Tracking the Introduction of the Village Phone Product in Rwanda

Michael Futch and Craig McIntosh

June 2009

Abstract:
This paper presents the results from a quantitative impact study of the Grameen/MTN Village Phone in
Rwanda conducted between June 2006 and August 2007. We find that the introduction of a Village
Phone had a substantial impact on reported access to telecommunications for local entrepreneurs. Whilethe introduction of phones did not follow the intended randomized design, we compare the changes
observed in 94 study communities that receive the phones to the 284 that did not. We find that theplacement of a Village Phone in a community was associated with increased use of phones to transmit
news and a greater propensity for farmers to arrange their own transit. Despite this improvement inaccess to telephony, the actual prices received by farmers were not affected. Impacts at the household
level were muted by the relatively small size of Village Phone businesses and airtime usage rates,
implying that profit must be transferred from other sources in order to pay off the phone in six months.
Reported labor time in household enterprise increased dramatically for Village Phone operators, but
positive impacts on consumption or overall business profits were not found.

1. INTRODUCTION.
Mobile phones appear to have transformed developing economies, rapidly becoming themost ubiquitous high-tech consumer item in the poor countries of the world. Gauging the real
impact of mobile telephony on economic activity is an empirical challenge, however: telecom
providers are savvy at building new infrastructure only where demand exists or where growthprospects are high, and hence we tend to see a positive correlation between telephonic andeconomic expansion even if the relationship is not causal. Only a limited number of studies havebeen able to use panel data to construct before-after measurements of impacts, and studies such
as Jensen (2007) and Aker (2008) typically focus on prices and do not examine a broad range ofcommunity impacts.
This paper presents the results of a study intended to measure these broader economic
impacts of mobile telephony. We track a group of potential Village Phone operators (VPOs) andthe communities in which they reside as the Village Phone (VP) program is introduced in
Rwanda. The VP product, known locally as “Tel’imbere,” is a result of collaboration between
the Grameen Foundation and MTN Rwanda (MTNR), a part of major African mobile phone
provider MTN Group. The Village Phone operates in rural areas and links a car battery to a
standard mobile handset, which is then connected to an antenna that can be mounted on the roofof a kiosk. This then serves as a pay phone for the community, with the operator charging fixed
rates and receiving set profits as a function of call destination and airtime usage on the phone.
Car batteries are easily charged in market centers, and one charge runs the phone for roughly 2months. The antenna allows VP to operate beyond the range in which a typical mobile phonegets coverage, up to roughly 30 kilometers from the base station. Collaborations withmicrofinance institutions (MFIs) provide financing to operators so that they can purchase the1phones and pay off the loan with the profit stream from airtime usage. During the study period,
the term of the loan offered by MFIs was six months, and the total cost of the loan plus financing
was 261 US Dollars.
The intended design of this study was a randomized controlled trial, in which the phones
would be phased in to a group of 380 pre-selected villages in an experimental fashion. In theend, competitive pressures in the MFI sector as well as operational glitches in the rollout process
undermined the randomized structure. When we returned to do a follow-up study 15 months
after the baseline we found that 94 communities had received phones, but that the actual rollout
bore little resemblance to the intended design. We therefore treat our data as entirely nonexperimental, paying careful attention to the actual determinants of the receipt of a phone, andwe interpret these impact results with more caution than would be warranted in a randomized
evaluation.
We utilize a detailed household survey and an extensive community survey conductedwith entrepreneurs in the markets served by VPOs to draw conclusions about the effects of thephones. The household survey covers business outcomes such as profits, labor inputs, sales, andcustomer foot traffic at the enterprise level, as well as tracking more conventional impact
indicators such as consumption and schooling at the household level. The community survey
inquired after a wide range of potential impacts from telephony, including market availability,
prices received by local farmers, access to phones, and the means used to transport local goods tonational markets and communicate with outside entities such as police and health care providers.
Our results show clear mechanical effects of the phones at the community level:
entrepreneurs report local access to any kind of telephony rising from just over 50% to over 90%
when the VP is installed. The travel time to reach a phone reported by local entrepreneurs

2plummets from 56 minutes to 10 minutes in these communities. We see a moderate but
significant increase in the percentage of local farmers who report arranging for their own
transport to market (from 27% to 40%) and local entrepreneurs are more likely to pass news via
cell phone. However, the community analysis shows absolutely no impact of the phones on
trading activity or availability of goods in local markets, and household-level impacts are
repressed by the fact that airtime usage in Rwanda is so low as to indicate that few VPOs realize
sufficient profits to pay off their six month loans from that source alone. The sole significant
impact at the household level is an increase in the time reportedly worked in both VP and nonVP enterprises of operators. Therefore in the context of Rwanda, a densely populated country
with moderate pre-existing phone coverage, the actual use of Village Phones by clients issufficiently low as prevent them from being highly profitable, or from being drivers of local
commerce.

2. BACKGROUND.
Cross-country evidence on the impacts of information and communication technology(ICT) improvements have tended to show large impacts on economic growth, whether analyzing
fixed-line upgrading in developed countries (Röller & Waverman 2001) or the introduction ofmobile phones in developing countries (Waverman et al. (2005), or see Donner (2008) for an
excellent review of recent studies). Wallsten (2001) finds that the key determinant of broadbased, low-cost access to telephony in African and Latin American countries is the degree ofcompetition, whereas the effects of deregulation are more muted.1

1

Despite the rapid scale-up of mobile access on an African continent otherwise short on infrastructural successstories, data from the PingER project shows that the internet digital divide between Africa and the rest of the worldis large and growing (Zennaro et al 2006).

3

More micro-level studies of the impacts of ICTs have found a decrease in price volatility
across markets (Eggleston et al 2002; Jensen 2007; Abrahams 2008; Aker 2008), decreases in the
profits taken by middlemen (Goyal 2008) and increases in the efficiency of specific institutions
using ICTs (Athey & Stern 2002 for IT in US hospitals). The few studies that have looked
directly at the impact of mobile telephony on Small and Medium-sized Enterprise (SME) profits
in Africa have found divergent effects. Esselaar et al (2008), find huge effects of ICTs in
informal African enterprises, and criticize Choudhury & Wolf (2003) for failing to control
sufficiently for the degree of informality (informal businesses are more profitable and less likely
to use ICTs than formal businesses, and so this may create downward bias). However, theEsselaar et al study makes no mention of the substantial selection bias in the decision of an
informal business to buy a mobile phone, which itself may create substantial upward bias.
Other impacts of ICT introduction in Africa found in the literature are improved networking withoverseas migrants in Senegal (Tall 2004) and limited improvements in networking between
upper-level NGOs in Tanzania (Mercer 2004). Molony (2007), on the other hand, argues that thecontinued importance of trust in the overwhelmingly informal commercial interactions on thecontinent will continue to make face-to-face communication necessary.
The Grameen Village Phone program in its home country, Bangladesh, was the subject of
an in-depth analysis by Richardson et al. (2000). The study looked at a broad range of
qualitative impacts, focusing particularly on the gender matching between the VPO and the usersof the phone. Also, given the heavy international migration of Bangladeshis to the Persian Gulf
and other parts of the world, it found that a predominant use of the VPs was to communicate
with overseas diaspora. This issue is less salient in Rwanda, a country that has many external
refugees, a few of whom currently reside in places with good telecommunications access (that is,4areas like the Kivu region of the Democratic Republic of the Congo). The closest study to oursin terms of research subjects is Donner (2007), who conducts a study in Kigali of entrepreneurs
much like those studied in our analysis, and suggests that mobile phones create an importantavenue through which microentrepreneurs, particularly those with no landline phone, can create
new networks of customers.
The technical capabilities of the Village Phone suggest that they will operate withoutcompetition from other phones, and thereby generate a marked improvement in the quality of
local telephony. Further, this is a study of the pilot of Rwanda’s VP program, and therefore
virtually no competition from pre-existing VP phones exists. In reality, however, most of therollout in Rwanda occurs in areas that had some pre-existing mobile coverage, and it is notuncommon to see people talking on standard handsets in front of a VP enterprise. In suchlocations the introduction of a Village Phone creates no new access to telephony, but may stillcause access to telephony to become both cheaper and more convenient for households that do
not own a handset. This effect is likely to be more pronounced in the future if the highly
structured pricing of the VP product undercuts high prices for private rental of time on handsets.
Further undermining the magnitude of the ‘shock’ generated by the introduction of VP isa pre-existing product called Tuvugane, which also uses the MTNR network. The Tuvugane
handset is the size of an office phone with a small internal battery that can be charged overnight.
It lacks the external antenna of the Village Phone product, and so has a more limited ability toextend telephony beyond the boundaries of the standard coverage network. These phones wereubiquitous in urban areas and had already penetrated into relatively rural centers as of 2006 when
the baseline survey was conducted. Our data present multiple forms of evidence that Tuvugane
was the primary competitor of VP’s Tel’imbere in Rwanda. Indeed, even after the introduction5

of the VP product, Tuvugane is reported as the nearest public phone by entrepreneurs in morethan 60 percent of Tel’imbere markets. Therefore we must be careful not to interpret theseresults as the impact of access to a phone, but rather the impact of the improvement in telephonygenerated by the VP. While we see some real evidence of the improvement, we do not find the
sharp effects that some other studies have found. In the conclusion we discuss possible reasonsfor this, and the paper tests for whether impacts are stronger in environments with no previous
coverage.

3. VILLAGE PHONE PRODUCT DESIGN.
The Village Phone product consists of a standard Nokia handset that is plugged into a carbattery for power, and a roof-mounted antenna to extend the range of the phone. The phone isprovided to the operator via a six-month loan from local microfinance institutions (MFIs). The
total cost of the loan is $261 including interest costs, which translates into an average monthly
payment of $43.50. During the period of our study there were three MFIs providing financing
for the VP product: CARE, which makes small loans to relatively poor borrowers in large jointliability groups, and Vision Finance and Urwego, both MFIs that feature smaller groups andlarger loans to wealthier individuals.2 While more than half of the individuals in the study
sample were clients of CARE, the fraction actually receiving phones is roughly evenly split
across the three MFIs.
The loan is then supposed to be repaid using profits generated by the difference between
the retail price charged to customers and a discounted price charged to the operators by MTNR.
The cost to users of the Village Phone (Column 1 in Table 1) is based on rates on pre-paid
2

We find that the average CARE client has annual per capita expenditures of just under $200, while the figures for
Vision Finance and Urwego are $280 and $270, respectively. All three are non-profit organizations.

6

airtime cards, and is between standard per-second billing rates and ‘pay-as-you-go’ rates charged
to individuals who own their own MTNR handsets. The cost of using a Village Phone depends
on both the destination and duration of the phone call. The most inexpensive rate is for calls todomestic MTNR clients, while the highest rate is for international calls outside of East Africa.
The cost paid by the VPO to MTNR, however, varies depending on how long the Village Phone
has been in operation. MTNR offers a lower cost per unit for the first six months of operation(column II) to help offset the burden of repaying the loan. After 6 months the cost to the VPO
rises to those prices listed in column 4. The profit per unit during each respective time period is
also given in the table.
Table 1. Village Phone Pricing Schedule, US Dollars per minute.
RetailPrice

MTN
RwandaTel
East Africa
International
SMS
SMS International
MTN Info +

I
0.28
0.34
0.69
0.96
0.10
0.24
0.12

VP Enterprise,First 6 Months
Cost
Profitper minper min
II
III
0.17
0.11
0.28
0.07
0.48
0.21
0.62
0.34
0.06
0.04
0.14
0.10
0.07
0.05

VP Enterprise,
After 6 Months
Cost
per min
IV
0.22
0.30
0.56
0.81
0.08
0.19
0.10

Profitper min
V
0.06
0.04
0.12
0.15
0.02
0.05
0.02

In order for clients to make phone calls using Village Phone, the VPO must load airtime
onto the phone using MTNR scratch cards beforehand. When a call is made the cost is deductedfrom the Village Phone account according to the destination, duration and whether the phone has
been in operation for 6 months. After the call is made the VPO would retain the full price paidby the client. VPOs have complained that because the individual phone calls are not prepaid andthe calls are not metered, clients do not know the cost beforehand and will often not have thenecessary funds after the call is made. In order to receive proper remuneration, it is also the
responsibility of the VPO to correctly determine the appropriate rate based on the destination of7

the call. While there is anecdotal evidence of VPOs charging additional fees to phone users(particularly for receiving calls, which are not charged airtime fees in Rwanda, and for calls that
require the VPO to send someone to fetch the user), these fees are not a part of the formal VP
product.

4. STUDY DESIGN.
4.1. The Rollout of the Village Phones.
The intended design of the study was a randomized sequencing of the rollout of thephones. Combined with a baseline survey conducted prior to the rollout and a follow-up
conducted two thirds of the way through, this would have provided us with a clean experiment.
We selected our sample during Spring of 2006 by asking the three collaborating MFIs to identify
individuals who were interested in becoming operators, to whom they were willing to makeloans, and who belonged to groups that would be willing to undertake a VP loan with that
individual. We had to exert some pressure to obtain the desired sample size for the study (400groups), and in the end fell slightly short of that number. The sample therefore consists of‘likely’ clients in acceptable microfinance borrowing groups, who had expressed interest inoperating a Village Phone at the time of the baseline.
As discussed in the introduction, the rollout did not proceed as designed. Out of the 378
individual/community surveys conducted in the baseline (June 2006), 94 of them had received
phones as of the follow-up (August 2007). Nevertheless, the identity of those who received
phones bears only a weak resemblance to the intended experimental structure. Intended
treatment status was considered as an instrumental variable for actual treatment, but the
proportion of designated treatment individuals that actually received VP was 0.29 versus 0.22 for8those designated as controls. Furthermore, the difference between these means was statisticallyinsignificant, diminishing the predictive power of the first stage of an IV regression.3 Figure 1
maps the actual distribution of treatment and control locations along with MTNR coverage andthe profits of operators; while there is no obvious spatial pattern, the non-randomized nature of
the rollout presents several problems that we now discuss in more detail.

Figure 1. Map of MTNR coverage, cell towers, and the profits of Village Phone Operators.

Squares indicate locations of VPOs, size denotes VPO profit (US$/month).
Circles indicate the location of MTNR towers, x gives the location of untreated communities.

3

Note that non-VP communities that were intended to receive the treatment gained new access to the Tuvugane
product in almost the same proportion as those that were intended to be controls (55 percent vs. 58 percent).

9

From conversations with Grameen and the collaborating MFIs, three main factors drove
the collapse of the randomization and the actual provision of the phones. The first of these wasan increase in default across the Rwandan microfinance sector driven by the entrance of a host ofnew, unregulated institutions. Lenders were unwilling to extend Village Phone financing toborrowing groups that had encountered repayment problems, even if they were designated to betreated under the research design. Secondly, a shifting business climate meant that individuals
who had expressed interest in operating a phone at the time of the baseline no longer wanted
them at the point at which they were actually offered. Finally, pressure was applied to the MFIs
at the time of baseline to provide a sufficient number of potential operators, and some of theindividuals put forward may have been improperly vetted. This may have resulted in our
drawing a non-representative sample of operators. With these factors causing fewer phones tobe rolled out to the treatment than expected, operational pressures led to some control units
receiving phones as well.
The fact that those selecting out of the treatment may have suffered either fromrepayment shocks or from local business shocks might lead us to think that the operators who
actually received the phones were working in a more favorable climate than those who did not.
In other words, we may expect the bias created by this non-experimental design to be upwards,
meaning that the operators and communities that received phones would appear to be doing
better even had they not received the phones. Given that we find few positive impacts of thephones, particularly at the household level, this upward bias if anything would reinforce theoverall lack of impacts.
The standard method in the impact literature for estimating treatment effects where bothbaseline and follow-up data are present is a ‘difference in differences’ (DID) regression: this is a10

comparison of changes in the treatment to changes in the control. Statistically, we can write the
DID test without control variables as:(1)

Yit = β 0 + α1Treati + α 2 Per 2t + δ (Treati * Per 2t ) + ε it

Here Yit is some outcome variable, i indexes the time period (1 or 2), t indexes the individual
unit of observation, Treati is a binary variable indicating whether a unit will ever be treated,
Per 2t is a binary variable for the follow-up period, and the coefficient δ measures the DID;
changes observed only in the treatment and not in the control. In order to present the data in astransparent a fashion as possible, our tables provide the pre- and post-treatment means ofoutcome variables, partitioning across communities that do and do not receive phones. The test
statistic given for the DID is the t-statistic on δ from above.
There is one interesting issue that becomes tractable due to the failure of randomization,and this is a direct observation of the process through which VPOs are selected. If we take ourbaseline group as a sample of individuals who were an attractive target market for the product,
there is substantial interest in understanding what type of person and what type of village end up
receiving phones in reality. For this reason we present all tables with baseline means separately
for those who will and those who will not receive phones, as well as a t-test of the difference. In
this way we gain insight into the kinds of markets in which uptake for the phones will be highest,
although it is important to note that the sample for this study is not representative of Rwanda, butrather is representative only of the ‘likely’ markets as identified by the process through which we
selected the baseline sample.
4.2. Data.
The baseline survey was administered in May-June 2006, and the follow-up in AugustSeptember 2007. The survey selected a sample of 378 groups across CARE, Urwego, and11

Vision Finance. The Operator Survey was administered to the individuals identified as theputative operator in the baseline survey. Wherever possible the follow-up survey wasadministered to the same individual. If that individual could not be found in the follow-up andthere was no phone in the community, the follow-up team attempted to survey a different
member of the same MF group. If the community had received a handset, the follow-up survey
tracked the actual operator of the phone in addition to the baseline individual. The Operator
Survey collects information on a standard set of household and enterprise outcomes, including
consumption, expenditures, schooling, household composition, assets, phone usage, andinvestment and cash-flow in household enterprises.
The Community Survey was administered by bringing together two individuals,
identified by the surveyors as the most knowledgeable about local market conditions, and asking
them to answer a wide variety of community-level questions regarding prices, market
availability, local farming activity, and access to local telecommunications.4 We describe here
impacts on all community outcomes except prices, which are considered in a separate paper.
The complete survey includes 764 observations across the two waves. To ensure that we
found all relevant operators, we used two methods. First, during the follow-up survey we not
only asked whether our baseline surveyees had received phones, but whether anyone in that
community who was not surveyed in the baseline had. This allows us to correctly identify
community-level treatment even if the operator is not the person we interviewed in the baseline.
Second, we used a list of concurrent rollouts prepared by MTNR to call all VP phones
introduced during the study period in order to verify operator identify and location.
4

In some cases these community surveys are identical between two respondents, indicating a ‘consensus’ answer,
and in other cases they differ. To deal with this we cluster standard errors at the locality level when running
regressions from the community-level questionnaire, which provides answers basically identical to collapsing thesesurveys to the community/period level.

12In the household-level (and enterprise-level) analysis, we wished to track the same
individual from baseline to follow-up. In this case, we are interested only in individuals who
were surveyed in both waves, and so we consider as ‘treated’ only those for whom baseline andfollow-up surveys are available and who received phones by the follow-up. This creates abalanced panel dataset on 290 individuals, of whom 49 received phones between the twosurveys.5 At the community level, we are not interested in whom exactly received a phone,rather whether anyone in the community received one. Therefore for the community-level
analysis we create a dataset where the unit of analysis is the location rather than the individual,
and this results in a balanced panel dataset of 378 observations, of which 94 receive the‘community’ treatment.
The survey data is relatively ‘noisy’, and so we use a variety of techniques to try todampen variation caused by outliers. We present most of the analysis using simple comparisonsof means and differences-in-differences, and have experimented with trimming outliers(particularly for the self-reported financial calculations), so any results discussed are robust tothese changes.
We have three different ways of identifying operator profits. Following de Mel,McKenzie, and Woodruff (2008) we prefer the use of self-reported profits to trying to calculate
profits mechanically, but we present results using net revenue minus net costs from thehousehold survey as well as using self-reported profits. In addition to these two methods, the
MTNR data provides the phone number, duration, and time of every call made into or out of
Village Phones from January-September 2007. In combination with the billing structure, this5

Individuals who do not themselves receive phones but reside in communities that do are dropped from the‘individual’ dataset because they may receive an indirect treatment effect. Results are not sensitive to the inclusionof these observations. We are missing location information on 31 individuals, resulting in fewer observations inregressions that include spatial data.

13

gives a completely error-free means of calculating direct profits from airtime usage, although thismeasure does not subtract off the operating costs of battery charging and upkeep of the premises,
and misses potential increased profits from increased foot traffic or additional, unofficial
charges.6 This allows us to examine how airtime-driven profits relate to a variety of othercharacteristics for operators. We also use these raw data files to calculate simple statistics suchas the number of calls per month, average duration of calls, and so on.
We attempted to record the location of all communities with paper maps, from which
coordinates were then read as latitude/longitude figures into ArcView for GIS analysis. In theend we were unable to get definitive locations on 48 surveyees, and so the location data ispresent for 343 of our 391 operators. This can be combined with maps from MTNR that show
the location of their cell phone towers as of the time of the baseline, and GIS layers on road
networks to calculate distances. Further, we can define variables such as ‘number of VPOs
within 5 km’ by drawing circles around the location of each VPO. We now proceed to an
analysis of the data.

5. RESULTS.
5.1. Community level.
Comparison of the first three columns of Table 2 gives a sense of the selection effects of
the introduction of phones during our study period. Communities that would receive a VP by thefollow-up were, as of the time of the baseline, more isolated from telecommunications than theircounterparts that would not receive VPs. The communities that would see the establishment of
VP enterprises were not economically isolated, as we see that they were more likely to be in
6

For example, mobile users in Rwanda do not pay to receive calls, but there are reports that it is not uncommon for
VPOs to charge users of the phone for this.

14

villages hosting markets at the time of the baseline. Thus, the communities that actually receive
phones are entrepreneurial centers with poor pre-existing telecommunications access. Overall,however, we test the community-level selection process using a wide battery of characteristics
and we find selection effects to be limited. Those baseline characteristics that are associatedwith receiving phones are not associated with differential rates of change in the control groups,
and hence we suggest that little selection bias will be present in the DID impact estimates at thecommunity level (see Appendix A2 for the selection analysis, and A9 for regressions using thesesame variables to explain changes in outcomes in the control communities).

Table 2. Community-Level Analysis.
Baseline Outcome:Followup Outcome:T-stat on
NonBaseline
NonVPOs
VPOs Difference: VPOs
VPOs
Outcome:Phone call from this center possible
0.636
0.512
(1.92)
0.809
0.907
Distance to make a phone call (km)43.21856.589(1.52)
27.087
10.568Nearest phone is Tuvugane
0.799
0.862
(1.34)
0.706
0.616
Amount spent on phone calls
9.650
9.471
(0.10)
10.562
10.871Market meets in this center
0.325
0.483
(2.46)*
0.478
0.516# of traders that run business out of this center
5.926
6.684
(0.80)
7.672
9.670# of people who live here but trade in different market
0.467
0.391
(1.09)
0.418
0.525
Most common method of passing news (3:travel, 1:phone)
1.478
1.651
(1.98)*
1.314
1.247
Producers arrange own transport
0.467
0.391
(1.09)
0.418
0.525
Producers know the Kigali price before selling
0.177
0.174
(0.06)
0.175
0.242
Highest market price received by farmers over last year
0.379
0.348
(0.65)
0.344
0.351
Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%; estimated via Equation (1).
Baseline T-tests conducted on 326 community-level observations, DID has 670 panel observations.
For all yes-no questions, answer is coded as 1=yes and 0=no, so outcome gives the share (out of 1) indicating 'yes'.
All currency amounts are in 2007 US Dollars.

Difference T-stat onin
Difference
Differencesin
Differences
0.222(3.17)**-29.890(3.02)**-0.153(2.00)*
0.488(0.15)-0.120(1.63)
1.240(0.60)
0.183(1.98)*-0.240(2.03)*
0.183(1.98)*
0.070(0.96)
0.038(0.80)The DID impact of the phone is given by a comparison of changes in centers that see theintroduction of a VP versus changes in centers that do not. The starkest impacts arise in thereported access to phone service in local markets, and the distance traveled to reach the closest
phone. Reported phone access rises from 51% to 90% at the village level for communities thatreceive phones. The average travel time for the surveyed entrepreneurs to use a phone fell from56 minutes to 10 minutes in these communities, and the distance traveled falls by 30km, an 80%
15decrease. The likelihood that the closest reported phone is Tuvugane falls from 86% in thebaseline survey, but even after the intervention we still have 60% of surveyees reporting
Tuvugane as closest. Despite this increase in access, the direct price of telephony is notdecreased, and the total amount that entrepreneurs report spending on telecommunications also
does not change.
An additional interesting impact at the community level is on the ways in which local
farmers are able to get their goods to the market. The probability of farmers in local areas
arranging for their own transport was 26.5% for both treatment and control, but only in thetreatment that probability rises to 40%. While this effect is significant, it does not translate into
better information over prices or actual farmgate prices received by farmers (while point values
are all positive in the DID, they are not significant). This result is consonant with Molony(2008) who finds that interlinked provision of credit by established buyers prevents farmers fromrealizing informational gains that would otherwise be generated by mobile telephony. We seethat while VPs were attracted to markets, they did not seem to attract markets.
In summary, the phones had sizable impacts on reported access to telecommunications,
including travel times and distances, and had muted but detectable impacts on how news istransferred and how transport is arranged.
5.2. Enterprise level.
The enterprise-level analysis is based on a survey module eliciting separate financial
details for each different business activity conducted by the household. A new VP creates a newobservation at the enterprise level, and this makes panel comparison of VP versus non-VP
enterprises difficult. We approach this problem in two simple ways. The first three columns ofTable 3 use only the follow-up data, and compare the attributes of VP businesses to the attributes

16

of all other enterprises. This shows clearly that the VP business is smaller than the otherenterprises run by these entrepreneurs in every respect but one: the time the entrepreneur reports
working in the business. That is to say, the VP enterprise is uniquely time consuming and yet
generates revenues, costs, and profits that are lower than other enterprises. The implicit wage inthese enterprises is therefore very low, and yet clearly ‘working’ at a VP enterprise requires onlythat the phone be turned on, and therefore likely permits a high degree of multi-tasking.
Whether from the supply or the demand side of the market, the VP business is likely to bea second business. Only 5 VPOs got phones while not having been operating a baseline
business, which represents just 7% of the eventual sample of VPOs. Virtually no VPOs reportprofits greater than $90/month, while 20% of non-VPO enterprises make more money than this.
While VP businesses have roughly the same modal rate of profit as non-VPOs, the average VPO
profit is $20/month, well short of the $48/month reported in the average non-VPO business (seeTable 6).7
Table 3. Enterprise-Level Analysis.

Outcome (each enterprise is a separate observation):
Profit (self-reported)
Rank of business (1-3, 1 is 'most important')
Amount of loan invested in this business
Monthly Costs in enterpriseMonthly Revenue in enterprise
Total value of Stock in enterprise
Wage bill in enterprise
Hours worked by the entrepreneur in this business

Simple t-test of differences across secondperiod businesses, VP vs. non-VP.

Impact of the entry of VP businesses on all other enterprises
run by VPO, as a DID:

Non-VP
VP
enterprises,
enterprises,
followup data followup data
47.58
19.47
1.15
1.66
40.96
31.76
142.7335.22
231.1462.18
133.9033.16
123.83
33.41
43.04
59.59

Averagechange 20062007 without
VP entry-12.399-0.058-63.280
1.162-13.409-17.471-25.514
7.013

T-stat on
difference(3.64)**(6.65)**(0.54)(3.24)**(4.50)**(2.76)**(2.88)**(3.67)**

Averagechange 2006- Difference inT-stat on
2007 with VP Differences, Difference inentry
VP vs. non-VP Difference-18.262-5.863(0.43)-0.0520.006(0.06)
0.201
63.481(2.32)*-16.315-17.477(0.36)-87.520-74.111(1.18)-77.514-60.043(0.89)-50.623-25.109(0.50)
28.91121.898(4.29)**

Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%. All currency amounts are in US Dollars.
Followup comparisons conducted on 237 enterprises, DID has 444 panel observations on non-VP enterprises.

7

We had initially suspected that these small reported profits might be due to the fact that clients were deducting
debt-servicing costs; meaning that they were mentally subtracting the loan payment out of the reported profit.
However, on closer inspection only 20% of the VPOs who appear in the follow-up survey have had their phones forless than 6 months, meaning that those still servicing their loan are a minority in the data. Examining revenues
instead of profits, the picture is similar: average monthly revenues are $62(VP) versus $230 (non-VP), and thisfigure is free of any influence of outstanding debt in reporting.

17Columns 4-7 of Table 3 give the impact of the entry of a VP enterprise on the non-VP
enterprise of operators. It is therefore identified off of those individuals who start a VP
enterprise as a second business, and we analyze only the first business. The large increases inlabor time picked up among VP operators show up even in their non-VP businesses, suggestingfor example that a shopkeeper who also installs a phone keeps longer hours in both enterprises.
Reported credit access to this non-VP enterprise also benefits, presumably as a result of theadditional line of credit accessed through the VP financing. Reported profits are no higher,
however, failing to give evidence of an expected impact of the VP businesses: that increasedfoot traffic would benefit the VPO’s other business as well.
Analysis of profits in household enterprises is always complicated because of imperfectrecall and recordkeeping, as well as fungibility with the consumption side of the household. In
our case the repayment of debt on the phone adds an additional layer of complexity. Even
though the MFI loan had already fully been paid off in 80% of our VPO households at follow-up,
if the full costs were still being covered then the household might report lower profits for all
enterprises. Rather than relying on survey questions to try to understand how households
amortized profits, we use call data from MTNR to calculate the exact profits transferred to theoperators. This quantity is a mechanical function of airtime by destination as given in Table 1,
and gives the upper bound on the profits directly from the VP enterprise (if there were no costs),
ignoring completely the incidental effect on the profits of other enterprises.

18

Figure 2. Profitability Analysis, By Distribution of Airtime Usage.

Figures are in US dollars. The red line indicates the total cost including interest of servicing the loan, and the figure plots cumulative profit overmonths of operating the phone by percentile of phone usage (p50 is the median phone usage rate). When the curves cross the red line thisindicates that phone has paid for itself according to a simple and conservative measure of profits.

The total cost of the six-month loan that finances the VP is $261 including interest costs.
This translates into an average monthly payment of $43.50. We use the fee schedule per time
unit in the VP product, along with exhaustive data from MTNR on call durations anddestinations to calculate revenues and profits from the VP enterprise for operators. The averagemechanical profits from operating the phone are $30.50 per month in our sample, meaning thatthe profits from the phone only cover 70% of the debt-servicing costs.8 Thus, the average client
has to transfer profit into the VP enterprise from other sources in order to make the loan
payments. We do not find strong spillover effects of the VP enterprise on profits from otherenterprises, meaning that the margin between profits and loan payments likely represents a
negative net income shock to households during the course of the loan.
8

Our mechanical profit estimate does not include costs such as the need to charge the battery, nor sources ofrevenue such as informally billing customers for receiving calls or charging other mobile devices from the
Tel’imbere battery.

19

After the six months the household owns the phone outright, and hence can recoup thisinvestment fully. Figure 2 takes one approach to this accounting problem; showing the trajectoryof aggregate profit generated by the phone for different percentiles of phone usage. The red line
indicates the threshold at which operators break even, and shows that while a client in the 75th
percentile of call volume pays off the phone in the seventh month, the median client is still
paying off the phone at 9 months. We have few phones in our sample with a duration ofownership longer than this, and hence we should not expect to see any strong income effects ofthe phones on the basis of simple profit calculations. Clearly, this has to do with a low average
usage of the phones: 15,000 seconds per month sounds like a substantial figure, but thistranslates into an average monthly airtime use of only 4 hours and 15 minutes (a forty-hour per
week business is open ~170 hours per month).

5.3. Household level.
Turning first to selection effects, Table 4 shows very clearly that entrepreneurs who had
more businesses, businesses with more clients, higher revenues, and larger business stocks weremore likely to take the phones. Using pre-treatment data to make the comparison, the average
person who would eventually get a phone had three times the costs, twice the revenue, and morethan twice as many customers daily as those who would not eventually get phones. Baseline
owners of shops, restaurants, and bars are the most likely to receive phones, and farmers are least
likely.

20

Table 4. Household-Level Analysis.

Total for Entire Household of:Annual Expenditures per capita# times per week meat is consumed
Child enrollment rate
Number of Household Enterprises
Total Costs per month
Total Revenue per monthSelf-reported monthly profits
Total value of business stock# clients on a good day# paid employees
Total hours across all businesses worked/week
Size of current loan
Amount paid on current loan

Baseline Outcome:T-stat on
Baseline
Non-VPOs VPOs
Difference:
236.196
197.567(0.81)
0.859
0.980(0.74)
0.795
0.820(0.40)
0.851
1.266(3.88)**
161.592
443.848(2.46)*
226.685440.619(2.64)**
50.61486.740(1.78)
163.250461.693(3.16)**
26.97564.082(3.81)**
1.095
1.489
(1.21)
36.79437.954(0.34)
155.435251.937(2.04)*
128.478
159.274(0.71)

Followup Outcome:
NonVPOs
246.245
1.250
0.814
0.644
108.605160.305
38.959269.00223.8091.211
47.571333.945259.482

VPOs
261.488
1.490
0.800
2.001
246.289388.831
68.249285.269
70.4291.183
117.563418.733
517.892

Differencein
Differences
53.8720.119-0.0390.942-144.572
14.592-6.836-282.176
9.513-0.42268.832-11.714227.614

T-stat on
Differencein
Differences(0.68)(0.44)(0.44)(6.46)**(1.15)(0.14)(0.27)(1.03)(0.66)(0.46)(8.27)**(0.12)(2.04)*

Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%. All currency amounts are in US Dollars.
Baseline comparisons conducted on 290 households, DID has 580 panel observations.

The relatively small size and low profits of VP enterprises should temper expectations fordramatic impacts on outcomes such as consumption or school enrolment at the household level.
Indeed, Table 4 shows such impacts to be non-existent. Results not reported in Table 4 look at
each element of the consumption survey separately, as well as outcomes such as electricity in thehome and type of home construction and again find no impacts. The strongest impact seen at thehousehold-level echoes the result from the enterprise-level analysis: a dramatic increase in labortime across all businesses, with no corresponding change in profits or the number of employees.
The increase in the number of household enterprises is statistically indistinguishable from one,
implying that the VP enterprises do not ‘crowd out’ other businesses. Had this coefficient beenless than one it would imply that other enterprises were being closed as a result of the initiationof a VP business). The amount of debt retired by VPOs is roughly twice that for non-VPOs,
illustrating the heavier loan burden and the rapid repayment schedule associated with theoperation of a VP enterprise.

21

The result on labor time is so dramatic as to warrant some additional discussion. First,
what it means to be ‘working’ when operating a VP business may be simply that the phone is on
and the operator is ‘on call’ (the average VPO reports putting 60 hours of work a week into the
VP business alone). Second, this result will be driven in part by double-counting, since a major
benefit of the VP business is that it can be run jointly with a retail or service business. The
possibility of double-counting makes calculation of impacts on implicit wages difficult, but
clearly implicit wages have fallen in VPO households.9 The overall effect seen in VP and otherhousehold enterprises is that operators graduate from running 1.25 businesses into which theyput 28 hours each, to running 2 businesses into which they put an average of 50 hours each. The
VPO, therefore, far from ‘crowding out’ time in other enterprises, actually ‘crowds it in’. The
magnitude of this result falls if we trim outliers, but it remains strongly significant with any
reasonable degree of trimming. Figure 3 illustrates this impact a different way, showing thehistogram of total hours worked in VP household enterprises in the baseline (before they had aphone) and in the follow-up (once they had the phone).

9

To check this, we calculated implicit wages two different ways: first, we took the total profits across household
enterprises and divided them by the number of hours worked by household members in these enterprises. Thismethod assumes no double-counting. We then assumed perfect double-counting, and instead calculated implicit
wages dividing the sum of enterprise profits but the maximum number of hours worked in any enterprise. Both ofthese measures show a significantly negative DID impact.

22

0

.01

Density
.02

.03

.04

Figure 3. Hours Worked by Operators per Enterprise/Week, Baseline vs. Follow-up.

0

50
100
Hours Worked per Week
Baseline (Pre-Phone)

150

Followup (Post-Phone)

Given the fact that households that would become VPOs were operating larger
enterprises in the baseline anyway, we may wonder whether mean reversion is preventing us
from seeing an impact. That is, if bigger enterprises would have grown more slowly between
baseline and follow-up in the absence of the VP program, our estimates of the ‘impact’ of thephones would be biased downwards. We check for this by regressing the change in enterprisesize on the baseline enterprise size in the control group (where no treatment effects are present),
and find that indeed such mean reversion is present. We therefore run multivariate impact
regressions, controlling for baseline enterprise size in a DID regression. These results (notreported) show that the apparent impacts of the VP on household entrepreneurial outcomes are
improved when we control for baseline characteristics (particularly imputed profit, equal torevenues minus costs); in no case do they become statistically significant.

23

6. TARGETING & MARKET ANALYSIS.
6.1. Which types of communities and VPOs are the most attractive markets?
The central targeting question for the VP program relates to the kinds of entrepreneurs,
communities, and locations that are the best market for their product. An easy way to answer
this is to look at phone usage across the many characteristics captured in our data and to examine
which attributes correlate with heavy airtime usage.
The results of this analysis found surprisingly few significant characteristics. Perhaps
because all of our sampled communities were pre-identified as ‘good’ markets (and therefore wehave insufficient variation in the sample) we have found very few household or community
characteristics correlated with the usage per phone.10
Table 5. Community-Level Determinants of VP Profitability.
Average Monthly Number of calls
Profitper month
How many months VP has been operating
Km to closest MTN tower
Km to closest paved road
Number of VPOs within 5km
Distance to closest other VPO-1.310(1.01)
0.001(1.68)-0.001(1.90)-6.180(2.23)*-0.002(2.00)*-40.727(1.82)
0.013(1.55)-0.012(2.08)*-112.843(2.36)*-0.032(2.08)*

Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%.
Regressions run on the 85 VPOs for whom all location data is present.

Perhaps the most obvious characteristic that we would expect to drive the intensity ofphone usage would be the quality of pre-existing mobile coverage (only four communities hadlandline access as of the baseline, and so we focus on mobile access). This is naturally proxiedfor by distance to the closest MTNR tower. We might expect an inverted-U relationship,

10

These results are not presented here, but are available upon request.

24

whereby those very close to towers see little usage because non-VP mobiles are ubiquitous andthose too far from mobile towers are sufficiently isolated as to have less demand for telephony.
We examine this relationship by regressing the number of calls per VPO per month on
the distance from the closest MTNR tower and the square of this distance. Figure 11 plots theresulting relationship; while it is true that every VPO with more than 1200 calls per month lies in
the intermediate distance range, the quadratic term is not statistically significant. So in the end
we find less evidence of a direct impact of distance from mobile infrastructure on call volume
than anticipated.

6.2. Do Profits Decay over Time?
One straightforward question that can be asked with the profit data calculated from the
MTNR call records is whether we observe ‘decay’ in profits over the course of time as the cell
phone network expands. There is a technical issue in calculating these profits, because the pay
structure gives smaller payments to operators after 6 months (the expected duration of loan
servicing). This causes the apparent airtime profit to fall discretely at 7 months (see Figure 2).
This can be dealt with in a simple way by holding the fee structure constant at the rate used in thefirst six months, in which case the effects of this billing change are eliminated. With thiscorrection made, Figure 4 shows a box-and-whisker plot of revenues to the VPO according to theamount of months the phone has been operating, and finds no decay over the course of our(admittedly short) time period. While one would want a substantially longer period of
observation to make predictions about the future, at least in these early 9 months of the Rwanda
VP program there has been no decay in profits over time.

25

0Seconds of airtime per VPO/month
20,000 40,000 60,000 80,000

100000Figure 4. Airtime Usage, by Months Phone has been in Operation.

1

2

3

4

5

6

7

8

9

Number of months phone has been in operation

6.3. Saturation Effects between VP businesses.
One area in which local-level characteristics appear to have important impacts on callvolume is the ‘congestion’ effect generated by putting too many VPOs in too small a local area.
Since we have the lat/long coordinates of the VPOs, we can examine the density of VPOs to see
whether having many VPOs nearby to each other exerts a ‘poaching’ effect on the client base.
(This question is related to the size of the ‘catchment area’ from which a VPO draws clients; if
the client base is extremely local than we expect sharp congestion effects with immediate colocation).

26

0

Average Monthly Profit from VP, US$
20
40
60
80

100

Figure 5. VPO Profits by Number of Other VPOs within 5 km.

0

1

2

3

4

Numbers of observations are 316, 211, 94, 30, and 16 for the respective market saturations

Figure 5 plots the raw relationship between VPO profit and the local saturation of VPOs.
We see that having two additional VPOs within 5 kilometers appears to have no effect on
average profits, but three or more results in extremely low averages. The interpretation of thisrelationship is complicated by the fact that while being ‘remote’ indicates little competition, it
may also indicate an isolated or dispersed market. As a way of trying to disentangle these two
effects, Table 13 controls in a linear fashion for the distance to the nearest VPO (which we argue
proxies for remoteness) and the number of VPOs within 5 kilometers (competition). The effects
on these two measures are opposite; linear distance from a VPO is a bad thing (remoteness) but
conditional on this relationship, having more VPOs located in your immediate areas is also a badthing. Hence we see strong evidence of congestion effects in the data.

27

6.4. Does preexisting telephony alter the impact of Village Phone?
A final source of heterogeneity that could be important in mediating the impact of VP isthe pre-existence of telephony in the community. We might imagine that sharper impacts would
be found when VP is introduced into communities that had no prior access to telephones (mobile
or landline). To investigate this, we use an interaction analysis to test for differential impacts ofthe treatment in communities with no baseline access to phones.
Table 6. Differential Impact in Communities Without Baseline Phone Access.

Round 2 * Treatment
No previous phone accessNo access * Round 2 * Treatment
Round 2Treated everConstant
Observations
R-squared

Expenditures
per capita Business costs
53.087-144.572(0.67)(1.16)
11.56-51.306(0.36)(1.00)-60.488-321.457(0.75)(2.56)*
10.057-52.987(0.31)(1.03)-65.692
149.536(1.00)(1.43)
242.29134.555(8.49)**(2.97)**
566
580
0
0.04

Outcome:Business
revenue
14.592(0.14)-19.216(0.46)-72.073(0.70)-66.38(1.58)
184.936(2.15)*
216.559(5.83)**
580
0.04

Business
profit-6.836(0.27)-13.953(1.33)-31.815(1.24)-11.655(1.11)
23.864(1.12)
43.261(4.68)**
580
0.02

Hours
worked68.867(8.30)**-4.282(1.06)
17.539(2.10)*
10.716(2.63)**
8.824(1.27)
34.68(10.27)**379
0.37

Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%.

The overall decrease in costs and increase in hours worked are both found to be larger incommunities where no telephonic access existed. However, consumption in operators’households and profits in their business are not found to be significantly different, and since
there is no overall effect on these central outcomes we conclude that the lack of impact iscommon both to locations with and without access to phones as of baseline.

28

7. CONCLUSION.
This paper presents the results of the 2006-2007 study of the Rwanda Village Phone
product. We find a substantial improvement in access to telecommunications (reported local
access, travel times, use of phones to pass news, and farmers arranging for their own transport tomarkets), but little evidence that this access translates into improved final outcomes such as
prices received by farmers or operator income. In all, the results may be surprising for the lackof these broad final impacts. While some of the survey data is ‘noisy’ (which would result in theinability to find statistically significance even for large impacts), we fail to find impacts even formany precisely-measured outcomes.
The failure of the randomization of the rollout of Village Phone undermines ourconfidence in the cleanliness with which these impacts are measured. It is likely that groups
with repayment problems were more likely to wind up not receiving phones, and that operatorswith larger businesses in more attractive markets were more likely to receive them. Both ofthese factors, however, would lead us to believe that the results presented here are overestimatesof the real effects, and hence this does not seem a likely explanation for the lack of observed
impacts. Nonetheless, it is possible that since the areas receiving phones were more likely tohave been entrepreneurial centers as of baseline, markets were already functioning well there andthus they would have seen smaller improvements even in the absence of the treatment.
At the operator level, the most likely explanation for the lack of individual-level
outcomes is the low average phone usage found in our sample. This implies that the median
operator earns only about 70% of the total amount due on their loan during the six month
repayment period in Rwanda. If we compare the airtime usage figures to other Village Phone
programs in Africa we see that indeed airtime usage is lowest in Rwanda, where the average29operator has 8.8 minutes per day of outgoing calls, compared to Cameroon where that figure is
10.5 minutes and Uganda where it is 13 minutes. Therefore the profits realized by operator
households are low both in an absolute sense (compared to the term of the loan) and in a relative
sense.
The Grameen Foundation has already responded to this problem in Rwanda, encouraging
collaborating MFIs to extend the repayment period for MFI financing from six months to one
year, and increasing the margins realized by VPOs after the loan is repaid. In the VP program in
Cameroon, changes to the pricing and repayment structure have decreased the break even daily
airtime usage from 14 minutes per day to only 6 minutes, meaning that one would expect
substantially stronger household benefits to operators from the product currently in the field thanthe product studied in this paper.
At the community level, the relatively small impacts may best be explained by the preexistence of a very similar product (Tuvugane), aimed at a similar segment of the market andsharing an almost identical retail pricing structure. The relative ubiquity of Tuvugane in Rwanda
suggests that the welfare gains that we expect to accompany the creation of widespread, low-costaccess to telephony at the village level had already occurred as of our baseline. Indeed, it isnoteworthy that despite the significant 20% decrease in the number of local entrepreneurs
reporting Tuvugane as the closest phone, over 60% of those in communities with Village Phone
still report Tuvugane as the closest phone in the follow-up. This, compounded with the small
size and relatively good transportation infrastructure of Rwanda, seems to have precluded the
sharp impacts of telephony seen in studies such as Jensen (2007). Again, this suggests that the
impact of Village Phone at a community level may be larger when it is introduced in one of themany African countries without a similar product already on the market.

30

Nonetheless, the communities into which the phones go do see some demonstrable
benefits. Most striking are the improvements in phone access, travel times, and travel distances
reported by local entrepreneurs. There is intriguing (if weaker) evidence of a shift towards theuse of telephones to transmit news between villages, to arrange transport of harvests to market,
and to contact the police. We therefore find significant evidence of improvements in theintermediate goal of access to telephony, but not in the ultimate outcomes such as incomes or
prices received by farmers. In conclusion, airtime usage in Rwanda is sufficiently low as toprevent the VP business from being very profitable for operators. However, communities see
real improvements in telecommunications access and some shifts in local-level behavior arisingfrom this improved access.

31

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33

DATA APPENDIX.
A1. Basic Comparison of Client-Level Characteristics across MFIs:
Baseline Outcome:Annual Expenditures per capita# times per week meat is consumed
Fraction with electricity
Fraction homeowners
Total value of non-business assets
Annual wages by all household members
Enrollment rate for girls
Enrollment rate for boys
Fraction with landline phone
Fraction with mobile phone

CARE

URWEGOVISIONFINANCE

195.48
0.66
0.01
0.92
114.03
191.16
0.80
0.81
0.01
0.17

268.10
1.11
0.18
0.82
472.80260.99
0.87
0.86
0.03
0.50

278.57
1.20
0.20
0.78
265.04371.47
0.85
0.74
0.02
0.48

Number of Household Enterprises
Total Costs per month
Total Revenue per monthSelf-reported monthly profits
Total value of business stock
Purchases of new capital equipment past month
Liquidation of capital equipment past month# clients on a good day# clients on a bad day# paid employees
Total hours across all businesses worked/week
Size of current loan
Amount paid on current loan

0.67
66.55
114.95
26.97
98.98
6.15
1.04
13.23
5.99
0.77
31.66
99.40
91.48

1.47
295.42485.9587.47
457.91
70.24
2.00
52.24
20.50
2.24
39.16
323.52258.201.15
446.73449.70100.54327.14183.54
1.77
63.49
24.35
1.44
42.14
243.67159.26

Total household savings
Fraction of individuals who received phones

418.230.11

217.480.32

541.350.22

All currency amounts are in US Dollars.

34

DETERMINANTS OF SELECTION.
A2. Household Comparison of Baseline Characteristics(are those who will receive phones different from those who will not?).
Baseline (untreated) Outcome:Annual Expenditures per capita# times per week meat is consumed
Fraction with electricity
Fraction homeowners
Total value of non-business assets
Annual wages by all household members
Enrollment rate for girls
Enrollment rate for boys
Fraction with landline phone
Fraction with mobile phone
Number of Household Enterprises
Total Costs per month
Total Revenue per monthSelf-reported monthly profits
Total value of business stock
Purchases of new capital equipment past month
Liquidation of capital equipment past month# clients on a good day# clients on a bad day# paid employees
Total hours across all businesses worked/week
Size of current loan
Amount paid on current loan
Total household savings

Non-VPOs
236.196
0.859
0.096
0.867
176.707259.020
0.822
0.795
0.012
0.290

VPOs
197.567
0.980
0.063
0.837
349.036228.010
0.840
0.820
0.040
0.367

T-stat on
difference(0.81)(0.74)(0.73)(0.56)(1.62)(0.20)(0.29)(0.40)(1.39)(1.07)

0.851
161.592226.685
50.614163.250
42.392
1.266
26.975
11.2161.095
36.794155.435
128.478441.0871.266
443.848440.61986.740461.693188.9451.939
64.08223.7551.489
37.954251.937
159.274
366.198(3.88)**(2.46)*(2.64)**(1.78)(3.16)**(1.84)(3.47)**(3.81)**(2.94)**(1.21)(0.34)(2.04)*(0.71)(0.25)

Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%
All currency amounts are in US Dollars.

35

A3. Community-level Comparison of Baseline Characteristics 1(are communities that will receive phones different from those that won’t?)Baseline (untreated) Outcome:Phone call from this center possible# of different places to make call in this center
Possible to buy airtime cards in this center
Land line connections in this cell# of people with land line phones in this cell# of people with mobile phones in this cell
Distance to make a phone callThat phone is Tuvugane
That phone is Landline
Distance to reach a Tuvugane phone

Non-VPOs

VPOs

T-stat on
difference

0.636
2.229
0.468
0.171
1.800
0.920
43.2180.799
0.209
48.028

0.512
2.510
0.519
0.184
1.577
0.897
56.5890.862
0.116
54.061(1.92)(0.37)(0.63)(0.23)(0.19)(0.52)(1.52)(1.34)(1.95)(0.73)

Center on a paved roadIf not, distance to nearest paved road
Travel time to Kigali
Travel cost to Kigali
Electricity in this sector
Market meets in this center
If not, distance to nearest market
How often does nearest market meet
# of people opening new business in last year
Most common method of passing news

0.226
116.596191.733
1348.391
0.309
0.325
48.742
1.885
3.078
1.478

0.190
129.672220.640
1442.442
0.306
0.483
41.189
2.105
3.418
1.651

(0.68)(1.03)(1.60)(0.76)(0.05)(2.46)*(1.29)(1.24)(0.65)(1.98)*

Amount spent on phone calls for business transport
Amount spent on phone calls for price inquiries
Amount spent on phone calls for market availability
Amount spent on phone calls for emergencies
Amount spent on phone calls arranging remittences
Amount spent on phone calls for social purposes
Amount spent on phone calls
Surveyed community member owns phone
Price charged for friend phone use
Price charged for landline phone use
Price charged for mobile phone use
Price charged for tuvugane use
Price charged for telimbere use
Made international calls in last 6 months
Number of times household member ill last 6 months# of times household member been to health clinic 6 mo
Used phone to contact clinic# of times household member been hospitalized in 6 mo
Used phone to contact hospital# of times household member purchased medication 6 m
Used phone to purchase medication
Distance to nearest health care facility
Nearest health care facility has phone# of crimes in this center in last year
How many involved police
How were police contacted 1-4
If needed, would contact police by landline
If needed, would contact police by mobile
If needed, would travel to police station

5.029
3.582
3.389
1.572
2.259
3.553
9.650
0.483
0.682
0.158
0.614
0.083
0.065
0.218
3.642
3.022
0.063
1.708
1.980
3.355
0.025
52.028
1.564
2.349
1.588
2.091
0.094
0.534
0.653

6.701
3.831
4.044
1.414
2.904
3.723
9.471
0.477
0.595
0.126
0.470
0.137
0.028
0.206
4.230
3.576
0.068
1.455
1.979
4.711
0.034
50.043
1.517
3.982
2.616
2.187
0.050
0.400
0.667

(1.11)(0.28)(0.49)(0.47)(0.63)(0.32)(0.10)(0.09)(0.63)(0.40)(0.74)(0.72)(1.08)(0.22)(1.32)(1.38)(0.16)(1.43)(0.07)(1.84)(0.40)(0.34)(0.80)(1.80)(1.51)(0.55)(1.37)(2.10)*(0.22)

36

A4. Community-level Comparison of Baseline Characteristics 2(are communities that will receive phones different from those that won’t?)Baseline (untreated) Outcome:Local cooperative in center# of traders for a given commodity# of traders that run business out of this center
Producers arrange own transport
Producers arrange own transport more than a year ago
Cell member used fertilizer or chemicals on this crop
Producers know the Kigali price before selling
Highest price received by farmers last harvest
Lowest price received by farmers last harvest
Commodity for sale at local market
Highest market price over last year
Lowest market price over last year
Beef in center
Beef in market
High price last 6 months
Low price last 6 months
Airtime cards in center
Airtime cards in market
High price last 6 months
Low price last 6 months
Soda in center
Soda in market
High price last 6 months
Low price last 6 months
Condom in center
Condom in market
High price last 6 months
Low price last 6 months

37

Non-VPOs
0.346
11.114
5.926
0.467
0.262
0.709
0.177
0.324
0.225
0.991
0.379
0.271

VPOs
0.414
13.549
6.684
0.391
0.269
0.777
0.174
0.304
0.229
0.994
0.348
0.270

T-stat on
difference(1.07)(1.16)(0.80)(1.09)(0.13)(1.25)(0.06)(0.43)(0.14)(0.38)(0.65)(0.03)

0.646
1.077
1.500
1.330
0.415
1.278
0.928
0.917
0.966
1.038
0.348
0.300
0.882
1.068
0.098
0.087

0.679
1.128
1.546
1.356
0.459
1.279
0.932
0.917
0.988
1.023
0.348
0.306
0.919
1.086
0.094
0.082

(0.55)(1.26)(0.89)(0.54)(0.69)(0.02)(0.57)(.)(1.36)(0.83)(0.11)(1.08)(1.01)(0.56)(0.69)(1.13)IMPACTS.
A5. Household Impacts of Phones, Difference-in-differences(are the changes for those who received phones different than the changes for those who didn’t?).

Magnitude of the Change in Outcome:Annual Expenditures per capita# times per week meat is consumed
Fraction with electricity
Fraction homeowners
Total value of non-business assets
Annual wages by all household members
Enrollment rate for girls
Enrollment rate for boys
Fraction with landline phone
Fraction with mobile phone
Number of household enterprises
Total Costs per month
Total Revenue per monthSelf-reported monthly profits
Total value of business stock
Purchases of new capital equipment past month
Liquidation of capital equipment past month# clients on a good day# clients on a bad day# paid employees
Total hours across all businesses worked/week
Size of current loan
Amount paid on current loan
Total household savings

Average change Average change Difference in2006-2007
2006-2007
Differences,T-stat on
among
among
VPOs vs. non- Difference in
Non-VPOs
VPOs
VPO
Difference
10.04963.92153.872(0.68)
0.391
0.510
0.119
(0.44)-0.0110.081
0.092(1.42)
0.095
0.080-0.015(0.23)
134.443-59.539-193.982(0.79)-19.252
219.116238.368(1.18)
0.026
0.092
0.066
(0.79)
0.019-0.020-0.039(0.44)
0.000-0.041-0.041(1.57)
0.086
0.224
0.138
(1.32)-0.207-52.987-66.380-11.655
105.752-42.255
414.548-3.166-1.6060.116
10.777178.510131.004-89.105

0.735-197.559-51.788-18.491-176.424-179.209
0.551
6.347
4.959
-0.30679.609166.796358.618-97.888

Absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%
All currency amounts are in US Dollars.

38

0.942
-144.572
14.592-6.836-282.176-136.954-413.997
9.513
6.565
-0.42268.832-11.714227.614-8.783(6.46)**(1.15)(0.14)(0.27)(1.03)(1.71)(0.45)(0.66)(1.06)(0.46)(8.27)**(0.12)(2.04)*(0.03)

A6. Household Impacts, using Control Variables:(controlling for baseline characteristics, are the changes for those who received phones different
than the changes for those who didn’t?).
Outcome variable:

Control Variable:
Impact of Receiving VP
Baseline Business size
Baseline hours worked
Baseline number of clients
Distance to MTN cell phone tower (km)Distance to Road (km)No. of dependents in hh
HH head Age
HH head Age squared
Dummy for followup
Dummy for ever treatedConstant
Observations
R-squared

39

SelfReported
Profit

ImputedProfitRevenue-8.914(0.31)
0.044(5.26)**
0.171(0.73)
0.383(3.80)**
0.662(0.58)-2.706(2.64)**-0.228(0.11)
5.417(1.64)-0.066(1.60)-10.050(0.91)
11.386(0.56)-61.174(0.98)

217.759(1.41)-0.054(1.16)
2.517(1.98)*-0.267(0.49)-3.599(0.58)
2.816(0.50)
16.838(1.52)-8.415(0.47)
0.063(0.28)-10.728(0.18)-102.73(0.92)
153.738(0.45)

502
502
0.14
0.02
(t-statistics in parentheses)

Costs

Expenditur
es perCapitaHours
Worked77.309(0.70)
0.127(3.85)**
1.783(1.96)
2.040(5.19)**
0.835(0.19)-9.028(2.26)*-1.126(0.14)
18.820(1.46)-0.253(1.56)-69.948(1.62)
83.204(1.05)-147.838(0.61)-140.450(0.99)
0.180(4.29)**-0.734(0.63)
2.307(4.60)**
4.434(0.77)-11.844(2.32)*-17.964(1.77)
27.235(1.65)-0.316(1.53)-59.220(1.07)
185.933(1.83)-301.576(0.97)

38.397(0.47)
0.129(5.29)**
0.614(0.91)
0.662(2.28)*-7.340(2.22)*-0.827(0.28)-22.402(3.81)**
19.538(2.04)*-0.207(1.73)
5.587(0.18)-102.333(1.74)-29.654(0.17)

69.972(7.89)**
0.001(0.41)
0.558(6.28)**
0.005(0.17)
0.157(0.38)-0.098(0.27)
0.223(0.31)-0.835(0.62)
0.009(0.50)
14.614(3.43)**-0.532(0.08)
31.77(1.26)-0.316(0.29)
0.000(0.56)
0.018(2.04)*
0.002(0.42)
0.103(2.36)*-0.052(1.35)
0.045(0.58)
0.181(1.44)-0.002(1.46)
0.202(0.48)-0.051(0.07)-3.541(1.50)

1.048(7.10)**
0.000(1.77)
0.009(7.20)**
0.001(2.17)*
0.009(1.55)-0.017(3.15)**-0.007(0.68)
0.027(1.57)
0.000(1.80)-0.189(3.29)**
0.233(2.19)*
0.226(0.70)

502
0.18

502
0.13

500
0.13

320
0.43

502
0.03

502
0.37

Number of
Employees businessesA7. Community-level Impacts 1(are the changes in communities that receive phones different from those that don’t?)

Magnitude of the Change in Outcome:Phone call from this center possible# of different places to make call in this center
Possible to buy airtime cards in this center
Land line connections in this cell# of people with land line phones in this cell# of people with mobile phones in this cell
Distance to make a phone callThat phone is Tuvugane
That phone is Landline
Distance to reach a Tuvugane phone

Average change Average change Difference in2006-2007
2006-2007
Differences,T-stat on
among
among
VPOs vs. non- Difference in
Non-VPOs
VPOs
VPO
Difference
0.173
0.395
0.222
(3.17)**
0.428
2.054
1.626
(1.14)
0.236
0.187-0.049(0.53)-0.066-0.0230.043(0.63)-0.110-0.369-0.259(0.19)
0.035
0.092
0.057
(1.17)-16.131-46.021-29.890(3.02)**-0.093-0.246-0.153(2.00)*-0.0920.011
0.103(1.71)-30.834-38.702-7.868(0.79)

Center on a paved roadIf not, distance to nearest paved road
Travel time to Kigali
Travel cost to Kigali
Electricity in this sector
Market meets in this center
If not, distance to nearest market
How often does nearest market meet
# of people opening new business in last year
Most common method of passing news

0.047
4.919
153.81136.448
0.011
0.153
2.928
0.078
0.686
-0.1640.099-21.790
24.696-118.060
0.086
0.033
1.858
0.073
-0.396-0.4040.052-26.709-129.115-154.508
0.075-0.120-1.070-0.005-1.082-0.240(0.96)(1.87)(1.66)(1.10)(1.52)(1.63)(0.09)(0.02)(1.51)(2.03)*

Amount spent on phone calls for business transport
Amount spent on phone calls for price inquiries
Amount spent on phone calls for market availability
Amount spent on phone calls for emergencies
Amount spent on phone calls arranging remittences
Amount spent on phone calls for social purposes
Amount spent on phone calls
Surveyed community member owns phone
Price charged for friend phone use
Price charged for landline phone use
Price charged for mobile phone use
Price charged for tuvugane use
Price charged for telimbere use
Price charged for other phone use
Made international calls in last 6 months
Number of times household member ill last 6 months# of times household member been to health clinic 6 mon
Used phone to contact clinic# of times household member been hospitalized in 6 mont
Used phone to contact hospital# of times household member purchased medication 6 mo
Used phone to purchase medication
Distance to nearest health care facility
Nearest health care facility has phone# of crimes in this center in last year
How many involved police
How were police contacted 1-4
If needed, would contact police by landline
If needed, would contact police by mobile
If needed, would travel to police station-2.189-1.634-1.804-1.249-1.377-0.002
0.912
0.219
0.029
-0.1220.490-0.015
0.024
0.004
0.102
-0.855-0.4710.099-1.225-0.022-1.0140.022-1.670
0.172
1.048
1.209
-0.390
0.461
0.399
0.167

-4.184-1.795-2.970-1.046-1.515
0.844
1.400
0.257
-0.314-0.0900.490-0.023
0.031
0.004
0.135
-1.819-1.4130.064-1.107-0.058-2.9150.016-6.9230.036-1.229-0.498-0.278
0.431
0.525
0.023

-1.995-0.161-1.1660.203-0.138
0.846
0.488
0.038
-0.3430.032
0.000-0.008
0.007
0.000
0.033
-0.964-0.942-0.0350.118-0.036-1.901-0.006-5.253-0.136-2.277-1.7070.112-0.0300.126-0.144(1.15)(0.16)(0.80)(0.50)(0.13)(0.83)(0.15)(0.49)(1.65)(0.38)(.)(0.09)(0.18)(.)(0.40)(1.87)(2.09)*(0.66)(0.59)(1.12)(2.53)*(0.16)(0.77)(1.51)(1.94)(1.62)(0.45)(0.26)(1.72)(1.33)

40

A8. Community-level Impacts 2(are the changes in communities that receive phones different from those that don’t?)

Magnitude of the Change in Outcome:Averagechange 20062007 among
Non-VPOs

Difference in T-stat on
Averagechange 2006- Differences, Differencein2007 among VPOs vs. nonDifference
VPO
VPOs

Local cooperative in center-0.066-0.109-0.043(0.49)# of traders for a given commodity

1.701-0.240-1.941(0.61)# of traders that run business out of this center

1.746

2.986

1.240

(0.60)

Producers arrange own transport-0.049

0.134

0.183

(1.98)*

Producers arrange own transport more than a year ago-0.066-0.0390.027(0.32)

Cell member used fertilizer or chemicals on this crop

-0.106-0.0580.048(0.60)

Producers know the Kigali price before selling-0.002

0.068

0.070

(0.96)

Highest price received by farmers last harvest-0.019

0.011

0.030

(0.65)

Lowest price received by farmers last harvest

0.016

0.029

0.013

(0.41)

Commodity for sale at local market-0.021-0.0120.009(0.69)

Highest market price over last year

-0.035

0.003

0.038

(0.80)

Lowest market price over last year

0.002

0.016

0.014

(0.41)

Beef in center

0.056

0.036

-0.020(0.29)

Beef in market-0.026-0.084-0.058(1.26)High price last 6 months

0.072

0.039

-0.033(0.71)

Low price last 6 months

0.048

0.045

-0.003(0.07)

Airtime cards in center

0.298

0.337

0.039

(0.58)

Airtime cards in market-0.192-0.1820.010(0.17)High price last 6 months

0.007

0.006

-0.001(0.10)

Low price last 6 months

0.000

0.000

0.000

(.)

Soda in center

0.014

0.001

-0.013(0.60)

Soda in market-0.030-0.0010.029(1.17)High price last 6 months

0.025

0.026

0.001

(0.17)

Low price last 6 months

0.068

0.062

-0.006(0.99)

Condom in center

0.028

0.010

-0.018(0.46)

Condom in market-0.015-0.020-0.005(0.10)High price last 6 months

0.011

0.012

0.001

(0.10)

Low price last 6 months

0.016

0.017

0.001

(0.14)

41

A9. Changes in non-VPO households(Do variables that predict treatment lead to differential changes in the controls?)
Revenue
ChangeAble to make a call from this center
Number of Household Enterprises
Total Costs per month
Total Revenue per monthSelf-reported monthly profits
Total value of business stock
Purchases of new capital equipment past month
Liquidation of capital equipment past month# clients on a good day# clients on a bad day# paid employees
Total hours across all businesses worked/week
Size of current loan
Amount paid on current loan
Total household savings
Constant
Observations
R-squared

42-101.348-7.9760.133-0.782-0.9020.004-0.127-20.599-0.564
1.604
0.745
5.18
0.392
-0.183-0.007194.491234
0.38

t-stat
-1.31
-0.07
-1.39
(5.03)**
-1.47
-0.05
-0.9
-0.4
-0.36
-0.44
-0.03
(2.45)*
-1.13
-0.5
-0.35
-1.55

ProfitChange-27.881-11.740.026
0.001-1.1290.011-0.0070.697
0.091-0.332-0.427
0.307
0.045
0.012
-0.00179.216234
0.62

t-stat
-1.9
-0.53
-1.41
-0.02
(9.69)**
-0.68
-0.27
-0.07
-0.31
-0.48
-0.09
-0.77
-0.68
-0.17
-0.38
(3.32)**