Tsukuba Economics Working Papers
No. 2019-002

The Effect of Mobile Money Usage on Borrowing, Saving, and Receiving
Remittances: Evidence from Tanzania
byAskar Ismailov
Albert Benson Kimaro
Hisahiro Naito
November 2019

UNIVERSITY OF TSUKUBA
Graduate School of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Effect of Mobile Money Usage on Borrowing,
Saving, and Receiving Remittances: Evidence from
Tanzania
Askar Ismailov ∗†Central Bank of The Republic of Uzbekistan
University of Tsukuba
Albert Benson Kimaro ∗‡Ministry of Agriculture, Government of Tanzania
University of Tsukuba
Hisahiro Naito ∗§Graduate School of Humanities and Social Sciences
University of Tsukuba
November 20, 2019∗

This research was conducted while A.I. and A.K. were affiliated with the University of Tsukuba.
A.I. appreciates the financial support from the Asian Development Bank; A.K. appreciates the financial support from the Joint Japan World Bank Graduate Scholarship; and H.N. appreciates the
support from the university’s research fund. The opinions expressed in this paper do not represent
the opinions of the Asian Development Bank, the Japanese government, the World Bank, or any organizations with which A.I., A.K., and H.N. are affiliated. The authors are responsible for all remaining
errors. The authors appreciate the comments from Yuko Nakano, Zeng Fei, and Mari Minowa.
†
Email: asqar.ismailov@gmail.com Address: 6 Islom Karimov Street, Tashkent, Uzbekistan Postal
code: 100001‡
Email: albenson10@gmail.com Address: Tanzania Cooperatives Development Commission, 41101
Uhuru Street, P.O Box 201 Dodoma, Tanzania§
Email: naito@dpipe.tsukuba.ac.jp Address: Graduate School of Humanities and Social Sciences,1-1-1, Tennodai, Tsukuba City, Ibaraki Prefecture Japan Postal Code: 305-8573.

Abstract
This study examines the effect of the use of mobile money services on financial behavior
using data from Tanzania. We estimate the causal effect of the use of mobile money
on borrowing, receiving remittances, and saving by applying a two-stage least squares
estimation using the shortest distance to the border of areas with multiple mobile phone
networks, which is a proxy for accessibility to a mobile network, as an instrumental
variable. We find that when a household experiences a negative shock, non-users ofmobile money increase borrowing, while mobile money users do not. Further, the use ofmobile money increases the probability of saving in mobile money savings accounts andof receiving remittances, while it decreases the probability of 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 andthose who do not. This evidence suggests that the use of mobile money increases thereceipt of remittances regardless of negative shocks and changes the saving portfolio,
allowing a household 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, the negative shock does not decrease the livelihoodof mobile money users, while it does reduce that of non-users.

1

Introduction

In June 2019, Facebook, the largest social network service provider in the world, announced a plan to introduce a cryptocurrency, Libra. Facebook stated that 1.7 billion
people do not use banks and that Libra could reach many of them (Facebook, 2019).
Although many regulating organizations issued concerns about Libra, any regulationneeds to be balanced between its cost and benefit. To those without a bank account,providing access to financial services could change their lives dramatically. For example, in many sub-Saharan African countries, a substantial percentage of households
do not use banks, as Facebook recognizes. Lacking a formal bank account makes it
difficult for households to save safely and prepare 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 a mobile phone to transfer money to anotherholder with a different SIM card.1 In addition, mobile money operators often offera savings account in which customers can save with a reasonable interest rate bydepositing money with the nearest mobile money agent.
According to a financial inclusion survey by th World Bank (World Bank, 2014),
only 55, 19, and 17 percent of adults have a bank account in Kenya, Tanzania, andZimbabwe, respectively, whereas 58, 32, and 32 percent of adults already have mobile
money accounts. Mobile money has thus proliferated at an accelerated rate. In Tanzania, mobile money was officially introduced in 2008. In 2009, the user rate of mobile
money was just 1.1 percent; however, this rose to 49.9 percent in 2013 and 55.8 percent
in 2017. The function of mobile money has also expanded. For example, 51 percent
of adult household heads in Tanzania have saved in the past 12 months, one-third ofwhich have saved in a mobile money savings account (see also Table 2 in Section 4).
This high speed of market penetration and expanding function of mobile moneyis attracting the attention of policymakers and researchers for several reasons. First,
in developing countries, the lack of access to a safe method of saving can lead to1

A mobile money account is attached to a mobile phone SIM card, not the mobile 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.

1insufficient saving, which makes it difficult for a household to buffer negative shocks.
Hence, the availability of saving methods through mobile money might make it easier tosmooth consumption. Second, mobile money operators often partner with traditionalbanks. As a result, people in developing countries can access traditional banking services through mobile money accounts. Third, to overcome the lack of access to credit
in developing countries, which is one of the key problems for households, mobile money
operators have started to offer borrowing services. Fourth, given the recently expandingrole of mobile money in an economy, governments in developing countries have started
to recognize that mobile money transactions are an attractive target for taxation. For
example, Kenya, Tanzania, and Uganda, which have developed successful domestic mobile money industries, have started to impose taxes on mobile money transfer fees ortransfer amounts (Rukundo, 2017; Ndungu, 2019). In addition, regulators in the financial sector aim to balance the regulation of traditional financial sectors with that in themobile money industry (Klein and Mayer, 2011; Lal and Sachdev, 2015; Khiaonarong,2014). To evaluate the economic cost of taxes and regulation on the mobile money
industry, it is thus critical to evaluate how mobile money affects economic activity.
Fifth, to evaluate the impact of a new financial service such as Libra, the evaluationof the broader impact of mobile money is useful. For these reasons, it is beneficial to
study how the use of mobile money affects financial behavior such as borrowing andsaving in developing countries.
Theoretically, there are several channels through which the use of mobile money
affects borrowing. First, when a household lacks access to a formal financial institution,
the availability 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, theuse of mobile money allows it to switch the source of that borrowing to a mobile
money operator (substitution effect). This, however, would not affect the probabilityof borrowing. Third, having a mobile money account makes it easy for a household toborrow from relatives or friends in the face of negative shocks because of the low transfer
fee, which increases borrowing (connection effect). Fourth, the presence of low-costmoney transfers might increase the possibility of households forming mutual insurancegroups (Jack and Suri, 2014) (insurance effect). This insurance effect is likely to lower2the need for borrowing, although it will increase remittances (Ratha et al., 2003; Yang
and Choi, 2007). 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, when a household can receive more remittances, the need forborrowing falls (income effect).
Regarding saving behavior, similar arguments hold. The new source effect will
increase saving. The substitution effect will not change the total amount of saving but
the composition of different 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
and show that the use of mobile money mitigates the effect of experiencing negative
shocks on households borrowing. We also find that the use of mobile money changes
a saving portfolio from less liquid saving to more liquid saving. In addition, the useof mobile money increases the probability of receiving remittances, regardless of experiencing a negative shock. This evidence suggests that the use of mobile money helps
households prepare for future negative shocks; hence, users of mobile money do notneed 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 increasein mobile money usage, researchers have started to examine its effect on the economy.
Aker et al. (2016) and Muralidharan et al. (2016) analyze the role of the secure payment method in Niger and India, respectively. Blumenstock et al. (2015) conduct a
randomized experiment to test the effectiveness of using mobile money to pay salaries.
Dupas and Robinson (2013a) analyze the role of mobile money as a secure way to deposit daily cash in micro-enterprises in Kenya. Jack and Suri (2014) theoretically showthat the development of mobile money decreases the transaction cost of risk sharing
and increases the means to absorb a negative income shock on a household throughan increase in remittances. Additionally, the authors empirically demonstrate that,in Kenya, a household that uses mobile money does not decrease consumption when3

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 thatexperience negative shocks. Suri and Jack (2016) analyze the long-run effect of theuse of mobile money and find that 2 percent of Kenyan households have moved out ofpoverty since its availability in the country because of increases in saving and financial
resilience.
Second, several studies examine the effect of having a bank account on financial
behavior. Burgess and Pande (2005) find that the state-led bank expansion in rural
India has reduced poverty. Bruhn and Love (2009) analyze the expansion of a Mexicanbank that offered both saving and credit products. They estimate that the new bankopening 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 the effect of a large financial inclusion
program in India and find that the region exposed to the program now lends more toborrowers. Dupas et al. (2018) analyze the effect of having a bank account on saving
using field randomization in three countries, Uganda, Malawi, and Chile. They findno discernible intention-to-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 financial behavior, several considerations are needed. First, using mobile money is a choice variable. A financiallydistressed household might set up a mobile money account or it might not. This would
introduce endogeneity bias. Second, an important variable that is not among the control variables, which might affect the financial decision, could be correlated with mobile
money usage. This would lead to omitted variable bias. To treat those problems, inthis study, we apply two-stage least squares (2SLS) estimation and use information on
the network strength of G2 mobile phone networks as an instrumental variable.
For the instrumental variable, we first map the areas covered by the mobile networksof Vodacom Tanzania and Tigo Mobile, whose market shares for mobile money are 54
percent and 29 percent, respectively. Then, we extract the intersection of the areas
covered by the networks of both mobile money operators and calculate the shortest
4

distance from each households location to the border of the intersection of the networks
using GPS information on each household location. We use the shortest distance as
the instrumental variable.2
In addition, to reduce the risk of the bias that comes from omitting importantvariables, we restrict the sample to the heads of the households who live within ± 10
km of the border of areas with multiple mobile networks. It is likely that households
who live within a 20 km bandwidth are relatively homogeneous. To guarantee theexogeneity of the instrumental variable further, we include as control variables observed
demographic characteristics, 30 region dummies, and the shortest distance to different
types of financial institutions. To control for the difference in economic activity indifferent areas, we calculate the population density and average intensity of night lights
within a 5 km radius of each household and include them as control variables. In
the robustness check, we restrict the sample to heads of households who live within± 7.5 km of the border of areas with multiple mobile networks. We also control for
potential heterogeneity by including 170 district dummies in some of the specifications.
Our analysis shows that our estimation results are robust to those robustness checks(Section 6).
The organization of the remainder of this paper is as follows. In Section 2, we
explain the institutional background in Tanzania. Section 3 explains our main modeland identification strategy. Section 4 describes the data and how we code each variable.
Section 5 shows the main regression results. In Section 6, we present several robustness
checks and demonstrate that our estimation results are robust. Section 7 concludes.
2

Institutional Background in Tanzania

In 2007, mobile network operators were allowed to offer payment services under theamendment to the Bank of Tanzania Act (2006). In 2008, Vodacom Tanzania introduced mobile money services through M-Pesa products, and Tigo Mobile and Airtel
introduced Tigo Pesa and Airtel Money in 2009, respectively. In 2010, Zantel introduced Ezy Pesa. By 2015, Vodacom had reached a market share of 54 percent with
M-Pesa mobile money services followed by Tigo Pesa, 29 percent, Airtel Money, 13
2

If a household is located inside the intersection, we assign a negative value to the distance.

5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.
In similar ways, 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 or
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.
3

Model and Identification Strategy

We consider the following model based on previous studies (Jack and Suri, 2014; Munyegera and Matsumoto, 2016):
yi = β0 + β1 Mobilei + γxi + 1i(1)where yi is the outcome variable such as the borrowing, saving, or receiving a remittance dummy. If a household borrowed (saved) in the past 12 months, this is equal to6

one and zero otherwise. For the receiving a remittance dummy, if a household received
a remittance in the past 12 months, it is equal to one and zero otherwise. Mobile iis a dummy variable equal to one if household i uses mobile money. xi is a vectorof the household characteristics such as the education level of the head of the household, age of the household head, household size, and source of income as well as thegeographical variables such as the region or district dummy and distance to different
types of financial institutions (e.g., commercial banks, community banks, and microfinance institutions). xi also includes a negative shock dummy since one of the motivesof borrowing is experiencing a negative shock. Additionally, xi includes the mobile
phone ownership dummy following the specification used in Jack and Suri (2014) and
Munyegera and Matsumoto (2016).3 Our parameter of interest is β1 .
In addition to equation (1), we estimate the following model as an extension:yi = β0 + β1 Mobilei + β2 Negativei + β3 Mobilei × Negativei + γxi + 1i(2)where Negativei is a dummy variable indicating whether a household experienced atleast one negative shock in the past 12 months. In equation (2), β2 shows the extent towhich 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 for a household that uses mobile money comparedwith a household that does not use mobile money. β1 shows the extent to which theuse of mobile money affects saving, borrowing, or receiving remittances for those who
did not experience a negative shock in the past 12 months.
Using the ordinary least squares (OLS) estimation to estimate equation (1), there
are several reasons why the estimated coefficient of β1 would be inconsistent. 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
3

Although the mobile phone ownership dummy is included as a control variable in previous studies of the effect of the use of mobile money on consumption (Jack and Suri, 2014; Munyegera andMatsumoto, 2016), we recognize that this dummy is also an endogenous variable; hence, including it
as a control variable would introduce bias in the estimated coefficient of β1 even when mobile moneyuse is instrumented. We discuss the effect of including the mobile phone ownership dummy on theestimation of β1 later in this subsection.

7

dummy and error term 1i would be positively correlated, and estimating (1) using
OLS would thus generate upward bias.
Second, a household that uses mobile money may differ from a household that does
not in terms of other characteristics. When some of the households’ characteristics
that influence saving behavior and mobile money is not observed, estimating (1) using
OLS generates an inconsistent estimate of β1 .
To solve the endogeneity bias and omitted variable bias resulting from applying
OLS to (1), we estimate (1) using a 2SLS estimation. For the instrumental variable,
we use the shortest distance from each household’s location to areas in which multiple
mobile networks are available. The basic idea of using this distance as the instrumental
variable is that if a household’s connection to a mobile phone network is better, it has
more incentive to use mobile money because using a mobile money service requires tohave a good connection to a mobile phone network.
As in many developed countries, the area classified as being able to connect to amobile network by each operator does not imply that a customer within this area canalways connect her or his mobile phone to that network and that a customer outside
this area cannot. Instead, mobile phone operators theoretically calculate the strengthof the radio of the mobile network and calculate the probability that a household canconnect to a mobile network. Then, a mobile phone operator draw a threshold line todetermine whether a household can access to mobile phone network with a reasonableprobability. Thus the shortest distance from the border of the intersection of the two
network available areas is likely to a good proxy of the accessibility to a mobile phone
network. If a household’s location is far outside the area in which multiple mobile
networks are available, it is likely that the network connection is unstable or weak.
When a household cannot connect his or her mobile phone to the mobile network
easily, then he or she is unlikely to use mobile money; by contrast, a household that
can access the mobile network easily is more likely to use mobile money.
Figure 1 shows the relationship between the shortest distance to areas in which
multiple networks are available and usage 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 shows a clear relationshipbetween the shortest distance to the border of areas in which multiple mobile networks
8Figure 1: The First Stage Relationship of 2SLS.
The Shortest Distance to the Border of Multiple Network Areas

.8
.6
.4
.2
0

Mobile Money Use Rate

1and Mobile Money Usage Rate

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

2

4

6

8 10 12 14 16 18 20

Distance From the Border of the Multiple Network Area
Notes: The multiple mobile network area is the area in which both networks of Vodacom
Tanzania and the Tigo mobile are accessible. The mobile money use rate is the proportionof households that use mobile money. For each 0.5 km, the average mobile money use
rate is calculated. The reference vertical lines are shown at the point where the distanceis equal to -10 km or 10 km. The above graph shows that when the distance is within[-10,10], the relationship between the distance and the average mobile money use rateis 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 thefitted line above, all observations within [-20,20] are used.

are available and mobile money usage rate. This is consistent with our hypothesis thata household located within an area with multiple mobile networks, or close to such anarea, has a stronger network connection and is thus more likely to use mobile money.
However, one might argue that the relationship between the shortest distance toareas with multiple mobile networks and mobile money usage rate is generated by otherunobservable factors such as local economic activity instead of the strength of the radioof the mobile network. If a mobile network company sets up the network in an area inwhich a household has higher education or income, the relationship in Figure 1 may

9not be driven by the strength of the network of the mobile phone operator, but byother characteristics. In addition, in an area close to or inside the areas with multiple
mobile networks, there might be more financial institutions such as commercial banks.
Since those characteristics may affect saving, using the shortest distance to areas withmultiple mobile networks as an instrumental variable could introduce bias into the
2SLS estimation.
To reduce the possibility that other characteristics are correlated with both the
distance and the dependent variable as much as possible, we include various control
variables and estimate the following first-stage equation in the 2SLS regression:
Mobilei =α0 + α1 Distancei + α2 xi + 2i(3)where xi is the control variables and Distancei is each household’s shortest distance tothe areas in which multiple mobile networks are available. As xi , we include the ownership of the mobile phone, the age of the household head, family size, the education levelof the household head, sources of household income (eight dummies for 9 categories),
the region dummy (30 region dummies), average night lights, the population densityof the area around the household, and the distance to commercial banks, community
banks, and microfinance institutions. In the robustness checks, we also include the district dummy (170 district dummies), the transportation equipment ownership dummy
and its interaction to distance to financial institution as control variables4
Figure 2 shows the relationship between each household’s shortest distance to areas
with multiple mobile networks and mobile money usage rate after controlling for theeffect of those control variables. For the horizontal axis, we measure the residual from
regressing each household’s shortest distance to areas with multiple mobile network
accessibility on control variables. For the vertical axis, we measure the residual from
regressing the mobile money usage dummy on those control variables.
As Figure 2 shows, even after controlling for the effect of those covariates, there isa clear relationship between the usage rate of mobile money and the shortest distanceof the household to areas with multiple mobile networks. In particular, Figure 1 and4

we include ownership of transportation equipments and its interaction to distance to financial
institution to control the time distance to financial institutions. We do not include those variable inthe base specification due to the potential endogeneity of transportation equipments.

10Figure 2: 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 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 vertical axis is the residual of the regression regressing the distance on all thecovariates. 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 andthe average mobile money use rate is almost on the same line. The estimated coefficient of
the slope of the fitted line is -0.011 and the robust standard error is 0.00039. R2 =0.52. For
the estimation of the fitted line above, all observations within [-20,20] are used.

Figure 2 show that the relation between the distance to areas with multiple mobile
networks is almost linear when the distance is within 10 km. When the sample includes
households whose distance to an area with multiple mobile networks is in [−20, −10]or [10, 20], the variance rises. This is partly because the number of households whose
distance to areas with multiple mobile networks is in [−20, −10] or [10, 20] is small.
When we restrict the sample to the households whose distance to the multiple mobile
network area is in [−20, −10] or [10, 20], we have only 270 households, while the numberof households whose distance is in [−10, 10] is 4127.
Due to this relatively small sample size of households located outside [−10, 10] andlarge variance in the mobile money usage rate in this group, we restrict the sample to11households whose distance to areas with multiple mobile networks is in [−10, 10]. This
restriction is likely to guarantee that these sample households are more homogeneous.
To examine the robustness of our result, we also use the sample where the distanceto the area with multiple mobile networks is within 7.5 km and within 15 km. Our
robustness checks show that our estimates are robust with those two sub-sample5 .
For the instruments of estimating equation (2), we use each household’s shortest
distance to areas with multiple mobile networks and its interaction with the negative
shock dummy as the instrumental variables. We also show that the negative shock isorthogonal to the instrumental variable. More specifically, we estimate the following
equation as the first stage of equation (2):
Mobilei =α0 + α1 Distancei + α3 Distancei × Negative Shocki + α3 xi + 2i
4(4)

Dataset and Summary Statistics

DatasetWe use the nationally representative survey of FinScope Tanzania 2017. This dataset
is commissioned by the Financial Sector Deepening Trust in partnership with the Bankof Tanzania, Ministry of Finance and Planning, National Bureau of Statistics, Officeof Chief Government Statistician Zanzibar, representatives of providers of financial
services, and nongovernmental organizations and other private sector players. The
survey was conducted by Ipsos Tanzania under the technical advisory of Yakini Development Consulting. The Tanzania Population and 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 the application 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 were selected at random, and in the third stage, from the list of all
adult household members in 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 respondent to the interview.
5

For this robustness check, please see Section 6.5

12

The FinScope Tanzania 2017 dataset provides detailed information on financial
behavior such as borrowing, saving, and receiving remittances. It also has information
on GPS location as well as the demographic characteristics of the households surveyed.
Regarding saving behavior, the survey asks about the use of different saving methods in the past 12 months, such as saving in livestock, saving in cash at home, saving inbank and saving in saving groups. We divide saving groups into two categories: savinggroups that use mobile money technology to collect money from members and savinggroups that do not. For each saving method, we make a dummy variable which is equal
to one if a household saved in a corresponding saving method in the past 12 months
and zero otherwise. Similarly, the survey asks about different sources of borrowing
such as banks, microfinance institutions, friends and relatives, and saving groups. We
also make a dummy variable for each source of borrowing.
Regarding financial difficulty to pay 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
make a financial difficulty to pay regular expenses dummy and set it equal to one if ahousehold chooses the first two choices and set it equal to zero otherwise.
Regarding negative shocks, the survey asked about several types of negative shocks:
having unforseen large expenses in the last 12 months, experiencing unforseen drop of
the price of the output in the last 12 months and unforseen drop of the volume of theoutput in the last 12 months. We code the negative shock dummy equal to one if ahousehold experiences at least one of the above negative shocks.6
The education level of the heads of households is classified into eight categories.
The variables representing the income sources of the households are classified into nine
categories.
The shortest distance to areas with 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 phone network availability maps using ArcGIS and extract
6

The survey asked additionally whether a household received less money than expected. However,since this question might include a case where a household received less remittance than a household
expected and since we are also interested in the effect of intrinsic negative shocks on the receipt ofremittance, we do not include this case in the category of negative shocks.

13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 thenegative value if a household is located inside the intersection (Figure A1).
For each financial institution (commercial banks, community banks, and microfinance institutions), the Financial Sector Deepening Trust has surveyed financial sectors
and published a GIS map of all branches of commercial banks, saving cooperatives,
microfinance institutions, and postal offices in Tanzania in the Financial Access Maps
Dataset. We calculate the distance variables representing the distance of each household to the nearest bank branch, community bank branch, and microfinance institution
office using the Financial Access Maps Dataset (Figure A2).
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). To measure local economic activity, we use the night lights data
produced by the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (National Geophysical Data Center, 2019) following
the work by Henderson, Storeygard and Weil (2012). To prevent endogeneity, namelythat mobile money affects economic activity and thus night lights, we use night lights
data from 2007, the year before mobile money was introduced in Tanzania.
Summary Statistics
Tables 1 and 2 provide the summary statistics of the main variables. In total, 58
percent of heads of households use the mobile money service, while 71 percent own amobile phone. For each household, we measure the shortest distance to the border of
the area in which multiple mobile networks (Vodacom and Tigo mobile) are available.
This distance becomes negative when a household live within such an area. The meanof the shortest distance to areas with a multiple mobile network is -1.6 km. The shareof households who live in an area in which multiple mobile networks are available is 75
percent.
Table 2 calculates the summary statistics of the variables of financial activity inthe past 12 months. About 50 percent of households did not save at all in the past
12 months and 43 percent borrowed. Almost half (49 percent) of households received
14

Table 1. Summary Statistics
VARIABLES
Mobile money and Network

Mean

Std. Dev.

Mobile money use dummy

0.58

0.49

Mobile Phone Owners

0.71

0.45

Living in Multiple Networks Area

0.75

0.44

Distance from the multiple network area(km)

-1.60

3.79

Households' Size

4.03

2.42

Age of Household Head

39.76

12.08

Household head being Male

0.63

0.48

Negative shock dummy

0.584

0.493

Population Density

0.01890.0768Night Light Index

5586

22350

Distance to nearest commercial bank (km)

24.2

22.3

Distance to nearest community bank (km)

170.5

122.8

Distance to nearest micro-finance (km)

39.8

39.4

Demographic Characteristics and Negative ShockPopulation density and Distance

Notes: The sample is restricted to households in FinScope Tanzania in which
the age of the head is 15 to 65 years and whose distance to the border of areas
with multiple mobile networks is less than or equal to 10 km. This distanceis classified as a negative value if a household lives inside areas with multiple
mobile networks. N=4127.

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, 12.8 percent of households saved in cash, 2.1 percent
saved in saving groups that use mobile money technology to collect money from members, and 4 percent saved in saving groups that do not use mobile money technology.
4.9 percent saved in livestock and other real assets.
Among those who borrowed, more than 90 percent of households borrowed frominformal 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 fromsaving groups that do not use mobile money technology, and 4 percent borrowed money
15

Table 2. Summary Statistics of Financial Behavior in the Last 12 Months
VARIABLES
Saved, Borrowed and Received Remittance
Saved in the last 12 months
Borrowed in the last 12 months
Received Remittance in the last 12 months
Saving Methods
Saved in Mobile Money Account
Saved in Bank
Saved in SACCO
Saved in Micro Finance Instituion
Saved in Saving Group with MM technology
Saved in Saving Group without MM technology
Saved in Cash
Saved to Relatives, Church or Communities
Saved to Livestocks and other real assets
Borrowing Methods
Borrowed from Mobile Money Operator
Borrowed from Bank
Borrowed from SACCO
Borrowed from Micro-financial institutions
Borrowed from Saving Group with MM technology
Borrowed from Saving Group without MM technology
Borrowed from Relative and Friends

Mean

Std. Dev.

0.511
0.489
0.434

0.500
0.500
0.496

0.190
0.100
0.012
0.008
0.040
0.128
0.213
0.045
0.049

0.393
0.300
0.108
0.088
0.197
0.334
0.409
0.206
0.216

0.025
0.015
0.009
0.012
0.039
0.126
0.333

0.155
0.123
0.093
0.109
0.194
0.332
0.471

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 moneyfrom members. Each variable is equal to one if the condition applies. N=4127.

from saving groups that use mobile money technology. Only 2.5 and 1.5 percent ofhouseholds borrowed from banks and mobile money operators, respectively.
5
5.1

Results
Effect on Borrowing

In Table 4, 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
16

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 dummy is coded one if a household
experienced at least one negative shock in the past 12 months and zero otherwise.
Table 3. Estimation Results of the OLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Borrowing
Borrowing Dummy

Dependent Variable
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
Mobile Phone Ownership
Population Density & Night Lights
Region (31 regions)
Demographic Characteristics
Distance to Financial Institutions
N

(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

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

4,127

4,127

4,127

4,127

Yes
Yes
Yes
Yes
Yes
4,127

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

In all the tables below, the sample is restricted to the head of the household. To
17

Table 4. First-Stage Estimation Results of the 2SLS Estimation
Use of Mobile Money Dummy

Endogenous Variable
Distance from Network Area
R-squared
Kleibergen-Paap Rank Wald

(3)
(4)
(5)
(1)
(2)
-0.0153*** -0.0146*** -0.0130*** -0.0106*** -0.00909***(0.00178) (0.00178) (0.00185) (0.00180) (0.00186)
0.226
0.235
0.294
0.353
0.356
73.93
67.56
49.22
34.23
24.01

Control Variables
Yes
Yes
Yes
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Yes
Population Density& Night Light
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. *** p<0.01, ** p<0.05, *p<0.1.

control for the types of jobs of each household head, we include the source of income
dummy (eight categories) of each household in all specifications. In column (2), tocontrol for the economic activity at each household’s location, we include population
density in 2015 and average index of night lights in 2007 at the household’s location.
In addition, in Column (3), we include the region dummy (31 regions). In Column(4), we add the demographic characteristics as the control variables. For the demographic characteristics, we include the education level, age, and gender of the headof the household as well as household size. In Column (5), we include the distanceto commercial banks, community banks, and microfinance institutions. Panel A of
Table 3 shows that the effect of the use of mobile money increases the probability ofborrowing by about 11–14 percentage points in all the specifications. The estimatedcoefficient is stable and statistically significant in all specifications.
One of the reasons that a household borrows money is experiencing a negative shock.
Panel B of Table 3, which includes the negative shock dummy in the explanatory
variables, shows that experiencing a negative shock does not change the estimatedcoefficient of the use of mobile money dummy. As shown in Panel B of Table 3, theuse of mobile money increased the probability of borrowing by 10–13 percentage points
and the experience of a negative shock increases the probability of borrowing by 18-19
18

percentage points.
In Panel B of Table 3, we assume that the effect of a negative shock on borrowing
is similar between those who use mobile money and who do not. However, the effect ofa negative shock can differ between mobile money users and non-mobile money users.
Panel C of Table 3 examines the effect of experiencing a negative shock, the useof mobile money, and their interaction on the probability of borrowing. This panel
shows that when a household experiences a negative shock, the probability of borrowing
increases by 18 percentage points and the use of mobile money increases the probabilityof borrowing by about 10 percentage points. The estimated coefficient of the interaction
term is small and statistically insignificant. Thus, the effect of experiencing at least one
negative shock on the probability of borrowing for mobile money users is not different
from the ones of non-users.
However, Table 3 could be subject to endogeneity bias because the use of mobile
money is a choice variable instead of a randomly assigned variable. To overcome endogeneity bias, we apply a 2SLS estimation. Table 4 shows the estimation results of thefirst stage of the 2SLS estimation; the second stage is in Panel A of Table 5. In Table 4,
the dependent variable is the mobile money use dummy and the excluded instrumental
variable is the shortest distance to areas in which multiple mobile networks are available. The estimated coefficients of the instrumental variable are highly statisticallysignificant and stable. Column (1) of Table 4 shows that if a household is located 10
km away from the border of areas with multiple mobile networks, the probability ofusing mobile money decreases by 15 percentage points. 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 10.7
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 experiencing anegative shock as a control variable. In contrast to the corresponding OLS estimationin Panel A of Table 3, the estimated coefficient of the use of mobile money dummy on
the probability is small, has the opposite sign, and is statistically insignificant. The
7

To save space, the first-stage results that correspond to Panels B and C of Table 5 are shown on
Appendix A.

19

size of the estimated coefficients are also economically insignificant. In Column (5), theestimated coefficient is equal to -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, and statistically insignificant. In contrast, the estimated coefficients of experiencing a negative shock are statistically and economically significant,positive, and stable. Column (5) shows that experiencing a negative shock increases
the probability of borrowing by about 18.5 percentage points.
Panel B of Table 5 assumes that the effect of a negative shock is the same between
mobile money users and non-mobile money users. Panel C of Table 5 relaxes thisassumption and introduces the interaction term of the negative shock dummy andmobile money user dummy. For the instrumental variable of the interaction term,
we use the negative shock dummy times the distance to areas with multiple mobile
networks.
Panel C of Table 5 shows that for non-mobile money users, experiencing a negative
shock increases the probability of borrowing by about 47 percentage points. However,since the estimated coefficient of the interaction term is -50 percentage points, the effectof a negative shock is almost zero for mobile money users. Since about 58 percent of
the households in our dataset use mobile money, the average effect of a negative shockon the whole population is 0.47 − 0.58 × 0.5 = 0.185. This is close to the estimatedcoefficients of the negative shock dummy 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 and who do not. For those who do not experience a
negative shock, the use of mobile money increases the probability of borrowing by 23
percentage points, although its statistical precision is low. For those who experiencea negative shock, the use of mobile money decreases the probability of borrowing by
50 percentage points. Since 59 percent of the households in our dataset experiencea negative shock, the average effect of using mobile money on borrowing is 0.23 −0.59 × 0.5 = −0.06, 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 the different effect of anegative shock for users and non-users of mobile money. One might wonder if ourestimation strategy relies on the functional form and whether the results are robust.
20

Table 5. Estimation Results of the 2SLS Estimation
Estimated Coefficients of the 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
Mobile Money × Negative Shock
Negative ShockR-squared
Kleibergen-Paap Rank Wald

(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

Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Population Density& Night Light
Yes
Yes
Yes
Region (31 regions)
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.

Yes
Yes
Yes
Yes
Yes
4,127

For a robustness check, we examine the effect of a negative shock for mobile money users
and non-mobile money users graphically. More specifically, based on our instrumental
variable, namely each households distance to areas with multiple mobile networks,

21

Figure 3: The Differential Effect of Negative Shocks across Households

.5
.4
.3
.2
.1

estimated coefficient of negative shockwith a Different Probability of Using Mobile Money

.4

.5

.6

.7

average mobile money use rate

.8Notes: The sample is divided into 10 groups based on each household’s distance to areas
with multiple mobile networks. For each group, equation (5) is estimated by OLS. Then,the size of the estimated coefficient of the negative shock dummy is measured on the verticalaxis. For the horizontal axis, for each group, the average probability of using mobile moneyis calculated and measured on the horizontal axis. The size of each circle is the sample sizeof each group. For those 10 plots, weighted OLS is applied. The slope of the fitted line is-0.57 and the R-squared is 0.38. The vertical intercept at the horizontal value being 0 is
0.51 and the vertical value at the horizontal value being 1 is equal to -0.07.

we divide the main sample into 10 groups from -10 km to 10 km in increments of 2
km. Then, for each group, we estimate the following equation and plot the estimatedcoefficient of the negative shock dummy on the vertical axis:
Borrowingi = β0 + β1 Negative Shocki + γxi + 1i(5)

In the third step, for each group, we calculate the average use rate of mobile money
and plot it on the horizontal axis. Figure 3 illustrates the plotted x-axis and y-axis,
showing how the effect of a negative shock differs for different values of the use of mobile
money rates. The variation of the horizontal axis is driven by the different values of
the instrument. Thus, the slope in Figure 3 should be similar to the interaction term
estimated in Panel C of Table 5. Figure 3 shows that as the average use rate of mobile

22money falls, the effect of a negative shock rises. In the figure, the slope of the fitted
line is -0.57 and the vertical intercept is 0.51. The vertical value at the horizontal value
being 1 is equal to -0.07. This implies that when the average use rate of mobile money
is zero, the effect of experiencing a negative shock on the probability of borrowing is
0.57; moreover, when the average use rate of mobile money is equal to one, the effectof experiencing a negative shock is -0.07. These results are consistent with Panel C of
Table 5.
Table 5 and Figure 3 show that the effect of an experience of negative shock on borrowing for mobile money user is almost equal to zero while for non-user an experienceof negative increases the probability of borrowing by 47 percentage points.
5.2

Orthogonality of Negative Shocks to the Instrumental Variable

One natural question from Figure 3 and Panel C of Table 5 is whether a householdthat is far away from areas with multiple mobile networks experiences different types ofnegative shocks than a household located inside those areas. To examine whether sucha case is plausible, we estimate the effect of the distance from the areas with multiple
networks on the frequency of negative shocks. If the nature of a negative shock differs
by location, its frequency is also likely to be different. For this purpose, in Table
6, we regress the negative shock dummy on the distance from areas with multiple
mobile networks with several control variables by OLS. The estimated coefficients are
economically and statistically insignificant. This shows that when the distance toareas with multiple mobile networks is 10 km away, the probability of experiencing anegative shock falls only by 2.1 percentage points (P-value=0.3). Thus, the frequencyof experiencing a negative shock is the same in households with different locations.
This indicates that the criticism that the nature of the negative shocks is different at
different location is unlikely.
5.3

Effect on Remittances and Savings

In Section 5.2, we have shown that the use of mobile money mitigates the effect ofnegative shocks on borrowing. More specifically, for non-users of mobile money, anegative shock increases the probability of borrowing, whereas it does not increase the23Table 6. Orthogonality of Negative Shocks: The Effect of Distance on Negative Shocks (OLS)
Negative Shock DummyDistance 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
Yes
Yes
Region (31 regions)
Yes
Demographic Characteristics
Distance to Financial Institutions
4,127
4,127
4,127
N
Notes: Robust standard error in parenthesis. Notes in Table 3 apply.

(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

probability of borrowing for users.
There are several possible reasons for this difference. The first is that with theavailability of mobile money, households can form mutual insurance networks easily andthus receive remittances when they experience a negative shock because the availabilityof mobile money lowers the cost of transferring remittances. The second reason is thatthe use of mobile money increases the probability of receiving remittances regardlessof experiencing a negative shock because of altruism by friends and relatives. Large
remittances increase household resources and reduce demand for borrowing when ahousehold experiences a negative shock. The third reason is that a household withaccess to mobile money can increase its total savings. The fourth reason is that ahousehold can change a portfolio from savings through less liquid savings (e.g., savingsin livestock and savings in the church and communities) to savings in mobile moneyand banks. As the above-mentioned channels are not mutually exclusive, it is difficult
to identify which factor is the exact mechanism that generates the results in Table 5
and Figure 3.
To narrow the possible mechanisms that generate the results in Table 5 and Figure 3, we first examine whether the use of mobile money increases the probability ofreceiving remittances, and in Table 7, whether the use of mobile money increases receiving remittances when a household experiences a negative shock. In Table 7, the24dependent variable is the receipt of the remittance dummy, which is equal to one if
a household received a remittance in the past 12 months and zero otherwise. The
estimated coefficient shows that the use of mobile money increases the probability ofreceiving remittances by 72 percentage points. The estimated coefficient of experiencing a negative shock is 12.8 percentage points, but its statistical precision is low. In
addition, the estimated coefficient of the interaction term of the negative shock dummy
and use of mobile money dummy is negative (and statistically insignificant). Table 7
suggests that a mobile money user receives more remittances, but this receipt of remittances is not correlated with experiencing a negative shock. In particular, there isno evidence that a mobile money user receives remittances with a higher probability
when a household experiences a negative shock.
Another possible channel through which a mobile money user does not need toborrow when he or she experiences a negative shock is that a household can change asaving portfolio, so that it can prepare for a future negative shock. As a result, when it
experiences a negative shock, it does not need to borrow. To examine whether such ahypothesis is supported, we examine the probability of saving for each saving method.
Since we are interested in the average effect of using mobile money on each 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 asa control variable.8

8

To save the space, we report only the estimated coefficient of the use of mobile money dummy.
But the negative shock dummy is included in the control variable in all specification. In all cases,
the estimated coefficient of the negative shock dummy is very small and statistically insignificant.
In addition, we also estimated the specifications where the interaction term of the negative shockdummy and the use of mobile dummy is included. In all specification, the estimated coefficient of theinteraction term is economically very small and statistically insignificant. The estimation results areavailable from the author upon request.

25

Table 7. Second-Stage Estimation Results of the 2SLS Estimation
Estimated Coefficients of Mobile Money Use, Negative Shocks, and Their Interaction on the
Receipt of Remittances
Panel A
Receipt of Remittance Dummy

Dependent variable
Use of Mobile Money Dummy
Mobile Money × Negative Shock
Negative Shock(1)
0.698***(0.126)-0.107(0.160)0.0958(0.0932)(2)0.703***(0.130)-0.104(0.160)0.0943(0.0934)(3)
0.689***(0.148)-0.145(0.159)0.113(0.0926)(4)0.681***(0.176)-0.158(0.157)0.125(0.0921)(5)0.752***(0.209)-0.157(0.160)0.120(0.0947)

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

0.261
25.24
4,127

0.260
24.35
4,127

0.247
20.57
4,127

0.255
15.23
4,127

Yes
Yes
Yes
Yes
Yes
0.243
10.83
4,127

Control Variables
Mobile Phone Ownership
Population Density& Night Light
Region (31 regions)
Demographic Characteristics
Distance to Financial Institutions
R-squared
Kleibergen-Paap Rank WaldN
Notes: Robust standard errors in parentheses. Notes of Table 3 apply. *** p<0.01, ** p<0.05, *p<0.1.

26

Table 8. Second-Stage Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Various 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***-0.334***-0.351**(0.0912)(0.114)(0.142)-0.361**(0.173)-0.179(0.110)

Saving in Cash-0.193*-0.0595-0.0793(0.116)(0.137)(0.170)-0.00983(0.204)0.640***(0.104)

Saving in Mobile Money Account0.724***
0.682***
0.623***(0.108)(0.136)(0.165)0.600***(0.191)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***
0.200***
0.214***
0.220**(0.0512)(0.0537)(0.0653)(0.0827)(0.102)

Panel E.
Use of Mobile Money Dummy

Saving in Saving Groups without MM Technology
0.03610.01950.02130.01440.110(0.0931)(0.0971)(0.116)(0.142)(0.171)Panel F.
Use of Mobile Money Dummy
Control Variables
Negative Shock Dummy
Mobile Phone Ownership
Population Density& Night Light
Region (31 regions)
Demographic Characteristics

Saving at least in one method
0.334**
0.421***
0.346*(0.135)(0.163)(0.199)0.365***(0.130)
Yes
Yes

Yes
Yes
Yes

Distance to Financial Institutions

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

0.370
(0.242)
Yes
Yes
Yes
Yes
Yes
Yes

Kleibergen-Paap Rank Wald

75.08
68.71
49.05
33.98
23.56
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.

27

Panel A of Table 8 shows that the use of mobile money decreases the probability ofsaving in less liquid savings by 30 percentage points. We code the method of saving aless liquid one if a household saves in livestock or through the church and communities.
Panel B of Table 8 shows that the use of mobile money does not change the probabilityof saving in cash. In Panel B, the estimated coefficient is less than 3 percentage points
and is statistically insignificant.
In contrast, Panel C and Panel D of Table 8 show that the use of a mobile money
increases saving in a method which uses mobile money technology. More specifically,
the use of mobile money increases the probability of saving in a mobile money accountand saving in a saving group that uses mobile money as a form of collection by 60
percentage points and 22 percentage points, respectively. Panel E shows that the useof mobile money does not increase the probability of saving in a saving group that does
not use mobile money for collection of money from members.
Finally, Panel F shows the effect of the use of mobile money on the probability ofsaving in at least one method. Columns (1) to (4) show that the estimated coefficientsare stable, with the use of mobile money affecting the probability of saving in atleast one method by 33–42 percentage points. In column (5), the estimated coefficient
becomes insignificant although the size of the estimated coefficient is very similar to theones in column (1) to column (4). In the robustness check, we find that although theestimated coefficients are similar, they are often statistically insignificant. For example,
in Table B4 Panel F, the estimated coefficients are significant only for column (1) and(2). This suggests that we do not find a strong evidence that the use of mobile money
increases the probability of saving at least in one asset.
5.4

Effect of Mobile Money on Livelihood

The final question is how much the use of mobile money affects households’ livelihood.
Table 9 examines the effect of the use of mobile money on the probability of experiencing financial difficulty to pay regular expenses. To examine the effect of the use ofmobile 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 mobile
money usage. In Panel A, the estimated coefficient of the use of mobile money dummy,which measures the effect of the use of mobile money for a household that experiences28

a negative shock, ranges from -0.5 to -0.68 (statistically significant). Panel A also
shows that when a household does not experience a negative shock, the probabilityof experiencing financial difficulty decreases by 23 percentage points. The estimatedcoefficient of the interaction term implies that the effect of the use of mobile moneyon experiencing financial difficulty for those who do not experience a negative shock is
28 percentage points smaller than for those who experience a negative shock, although
the estimated coefficients are not statistically precise. To see the effect of experiencing
a negative shock on difficulty to pay regular expenses for those who use the mobile
money, we examine whether the sum of the coefficient of the interaction term andnon-negative shock dummy is equal to zero. The bottom raw of Panel A shows thechi-squares and its p-value. The calculated P-value implies that we cannot reject thenull hypothesis the effect of experiencing a negative shock on financial difficulty for
those who use mobile money is equal to zero.
Panel B shows the estimates of the effect of the use of mobile money and a nonnegative shock without the interaction term. Without this term, the estimated coefficient of the use of mobile money is the average effect of the use of mobile money.
Panel B shows that the use of mobile money decreases the probability of experiencing
financial difficulty by 49 percentage points. Panel B also shows that the effect of experiencing a negative shock will increases the financial difficulty to pay regular expenses
by 6 percentage points.
6
6.1

Robustness Checks
Controlling District Fixed Effect

In the analysis in section 5, we have included regional dummy (30 region dummies) inaddition to population density and average night light of the area in which a household
is located. The idea of including the population density and the average night light is
to control the difference of the economic activity of the areas in which households arelocated. One might argue, however, that to control the difference of economic activityof different areas by the region fixed effect, the population density and the averagenight lights is not sufficient.
In this robustness checks, we include the district dummy (170 district dummies ) to29

Table 9. Second-Stage Estimation Results of the 2SLS Estimation
Estimated Coefficients of the Mobile Money Use Dummy on Financial Difficulty
Financial Difficulty to Pay Regular Expenses
Dependent variable
Variable
Panel A.
Use of Mobile Money Dummy

(1)

(2)

(3)

(4)

(5)

-0.495***-0.501***-0.638***-0.678***-0.629**(0.155)(0.160)(0.189)(0.230)(0.265)
0.258
0.255
0.265
0.290
0.284
Mobie Money × No Negative Shock(0.172)(0.173)(0.177)(0.179)(0.178)-0.209**-0.208**-0.215**-0.231**-0.225**No Negative Shock(0.103)(0.105)(0.105)(0.0998)(0.100)
Kleibergen-Paap Rank Wald
25.24
24.35
20.57
15.23
10.83
Testing Coef. of Mobiee 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 DummyNo Negative Shock
Kleibergen-Paap Rank Wald

-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

Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Yes
Population Density& 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. The last column of Panel A tests the null hypothesisthat the sum of the coefficient of the non-negative shock dummy and the interaction term of themobile money usage dummy and non-negative shock dummy is equal to zero. The chi-squared
value and its P-value are shown in the last row. *** p<0.01, ** p<0.05, * p<0.1.

control the difference of economic activity of different areas. Table B1-Table B5 show
the first stage and the second stage results when we include the 170 district dummies ascontrol variables in addition to other control variables. The estimated coefficients andtheir standard errors are quite similar to the ones in Section 5. The only difference is30that Kleibergen-Paap Rank Wald statistics which tests the strength of the first stage of
2SLS estimation. When we include all control variables and 170 district dummies, theKleibergen-Paap Rank Wald statistics become 9.1, which is slightly smaller than thethreshold value, 10. This is likely due to including so many control variables. However,
all estimated coefficients and the standard errors do not change substantially comparedwith the one with a smaller set of control variables and 170 district dummies. Thissuggests that even controlling the district fixed effect, our results are quite robust.
6.2

Endogeneity of Mobile Phone Ownership

In all analysis in section 5, we have included the mobile phone ownership dummy as acontrol variable in xi . Although the inclusion of the mobile phone ownership dummy
follows the literature (Jack and Suri, 2014; Munyegera and Matsumoto, 2016), its
inclusion could bias the estimate of β1 because mobile phone ownership is the outcome
variable and controlling for it introduces bias when estimating the causal effect (Angristand Pischke, 2008). To observe how this occurs, assume that mobile phone ownership isa negative function of our instrumental variable, distance to areas with multiple mobile
networks, and a positive function of unobserved characteristics z1i . Assume that z1i is avariable such as unobserved income and familiarity with technology, which is positively
correlated with financial activity. When the distance decreases, the probability of
the use of mobile money increases. However, it also affects the probability of mobile
phone ownership positively. 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 on mobile
phone ownership) will give a downward bias to the estimate of β1 , even if we were to
use the instrumental variable for mobile money use. On the other hand, if we were
to exclude mobile phone ownership in the 2SLS estimation as a control variable in xi ,the coefficient of the mobile money use dummy would capture not only the effect ofmobile money use but also the effect of mobile phone ownership, since the instrumental
variable (mobile phone network coverage dummy) is also correlated with mobile phone31

ownership.9 To address this problem, we run the 2SLS estimation without the mobile
phone ownership dummy as one of the control variables. Tables B1–B6 in Appendix
B show that the estimated coefficient of mobile money use in the 2SLS estimation
does not change to a large extent. This implies that the bias induced by including themobile phone ownership dummy is unlikely to be serious.10
6.3

Controlling for Income

In Section 5, we have included the negative shock dummy to examine the different
effect of the use of mobile money. In the analysis, we did not include income as acontrol variable for two reasons. First, income is the outcome variable if a householdthat received remittances through mobile money invested in productive assets andgrew its income. Including the outcome variable would thus cause the same problem
as including the mobile phone ownership dummy, as we discuss 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 in a downward direction.
On the other hand, one might argue that the instrumental variable is correlatedwith income and thus that 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 is upwardly biased.
Column (1) of each table in Appendix C corresponds to Column (5) in Section 5.
Column (2) of each table shows the estimated coefficients when we control for income.
The tables in Appendix C show that controlling for the effect of income does not affectthe estimated coefficient. This suggests that the bias caused by excluding income from9

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.
10
Another possible reason behind the similarity between the 2SLS estimates of β1 with and without
the mobile phone ownership dummy is that z1i is not positively correlated with financial behavior
but is negatively correlated with financial behavior. The assumption of a positive correlation between
z1i and financial behavior is not testable, since we cannot observe z1i . However, we can still check
whether the observable variables that affect mobile phone ownership positively are correlated withfinancial behavior following Altonji et al. (2005). When we regress saving or borrowing and mobile
phone ownership on the education of respondents and the wage earner dummy, we find that thosevariables are positively correlated with both mobile phone ownership and financial behavior (saving
and borrowing).

32

the control variables is not serious.
6.4

Controlling Time Distance to Financial Institutions

In the analysis in Section 5, we have included as the control variable the physical distance to several financial institutions such as distance to commercial bank, distanceto community bank and distance to micro-finance institution. Note that the time distance depends on the ownership of transportation equipment (bicycle, motorcycle, andcar) and that the ownership of that equipment is the outcome variable. A householdthat receives a substantial amount of remittances might purchase a motorcycle. Dueto this endogeneity, we use the physical distance as a control variable. To examine
the sensitivity of our analysis by controlling for the time distance, we re-run the regression by controlling for the time distance instead of the physical distance. To doso, the ownership of transportation equipment is included a control variable and they
are interacted with the physical distance. Column (3) of each table in Appendix C
shows that the estimation results when transportation equipment and the interaction
term with the physical distance are included in the control variable. The column (3)of Tables in Appendix C shows that the estimated coefficients and standard errors arequite similar to the ones in Section 5.
6.5

Using Different Sub-samples

In the analysis in Section 5, we have used households whose distance to areas withmultiple mobile networks is less than or equal to 10 km because of the clear first-stage
relationship and relatively large sample of this group. One might ask whether ourestimates are sensitive to the selection of the sample. In column (4) of each table inAppendix C, we used, as the sample, the household whose distance to the areas withmultiple mobile networks is less than or equal to 7.5 km. The estimated coefficientsand the standard errors are quite similar to the one obtained in Section 5. In column
5, we used, as the sample, the household whose distance to the areas with multiple
mobile networks is less than or equal to 15 km. Again, our estimated coefficients are
similar to the ones obtained in Section 5. This suggest that as long as we choose thedistance to the areas with multiple mobile network as small as possible, the household

33selected are similar and our estimated coefficients do not change so much.
7

Discussion and Conclusion

The findings by this study can be summarized as follows. First, we show that the useof mobile money decreases the effect of a 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 notincrease for mobile money users. Second, we find that the use of mobile money increases
the probability of receiving remittances by 75 percentage points and that its effect isindependent of experience of a negative shock. This indicates that remittances areunlikely to work as insurance. Third, the use of mobile money affects the compositionof saving methods. In particular, the use of mobile money decreases the probability ofsaving using less liquid saving methods such as saving through livestock, the church, andcommunities by 36 percentage points while the use of mobile money increases saving
in a mobile money account 36 percentage points as well as in saving groups that adopt
mobile money technology by 22 percentage points. These evidences suggest that the useof mobile money increases the receipt of remittances regardless of negative shocks andchanges the saving portfolio, so a household can prepare for negative shocks. Hence, ahousehold that uses mobile money does not need to increase borrowing in the face of anegative shock. Consistent with interpretation, we find that the negative shock increasefinancial difficulty to pay regular expenses for non-users by 23 percentage points while
it does not affect the financial difficulty to pay regular expenses for users.
Regarding the relationship to the literature on the effect of mobile money on economy, our analysis is consistent with previous analyses that show that mobile money
affects many dimensions of economic activities (Jack and Suri, 2014; Munyegera andMatsumoto, 2016). Our results, however, slightly differ from studies in the literaturethat show that the use of mobile money works like insurance in the sense that theyshow that a household receive remittance more when a household experience a negative
shock. In contrast, our analysis suggests that the use of mobile money help to absorb
shocks through an increase of remittance in normal periods and by change of saving
portfolio. Our result is consistent with the work of Naito (2017), which shows that in34

Zimbabwe the receipt of remittances does not depend on experiencing a negative shock
but the remittance helps to mitigate financial difficulty sending children to school.
Regarding the relationship to the literature on the effect of having a bank accounton economic activity and welfare, our results is consistent with the previous results
which show that a bank account affects financial behavior in a reasonable magnitude.
For example, using randomized experiments, Dupas et al. (2018) find a large treat
effect on treated, which is consistent with our finding. Our finding contributes to thisliterature showing that mobile money change saving portfolio and help household toabsorb negative shock through self-preparation.
Mobile money is becoming an important policy issue. The governments of developing countries recognize that mobile money transfers are an attractive tax basebecause of their size and the ease of understanding the transaction(Rukundo, 2017;
Ndungu, 2019). Financial regulation aims to balance the traditional banking sector and mobile money operators(Klein and Mayer, 2011; Khiaonarong, 2014; Lal andSachdev, 2015). The introduction of Libra by Facebook could affect millions of nonbank users(Facebook, 2019), and the regulators of the financial sector has many concerns about the introduction of Libra.
On the other hand, for any regulation and taxation policy, their cost and benefit
need to be balanced. Our analysis shows that mobile money affects various financial
behaviors. For example, using mobile money means that many households do not needto borrow in the face of negative shocks. They receive more remittances and reduce
less liquid saving as well as increase savings in mobile money savings accounts and insaving groups that use mobile money technology.
In poor countries, the lack of access to credit and the lack of safe and liquid saving
methods are often policy targets. Mobile money solves those issues to some degree.
This implies that the efficiency cost of taxation on mobile money transactions andstrict regulations on the mobile money industry will not be low. The regulators ofmobile money operators and tax planning authorities in developing countries therefore
need to be careful when implementing tax and regulation policies on mobile money
and mobile money industry.

35

References
Agarwal, Reena and Andrew W Horowitz, “Are international remittances altruism or insurance? Evidence from Guyana using multiple-migrant households,”
World development, 2002, 30 (11), 2033–2044.
Agarwal, Sumit, Shashwat Alok, Pulak Ghosh, Soumya Ghosh, Tomasz
Piskorski, and Amit Seru, “Banking the Unbanked: What Do 255 Million New
Bank Accounts Reveal about Financial Access?,” Columbia Business School Research
Paper, 2017, (17-12).
Aker, Jenny C, Rachid Boumnijel, Amanda McClelland, and Niall Tierney,“Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money
Cash Transfer Experiment in Niger,” Economic Development and Cultural Change,
2016, 65 (1), 1–37.
Altonji, Joseph G, Todd E Elder, and Christopher R Taber, “Selection on
observed and unobserved variables: Assessing the effectiveness of Catholic schools,”
Journal of political Economy, 2005, 113 (1), 151–184.
Angrist, Joshua D and Jörn-Steffen Pischke, Mostly harmless econometrics: An
empiricist’s companion, Princeton university press, 2008.
Blumenstock, Joshua E, Michael Callen, Tarek Ghani, and Lucas Koepke,“Promises and pitfalls of mobile money in Afghanistan: evidence from a randomized
control trial,” in “Proceedings of the Seventh International Conference on Information and Communication Technologies and Development” ACM 2015, p. 15.
, Nathan Eagle, and Marcel Fafchamps, “Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters,” Journal of Development
Economics, 2016, 120, 157–181.
Bruhn, Miriam and Inessa Love, “The Economic Impact of Banking the Unbanked:
Evidence from Mexico,” Policy Research Working Paper, 2009, 4981.

36

Burgess, Robin and Rohini Pande, “Do rural banks matter? Evidence from theIndian social banking experiment,” American Economic Review, 2005, 95 (3), 780–
795.
Center for International Earth Science Information Network - CIESIN Columbia University, Gridded Population of the World, Version 4 (GPWv4):
Population Count, Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC)., 2016.
Dupas, Pascaline and Jonathan Robinson, “Savings constraints and microenterprise development: Evidence from a field experiment in Kenya,” American Economic
Journal: Applied Economics, 2013, 5 (1), 163–92.
and, “Why don’t the poor save more? Evidence from health savings experi-ments,” American Economic Review, 2013, 103 (4), 1138–71.
, Dean Karlan, Jonathan Robinson, and Diego Ubfal, “Banking the Unbanked? Evidence from three countries,” American Economic Journal: Applied
Economics, 2018, 10 (2), 257–97.
Facebook, “Welcome to Official Libra White Paper,” https://libra.org/en-US/whitepaper/ 2019.
Henderson, J Vernon, Adam Storeygard, and David N Weil, “Measuring
economic growth from outer space,” American economic review, 2012, 102 (2), 994–
1028.
Jack, William and Tavneet Suri, “Risk sharing and transactions costs: Evidence
from Kenya’s mobile money revolution,” The American Economic Review, 2014, 104(1), 183–223.
Khiaonarong, Tanai, “Oversight issues in mobile payments,” Working Paper 14/123,
IMF 2014.
Klein, Michael and Colin Mayer, Mobile banking and financial inclusion: The
regulatory lessons, The World Bank, 2011.
37

Lal, Rajiv and Ishan Sachdev, “Mobile Money Services-Design and Development
for Financial Inclusion,” Working Paper 15-083, Harvard Business School 2015.
Munyegera, Ggombe Kasim and Tomoya Matsumoto, “Mobile money, remittances, and household welfare: panel evidence from rural Uganda,” World Development, 2016, 79, 127–137.
Muralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar, “Building
state capacity: Evidence from biometric smartcards in India,” American Economic
Review, 2016, 106 (10), 2895–2929.
Naito, Hisahiro, “Does Mobile Money Helps Childrens Schooling,” Working Paper,
Univesity of Tsukuba, Program in Economic and Public Policy, 2017.
National Geophysical Data Center, Version 4 DMSP-OLS Nighttime Lights Time
Series, National Oceanic and Atmospheric Administration, 2019.
Ndungu, Njuguna S, “Taxing Mobile Phone Transactions in Africa: Lessons from
Kenya,” Brookings Institution Policy Brief, 2019.
Ratha, Dilip et al., “Workers remittances: an important and stable source of external
development finance,” Global development finance, 2003, 2003, 157–175.
Riley, Emma, “Mobile money and risk sharing against village shocks,” Journal of
Development Economics, 2018, 135, 43–58.
Rukundo, Solomon, “Taxation of the Telecommunications Sector: A Focus on Policy
Issues and Considerations in Taxation of Mobile Money in Uganda,” in “Africa Tax
Research Network 2017 Congress at Antananarivo, Madagascar” 2017, pp. 4–6.
Suri, Tavneet and William Jack, “The long-run poverty and gender impacts ofmobile money,” Science, 2016, 354 (6317), 1288–1292.
Vanwey, Leah K, “Altruistic and contractual remittances between male and femalemigrants and households in rural Thailand,” Demography, 2004, 41 (4), 739–756.
World

Bank,

“Financial

Inclusion

Data

http://datatopics.worldbank.org/financialinclusion/ 2014.
38

GlobalFindex,”Yang, Dean and HwaJung Choi, “Are remittances insurance? Evidence fromrainfall shocks in the Philippines,” The World Bank Economic Review, 2007, 21 (2),219–248.

39

Figure A1: Locations of Households in the Sample and the Areas Where Vodacom
and Tigo Mobile are Accessible in Tanzania

Notes: Green circles show the location of the households surveyed in Finscope data set.
The blue areas show the areas where the networks of both Vodacom and Tigo mobile areaccessible.

40

Figure A2: The Locations of Households of the Sample and Financial Institutions in Tanzania
!

!
!

!
!
!

!
!
!!
!
!

!
!
!
!

!

!
!

Notes: Green circles show the location of the households surveyed in Finscope data set. The
red circles show the location of the financial institutions (commercial banks, community
banks and microfinance institutions.

41

( Online Appendices A, B and C are included for the purpose of refereeing.
They are not for publication. They will be put on the online data page.)
Online Appendices A: First-Stage Results Corresponding to Panel B andPanel C of Table 5
Table A1. First-Stage Estimation Results of the 2SLS Estimation (2)Use of Mobile Money Dummy

Endogenous Variable
(1)

(2)

(3)

-0.0154***-0.0147***-0.0129***-0.0105*** -0.00900***(0.00178)(0.00178)(0.00185)(0.00180)(0.00185)

0.0655***0.0628***0.0511***

0.0425***0.0434***(0.0144)(0.0144)(0.0143)(0.0137)(0.0137)

R-squared

0.230

0.238

0.296

0.354

0.358

Kleibergen-Paap Rank Wald

73.93

67.56

49.22

34.23

24.01

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Distance from Network Area
Negative shock dummy

(4)

(5)

Control Variables
Mobile Phone Ownership
Population Density & Night Light
Region (31 regions)
Demographic Characteristics
Distance to Financial Institutions
N

Yes
4,127

4,127

4,127

4,127

4,127

Notes: Robust standard errors in parentheses. Notes in Table 3 apply. This first-stage resultscorrespond to the second-stage estimation shown in Panel B of Table 6. *** p<0.01, ** p<0.05, *p<0.1.

42

Table A2. First-Stage Estimation Results of the 2SLS Estimation (3)Use of Mobile Money Dummy

Endogenous Variable
Distance from Network Area
Distance from Network Area ×
Negative shock
Negative shock
R-squared
Endogenous Variable
Distance from Network Area
Distance from Network Area ×
Negative shock
Negative shock
R-squared
Control Variables
Mobile Phone Ownership
Population Density & Night Light
Region (31 regions)
Demographic Characteristics
Distance to Financial Institutions
Kleibergen-Paap Rank Wald

(1)
(2)
(3)
(4)
(5)
-0.0172*** -0.0166*** -0.0146*** -0.0120*** -0.0106***(0.00261) (0.00261) (0.00260) (0.00253) (0.00257)
0.00315
0.003140.00291
0.00250
0.00276(0.00349) (0.00349) (0.00345) (0.00332) (0.00332)
0.0706*** 0.0679*** 0.0558*** 0.0466*** 0.0478***(0.0158)(0.0157)(0.0157)(0.0151)(0.0150)
0.230
0.238
0.296
0.354
0.358
Use of Mobile Money Dummy × Negative Shock
(1)
(2)
(3)
(4)
(5)
0.00374*** 0.00406*** 0.00485*** 0.00635*** 0.00709***(0.000793) (0.000796) (0.000996) (0.00110) (0.00117)-0.0201*** -0.0201*** -0.0204*** -0.0206*** -0.0205***(0.00252) (0.00252) (0.00254) (0.00250) (0.00250)
0.570***
0.569***
0.564***
0.560***
0.560***(0.0108)(0.0108)(0.0111)(0.0110)(0.0110)
0.463
0.465
0.482
0.501
0.503
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

25.24
4,127

24.35
4,127

20.57
4,127

15.23
4,127

Yes
Yes
Yes
Yes
Yes
10.83
4,127

N
Notes: Robust standard errors in parentheses. Notes in Table 3 apply. This first-stage resultscorrespond to the second-stage estimation shown in Panel C of Table 6. *** p<0.01, ** p<0.05, *p<0.1.

43

Online Appendices B: Controlling with District Dummy.
Table B1. First-Stage Estimation Results of 2SLSUse of Mobile Money Dummy

Endogenous Variable
Distance from Network Area
R-squared
Kleibergen-Paap Rank Wald

(1)
(2)
(3)
(4)
(5)
-0.0153*** -0.0146*** -0.0112*** -0.00961*** -0.00929***(0.00178) (0.00178) (0.00219) (0.00212) (0.00217)
0.226
0.235
0.333
0.384
0.387
73.93
67.56
25.92
20.54
18.29

Control Variables
Mobile Phone Ownership
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Population Density & Night Light
YesDistricts (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.

44

Table B2. 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)

-0.167(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

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.

45

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 RemittancePanel A
Receipt of Remittance Dummy

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)(2)0.703***(0.130)-0.104(0.160)0.0943(0.0934)(3)0.744***(0.186)-0.0328(0.165)0.0429(0.0979)(4)0.761***(0.215)-0.0565(0.163)0.0600(0.0964)(5)0.843***(0.233)-0.0579(0.168)0.0557(0.0997)

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes

0.261
25.24
4,127

0.260
24.35
4,127

0.211
12.83
4,127

0.218
10.24
4,127

Yes
Yes
Yes
Yes
Yes
0.188
9.107
4,127

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

46

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.

47

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.

48

4,127

4,127

Online Appendices C: Estimation with Different Specifications
Table C1. First-Stage Estimation Results of 2SLSUse of Mobile Money Dummy
Endogenous Variable
(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

(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.

49

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.

50

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.

51

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.

52

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.

53