Payment Mechanisms and Anti-Poverty Programs:
Evidence from a Mobile Money Cash Transfer Experiment in Niger
Jenny C. Aker, Rachid Boumnijel, Amanda McClelland and Niall Tierney*

November 2016
Abstract: Cash transfers have become an increasingly important component of social protection
policies in both developed and developing countries. While such programs are often
implemented electronically in developed countries, in many developing countries with weakfinancial infrastructure, such transfers are distributed manually, resulting in significant costs toprogram recipients and the public sector alike. The introduction of mobile money systems inmany developing countries offers new opportunities for distributing cash transfers. Using datafrom a randomized experiment of a mobile money cash transfer program in Niger, we findevidence of benefits of this new system: Household diet diversity was 9-16 percent higher
among households who received mobile transfers and children ate 1/3 more of a meal per day.
These results can be partially attributed to time savings associated with mobile transfers, as
program recipients spent less time traveling to and waiting for their transfer. They are also
associated with shifts in intra-household bargaining power for women. These results suggest thatelectronic transfers may address key logistical challenges in implementing cash transfer
programs in developing countries, but that sufficient investment in the payments infrastructure isneeded.
Keywords: Unconditional cash transfers, mobile money, electronic transfers, Africa
JEL codes: O1, H84, I38, O33*

Jenny C. Aker, The Fletcher School, Tufts University and International Food Policy Research Institute (IFPRI),
160 Packard Avenue, Medford, MA 02155; Jenny.Aker@tufts.edu. Rachid Boumnijel, Relief International,
Rachid.Boumnijel@ri.org. Amanda McClelland, International Federation of the Red Cross and Red CrescentSocieties, amanda.mcclelland@ifrc.org, Niall Tierney, Deputy Head of Development, Irish Aid Ethiopia
niall.tierney@dfa.ie. We thank Concern Worldwide in Niger for their support in all stages of this project and would
especially like to thank Adamou Hamadou and the data collection team in Niger. We greatly appreciate commentsfrom Randall Akee, Steven Block, Simone di Castri, Pascaline Dupas, Frederico Finan, Alain de Janvry, Kelsey
Jack, Robert Jensen, Charles Kenny, Stephan Klasen, Alan Gelb, Jessica Goldberg, Christopher Ksoll, Jake Kendall,
Jeremy Magruder, Craig McIntosh, Pau, Julie Schaffner, Gabrielle Smith, Tavneet Suri, Frank-Borge Wietzke andparticipants at seminars at the Center for Global Development, UC-Berkeley, MWIDC, UC-Davis, UC-Santa Cruz,
UC-Irvine, University of Southern California and the University of Gottingen. We are grateful for financial support
from the European Commission, Irish Aid, the Hitachi Center and Tufts University. All errors are our own.

.

Conditional and unconditional cash transfers have become an increasingly common
component of social protection policies in both developed and developing countries (World Bank
2009, DFID 2011). As of 2013, 119 developing countries had some type of cash transfer
program in place (Gentilini et al 2014). In higher and middle-income countries, such programs
are often implemented electronically, either via bank transfers or pre-paid debit cards. Yet indeveloping countries with limited financial infrastructure, cash transfer programs often requirephysically distributing cash in small denominations to remote rural areas. This can result in
substantial costs for both the implementing agency and program recipients, thereby affecting thepotential effectiveness of cash transfers as compared with other anti-poverty programs andresulting in hidden costs to program recipients.
The introduction of mobile phone-based money transfer systems (m-transfers or mobile
money) in many developing countries offers an alternative infrastructure for delivering suchtransfers. By transferring money via the mobile phone, mobile money could potentially reducethe costs and leakage associated with social protection programs.1 In addition, m-transfer
systems may prove easier for transfer recipients to collect their transfers, provided that they haveeasy access to m-transfer agents (Blumenstock, Callen and Ghani 2015). Beyond their costsaving potential, m-transfer systems may have broader implications for economic developmentby increasing access to informal private transfers (Blumenstock, Eagle and Fafchamps 2016,
Jack and Suri 2014) or serving as an alternative savings device (Mas and Mayer 2012, Mbiti and
Weil 2011).
This potential “win-win” scenario, in which the public sector could lower the costs ofimplementing anti-poverty programs and the poor could receive other benefits, is attractive forpolicymakers, donors and implementing agencies alike (Banerjee et al 2013). In 2012, the“Better than Cash” Alliance was formed, advocating for governmental and non-governmentalorganizations to move to digital payments for payroll, government benefits and humanitarian aid,
citing cost savings, transparency and financial inclusion as potential benefits.2 This has been

1

Muralidharan et al (2016) evaluate the impact of distributing cash transfer programs via biometric Smartcards andfind that the electronic payment infrastructure reduced leakage.
2
http://betterthancash.org/about/. The “Better than Cash” Alliance seeks to “empower people through electronic
payments.”1

echoed by a number of other organizations, such as the Bill and Melinda Gates Foundation,
which has made digital payments a key aspect of its financial inclusion strategy.
What is surprising about the calls for a shift to electronic payments is the scarcity ofrigorous empirical evidence to support these claims. A priori, it is not clear that electronictransfers will unambiguously improve welfare. For example, much of the proposed cost savings
associated with electronic transfers depends upon the existence of a well-functioning electronic
distribution system, such as a mobile agent network that allows recipients to “cash out”. Whileapproximately 271 m-money platforms have been deployed in 93 countries worldwide, adoption
has remained low in most countries (GSMA 2015, Evans and Pirchio 2015).3 In the absence ofphysical access points, using m-money to distribute cash transfers might actually increase costs
for recipients who cannot access m-transfer agents or use the technology (MacAuslan 2010).4
Furthermore, electronic transfers could increase the likelihood of leakage if m-transfer agentscan more easily extract the transfer from program recipients than public sector agents.
Using a randomized experiment, we examine the effects of using mobile money indelivering a cash transfer program in Niger. In response to a devastating drought, targetedhouseholds in 96 villages received a monthly unconditional cash transfer, with women as theprimary beneficiary. The first delivery channel provided the cash transfer manually, whereby
cash was distributed in individual envelopes (the standard mechanism). The second delivery
channel provided the cash transfer electronically, whereby program recipients received thetransfer via the m-transfer system, as well as a m-transfer-enabled mobile phone. The third
delivery mechanism was the same as the manual cash mechanism, but households also received a
m-transfer-enabled mobile phone. As we were unable to collect data from a pure comparisongroup, our analysis focuses on the relative costs and benefits of different transfer mechanisms.
Overall, our results provide evidence that the m-transfer system had benefits:
Households in the m-transfer group used their cash transfer to buy more diverse types of goods
3Outside of Kenya, the most notable m-money success story, m-money adoption has remained low in other Africancountries, estimated at 10 percent of mobile phone users (GSMA 2015). This hides substantial geographic variation,with m-money adoption ranging from 19% in West Africa to 55% in East Africa (GSMA 2015). Nevertheless, not
all registered m-money accounts are considered to be active.
4
While the number of registered m-money agents has grown substantially over the past few years, maintaining active
agents – namely, those who can cash in and cash out - has remained a challenge for a number of providers (GSMA2015).

2and were more likely to purchase protein and energy-rich foods. These diverse uses of thetransfer also resulted in a 9-16 percent improvement in diet diversity, primarily due to increased
consumption of beans and fats, and children consumed 1/3 more of a meal per day. We do notfind evidence that m-transfer households reduced their ownership of other durable and nondurable goods, suggesting that other household members were not decreasing their contribution
to household goods as a result of the transfer.
These results can be partially explained by less time spent by m-transfer program
recipients in obtaining their transfer, as well as increased bargaining power for women. Mtransfer program recipients traveled shorter distances to obtain their transfer as compared with
their manual cash counterparts.5 While the magnitude of average time savings was relatively
small – approximately 2.5 days over a five-month period – we believe that this is a conservativelower bound on actual time savings. In addition, this savings occurred during a period of theyear when opportunity costs were high, implying that the time savings could have enabled mtransfer program recipients to engage in other productive activities or invest more time in childcare. While we do not have data on the latter channel, we have some evidence in support of theformer claim: M-transfer households were more likely to cultivate marginal cash crops that areprimarily grown by women.6
In addition to the time savings, we provide suggestive evidence that the m-transfer
mechanism affected intra-household decision-making. Program recipients, all of whom were
women, reported that the m-transfer was less observable to other household members, thereby
allowing them to temporarily conceal the arrival of the transfer. We find that m-transfer program
recipients were more likely to travel to weekly markets and spend more on children’s clothing
than those in the other treatment arms. In addition, the improved diet diversity results werestronger six months after the program, well after the cash transfer had been spent. The results,

5This result would not be surprising in a context such as Kenya, with over 50,000 mobile money agents,
approximately 10 percent of the total number of agents in sub-Saharan Africa. In West Africa, there are fewer than
45 mobile money agents per 1,000 people in countries such as Senegal and Ivory Coast, two countries with the mostwell developed m-money platforms (Lonie et al 2015). This is less than the number of Western Union or
MoneyGram points of sale in such countries, which are in direct competition with mobile money.
6Unlike Muralidharan et al (2016), we do not find evidence that the m-transfer mechanism had any impacts onleakage.

3

taken together, suggest that the m-transfer technology might have shifted women’s bargaining
power within the household.
Our paper makes two substantive contributions. First, while there has been substantial
literature on the costs of transfer programs (Caldes, Coady and Maluccio 2004, Handa and Davis
2006), there is scant literature on the relative benefits and costs of electronic versus manualtransfers. More recently, Muralidharan et al (2016) have found that electronic transfers
combined with biometric identification resulted in significant cost reductions for beneficiaries,
whereas Blumenstock, Callen, and Ghani (2015) found that mobile salary payments significantly
reduced firms’ costs in areas where adequate mobile network and agent coverage existed.
Second, our experiment adds to a strand of literature on the impact of m-transfer systems on
household welfare (Jack and Suri 2014, Blumenstock, Eagle and Fafchamps 2016). That
literature has primarily focused on the use of m-money for private transfers. Yet both of thesestrands of literature are unable to disentangle the impact of the technology from the transfer
mechanism. In contrast, our experiment exogenously varies access to both the mobile phonehandset and the m-transfer technology, thereby allowing us identify different behavioral
responses to each.
Combined with these studies, our results suggest that the use of technology for antipoverty programs can help to address key logistical challenges in implementing such programs.
In our context, the m-transfer intervention also improved program performance: It greatly
reduced program recipients’ costs as compared with the manual cash transfer, and variable costs
were 20 percent lower. Yet this requires the existence of an active m-transfer agent
infrastructure, which remains a challenge in some of the poorest countries in the word, wheresuch systems could have the greatest potential impact in reducing transaction costs (GSMA2015). In addition, it is not clear that such systems will improve households’ financial inclusion
or generate longer-term benefits, as its proponents suggest. Compared to the manual cashtransfer mechanisms, the initial costs of the m-transfer delivery system were higher, primarily
due to the costs of mobile phones.
The rest of the paper proceeds as follows. Section I describes the context and theexperimental design. Section II describes the different datasets and estimation strategy. We

4discuss the results in Section III before discussing mechanisms (Section IV) and alternative
explanations (Section V). Section VI concludes.
I.

Setting and Research Design
Niger, a landlocked country located in West Africa, is one of the poorest countries in theworld. With a per capita GNP of US$360 and an estimated 85 percent of the population livingon less than US$2 per day, Niger is one of the lowest-ranked countries on the United Nations’
Human Development Index (UNDP 2011). Rainfall ranges from 200 millimeters (mm) per year
in the northern regions to 800 mm in the south and is subject to high intra- and inter-annual
variability (Nicholson, Some and Kone, 2000). For example, Niger experienced six droughts
between 1980 and 2005 (Government of Niger 2007). In 2009/2010, the time period of thisstudy, Niger experienced both drought and harvest failures, with 2.7 million people classified asvulnerable to extreme food insecurity (FEWS NET 2010).
The first m-transfer system in Niger was introduced in January 2010. Known as “Zap”,
the product was developed by the primary mobile phone service provider (Zain, now Bhartia
Airtel). Like most m-transfer systems, Zap allowed users to store value in an account accessibleby the handset, convert cash in and out of the account and make transfers by using a set of text
messages, menu commands and personal identification numbers (PINs) (Aker and Mbiti 2010).
While mobile phone coverage has grown substantially in Niger over the past decade, initialcoverage, usage and growth of Zap was limited and geographically focused in the capital city
(Niamey) and regional capitals.7 The cost of making a $USD45 transfer using Zap cost USD$3
during this period.8
A. Cash Transfer Delivery Mechanisms
In response to the 2009/2010 drought and food crisis in Niger, an international nongovernmental organization, Concern Worldwide, designed a short-term social protection
program. The program sought to prevent increases in malnutrition and asset depletion by

7

Since the introduction of m-money into Niger in 2010, there have been three m-money deployments. Nevertheless,m-money adoption in Niger is still estimated at 5 percent.
8
In 2010, it cost $1.50 to make a transfer between $20-$40, whereas a transfer greater than $USD 40 cost $3.

5

providing unconditional cash transfers to approximately 10,000 drought-affected households
during the “hungry season”, the five-month period before the harvest.
The first experimental treatment was the manual cash intervention (Cash), whereby
households received an unconditional cash transfer of CFA 22,000 per month (approximately$US45) over a five-month period.9 The total value of the transfer was slightly less than 2/3 ofthe total annual GDP per capita, larger than cash transfer programs in Latin America and subSaharan Africa (Handa and Davis 2006, Garcia and Moore 2012).10 Payments were made on amonthly basis, with cash counted into individual envelopes and transported via armored vehicles
to distribution centers.11 As is common in these types of programs in Niger, one village waschosen as a distribution point for a group of villages, although Concern tried to ensure that the
cash distribution points were as close as possible to each village (Niang et al 2012, Hoddinott et
al 2014). Program recipients were informed of the date and location of their cash transfer via aphone call, primarily by contacting a point person within the village the day before or themorning of the transfer, and had to travel to their designated location on that given day to receive
the cash transfer. The manual cash transfer system was similar to that of other anti-poverty
programs in Niger at the time.
The two additional interventions were variants of the basic intervention, one of which
was aimed at reducing the costs of distributing cash to remote, sparsely-populated and in somecases insecure rural areas. In the second experimental treatment (Zap), program recipients
received their cash transfer via the mobile phone. On the day of the cash transfer, Zap program
recipients would receive a message with a special “beep” on their mobile phone, informing themthat the transfer had arrived. After receiving this notification, program recipients had to take themobile phone to an m-transfer agent located in their village or a nearby village to obtain their
9

The value of the transfer varied monthly, with three initial transfers of CFA 20,000 ($US 40) and two final transfersof CFA 25,000 ($US 50).
10
Conditional cash transfer programs in Latin America range from 8-25 percent of average per capita annual income(Handa and Davis 2006). The size of unconditional cash transfer programs in sub-Saharan Africa varies
considerably, ranging from US$8 per month in Mali to US$37 and US$42 per month in Kenya and Rwanda,
respectively (Garcia and Moore 2012). These represent between 20-40 percent of per capita income in thosecountries. The program was the same magnitude as the government’s safety net program, which provided
CFA10,000 per month over 12 months, although the timing was concentrated during the hungry season.
11Despite the fact that Niger is one of the largest countries in Africa, the total road network was estimated to be
15,000 km as of 2005, of which only 8 percent was paved. There is less than 1 bank for every 100,000 people,
making it one of the most “unbanked” countries in sub-Saharan Africa (Demirguc-Kunt and Klapper 2012).

6

transfer.12 The m-transfer agent would then remove the value of the cash transfer and “cash out”,
paying the value of the cash transfer to the program recipient. As less than 30 percent ofhouseholds in the region owned mobile phones prior to the program, Concern also provided
program recipients with mobile phones equipped with a m-money account and training on how
to use the technology. In addition, as Zap was introduced into Niger a few months’ prior to theintervention, there were a limited number of Zap agents in rural areas. Concern therefore
encouraged the mobile phone operator to register m-money agents within the program area, but
did not have any control over the location or density of those agents. The second intervention
thereby differed from the Cash intervention with respect to the transfer delivery mechanism, as
well as the provision of the handset and the m-transfer technology.
In an effort to disentangle the impact of electronic delivery mechanism from that of themobile phone, we also implemented a third experimental treatment (Mobile). The Mobileintervention mirrored the manual cash intervention, but program recipients also received amobile money-enabled mobile phone and training on how to use it.
As these treatments differ in the type of delivery mechanism and technology provided(m-transfer or a mobile phone), comparing outcomes across the different treatments will allow us
to estimate the additional costs and benefits of using the m-transfer technology in the context of asocial protection program. In particular, comparing outcomes between the Mobile and Cashgroups allows us to measure the additional effect of mobile phone ownership, whereas
comparing outcomes between the Zap and Mobile interventions allows us to estimate theadditional effect of the m-transfer delivery mechanism. 13
Due to the humanitarian nature of the intervention and the political situation at the time,
we were unable to collect data from a pure comparison group. Hence, while we can estimate thecausal effect of the m-transfer mechanism as compared with the manual cash mechanism, wecannot estimate the causal impact of the cash transfer program.
12

While the transfer fee and the first withdrawal fee were paid by Concern, program recipients would have had to
pay the “cash out” (withdrawal) fee for any additional withdrawals. This would have cost approximately $US .25
for each withdrawal.
13
Concern also implemented a seed distribution program in approximately one-third of targeted villages, whereby
recipient households could replace two of their cash transfer payments with the equivalent value in seeds (alsoprovided by Concern). There is not a statistically significant difference in the presence of a seed distribution
program across treatments (Table 1).

7

B. Experimental Design
Prior to the intervention, Concern Worldwide identified 116 intervention villages in one
region of Niger. Eligible villages were those classified by the Government of Niger as having
produced less than 50 percent of their consumption needs during the 2009 harvest.14 Of these,some villages were prioritized for either the Cash or Zap intervention based upon their lack ofmobile phone coverage (Cash) and proximity to the Niger-Mali border (Zap), thereby reducing
our sample size to 96 eligible villages. Among these villages, we first stratified byadministrative division (commune) and then randomly assigned villages to the Cash, Mobile or
Zap interventions. In all, 32 villages were assigned to the Cash group, 32 to the Mobile groupand 32 to the Zap group.15
Prior to program assignment, eligible households within each village were identified by avillage-level vulnerability exercise. Using indicators such as livestock ownership, landholdings
and the number of household members, households were classified into four vulnerability
categories (A, B, C and D), with C and D as the most vulnerable categories. Households fromthe C and D categories were selected for the program. The number of recipient households pervillage ranged from 12 to 90 percent of the village population, covering an average of 45 percentof the population. In all treatments, the cash transfer (as well as the mobile phone and training in
the Zap and Mobile treatments) was provided to the woman within the household.16 The study
timeline is presented in Figure 1.
C. Why should m-transfers matter?
We expect that the m-transfer delivery mechanism might affect household outcomesthrough different channels. First, if the m-transfer mechanism reduces program recipients’ costs
14

To calculate a food “deficit”, the Government of Niger estimated village-level millet production and compared thiswith estimated consumption “needs”, defined as 190 kg of millet/capita/year. A village that produced less than 50
percent of its estimated consumption needs was considered to be food deficit, and was therefore eligible forassistance in 2009/2010.
15
The average distance between villages of different treatments was 48 km, with a minimum distance of 3 km.
16Concern Worldwide only distributed the cash transfer in the Cash and Mobile treatments to the female program
recipient (after presenting the beneficiary ID card), unless the program recipient had a disability. The requirement
was the same for the Zap treatment group: m-money agents were instructed to only “cash out” to the intended
program recipient, upon presentation of the mobile phone, PIN number and beneficiary ID card. If there were
multiple wives within a household (28% of households), the transfer was provided to the first wife.

8

involved in obtaining the transfer or their uncertainty with respect to these costs, then this could
reduce program recipients’ opportunity costs during a time of year when such costs wererelatively higher. Alternatively, if the new technology makes it more difficult for program
recipients to access their cash – either due to the limited number of m-transfer agents, difficultyin using the technology or charging the phone – this could increase costs for the Zap households
to obtain the cash transfer program. Furthermore, while we might expect such transfers to
reduce leakage (Muralidharan et al 2016), electronic transfers could increase the likelihood ofleakage if m-transfer agents can more easily extract the transfer from program recipients.
Second, the m-transfer system could simply change the way in which households spend
the cash transfer. For example, if Zap program recipients obtain their cash from an agent andkiosk-owner within the village, program recipients might be exposed to different products or
prices at the kiosk. The m-transfer technology could also encourage program recipients to store
some of the transfer on their phone, thus increasing the mental costs associated with unplanned
expenditures (Dupas and Robinson 2013).
Third, access to the mobile phone technology could increase households’ access to
information, thereby allowing them to improve their decision-making with respect to agriculture,
migration and consumption. Since program recipients in both the Zap and Mobile treatments
received mobile phones, this should only be a potential channel if Zap households used theirhandsets in different ways, or if women had greater control of the phone. In particular, having
access to the m-transfer technology on the mobile phone could have provided households with analternative means of receiving informal private transfers, thereby helping households to better
cope with risks and shocks (Blumenstock, Eagle and Fafchamps 2015, Jack and Suri 2014).
Finally, since m-transfers reduce the observability of the amount and timing of the cashtransfer, this could affect women’s bargaining power within the household, thus changing theintra-household allocation of resources (Duflo and Udry 2004, Doss 2006, Doepke and Tertilt
2014, Ashraf 2009, Ashraf et al 2014, Morawczynski and Pickens 2009.).17 Reducing the

17Ashraf et al (2014) provide a voucher for concealable contraceptives either to women alone or jointly with theirhusbands, and find that women who were privately provided a voucher were more likely to use the contraceptives
and have fewer births.

9

transfer’s observability could also affect inter-household sharing, thereby leaving more income
available for the household (Jakiela and Ozier 2016).
II.

Data and Estimation Strategy

A. DataThis paper uses four primary datasets. The first dataset is a household survey of 1,152
program recipients in 96 intervention villages across three rounds. The primary respondent for
the household surveys was the program recipient for participating households. The baseline
survey was conducted in May 2010, with follow-up surveys in December 2010 and May 2011.
The research team located approximately 93 percent of respondents for the follow-up surveys,
and attrition was not differential across the experimental arms either in December 2010 or May
2011 (Table A1; Tables A1-A7 available online). The main sample in this paper therefore
consists of those households who were located during the follow-up surveys: 1,082
respondents.18
The household survey included modules on household demographics, food security
agricultural production and sales, mobile phone usage, asset ownership and shocks. For thefollow-up surveys, we also included modules on the uses of the cash transfer. As the surveys
were conducted during a food crisis and over a short time frame, we were mindful of the timeburden on respondents. As a result, the household surveys did not include a full income andexpenditure module, so we are unable to measure the impact of the program on total household
expenditures. Rather, we collected data on proxies for well-being, such as asset accumulation(as a wealth proxy) and food security measures.
The second dataset is a village-level survey, collected during the same periods as thehousehold-level surveys. The village surveys collected information from a focus group of male18

The number of observations in each table is not always equal to 1,082. First, if the survey team was not able tofind the respondent but found another household member, the team interviewed the other household member, asking
household-specific (not respondent-specific) questions. Second, sections that are related to the uses of the transfer(e.g., Tables 3, 5 and 6) are conditional on the household having received a transfer, which is approximately 1,047
households. Finally, there are some missing observations for specific variables, and some tables include several
rounds of data. Thus, the number of observations in the table is the maximum number of observations for a set of
dependent variables.

10and female village residents on topics such as mobile phone coverage, access to markets and thenumber of Zap agents.
The third dataset includes weekly price information for six products in forty-five markets
between May 2010 and January 2011, as well as the date of each cash transfer in the village. We
use these data to test for differential effects of the cash transfer delivery mechanism on local
market prices and supply.
The final dataset is anthropometric data among children under five, collected in May
2011. These data were collected from program recipient households from a randomly chosen
subset of intervention villages, for a total sample of 30 villages and 691 households.
B. Pre-Program Balance of Program Recipients
Tables 1 and 2 present the baseline characteristics for the sample. 19 Column 1 presents
the sample mean and standard deviation for a series of characteristics. To test for balance acrossgroups, Columns 2–3 present the coefficient estimates and standard errors of the difference
between the baseline mean in the Zap or Mobile treatments and the mean in the Cash treatment.
Since randomization was done at the village level, the standard errors are clustered at the villagelevel. In addition, controls for the presence of the seed program and stratification fixed effects
are included.
A few characteristics are worth mentioning. The average household size was nine and amajority of respondents were members of the Hausa ethnic group (Table 1). 28 percent ofhouseholds were in polygamous marriages. Only 29 percent of households owned a mobile
phone prior to the start of the program, yet 61 percent of respondents had used a mobile phone in
the few months prior to the baseline. 98 percent of households had experienced drought in thepast year. 35 percent of villages had a weekly market, and 26 percent had a seed distribution
program (Panel E).
Turning to key outcome variables (Table 2), household diet diversity was 3 (out of 12
food categories), and households reported having sufficient food for approximately 2 out of the19

Due to an administrative error, the survey team only conducted the baseline survey in 93 villages (rather than 96).
Thus, the total number of baseline observations should be 1,106, rather than 1,152; however, there are missing
baseline observations for some variables.

11

past 6 months. This is unsurprising, as 97 percent of households relied upon agriculture as a
primary income source and nearly all of them had been affected by drought.
Looking at the differences across treatments, the randomization appears to have beensuccessful in creating comparable groups along observable dimensions. Differences in preprogram household characteristics and outcomes are small and generally not statisticallysignificant. Zap program recipients were older, less likely to be from the Hausa ethnic group andmore likely to raise livestock as compared with the Mobile group (Panels A-D).20 Overall, there
are 30 dependent variables in Tables 1 and 2. Of these, only two coefficients were statisticallysignificant at the 10 percent level and 1 coefficient at the 5 percent level, comparable with what
we would expect with random assignment.
C. Estimation Strategy
To estimate the impact of different cash transfer delivery mechanisms on a variety ofoutcomes, we use a simple reduced form regression specification of the following form:(1)

Yiv = b0 + b1Zapv + b2Mobilev + X’iv0g +seedv + qC + eiv

The variable Yiv represents the outcome of interest (costs, uses of the cash transfer, food security
and assets) of individual or household i in village v after the transfer. Zapv is an indicatorvariable for whether the village was assigned to the m-transfer program, whereas Mobilev is anindicator variable for whether the village was assigned to the Mobile group. qC are geographic
fixed effects at the commune level, the level of stratification. As a robustness check, we also
include a vector of baseline covariates that differed at baseline, X’iv0, such as age. 21 Finally, we
control for the presence of a seed distribution program at the village level. The error term
consists of eiv, which captures unobserved individual or household characteristics oridiosyncratic shocks. We cluster the error term at the village level to account for the program
design, and also correct for heteroskedasticity. The coefficients of interest are b1 and b2, theintent-to-treat (ITT) effect of the different transfer mechanisms (as compared with the basic cash
20

In this context, the differences in ethnicity and livestock-raising are correlated, as the Fulani and Touareg ethnic
groups are primarily pastoralists.
21
The results presented in the tables do not include baseline covariates. Results are largely robust to including
covariates that differed at baseline (age, livestock-raising and growing cowpea), although the individual coefficientsand levels of statistical significance may vary slightly.

12intervention) on the outcome of interest, under the assumption that they are conditionallyorthogonal to eiv. Most regression specifications presented in this paper use the December 2010
household data, immediately after the transfer. When household data are available for both
December 2010 and May 2011, we pool the data and include a linear time trend. We also
conduct separate analyses by time period, which allows us to measure the immediate and longerterm effects of the program.
Equation (1) is our preferred specification for most outcomes, as some of the data were
not collected during the baseline. For those outcomes for which baseline data are available, we
also use an Analysis of Covariance (ANCOVA) specification, which controls for baseline valueof the outcome variable. In cases where an outcome variable has high variability and low
autocorrelation, as is the case with our food security measures, the ANCOVA model is preferredover difference-in-differences (McKenzie 2012).
III.

Results

A. Uses of the Transfer
As the cash transfer was unconditional, program recipients were free to spend the cashtransfer how they wished. Overall, households in the manual cash villages used their transfer topurchase 4.32 different categories of goods and services, including staple grains (99%), oil(68%), condiments (68%), cowpeas (42%), meat (39%), health expenses (30%), seeds (18%),
school fees (7%), debt reimbursement (6%), clothing (4%) and hiring labor (1%). (Respondents
could list more than one use of the cash transfer, so the total can exceed 100%.) Consistent withother studies on cash transfers, fewer than 1% of households used the cash transfer to buy“temptation goods”, defined in this context as sweets (such as doughnuts and cookies) and tea(Evans and Popova 2014). Thus, program recipients primarily used the transfer to ensure
immediate consumption needs, as well as make limited agricultural investments and avoid asset
depletion. These purchasing patterns, especially purchases of bulk grain, are similar to thosefound in Hoddinott et al (2014) in the context of a cash transfer program Niger.
As we do not have a full expenditure module, we are unable to show the impact of thetransfer on total expenditures or the quantities demanded. Nevertheless, we do have data on theuses of the transfer. While this constrains our analysis, these outcomes can provide important
insights, as the transfer represented a significant income shock to recipient households. In

13addition, since households only had two income sources prior to the program and did not receive
other public aid, it is reasonable to assume that households’ marginal propensity to consume washigh, and that the uses of the transfer would approximate overall expenditures during this period.
Table 3 shows the different uses of the cash transfer by treatment group, using data from
December 2010. Overall, the results paint a picture of more diverse uses of the cash transfer by
Zap households, primarily for food items. Households in Zap villages purchased .78 more typesof food and non-food items as compared with the Cash group, and .85 more types of items ascompared with the Mobile group, with a statistically significant difference between each pair.
While the likelihood of purchasing staple grains did not differ by the transfer mechanism,
program participants in the Zap group were 18-20 percentage points more likely to purchase nonstaple grains (rice and corn), 9-10 percentage points more likely to purchase cowpea and 11-18
percentage points more likely to purchase condiments, oil and meat as compared with those in
the Cash and Mobile groups (Panel A). Most of these differences are statistically significant at
the 1 percent level. Yet there are no statistically significant differences in the uses of the cashtransfer with respect to school fees, health expenses and clothing (Panel B).22 These patterns are
similar when restricting the analysis to the last transfer (Table A2).23
B. Food Security and Nutritional Status
While the results in Table 3 suggest that Zap households used the cash transfer
differently, especially with respect to food items, without a full expenditure module, this would
not necessarily indicate a net welfare improvement. In particular, Zap program recipients’
spouses could have contributed fewer public or private goods to the household as a result of the
cash transfer, potentially distorting the uses of the transfer or consumption. While we are unable
to assess the impact of the program on household expenditures, Table 4 estimates the impact of22

A potential concern with self-reported measures is that program recipients could simply list the first (or largest)
expenditures made after receiving the transfer, which could differ by treatment groups. Thus, we might see atreatment effect on measured expenditures rather than actual expenditures. This concern is alleviated by the way inwhich the question was measured; after program recipients cited specific categories, enumerators were instructed togo through a comprehensive list of all potential categories and ask the recipient if they spent the cash transfer on thatparticular category.
23
Measuring the impact of the different treatments on the intensive margin of food expenditures reveals that Zap
households spent slightly more on other grains, cowpea and oil and slightly less on staple grains as compared with
Mobile and Cash households. However, these results are not statistically significant, and the magnitudes are
relatively small (between $.10 and $1.00).

14

the different treatments on proxy measures of well-being, namely, household food security andnutritional status.24
Table 4 (Panel A) shows the estimates of the effect of different transfer delivery mechanisms
on household food security, using pooled data from December 2010 and May 2011. Zap
households had diet diversity scores that were .28-.51 points higher than the Cash and Mobile
households, with a statistically significant difference at the 10 and 1 percent levels,
respectively.25 Focusing on particular food groups, Zap households were 6 percentage points
more likely to consume beans, 9-11 percentage points more likely to consume fats and 3
percentage points more likely to consume fruits than Cash and Mobile households. All of thesedifferences are statistically significant at the 1 or 5 percent levels. These results are also largely
robust to using an ANCOVA specification and the December 2010 or May 2011 data separately(Table A3 and Figure A1).26 Overall, these effects represent a 30-percent increase in
consumption of beans and fats, particularly important food groups given the high prevalence ofprotein-energy malnutrition in Niger (DHS 2012). In addition, the increased food consumption
categories are broadly correlated with the more diverse purchases observed in Table 3.27
Panel 4 (Panel B) shows the results of the cash transfer delivery mechanism on childnutritional outcomes, using data from May 2011. While children in Cash villages ate 3.17 meals
per day, children in Zap households ate an additional 1/3 of a meal as compared with those in the24

The primary food security measure used is the household diet diversity index, a standard index developed by the
Food and Nutrition Technical Assistance (FANTA). The index asks about household-level consumption of specificfood groups over the past 24 hours, including cereals, tubers, legumes, milk, fish, meat, oils, condiments, eggs,
fruits, vegetables and sugar (FANTA 2006). A more varied diet is associated with a number of improved outcomesin areas such as birthweight (Rao et. al. 2001), child anthropometric status (Allen et. al., 1991; Hatloy, Hallund,
Diarra and Oshaug, 2000; Onyango, Koski and Tucker, 1998) and food expenditures (Hoddinott and Yohannes
2002, Thorne-Lyman et al 2010).
25
While Mobile households had diet diversity that was .23 points lower than the Cash households, this seems to be
primarily driven by the lower likelihood of consumption of condiments amongst Mobile households.
26
The diet diversity results are broadly consistent when using only the December 2010 or the May 2011 data,suggesting that these results hold in the short and longer-term (Table A3). While overall diet diversity is higher in
December 2010, it drops in May 2011, consistent with the hungry period. Yet the impact of the m-transfer
mechanism on the diet diversity is stronger in May 2011, six months after the end of the program.
27
While Zap households were more likely to purchase meat (Table 3), this did not translate into a higher likelihoodof meat consumption (Table 4). This could be partially due to the different reference periods for the outcome
measures. Table 3 asked households how they used the cash transfer (the last transfer was in October 2010),
whereas Table 4 asked about household consumption in the past 24 hours (which occurred in December 2010).
While grains, oils and beans are storable, meat is perishable unless dried. Thus, we would not necessarily expect tosee an increase in meat consumption two months’ after the last cash transfer.

15

Mobile and Cash groups, with a statistically significant difference at the 5 percent level.28
Similarly, children in Zap households had diet diversity scores that were 12-14 percent higher ascompared with those in the Mobile and Cash groups, although this was only statisticallysignificant for one pairwise comparison. Yet these improvements did not translate into changes
in nutritional status, as measured by weight-for-height z-scores. This could be partially due tothe imprecision of the estimates, as nutritional data were only collected for a subset of villages.
In addition, improved child nutritional status is only positively correlated with diet diversity
when diet diversity is greater than three food groups, which is not the case in our context(Labadarios et al 2011).
While we would not necessarily expect asset accumulation as a result of the cash transfer
program, we test for impacts on household asset ownership to verify that other household
members were not reducing their contribution to household public or private goods (Table 4,Panel C). Excluding mobile phones, Zap households owned .12-.31 more asset categories ascompared with those in the Cash and Mobile group, respectively, although only the latter
pairwise comparison is statistically significant at conventional levels. The Zap transfer
mechanism did not have a strong impact upon households’ durable (carts, plows, bikes andmotos) or non-durable asset ownership (flashlights, petrol lamps and radios), although nondurable asset ownership was higher in Zap households as compared with Mobile households.
Overall, these results suggest that household members were not reducing their contribution of
public or private goods to the household as a result of the cash transfer. These results are
consistent when using an ANCOVA specification or using data from the December or May
rounds (Table A3).
IV.

Potential Mechanisms

One of the core results in this paper is that receiving a cash transfer via mobile money led todifferent uses of the transfer and increased household diet diversity, primarily for certain foodgroups. Our experimental design allows us to conclude that these results are due to the mtransfer mechanism, and not to the mobile phone. This section presents evidence on the channels
through which the observed impacts occurred.
28

The statistical significance for the Zap-Mobile comparison diminishes once commune fixed effects are excluded.

16

A. Reduced Costs of Obtaining the Transfer
A key claim of those supporting the use of electronic transfers is that they will reduce thecosts of implementing the program, including leakage. Yet whether these cost reductions accrue
to program recipients depend upon the local electronic payments infrastructure. While Concern
Worldwide tried to minimize the distance that Cash and Mobile program recipients had to travel
to obtain their transfer, a standard practice in such programs, Concern did not have control overthe placement of registered m-money agents, which was managed by the mobile phone operator.
Figure 2 shows the recipients’ travel costs related to obtaining the cash transfer. As both the
Mobile and Cash groups received the cash transfer via the same mechanism, we pool the twogroups. Overall, program participants in Zap villages incurred significantly fewer costs to obtainthe transfer. Whereas Cash and Mobile program recipients travelled approximately 4 km (roundtrip) from their home village to obtain the transfer, Zap program recipients only travelled 2 km to“cash out” at the nearest agent, with a statistically significant difference at the 1 percent level.29
This is equivalent to a travel time savings of approximately 1 hour for each cash transfer, or 5hours over the entire program. However, this analysis excludes the Cash program recipients’
waiting time during the transfer, which averaged four hours per cash transfer, as compared with30 minutes for Zap recipients.30 Including wait time, the average cost savings to Zap program
recipients over the program period would have been approximately 20 hours. Based upon anaverage daily agricultural wage of US $3, this would translate into US $7.50 over the life of the
program, equivalent to approximately 20-kg of grain at the time.
While the Zap transfer mechanism reduced recipients’ costs of obtaining the transfer,contrary to Muralidharan et al (2016), we do not find any effects on leakage. Overall, leakage
was very low: 98% of Cash recipients reported receiving their transfer and received CFA 95,000
over 4.4 transfers (Table 5). Only one of these differences was statistically significant across thethree treatment arms. Part of the difference between the cash transfer balance received ($US200) as compared with the target ($US 225) can be explained by the seed distribution program,
29

Hoddinott et al (2014) find that the average travel and wait time for cash transfers in Niger was one hour, using a
portable ATM system.
30
While the average wait time for manual cash transfers was four hours, this ranged from 1 to 8 hours. The
corresponding wait time for Zap recipients was 15 minutes to 1 hour. (Personal correspondence with Concern).

17whereby part of cash transfer was replaced by an equivalent value of seeds.31 Yet even if thesedifferences were due to actual leakage, this would represent between 4-10 percent of the total
value of the transfer ($US 1.50 to $7.50).
While the time savings results are small in magnitude, this occurred during the agricultural
planting season, when program recipients’ opportunity costs were relatively higher. Thissuggests that the estimates are a lower bound, as most Cash program recipients had to travelfrom their fields – often located 1.5 hours from their village – before obtaining the transfer. Yet
Zap recipients had greater flexibility in choosing a time to cash out, which could have freed up
their time to engage in more productive agricultural activities or spend more time on childcare or
food preparation. While we do not have data on the latter, we have some suggestive evidence ofthe former: Zap program recipients were 7-13 percentage points more likely to plant okra andvouanzdou than their Cash and Mobile counterparts (Table A4). There are few differences
across treatments in planting other staple food and cash crops, and this did not translate intohigher production or sales. As okra and vouandzou are marginal cash crops grown by women,
this suggests that some of the time savings could have been used for agricultural activities.32
B. Differential Timing and Location of the “Cash Out” and Expenditures
The differential uses of the transfer and diet diversity by Zap households could also be due tochanges in the timing and location of household expenditures. Unsurprisingly, almost all of the
Cash households received their transfer on the same day that it was available, as mosthouseholds did not have a choice (Table 6, Panel A). By contrast, Zap households were 36-39
percentage points less likely to receive their cash on the same day that it was available, “cashingout” 1-4 days after receiving the notification. While the lag time between being informed of thetransfer and actually receiving the transfer was only 1-2 days longer in Zap villages, these31

In an effort to reduce the likelihood of theft during the manual cash distribution, Concern Worldwide monitored allactivities and hired security to guard the cash during transport. Those costs represented over 60 percent of themanual cash transfer distribution budget.
32This increase in the likelihood of planting these cash crops did not appear to have a strong income effect, as theamount harvested and sold was extremely small. . As women had to negotiate access to land with their spouses tocultivate these crops, this suggests a potential shift in intra-household bargaining, which is discussed below. Whilehouseholds in the Zap and Mobile treatments were more likely to sell cowpea, this is compared to a mean of zero in
the Cash group.

18

additional days provided program recipients with greater flexibility during a particularly busy
time of year.
Zap program recipients also had the option of withdrawing their transfer in smaller amounts,
thereby allowing them to use the mobile phone as a savings device and avoid unplanned
expenditures. The cash-out data reveals that this was not the case. For all transfers, over 98% of
Zap households withdrew the entire amount of their cash transfer at one time (Personal
Correspondence with Zain 2011). This could, in part, be due to the fact that Zap households
would have had to pay a fee for any additional withdrawals. In fact, less than 5 percent ofhouseholds had any value remaining in their mobile phone one month after the last transfer, andthose that did saved less than US $0.15.
The slight change in the timing of the withdrawal could have modified the timing andlocation of household expenditures. Yet 60 percent of Cash households reported spending theirtransfer all at one time (Table 6, Panel B), without a statistically significant difference in thetiming of purchases among the treatments. The results are similar for the location of
expenditures: Zap households did not have significantly different purchasing patterns ascompared with their Mobile and Cash counterparts (Table 6, Panel C). Overall, these results
suggest that while the m-transfer mechanism increased the lag time between learning about the
cash transfer and receiving the cash, it did not change when or where households spent themoney.33
C. Increased Use of Mobile Phones
With access to the mobile phone handset, Zap households could have been better informed
about agricultural prices, thereby affecting their purchasing decisions and diet diversity. While
this channel should, in theory, yield similar results for both the Zap and Mobile groups, inpractice, Zap program recipients could have felt a greater sense of “ownership” over the mobile
phone, as the transfer was specifically linked to the handsets.
33

The “innovation” of the m-transfer technology could have increased program recipients’ awareness of the cashtransfer program, thereby reducing uncertainty about the cash transfer and allowing households to more optimally
allocate expenses over time. Yet only 13 percent of Cash program participants could correctly cite the total amountor duration of the cash transfer prior to the program, with no statistically significant difference between the Zap,
Cash and Mobile treatments (Table 6, Panel D).

19

Table 7 shows the impact of the different transfer mechanisms on mobile phone usage, using
pooled data from December 2010 and May 2011. Unsurprisingly, mobile phone usage washigher amongst the Zap and Mobile households as compared with the Cash households (Panel
A). While Zap and Mobile program recipients were more active mobile phone users, especiallyfor personal communications, there were no statistically significant differences in mobile phone
usage for commercial activity or asking for help (Panel A). Thus, while we cannot rule out thatthe mobile phone handset affected Zap households’ outcomes in some unobservable ways, it did
not lead to increased access to market information, at least in the short-term. 34
Access to the m-transfer technology also did not affect Zap program recipients’ access to
private transfers (Table 7, Panel B). While the probability and intensity of seasonal migration
was slightly higher among Zap households, this did not translate into changes in remittances:
There were no statistically significant differences in the frequency or amount of remittances
received or the remittance mechanism. These findings are robust when limited to one time
period and also when conditioning on those households affected by a shock (not shown). These
results are not surprising, as the agent network was not widespread at the time and the m-transfer
system could not be used for transfers to Nigeria, the destination for a majority of migrants.35
D. Inter- and Intra-Household Dynamics
Unlike the manual cash mechanism, the Zap transfer mechanism made it more difficult forothers to immediately observe the arrival of the transfer, as the program recipient was notified of
the transfer arrival via a discrete beep. This was particularly relevant for Zap transfer recipients,
many of whom wore their phones around their necks so that they could be notified of the mtransfer at any place or time.36 In theory, this could have allowed Zap program recipients to haveprivate information about the arrival of transfer, thereby affecting their bargaining power with34

The regressions in Table 7 (Panel A) were estimated by imputing a zero value for those recipients who had notused a mobile phone. We also estimated this table conditional on whether the recipient had used a mobile phone
since the last harvest. The results are largely robust to this alternative specification.
35
The regressions in Table 7 (Panel B) were estimated by imputing a zero value if the household did not have any
migrants. We also estimated these results conditional on whether the household had a migrant. The results arelargely robust to this specification.
36
While we do not have administrative data on the actual timing of the m-transfers or the time of the day that the
beeps were received, Concern Worldwide transferred the money during the day. These are times when female
program recipients would typically be away from home, searching for water and firewood or working in the field.

20

respect to how the cash transfer was used within or outside of the household.37 While the Zap
treatment did not have an impact on the likelihood of inter-household sharing (Table A5), it did
appear to affect intra-household dynamics. In focus group interviews, Zap program recipients
reported that they did not immediately inform their spouses about the arrival of the transfer, but
waited until they were in the privacy of their home. By contrast, since Cash and Mobile program
recipients often obtained their transfer with other household members, they immediately
provided the cash transfer to that family member, with little opportunity to discuss how thetransfer would be used.
Table 8 tests for the impact of the Zap intervention on intra-household decision-making.38
Overall, 53 percent of program recipients in the Cash villages reported that they were
responsible for spending at least part of the cash transfer, and almost all recipients (99 percent)stated that they were consulted on how to spend the cash transfer. There was no statisticallysignificant difference in either of these outcomes among the three groups (Panel A).39
Table 8 also assesses the impact of the m-transfer on other proxy indicators of intrahousehold decision-making, many of which are common in much of the female empowerment
literature (Deopke and Tertilt 2014, Bobonis 2009, Attanasio and Lechene 2002). Zap program
recipients were more likely to travel to weekly markets and to be involved in the sale ofhousehold grains than Mobile or Cash households (Panel B). The Zap intervention also affected
women’s and children’s clothing expenditures for Muslim festivals (Panel C): Zap households

37

The program was implemented an area of Niger where socio-cultural norms often do not permit younger, married
women of the Hausa ethnic group to travel to markets (Coles and Mack 1991). As most of the women in our sample
are married and less than 45 years old, we would not expect to find strong effects of the m-transfer mechanism on
women’s visible control over the cash transfer, such as spending it on their own.
38
In order to formally test for differences in intra-household decision-making, we would ideally want to test
outcomes across each of the three interventions between households with male and female program recipients. As
all program recipients were women, we are unable to do this. In addition, a large number of zero expenditures formales does not allow us to compute the ratio of women to male expenditures.
39
Program recipients who were primarily responsible for spending the cash transfer were female-headed households(13 percent of the sample) and those from the Fulani and Touareg ethnic groups, for whom the travel restriction was
not a primary constraint. Nevertheless, even in these cases, fewer than 2 percent of these recipients were solely
responsible for spending all of the cash transfer. We do not have sufficient variation to estimate heterogeneous
results by female-headed household or ethnicity.

21

were 7-10 percentage points more likely to spend money on women’s and children’s clothing
and spent approximately 40 percent more as compared with the Cash and Mobile treatments.40
Taken together, the results in Table 8 provide suggestive evidence that the m-transfer
mechanism affected intra-household decision-making, thereby affecting households’ uses of thetransfer and food security. This could have happened in one of three ways. First, male membersof Zap households could have been less likely to spend the transfer on temptation goods. Second,
women might have been better able to hide the transfer amount from their husbands, allowing
them to purchase more diverse foodstuffs on their own. Finally, women in Zap households
might have been better able to convince their husbands to spend more on higher quality foods or
to invest extra effort in finding lower prices for staple foods, thereby freeing up income.41 As we
do not have data on temptation good spending, we are unable to rule out the first hypothesis. The
second hypothesis is not supported by the data on the location of expenditures (Table 6). We
posit that the third mechanism is potentially at work, although we only have qualitative data to
support this.42
V.

Alternative Explanations

There are several potential confounds to the above findings. A first potential confounding
factor could emerge if the registration of Zap agents (who were already village residents ortraders) provided new types of financial or commercial services to households, thereby
improving Zap households’ access to foodstuffs and agricultural inputs. Table 9 (Panel A)
shows that this is not the case. Only 3 percent of Cash villages had a Zap agent, and there was
not a statistically significant difference in the likelihood or number of Zap agents between theZap, Mobile and Cash villages. While these results are not very precisely measured, they40

While much of the literature calculates the ratio of male to female or male to child clothing expenditures(Lundberg, Pollak and Wales 1997), very few households in our sample spent money on male clothing, so weconcentrate on women and children’s clothing expenditures.
41
An additional test of the intra-household bargaining channel would be to measure the impacts of the Zap programfor male and female-headed households. If the program truly changed intra-household bargaining, then the effects
should only be apparent for male-headed households. While we conduct this analysis, we cannot conclude that there
is a statistically significant difference between the two groups. This could be partly due to the limited number offemale-headed households in our sample (less than 15 percent), as well as the fact that the male and female-headedhouseholds differ in observable and unobservable ways.
42Qualitative and quantitative evidence suggest that men in Zap households were willing to travel farther to larger
markets, suggesting that the third mechanism could have taken place.

22

suggest that Zap agents were evenly distributed across different villages, and that differential
access to m-transfer agents is not driving the results.
In Figure 2 and Tables 3 and 4 (our primary outcomes of interest), we examined the impactof the Zap program on multiple outcomes for three treatment interventions. This raises concernsthat the observed effects cannot be attributed to the Zap intervention, but are simply observed bychance among all of the different outcomes. Following Sankoh et al (1997), we use a Bonferroni
correction that adjusts for the mean correlation among outcomes, focusing on the key outcomesof interest. Using an alpha of 10 percent, and assuming an inter-variable correlation of .08 (fortransfer uses) to .35 (for household diet diversity), the Bonferroni p-value (adjusting forcorrelation) would therefore be between .002 and .005.43 The outcomes that remain significant
at these adjusted levels are program recipients’ cost of obtaining the cash transfer (Figure 2), thedifferent uses of the cash transfer in Table 3 (with the exception of cowpea and condiments) andthe increased consumption of fats and higher diet diversity (for the Zap group as compared with
the Mobile group) in Table 4. Thus, we are confident that the Zap intervention affected
recipients’ costs in obtaining the cash transfer, recipients’ uses of the transfer, fat consumption
and household diet diversity.44
A final potential confounding factor is the differential effect of the transfer mechanism onfood prices (Cunha et al 2013). If the manual cash transfer mechanism put greater inflationarypressure on local markets as compared with the m-transfer mechanism, this could have reduced
the value of the cash transfer in those villages and decreased the number of goods purchased or
consumed. Or, if the m-transfer increased prices more quickly and producers were price elastic,
this could have encouraged households to consume goods that were less affected by the priceincrease, or increased incentives for traders to supply those goods.45 Table A6 shows that

43

The Bonferroni correction without accounting for inter-outcome correlation would yield an adjusted p-value of.001. However, in the case of correlated outcome variables, the mean correlation between outcome variables can be
included as a parameter in the Bonferroni adjustment (Sankoh et al 1997, Simple Interactive Statistical Analyses). A
mean correlation of zero would yield the full Bonferroni adjustment, whereas a mean correlation of one would meanno adjustment. A less restrictive test than the Bonferroni adjustment would be the use of False Discovery Rate
(FDR) techniques (Anderson 2008).
44
Fat consumption and household diet diversity are only robust to the Bonferroni correction for the Zap-Mobile
pairwise comparison.
45
For a discussion of the potential impact of m-money on the velocity of money and inflation, see Jack, Suri andTownsend (2010).

23different cash transfer delivery mechanisms did not have differential impacts on the number ofwholesalers and semi-wholesalers or the number of products available on the market.
As 63 percent of program recipients’ purchases in took place in weekly markets, primarily
outside of the village, we collected weekly market price data between May and September 2010
from 45 markets in the region. The randomized nature of the program implies that some markets
were linked to both Cash and Zap villages, almost certainly violating the stable unit treatment
value assumption.
Table A7 presents the results of a regression of the log of weekly prices in market on the cashtransfer delivery mechanisms, controlling for market and time fixed effects.46 Overall, thepresence of a cash transfer in a particular market during the week did not have a statisticallysignificant impact upon food prices (Panel A). The absence of an impact suggests that local
supply on these markets was able to absorb the potential additional demand from the cashtransfer program, especially for staple food crops, although this is not a causal interpretation. 47
Panel B measures the impact of the cash transfer delivery mechanism on weekly food prices.
Overall, the results are consistent with those in Panel A. The presence of the m-transfer or
manual cash transfer program in a nearby village did not have an impact upon staple grain,
cowpea or vegetable oil prices, and there is no statistically significant difference between the
cash transfer mechanisms, with the exception of rice.48 Overall, the analysis suggests that were
not strong price effects due to the different transfer mechanisms.
VI.

Conclusion

46The regression estimated in Table A7 is the following: ln(pij,t) = α + β1zapj,t + β2cashj,t + θt + θj + εij,twhere ln(pij,t)is the log price of agricultural good i in market j at week t, zapj,t is an indicator variable equal to one if a village
within a 10 km-radius of the market received a transfer via zap during week t, 0 otherwise; cashj,t is an indicatorvariable equal to one if a village within a 10-km radius of the market received a manual cash transfer during week t,
0 otherwise; θt represents a full set of time fixed effects, either monthly or weekly, whereas θj are a set of marketlevel fixed effects, which capture market characteristics. Standard errors are clustered at the market level.
47
While the results in Table A7 suggest that markets were able to respond to increased demand, these results only
show relative changes in prices. Welfare could have decreased on these markets due to the cash transfer if the influxof cash increased prices higher than average prices during this period of year. This requires a comparison of average
prices on these markets during the previous (non-drought) marketing season, which we do not have.
48
The one difference was for retail rice prices: the presence of a Zap transfer during a particular week increased rice
prices by 2 percent, with a statistically significant difference between the electronic and manual cash transfer
mechanisms. This is consistent with the finding that Zap households were more likely to purchase other cereals.
Given the price of a bag of millet, households typically purchased small quantities (e.g., 1 kg) of rice. Rice is notproduced in these areas of Niger, so the increase in rice prices could not have increased incentives to produce rice.

24

Cash transfer programs are an important part of the social protection policies in many
developing countries. While there is significant evidence on the impact of such programs on
improving specific outcomes, there is more limited evidence on their cost-effectiveness ascompared with other types of interventions. Yet understanding the costs and benefits ofimplementing these programs is particularly important in countries where distributing cash
involves significant logistical, operational and security costs, as is the case in many countries
sub-Saharan Africa.
An intervention that provided a cash transfer via the mobile phone strongly reduced the costsof program recipients in obtaining the cash transfer. Yet a key question for the introduction of a
new technological payment infrastructure is the cost of such programs to the implementing
agency.49 While the initial costs of the Zap program were significantly higher, primarily due tothe purchase of mobile phones, the per-transfer costs of the Zap transfer mechanism wereapproximately 20 percent lower than per-transfer cost of the manual cash distribution (Figure
A2). Across all transfers, the per recipient cost was US$16.43 in Cash and Mobile villages, ascompared with a per recipient cost in Zap villages of US$24.14, or US$7.70 more per recipient.50
Excluding the cost of the mobile phones, the per-recipient cost of the Zap intervention fell to$6.78 per recipient (not shown).51 This suggests that mobile phones could be a simple and lowcost way to deliver cash transfers once the necessary infrastructure is in place.
Yet beyond the cost reductions, distributing cash transfers electronically also affectedhousehold behavior: Zap households bought more types of food items and increased their dietdiversity, all while retaining their durable and non-durable assets. These observed differences49

The initial investment costs of the Zap program included expenses for identifying program recipients, purchasing
mobile phones and training recipients in how to use them, the latter of which were only for the Zap intervention.
Variable costs for the manual cash distributions included transport and security costs, as well as costs associatedwith counting the cash into individual envelopes. Equivalent variable costs for the m-transfer mechanism included
the fees associated with making the transfers and staff time in managing the transfer process. The key variable costs
for the Zap intervention included the transfer cost to the program recipients and program recipients’ withdrawal fees.
50
The annual per recipient costs of cash transfer programs in sub-Saharan Africa range from US$7 (Malawi) to
US$35 (Ethiopia) (DFID 2011).
51
The cost comparison focuses on the cost to the Concern Worldwide for implementing the program, rather than thecosts to the private sector (such as the cost of maintaining the agent network, which were managed solely by themobile phone operator). While this might omit some important costs, this is fairly standard in cost comparisons; forexample, most electronic transfer programs do not compare the costs for maintaining a banking sector or
SmartCards infrastructure after the initial investment.

25

are primarily due to the m-transfer intervention and not to the mobile phone, suggesting that a
program that jointly distributes mobile phones and cash transfers would not yield the same
impacts.
While these results are promising, they suggest that electronic transfers may not lead to
improved financial inclusion for all households in all contexts, as proponents might suggest.
Unlike the mobile money “revolution” in Kenya, mobile money registration and usage has notgrown as quickly in other parts of sub-Saharan Africa, including Niger. This suggests that
substantial investment to register clients and agents would be required to establish mobile
payment systems. In addition, while program recipient households in our study used mobile
money to receive their transfer, they did not use it to receive remittances or to save, twoimportant aspects of financial inclusion. This is potentially related to the limited m-money agent
network in the country, a common issue in other West African countries.
Like many field experiments, the generalizability of our results may be limited. Our
study exploits variation in the transfer mechanism during a food crisis, when the marginal utilityof income can be high. In addition, as we do not have full income and expenditure data, we areunable to estimate the Engel curves of these households, a relevant question for many cashtransfer programs (Attansio et al 2012). And finally, since Niger is one of the poorest countries
in the world, with low rates of literacy, financial inclusion and mobile money adoption, thecontext might be different from other countries where governments are considering electronic
payments. Nonetheless, Niger’s educational, financial and mobile money indicators are notvastly different from other countries in West Africa, suggesting that our results might be
informative for those contexts (UNESCO 2012, Demirguc-Kunt and Klapper 2012).52
Despite these caveats, the widespread growth of mobile phone coverage and m-transfer
services in developing countries suggests that these constraints could be overcome. In addition,
the benefits of the program in a context such as Niger -- a country with limited roadinfrastructure, low literacy rates and high financial exclusion -- suggests that the approach could
thrive in less marginalized countries and during periods when the marginal utility of income is52

For example, countries in the West Africa region have some of the lowest educational indicators in the world, andfewer than 10 percent of individuals had used mobile money in 2012, ranging from 1-2% in Togo and Ghana to 19%
in Liberia (Demirguc-Kunt and Klapper 2012).

26

lower. This is particularly the case in other areas of West Africa, such as Burkina Faso, Mali,
northern Ghana and Senegal, which have similar education and mobile money indicators.
Nevertheless, transferring these responsibilities to the private sector could potentially increase
the likelihood of corruption or leakage, especially if m-transfer agents exert some type of powervis-à-vis program recipients. In addition, such programs could potentially crowd out certain
segments of the private sector, especially smaller traders and shopkeepers who are unableregister as agents.

27

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31

Figure 1. Timeline of Data Collection and Implementation
Year

January

March

April

May

2010

Villageselection

Identificationand selectionof program
recipients

Programpreparation

Baselinehousehold andvillage-level
survey in 93
villages

June

July

AugustSeptember

Hungry season and planting season
Monthly cash transfers in 116 villages

OctoberNovemberHarvest period

Price data collected in 45 markets in all communes
2011

Final
household andvillage-level
survey in 96
villages
Nutrition
survey in 30
villages

Hungry season and planting season

32

Harvest period

December
Midterm
household andvillage-level
survey in 96
treatment
villagesFigure 2. Program Recipients’ Costs of Obtaining the Cash Transfer by Cash Transfer Delivery
Mechanism

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Distance (km) to Cashpoint

Distance (hours) to Cash Point
Cash/Mobile

Zap

Notes: This figure shows the mean cost (in km and hours), by transfer mechanism, for program recipients’ travel to the nearest
cash point to obtain their cash transfer. Data were obtained from the household surveys and Concern Worldwide’s list of
distribution points for the manual cash villages.

33

34

Table 2: Baseline Individual and Household Covariates (by Treatment Status)(1)

Panel A: Socio-Demographic Characteristics
Age of respondent
Polygamous household
Respondent is member of Hausa ethnic group
Number of household members
Number of household members under 15 years
Percentage of Household Members with Some Education
Panel B: Household Income Sources and Assets
Agriculture is an income source
Livestock is an income source
Remittances are an income source
Number of asset categories owned (out of 12)
Panel C: Mobile Phone Ownership and Usage
Household owns mobile phone
Respondent has used mobile phone since last harvest
Respondent made or received call since last harvest
Respondent sent or received m-money transfer since last
harvestPanel D: Shocks
Household experienced drought in past year
Household experienced crickets in past year
Number of household observations
Panel E: Village-Level Covariates
Market located within the village
35

Cash
Mean
(s.d.)(2)
ZapCashCoeff(s.e.)(3)
MobileCash
Coeff(s.e.)(4)
ZapMobileCoeff(s.e.)

33.22(11.05)0.28(0.45)
0.81(0.39)
9.31(4.95)
5.65(3.42)
0.58(0.32)

1.90(1.21)
0.04(0.04)-0.05(0.08)-0.40(0.63)-0.35(0.38)-0.01(0.03)-0.90(1.24)
0.02(0.04)
0.08(0.06)-0.21(0.52)-0.11(0.34)
0.04(0.03)

2.79**(1.24)
0.01(0.04)-0.13(0.08)-0.18(0.50)-0.24(0.34)-0.04(0.03)

0.98(0.15)
0.61(0.49)
0.34(0.47)
3.62(1.60)-0.01(0.01)
0.06(0.06)-0.01(0.04)
0.07(0.18)-0.01(0.01)-0.03(0.06)-0.05(0.04)-0.15(0.16)-0.00(0.01)
0.09*(0.05)
0.04(0.04)
0.22(0.14)

0.29(0.45)
0.61(0.49)
0.61(0.49)

0.04(0.05)
0.06(0.05)
0.06(0.05)-0.03(0.04)
0.00(0.05)-0.00(0.05)

0.06(0.04)
0.05(0.04)
0.05(0.04)

0.00(0.00)

0.00(0.00)

0.00(0.00)-0.00(0.00)

0.98(0.15)
0.81(0.39)-0.00
0.01(0.01)(0.01)-0.02-0.04(0.04)(0.05)
1106-0.01(0.01)
0.01(0.04)

0.35

-0.04

-0.04

0.01

(0.49)0.97(0.18)
0.26(0.44)

School located within village
Presence of a seed distribution program
Number of village observations(0.13)
0.01(0.04)
0.04(0.08)(0.13)-0.04(0.05)-0.04(0.08)(0.12)
0.05(0.05)
0.08(0.09)

93

Notes: This table presents a comparison of individual and household covariates in each of the different treatment areas.
Column 1 shows the mean and s.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the differencein conditional means between the different treatments and the cash households. Column 4 shows the conditional difference
in means between the Zap and Mobile treatment households. All regressions control for commune-level fixed effects and thepresence of a seed program in the village, the level of stratification prior to randomization. Heteroskedasticity-consistent s.e.
are clustered at the village level(for Panels A-C) are presented in parentheses. *** significant at the 1 percent level, **significant at the 5 percent level, * significant at the 10 percent level.

36

Table 3: Baseline Individual and Household Outcomes (by Treatment Status)(1)

Panel A: Food Security Outcomes and Coping Strategies
Number of months of household food provisioning (scale of 6)
Household diet diversity index (scale of 12)
Panel B: Migration and Remittances
One household member migrated since the last harvest
Number of remittances received since the past harvest
Received remittance via m-money transfer (zap)Panel C: Agricultural Production and Livestock
Cultivate land
Produce millet
Quantity of millet produced (kg)
Produce cowpea
Quantity of cowpea produced (kg)
Produce vouandzou or okra
Number of observations

Cash
Mean
(s.d.)(2)
ZapCashCoeff(s.e.)(3)
MobileCash
Coeff(s.e.)(4)
ZapMobileCoeff(s.e.)

1.93(1.56)
3.10(2.03)

0.19(0.15)
0.04(0.19)

0.14(0.14)-0.08(0.18)

0.06(0.14)
0.12(0.16)

0.47(0.50)
0.71(1.89)
0.02(0.13)

0.03(0.05)
0.24(0.20)
0.00(0.01)

0.02(0.05)
0.08(0.17)
0.01(0.01)

0.01(0.05)
0.16(0.19)-0.00(0.01)

0.98(0.13)
0.97(0.17)
267
(363)
0.87
(0.34)
9.06
(30)
0.54
(0.50)-0.02-0.01(0.01)(0.01)-0.01
0.01(0.01)(0.01)
19.97-16.25(52.86) (43.77)-0.03-0.07*(0.03)(0.04)
2.34
0.98(2.44)(2.73)
0.00-0.02(0.06)(0.05)
1105-0.01(0.01)-0.01(0.01)
36.22(40.03)0.05(0.04)
1.36(2.49)
0.02(0.05)

Notes: This table presents a pre-treatment comparison of individual and household outcomes in each of the different treatment areas.
Column 1 shows the mean and s.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the average difference
between the different treatments and the cash households. Column 4 shows the average difference between the Zap and Mobile
treatment households. All regressions control for commune-level fixed effects and the presence of a seed program in the village.
Heteroskedasticity-consistent s.e. clustered at the village level are presented in parentheses. *** significant at the 1 percent level, **significant at the 5 percent level, * significant at the 10 percent level.

37

Table 4: Uses of the Cash Transfer

Panel A: Uses of Cash Transfer for Food Items
Number of food and non-food items purchased with cash transfer
Transfer used to buy staple grains (millet, sorghum)
Transfer used to buy other grains (corn, rice)Transfer used to buy cowpea
Transfer used to buy condiments
Transfer used to buy oil
Transfer used to buy meat
Panel B: Uses of Cash Transfer for Non-Food Items
Transfer used to pay school fees

(1)
Cash
averageMean(s.d.)
4.32(2.46)
1.00(0.05)
0.56(0.50)
0.40(0.49)
0.68(0.47)
0.68(0.47)
0.38(0.49)
0.07(0.26)
0.30(0.46)
0.04(0.20)

Transfer used to pay health expenses
Transfer used to buy clothes
Number of observations(2)
Zap-Cash(3)(4)
MobileZapCash
MobileCoeff(s.e.) Coeff(s.e.) Coeff(s.e.)
0.78***(0.24)-0.01(0.01)
0.18***(0.05)
0.09*(0.05)
0.11**(0.05)
0.13***(0.05)
0.16***(0.04)-0.07(0.24)
0.00(0.01)-0.02(0.05)-0.01(0.05)-0.02(0.04)-0.01(0.05)-0.02(0.04)

0.85***(0.25)-0.01(0.01)
0.20***(0.06)
0.10**(0.05)
0.12***(0.04)
0.15***(0.05)
0.18***(0.04)-0.02(0.02)-0.01(0.03)
0.01(0.02)-0.01(0.02)-0.03(0.04)
0.00(0.03)-0.01(0.02)
0.03(0.03)
0.00(0.02)

1047

Notes: This table presents a simple difference comparison of households in each of the different treatment areas. Column 1 shows the meanand s.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the average difference between the different treatments andthe cash households. Column 4 shows the average difference between the Zap and Mobile treatment households. All regressions control for
geographic-level fixed effects and for the presence of a seed distribution program in the village. Heteroskedasticity-consistent s.e. clustered atthe village level are presented in parentheses. *** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10
percent level.

38

Table 5: Impact on Food Security, Nutritional Status and Asset Ownership

Panel A: Food Security
Household diet diversity score (out of 12)
Consumption of:Grains
Beans
Fats
Meat
Condiments
Fruit
Number of observations
Panel B: Child Nutritional Status
Number of meals eaten by children under 5 in past 24 hours
Diet diversity of children under 5
Weight-for-height z-score
Number of observations
Panel C: Durable and Non-Durable Goods
Number of asset categories owned (out of 11, excluding mobile phones)
Durable assets (plows, carts, bikes and motos)
Non-durable assets (flashlights, petrol lamps and radios)
Number of observations

(1)
Cash
averageMean(s.d.)(2)
ZapCashCoeff(s.e.)(3)
MobileCash
Coeff(s.e.)(4)
ZapMobileCoeff(s.e.)

3.17(1.70)

0.28*(0.15)-0.23*(0.13)

0.51***(0.14)

0.99(0.10)
0.18(0.39)
0.29(0.45)
0.06(0.24)
0.36(0.48)
0.02(0.15)

0.00
0.00(0.01)(0.01)
0.06**-0.01(0.03)(0.02)
0.09**-0.02(0.03)(0.03)
0.02-0.00(0.02)(0.02)-0.01 -0.08**(0.05)(0.04)
0.03**-0.01(0.01)(0.01)
2167

0.00(0.00)
0.07**(0.03)
0.11***(0.03)
0.03*(0.01)
0.07*(0.04)
0.03***(0.01)

3.17(1.71)
2.33(1.80)-1.15(0.96)

0.33**
0.05(0.15)(0.14)
0.20-0.22(0.23)(0.18)
0.06-0.03(0.12)(0.15)
543

0.28**(0.12)
0.42**(0.17)
0.09(0.13)

3.05(1.28)
0.18(0.49)
1.63(0.87)

0.12-0.19*(0.11)(0.11)-0.01 -0.07**(0.04)(0.03)
0.12-0.08(0.07)(0.07)
2210

0.31***(0.09)
0.05(0.03)
0.20***(0.07)

Notes: This table presents the simple difference estimates for each of the different treatment areas. Column 1 shows the mean and s.d. ofthe basic treatment (cash) households, whereas Columns 2 and 3 show the difference in means between the different treatments and the
cash households. Column 4 shows the difference in means for zap and Mobile treatments. All regressions control for commune-level
fixed effects and the presence of a seed program in the village. Heteroskedasticity-consistent s.e. clustered at the village level arepresented in parentheses. *** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.
Panels A and C include pooled data from the December and May rounds, whereas Panel B only includes data from the May round.

39

Table 6: Leakage(1)
Cashaverage
Program recipient received cash transfer

0.98(0.16)
4.44(1.27)
95,637(30844)

Number of transfers received
Amount of money received (CFA)
Number of observations(2)
ZapCash(3)
MobileCash(4)
ZapMobile

-0.02
0.02
-0.04**(0.02)(0.01)(0.02)
-0.12
-0.03
-0.09
(0.11)(0.13)(0.13)-501.70-454.96-46.74(2,762.16) (3,137.43) (2,903.12)
1079

Notes: This table presents a simple difference comparison of households in each of the different treatment areas. Column
1 shows the mean and s.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the average
difference between the different treatments and the cash households. Column 4 shows the average difference between the
Zap and Mobile treatment households. All regressions control for geographic-level fixed effects and for the presence of aseed distribution program in the village. Heteroskedasticity-consistent s.e. clustered at the village level are presented inparentheses. *** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

40

Table 7: Location, Knowledge and Timing of Cash Transfer Expenses

Panel A: Timing of Receipt of Transfer
Obtained transfer the same day

Panel B: Timing of Expenditures
Spent money all at once
Spent money at least two times

(1)
Cash
average(2)
ZapCash(3)
MobileCash(4)
ZapMobile

0.89-0.39***-0.03(0.32)(0.06)(0.03)

0.36***(0.05)

0.60(0.49)
0.40(0.49)-0.03(0.04)
0.03(0.04)-0.03(0.04)
0.03(0.04)

0.00(0.04)-0.00(0.04)

0.42(0.49)
0.23(0.42)
0.63(0.48)

0.04(0.06)-0.01(0.09)
0.03(0.08)-0.04(0.05)
0.03(0.08)-0.00(0.08)

0.08(0.06)-0.04(0.08)
0.03(0.08)

0.13(0.33)
0.12(0.33)

0.01-0.01(0.03)(0.02)
0.02-0.02(0.03)(0.02)
1047

0.02(0.03)
0.04(0.03)

Pane C: Location of Expenditures
Spent transfer at kiosk in village
Spent transfer at market within village
Spent transfer at market outside village
Panel D: Knowledge of Cash Transfer
Knew correct amount of cash transfer
Knew correct duration of cash transfer
Number of observations

Notes: This table presents the simple difference estimates for each of the different treatment areas. Column 1 shows the mean ands.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the difference in means between the different
treatments and the cash households. Column 4 shows the difference in means for the Zap and Mobile treatments. All regressions
control for commune-level fixed effects and the presence of a seed program in the village. Heteroskedasticity-consistent s.e. clusteredat the village level are presented in parentheses. *** significant at the 1 percent level, ** significant at the 5 percent level, *significant at the 10 percent level.

41

Table 8: Mobile Phone Ownership and Usage

Panel A: Mobile Phone Ownership and Usage
Program recipient used mobile phone since last harvest
Made or received calls
Sent or received a "beep"
Received credit via Zap
Communicate with family/friends inside Niger
Communicate with family/friends outside Niger
Communicate with commercial contacts inside Niger
Used mobile phone to communicate death/ceremony
Used mobile phone to obtain price information
Used mobile phone to ask for help/support
Number of observations
Panel B: Migration, Remittances and Mobile Transfers
At least one household member migrates
Percentage of household members who migrated
Household received remittances as income
Amount of remittances received for last transfer (CFA)
Number of remittances since last harvest
Received remittance via Western Union
Received remittance via friend
Received remittance via Zap
42

(1)
Cash
average(2)
ZapCash(3)
MobileCash(4)
ZapMobileMean(s.d.)

Coeff(s.e.)

Coeff(s.e.)

Coeff(s.e.)

0.46(0.50)
0.45(0.50)
0.03(0.17)
0.00(0.07)
0.18(0.39)
0.16(0.36)
0.00(0.00)
0.07(0.26)
0.01(0.11)
0.07(0.26)

0.33*** 0.15***(0.04)(0.04)
0.30*** 0.15***(0.04)(0.04)
0.12*** 0.04***(0.02)(0.01)
0.19*** 0.03**(0.02)(0.01)
0.29*** 0.13***(0.04)(0.03)
0.09***
0.02(0.03)(0.02)
0.01** 0.01***(0.00)(0.00)
0.12*** 0.08***(0.03)(0.02)-0.00
0.01(0.01)(0.01)
0.04
0.03(0.03)(0.02)
2116

0.18***(0.04)
0.15***(0.04)
0.08***(0.02)
0.16***(0.02)
0.16***(0.04)
0.07***(0.02)-0.00(0.01)
0.04(0.03)-0.01(0.01)
0.00(0.02)

0.39(0.49)
0.05(0.08)
0.21(0.41)
4216(12385)0.52(2.45)
0.06(0.25)
0.10(0.30)
0.00(0.04)

0.08*(0.05)
0.02*(0.01)
0.05(0.03)
493.24(842.57)
0.19(0.16)-0.01(0.01)
0.04(0.02)
0.00(0.00)

0.03(0.04)
0.01(0.01)
0.04(0.03)
267.92(825.93)0.19(0.14)
0.02(0.01)
0.01(0.02)
0.00(0.00)

0.05(0.05)
0.01(0.01)
0.01(0.03)
225.33(875.49)-0.00(0.12)-0.02(0.01)
0.03(0.02)-0.00(0.00)

Number of observations

2217

Notes: This table presents the simple difference estimates for each of the different treatment areas. Column 1 shows themean and s.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the difference in means between
the different treatments and the cash households. Column 4 shows the difference in means for the Zap and Mobile
treatments. All regressions control for commune-level fixed effects and the presence of a seed program in the village.
Heteroskedasticity-consistent s.e. clustered at the village level are presented in parentheses. *** significant at the 1
percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

43

Table 9: Intra-Household Decision-Making

Panel A: Decision-Making Regarding Cash Transfer
Program recipient responsible for spending part of cash transfer
Program recipient involved in deciding how to spend the transfer
Number of observations
Panel B. Women's Involvement in Agriculture
Program recipient visited market in past week

(1)
Cash
average(2)
Zap-Cash(3)
MobileCash(4)
ZapMobileMean(s.d.)

Coeff(s.e.)Coeff(s.e.)

Coeff(s.e.)

0.53(0.50)
0.99(0.09)-0.01(0.03)
0.01(0.01)-0.03(0.04)
0.01(0.01)

0.02(0.04)-0.00(0.01)-0.04(0.04)-0.02(0.03)

0.14***(0.04)
0.06**(0.03)

0.09*
0.03(0.05)(0.05)
1,749.80** -236.89(863.55)(780.32)1022

0.06(0.05)
1,986.70**(825.78)1040

Program recipient responsible for selling grains for household
Number of observations
Panel C: Clothing Expenditures for Muslim Festivals
Household spent money on women or children's clothing for festivals
Amount spent on children's clothing for festivals (CFA)
Number of observations

0.19(0.39)
0.15(0.36)

0.09**(0.04)
0.04(0.03)
1063

0.44(0.50)
4531(8087)

Notes: This table presents the simple difference estimates for each of the different treatment areas. Column 1 shows the mean and s.d. of the basic
treatment (Cash) households, whereas Columns 2 and 3 show the difference in means between the different treatments and the cash households.
Column 4 shows the difference in means for the Zap and Mobile treatments. All regressions control for commune-level fixed effects and thepresence of a seed program in the village. Heteroskedasticity-consistent s.e. clustered at the village level are presented in parentheses. ***significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

44

Table 10. Alternative Explanations
(1)

(2)

(3)

(4)

Cash
average

ZapCashMobileCash

ZapMobile

Mean (s.d.)Coeff(s.e.)

Coeff (s.e.)Coeff(s.e.)

0.25(0.44)
0.03(0.18)
0.07(0.38)-0.02(0.11)-0.05(0.03)-0.10(0.08)-0.13(0.10)
0.01(0.05)-0.05(0.11)

0.11(0.09)-0.06(0.05)-0.05(0.05)-0.02(0.05)-0.02(0.03)-0.02(0.05)
0.02(0.03)

Panel A: Access to Village-Level InfrastructureMarket located within the village
Zap agent in village
Number of Zap agents in village
Number of observations
Panel B: Shocks
Household was affected by drought in 2010/2011

96
0.66(0.47)
0.69(0.46)

Household was affected by illness in 2010/2011
Number of observations-0.04(0.05)-0.00(0.03)
1093

Notes: This table presents the simple difference estimates for each of the different treatment areas. Column 1 shows themean and s.d. of the basic treatment (Cash) households, whereas Columns 2 and 3 show the difference in means between the
different treatments and the cash households. Column 4 shows the difference in means for the Zap and Mobile treatments.
All regressions control for commune-level fixed effects and the presence of a seed program in the village.
Heteroskedasticity-consistent s.e. clustered at the village level are presented in parentheses. *** significant at the 1 percent
level, ** significant at the 5 percent level, * significant at the 10 percent level.

45