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Growth Week 2011: Country Session 6 - Rwanda
 

Growth Week 2011: Country Session 6 - Rwanda

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The Country Session on Rwanda comprised three presentations on topics such as agronomy practices amongst coffee farmers and the Land Tenure Regularization programme. The presentations were followed ...

The Country Session on Rwanda comprised three presentations on topics such as agronomy practices amongst coffee farmers and the Land Tenure Regularization programme. The presentations were followed by an open discussion. The presentations in this slideshare are from Marcel Fafchamps and Eliana La Ferrara.

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    Growth Week 2011: Country Session 6 - Rwanda Growth Week 2011: Country Session 6 - Rwanda Presentation Transcript

    • 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 2011Joshua 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 chargedJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 2 / 22
    • The Earthquake Magnitude 6 earthquake in Western Rwanda on February 3, 2008 43 dead and 1,090 injured. 2,288 houses destroyedJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 3 / 22
    • 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.Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 4 / 22
    • 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 citiesJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 5 / 22
    • 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)Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 6 / 22
    • 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 levelJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 7 / 22
    • 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 shockJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 8 / 22
    • 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 prepaidJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 9 / 22
    • 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 UnionJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 10 / 22
    • 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.Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 11 / 22
    • 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 4,797,080Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 12 / 22
    • 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 6,645,531Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 13 / 22
    • Table 6. Net transfers and wealth District Cell tower User Dyad Coef. St.Error Coef. St.Error Coef. St.Error Coef. St.Error 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 Day dummies yes yes yes yes Fixed effects district tower user directed dyad Number of observations 1,680 9,240 6,645,531 4,797,080Joshua Blumenstock Nathan Eagle Marcel Fafchamps Mobile Phones UC Berkeley Santa Fe Institute September 2011 14 / 22
    • Transfers and number of pre-existing contacts Table 7. Net transfers and number of contacts District Cell tower User Dyad Coef. St.Error Coef. St.Error Coef. St.Error Coef. St.Error 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 Day dummies yes yes yes yes Fixed effects district tower user directed dyad Number of observations 1,680 9,240 6,645,531 4,797,080Joshua Blumenstock Nathan Eagle Marcel Fafchamps Mobile Phones UC Berkeley Santa Fe Institute September 2011 15 / 22
    • Transfers and reciprocity Table 8. Net transfers and recipients 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 4,381,704Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 16 / 22
    • 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 recipientFigure 3: Relationship between the geographic structure of an individual’s network and her propensity toreceive a transfer after the earthquake.5 Robustness and LimitationsA Functional form assumptionsWe briefly show that our central results are not sensitive to the precise econometric specifications, or to thechoice of time window (which in most regressions is restricted to the period starting one month before theearthquake and ending one month after the earthquake). Table 8 presents estimates of the average treatmenteffect of model (1) using the full dataset from October 2006 until July 2009 under a variety of econometricspecifications. Column (1) gives the standard OLS results with no control variables Xrt , time fixed effectsθt , or tower fixed effects πr . Column (2) includes time-varying controls to account for regional variationin 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 magnitudesimilar 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 74895Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 17 / 22
    • 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)Joshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 18 / 22
    • 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 cityJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 19 / 22
    • 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 providersJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 20 / 22
    • 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 cashJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 21 / 22
    • 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 areasJoshua Blumenstock Nathan Eagle Marcel Fafchamps UC Berkeley Mobile Phones Santa Fe Institute September 2011 22 / 22
    • Evaluating the impact ofLand 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
    • 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 TenureRegularization (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
    • explaining process & map
    • field demarcation with neighbors
    • locating parcels on the map
    • processing claims receipts
    • Research questions
    • 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
    • Evaluation design
    • RCT Program is national  cannot randomize Treatment vs No treatment However, can randomize ORDER in which different locations get LTRPolitical constraints No Kigali province, Kirehe & Rubavu districts “Early” and “late” locations randomized, others not
    • Treatment Firstlocations to receive LTRControl Last locations to receive LTRCombine randomization & panel analysis Baseline IE survey in Jan. 2011 Follow up in Dec. 2011-Jan. 2012
    • DesignAdmin structure in Rwanda Provinces (4) + City of Kigali Districts (30, 3 in Kigali) Sectors (416) Cells (2,146) Villages or ImiduguduFeasibility 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 districtsWithin 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
    • Treatment and control sectors
    • Survey instrument
    • 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)
    • Results… next year!