Tsukuba Economics Working Papers
No. 2021-002

The Effect of Mobile Money on Borrowing and Saving: Evidence from Tanzania
byHisahiro Naito
Askar Ismailov
Albert Benson Kimaro

March 2021

UNIVERSITY OF TSUKUBA
Program in Economic and Public Policy
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Effect of Mobile Money on Borrowing and Saving: Evidence from Tanzania?
Hisahiro Naitoa,1 , Askar Ismailovb,2 , Albert Benson Kimaro c,3,∗a University of Tsukuba, Japanb Central Bank of Uzbekistan, Uzbekistan and University of Tsukuba, Japanc Ministry of Finance, Tanzania and University of Tsukuba, Japan

Abstract
This study examines the effect of the use of mobile money services on borrowing and saving using data from Tanzania. We
estimate the causal effect of the use of mobile money on borrowing, saving, and receiving remittances by applying a two-stage
least squares estimation using the shortest distance to the border of the areas with multiple mobile networks, which is a proxy foraccessibility to a mobile network, as an instrumental variable, while controlling for distance to financial institutions, population
density of the residence, night light luminosity, and other important covariates. We find that when a household experiences anegative shock, mobile money non-users increase borrowing, while mobile money users do not. Further, the use of mobile money
increases the probability of saving in mobile money savings accounts and receiving remittances, while it decreases the probabilityof saving in less liquid assets such as livestock. On the other hand, we find that the effect of the use of mobile money on receiving
remittances is the same for those who experience a negative shock and those who do not. These results indicate that the useof mobile money increases the receipt of remittances regardless of negative shocks and changes the saving portfolio, allowing ahousehold to prepare for negative shocks. Hence, a household that uses mobile money does not need to increase borrowing in
the face of a negative shock. Consistent with this interpretation, we find that experiencing a negative shock does not decrease thelivelihood of mobile money users, while it does reduce that of non-users.
Keywords: Mobile Money, Saving, Borrowing, Remittance, Financial Behavior, Portfolio Choice

1. Introduction

5

In many sub-Saharan African countries, a substantial percentage of households do not use banks. Lacking a formal bankaccount makes it difficult for households to save safely and pre10? This research was conducted while A.I. and A.K. were affiliated with the
University of Tsukuba. H.N. conceptualized the research, designed the research, cleaned the data, analyzed the data, wrote the draft and wrote the paper.
A.I. collected the data, cleaned the data, analyzed the data and participatedin editing. A.K. helped collect the data. H.N. is responsible for any remain- 15
ing errors. A.I. appreciates the financial support from the Asian Development
Bank Japan Study Program, A.K. appreciates the financial support from the
Joint Japan World Bank Graduate Scholarship, and H.N. appreciates the financial support from the research fund of the University of Tsukuba. The opinions expressed in this paper do not represent those of the Asian Development
Bank, the Japanese government, the World Bank, or any organizations with 20
which A.I., A.K., and H.N. are affiliated. The authors appreciate the comments from Suanna Oh, Mari Minowa, Yuko Nakano, and Zeng Fei Yu as
well as the conference participants of the Western Economic Association International (2021) and anonymous referees. The Supplemental Information ofthis paper is available at https://github.com/hisahironaito/Mobile_money_saving_borrowing.
∗ Corresponding author
Email addresses: naito@dpipe.tsukuba.ac.jp (Hisahiro Naito ),asqar.ismailov@gmail.com (Askar Ismailov ), albenson10@gmail.com(Albert Benson Kimaro )1 Address: 6 1-1-1 Tennodai University of Tsukuba, Tsukuba City, Ibaraki
Prefecture Japan, Postal Code 305-8571
2 Address: 6 Islom Karimov Street, Tashkent, Uzbekistan Postal code:
1000013 Address: 41101 Uhuru Street, PO Box 201 Dodoma, Tanzania

Preprint submitted to Elsevier

pare for potential future negative shocks. However, recent technological developments have started to change the financial access of non-bank users due to the development of mobile money
technology. Mobile money allows the holder of a SIM card of amobile phone to transfer money to another holder with a different SIM card.4 In addition, mobile money operators often offera savings account in which customers can save with a reasonable interest rate by depositing money with the nearest mobile
money agent.5
According to a financial inclusion survey by the World Bank
(World Bank, 2014), only 55, 19, and 17 percent of adults havea bank account in Kenya, Tanzania, and Zimbabwe, respectively, whereas 58, 32, and 32 percent of adults already havemobile money accounts. Mobile money has also proliferated at
an accelerated rate. In Tanzania, mobile money was officiallyintroduced in 2008. In 2009, the user rate of mobile money wasjust 1.1 percent; however, this rose to 32 percent in 2013 and4 A mobile money account is attached to a mobile phone SIM card, not themobile phone itself. In developing countries, each individual often owns a SIM
card, but shares a mobile phone with others, especially in rural areas. Hence,even in such cases, it is possible for each individual to hold his or her own
mobile money account as long as he or she owns a SIM card.
5 The cost of the equipment needed to become a mobile money agent is much
lower than the cost of setting up a bank branch or ATM. One needs only a personal computer and mobile network access to become a mobile money agent.
In sub-Saharan countries, owners of small grocery shops often become mobile
money agents. This implies that mobile money agents are more available thanbank branches and ATMs.

June 7, 2021

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55.8 percent in 2017. The function of mobile money has also
expanded. For example, 51 percent of adult household heads in 80
Tanzania have saved in the past 12 months, one-third of whichhave saved in a mobile money savings account (see also Table
2 in Section 3).
Given this high speed market penetration and the expanding
function of mobile money, a natural question is to what degree 85
mobile money affects the borrowing and saving of households.
In developing countries, the lack of access to a safe method ofsaving can lead to insufficient saving. Insufficient saving andinsufficient credit access in the face of negative shocks makeit difficult for a household to buffer negative shocks. Hence, 90
the availability of safe saving methods through mobile money
might make it easier to smooth consumption.
Theoretically, there are several channels through which theuse of mobile money affects borrowing. First, when a household lacks access to a formal financial institution, the availabil- 95
ity of borrowing from a mobile money operator might increase
borrowing (new source effect) when it experiences a negative
shock. Second, on the contrary, if a household already has access to borrowing from a formal financial institution, the use ofmobile money allows it to switch the source of that borrowing100
to a mobile money operator (substitution effect). This, however,would not affect the probability of borrowing. Third, having amobile money account makes it easy for a household to borrowfrom relatives or friends in the face of negative shocks becauseof the low transfer fee, which increases borrowing (connection105
effect). Fourth, the presence of low-cost money transfers might
increase the possibility of households forming mutual insurancegroups (Jack and Suri, 2014) (insurance effect). This insurance
effect is likely to lower the need for borrowing, although it will
increase remittances (Ratha et al., 2003; Yang and Choi, 2007).110
Fifth, a mobile money user can receive more remittances because of the low cost of transferring money for altruistic reasons(Agarwal and Horowitz, 2002; Vanwey, 2004). Hence, whena household can receive more remittances, the need for borrowing falls (income effect). Finally, mobile money can allow
households to save in a safe and liquid way, thereby preparing
them for negative shocks and thus decreasing demand for borrowing.
Regarding saving behavior, similar arguments hold. The newsource effect will increase saving. The substitution effect will
not change the total amount of saving but the composition ofdifferent saving methods will. The insurance effect will decrease the need for saving. The income effect is likely to increase saving. Thus, from these theoretical points, it is not clear
whether the use of mobile money will increase or decrease borrowing and saving.
In this study, we examine the effect of the use of mobile
money on financial behavior (borrowing and saving) and showthat the use of mobile money mitigates the effect of experiencing negative shocks on households’ borrowing. More specifically, we find that when a household experiences a negative
shock, non-users of mobile money increase borrowing, whilemobile money users do not. We also find that the use of mobile
money affects the composition of saving methods. In particular,
the use of mobile money decreases the probability of saving us2

ing less liquid saving methods such as saving through livestock,churches, and communities, while the use of mobile money increases saving in a mobile money account. In addition, we
find that the use of mobile money increases the probabilityof receiving remittances, regardless of experiencing a negative
shock. This evidence suggests that the use of mobile money
helps households prepare for future negative shocks through achange of saving portfolio; hence, users of mobile money do
not need to borrow when they experience such shocks. Consistent with this interpretation, we find that a negative shock does
not decrease the livelihood of mobile money users, whereas it
does reduce that of non-users.
Our study is related to several strands of the literature. Given
the rapid increase in mobile money usage, researchers havestarted to examine its effect on the economy (Aker et al., 2016;
Muralidharan et al., 2016; Asongu and Asongu, 2018; Asongu,
2018; Okello Candiya Bongomin et al., 2018; Okello Candiya Bongomin and Munene, 2021; Blumenstock et al., 2015;
Dupas and Robinson, 2013a; Jack and Suri, 2014; Munyegera
and Matsumoto, 2016; Blumenstock et al., 2016; Riley, 2018;
Gosavi, 2018; Suri and Jack, 2016; Abiona and Koppensteiner,
2020; Riley, 2020).6 To the best of our knowledge, however, no
study focuses solely on the effect of mobile money on household saving, saving methods, and borrowing.
Second, several studies examine the effect of having a bankaccount on financial behavior. Burgess and Pande (2005) findthat the state-led bank expansion in rural India has reduced
poverty. Bruhn and Love (2009) analyze the expansion of a
Mexican bank that offered both saving and credit products.
They estimate that the new bank opening led to 7 percent higher
income for both men and women. Dupas and Robinson (2013b)
show that providing a safe place to save increases health-related
saving by 60 percent in Kenya. Agarwal et al. (2017) analyze
6 Aker et al. (2016) and Muralidharan et al. (2016) analyze the role ofthe secure payment method in Niger and India, respectively. Asongu andAsongu (2018) examine the effect of mobile money usage on economic development. Asongu (2018) analyze the determinants of mobile money penetrationin African countries. Okello Candiya Bongomin et al. (2018) and Okello Candiya Bongomin and Munene (2021) examine the role of the social context for
the adoption of mobile money. Blumenstock et al. (2015) conduct a randomized
experiment to test the effectiveness of using mobile money to pay salaries. Dupas and Robinson (2013a) analyze the role of mobile money as a secure way todeposit daily cash in microenterprises in Kenya. Jack and Suri (2014) theoretically show that the development of mobile money decreases the transaction costof risk sharing and increases the means to absorb a negative income shock on ahousehold through an increase in remittances. Additionally, the authors empirically demonstrate that, in Kenya, a household that uses mobile money does notdecrease 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 smooth consumption for households that experience negative shocks. Gosavi (2018) studies the effect of the usage of mobile money forfirms’ financing. Suri and Jack (2016) analyze the long-run effect of the use ofmobile money and find that 2 percent of Kenyan households have moved outof poverty since its availability in the country because of increases in saving
and financial resilience. Abiona and Koppensteiner (2020) analyzes the effectof the use of mobile money on education expenditure in Tanzania. Riley (2020)
finds, using field experiments, that disbursing loans through a mobile money
account to female business borrowers has a more significant effect on profit
than disbursing loans in cash.

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the effect of a large financial inclusion program in India and findthat the region exposed to the program now lends more to borrowers. Dupas et al. (2018) analyze the effect of having a bankaccount on saving using field randomization in three countries,
Uganda, Malawi, and Chile. They find no discernible intentionto-treat effects on savings, but a large treatment-on-the-treatedeffect due to the low take-up rate.
To study the effect of the use of mobile money on financial165
behavior, several considerations are needed. First, using mobile
money is a choice variable. A financially distressed household
might set up a mobile money account. This would introduce endogeneity bias. Second, an important variable that is not among
the control variables, which might affect the financial decision,170
could be correlated with mobile money usage. This would leadto omitted variable bias. To treat those problems, in this study,
we apply two-stage least squares (2SLS) estimation and use information on accessibility to G2 mobile networks as an instru175
mental variable.
To measure accessibility to mobile networks, we first map
the areas covered by the mobile networks of Vodacom Tanzania
and Tigo Mobile, whose market shares for mobile money are
54 percent and 29 percent, respectively. Then, we extract theintersection of the areas covered by the networks of both mobile180
money operators and calculate the shortest distance from eachhousehold’s location to the areas covered by both mobile phonecompanies using GPS information on each household location.
To make the sample relatively homogeneous, we restrict it tohouseholds who live within 10 km of the border of the area with185
multiple mobile networks regardless of whether they live insideor outside the network area. We assume that if a household iswithin the areas covered by multiple mobile networks, such ahousehold has a better chance of accessing a mobile network.7
We use this shortest distance as the instrumental variable while190
controlling for many covariates such as the distance to different
types of financial institutions, night light luminosity, population
density, district fixed effects, income sources, and demographic
characteristics. Including such control variables implies that we
195
are not comparing urban residents with rural residents.
The remainder of this paper is organized as follows. In Section S1 of the Supplemental Information, we explain the institutional background of Tanzania. Section 2 explains our mainmodel and identification strategy. Section 3 describes the dataand how we code each variable. Section 4 shows the main re-200
gression results. Section 5 and Section S5 of the Supplemental Information discuss the results of a variety of robustness
checks. Section 6 summarizes the results and Section 7 concludes.
205

2. Estimation Model
160

2.1. Identification Strategy
We consider the following model based on previous studies210(Jack and Suri, 2014; Munyegera and Matsumoto, 2016):
7 If a household is located inside the intersection, we assign a negative value
to the distance.

3yi = β0 + β1 Mobilei + β2 Negativei+β3 Mobilei × Negativei + γxi + 1i(1)where yi is the outcome variable such as the borrowing, saving, or receiving remittances dummy. If a household borrowed(saved) in the past 12 months, these dummies are equal to one
and zero otherwise. For the receiving remittances dummy, if
a household received a remittance in the past 12 months, it isequal to one and zero otherwise. Mobilei is a dummy variableequal to one if household i uses mobile money. Negativei is adummy variable indicating whether a household experienced atleast one negative shock in the past 12 months. xi is a vector of
the control variables. The parameters of interest are (β1 , β2 , β3 ).
β1 shows the extent to which the use of mobile money affects
saving, borrowing, or receiving remittances for those who did
not experience a negative shock in the past 12 months. β2 showsthe extent to which experiencing at least one negative shock
affects saving, borrowing, or receiving remittances in the past
12 months for those who do not use mobile money. β3 showsthe extent to which experiencing at least one negative shock affects saving, borrowing, or receiving remittances differently fora household that uses mobile money compared with a household that does not.
When we use ordinary least squares (OLS) estimation to estimate equation (1), the estimated coefficient of (β1 , β2 , β3 ) would
be inconsistent for several reasons. First, having a mobile
money account is a choice variable. In other words, a household wishing to save might decide to open such an account. In
this case, the mobile money dummy and error term 1i would bepositively correlated, and estimating (1) using OLS would thus
generate upward bias.
Second, a household that uses mobile money may differ froma household that does not in terms of other characteristics.
When some of the households’ characteristics that influencesaving behavior and mobile money are not observed, estimating (1) using OLS would generate an inconsistent estimate ofβ1 .
To solve the endogeneity bias and omitted variable bias resulting from applying OLS to (1), we use 2SLS estimation.
For the instrumental variable, we use the shortest distance fromeach household’s location to the areas in which multiple mobile networks are available while controlling for the distanceto all types of financial institutions, sources of income, demographic characteristics, and average economic activity indicesat the household location measured by night light luminosity,
the built-up rate, and population density. The basic idea of using this distance as the instrumental variable is that if a household’s connection to a mobile network is better, it has more
incentive to use mobile money because using a mobile money
service requires a good connection to a mobile network.
As in many developed countries, the area classified as being
able to connect to a mobile network by each operator does not
imply that a customer within this area can always connect hisor her mobile phone to that network and that a customer outside
this area cannot. Instead, mobile phone operators theoretically260

Figure 1: The First-Stage Relationship of 2SLS.
The Shortest Distance to the Border of Multiple Network Areasand Mobile Money Usage Rate

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Mobile Money Use Rate

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calculate the strength of the radio of the mobile network andthen the probability that a household can connect to the mobile
network. Then, a mobile phone operator draws a threshold line
to determine whether a household can access the mobile network with a reasonable probability. Thus, the shortest distancefrom the border of the areas covered by two mobile network240
operators is likely to be a good proxy of accessibility to a mobile network. If a household’s location is far outside the area inwhich multiple mobile networks are available, it is likely thatthe network connection is unstable or weak. When a household
cannot connect his or her mobile phone to the mobile network245
easily, then he or she is unlikely to use mobile money; in contrast, a household that can access the mobile network easily ismore likely to use mobile money.
Figure 1 shows the relationship between the shortest distanceto the areas in which multiple networks are available and usage250
rate of mobile money. To make this figure clearer, we restrict
the sample to households whose shortest distance to the borderof the multiple network areas is less than 20 km. Figure 1 showsa clear relationship between the shortest distance to the borderof the areas in which multiple mobile networks are available255
and mobile money usage rate. This is consistent with our hypothesis that a household located within an area with multiple
mobile networks, or close to such an area, has a stronger network connection and is thus more likely to use mobile money.

0

215-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

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8 10 12 14 16 18 20

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Distance From the Border of the Multiple Network Area

Notes: The multiple mobile network area is the area in which
the networks of both Vodacom Tanzania and Tigo Mobile are accessible. The mobile money use rate is the proportion of households that use mobile money. For each 0.5 km, the average mobile money use rate is calculated. The reference vertical lines areshown at the point where the distance is equal to -10 km or 10 km.

tion:
Mobilei = α0 + α1 Distancei + α2 Negativei ++ α3 Distancei × Negativei + α3 xi + 2i(2)where Distancei is the shortest distance for each household tothe areas in which multiple mobile networks are available. xi
is a vector of the control variables. As xi , we include the characteristics of respondents, household characteristics such as theeducation level of the head of the household, age and genderof the household head, household size, and income source aswell as the geographical variables such as the region dummy(30 region dummies). To control for urbanicity, we use the average night light luminosity in 2007 and the average built-up
rate in 2010 within a 5 km radius of each household’s location using satellite imagery data (Wang et al., 2019; NOAA National Geophysical Data Center, 2019). To prevent endogeneity, namely, that mobile money affects economic activity andthus night light luminosity, we use the night light luminosity
data from 2007, the year before mobile money was introducedin Tanzania. We also calculate the population density within
a 5 km radius of each household’s location using the population density map with a 1 km resolution and include it as acontrol variable (Center for International Earth Science Information Network - CIESIN - Columbia University, 2016). Further, we calculate the shortest distance to commercial banks,
community banks, and microfinance institutions using the GPS
information of those financial institutions and GPS information
on household location and include them as control variables.
Additionally, xi includes the mobile phone ownership dummy
following the specification used in Jack and Suri (2014) and
Munyegera and Matsumoto (2016).8 In the robustness checks,
we also include the district dummy (170 district dummies), thetransportation equipment ownership dummy, and its interaction
with distance to financial institutions as control variables.9 Figure S1 in the Supplemental Information shows the first-stage
relationship between the distance to the area with multiple mobile networks and use of mobile money after controlling for theeffect of the control variables. It shows that even after controlling for the effect of the control variables, there is a clear relationship between this distance and the use of mobile money.
Figure 1 shows that for the outside sample [−10, 10], the variance in the mobile money usage rate becomes large because of
the relatively small sample of households.10 Thus, we restrict
the sample to households whose distance to the areas covered
8 Although the mobile phone ownership dummy is included as a control vari-able in previous studies of the effect of the use of mobile money on consumption (Jack and Suri, 2014; Munyegera and Matsumoto, 2016), we recognize that
this dummy is also an endogenous variable; hence, including it as a control variable would introduce bias into the estimated coefficient of β1 even when mobile
money use is instrumented. We discuss the effect of including the mobile phoneownership dummy on the estimation of β1 in S5.2 of the Supplemental Information.
9 We include the ownership of transportation equipment and its interaction
with distance to financial institutions to control for time distance to financial
institutions. We do not include those variables in the base specification becauseof the potential endogeneity of transportation equipment.
10 When we restrict the sample to households whose distance to the multiple

The above graph shows that when the distance is within [-10,10],
the relationship between the distance and average mobile money
use rate is almost on the same line. The estimated coefficient of
the slope of the fitted line is -0.0188 and the robust standard error
is 0.0003599. R2 =0.75. For the estimation of the fitted line above,
all the observations within [-20,20] are used.

Therefore, as the first stage, we estimate the following equa4

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by multiple mobile networks is in [−10, 10]. This restriction is330
likely to guarantee that these sample households are homogeneous. To examine the robustness of our results, we also use
the sample where the distance to the area with multiple mobile
networks is within 7.5 km and within 15 km. Our robustness
checks show that our estimates are robust to those two subsamples.11
At this point, we must emphasize that our instrument is not
the distance from the city center and that we are not comparing urban residents with rural residents. First, the border of thearea where multiple mobile networks are available is far fromthe center, and we restrict the sample to households that live
within 10 km (inside or outside the network area) of the borderof the area with multiple network access. Second, we control
for the distance from all types of financial institutions. Third, tocontrol for the possibility that different locations have different
levels of economic activity, we include night light luminosity in2007 and the built-up rate in 2010, which were originally created using satellite images, as control variables. Fourth, we include population density, which was created using census datawith detailed administration information, at each location asa control variable. Fifth, we include information on income
source (nine categories) such as farming and formal employment as control variables. Including all those control variables
is likely to ensure that we are comparing similar households
whose distance to the border of the area with multiple mobile
networks is different. In addition, we check the stability of theestimated coefficients of the main variables by adding the control variables gradually. The stability of the estimated coefficient suggests that the error term is not likely to be correlatedwith those control variables, implying that the omitted variable
effect is small.

3. Dataset and Summary Statistics

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DatasetWe use the nationally representative survey of FinScope Tanzania in 2017. See S4 of the Supplemental Information for a de335
tailed explanation of this dataset. The FinScope Tanzania 2017
dataset provides detailed information on financial behavior suchas borrowing, saving, and receiving remittances. It also has information on GPS location as well as the demographic characteristics of the surveyed households. In our analysis, we restrict340
the sample to households in which the head of the household
was the respondent to the interview.
Regarding saving behavior, the survey asked about the use ofdifferent saving methods in the past 12 months, such as saving
in livestock, saving in cash at home, saving in banks, and saving
345
in saving groups. We divide saving groups into those that use
mobile money technology to collect money from members andthose that do not. For each saving method, we create a dummy
variable equal to one if the household saved using that methodin the past 12 months and zero otherwise. Similarly, the survey
asked about different sources of borrowing such as banks, microfinance institutions, friends and relatives, and saving groups.
We also create a dummy variable for each source of borrowing.
Table 1. Summary Statistics
Variables
Mobile Money and Network
Mobile Money Use Dummy
Mobile Phone Ownership
Living in a Multiple Networks Area
Distance from the Multiple
Networks Area (km)Demographic Characteristics
Households’ Size
Age of the Household HeadHousehold Head being Male
Negative Shock Dummy
Population Density and Distance
Population Density
Night Light Luminosity
Distance to the Nearest
Commercial Bank (km)Distance to the Nearest
Community Bank (km)Distance to the Nearest
Microfinance Institution (km)Mean

Std. Dev.

0.58
0.71
0.75
-1.60

0.49
0.45
0.44
3.79

4.03
39.76
0.63
0.584

2.42
12.08
0.48
0.493

0.01895586
24.2

0.0768
22350
22.3

170.5

122.8

39.8

39.4

Notes: The sample is restricted to households in which the respondent of theinterview is the head of the household in FinScope Tanzania and in which
the age of the head is 15 to 65 years and whose absolute value of the distanceto the border of the areas covered by multiple mobile networks is less thanor equal to 10 km. This distance variable is coded as a negative value if
a household lives inside the areas covered by multiple mobile networks.
N=4127.

Regarding the difficulty paying regular expenses, the responses are categorized into five groups: always struggle to pay
unexpected expenses, very often struggle, sometimes struggle,
rarely struggle, and never struggle. Based on this information,
we create a difficulty paying regular expenses dummy and set
it equal to one if a household chooses the first two choices andzero otherwise.
Regarding negative shocks, the survey asked about several
types of negative shocks in the past 12 months: having unforeseen large expenses, experiencing an unforeseen drop in theprice of the output, and experiencing an unforeseen drop in thevolume of the output. We code the negative shock dummy equal
to one if a household experiences at least one of the above negative shocks.12
12 The survey additionally asks whether a household received less money than

expected. However, since this question might include cases where a household
received less remittances than expected and since we are also interested in theeffect of intrinsic negative shocks on the receipt of remittances, we do not include this case in the category of negative shocks.

mobile network area is in [−20, −10] or [10, 20], we have only 270 households,
while the number of households whose distance is in [−10, 10] is 4127.
11 For this robustness check, see S5.4 in the Supplemental Information.

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Figure 2: Locations of the Households and Financial Institution
and the Areas Where Vodacom and Tigo Mobile are Accessible in Tanzania
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Areas with Multiple Mobile Networks and Location of the Households(a) Locations of the Households and Financial Institution

Notes: The green circles in (a) show the locations of the households surveyed in the FinScope dataset. The blue areas show the areas where
the networks of both Vodacom and Tigo Mobile are accessible. The green circles in (b) show the locations of the households surveyed in the
FinScope dataset. The red circle shows the location of the financial institution.

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The shortest distance to the areas covered by multiple mobile networks is calculated as follows. First, we choose two of
the largest mobile money operators in Tanzania, Vodacom andTigo Mobile, whose market shares are 54 percent and 29 percent, respectively. Then, we overlay their 2G mobile network380
availability maps using ArcGIS and extract their intersection.
Using GPS information on each household’s location, we calculate the shortest distance to the border of the intersection of
the networks. We use a negative value if a household is locatedinside the intersection (Figure 2(a)).
For each financial institution (commercial banks, community banks, and microfinance institutions), the Financial Sector385
Deepening Trust has surveyed financial sectors and published a
GIS map of all the branches of commercial banks, saving cooperatives, microfinance institutions, and postal offices in Tanzania in the Financial Access Maps Dataset. We calculate thedistance variables representing the distance of each householdto the nearest bank branch, community bank branch, and mi-390
crofinance institution office using the Financial Access Maps
Dataset (Figure 2(b)).

work is -1.6 km. The share of households that live in an area inwhich multiple mobile networks are available is 75 percent.

Summary Statistics
Tables 1 and 2 provide the summary statistics of the main variables. In total, 58 percent of the heads of the households use the395
mobile money service, while 71 percent own a mobile phone.
For each household, we measure the shortest distance to theborder of the area in which multiple mobile networks (Vodacom and Tigo Mobile) are available. This distance becomes
negative when a household lives within such an area. The mean400of the shortest distance to the areas with a multiple mobile net-

Among those who borrowed, more than 90 percent of households borrowed from informal sources such as friends, relatives, and saving groups. Specifically, 33 percent borrowed
from friends and relatives in the past 12 months, 12.6 percent
borrowed from saving groups that do not use mobile money
technology, and 4 percent borrowed money from saving groups
that use mobile money technology. Only 2.5 and 1.5 percent ofhouseholds borrowed from banks and mobile money operators,
respectively.

6

Table 2 shows the summary statistics of the variables of financial activity in the past 12 months. About 50 percent ofhouseholds did not save at all in the past 12 months and 43 percent borrowed. Almost half (49 percent) of households received
remittances.
Regarding formal saving, 19 percent of households saved using a mobile money account and 10 percent saved in a bank.
Thus, among those who saved, more than one-third of households saved in mobile money savings accounts.
Regarding informal saving, 21.3 percent of households saved
in cash, 4 percent saved in saving groups that use mobile money
technology to collect money from members, and 12.8 percent
saved in saving groups that do not use mobile money technology. 4.9 percent saved in livestock and other real assets.

Table 2. Summary Statistics of Financial Behavior
in the Past 12 Months
Variables

Mean

Saved, Borrowed, and Received Remittances
Saved in the past 12 months
0.511
Borrowed in the past 12 months
0.489
Received Remittances
0.434
in the past 12 months
Frequency of Receiving
14.86
Remittances in the past 12month410

Std. Dev.
0.500
0.500
0.496

415

57.52
420

Saving Methods
Saved in Mobile
Money Account
Saved in Bank
Saved in SACCO
Saved in MicrofinanceInstitution
Saved in Saving Group
with MM technology
Saved in Saving Group
without MM technology
Saved in Cash
Saved in Relatives,
Church, or Communities
Saved in Livestockand other Real Assets
Borrowing Methods
Borrowed from Mobile
Money Operator
Borrowed from Bank
Borrowed from SACCO
Borrowed fromMicrofinance Institution
Borrowed from Saving Group
with MM technology
Borrowed from Saving Group
without MM technology
Borrowed fromRelatives and Friends

0.190

0.393

0.100
0.012
0.008

0.300
0.108
0.088

0.040

0.197

0.128

0.334

0.213
0.045

0.409
0.206

0.049

0.216

0.025

0.155

0.015
0.009
0.012

0.123
0.093
0.109

0.039

0.194

0.126

0.332

0.333

0.471

425

430

435

440

445

450

Notes: The same notes as Table 1 apply. All the variables are dummy
variables. SACCOS denotes the Saving and Credit Cooperative Society.
Saving Group with MM technology denotes a saving group that uses mobile money to collect money from members. Each variable is equal to one
if the condition applies. N=4127.
455

4. Results
4.1. Effect on Borrowing
460

405

In Table 4 and Table 5, we estimate the effect of using mobile
money and experiencing a negative shock on borrowing. The
dependent variable is the borrowing dummy, which is equal to
one if a household borrowed in the past 12 months and zero otherwise. The main explanatory variables are the use of mobile

money dummy and negative shock dummy. The use of mobile
money dummy is equal to one if the head of the household uses
mobile money and zero otherwise. The negative shock dummies are coded one if a household experienced at least one negative shock in the past 12 months and zero otherwise. The sample is restricted to the head of the household. To control for thetypes of jobs of each household head, we include the income
source dummy (nine categories) of each household in all the
specifications. In Column (2), to control for the economic activity at each household’s location, we include population density in 2015 and average night light luminosity in 2007 at thehousehold’s location. In addition, in Column (3), we include
the region dummy (30 region dummies). In Column (4), we
add the demographic characteristics as control variables, suchas the education level, age, and gender of the head of the household as well as household size. In Column (5), we include thedistance to commercial banks, community banks, and microfinance institutions.
Table 3 shows the OLS estimation results. Table 4 showsthe estimation results of the first stage of the 2SLS estimation. In Table 4, the dependent variable is the mobile moneyuse dummy and the excluded instrumental variable is the shortest distance to the areas in which multiple mobile networks areavailable. The estimated coefficients of the instrumental variable are highly statistically significant and stable. Column (1)of Panel A of Table 4 shows that if a household is located 10 km
away from the border of the areas covered by multiple mobile
networks, the probability of using mobile money decreases by
15 percentage points.13 Column (5) shows that after controlling for all the control variables, this probability changes to 9
percentage points. The Kleibergen–Paap Rank Wald statistics,
which are the heteroscedasticity robust version of the F-test of
the weak instrument, are greater than 10.
Table 5 shows the second-stage results of the 2SLS estimation. Panel A of Table 5 shows the effect of the use of mobile
money when we do not include the variable of experiencing anegative shock as a control variable. In contrast to the corresponding OLS estimation in Panel A of Table 3, the estimatedcoefficient of the use of mobile money dummy on the borrowing dummy variable is small, has the opposite sign, and is statistically insignificant. The sizes of the estimated coefficientsare also economically insignificant. In Column (5), the estimated coefficient is -0.0138. Panel B controls for experiencing
a negative shock. The estimated coefficients of the use of mobile money in Panel B of Table 5 are still small, negative, andstatistically insignificant. Thus, the difference in the estimatedcoefficient of the use of mobile money between the OLS estimation and 2SLS estimation means that the OLS estimationof the use of mobile money on the borrowing dummy variable
is upward biased, suggesting that households that use mobile
money are those who need to borrow. In contrast, the estimatedcoefficients of experiencing a negative shock are statisticallyand economically significant, positive, and stable in the 2SLS
13 To be able to compare the effect of distance on using mobile money graphically, we consider a thought experiment of increasing distance by 10 km insteadof 1 km.

7

Table 3. Estimation Results of the OLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Borrowing
Dependent Variable

Borrowing Dummy
(1)

(2)

(3)

(4)

(5)

0.134***(0.0174)
0.041

0.132***(0.0176)
0.041

0.141***(0.0179)
0.041

0.110***(0.0186)
0.055

0.111***(0.0187)
0.056

0.121***(0.0173)
0.188***(0.0162)0.071

0.119***(0.0174)
0.187***(0.0162)0.072

0.130***(0.0178)
0.183***(0.0163)0.069

0.100***(0.0185)0.178***(0.0163)0.082

0.101***(0.0185)0.178***(0.0163)0.082

0.122***(0.0246)-0.00136(0.0307)0.189***(0.0234)
0.071

0.119***(0.0247)-0.000530(0.0307)0.188***(0.0234)0.072

0.137***(0.0250)-0.0111(0.0304)
0.189***(0.0234)
0.069

0.104***(0.0255)-0.00593(0.0302)0.182***(0.0233)
0.082

0.104***(0.0256)-0.00626(0.0302)0.181***(0.0233)
0.083

Mobile Phone Ownership
Population Density & Night Lights
Region (31 Regions)
Demographic Characteristics
Distance to Financial Institutions

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

N

4,127

4,127

4,127

4,127

4,127

Panel AUse of Mobile Money Dummy
R-squared
Panel BUse of Mobile Money Dummy
Negative ShockR-squared
Panel CUse of Mobile Money Dummy
Mobile Money × Negative Shock
Negative ShockR-squared
Control Variables

Notes: Robust standard errors in parentheses. All the specifications include the income source
dummy. For income source, there are nine categories. For the demographic characteristics, theeducation level, age, and gender of the head of the household and household size are included. ***p<0.01, ** p<0.05, * p<0.1.

465

470

475

estimation. Column (5) of Panel B of Table 5 shows that experiencing a negative shock increases the probability of borrowing480
by about 18.5 percentage points.
Panel B of Table 5 assumes that the effect of a negative shockis the same between mobile money users and non-users of mobile money. Panel C of Table 5 relaxes this assumption andintroduces the interaction term of the negative shock dummy485
and mobile money use dummy. For the instrumental variable ofthe interaction term, we use the negative shock dummy times
the distance to the areas covered by multiple mobile networks.
Panel C of Table 5 shows that for non-mobile money users,
experiencing a negative shock increases the probability of bor-490
rowing by about 47 percentage points. However, since theestimated coefficient of the interaction term is -50 percentage
points, the effect of a negative shock is almost zero for mobile money users. Since about 58 percent of the households inour dataset use mobile money, the average effect of a negative495
8shock on the whole population is 0.47 − 0.58 × 0.5 = 0.18.
This is close to the estimated coefficients of the negative shockdummy in Panel B of Table 5.
Similarly, the effect of the use of mobile money on borrowing is different for those who experience a negative shock andthose who do not. For those who do not experience a negative shock, the use of mobile money increases the probabilityof borrowing by 20.5 percentage points, although its statisticalprecision is low. For those who experience a negative shock, theuse of mobile money decreases the probability of borrowing by
50 percentage points. Since 59 percent of the households in ourdataset experience a negative shock, the average effect of using mobile money on borrowing is 0.205 − 0.59 × 0.5 = −0.09,
which is close to the estimated coefficients of the mobile moneyuse dummy in Panel B of Table 5.
Panel C of Table 5 uses the interaction term to examine thedifferent effects of a negative shock for users and non-users ofTable 4. First-Stage Estimation Results of the 2SLS Estimation
Endogenous Variable
Panel AUse of Mobile Money Dummy
(1)

(2)

(3)

(4)

(5)

Distance from Network Area

-0.0153***(0.00178)-0.0146***(0.00178)-0.0130***(0.00185)-0.0106***(0.00180)-0.00909***(0.00186)

R-squared
Kleibergen-Paap Rank Wald

0.226
73.93

0.235
67.56

0.294
49.22

0.353
34.23

0.356
24.01

-0.0154***(0.00178)
0.0655***(0.0144)-0.0147***(0.00178)
0.0628***(0.0144)-0.0129***(0.00185)
0.0511***(0.0143)-0.0105***(0.00180)0.0425***(0.0137)-0.00900***(0.00185)
0.0434***(0.0137)

0.230
73.93

0.238
67.56

0.296
49.22

0.354
34.23

0.358
24.01

-0.0172***(0.00261)
0.00315(0.00349)
0.0706***(0.0158)-0.0166***(0.00261)
0.00314(0.00349)
0.0679***(0.0157)-0.0146***(0.00260)
0.00291(0.00345)
0.0558***(0.0157)-0.0120***(0.00253)
0.00250(0.00332)
0.0466***(0.0151)-0.0106***(0.00257)
0.00276(0.00332)
0.0478***(0.0150)

0.230
25.24

0.238
24.35

0.296
20.57

0.354
15.23

0.358
10.83

Mobile Phone Ownership
Population Density & Night Lights
Region (31 Regions)
Demographic Characteristics
Distance to Financial Institutions

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

N

4,127

4,127

4,127

4,127

4,127

Panel B
Distance from Network Area
Negative Shock Dummy
R-squared
Kleibergen-Paap Rank Wald
Panel CDistance from Network Area
Distance from Network Area ×
Negative Shock
Negative ShockR-squared
Kleibergen-Paap Rank Wald
Control Variables

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

500

505

510

mobile money. For a robustness check, we examine the effectof a negative shock on mobile money users and non-mobile515
money users graphically. More specifically, we divide the sample households into those that experienced a negative shock andthose that did not. Then, using the shortest distance from theborder of the area with multiple mobile networks, we make 40
bins, where the size of each bin is 0.5 km. For households that520
did not experience a negative shock, we calculate the average
probability of borrowing (red circle). Similarly, we calculate
the average probability of borrowing for households that experienced at least one negative shock in the same bin (blue square).
Figure 3(a) shows that for households that are likely touse mobile money (households whose distance is negative andlarge), the effect of a negative shock on borrowing is relatively525
small. For households unlikely to use mobile money (distanceto the border is positive and large), the effect of negative shocks
is relatively large.
Figure 4 shows this exercise more concretely using regres9sion analysis. First, we divide the main sample into 20 groups
from -10 km to 10 km in increments of 1 km. Then, for eachgroup, we regress the borrowing dummy on the negative shockdummy and a set of control variable. Next, we plot the estimated coefficient of the negative shock dummy on the verticalaxis. In the third step, for each group, we calculate the averagemobile money use rate and plot it on the horizontal axis. Figure
4 shows that as the average use rate of mobile money falls, theeffect of a negative shock rises.

In summary, Table 5 shows that the effect of experiencing anegative shock on borrowing is almost equal to zero for mobile
money users, while for non-users, it increases the probability ofborrowing by 47 percentage points. Figure 3 and Figure 4 areconsistent with those results. In the following subsections, weinvestigate why we observe this pattern of the effect of the useof mobile money and negative shocks on borrowing.

Table 5. Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Borrowing
Dependent Variable

Borrowing Dummy
(1)

(2)

(3)

(4)

(5)

-0.167(0.138)-0.02973.93-0.194(0.146)-0.03967.56-0.0456(0.166)
0.016
49.22

-0.0567(0.204)
0.037
34.23

-0.0138(0.244)
0.046
24.01

-0.144(0.133)0.205***(0.0186)
0.018
75.08

-0.167(0.140)0.205***(0.0187)
0.010
68.71

-0.0568(0.164)0.192***(0.0185)
0.044
49.05

-0.0744(0.202)0.186***(0.0186)
0.062
33.98

-0.0573(0.243)0.185***(0.0196)
0.066
23.56

0.0968(0.152)-0.469**(0.195)0.472***(0.114)-0.04925.24

0.0750(0.157)-0.471**(0.196)0.473***(0.115)-0.05924.35

0.211(0.180)-0.505***(0.189)0.480***(0.111)-0.02520.57

0.182(0.214)-0.490***(0.187)
0.466***(0.110)-0.00515.23

0.205(0.250)-0.501***(0.189)0.470***(0.112)-0.002
10.83

Mobile Phone Ownership
Population Density & Night Lights
Region (31 Regions)
Demographic Characteristics
Distance to Financial Institutions

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

N

4,127

4,127

4,127

4,127

4,127

Panel AUse of Mobile Money Dummy
R-squared
Kleibergen-Paap Rank Wald
Panel BUse of Mobile Money Dummy
Negative Shock Dummy
R-squared
Kleibergen-Paap Rank Wald
Panel CUse of Mobile Money Dummy
Mobile Money × Negative Shock
Negative Shock Dummy
R-squared
Kleibergen-Paap Rank Wald
Control Variables

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

530

4.2. Orthogonality of Negative Shocks to the Instrumental Variable

4.3. Effect on Remittances

545

535

540

One natural explanation of Panel C of Table 5 and Figure 3
is that a household that is far away from the areas covered bymultiple mobile networks experiences different types of nega-550
tive shocks than a household located inside those areas. Table
S2 of the Supplemental Information examines such a possibility
by regressing the frequency of negative shocks on the distancefrom the area with multiple mobile networks. The regression results show that the frequency of negative shocks is almost equal555
to zero and statistically insignificant, suggesting that different
negative shocks are unlikely to arise in different locations.
10

Several mechanisms may explain the difference in borrowing
patterns between non-user and users of mobile money. First,
with the availability of mobile money, households can form
mutual insurance networks easily and thus receive remittances
when they experience a negative shock because the availability of mobile money lowers the cost of transferring remittances.
Second, the use of mobile money increases the probability of receiving remittances regardless of experiencing a negative shock
due to the altruism of relatives and friends. A large amount of
past remittances allows households to have sufficient savings.
Sufficient savings can reduce demand for borrowing when ahousehold experiences a negative shock. The third channel isthat a household can change from saving through less liquidsavings (e.g., savings in livestock and in the church and com-Figure 3: The Probability of Borrowing and Receiving Remittances for Households with

1
0

0

Probability of Borrowing
.2
.4
.6
.8

Probability of Receiving Remittance
.2
.4
.6
.8

1

and without a Negative Shock

-10

-5
0
5
Distance From the Border of the Multiple Network Area
Households with no shock
Households with shocks

10

-10

Fitted line
Fitted line-505Distance From the Border of the Multiple Network Area
Households with no shock
Households with shocks(a) Probability of Borrowing and Distance10

Fitted line
Fitted line(b) Probability of Receiving Remittances and Distance

Notes: Using the shortest distance from the border of the areas covered by multiple mobile networks, we make 40 bins, where the size of each bin
is 0.5 km. For each bin, we calculate the average probability of borrowing (a) and receiving remittances (b) for two types of households: those thatexperienced at least one negative shock (blue square) and those that did not (red circle). The size of the circles and squares is the sample size ofeach cell. In (a), the slopes of the two lines have the opposite signs, while in (b), the slopes of the two lines have the same signs and the two lines
are parallel. This shows that in (a) when a household is likely to use mobile money (distance is negative and large), the difference between the two
fitted lines becomes small, showing that the effect of a negative shock on the probability of borrowing is small. In (b), when a household is likely
to use mobile money, the difference between the two fitted lines does not change, showing that the effect of a negative shock on the probability ofreceiving remittances does not change.

Figure 4: The Differential Effect of Negative Shocks
across Households with a Different Probability
560

estimated coefficientav of negative shock
.2
.4

.6

of Using Mobile Money

565

0

570

.2

.4

.6
.8
average mobile money use rate

1

Notes: The sample is divided into 20 groups based on each household’s distance to the areas covered by multiple mobile networks.
For each group, the borrowing dummy is regressed on the negative
shock dummy using OLS. The size of the estimated coefficient of
the negative shock dummy is measured on the vertical axis. For
each group, the average probability of using mobile money is calculated and measured on the horizontal axis. The size of eachcircle is the sample size of each group.

munities) to saving in mobile money and banks.14

575

580

585

14 The abovementioned channels are not mutually exclusive. It is difficult to
identify which factor is the exact mechanism that generates the results in Table

To narrow the possible mechanisms that generate the resultsin Table 5, Figure 3(a), and Figure 4, we first examine in Table
6 whether the use of mobile money increases the probability
and frequency of receiving remittances when a household experiences a negative shock. Table 8 shows the 2SLS estimation
results. In Panel A, the dependent variable is the receiving remittances dummy, which is equal to one if a household received
a remittance in the past 12 months and zero otherwise. In PanelB, the dependent variable is the frequency of receiving remittances in the past 12 months.
In Panel A, the estimated coefficients show that the use ofmobile money increases the probability of receiving remittances by 75 percentage points. The estimated coefficient ofexperiencing a negative shock is small (12 percentage points)
compared with the effect of the use of mobile money and statistically insignificant.
In addition, the estimated coefficient of the interaction term issmall, negative, and statistically insignificant. This implies thatwhen a household experiences a negative shock, the probability
that a user of mobile money receives a remittance is lower thanthat of a non-user. Thus, the pattern of receiving remittances isunlikely to explain the pattern of borrowing.
In Panel B, the effect of the use of mobile money is large
but the estimated coefficient of the negative shock and its interaction term are small and statistically insignificant. Thus,
both Panel A and Panel B of Table 6 show that the effect of anegative shock on receiving remittances and the frequency ofremittances are unlikely to depend on the use of mobile money.
5 and Figure 3.

11

Table 6. The Effect of the Use of Mobile Money, Negative Shocks, and Its Interaction
on the Receipt and Frequency of the Receipt of Remittances (2SLS Estimation)
Dependent Variable
(1)

(2)

Panel AUse of Mobile Money Dummy
Mobile Money × Negative Shock
Negative ShockR-squared
Kleibergen-Paap Rank Wald

(4)

(5)

Receipt of Remittances Dummy
0.698***(0.126)-0.107(0.160)0.0958(0.0932)

0.703***(0.130)-0.104(0.160)0.0943(0.0934)

0.689***(0.148)-0.145(0.159)0.113(0.0926)

0.681***(0.176)-0.158(0.157)0.125(0.0921)0.752***(0.209)-0.157(0.160)0.120(0.0947)

0.261
25.24

0.260
24.35

0.247
20.57

0.255
15.23

0.243
10.83

Panel BUse of Mobile Money Dummy(3)

Frequency of the Receipt of Remittances
52.67***(15.19)5.217(19.60)-5.337(10.98)51.96***(15.42)5.031(19.55)-5.238(10.95)50.63***(18.09)1.230(19.42)-3.202(10.84)54.51**(22.08)0.339(19.61)-2.500(11.05)43.25*(26.05)-1.153(19.25)-1.020(10.89)-0.04225.24-0.03824.35-0.03120.57-0.036
15.23

0.004
10.83

Mobile Phone Ownership
Population Density & Night Lights
Region (31 Regions)
Demographic Characteristics
Distance to Financial Institutions

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

N

4,127

4,127

4,127

4,127

4,127

Mobile Money × Negative Shock
Negative ShockR-squared
Kleibergen-Paap Rank Wald
Control Variables

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. Both Panel A and Panel B show
the estimation results of 2SLS estimation when the dependent variable is the receipt of remittance dummy
and the frequency of receiving remittances. *** p < 0.01, ** p<0.05, * p < 0.1.

590

Figure 3(b) shows the reduced-form relationship between the
distance from the border of the area with multiple mobile networks and average probability of receiving remittances. The
red circles show the average probability of the receipt of remittances for households that did not experience negative shocks.605
The blue circles show the average probability for households
that experienced negative shocks.

are interested in the average effect of using mobile money oneach saving method, we do not include the interaction term of
the use of mobile money dummy and negative shock dummy.
However, we include the negative shock dummy itself as a control variable.15
Panel A of Table 7 shows that the use of mobile money decreases the probability of saving in less liquid savings by 30
percentage points. We code the method of saving as less liq-

4.4. Effect on Saving and Saving Methods
595

600

15 To save space, we report only the estimated coefficient of the use of mobile
money dummy. However, the negative shock dummy is included as a control
variable in all the specifications. In all the cases, the estimated coefficient of
the negative shock dummy is small and statistically insignificant. In addition,
we estimate the specifications where the interaction term of the negative shockdummy and use of mobile dummy are included. In all the specifications, theestimated coefficient of the interaction term is economically small and statistically insignificant. The estimation results are available from the author uponrequest.

Another possible channel through which a mobile money user
does not need to increase borrowing when he or she experiencesa negative shock is changing the saving portfolio to prepare fora future negative shock. As a result, when he or she experiences a negative shock, he or she does not need to borrow. To
examine whether such a hypothesis is supported, we examine
the probability of saving under each saving method. Since we
12

610

615

620

625

630

635

uid if a household saves in livestock or through the church andcommunities. Panel B of Table 8 shows that the use of mo-665
bile money does not change the probability of saving in cash.
In Panel B, the estimated coefficient is less than 3 percentage
points and statistically insignificant.
In contrast, Panels C and D of Table 7 show that the use ofmobile money increases saving in a mobile money technology.670
More specifically, the use of mobile money increases the probability of saving in a mobile money account and saving in asaving group that uses mobile money as a form of collection by
60 and 22 percentage points, respectively. Panel E shows thatthe use of mobile money does not increase the probability ofsaving in a saving group that does not use mobile money for thecollection of money from members.
Finally, Panel F shows the effect of the use of mobile money675
on the probability of saving in at least one method. Columns(1) to (4) show that the estimated coefficients are stable, withthe use of mobile money affecting the probability of saving inat least one method by 33–42 percentage points. In Column(5), the estimated coefficient becomes insignificant, although
the size of the estimated coefficient is similar to that in Columns(1) to (4). In the robustness check, we find that although the680
estimated coefficients are similar, they are often statistically insignificant. For example, in Panel F of Table B4, the estimatedcoefficients are significant only for Columns (1) and (2). Thissuggests that we do not find strong evidence that the use of mobile money increases the probability of saving in at least one
685
asset.

4.5. Effect of Mobile Money on Livelihood

640

645

650

655

660

The final question is the extent to which the use of mobile
money affects households’ livelihood. Table 8 examines the690
effect of the use of mobile money on the probability of experiencing difficulty paying regular expenses. To examine theeffect of the use of mobile money on those who experience a
negative shock, we include the non-negative shock dummy instead of the negative shock dummy and its interaction with mo-695
bile money use. In Panel A, the estimated coefficient of theuse of mobile money dummy, which measures the effect of theuse of mobile money for a household that experiences a negative shock, ranges from -0.5 to -0.68 (statistically significant).
Panel A also shows that when a household does not experience700
a negative shock, the probability of experiencing financial difficulty decreases by 23 percentage points. The estimated coefficient of the interaction term implies that the effect of a negative shock for a mobile money user is almost equal to zero(-0.23+0.28=0.05). To see the effect of experiencing a negative705
shock on the difficulty in paying regular expenses for those who
use mobile money, we examine whether the sum of the coefficient of the interaction term and non-negative shock dummy isequal to zero. The bottom row of Panel A shows the chi-square
and its p-value. The calculated P-value implies that we can-710
not reject the null hypothesis that the effect of experiencing anegative shock on financial difficulty for those who use mobile
money is zero.
Panel B shows the estimates of the effect of the use of mobile
13

money and a non-negative shock without the interaction term.
Without this term, the estimated coefficient of the use of mobile
money is the average effect of such use. Panel B shows that theuse of mobile money decreases the probability of experiencing
financial difficulty by 49 percentage points. It also shows thatexperiencing a negative shock increases the difficulty paying
regular expenses by 6 percentage points.

5. Robustness Checks
In Section S5 of SI, we conduct variety of robustness checks
which include controlling district fixed effect, controlling income, controlling time distance to financial institution, controlling the pattern of mutual help of community of each respondent, using different subsample and checking the sensitivity of
estimated coefficients by the inclusion of mobile phone ownership dummy. Our robustness checks show that the estimation
results are quite robust.

6. Summary and Discussion
The findings of this study can be summarized as follows. First,
we show that the use of mobile money decreases the effect ofa negative shock on borrowing. For non-mobile money users,
when they experience a negative shock, the probability of borrowing increases by about 50 percentage points, while the probability of borrowing does not increase for mobile money users.
Second, we find that the frequency of negative shocks is not correlated with the distance from the border of mobile networks or
use of mobile money. Third, we find that the use of mobile
money affects the composition of saving methods. In particular, it decreases the probability of saving using less liquid saving
methods such as saving through livestock, the church, and communities by 36 percentage points, while it increases saving ina mobile money account by 36 percentage points as well as insaving groups that adopt mobile money technology by 22 percentage points. Fourth, we show that the use of mobile money
increases the probability of receiving remittances by 75 percentage points and that its effect is independent of experiencing
a negative shock. This evidence suggests that the use of mobile
money changes the saving portfolio, thereby allowing a household to prepare for negative shocks. Hence, a household thatuses mobile money does not need to increase borrowing in theface of a negative shock, whereas a household that does not use
mobile money needs to increase borrowing when such a shock
occurs. Consistent with this interpretation, we find that experiencing a negative shock increases the difficulty paying regularexpenses for non-users by 23 percentage points, while it does
not affect the difficulty paying regular expenses for users.
Regarding the effect of mobile money on the economy, ouranalysis is consistent with previous analyses that show that mobile money affects many dimensions of economic activities. In
particular, our results are consistent with previous studies thatshow that the use of mobile money mitigates negative shocks(Jack and Suri, 2014; Blumenstock et al., 2016; Riley, 2018).

Table 7. Second-Stage Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Various Saving Methods
Dependent Variable
(1)

(2)

Panel A.
Use of Mobile Money Dummy(3)-0.240***(0.0866)-0.250***(0.0912)-0.179(0.110)Panel E.
Use of Mobile Money Dummy-0.193*(0.116)0.640***(0.104)0.623***(0.108)-0.361**(0.173)-0.0595(0.137)-0.0793(0.170)-0.00983(0.204)0.724***(0.136)0.682***(0.165)0.600***(0.191)

Saving in Saving Groups with MM Technology
0.190***(0.0512)0.196***(0.0537)0.200***(0.0653)0.214***(0.0827)0.220**(0.102)

Saving in Saving Groups without MM Technology
0.0361(0.0931)0.0195(0.0971)

Panel F.
Use of Mobile Money Dummy-0.351**(0.142)

Saving in Mobile Money AccountPanel D.
Use of Mobile Money Dummy-0.334***(0.114)

Saving in Cash

Panel C.
Use of Mobile Money Dummy(5)

Less Liquid Saving

Panel B.
Use of Mobile Money Dummy(4)0.0213(0.116)0.0144(0.142)0.110(0.171)

Saving at least in one method
0.365***(0.130)0.334**(0.135)0.421***(0.163)

0.346*(0.199)0.370(0.242)

Negative Shock Dummy
Mobile Phone Ownership
Population Density & Night Lights
Region (31 Regions)
Demographic Characteristics
Distance to Financial Institutions

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes
Yes

Kleibergen-Paap Rank Wald
N

75.08
4,127

68.71
4,127

49.05
4,127

33.98
4,127

23.56
4,127

Control Variables

Notes: Robust standard errors in parentheses. Notes of Table 3 apply. All panels show the 2ndstage results of 2SLS estimation. *** p<0.01, ** p<0.05, * p<0.1.

715

720

725

However, they differ slightly from those of studies that showthat the use of mobile money works like insurance, as they high-730
light that a household receives remittances more when experiencing a negative shock. In contrast, our analysis indicates thatthe use of mobile money increases the receipt of remittances,
but its effect on the receipt of remittances is independent of negative shocks.
735
A natural question is why we have different results on the effect of the use of mobile money on the receipt of remittances
for households with and without negative shocks. In Tanzania, mobile money was introduced in 2008 and our survey was
conducted in 2017. Thus, a household that has been using mo-740
bile money in the intervening nine years could have saved sufficiently in liquid assets through increased remittances. Such a
14household does not need to decrease consumption in the presence of negative shocks. In addition, the negative shocks inour survey may be relatively small and the increased liquid savings through a change in the saving portfolio in previous years
could offset the negative shock. Naito (2017) shows that the effects of the use of mobile money on the receipt of remittancesin Zimbabwe are similar regardless of whether a household experiences a negative shock, which is consistent with our results.
If this is so, the difference between the literature and our result
indicates that the timing of the survey is critical. This should be
explored in future research.
Regarding the effect of having a bank account on economic
activity and welfare, our results are consistent with previous
results that show that a bank account affects financial behav-

Table 8. Second-Stage Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Financial Difficulty
Dependent Variable
Variable

Financial Difficulty Paying Regular Expenses
(1)

(2)

(3)

(4)

(5)

-0.495***(0.155)0.258(0.172)-0.209**(0.0998)25.24-0.501***(0.160)0.255(0.173)-0.208**(0.100)24.35-0.638***(0.189)0.265(0.177)-0.215**(0.103)20.57-0.678***(0.230)0.290(0.179)-0.231**(0.105)15.23-0.629**(0.265)0.284(0.178)-0.225**(0.105)
10.83

Panel A.
Use of Mobile Money Dummy
Mobile Money × No Negative ShockNo Negative Shock
Kleibergen-Paap Rank Wald
Joint F-test
Testing Coef. of Mobile Money × No Negative Shock+ Coef. of No Negative Shock=0
Chi-squared
0.419
0.387
0.362
0.539
0.557
P-value
0.518
0.534
0.547
0.463
0.456
Panel BUse of Mobile Money Dummy-0.370***(0.120)-0.0622***(0.0167)75.08-0.377***(0.126)-0.0628***(0.0167)68.71-0.513***(0.156)-0.0642***(0.0174)49.05-0.540***(0.195)-0.0660***(0.0177)
33.98-0.494**(0.230)-0.0636***(0.0184)
23.56

Mobile Phone Ownership
Population Density & Night Lights
Region (31 Regions)
Demographic Characteristics
Distance to Financial Institutions

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

N

4,127

4,127

4,127

4,127

4,127

No Negative Shock
Kleibergen-Paap Rank Wald
Control Variables

Notes: Robust standard errors in parentheses. Panel of the Join F-test tests the null hypothesis that the sumof the coefficient of the non-negative shock dummy and the interaction term of the mobile money usage
dummy and non-negative shock dummy is equal to zero. The chi-squared value and its P-value are shown.
*** p<0.01, ** p<0.05, * p<0.1.

745

ior to a reasonable degree. For example, using randomized experiments, Dupas et al. (2018) find a large treatment effect on
the treated, which is consistent with our finding. Our findings
thus contribute to this literature by showing that mobile moneychanges saving portfolios and helps households absorb negative
shocks through preparation.

Khiaonarong, 2014; Lal and Sachdev, 2015).

7. Conclusion
750

755

Mobile money is becoming an important policy issue. The gov-760
ernments of developing countries recognize that mobile moneytransfers are an attractive tax base (Rukundo, 2017; Ndung’u,2019) and financial regulators are planning to more strictly regulate the mobile money industry to balance the traditional banking sector and mobile money operators (Klein and Mayer, 2011;765
15

On the other hand, in poor countries, the lack of access tocredit and lack of safe and liquid saving methods are often
policy targets. Mobile money solves these issues to some degree. This implies that the efficiency cost of taxation on mobile money transactions and stricter regulations on the mobile
money industry will not be low. Regulators of mobile money
operators and tax planning authorities in developing countries
therefore need to be careful when implementing tax and regulation policies on mobile money and the mobile money industry.

References
835

References
770

775

780

785

790

795

800

805

810

815

820

825

830

Abiona, O., Koppensteiner, M.F., 2020. Financial inclusion, shocks, and840
poverty: Evidence from the expansion of mobile money in tanzania. Journalof Human Resources , 1018–9796R1.
Agarwal, R., Horowitz, A.W., 2002. Are international remittances altruism
or insurance? evidence from guyana using multiple-migrant households.
World development 30, 2033–2044.
845
Agarwal, S., Alok, S., Ghosh, P., Ghosh, S., Piskorski, T., Seru, A., 2017.
Banking the unbanked: What do 255 million new bank accounts reveal about
financial access? Columbia Business School Research Paper .
Aker, J.C., Boumnijel, R., McClelland, A., Tierney, N., 2016. Payment mechanisms and antipoverty programs: Evidence from a mobile money cash trans-850
fer experiment in niger. Economic Development and Cultural Change 65,
1–37.
Asongu, S., Asongu, N., 2018. The comparative exploration of mobile money
services in inclusive development. International journal of social economics.
855
Asongu, S.A., 2018. Conditional determinants of mobile phones penetrationand mobile banking in sub-saharan africa. Journal of the Knowledge Economy 9, 81–135.
Blumenstock, J.E., Callen, M., Ghani, T., Koepke, L., 2015. Promises and pitfalls of mobile money in afghanistan: evidence from a randomized control860
trial, in: Proceedings of the Seventh International Conference on Information and Communication Technologies and Development, ACM. p. 15.
Blumenstock, J.E., Eagle, N., Fafchamps, M., 2016. Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters. Journalof Development Economics 120, 157–181.
865
Bruhn, M., Love, I., 2009. The economic impact of banking the unbanked:
Evidence from mexico. Policy Research Working Paper 4981.
Burgess, R., Pande, R., 2005. Do rural banks matter? evidence from the indiansocial banking experiment. American Economic Review 95, 780–795.
Center for International Earth Science Information Network - CIESIN Columbia University, 2016. Gridded Population of the World, Version
4 (GPWv4): Population Count. Palisades, NY: NASA Socioeconomic
Data and Applications Center (SEDAC). URL: http://dx.doi.org/10.
7927/H4X63JVC.
Dupas, P., Karlan, D., Robinson, J., Ubfal, D., 2018. Banking the unbanked?
evidence from three countries. American Economic Journal: Applied Economics 10, 257–97.
Dupas, P., Robinson, J., 2013a. Savings constraints and microenterprise development: Evidence from a field experiment in kenya. American Economic
Journal: Applied Economics 5, 163–92.
Dupas, P., Robinson, J., 2013b. Why don’t the poor save more? evidence fromhealth savings experiments. American Economic Review 103, 1138–71.
Gosavi, A., 2018. Can mobile money help firms mitigate the problem of accessto finance in eastern sub-saharan africa? Journal of African Business 19,
343–360.
Jack, W., Suri, T., 2014. Risk sharing and transactions costs: Evidence fromkenya’s mobile money revolution. The American Economic Review 104,
183–223.
Khiaonarong, T., 2014. Oversight issues in mobile payments. Working Paper
14/123. IMF.
Klein, M., Mayer, C., 2011. Mobile banking and financial inclusion: The regulatory lessons. The World Bank.
Lal, R., Sachdev, I., 2015. Mobile Money Services-Design and Development
for Financial Inclusion. Working Paper 15-083. Harvard Business School.
Munyegera, G.K., Matsumoto, T., 2016. Mobile money, remittances, andhousehold welfare: panel evidence from rural uganda. World Development
79, 127–137.
Muralidharan, K., Niehaus, P., Sukhtankar, S., 2016. Building state capacity:
Evidence from biometric smartcards in india. American Economic Review
106, 2895–2929.
Naito, H., 2017. Does mobile money helps children’s schooling. Working
Paper, Univesity of Tsukuba, Program in Economic and Public Policy .
Ndung’u, N.S., 2019. Taxing mobile phone transactions in africa: Lessons fromkenya. Brookings Institution Policy Brief .
NOAA National Geophysical Data Center, 2019. Version 4 DMSP-OLS Night-

16

time Lights Time Series. National Oceanic and Atmospheric Administration.
Okello Candiya Bongomin, G., Munene, J.C., 2021. Analyzing the relationshipbetween mobile money adoption and usage and financial inclusion of msmes
in developing countries: Mediating role of cultural norms in uganda. Journalof African Business 22, 1–20.
Okello Candiya Bongomin, G., Ntayi, J.M., Munene, J.C., Malinga, C.A.,
2018. Mobile money and financial inclusion in sub-saharan africa: the moderating role of social networks. Journal of African Business 19, 361–384.
Ratha, D., et al., 2003. Workers’ remittances: an important and stable source of
external development finance. Global development finance 2003, 157–175.
Riley, E., 2018. Mobile money and risk sharing against village shocks. Journalof Development Economics 135, 43–58.
Riley, E., 2020. Resisting social pressure in the household using mobile money:
Experimental evidence on microenterprise investment in uganda. Universityof Oxford, May 25.
Rukundo, S., 2017. Taxation of the telecommunications sector: A focus onpolicy issues and considerations in taxation of mobile money in uganda, in:Africa Tax Research Network 2017 Congress at Antananarivo, Madagascar,
pp. 4–6.
Suri, T., Jack, W., 2016. The long-run poverty and gender impacts of mobile
money. Science 354, 1288–1292.
Vanwey, L.K., 2004. Altruistic and contractual remittances between male andfemale migrants and households in rural thailand. Demography 41, 739–
756.
Wang, P., Huang, C., de Colstoun, E.C.B., Tilton, J.C., Tan, B., 2019. Global
Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. Palisades, NY: NASA Socioeconomic Data and Applications Center(SEDAC). URL: https://doi.org/10.7927/H4DN434S.
World Bank, 2014.
Financial inclusion data global findex.
http://datatopics.worldbank.org/financialinclusion/.
URL: http://datatopics.worldbank.org/financialinclusion/.
Yang, D., Choi, H., 2007. Are remittances insurance? evidence from rainfall
shocks in the philippines. The World Bank Economic Review 21, 219–248.

The Effect of Mobile Money on Borrowing and Saving: Evidence from
Tanzania
Hisahiro Naito
Askar Ismailov
Albert Benson Kimaro
Supplemental Information
S1

Institutional Background in Tanzania

In 2008, the Bank of Tanzania issued letters of no objection to the partner banks of
Vodacom’s M-PESA and Zantel’s Z-Pesa (Di Castri & Gidvani, 2014), which allowed
Vodacom and Zantel to start mobile money services. Following this no objection letter,in the same year, Vodacom Tanzania introduced mobile money services through MPesa products and Tigo Mobile and Airtel introduced Tigo Pesa and Airtel Moneyin 2009, respectively. In 2010, Zantel introduced Ezy Pesa. By 2015, Vodacom hadreached a market share of 54 percent with M-Pesa mobile money services followed by
Tigo Pesa (29 percent), Airtel Money (13 percent), and Ezy Pesa (4 percent).
Regarding the content of the services of those products, the bilateral connections
between mobile network operators and bank payment systems have enabled customers
to transfer funds between bank accounts and mobile wallets in both directions. Morespecifically, mobile money users can save to their bank account in three ways. First,
those with a smartphone can use mobile money applications (Tigo Pesa, MPESA,HaloPesa, TTCL Pesa) to send to or save money in bank accounts. Second, thosewith GSM cellular phones can use Unstructured Supplementary Services Data (USSD),
which has the option to send to or save money in bank accounts. Third, mobile moneyusers can visit their nearest mobile money agent to save money to their bank accounts.
Similarly, mobile money users can withdraw money from their bank accounts.
Regarding saving, mobile money providers offer safe interest-bearing savings accounts, and these increased the proportion of Tanzanians who saved using mobile
financial services by 20 percent from 2011 to 2014. The interest rates of those mobile
money operators are generally above the average interest rates provided by banks. A
mobile money savings account allows each customer to save up to 3 million TZS or1

1,400 USD (World Bank, 2017b).
Regarding borrowing, although borrowing from mobile money operators has become
feasible, its size remains small, as we show in the next section. Only 2.5 percent ofhouseholds borrow from mobile money operators. Among those who borrow from any
source, 90 percent borrow from the informal sector. This suggests that the direct effectof the use of mobile money on borrowing from mobile money operators is minor.
S2

Explanation of the Dataset

The FinScope Tanzania 2017 dataset was commissioned by the Financial Sector Deepening Trust in partnership with the Bank of Tanzania, Ministry of Finance and Planning, National Bureau of Statistics, Office of Chief Government Statistician Zanzibar,
representatives of providers of financial services, and non-governmental organizations
and other private sector players. The survey was conducted by Ipsos Tanzania under
the technical advisory of Yakini Development Consulting. The Tanzania Populationand Housing Census 2012 was used as a base sampling frame to achieve a representative individual-based sample for the population aged 16 years and older through theapplication of a three-stage stratified sampling approach.
In the first stage of the three-stage sampling, the enumeration areas were randomly
sampled. In the second stage, from the sampled enumeration areas, 10 households wereselected at random, and in the third stage, from the list of all adult household membersin the sample, one adult household member was randomly selected to be interviewed.
In our analysis, we restrict the sample to households where the head of the household
was the respondent to the interview.
The education level of the heads of the households is classified into eight categories.
The variables representing household income sources are classified into nine categories.
Population density is created using the dataset of the population count of Tanzania
for 2015 provided by the Center for International Earth Science Information Network(Center for International Earth Science Information Network - CIESIN - Columbia
University, 2016). This dataset is created from the detailed geographical informationof the census dataset, and the resolution is 1 km. To calculate population density
at each household location, we form a circle with a radius of 5 km and calculate the2population density of each circle.
We use the night light luminosity data provided by the National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA National Geophysical Data Center, 2019) following Henderson et al. (2012). To prevent endogeneity,
namely, that mobile money affects economic activity and night light density, we use thenight light luminosity data from 2007, the year before mobile money was introducedin Tanzania. The average built-up rate is created using the dataset provided by Wanget al. (2019), which was created using Landsat satellite imagery data. For both thenight light luminosity data and the built-up rate data, we form a circle with a 5 km
radius at the location of each household and calculate night light luminosity and thebuilt-up rate.

3

S3

Relationship between the Distance to the Areas with Multiple Mobile
Networks and Use of Mobile Money After Controlling for the Effect of
the Control Variables

Figure S1 shows the relationship between each household’s shortest distance to theareas covered by multiple mobile networks and the mobile money usage rate after
controlling for the effect of the control variables. On the horizontal axis, we measure
the residual from regressing each household’s shortest distance to the areas covered bymultiple mobile network accessibility on the control variables. On the vertical axis, wemeasure the residual from regressing the mobile money usage dummy on those control
variables.
Figure S1: Shortest Distance to the Border of Multiple Network Areas and the Use

.5
0
-.5
-1

Residual of Mobile Money Use Rateof Mobile Money after Controlling for the Effect of the Covariates-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

2

4

6

8 10 12 14 16 18 20

Residual of Distance

Notes: The horizontal axis is the residual of the regression regressing the distance on all
the covariates. The vertical axis is the residual of the regression regressing the mobile use
dummy on all the covariates. The size of the bin is 0.5 km. The reference vertical lines
are shown at the point where the distance is equal to -10 km or 10 km. The above graphshows that when the distance is within [-10,10], the relationship between the distance andaverage mobile money use rate is almost on the same line. The estimated coefficient of theslope of the fitted line is -0.011 and the robust standard error is 0.00039. R2 =0.52. For theestimation of the fitted line above, all the observations within [-20,20] are used.

4

S4

Orthogonality of a Negative Shock to the Distance from the Areas with
Multiple Mobile Networks

One natural question from Figure 3 and Panel C of Table 5 is whether a household thatis far away from the areas covered by multiple mobile networks experiences different
types of negative shocks than a household located inside those areas. To examine
whether such a case is plausible, we estimate the effect of the distance from the areas
covered by multiple networks on the frequency of negative shocks. If the nature of anegative shock differs by location, its frequency is also likely to be different. For thispurpose, in Table 6, we regress the negative shock dummy on the distance from theareas covered by multiple mobile networks with several control variables using OLS.
The estimated coefficients are economically and statistically insignificant. This showsthat when the distance to the areas covered by multiple mobile networks is 10 km
away, the probability of experiencing a negative shock falls only by 2.1 percentage
points (P-value=0.3). Thus, the frequency of experiencing a negative shock is similar
in households with different locations, suggesting that the criticism that the nature ofnegative shocks is different at different locations is not justified.
Table S1. Orthogonality of Negative Shocks: The Effect of Distance on Negative Shocks (OLS)
Negative Shock Dummy

Dependent Variable
Distance from Network Area
R-squared(1)
0.00174(0.00195)
0.108

(2)
0.00192(0.00196)
0.109

(3)
-0.000753(0.00202)
0.154

Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Population Density & Night Light Luminosity
Yes
Yes
Region (31 Regions)
Yes
Demographic Characteristics
Distance to Financial Institutions
4,127
4,127
4,127
N
Notes: Robust standard error in parentheses. Notes of Table 3 apply.

5(4)-0.00100(0.00204)
0.162

(5)
-0.00214(0.00211)
0.163

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes
4,127

4,127

S5

Robustness Checks

S5.1

Controlling for the District Fixed EffectIn our estimation, we included 30 region dummies in addition to the population density
and average night light luminosity of the area in which a household is located. The ideaof including population density and average night light luminosity is to control for thedifference in the economic activity of areas. One might argue, however, that controlling
for the difference in economic activity using the region fixed effect, population density,and average night light luminosity is insufficient.
In this robustness check, we include 170 district dummies to control for the difference in the economic activity of areas. Tables B1–B5 show the first- and second-stage
results when we include these district dummies as control variables in addition to theother control variables. The estimated coefficients and their standard errors are similar to those in Section 5. The only difference is that we use Kleibergen–Paap Rank
Wald statistics to test the strength of the first stage of the 2SLS estimation. When
we include all the control variables and 170 district dummies, the Kleibergen–Paap
Rank Wald statistics become 9.1, which is slightly smaller than the threshold value of
10. This is likely due to the inclusion of so many control variables. However, none of
the estimated coefficients or standard errors change substantially compared with thosewith a smaller set of control variables and 170 district dummies. This suggests thateven when controlling for the district fixed effect, our results are robust.

6

Table B1. First‐Stage Estimation Results of 2SLS :Including District Dummies (171 Districts) as Control Variables
Use of Mobile Money Dummy
Endogenous Variable
(1)
(2)
(3)
(4)
(5)
Distance from Network Area
‐0.0153*** ‐0.0146*** ‐0.0112*** ‐0.00961*** ‐0.00929***(0.00178) (0.00178) (0.00219) (0.00212) (0.00217)
R‐squared
0.226
0.235
0.333
0.384
0.387
Kleibergen‐Paap Rank Wald
73.93
67.56
25.92
20.54
18.29
Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Yes
Population Density & Night Light
Yes
Yes
Yes
Yes
Districts (171 Districts)
Yes
Yes
Yes
Demographic Characteristics
Yes
Yes
Distance to Financial Institutions
Yes
4,127
4,127
4,127
4,127
4,127
N
Notes: Robust standard errors in parentheses. Notes in Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

7

Table B2. The Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use Dummy on Borrowing:
Including District Dummies (171 Districts) as Control Variables
Borrowing Dummy

Dependent variable
Panel AUse of Mobile Money Dummy
R‐squared
Kleibergen‐Paap Rank Wald
Panel BUse of Mobile Money Dummy
Negative shock dummy
R‐squared
Kleibergen‐Paap Rank Wald
Panel CUse of Mobile Money Dummy
Mobie Money × Negative Shock
Negative Shock

(1)

(2)

(3)

(4)

(5)

‐0.167(0.138)‐0.02973.93‐0.194(0.146)‐0.03967.56‐0.169(0.230)‐0.02825.92‐0.172(0.263)
0.003
20.54

‐0.0865(0.276)
0.032
18.29

‐0.144(0.133)0.205***(0.0186)
0.018
75.08

‐0.167(0.140)0.205***(0.0187)
0.010
68.71

‐0.152(0.222)0.201***(0.0204)
0.014
26.21

‐0.160(0.255)0.194***(0.0200)
0.039
20.66

‐0.112(0.272)0.190***(0.0205)
0.055
18.11

0.0968(0.152)‐0.469**(0.195)0.472***(0.114)‐0.04925.24

0.0750(0.157)‐0.471**(0.196)0.473***(0.115)‐0.05924.35

0.241(0.230)‐0.643***(0.198)0.567***(0.118)‐0.08012.83

0.216(0.266)‐0.613***(0.194)0.543***(0.115)‐0.04810.24

0.294(0.282)‐0.627***(0.194)0.547***(0.115)‐0.031
9.107

R‐squared
Kleibergen‐Paap Rank Wald
Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Population Density & Night Light
Yes
Yes
Yes
Districts (171 Districts)
Yes
Yes
Demographic Characteristics
Yes
Distance to Financial Institutions
4,127
4,127
4,127
4,127
N
Notes: Robust standard errors in parentheses. Notes in Table 3 apply. *** p<0.01,** p<0.05, * p<0.1.

8

Yes
Yes
Yes
Yes
Yes
4,127

Table B3. The Second Stage Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use, Negative Shock and Their Interaction
on Receipt of Remittance : Including District Dummies (171 Districts) as Control Variables
Panel A
Receipt of Remittance Dummy
(2)
(3)
(4)
0.703***
0.744***
0.761***(0.130)(0.186)(0.215)‐0.104‐0.0328‐0.0565(0.160)(0.165)(0.163)0.09430.04290.0600(0.0934)(0.0979)(0.0964)

Dependent variable
Use of Mobile Money Dummy
Mobie Money × Negative Shock
Negative Shock(1)
0.698***(0.126)‐0.107(0.160)0.0958(0.0932)(5)0.843***(0.233)‐0.0579(0.168)0.0557(0.0997)

Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Yes
Population Density & Night Light
Yes
Yes
Yes
Yes
Districts (171 Districts)
Yes
Yes
Yes
Demographic Characteristics
Yes
Yes
Distance to Financial Institutions
Yes
R‐squared
0.261
0.260
0.211
0.218
0.188
Kleibergen‐Paap Rank Wald
25.24
24.35
12.83
10.24
9.107
4,127
4,127
4,127
4,127
4,127
N
Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

9

Table B4. The Second Stage Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use Dummy on Varioius Saving Methods
Dependent variable
(1)

(2)

(3)

(4)

(5)

Panel A.
Use of Mobile Money Dummy-0.240***(0.0866)

Less Liquid Saving-0.250***-0.419**-0.443**(0.164)(0.191)(0.0912)-0.454**(0.206)-0.179(0.110)

Saving in Cash-0.193*-0.0554-0.0625(0.116)(0.182)(0.213)-0.0633(0.226)0.640***(0.104)

Saving in Mobile Money Account0.640***
0.623***
0.676***(0.108)(0.170)(0.195)0.592***(0.204)

Panel B.
Use of Mobile Money Dummy
Panel C.
Use of Mobile Money Dummy
Panel D.
Use of Mobile Money Dummy

Saving in Saving Groups with MM Technology
0.190***
0.196***
0.226***
0.234**
0.229**(0.0512)(0.0537)(0.0854)(0.101)(0.109)Panel E.
Use of Mobile Money Dummy

Saving in Saving Groups without MM Technology
0.03610.01950.04500.03360.141(0.0931)(0.0971)(0.149)(0.171)(0.185)

Panel F.
Use of Mobile Money Dummy
Control Variables
Negative Shock Dummy
Mobile Phone Ownership
Population Density & Night Light
Districts (171 Districts)
Demographic Characteristics

0.365***(0.130)Yes
Yes

Saving at least in one method
0.334**
0.329
0.265(0.135)(0.212)(0.243)0.213(0.260)

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes

Distance to Financial Institutions

Yes
Yes
Yes
Yes

Yes

Kleibergen-Paap Rank Wald

75.08
68.71
26.21
20.66
18.11
4,127
4,127
4,127
4,127
4,127
N
Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

10

Table B5. The Second Stage Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use Dummy on Financial Difficulty
Financial Difficulty to Pay Regular Expenses

Dependent variable
Variable

(1)

(2)

(3)

(4)

(5)

-0.495***-0.501***-0.772***-0.815***-0.756**(0.155)(0.160)(0.250)(0.288)(0.299)

0.258

0.255

0.331*

0.354*

0.355*(0.172)(0.173)(0.187)(0.188)(0.186)-0.209**-0.208**-0.257**-0.271**-0.269**(0.0998)(0.100)(0.111)(0.112)(0.110)-0.370***-0.377***-0.643***-0.678***-0.631**(0.120)(0.126)(0.219)(0.258)(0.273)

Panel A.
Use of Mobile Money Dummy
Mobie Money × No Negative ShockNo Negative Shock
Panel BUse of Mobile Money Dummy
Negative Shock Dummy-0.0622*** -0.0628*** -0.0683*** -0.0694*** -0.0665***(0.0167)(0.0167)(0.0196)(0.0197)(0.0199)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Control Variables
Mobile Phone Ownership
Population Density& Night Light
Districts (171 Districts)
Demographic Characteristics
Distance to Financial Institutions
N

Yes
4,127

4,127

4,127

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

11

4,127

4,127

S5.2

Endogeneity of Mobile Phone Ownership

In equation (1), we include the mobile phone ownership dummy as a control variable
in xi . Although the inclusion of this dummy follows the literature (Jack & Suri, 2014;
Munyegera & Matsumoto, 2016), it could bias the estimate of β1 because mobile phoneownership is the outcome variable and controlling for it introduces bias when estimating
the causal effect (Angrist & Pischke, 2008). To observe how this occurs, assume thatmobile phone ownership is a negative function of our instrumental variable, distance tothe areas covered by multiple mobile networks, and a positive function of unobserved
characteristics z1i . Assume that z1i is a variable such as unobserved income and familiarity with technology, which is positively correlated with financial activity. When the
distance decreases, the probability of using mobile money increases. However, it also
positively affects the probability of mobile phone ownership. Thus, a decrease in the
instrumental variable (distance) while controlling for mobile phone ownership implies
that z1i must decrease and that financial behavior decreases because of the fall in z1i .
Thus, the 2SLS estimation while controlling for mobile phone ownership (without the
instrumental variable of mobile phone ownership) will downward bias the estimate ofβ1 , even if we were to use the instrumental variable for mobile money use. On theother hand, if we were to exclude mobile phone ownership from the 2SLS estimation
as a control variable in xi , the coefficient of the mobile money use dummy would
capture not only the effect of mobile money use but also the effect of mobile phoneownership since the instrumental variable (mobile network coverage dummy) is also
correlated with mobile phone ownership.1 To address this problem, we run the 2SLS
estimation without the mobile phone ownership dummy as one of the control variables
and examine how the estimated coefficient of the use of mobile money is sensitive tothe inclusion of the mobile phone ownership dummy. Column (1) in Tables C1–C5
shows that the estimated coefficient of mobile money use in the 2SLS estimation does
not change without the mobile phone ownership dummy. This implies that the bias
induced by including the mobile phone ownership dummy is unlikely to be serious.2
1

One way to solve this problem is to find another instrumental variable correlated with mobile
phone ownership but not with financial behavior. However, finding another instrument is difficult.
2
Another possible reason behind the similarity between the 2SLS estimates of β1 with and without
the mobile phone ownership dummy is that z1i is actually negatively correlated with financial behavior.
The assumption of a positive correlation between z1i and financial behavior is not testable since we

12

S5.3

Controlling for Income

In Section 5, we included the negative shock dummy to examine the different effectsof the use of mobile money. In that analysis, we did not include income as a control
variable for two reasons. First, income is the outcome variable if a household thatreceives remittances through mobile money invested in productive assets and grewits income. Including the outcome variable would thus cause the same problem as
including the mobile phone ownership dummy, as discussed in the above subsection.
Second, information on income includes information on the negative shock. Thus, theestimated coefficient of the negative shock dummy while controlling for income would
bias the estimated coefficients of the negative shock dummy downward.
On the other hand, one might argue that the instrumental variable is correlatedwith income, and thus not including income in the control variables might bias ourestimates. In this case, the effect of the use of mobile money could include the effectof income, and the estimated coefficient would be upwardly biased.
Column (2) in Tables C1–C5 shows the estimated coefficients when we control forincome. We find that controlling for the effect of income does not affect the estimatedcoefficient. This suggests that the bias caused by excluding income from the control
variables is not serious.
S5.4

Controlling for Time Distance to Financial Institutions

In Section 4, we included as a control variable the physical distance to several financial
institutions such as commercial banks, community banks, and microfinance institutions. Such time distance depends on the ownership of transportation equipment (e.g.,
bicycle, motorcycle, and car), which is the outcome variable. A household that receives a substantial amount of remittances might purchase a motorcycle. Owing tothis endogeneity, we use the physical distance as a control variable. To examine thesensitivity of our analysis by controlling for the time distance, we re-run the regression
cannot observe z1i . However, we can still check whether the observable variables that affect mobile
phone ownership positively are correlated with financial behavior following Altonji et al. (2005). When
we regress saving or borrowing and mobile phone ownership on the education of respondents and thewage earner dummy, we find that those variables are positively correlated with both mobile phoneownership and financial behavior (saving and borrowing).

13

by controlling for the time distance instead of the physical distance. To do so, we include the ownership of transportation equipment interacted with the physical distanceas the control variables. Column (3) in Tables C1–C5 show the estimation results whentransportation equipment and the interaction term with physical distance are included
as control variables. We find that the estimated coefficients and standard errors are
similar to those in Section 4.
S5.5

Using Different Subsamples

In Section 5, we used households whose distance to the areas covered by multiple mobile
networks is less than or equal to 10 km because of the clear first-stage relationship andrelatively large sample of this group. One might ask whether our estimates are sensitive
to sample selection. In Column (4) of each table in Section S4.2, we use, as the sample,
the households whose distance to the areas covered by multiple mobile networks is lessthan or equal to 7.5 km. The estimated coefficients and standard errors are similar
to those obtained in Section 5. In Column (5), we use, as the sample, the households
whose distance to the areas covered by multiple mobile networks is less than or equal to
15 km. Again, our estimated coefficients are similar to those obtained in Section 5. Thissuggests that as long as we choose the distance to the areas covered by multiple mobile
networks as small as possible, the households selected are similar and our estimatedcoefficients change little.

14

Table C1. First-Stage Estimation Results of 2SLSUse of Mobile Money Dummy
(1)
(2)
(3)
(4)
-0.00912***-0.00896***
Distance from Network Area -0.00929***-0.00900***(0.00217)(0.00185)(0.00186)(0.00212)
R-squared
0.387
0.361
0.356
0.356
Kleibergen-Paap Rank Wald
18.29
23.62
25.92
17.82
Specifcation
Not Control Mobile Phone
Yes
Ownership
Control Income
Yes
Transportation equipment
Yes
Yes|Distance|≤ 7.5km|Distance|≤15km
4,127
3,867
4,127
4,127
N
Endogenous Variable(5)-0.00885***(0.00158)
0.362
31.41

Yes
4,236

Notes: Robust standard errors in parentheses. Column (1)-(5) use the specificaiotn of column (5)in Table 4 except the specification described above. Column (1) in the above table include allcontrol variables in Table 4 except mobile phone owership dummy. Coloum (2) include allcontrol variable in column (5) of Table 4 and income as control variables. Column (3) in theabove table includes the tranportation equipment (bicycle, motorcycle and car) ownershipdummy and their interaction with the phyiscial distance to financial instituions in addtion to allcontrol variables used in column (5) of Table 4. Column (4) in the above table uses as the
sample the housheolds whose shortest distance to the areas with multiple mobile network areas
is less than or equal to 7.5km. Column (5) uses as the sample the households whose shortest
distance to the areas with multiple mobile networks is less than or equal to 15 km. *** p<0.01, **p<0.05, * p<0.1.

15

Table C2. The Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use Dummy on Borrowing
Borrowing Dummy

Dependent variable
Panel AUse of Mobile Money Dummy
R-squared
Kleibergen-Paap Rank Wald
Panel BUse of Mobile Money Dummy
Negative shock dummy
R-squared
Kleibergen-Paap Rank Wald
Panel CUse of Mobile Money Dummy
Mobie Money × Negative Shock
Negative ShockR-squared
Kleibergen-Paap Rank Wald

(1)

(2)

(3)

(4)

(5)

-0.0865(0.276)
0.032
18.29

0.00216(0.246)
0.051
23.62

-0.0460(0.245)
0.040
23.94

-0.181(0.290)
0.000
17.82

0.0138(0.214)
0.052
31.41

-0.112(0.272)0.190***(0.0205)
0.055
18.11

-0.0417(0.245)0.183***(0.0198)
0.071
23.14

-0.0744(0.202)0.186***(0.0186)
0.062
33.98

-0.213(0.289)0.192***(0.0218)
0.019
17.58

-0.00508(0.212)0.181***(0.0185)
0.077
31.14

0.294
(0.282)-0.627***(0.194)0.547***(0.115)-0.0319.107

0.222(0.254)-0.510***(0.188)0.474***(0.111)
0.000
10.66

0.170
(0.251)-0.508***(0.191)0.476***(0.113)-0.01510.62

0.0276(0.306)-0.409*(0.240)0.422***(0.139)-0.0258.683

0.224(0.237)-0.350**(0.162)0.376***(0.0932)
0.049
15.64

Specifications
Not Control Mobile Phone
Yes
Ownership
Control Income
Yes
Transportation equipment
Yes
Yes|Distance|≤ 7.5km|Distance|≤15km
Yes
4,127
4,127
4,127
3,867
4,236
N
Notes: Robust standard errors in parentheses. This table is the robustness check of column(5) of Table 5 in section 5. Notes in Table C1 apply. *** p<0.01, ** p<0.05, * p<0.1.

16

Table C3. The Second Stage Estimation Results of 2SLS
stimated Coefficients of Mobile Money Use, Negative Shock and Their Interaction on
Receipt of RemittancePanel A
Receipt of Remittance Dummy

Dependent variable
Use of Mobile Money Dummy
Mobie Money × Negative Shock
Negative Shock

(1)

(2)

(3)

(4)

(5)

0.843***

0.746***

0.744***

0.705***

0.918***(0.233)(0.211)(0.186)(0.259)(0.204)-0.0579-0.150-0.03280.0645-0.178(0.168)(0.160)(0.165)(0.203)(0.141)0.05570.117

0.0429-0.00503

0.128(0.0997)(0.0944)(0.0979)(0.117)(0.0809)

Control Variables
Not Control Mobile Phone Ownership

Yes

Control Income

Yes

Transportation equipment

Yes
Yes

|Distance|≤ 7.5km|Distance|≤15km

Yes

R-squared

0.188

0.244

0.244

0.217

0.193

Kleibergen-Paap Rank Wald

9.107

10.66

10.62

8.683

15.64

N

4,127

4,127

4,127

3,867

4,236

Notes: Robust standard errors in parentheses. This table is the robustness check of column (5) of
Table 7 in section 5. Notes in Table C1 apply. *** p<0.01, ** p<0.05, * p<0.1.

17

Table C4. The Second Stage Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use Dummy on Varioius Saving Methods
Dependent variable
(1)

(2)

(3)

(4)

(5)

Panel A.
Use of Mobile Money Dummy-0.454**(0.206)

Less Liquid Saving-0.359**-0.396**-0.377*(0.175)(0.175)(0.196)-0.315**(0.149)-0.0633(0.226)-0.0145(0.207)

Saving in Cash-0.0475-0.0130(0.204)(0.240)-0.0937(0.180)0.592***(0.204)

Saving in Mobile Money Account0.604***
0.618***
0.661***(0.193)(0.192)(0.227)0.485***(0.153)

Panel B.
Use of Mobile Money Dummy
Panel C.
Use of Mobile Money Dummy
Panel D.
Use of Mobile Money Dummy

Saving in Saving Groups with MM Technology
0.220**
0.229**
0.214**
0.208*
0.132(0.109)(0.102)(0.102)(0.118)(0.0845)

Panel E.
Use of Mobile Money Dummy

Saving in Saving Groups without MM Technology
0.141
0.09820.07920.09970.184(0.185)(0.173)(0.171)(0.197)(0.145)

Panel F.
Use of Mobile Money Dummy
Control Variables
Negative Shock Dummy
Not Control Mobile Phone Ownership
Control Income
Transportation equipment|Distance|≤ 7.5km

0.213(0.260)Yes
Yes

Saving at least in one method
0.355
0.326
0.276
(0.243)(0.240)(0.280)0.195(0.209)

Yes

Yes

Yes

Yes

Yes
Yes
Yes

|Distance|≤15km
Kleibergen-Paap Rank Wald
N

Yes
75.08
4,127

68.71
4,127

26.21
4,127

18.11
3,867

20.66
4,236

Notes: Robust standard errors in parentheses. This table is the robustness check of column (5) of Table
8 in section 5. Notes in Table C1 apply. *** p<0.01, ** p<0.05, * p<0.1.

18

Table C5. The Second Stage Estimation Results of 2SLS
Estimated Coefficients of Mobile Money Use Dummy on Financial Difficulty
Financial Difficulty to Pay Regular Expenses

Dependent variable
Variable

(1)

(2)

(3)

(4)

(5)

-0.756**-0.617**-0.627**-0.595**-0.371*(0.299)(0.266)(0.266)(0.288)(0.202)

Panel A.
Use of Mobile Money Dummy
Mobie Money × No Negative ShockNo Negative Shock0.355*

0.278

0.278

0.373*

0.106(0.186)(0.176)(0.178)(0.214)(0.145)-0.269**-0.222**-0.221**-0.273**-0.115(0.110)(0.104)(0.105)(0.123)(0.0829)-0.631**-0.483**-0.489**-0.441*-0.335*(0.273)(0.232)(0.229)(0.259)(0.192)

Panel BUse of Mobile Money Dummy
Negative Shock Dummy-0.0665*** -0.0631*** -0.0619*** -0.0629*** -0.0554***(0.0199)(0.0186)(0.0182)(0.0195)(0.0167)

Control Variables
Not Control Mobile Phone Ownership

Yes

Control Income

Yes

Transportation equipment

Yes
Yes

|Distance|≤ 7.5km

Yes|Distance|≤15km
N

4,127

4,127

4,127

3,867

4,236

Notes: Robust standard errors in parentheses. This table is the robustness check of column (5) of
Table 9 in section 5. Notes in Table C1 apply. *** p<0.01, ** p<0.05, * p<0.1.

19

S5.6

Controlling for Social Networks and Community

Recently, several studies have pointed out the importance of social networks when
adopting mobile money (Okello Candiya Bongomin et al., 2018; Okello Candiya Bongomin & Munene, 2021). Given such studies, one might have another concern for ourestimation results: the shortest distance to the border of the areas covered by multiple
mobile networks might be correlated with some of the characteristics of communities.
More specifically, communities in which people have a strong tendency to help eachother might be correlated with the shortest distance to the border of the areas withmultiple mobile networks. If so, our 2SLS estimates measure not only the effect of
the use of mobile money but also the effect of such communities. To examine such a
possibility, we first construct a variable that measures the degree to which people incommunities help each other. Then, we include this constructed index as an additional
control variable in the regression.3 Table D1 in Appendix D shows our 2SLS estimation results when we include the community index as an additional control variable.
The estimated coefficient of the interaction term of the use of mobile money and adverse shock dummy changes from 0.50 to 0.49 when we include the community index.
Thus, the estimated coefficient of the interaction term of the negative shock and mobile
money dummy is virtually the same, even when controlling for the tendency of each
community to help its members. Thus, it is improbable that the effect of community
characteristics drives our results.

3

The questionnaire of the FinScope dataset asked for agree/disagree responses to the following
three statements: “You have people in the community that you can turn to for help if you need to”;
“People in your community have a strong sense of involvement in the community”; and “People inyour community rely on each other for support.” We count the number of agrees for each respondentand standardize it so that the mean becomes zero and the standard deviation becomes one. We use
this standardized value as the control variable.

20

Table D1. Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Borrowing by Including
Community Mutual Help Index as an Additional Control Variable
Borrowing Dummy

Dependent variable
Use of mobile money dummy
Mobile money × negative shock
Negative shock
Community mutual help
R‐squared
Kleibergen‐Paap Rank Wald(1)
0.0898(0.152)‐0.455**(0.194)0.465***(0.114)0.0265***(0.00857)‐0.04325.63(2)0.0680(0.157)‐0.457**(0.195)0.466***(0.115)0.0269***(0.00867)‐0.052
24.72

(3)
0.211
(0.180)‐0.488***(0.187)0.469***(0.110)0.0268***(0.00884)‐0.015
21.19

(4)
0.184
(0.214)‐0.477**(0.186)0.458***(0.109)0.0215**(0.00836)
0.003
15.53

(5)
0.209
(0.249)‐0.487***(0.187)
0.462***(0.111)
0.0212**(0.00860)
0.006
11.05

Control variables
Mobile phone ownership
Yes
Yes
Yes
Yes
Yes
Population density and night light
Yes
Yes
Yes
Yes
Region (31 regions)
Yes
Yes
Yes
Demographic characteristics
Yes
Yes
Distance to financial institutions
Yes
4,127
4,127
4,127
4,127
4,127
N
Notes: Robust standard errors in parentheses. Notes in Table 3 apply. Community Mutal Help
Index is a variable that measures the degree in which the community in which each respondent
reside help each other. This variable is constructed so that the mean of this variable is zero andits standard deviation is one. *** p<0.01, ** p<0.05, * p<0.1.

21

S5.7

Effect of the Use of Mobile Money on the Payment Method

Although this study focuses on financial behavior, mobile money can affect the payment
method used. For example, using a field experiment, Aker et al. (2016) analyze theeffect of utilizing mobile money as the payment method of the welfare system on
consumption behavior. In Table E1, we examine whether the use of mobile money
increases its use as a payment method for a type of good or service. We find thatmobile money is increasingly being used as a payment method for food and utilities.
Table E1. Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Use of Mobile Money Dummy on Using Mobile
Money as a Payment Method for Different type of Goods and Services
(1)

(2)

(3)

(4)

(5)

For
School
For
Fee
Food
0.271* ‐0.0156(0.145) (0.0544)

For
Medical
For
For
Bill
Rent
Utility‐0.00441 0.0489 0.270**(0.00450) (0.0379) (0.134)

Kleibergen‐Paap Rank Wald

0.162
24.01

0.011
24.01

‐0.00524.01‐0.059
24.01

0.133
24.01

Control variables
Mobile phone ownership
Population density and night light
Region (31 regions)
Demographic characteristics
Distance to financial institutions
N

Yes
Yes
Yes
Yes
Yes
4,127

Yes
Yes
Yes
Yes
Yes
4,127

Yes
Yes
Yes
Yes
Yes
4,127

Yes
Yes
Yes
Yes
Yes
4,127

Yes
Yes
Yes
Yes
Yes
4,127

Category
Mobile money
Dummy
R‐squared

Notes: Robust standard errors in parentheses. Notes in Table 3 apply. For eachcolumn, the dependent variable is the use of mobile money as a payment method
for the category of goods or services shown under the category raw. For example,
in column (1), the depedent variable is the use of mobile money dummy as apayment method for food. *** p<0.01, ** p<0.05, * p<0.1.

References
Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of political
22

Economy, 113(1), 151–184.
Angrist, J. D. & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s
companion. Princeton university press.
Center for International Earth Science Information Network - CIESIN - Columbia
University (2016). Gridded Population of the World, Version 4 (GPWv4): Population Count. Palisades, NY: NASA Socioeconomic Data and Applications Center(SEDAC).
Di Castri, S. & Gidvani, L. (2014). Enabling mobile money policies in tanzania: A’test
and learn’approach to enabling market-led digital financial services. Available at
SSRN 2425340.
Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growthfrom outer space. American economic review, 102(2), 994–1028.
Jack, W. & Suri, T. (2014). Risk sharing and transactions costs: Evidence from kenya’s
mobile money revolution. The American Economic Review, 104(1), 183–223.
Munyegera, G. K. & Matsumoto, T. (2016). Mobile money, remittances, and household
welfare: panel evidence from rural uganda. World Development, 79, 127–137.
NOAA National Geophysical Data Center (2019). Version 4 DMSP-OLS Nighttime
Lights Time Series. National Oceanic and Atmospheric Administration.
Okello Candiya Bongomin, G. & Munene, J. C. (2021). Analyzing the relationshipbetween mobile money adoption and usage and financial inclusion of msmes in developing countries: Mediating role of cultural norms in uganda. Journal of African
Business, 22(1), 1–20.
Okello Candiya Bongomin, G., Ntayi, J. M., Munene, J. C., & Malinga, C. A. (2018).
Mobile money and financial inclusion in sub-saharan africa: the moderating role ofsocial networks. Journal of African Business, 19(3), 361–384.

23

Wang, P., Huang, C., de Colstoun, E. C. B., Tilton, J. C., & Tan, B. (2019). Global
Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. Palisades,
NY: NASA Socioeconomic Data and Applications Center (SEDAC).

24