# Phone Sharing and Cash Transfers in Togo:
Quantitative Evidence from Mobile Phone Data

Emily L. Aiken  U.C. BerkeleyUSA<EMAIL_ADDRESS>,  Viraj Thakur  U.C.
BerkeleyUSA<EMAIL_ADDRESS>and  Joshua E. Blumenstock  U.C.
BerkeleyUSA<EMAIL_ADDRESS>
###### Abstract.

Phone sharing is pervasive in many low- and middle-income countries, affecting
how millions of people interact with technology and each other. Yet there is
very little quantitative evidence available on the extent or nature of phone
sharing in resource-constrained contexts. This paper provides a comprehensive
quantitative analysis of phone sharing in Togo, and documents how a large cash
transfer program during the COVID-19 pandemic impacted sharing patterns. We
analyze mobile phone records from the entire Togolese mobile network to
measure the movement of SIM cards between SIM card slots (often on different
mobile devices). First, we document the prevalence of sharing in Togo, with
22% of SIMs and 7% of SIM slots shared. Second, using administrative data from
a government-run cash transfer program, we find that phone sharing is most
common among women, young people, and people in rural areas. Finally, we find
that the delivery of cash aid via mobile money significantly increases phone
sharing among beneficiaries. We discuss the limitations of measuring phone
sharing with mobile network data and the implications of our results for
future aid programs delivered via mobile money.

phone sharing, mobile phone data, unconditional cash transfers

††ccs: Human-centered computing Empirical studies in ubiquitous and mobile
computing††ccs: Applied computing Sociology††ccs: Information systems Mobile
information processing systems

##  1\. Introduction

Over the past two decades, mobile phones have transformed how billions of
people communicate and interact. More recently, the rise of mobile money has
reshaped everyday financial transactions, including how people send and spend
money, apply for loans, and receive humanitarian assistance. In many low- and
middle-income countries (LMICs), phone sharing has played an integral role in
this digital transformation. Phone sharing is particularly pervasive in rural
Africa, where all members of a household or small community sometimes rely on
a single primary device (Gilwald, 2005; Wesolowski et al., 2012; Blumenstock
and Eagle, 2010).

The social practices that surround phone sharing are complex and nuanced. A
rich body of geographically diverse qualitative and ethnographic studies
highlights how phone sharing is mediated by complex social relationships and
obligations (Burrell, 2010); can restrict women’s empowerment (Porter et al.,
2020); leads to delicate interpersonal dynamics of reciprocity and payment
(Burrell, 2010; Ahmed et al., 2017); and helps people create and alter the
social spaces around them (Steenson and Donner, 2017). Yet, while there are
several qualitative studies of phone sharing dynamics, there is very little
quantitative evidence on phone sharing: to our knowledge, only a handful of
studies in the past two decades have provided systematic evidence on phone
sharing in LMICs, based on sample surveys from Rwanda, Kenya, Namibia, and
South Africa (Blumenstock and Eagle, 2010; Wesolowski et al., 2012; Gilwald,
2005).

Gathering more comprehensive evidence on phone sharing is particularly
important in the context of the rapid digitization of social protection
programs globally. With worldwide mobile phone penetration projected to exceed
70% in 2025 (GSMA, 2019), proponents of aid delivery via mobile money argue
that the transparency, traceability, and scalability of mobile aid payments
justify a switch from cash to mobile money in the aid sector (Davidovic et
al., 2020; Aker et al., 2016). Recently, the public health conditions of the
COVID-19 pandemic have rendered in-person delivery of aid payments infeasible,
leaving mobile money as the de facto standard for remote cash aid delivery in
LMICs. Gentillini et al. (2020) (Gentilini et al., 2020) estimate that 155
programs in 58 countries delivered aid via digital transfers during the
COVID-19 pandemic, prominently including the second wave of the Philippines’
Social Amelioration Program (reaching 14 million
households),111https://www.poverty-action.org/printpdf/48011 Pakistan’s Ehsaas
emergency cash program (reaching 12 million
households),222https://www.pass.gov.pk/ and Togo’s Novissi program (reaching
approximately one million individuals).333https://novissi.gouv.tg

While the promise of mobile aid delivery has been discussed at length in the
context of the COVID-19 pandemic (Davidovic et al., 2020; Aker et al., 2016;
Gronbach, 2021; Bazarbash et al., 2020; Palacios, 2020), it is important to
consider how mobile aid delivery interacts with heterogeneous patterns of
mobile phone uptake and use, including phone sharing. Existing work on phone
sharing suggests that aid delivered via mobile money may not reach the
neediest recipients if certain groups (particularly women and the economically
vulnerable) are disadvantaged in phone sharing relationships (Gilwald, 2005;
Burrell, 2010; Wesolowski et al., 2012; Porter et al., 2020). It is also
reasonable to hypothesize that the delivery of cash aid via mobile money may
change patterns of phone sharing, possibly increasing human contact when aid
is delivered in the context of public health crises like the COVID-19
pandemic.

This paper provides the first population-scale evidence on phone sharing, and
the first evidence on the impacts of cash aid delivery via mobile money on
phone sharing. We make three main contributions. First, by examining the
movement of SIM cards between mobile devices (SIM card slots) as recorded in
call detail records (CDR) from Togo, we estimate SIM and device sharing on
Togo’s entire mobile phone network, finding that 22% of SIM cards are shared
(used by more than one device), and 7% of devices are shared (used with more
than one SIM card). We note that our measure of phone sharing captures only
certain types of phone sharing — specifically, sharing that involves SIM
movement between devices — and therefore likely represents a lower bound on
true phone sharing in Togo. Second, by linking CDR to administrative data, we
study heterogeneity in phone sharing patterns by gender, age, and geography.
Our findings are largely concurrent with past ethnographic and survey-based
work on phone sharing, finding that sharing is highest among women (Rangaswamy
and Singh, 2009; Burrell, 2010; Blumenstock and Eagle, 2010; Wesolowski et
al., 2012), young people (Wesolowski et al., 2012), and people living in rural
areas (Sey, 2009; Wesolowski et al., 2012). Finally, we study how phone
sharing practices were impacted by a large cash transfer program that was
delivered via mobile money. This analysis reveals that the government’s mobile
money transfers led to a small but significant positive effect on phone
sharing, with similar increases across different demographic subgroups. We
conclude by discussing the limitations of measuring phone sharing with CDR and
the implications of our findings for future aid programs delivered via mobile
money.

###  1.1. Related Work

This paper builds on three bodies of literature: studies of phone sharing in
LMICs; studies using call detail records to analyze social dynamics,
particularly in LMICs; and studies evaluating the impacts of mobile cash
transfer programs.

####  1.1.1. Phone sharing

Several studies have used ethnographic (Rangaswamy and Singh, 2009; Burrell,
2010; Dodson et al., 2013; Ahmed et al., 2017; Sey, 2009) and survey-based
(Blumenstock and Eagle, 2010; Wesolowski et al., 2012; Gilwald, 2005)
approaches to study patterns of phone sharing in a variety of LMIC contexts,
ranging from rural Berber communities in Morocco (Dodson et al., 2013) to
urban neighborhoods in Bangladesh (Ahmed et al., 2017). Most geographically
relevant to our work in Togo is a study involving qualitative observation and
interviews with 22 mobile phone users in neighboring Ghana (Sey, 2009), which
suggests that peer-to-peer phone sharing in Ghana is more common in rural
areas than urban ones. It also emphasizes the importance of mobile payphones,
which are run by entrepreneurs or agents of the telecommunications companies.
People can use mobile payphones to make calls from their phone and/or SIM card
for a fee, leading to a set of “super user” phones and SIM cards that are
shared among hundreds or thousands of people.

Other ethnographic projects in India (Rangaswamy and Singh, 2009), Uganda
(Burrell, 2010), Morocco (Dodson et al., 2013), and Bangladesh (Ahmed et al.,
2017) have been more focused on the family and gender dynamics of phone
sharing practices. All four studies indicate that women are generally de-
prioritized in phone ownership and sharing arrangements within families, with
men frequently sharing their personal phones with wives and mothers
(Rangaswamy and Singh, 2009; Ahmed et al., 2017; Burrell, 2010), or
controlling the sharing dynamics of a family phone (Rangaswamy and Singh,
2009; Dodson et al., 2013; Ahmed et al., 2017). Burrell (2010) (Burrell, 2010)
also analyzes the gender dynamics of phone sharing outside of family units in
Uganda, in particular noting that in some cases “gifting” of phone time
between men and women is associated with expectations of reciprocity in the
form of money or sexual favors.

Three studies have used larger-scale survey-based approaches to estimate
nationally representative statistics on phone sharing in Africa. Gillwald
(2005) (Gilwald, 2005) presents data from nationally representative household
surveys in ten African countries conducted in 2004 and 2005, although
information on phone sharing is only published for four countries. 73.3% of
phone-owning survey respondents in Namibia report sharing their mobile phone
with friends at least occasionally; the corresponding statistic is 70% in
Rwanda, 50% in South Africa, and 48.3% in Botswana. In data from Namibia,
Gillwald (2005) finds that phone sharing is highest among low-income
households (for example, 34.9% of households in the lowest-income quartile
regularly share their phone with friends, in comparison to 7.27% of households
in the highest-income quartile). Gillwald (2005) also finds large differences
between rural and urban areas in phone sharing, with 61.8% of households in
rural Namibia sharing their phones at least occasionally with friends, in
comparison to 10.9% of households in major towns.

Wesolowski et al. (2012) (Wesolowski et al., 2012) analyzes data from a large
and nationally representative household survey in Kenya conducted in 2009 (N =
32,748), finding that while 85% of respondents report using a phone, only 44%
own their own phone; phone sharers who do not own their own device are
predominantly female (65%) and the spouses of heads of household (60%).
Wesolowski et al. (2012) also note that phone sharing (relative to ownership)
is highest among the poor, young people (under the age of 24), illiterate
people, farmers (in comparison to other occupations), and women. Blumenstock
and Eagle (2010) (Blumenstock and Eagle, 2010) conduct a similar exercise with
a phone survey of 901 respondents in Rwanda in 2009, reporting that around a
third of respondents share their mobile phone, with phone sharing
statistically significantly higher among women than men. No significant
differences were found in phone sharing by geography or level of education.

####  1.1.2. Work with call detail records

Our work also builds off a set of past studies that have used mobile phone
metadata to study social patterns. These studies have used Call Detail Records
(CDR, described in Section 2.1) to study population density (Deville et al.,
2014; Douglass et al., 2015), patterns of migration (Blumenstock, 2012;
Blumenstock et al., 2019; Chi et al., 2020), friendship formation (Onnela et
al., 2007; Eagle et al., 2009), social clustering (Hidalgo and Rodríguez-
Sickert, 2008; Barnett et al., 2016), and transportation networks (Järv et
al., 2012; Jahangiri and Rakha, 2015; Graells-Garrido et al., 2018). Most
relevant to our work is the use of CDR to study social and economic dynamics
in LMICs, including measuring poverty in Afghanistan (Blumenstock, 2018; Aiken
et al., 2020), Guatemala (Hernandez et al., 2017), Rwanda (Blumenstock et al.,
2015a), and Togo (Aiken et al., 2021); literacy rates in Senegal (Schmid et
al., 2017); women’s empowerment in Uganda (Slavchevska et al., 2021); and
employment shocks in South Asia (Sundsøy et al., 2017). However, to our
knowledge, no research has yet been published using call detail records to
study phone sharing.

####  1.1.3. The impact of mobile cash transfer programs

Finally, our work builds on a small literature studying the impact of
receiving cash transfers via mobile money. In particular, Aker et al. (2016)
(Aker et al., 2016) use a randomized controlled trial to compare the impacts
of a cash transfer program delivered via mobile money to the same program
delivered in person via cash. In addition to documenting positive impacts on
household food security and nutritional status for all beneficiaries, the
study documents a 9-16% increase in food security for households receiving
transfers via mobile money in comparison to households receiving transfers via
physical cash. The study also notes that women in households that received
mobile transfers had greater bargaining power in how the transfer was spent.
Blumenstock et al. (2015) (Blumenstock et al., 2015b) study a similar RCT
through which some employees of a large NGO in Afghanistan were randomly
assigned to receive salary payments in mobile money instead of cash. While
mobile money payments led to efficiency gains for the employer, there was
little evidence of positive benefits to employees. While these results may be
promising for the potential of cash transfer delivery via mobile money, no
studies to date have documented how mobile money transfers interact with
dynamics of phone sharing within families and communities.

##  2\. Methods

This section presents the data and empirical strategies used for our analysis
of phone sharing in Togo. Our analysis relies on call detail records (2.1)
matched to administrative data from the GiveDirectly-Novissi cash aid program
in Togo (2.2). Parts of our analysis also incorporate data from a large
randomized controlled trial associated with the GiveDirectly-Novissi program
(2.3). We use regression analysis with these data to estimate (1) demographic
differences in phone sharing, and (2) impacts of mobile cash transfers on
phone sharing (2.4, 2.5). We conclude the section by discussing the
limitations of measuring phone sharing with CDR (2.6).444The code for our
analyses is available in the GitHub repository located at
https://github.com/emilylaiken/togo-phone-sharing/.

###  2.1. Call Detail Records and Measures of Phone Sharing

Our analysis is based on records of mobile phone transactions in Togo, which
were obtained from Togo’s two mobile phone operators for the months of October
2020 to January 2021.555The analysis of these data is governed by a data
management plan and research protocol reviewed and approved by U.C. Berkeley’s
Committee for the Protection of Human Subjects. These Call Detail Records
(CDR) contain the metadata of each phone call or text message sent from a
mobile phone on the Togolese cell network in these five months. We use the
following information from both call and text records in our analysis:

  1. (1)

MSISDN: Mobile Station International Subscriber Directory Number; the phone
number (unique ID) for the SIM card placing the call666Since phone numbers are
personally identifying information, phone numbers are encrypted prior to
analysis.

  2. (2)

IMEI: International mobile equipment identify; the unique ID for the SIM card
slot the MSISDN is in when the call is placed

  3. (3)

Time stamp: Date and time when the transaction is placed

Note that based on these data, we only observe MSISDN and IMEI for outgoing
calls and texts (while our data also record the MSISDN for incoming
transactions, they do not record the IMEI for incoming transactions, so
incoming transactions are not incorporated in our analysis). Also note that
the definition of an IMEI is particularly important: rather than uniquely
identifying a physical mobile device, the IMEI uniquely identifies a SIM card
slot. This distinction is relevant to countries like Togo, where multi-SIM
phones are common.777While we are not aware of any quantitative studies of the
prevalence of multi-SIM phones, based on qualitative field observation they
are quite common. In nearby Nigeria, nearly half of all subscribers are
estimated to use multi-SIM phones (https://deviceatlas.com/blog/dual-sim-
smartphone-usage). We discuss the affordances and limitations of this
definition further in Section 2.6.

In total, we observe 1.51 billion transactions (1.06 billion calls and 445.54
million texts) assigned to 5.78 million unique MSISDNs and 9.41 million unique
IMEIs between October 1 and January 31, 2021. We remove transactions on seven
days that contain anomalous activity (two standard deviations above or below
the monthly mean) with respect to the number of active MSISDNs or
IMEIs.888Anomalous days are a mix of days with unusually low activity (likely
representing equipment malfunctions causing some mobile phone transactions to
be left out of the central databases of the mobile network operators), and
days of unusually high activity (for example, there are spikes in activity
around Christmas and New Years).

We construct two measures of phone sharing from these CDR. Our first measure
is SIM sharing: the extent to which SIMs are moved between devices (SIM card
slots). We measure SIM sharing by counting the number of unique IMEIs with
which a SIM card (MSISDN) is associated in each month. We consider a SIM to be
“shared” in a given month if it makes transactions with more than one IMEI in
that month. Our second measure of phone sharing is device sharing: the extent
to which devices (SIM card slots, identified by IMEIs) are used with multiple
SIMs. We consider a device “shared” in a given month if it makes transactions
associated with more than one MSISDN in that month.

###  2.2. The GiveDirectly-Novissi Aid Program and Associated Administrative
Data

We complement the call detail records with administrative data from
registrations for the GiveDirectly-Novissi aid program in Togo. The
GiveDirectly-Novissi program, administered by NGO GiveDirectly and the
government of Togo via the government’s Novissi social protection platform,
provided unconditional cash transfers to hundreds of thousands of Togolese
citizens between November 2020 and August 2021. Registration and payment for
GiveDirectly-Novissi was arranged entirely via mobile phones: applicants
filled out a short USSD (text-based) survey for registration, and eligible
applicants received five months of monthly cash transfers via mobile money.
Transfers for eligible applicants began immediately following registration.
Female beneficiaries received CFA 8,170 (USD 14.40) per month, and male
beneficiaries recieved CFA 7,000 (USD 12.34) per month. Aiken et al. (2021)
(Aiken et al., 2021) provide more details about the structure and targeting of
GiveDirectly-Novissi aid.

As part of the phone-based registration for GiveDirectly-Novissi aid,
applicants were required to provide their voter ID number for unique
identification. Aiken et al. (2021) (Aiken et al., 2021) estimate that between
83% and 98% of Togolese adults own a voter ID; information for the voter ID
was recorded in voter registration drives each year, with the most recent
drive occurring in 2020. The administrative information recorded with voter
IDs makes it possible to associate the following basic demographic information
with each SIM card that registered for GiveDirectly-Novissi:

  1. (1)

Gender (male or female)

  2. (2)

Prefecture of residence (the admin-3 unit; see Figure 1)

  3. (3)

Age

  4. (4)

Day of registration to GiveDirectly-Novissi

Note that a single SIM card could only register one voter ID on the USSD
registration platform, so each SIM card is uniquely tied to an individual via
their voter ID; people with multi-SIM phones could register one person on each
SIM.

In total, 485,977 SIMs registered for GiveDirectly-Novissi between November
2020 and August 2021. We restrict our analysis to 171,530 SIMs registered to
GiveDirectly-Novissi in November or December 2020.999Restricting to
registrations in November and December allows us to analyze phone sharing in
January among subscribers who received a cash transfer in January, in
comparison to a randomly selected treatment group, facilitating our evaluation
of the impacts of GD-Novissi transfers on phone sharing (Sections 2.5.2 and
3.3). For parts of our analysis — particularly those focusing on the impacts
of cash transfers on phone sharing — we restrict further to the 49,083
registrations that met the eligibility criteria for GiveDirectly-Novissi
benefits.101010Program eligibility was determined by location of residence and
the estimated wealth of the registered subscriber (Aiken et al., 2021). Of the
171,530 GiveDirectly-Novissi registrations in November and December 2020,
149,947 (87.43%) are in eligible geographies, and 49,083 (34.76% of
registrations in eligible geographies) are below the poverty threshold. Table
1 Column 1 provides a summary of the demographic characteristics of the
171,530 registrations to GiveDirectly-Novissi in November and December 2020.
Column 2 provides the demographic breakdown of the 49,083 aid-eligible
registrants.

###  2.3. Randomized Controlled Trial

The GiveDirectly-Novissi program was designed as a randomized controlled trial
(RCT), through which some beneficiaries received payments beginning in Fall
2020, and others received payments beginning in Spring 2021. There were two
reasons for randomizing the time of eligibility for GiveDirectly-Novissi
transfers: first, because the program did not have enough funding to pay all
eligible subscribers in Fall 2020, and second, because the program
implementers were interested in conducting an impact evaluation of
GiveDirectly-Novissi. Our analysis also leverages this randomization to
measure the impacts of cash transfers on phone sharing.

Of program registrants in November and December who met the two eligibility
criteria (geographic eligibility and poverty-based eligibility), 27,673
(56.38%) were assigned to the “treatment group” and were provided with monthly
cash payments, with the first payment delivered immediately upon registration
(November-December 2020), and the remaining four payments delivered every
thirty days. The remaining 21,410 (43.62%) were assigned to the control group,
and received the same total cash aid in lump transfers between May and August
2021. Much of our analysis relies on comparing outcomes between the treatment
and control groups, as treatment assignment was entirely at random. Table S1
confirms that in October 2020, prior to registration and cash transfer
delivery, there was no significant difference between treatment and control
groups in terms of demographic information or phone sharing.

###  2.4. Merging Datasets

####  2.4.1. Matching at the SIM level

For analysis of SIM sharing (whether a SIM is used in multiple devices), we
match GiveDirectly-Novissi registrations to phone sharing measures (which are
derived from CDR as described in section 2.1) directly via MSISDN. Both
GiveDirectly-Novissi registrations and the SIM sharing measure are uniquely
associated with an MSISDN, so matching between administrative data and phone
sharing at the SIM level is 1:1.

####  2.4.2. Matching at the IMEI level

Analysis of device sharing (whether a device is associated with multiple SIM
cards) requires linking between GiveDirectly-Novissi registrations and IMEIs.
The IMEI from which USSD registration is placed is not recorded in the
GiveDirectly-Novissi database, and we do not observe USSD transactions in our
CDR, so we are not able to directly match GiveDirectly-Novissi registrations
to devices via IMEIs. Instead, we assign each registration to the device used
by the registered SIM card on the day closest to program registration. For 56%
of registrants, the “nearest” device is used on the same day as
registration,111111In comparison, only 23% of registered SIMs make at least
one outgoing transaction on the average day in October, suggesting that
subscribers are substantially more likely to make a transaction on their day
of registration than on a random day. for 84% of registrants, it is within one
week, and for 97% of registrants it is within one month. For SIMs that are
associated with more than one device on the day of registration (5.50% of all
registrations), we use the device through which the SIM places the most
transaction on the closest day. For those with no unique maximum (1.57%), we
take one of the maximum devices at random.

Note that this matching strategy means that matching between registrations and
measures of device sharing is 1:N — one device can be associated with multiple
registrations, if it is the closest device to the date of registration for
multiple SIMs. 3.34% of devices in our dataset are linked to more than one
SIM; in our analysis of device sharing they appear once in the dataset for
each registration they are linked to. In Tables S2, S3, and S4, we evaluate
the sensitivity of our main results to this 1:N matching strategy, finding
that all results remain significant at the 0.05 level if we (1) include
devices associated with multiple registered SIMs only once in the dataset,
picking the associated registration at random, and (2) drop devices associated
with multiple registered SIMs altogether.

###  2.5. Empirical Strategy

We rely on regression analysis throughout for evaluating (1) differences in
phone sharing by demographic characteristics, and (2) impacts of treatment on
phone sharing.

####  2.5.1. Heterogeneity in phone sharing

To estimate differences in patterns of phone sharing by different demographic
subgroups (gender, age, and location), we use the dataset of GiveDirectly-
Novissi registrations in November and December matched to mobile phone data
from October 2020, before the aid program was launched on November 1, 2020.
Matching to CDR from October allows us to assess heterogeneity in phone
sharing before behavior was impacted by GiveDirectly-Novissi. At the SIM
level, this dataset contains 122,173 observations (after removing SIMs that do
not place at least one outgoing transaction in October). At the device level,
this dataset contains 105,443 observations (after removing devices that do not
place at least one outgoing transaction in October).

Our analysis quantifies the magnitude and significance of the differences in
these two measures of phone sharing between (1) men and women, (2) urban areas
(the prefectures of Lomé Commune, Agoe-Nyive, and Golfe — which make up the
capital city of Lomé — and the prefecture of Sokodé which contains the second
largest city of Tchaoudjo) and rural areas (the remaining 36 prefectures), and
(3) four age groups (age 18-30, 30-40, 40-50, and 50+).121212In order to
obtain a voter ID, applicants had to be at least age 18. Throughout, we employ
a basic specification (regressing phone sharing only on the relevant
demographic characteristic), and a specification that controls for the number
of transactions placed by each SIM or device (as our measure of phone sharing
may be confounded by the number of transactions placed, see Section 2.6).
These regression specifications are shown in Equation 1, where $y_{i}$ is an
indicator for whether observation $i$ is a shared SIM (or device)131313We
prefer these binary measures of phone sharing to, for example, the average
counts of devices per SIM and SIMs per deivce that are included in Table 2, as
the average count can be susceptible to a few “super-user” devices that are
associated with many SIMs. For example, among 105,443 devices matched to
GiveDirectly-Novissi registration data in October 2020, 165 devices (0.16%)
are associated with more than 1,000 SIMs in the month of October, skewing the
average measures high. It is possible that these “super-user” SIMs correspond
to the payphones identified in previous work in Ghana (Sey, 2009) that are
rented out for a price to people who do not own phones., $x_{i}$ is the
demographic variable of interest, $n_{i}$ is the number of transactions placed
by observation $i$ (included only in the specification controlling for number
of transactions), and $\epsilon_{i}$ is an idiosyncratic error term.

(1) $y_{i}=\beta_{0}+\beta_{1}x_{i}+\beta_{2}n_{i}+\epsilon_{i}$

####  2.5.2. Impact of mobile cash transfers on phone sharing

To estimate the impact of cash transfers delivered via mobile money on phone
sharing, we restrict our dataset to subscribers enrolled in the RCT (i.e. SIMs
and devices that are associated with registrations that met the geographic and
poverty requirements for GiveDirectly-Novissi, and were assigned to either
treatment or control groups).

We first employ a basic specification that uses phone sharing data only from
the month of January 2021, by which time all subscribers in the treatment
group had received at least one cash transfer. At the SIM level, this dataset
contains 43,323 observations (after removing SIMs that do not place at least
one outgoing transaction in January). At the device level, this dataset
contains 30,483 observations (after removing devices that do not place at
least one outgoing transaction in January). Treatment was assigned entirely at
random for the GiveDirectly-Novissi RCT, so a simple regression on treatment
is sufficient to estimate the treatment effect of cash transfers on phone
sharing. As in the earlier demographic analyses, we experiment with
controlling for the number of transactions. We also explore the addition of
demographic controls. These regression specifications are shown in Equation 2,
which includes an indicator $T_{i}$ for whether individual $i$ is in the
treatment group.

(2)
$y_{i}=\beta_{0}+\beta_{1}T_{i}+\beta_{2}n_{i}+\beta_{3}x_{i}+\epsilon_{i}$

We also employ a difference-in-differences specification in order to better
isolate changes in phone sharing over time (for a given SIM or device) caused
by the cash transfer. In the difference-in-differences specification, each
observation represents a subscriber-month ($N$ = 173,654 at the SIM level, $N$
= 147,419 at the device level), using phone sharing data from October,
November, December, and January. We regress monthly phone sharing $y_{it}$, an
indicator for whether observation $i$ is shared in month $t$, on $T_{it}$, an
indicator for whether observation $i$ received a cash transfer in month $t$,
while controlling for MSISDN fixed effects $\mu_{i}$ and month fixed effects
$\pi_{t}$, as well as $n_{it}$, the number of transactions placed by
observation $i$ in month $t$. We cluster standard errors at the SIM level for
SIM sharing and the device level for device sharing.

(3)
$y_{it}=\beta_{0}+\beta_{1}T_{it}+\beta_{2}\pi_{t}+\beta_{3}\mu_{i}+\beta_{4}n_{it}+\epsilon_{it}$

We return to the basic regression specification and data from January 2020 to
test for heterogeneous treatment effects by gender (men vs. women) and age
(under 30, 30-40, 40-50, or over 50).141414We cannot test for heterogeneous
treatment effects by geography (urban vs. rural), as only rural areas were
eligible for GiveDirectly-Novissi aid (see Table 1.) Specifically, to measure
heterogeneous treatment effects by gender, we regress SIM and device sharing
on treatment, number of transactions, a binary indicator for being female, and
the indicator for being female crossed with treatment. We employ an analogous
specification for testing for heterogeneous treatment effects by age group.
These specifications are shown in Equation 4, where $x_{i}$ is the demographic
information of interest (gender or age group).

(4)
$y_{i}=\beta_{0}+\beta_{1}T_{i}+\beta_{2}n_{i}+\beta_{3}x_{i}+\beta_{4}T_{i}\times
x_{i}+\epsilon_{i}$

###  2.6. Data Limitations

Before proceeding with the results of these analyses, we note several
limitations of the data and methods employed. These include:

  1. (1)

Limits to observation of phone sharing in CDR: The CDR only allow us to
observe phone sharing that involves a SIM card moving between devices (SIM
card slots). We therefore cannot observe other common types of phone sharing,
for instance when a single device and SIM card is shared between multiple
people (without switching SIM cards). In this sense, our measure of phone
sharing is a substantial underestimate of “true” phone sharing in Togo. We
likewise cannot track when subscribers change phones or SIM cards.151515By
using the phone number (MSISDN) as the unique identifier for SIM cards, we can
keep track of subscribers who switch SIM cards but transfer their phone number
to the new SIM. For this reason we use MSISDNs as our unique SIM identifier
rather than IMSI (international mobile subscriber identity numbers), which
cannot be transferred between SIM cards.

  2. (2)

Limits to IMEIs as device identifiers: IMEIs, our “device identifiers”,
uniquely identify SIM card slots rather than mobile devices. Our analysis
would therefore count the movement of a single SIM card between SIM card slots
in the same device as a type of phone sharing, so SIM sharing may be
overestimated where SIMs are moved within a physical device. Device sharing
may be underestimated where multiple SIMs are used with a single device, but
in different SIM card slots.

  3. (3)

Phone sharing is confounded by transaction count: Our measures of phone
sharing — whether a SIM is used in multiple SIM card slots and whether a SIM
card slot is associated with multiple SIMs — may be confounded by the number
of transactions placed by an SIM or device (SIM card slot). Phone sharing can
only be observed for subscribers placing at least one transaction; SIMs or
devices placing only one transaction in a given month will be assigned by
definition as “not shared.” To address this concern, we provide specifications
that control for the number of transactions placed by an SIM or device
throughout.

  4. (4)

Imperfect matching between GiveDirectly-Novissi registrations and SIM cards:
GiveDirectly-Novissi registrations are linked to SIM cards according to the
voter ID registered for GiveDirectly-Novissi with each SIM card. However, it
is not required that the owner of a phone register their voter ID on that
phone, and in some cases there may not be a designated owner for each SIM card
or device. For example, based on qualitative field observation of
GiveDirectly-Novissi registrations, it is common that in households with only
a single phone, the device and SIM cards inside it are owned by the man, but
he registers his wife’s voter ID for GiveDirectly-Novissi (women receive more
aid than men, which incentivizes this behavior). Given these patterns, the
matching between SIMs and Novissi registration data reflects only associations
rather than ownership.

  5. (5)

Imperfect matching of GiveDirectly-Novissi registrations and devices: In
addition to the issues with linking SIMs to GiveDirectly-Novissi registrations
described in (4) above, there is some further noise in matching devices to
GiveDirectly-Novissi registrations. As described in Section 2.4.2, the device
(IMEI) of registration is not recorded in Novissi registrations, and USSD
transactions for registration are not recorded in CDR. We therefore match
registrations to the “nearest” device (IMEI) associated with the SIM card of
registration, but in some cases it is likely that this matching does not
reflect the device that the registration was truly performed on.

  6. (6)

Limits to data representativity: Our dataset of CDR matched to GiveDirectly-
Novissi registrations is not nationally representative of Togo, nor
representative of the parts of Togo in which GiveDirectly-Novissi aid was
distributed. There is evidence of considerable self-selection in who registers
for GiveDirectly-Novissi aid (Aiken et al., 2021), and structural elements of
the program preclude certain populations from registering and therefore being
included in our analysis (for example, those without a voter ID or without
access to a phone could not register). Our analysis of CDR matched to
registration data therefore can only be interpreted as representative of the
population registering for GiveDirectly-Novissi; likewise our analysis of
treatment effects is only representative of the population eligible for
GiveDirectly-Novissi aid (where the eligible population is on average poorer
and more vulnerable than the applicant population (Aiken et al., 2021)).

##  3\. Results

###  3.1. Summary Statistics on Phone Sharing

Table 2 presents summary statistics on SIM and device sharing in Togo using
CDR from October 2020, prior to the launch of GiveDirectly-Novissi in November
2020. We present results for three groups: (1) all SIM cards and devices on
Togo’s two mobile phone networks, (2) all SIM cards and devices that
registered for the GiveDirectly-Novissi cash transfer program in November and
December 2020, and (3) all SIM cards and devices that met the eligibility
criteria for the GiveDirectly-Novissi RCT (see Section 2.3). 161616There are
fewer observations in column 2 of Table 2 than in Table 1 because we can only
measure phone sharing in the month of October for SIM cards and devices that
placed at least one transaction during the month that is recorded in our CDR;
not all registered subscribers make a transaction in every month. We find
substantial evidence of SIM and device sharing: 22.38% of devices are shared,
and 7.44% of devices are shared. There is higher phone sharing among those
registered for GiveDirectly-Novissi (33.13% shared SIMs and 11.98% shared
devices), perhaps because advertisement for the program was targeted towards
rural areas. We note, however, that across groups the median and modal SIM
card and device are not shared.

###  3.2. Heterogeneity in Phone Sharing

Table 3 evaluates differences in phone sharing among demographic groups using
data from October 2020, prior to the launch of the GiveDirectly-Novissi aid
program in November 2020. We examine heterogeneity by (1) gender (men vs.
women), (2) age group (18-30, 30-40, 40-50, and 50+), and (3) geography (urban
vs. rural, with urban defined as prefectures — admin-3 units — containing the
two major cities in Togo, and rural defined as all other prefectures). We find
that after controlling for the number of transactions, which can confound our
measures of phone sharing, SIM cards associated with women are not
statistically significantly more likely to be shared, but devices associated
with women are 1.84 percentage points (17.10%) more likely to be shared than
those associated with men (p ¡ 0.001). We find that SIMs and devices
associated with people age 18-30 are 6.91-8.47 points (26.09-3.98%) and
1.99-3.86 points (17.20-39.75%) more likely to be shared than those associated
with people over thirty (p ¡ 0.001), respectively. Finally, we find that SIMs
in rural areas are 10.03 points (53.27%) more likely to be shared than SIMs in
urban areas, and devices in rural areas are 3.16 points (36.92%) more likely
to be shared than devices in urban areas (p ¡ 0.001).

Figure 1 further illustrates heterogeneous phone sharing by geography, by
mapping the degree of SIM and device sharing by prefecture (admin-3 units).
While there are heterogeneous patterns of variation throughout the country, in
general SIM and device sharing are higher in the Northern rural parts of the
country in comparison with the more urban areas in the South.

###  3.3. Cash Transfer Impacts on Phone Sharing

To measure the impacts of receiving cash transfers via mobile money on phone
sharing, we restrict our dataset to SIMs and devices enrolled in the
GiveDirectly-Novissi RCT and use CDR from January 2021. The treatment group
for the RCT ($N$=27,673) received cash transfers every month for five months
after registration; since we restrict our analysis to subscribers registered
in November or December 2020, all treated subscribers received a cash transfer
in January 2021. The control group ($N$=21,410) began receiving cash transfers
in May 2021, so no control subscribers received cash in January 2021.

The first two columns of Table 4 present the results of regressing phone
sharing in January on treatment status. We find that after controlling for
number of transactions, treated SIM cards are 1.92 percentage points (8.16%)
more likely to be shared in January than control SIM cards (p ¡ 0.0001).
Treated devices are 0.94 points (12.32%) more likely to be shared in January
than control devices (p ¡ 0.01). We further find that the magnitude and
significance of these effects are robust to the inclusion of a variety of
demographic controls (Column 4).

To measure changes in phone sharing over time caused by cash transfers, we use
a difference-in-differences specification with CDR from October, November,
December, and January.171717We do not observe CDR after January so our
analysis is restricted to these four months, however, treated subscribers
continued to receive payments through April. Figure 2 provides visual
motivation for the differences-in-differences specification, plotting the
percentage of SIMs and devices shared by month, separately for treatment and
control groups. While treatment and control groups are nearly
indistinguishable for both measures of phone sharing in October, before the
aid program began, the treatment group has markedly more phone sharing in
November, December, and January, when cash transfers were distributed to some
or all treatment subscribers.

Observations for the differences-in-differences specification are SIM-months
(or device-months), and subscriber $i$ is “treated” in month $t$ if subscriber
$i$ received a cash transfer at anytime in month $t$; the differences-in-
differences specification also includes month and subscriber fixed effects.
Table 5 presents the results of the differences-in-differences specification.
As in the earlier set of results we observe positive and significant impacts
on phone sharing from treatment (treatment effects of 2.86 percentage points,
or 11.19%, for SIM sharing and 2.10 percentage points, or 21.63%, for device
sharing, p ¡ 0.001).

####  3.3.1. Heterogeneous treatment effects

We return to the basic regression specification to test for heterogeneous
treatment effects by gender (men vs. women) and age (under 30, 30-40, 40-50,
or over 50). Table 6 shows no statistically significant differences in impacts
of cash transfers on phone sharing by gender or age group.

##  4\. Discussion

This section expands on two particularly notable aspects of our results: the
concordance of our results with past work, in spite of differences in
measurement methods and study locations; and the implications of our results
for future aid programs delivered via mobile money.

###  4.1. Concordance with past work

In Section 3.2 we find that both SIM and device sharing are most common among
women, young people, and people in rural areas. Specifically, focusing on
device sharing, phones associated with women are 17.10% more likely to be
shared than phones associated with men; phones associated with people under 30
are 17.20-39.75% more likely to be shared than phones associated with people
over age 30, and phones in rural areas are 36.92% more likely to be shared
than phones in urban areas.

The gender dynamic has been studied extensively, both quantitatively and
qualitatively, in past work on phone sharing. Our finding that women use
shared phones more than men is consistent with Wesolowski et al. (2012)
(Wesolowski et al., 2012), which finds that nearly twice as many women as men
in Kenya report using a shared phone rather than a privately owned one (65% of
phone sharers are women, and 35% are men). It is also consistent with
Blumenstock et al. (Blumenstock and Eagle, 2010), in which 39% of female
respondents to a phone survey in Rwanda report sharing the phone on which they
answered the survey, in comparison to 25% of men. Our work is also consistent
with a set of qualitative and ethnographic studies that have documented higher
phone sharing among women than men in Bangladesh, India, and Uganda
(Rangaswamy and Singh, 2009; Burrell, 2010; Ahmed et al., 2017).

Differences in phone sharing by age groups has been studied less thoroughly in
past work. Our finding that young people (those under 30) share phones more
than older people (over 30) is, however, consistent with results from Kenya in
Wesolowski et al. (Wesolowski et al., 2012), which finds that 51% of survey
respondents ages 16-17 report using a shared phone (in comparison to 17% phone
ownership in the same group), 41% of respondents aged 18-24 report using a
shared phone (in comparison to 43% phone ownership), and 31-34% of respondents
aged 25-59 report using a shared a phone (in comparison to 41-53% ownership).
It is worth noting that Wesolowski et al. find that phone sharing increases
again after age 59, with 39-42% of respondents aged 60 and above reporting
using a shared a phone (in comparison with 23-29% phone ownership). We observe
a similar pattern, with phone sharing highest among those under 30, followed
by those over 50, then those ages 40-50, and finally lowest among those aged
30-40. It is possible that high levels of phone sharing among young people
reflect that young people cannot afford a personal phone, or that age
hierarchies in families or communities cause parents to share phones with
children rather than purchasing phones for young people (as observed by Ahmed
at al. (2017) in Bangladesh (Ahmed et al., 2017)).

Finally, our finding on differences in phone sharing by geography is also
consistent with Gillwald (2005) (Gilwald, 2005) in Namibia and Wesolowski et
al. (2012) (Wesolowski et al., 2012) in Kenya, both of which find that phone
sharing is more common in rural areas than urban ones. It is also consistent
with Sey (2009) (Sey, 2009), which suggests that phone sharing in Ghana is
higher in rural areas than in cities, though conclusions are limited by a
small sample size (N = 22). The large difference we identify in phone sharing
between urban and rural areas in Togo is not consistent with Blumenstock and
Eagle (2010), which finds no statistically significant difference between
urban and rural phone sharing in Rwanda.

The similarity in results between our work and past work on phone sharing,
particularly Wesolowski et al (2012), is especially notable considering that
the method used for measuring phone sharing in our paper is quite different
from methods employed in past studies. In particular, our CDR-based measure of
phone sharing only captures sharing that involves the movement of SIM cards
between devices, uses the SIM card slot as the unique device ID rather than an
ID for a physical mobile phone, and is subject to several sources of imperfect
matching between phone sharing measures produced from CDR and demographic data
(Section 2.6). The similarities between our results and past work suggest that
patterns of phone sharing can be quantitatively measured with CDR despite
these limitations.

###  4.2. Implications for policy

Our results, which document the impact of cash transfers on phone sharing and
highlight demographic heterogeneity in phone sharing, have implications for
future policies on mobile delivery of aid payments. To recap, we find that the
delivery of cash aid via mobile money significantly increases phone sharing,
with subscribers in the randomly assigned treatment group in the GiveDirectly-
Novissi program using a shared SIM card 8.16-11.19% more often than
subscribers in the control group, and using a shared device 12.32-21.63% more
frequently than subscribers in the control group. While these increases in
phone sharing are unlikely to negate all the health and economic benefits of
receiving cash transfers via a mobile phone (Davidovic et al., 2020; Aker et
al., 2016), the increase in person-to-person contact caused by mobile aid
delivery may be a concern for public health officials in the context of a
pandemic.

Moreover, our results highlight that it is important to consider phone sharing
dynamics in the design of mobile aid programs. If people who rely on shared
phones are less able to receive benefits than people with personal devices,
programs that deliver aid via mobile money risk disadvantaging groups that are
frequent sharers of mobile phones, including women, young people, and people
in rural areas. One option to address this issue is to allow any number of
people to receive cash transfers on a mobile phone — though this strategy may
be open to fraud (malicious actors registering other people on their phone and
keeping the payments). A middle ground is to limit registrations on a single
SIM card to a single individual, and provide financing for phone sharers to
purchase their own SIM card.181818SIM cards are typically much cheaper than
mobile devices. The GiveDirectly-Novissi program limited registrations on a
single SIM card to one individual. It is worth noting, however, that even if
policies are designed to ensure that phone sharers can register for aid
programs, phone sharers are at higher risk of having their aid payments taken
by other users of the same phone. This issue highlights the need for mobile-
based user interfaces that allow for private digital zones in the context of
shared use of mobile financial systems (Ahmed et al., 2017, 2019), as well as
the importance of foregrounding the needs of vulnerable groups in the design
of aid delivery systems.

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## Figures and Tables

Figure 1. SIM sharing (left) and device sharing (right) in October 2020, by prefecture (admin-3 unit, 40 prefectures in Togo). Figure 2. SIM sharing (red) and device sharing (green) from October to January 2020, separately for the treatment group (solid line) and control group (dashed line). | Matched to Administrative Data | Enrolled in RCT
---|---|---
| Number | Proportion | Number | Proportion
Panel A : Gender |  |  |  |
Female | 95,396 | 55.61% | 24,694 | 50.31%
Male | 76,134 | 44.39% | 24,389 | 49.69%
Panel B: Age Group |  |  |  |
18-30 | 67,424 | 39.30% | 16,081 | 32.76%
30-40 | 43,606 | 25.42% | 14,770 | 30.09%
40-50 | 29,415 | 17.15% | 9,428 | 19.21%
50+ | 31,095 | 18.13% | 8,804 | 17.94%
Panel C: Geography (urban vs. rural) |  |  |  |
Rural | 167,436 | 97.61% | 49,083 | 100.00%
Urban | 4,094 | 2.39% | 0 | 0.00%
N (Total) | 171,530 |  | 49,083 |
Table 1. Summary statistics on demographic breakdown for (1) GiveDirectly-Novissi registrations in November and December (left), which we use for our analysis of heterogeneity in phone sharing among demographic groups, and (2) GiveDirectly-Novissi beneficiaries enrolled in the RCT (right), which we use for our analysis of impacts of cash transfers on phone sharing. | Entire network | Matched to administrative data | Enrolled in RCT
---|---|---|---
Panel A: SIM sharing (Devices per SIM)
Mean | 1.35 (0.85) | 1.60 (1.15) | 1.50 (1.00)
Median | 1.00 | 1.00 | 1.00
Mode | 1.00 | 1.00 | 1.00
% of SIMs shared | 22.38% | 33.13% | 29.58%
N | 4,508,881 | 122,173 | 41,738
Panel B: Device sharing (SIMs per device)
Mean | 1.09 (1.86) | 4.24 (63.19) | 3.88 (60.67)
Median | 1.00 | 1.00 | 1.00
Mode | 1.00 | 1.00 | 1.00
% of devices shared | 7.44% | 11.98% | 9.56%
N | 5,600,581 | 105,443 | 35,500
Table 2. Summary statistics on phone sharing in October 2020 for (1) the entire mobile phone network (left), (2) observations matched to administrative data from GiveDirectly-Novissi (center), and (3) observations enrolled in the RCT (right). For the measures of mean devices per SIM and mean SIMs per device, standard deviations are shown in parentheses. | Gender | Age | Geography | Joint
---|---|---|---|---
| Basic | Control | Basic | Control | Basic | Control |
Panel A: SIM sharing |  |  |  |  |  |  |
Constant | 0.3419*** | 0.2875*** | 0.3832*** | 0.3340*** | 0.2733*** | 0.1883*** | 0.2309***
Number of mobile transactions |  | 0.0008*** |  | 0.0008*** |  | 0.0008*** | 0.0008***
Female | -0.0205*** | -0.0020 |  |  |  |  | 0.0027
Age: 30-40 |  |  | -0.0919*** | -0.0847*** |  |  | -0.0851***
Age: 40-50 |  |  | -0.0839*** | -0.0732*** |  |  | -0.0742***
Age: 50+ |  |  | -0.0814*** | -0.0691*** |  |  | -0.0708***
Rural |  |  |  |  | 0.0597*** | 0.1003*** | 0.1043***
$R^{2}$ | 0.0005 | 0.0254 | 0.0082 | 0.0319 | 0.0005 | 0.0267 | 0.0333
N | 122,173 | 122,173 | 122,173 | 122,173 | 122,173 | 122,173 | 122,173
Panel B: Device sharing
Constant | 0.1104*** | 0.1076*** | 0.1380*** | 0.1356*** | 0.0893*** | 0.0856*** | 0.0924***
Number of mobile transactions |  | 0.0000*** |  | 0.0000*** |  | 0.0000*** | 0.0000***
Female | 0.0182*** | 0.0184*** |  |  |  |  | 0.0200***
Age: 30-40 |  |  | -0.0383*** | -0.0386*** |  |  | -0.0396***
Age: 40-50 |  |  | -0.0284*** | -0.0291*** |  |  | -0.0307***
Age: 50+ |  |  | -0.0196*** | -0.0199*** |  |  | -0.0216***
Rural |  |  |  |  | 0.0316*** | 0.0316*** | -0.0347***
$R^{2}$ | 0.0008 | 0.01769 | 0.0024 | 0.0185 | 0.0003 | 0.0164 | 0.0199
N | 105,443 | 105,443 | 105,443 | 105,443 | 105,443 | 105,443 | 105,443
Table 3. Heterogeneity in patterns of phone sharing by (1) gender (male vs. female), (2) age group (18-30, 30-40, 40-50, and 50+, and (3) geography (urban vs. rural). For each outcome variable, we present two specifications: one regressing on just the demogrpahic variable(s) (left), and one regressing on the demographic variable(s) and controlling for the number of transactions associated with each SIM or device (right). In the far right column, we include a joint specification with all demographic information included. Statistical significance of regression coefficients are shown with stars: * $\rightarrow$ p ¡ 0.05, ** $\rightarrow$ p ¡ 0.01, *** $\rightarrow$ p ¡ 0.001. | Basic | Transactions Control | Demographic Controls
---|---|---|---
Panel A: SIM sharing |  |  |
Constant | 0.2764*** | 0.2352*** | 0.2831***
Treatment (received cash transfer) | 0.0193*** | 0.0192*** | 0.0196***
Number of mobile transactions |  | 0.0008*** | 0.0008***
Female |  |  | 0.0140***
Age: 30-40 |  |  | -0.0749***
Age: 40-50 |  |  | -0.0856***
Age: 50+ |  |  | -0.0846***
$R^{2}$ | 0.0004 | 0.0194 | 0.0268
N | 43,323 | 43,323 | 43,323
Panel B: Device sharing |  |  |
Constant | 0.0794*** | 0.0763*** | 0.0882***
Treatment (received cash transfer) | 0.0087** | 0.0094** | 0.0096**
Number of mobile transactions |  | 0.0000*** | 0.0000***
Female |  |  | 0.0063*
Age: 30-40 |  |  | -0.0183***
Age: 40-50 |  |  | -0.0236***
Age: 50+ |  |  | -0.0255***
$R^{2}$ | 0.0002 | 0.0244 | 0.0267
N | 30,483 | 30,483 | 30,483
Table 4.  Impacts of cash transfers delivered via mobile money on SIM sharing (panel A) and device sharing (panel B) in January 2020. We employ three regression specifications: the basic specification (left) which regresses phone sharing outcomes only on treatment, the transactions controls specification (middle) which controls for number of outgoing transactions by a SIM or device in January 2020, and the demographic controls specification which controls for the number of transactions alongside a set of demographic controls obtained from administrative data. Statistical significance of regression coefficients are shown with stars: * $\rightarrow$ p ¡ 0.05, ** $\rightarrow$ p ¡ 0.01, *** $\rightarrow$ p ¡ 0.001. | Basic | Transactions Control
---|---|---
Panel A: SIM sharing |  |
Constant | 0.2962*** | 0.2556***
Treatment x Month | 0.0305*** | 0.0286***
Number of mobile transactions |  | 0.0009***
Month: November | 0.0218** | 0.0230***
Month: December | 0.0050 | 0.0019
Month: January | -0.0254*** | -0.0299***
$R^{2}$ | 0.0031 | 0.0095
N | 173,654 | 173,654
Panel B: Device sharing |  |
Constant | 0.0975*** | 0.0971***
Treatment x Month | 0.0210*** | 0.0210***
Number of mobile transactions |  | 0.0000***
Month: November | 0.0391*** | 0.0391***
Month: December | 0.0000 | 0.0001
Month: January | -0.0241*** | -0.0239***
$R^{2}$ | 0.0106 | 0.0105
N | 147,419 | 147,419
Table 5. Difference-in-differences specification for estimating the impacts of cash transfers delivered via mobile money on SIM sharing (Panel A) and device sharing (Panel B). We employ two differences-in-differences specifications: a basic one (left), and one controlling for the number of outward transactions an SIM or device places in each month (right). Statistical significance of regression coefficients are shown with stars: * $\rightarrow$ p ¡ 0.05, ** $\rightarrow$ p ¡ 0.01, *** $\rightarrow$ p ¡ 0.001. Standard errors are clustered at the MSISDN level for Panel A and the IMEI level for Panel B. | Panel A: SIM sharing | Panel B: Device sharing
---|---|---
| Gender | Age | Gender | Age
Constant | 0.2274*** | 0.2910*** | 0.0735*** | 0.0910***
Treatment (received cash transfer) | 0.0135** | 0.0204* | 0.0072 | 0.0109
Number of mobile transactions | 0.0008*** | 0.0008*** | 0.0000*** | 0.0000***
Female | 0.0140* |  | 0.0057 |
Age: 30-40 |  | -0.0743*** |  | -0.0171**
Age: 40-50 |  | -0.0918*** |  | -0.0188**
Age: 50+ |  | -0.0810*** |  | -0.0313***
Female x Treatment | 0.0115 |  | 0.0046 |
Age: 30-40 x Treatment |  | -0.0025 |  | -0.0029
Age: 40-50 x Treatment |  | 0.0079 |  | -0.0097
Age: 50+ x Treatment |  | -0.0088 |  | -0.0089
$R^{2}$ | 0.0199 | 0.0266 | 0.0246 | 0.0260
N | 43,323 | 43,323 | 30,483 | 30,483
Table 6.  Heterogeneous impacts of mobile cash transfers on SIM sharing (Panel
A) and device sharing (Panel B) by gender (left) and age group (right) in
January 2020. For each specification we control for the number of mobile
transactions associated with the SIM or device in January 2020. Statistical
significance of regression coefficients are shown with stars: * $\rightarrow$
p ¡ 0.05, ** $\rightarrow$ p ¡ 0.01, *** $\rightarrow$ p ¡ 0.001.

## Supplementary Tables

| Treatment | Control | Difference
---|---|---|---
% Women | 0.50 (0.50) | 0.51 (0.50) | -0.01
Age | 38.01 (13.49) | 37.91 (13.53) | 0.10
Shared SIM | 0.29 (0.46) | 0.30 (0.46) | -0.01
Shared device | 0.09 (0.29) | 0.10 (0.30) | 0.00
Number of mobile transactions | 45.47 (70.02) | 45.93 (71.32) | -0.46
N | 27,673 | 21,410 | 49,083

Table S1. Pre-program differences between treatment and control groups for the
GiveDirectly-Novissi program, using Novissi registration data along with
mobile phone data from October 2020. Statistical significance of the
difference in means is determined with a t-test, and significance is shown
with stars: * $\rightarrow$ p ¡ 0.05, ** $\rightarrow$ p ¡ 0.01, ***
$\rightarrow$ p ¡ 0.001 (no differences are significant).

.

| Matched to Administrative Data | Enrolled in RCT
---|---|---
Panel A: 1:N Device Matching
Mean | 4.24 (63.19) | 3.92 (60.67)
Median | 1.00 | 1.00
Mode | 1.00 | 1.00
% of devices shared | 11.98% | 9.56%
N | 105,443 | 35,500
Panel B: 1:1 Device Matching
Mean | 1.33 (13.80) | 1.22 (8.28)
Median | 1.00 | 1.00
Mode | 1.00 | 1.00
% of devices shared | 11.24% | 8.92%
N | 101,475 | 34,332
Panel C: Uniquely Matched Devices Only
Mean | 1.15 (1.88) | 1.11 (0.84)
Median | 1.00 | 1.00
Mode | 1.00 | 1.00
% of devices shared | 10.76% | 8.54%
N | 98,152 | 33,281

Table S2. Sensitivity of the summary statistics on device sharing in October
2020 in Table 2 to the strategy for matching devices to administrative data.
Panel A reproduces the results shown in Panel B of Table 2, using the 1:N
matching strategy that we use throughout the main paper. Panel B reproduces
the same statistics, using a 1:1 matching strategy where for devices that
match to more than one phone number (and therefore more than one registration
entry), one phone number is selected at random for inclusion in the dataset.
Panel C again reproduces the same statistics, this time dropping any
registrations associated with devices that match more than one phone number

.

| Gender | Age | Geography | Joint
---|---|---|---|---
| Basic | Control | Basic | Control | Basic | Control |
Panel A: 1:N Device Matching |  |  |  |  |  |  |
Constant | 0.1104*** | 0.1076*** | 0.1380*** | 0.1356*** | 0.0893*** | 0.0856*** | 0.0924***
Number of mobile transactions |  | 0.0000*** |  | 0.0000*** |  | 0.0000*** | 0.0000***
Female | 0.0182*** | 0.0184*** |  |  |  |  | 0.0200***
Age: 30-40 |  |  | -0.0383*** | -0.0386*** |  |  | -0.0396***
Age: 40-50 |  |  | -0.0284*** | -0.0291*** |  |  | -0.0307***
Age: 50+ |  |  | -0.0196*** | -0.0199*** |  |  | -0.0216***
Rural |  |  |  |  | 0.0316*** | 0.0316*** | -0.0347***
$R^{2}$ | 0.0008 | 0.01769 | 0.0024 | 0.0185 | 0.0003 | 0.0164 | 0.0199
N | 105,443 | 105,443 | 105,443 | 105,443 | 105,443 | 105,443 | 105,443
Panel B: 1:1 Device Matching
Constant | 0.1037*** | 0.1020*** | 0.1308*** | 0.1293*** | 0.0864*** | 0.0839*** | 0.0910***
Number of mobile transactions |  | 0.0000*** |  | 0.0000*** |  | 0.0000*** | 0.0000***
Female | 0.0170*** | 0.0175*** |  |  |  |  | 0.0191***
Age: 30-40 |  |  | -0.0379*** | -0.0379*** |  |  | -0.0389***
Age: 40-50 |  |  | -0.0294*** | -0.0292*** |  |  | -0.0307***
Age: 50+ |  |  | -0.0200*** | -0.197*** |  |  | -0.0212***
Rural |  |  |  |  | 0.0269*** | 0.0280*** | -0.0301***
$R^{2}$ | 0.0007 | 0.0029 | 0.0025 | 0.0047 | 0.0002 | 0.0024 | 0.0059
N | 101,478 | 101,475 | 101,475 | 101,475 | 101,475 | 101,475 | 101,475
Panel C: Uniquely Matched Devices Only
Constant | 0.0992*** | 0.0918*** | 0.1261*** | 0.1196*** | 0.0861*** | 0.0737*** | 0.0796***
Number of mobile transactions |  | 0.0001*** |  | 0.0001*** |  | 0.0001*** | 0.0001***
Female | 0.0162*** | 0.0187*** |  |  |  |  | 0.0202***
Age: 30-40 |  |  | -0.0377*** | -0.0369*** |  |  | -0.0379***
Age: 40-50 |  |  | -0.0296*** | -0.0284*** |  |  | -0.0298***
Age: 50+ |  |  | -0.0209*** | -0.0194*** |  |  | -0.0209***
Rural |  |  |  |  | 0.0222*** | 0.0287*** | 0.0311***
$R^{2}$ | 0.0007 | 0.0036 | 0.0026 | 0.0051 | 0.0003 | 0.0029 | 0.0065
N | 98,152 | 98,152 | 98,152 | 98,152 | 98,152 | 98,152 | 98,152
Table S3. Sensitivity of the results on heterogeneity in patterns of device sharing by gender (left), age(middle), and geography (right) in Table 3 to the strategy for matching devices to administrative data. Panel A reproduces the results shown in Panel B of Table 3, using the 1:N matching strategy that we use throughout the main paper. Panel B and Panel C reproduce the same regression results, using the 1:1 matching strategy and restricting to only uniquely matched devices, respectively. | Basic | Transactions Control
---|---|---
Panel A: 1:N Device Matching |  |
Constant | 0.0794*** | 0.0763***
Treatment (received cash transfer) | 0.0087** | 0.0094**
Number of mobile transactions |  | 0.0000***
$R^{2}$ | 0.0002 | 0.0244
N | 30,483 | 30,483
Panel B: 1:1 Device Matching
Constant | 0.0726*** | 0.0693***
Treatment (received cash transfer) | 0.0081** | 0.0086**
Number of mobile transactions |  | 0.0001***
$R^{2}$ | 0.0002 | 0.0040
N | 29,472 | 29,472
Panel C: Uniquely Matched Devices Only
Constant | 0.0685*** | 0.0607***
Treatment (received cash transfer) | 0.0072* | 0.0074*
Number of mobile transactions |  | 0.0002***
$R^{2}$ | 0.0002 | 0.0036
N | 28,615 | 28,615
Table S4. Sensitivity of the results on the impacts of cash transfers on
device sharing in Columns 1 and 2 of Table 4 to the strategy for matching
devices to administrative data. Panel A reproduces the results shown in Panel
B of Table 4, using the 1:N matching strategy that we use throughout the main
paper. Panel B and Panel C reproduce the same regression results, using the
1:1 matching strategy and restricting to only uniquely matched devices,
respectively.