Growth Week 2011: Country Session 6 - Rwanda

Risk and Reciprocity Over the Mobile Phone Network:
Evidence from Rwanda

Joshua Blumenstock Nathan Eagle Marcel Fafchamps
UC Berkeley Santa Fe Institute Oxford University

September 2011

Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley
Mobile Phones Santa Fe Institute
September 2011 1 / 22

Motivation

Earthquakes and other natural disasters can have catastrophic e¤ects
Rely on friends, family, and neighbors for support in order to cope
Mobile phones have the potential to alleviate the short-term
consequences of a severe shock:
call for help (emergency services, stuck in rubble)
mobile money redeemed against food, shelter, health care
banks disrupted, ATM’ run out of cash
s
Households in developing world seldom hold large airtime balances
transfers of airtime/mobile money can provide tremendous help in
immediate aftermath of natural disaster
assuming that some cell towers remain in operation and phones are
charged


The Earthquake
Magnitude 6 earthquake in Western Rwanda on February 3, 2008
43 dead and 1,090 injured. 2,288 houses destroyed


Contribution

Data on all phone-to-phone airtime transfers ME2U in Rwanda
between 2006 and 2008
Earthquake caused a large and signi…cant in‡ux of airtime transfers to
people close to the epicenter.
highly signi…cant on the day of the earthquake and the following day
not on a number of “placebo" days
robust to di¤erent estimation strategies.
Total volume small, probably because mobile banking launched
shortly before earthquake
If similar earthquake today and same proportional response, mobile
money sent would be between USD$11,000 to $22,000.


Contribution

Additional data from phone interviews with 2,200 phone users
Construct measures of wealth and social network based on phone call
data

Findings:
More earthquake transfers go to richer individuals – probably because
they are more intensive users of telephones
More transfers to those with a large number of contacts living close
by, but not so close as to have been directly a¤ected by the
earthquake.
More transfers from people near a¤ected area, less from cities


Identi…cation and estimation

We construct gross and net transfers received in all locations on each
day before and after the earthquake
We compare transfers on the day of the earthquake to the average
transfer received by this location on other days just before and after
the earthquake
We vary the width of the time window to check robustness
We control for variation in transfers between di¤erent days for all
locations (e.g., day of the week, day of the month, seasonality)


Identi…cation and estimation

We do this at various levels of aggregation:
district
cell phone tower
individual phone number
pair of phone number (with some ME2U activity)
From a policy point of view, the district or cell tower analysis is
perhaps the most relevant
But it is also relevant to know who receives the transfers
Combining the two is often impossible because data is only available
either at the aggregate level, or from surveys
We can do both because we have the entire universe of ME2U
transfers in Rwanda at the individual level


Charity or reciprocity?

Do earthquake transfers ‡ow from the rich to the poor?
if charity, would expect the rich to help the poor
but richer phone users probably use phone more, so to them airtime is
more valuable in emergency
Do earthquake transfers come primarily from richer urban areas?
if charity, would expect help to come from more prosperous areas of
country
Do earthquake transfers ‡ow primarily between individuals with
previous history of transfers?
if reciprocity, would expect more transfers at earthquake between
individuals in reciprocal relationship
Do earthquake transfers fall with distance from epicenter (ignoring
the are directly a¤ected by earthquake)?
if monitoring is important, willingness to assist would fall with distance
from shock

Data

Primary dataset comes from Rwanda’ primary telecommunications
s
operator
Log of all airtime transfers from 2005 to end of 2008
Log of phone calls
50 billion transactions covering 1.5 million users over four years
All phones prepaid


Massive growth in recent years

Table: Mobile phone penetration: Number of mobile phones per 100 inhabitants.

2000 2001 2003 2005 2007 2009 Annual Gro
Rwanda 0.49 0.78 1.49 2.47 6.53 24.3 77.1%
South Africa 18.28 23.39 35.93 71.60 87.08 92.67 17.4%
United States 38.53 44.77 54.90 71.43 83.51 97.1 9.1%
Source: International Telecommunication Union


Dates covered All dates Earthquake window
1/1/05-12/31/08 12/1/07-4/1/08
Panel A: Aggregate tra¢ c Mean S.D.
# Me2U transactions 9,202,954 362,053
# unique users 1,084,085 119,745
# who send airtime 870,099 48,295
# who receive airtime 946,855 101,351
# who send and receive 732,869 29,901
# unique dyads 646,713 159,204
Panel B: Basic statistics 12/1/07-4/1/08
Transactions per user 6.05 12.05
Average distance (km) 13.51 27.67
Average value (RWF) 223.58 652.02
Notes: The window 1/1/2005-12/31/2008 encompasses the entire dataset. The wind
4/1/2008 is the same as is used in later individual-level regression. US$1=550RWF.


Transfers to a¤ected locations

Table 4. Average Effect of the Earthquake on Received ME2U Transfers (Gross)
District Cell tower User Dyad
Coef. St.Error Coef. St.Error Coef. St.Error Coef. St.Error
Earthquake shock 14169*** 1951 2832*** 177 9.48*** 0.740 11.92*** 0.585
Day dummies yes yes yes yes
Fixed effects district tower user directed dyad

Number of observations 1800 16,020 6,645,531 4,797,080


Table 5. Average Effect of the Earthquake on Received ME2U Transfers (Net)
District Cell tower User
Coef. St.Error Coef. St.Error Coef. St.Error
Earthquake shock 12823*** 1600 3053*** 116 10.01*** 1.082
Day dummies yes yes yes
Fixed effects district tower user

Number of observations 1800 16,020 6,645,531


Table 6. Net transfers and wealth
Earthquake shock 49,979*** 720 4,797*** 914 13.22*** 3.881 14.25*** 3.284
Wealth proxy of recipient * Shock 6.057*** 0.053 1.864* 1.016 18.96*** 5.213 13.69*** 2.126
Wealth proxy of recipient * Day of quake -0.002** 0.053 -0.154 0.136 -1.58*** 0.336 -0.54 0.396
Wealth proxy of recipient * In quake region n.a. n.a. 2.63*** 0.973 0.17 0.380
Wealth proxy of sender * Shock n.a. n.a. n.a. 6.00 5.996
Wealth proxy of sender * Day of quake n.a. n.a. n.a. 0.63* 0.369
Wealth proxy of sender * In quake region n.a. n.a. n.a. 0.03 0.415
Number of observations 1,680 9,240 6,645,531 4,797,080

Mobile Phones UC Berkeley Santa Fe Institute

Transfers and number of pre-existing contacts

Table 7. Net transfers and number of contacts
Earthquake shock 24,381*** 721 4,631*** 415 12.24*** 3.561 13.36*** 2.577
Degree of recipient * Shock 0.004*** 0.000 0.004** 0.001 0.052 0.033 0.034 0.033
Degree of recipient * Day of quake 0.000 0.000 -0.000 0.000 -0.004*** 0.001 -0.003 0.002
Degree of recipient * In quake region n.a. n.a. 0.009* 0.005 0.002 0.002
Degree of sender * Shock n.a. n.a. n.a. 0.008 0.006
Degree of sender * Day of quake n.a. n.a. n.a. 0.000 0.002
Degree of sender * In quake region n.a. n.a. n.a. -0.004* 0.002
Number of observations 1,680 9,240 6,645,531 4,797,080

Mobile Phones UC Berkeley Santa Fe Institute

Transfers and reciprocity

Table 8. Net transfers and recipient's ME2U past activity
Dyad
Coef. St.Error
Earthquake shock 10.103*** 0.784
ME2U sent in the past * Shock 0.187*** 0.021
ME2U sent in the past * Day of quake 0.022** 0.011
ME2U sent in the past * In quake region 0.002 0.011
ME2U received in the past * Shock -0.101 0.107
ME2U received in the past * Day of quake -0.021 0.020
ME2U received in the past * In quake region -0.052* 0.029
Day dummies yes
Fixed effects user
Number of observations 4,381,704


0.5
Shock X Distance coefficient
0.0
−0.5
−1.0
−1.5
−2.0

Interaction coefficient
Lowess smoother (f=.25)

0 50 100 150 200

Distance from recipient

Figure 3: Relationship between the geographic structure of an individual’s network and her propensity to
receive a transfer after the earthquake.

5 Robustness and Limitations

A Functional form assumptions

We briefly show that our central results are not sensitive to the precise econometric specifications, or to the
choice of time window (which in most regressions is restricted to the period starting one month before the

earthquake and ending one month after the earthquake). Table 8 presents estimates of the average treatment

effect of model (1) using the full dataset from October 2006 until July 2009 under a variety of econometric
specifications. Column (1) gives the standard OLS results with no control variables Xrt , time fixed effects
θt , or tower fixed effects πr . Column (2) includes time-varying controls to account for regional variation

in mobile phone use, column (3) adds regional fixed effects, and column (4) adds daily dummy variables.
Across all specifications, the estimated effect of the shock remains strong and significant, and of a magnitude

similar to that presented in Table 3.

21

Other shocks: ‡oods

Table 10. Effect of flood on transfers -- cell tower regressions
OLS with Cell tower Tower/
controls FE time FE
Affected by flood 933.040** 1029.241** 1068.659**
-316.98 -329.36 -375.45
Affected days 952.838*** 981.247***
-230.79 -206.75
In affected location 237.740*
-88.55
Total calls in location 0.075*** 0.065*** 0.103***
0 -0.01 -0.01
Outgoing transfers from location 0.678*** 0.637*** 0.527***
-0.03 -0.03 -0.04
R2 0.702 0.729 0.753
Number of observations 74895 74895 74895


Conclusion

Earthquake caused a large and signi…cant in‡ux of airtime transfers to
people close to the epicenter.
highly signi…cant on the day of the earthquake and the following day
not on a number of “placebo" days
robust to di¤erent estimation strategies.
Airtime transfers are not distributed equally
Wealthy receive the most at the time of the quake
Nature of the transfers:
Remittances: would expect ‡ows from Kigali-epicenter (not observed)
Altruism: would expect ‡ows from rich-poor (not observed)
Risk sharing: would expect ‡ows in reciprocal relationships (observed)


Policy implications

Research has shown that people a¤ected by large aggregate shocks
receive airtime transfers that probably enable them to:
call for help for self or another
regroup families
organize search e¤ort
organize support of a¤ected people (e.g., shelter, water, food)
reassure loved ones
These airtime transfers do not reach everyone:
mostly the rich
mostly well connected people, who know others outside the a¤ected
area
These airtime transfers do not come from everyone:
mostly from people nearby
not from the capital city


Policy implications

Policy makers can do better than that:
ensure transfers reach everyone in a¤ected area
ensure transfers come from those who can best a¤ord them
Suggestion:
automatic transfer of small amount of airtime by phone providers to all
phone numbers in a¤ected area
organized beforehand and triggered by agreed upon event
refunded ex post by government to phone providers


Policy implications

This can be organized
in any country
for any large shock such as earthquake, ‡ood, tsunami, volcanic
eruption, etc
as long as some cell towers are still standing
but towers are more resilient than many other installations (e.g.,
ATMs) because are located higher and often have own power supply
(e.g., solar panel)
But most likely to help in developing countries where the poor are
most likely
to have a zero airtime balance
to hold no cash


Policy implications

Mobile money
Large shocks also disrupt bank system, especially ATM’s
People run out of cash at a time when most need it – to pay for
water, food, shelter, care
Airtime can serve as substitute for money even where governments
have not authorized mobile money
Hence transfers can also serve as substitute for monetary transfers
Obviously would be even easier if governments authorize mobile
money
Doing so would also have many other bene…ts for the poor and would
help economic growth in more isolated, disadvantaged areas


Evaluating the impact of
Land Tenure Regularization:
Design and research questions

Daniel Ali
Klaus Deininger
Marguerite Duponchel
Markus Goldstein
Eliana La Ferrara

IGC Growth Week, 20 September 2011

In the next 15’
 Motivation
 Background on LTR program
 Research questions
 Impact evaluation design
 Structure of baseline survey

Motivation (1)
Land rights and productivity
 Insecure rights lower investment and
productivity
– Besley (1995), Goldstein and Udry (2008)

Mixed evidence on land registration
 does not increase productivity
– Bardhan and Mookherjee, West Bengal (2009) 
Quality of land matters
 does increase investment
– Deininger and Ali, Ethiopia (2011)
 is not cost effective
– Jacoby and Minten, Madagascar (2007)

 Need a better understanding of the
relationship between registration,
investment and productivity
(e.g., role of credit)

 Most existing studies exploit non-
experimental variation (politically difficult
to randomize registration!)

 We exploit random phase-in

Motivation (2)
More than productivity
 Unequal land rights across gender
 titling may affect bargaining and intra-hh
allocation

Land Tenure
Regularization (LTR)

Background on land in Rwanda
 Land scarcity, dependence on agriculture
 Highest popul. density in Africa
 Average parcel size =0.35 ha, significant
variation around this
 Environmental degradation; need for
investment
 Continued fragmentation; active land
market

Recent legislation
 1999 Inheritance legislation: Equal rights
to females

 2005 OLL
• Recognizes existing (customary) rights
• Equality for spouses; registration
compulsory
• Establishes institutional infrastructure
(NLC, DLBs, LCs at cell, sector, district
level)

National LTR program

 Participatory, low-cost methodology based
on photomaps

 Nation-wide program launched in 2010

 Currently 8mn. out of 12 mn. parcels
registered

9 steps to registration
 Notification to areas of LTR Programme
 Local information dissemination, public meetings
 Appoint & train Land Committees and Parasurveyors
 Demarcation: mark boundaries on a photo image
 Adjudication: record personal details, issue claims
receipt, record objections simultaneous with
demarcation
 Publication of adjudication record
 Objections & corrections period: final disputant lists
 Mediation period for disputes.
 Registration and Titling –preparation and issuance of
Documents

field demarcation with neighbors

Research questions
 How has LTR affected tenure security?
 What is the impact of increased security
on productivity?
 How has LTR affected investment? Who
within the hh has invested more?
 Channel: has LTR led to more access to
credit (land as collateral)?
 Has LTR led to more land mkt
transactions?

Research questions (cont’d)

 Intra-hh bargaining: has LTR changed
decision making within the hh?
 Gender: has LTR increased inheritance
rights of girls?
 Uncertainty about rights and land
disputes: has LTR led to fewer disputes?
 Implementation: How to leverage capacity
of village committee members to
maximize impact of LTR

RCT
 Program is national  cannot randomize
Treatment vs No treatment
 However, can randomize ORDER in which
different locations get LTR

Political constraints
 No Kigali province, Kirehe & Rubavu districts
 “Early” and “late” locations randomized,
others not

Treatment
 Firstlocations to receive LTR
Control
 Last locations to receive LTR

Combine randomization & panel analysis
 Baseline IE survey in Jan. 2011
 Follow up in Dec. 2011-Jan. 2012

Design
Admin structure in Rwanda
 Provinces (4) + City of Kigali
 Districts (30, 3 in Kigali)
 Sectors (416)
 Cells (2,146)
 Villages or Imidugudu

Feasibility requirement by govt:
complete whole sector once started
 Sector level randomization

Multi-site cluster randomized trial w/ 4 levels
 Before randomizing groups, blocking by
district is employed to improve statistical
power
 hh’s nested within enumeration areas
(umudugudu)
 nested within sectors
 nested within districts

Within each district sectors are randomly
assigned to “early” and “late” program
groups

 25 districts
 4 sectors per district
 3 cells per sector
 1 village per cell

 300 cells (villages) of which:
 150 treatment: LTR in Feb. 2011
 150 control: LTR in Jan-Feb. 2012

12 hh’s per cell (village)
 3600 hh’s

HH questionnaire
 Individual demographics, marital history
 Education
 Migration & displacement history
 Income, expenditures, assets, livestock
 Credit and remittances
 Social capital and decision making
 Perceptions and legal knowledge

 Separate answers by head and spouse

Parcel roster
 land rights, disputes, inheritance
 investments
 seasonal crop cover
 seasonal labour/non-labour inputs
 land sales

Community questionnaire
 Community level infrastructure
 Other government programs
 Individual interviews with members of
umudugudu committee:
Perceptions and legal knowledge, decision
making (survey and experimental)

Growth Week 2011: Country Session 6 - Rwanda

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Growth Week 2011: Country Session 6 - Rwanda