

5.3%

43

Table A2. Summary Statistics of Selected Variables of Bangladesh Data Set
VARIABLES(Main Dependent Variable)
Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)
Distance to nearest network border (km)(Control Variables)
Age of respondant
Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummy
N

(1)
mean

(2)
sd

(3)
min

(4)
max

0.155

0.362

0

1

-10.1612.84-44.61

26.69

33.99
0.440
0.902
0.386
0.426
0.283
0.601
7.394
1.607

7.922
0.496
0.298
0.487
0.495
0.451
0.490
0.971
0.792

22
0
0
0
0
0
0
6.012
0.693

50
1
1
1
1
1
1
10.83
3.809

0.791
0.236
0.724

0.407
0.424
0.447

0
0
0

1
1
1

3,995

44

Table A3. Summary Statistics of Selected Variables of Kenya Data Set
VARIABLES(Main Dependent Variable)
Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)
Distance to nearest network border (km)(Control Variables)
Age of respondant
Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummy
N

(1)
mean

(2)
sd

(3)
min

(4)
max

0.780

0.415

0

1

-2.66817.74-27.89

142.4

32.60
0.339
0.566
0.488
0.706
0.414
0.390
6.197
1.469

7.756
0.474
0.496
0.500
0.456
0.493
0.488
1.791
0.837

22
0
0
0
0
0
0
-2.461
0.705

50
1
1
1
1
1
1
10.01
3.654

0.800
0.364
0.841

0.400
0.481
0.366

0
0
0

1
1
1

2,034

45

Table A4. Summary Statistics of Selected Variables of Nigeria Data Set
VARIABLES(Main Dependent Variable)
Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)
Distance to nearest network border (km)(Control Variables)
Age of respondant
Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0)
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0)
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummy
N

(1)
mean

(2)
sd

(3)
min

(4)
max

0.0211

0.144

0

1

1.201

7.160

-14.06

49.52

33.54
0.503
0.710
0.425
0.732
0.408
0.441
5.873
1.759

7.853
0.500
0.454
0.494
0.443
0.491
0.497
1.615
0.886

22
0
0
0
0
0
0
2.836
0.694

50
1
1
1
1
1
1
10.18
3.949

0.594
0.379
0.697

0.491
0.485
0.460

0
0
0

1
1
1

2,034

46

Table A5. Summary Statistics of Selected Variables of Pakistan Data Set
VARIABLES(Main Dependent Variable)
Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)
Distance to nearest network border (km)(Control Variables)
Age of respondant
Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummy
N

(1)
mean

(2)
sd

(3)
min

(4)
max

0.0282

0.166

0

1

-1.937

21.53-27.83

166.9

34.88
0.497
0.888
0.331
0.515
0.327
0.552
6.260
2.251

7.942
0.500
0.315
0.471
0.500
0.469
0.497
1.421
0.921

22
0
0
0
0
0
0
2.188
0.693

50
1
1
1
1
1
1
10.53
4.143

0.690
0.130
0.653

0.463
0.337
0.476

0
0
0

1
1
1

4,538

47

Table A6. Summary Statistics of Selected Variables of Tanzania Data Set
VARIABLES(Main Dependent Variable)
Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)
Distance to nearest network border (km)(Control Variables)
Age of respondant
Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummy
N

(1)
mean

(2)
sd

(3)
min

(4)
max

0.452

0.498

0

1

-0.27012.67-16.00

67.24

33.42
0.356
0.602
0.489
0.777
0.333
0.516
5.296
1.389

8.046
0.479
0.490
0.500
0.416
0.471
0.500
1.964
0.696

22
0
0
0
0
0
0
0.535
0.744

50
1
1
1
1
1
1
9.866
3.660

0.709
0.122
0.692

0.454
0.327
0.462

0
0
0

1
1
1

2,029

48

Table A7. Summary Statistics of Selected Variables of Uganda Data Set
VARIABLES(Main Dependent Variable)
Mobile money use dummy (use=1, not=0)(Main Explanatory Variable)
Distance to nearest network border (km)(Control Variables)
Age of respondant
Gender dummy (Male=1, Female=0)
Marriage dummy (Married=1, Otherwise=0
Head dummy (Head=1, Otherwise=0)
Working dummy (Working=1, Otherwise=0
Location dummy (Urban=1, Rural=0)
Farmer dummy
Log of population density
Log of night light(Variables for Reference)
Multiple Networks coverage dummy
Bank account dummy
Mobile Phone Owership dummy
N

(1)
mean

(2)
sd

(3)
min

(4)
max

0.446

0.497

0

1

-3.1355.723-15.90

31.22

33.64
0.375
0.542
0.509
0.835
0.293
0.415
5.766
1.341

8.155
0.484
0.498
0.500
0.371
0.455
0.493
1.405
0.738

22
0
0
0
0
0
0
2.247
0.697

50
1
1
1
1
1
1
9.114
3.381

0.769
0.127
0.644

0.422
0.333
0.479

0
0
0

1
1
1

1,901

49