The use of mobile phones to produce better cash transfer outcomes in , Map 3: Cash Transfer Villages and Nearby Markets The Setting: Map 1: Niger Food Aid by Region Methods:

Applying Information Communication Technology (ICT) tools To further test these finding using GIS, I used the following tools: to the work of development practitioners could substantially improve development outcomes. Nonprofit and non- For the diet diversity question, I searched for nearby markets using the Near governmental organizations have employed mobile develop- tool. I did not set a distance tolerance, as I was looking at averages across vil- ment projects particularly during humanitarian crises. lages within treatment groups. Ideally, I would have used a Network Analyst tool to see distance by roads, but finding this data at the village level is chal- lenging. Concern International has employed the use of mobile phones

to deliver cash transfers during famine to households in need For land use statistics, I first set 7 km buffers around villages. I conducted a lit- of aid. To determine the impact of using mobile phones and a erature review to determine the appropriate buffer zone area. I then used the mobile money platform on outcomes of cash transfer pro- Zonal Statistics tool to pull land use from a raster grid of land use in Africa. grams, the first randomized controlled trial (RCT) was con- Map 2: Cash Transfer Villages by Treatment ducted. A RCT is a rigorous, econometric analysis of impact. To isolate a certain impact, RCTs are designed to include groups of participants who receive a certain intervention as well as groups who do not and therefore act as controls. In this example in Tahoua, Niger, 96 villages participated. Of the 96, Research Questions:

32 received the cash transfer through the traditional method of (1) Does the diet diversity outcome have more to do picking up the cash at a location nearby; 32 received the cash with proximity to markets than using Zap? Findings:

transfer using cell phones and the mobile money platform, (2) Does crop diversity have more to do with a villages Overall, there were no trends that would point to proximity to markets or land use having an Zap; and the final 32 acted as a placebo and received the cash land use than use of Zap? impact on the diet and crop diversity outcomes. Tables 1 – 3 contain the data on distance to transfer through the traditional method, but were also given markets in kilometers and village land use. Maps 1 and 2 illustrate the geospatial data used cell phones. After the monitoring and evaluation of the RCT, Table 2: Findings for Villages in Placebo Treatment to deliver the results. The analysis supports the findings in the paper that holding land use there were two important findings. First, households in villag- and distance to markets constant, outcomes can be attributed to the use of Zap. Distance to clos- es who used Zap were more likely to have high diet diversity. Village Majority Land Use est Market (km) These same Zap households were also more likely to grow a Moza Peul 15 Sparse herbaceous/ shrub cover Map 4: Cash Transfer Villages and Land Cover higher diversity of crops. Innélou 0 Bare Areas Inkarkada 0 Sparse herbaceous/ shrub cover Table 3: Findings for Villages in Cash Treatment Indiri 15 Sparse herbaceous/ shrub cover Table 1: Findings for Villages in Zap Treatment Maissoungoumi 24 Sparse herbaceous/ shrub cover Distance to clos- Village Majority Land Use Maifarinkaye zarma 4 Herbaceous Cover, closed - open est Market (km) Distance to closest 1 Herbaceous Cover, closed - open Ijali 14 Sparse herbaceous/ shrub cover Village Majority Land Use Market (km) Tamakass II 11 Herbaceous Cover, closed - open Tagigal 3 Sparse herbaceous/ shrub cover Mafari 18 Sparse herbaceous/ shrub cover Dabagui II 13 Sparse herbaceous/ shrub cover Azza Manano 4 Herbaceous Cover, closed - open Dangari 24 Sparse herbaceous/ shrub cover Takbalalan 5 Herbaceous Cover, closed - open Salkadama nomade 0 Sparse herbaceous/ shrub cover Garmazey 13 Sparse herbaceous/ shrub cover Dollé Mahamadou 1 Sparse herbaceous/ shrub cover Tamalwada 0 Sparse herbaceous/ shrub cover Galatan 5 Herbaceous Cover, closed - open Taguaye 8 Sparse herbaceous/ shrub cover Tamakass I 11 Sparse herbaceous/ shrub cover Abala Sani 18 Sparse herbaceous/ shrub cover Toudou taramna 5 Sparse herbaceous/ shrub cover Azza Makera 4 Herbaceous Cover, closed - open Kalfou Dabagui I 13 Herbaceous Cover, closed - open Tarjamatt 8 Sparse herbaceous/ shrub cover Sarou 1 20 Sparse herbaceous/ shrub cover Land Use Categories Amaloul Guidis 11 Sparse herbaceous/ shrub cover Taza 2 4 Sparse herbaceous/ shrub cover Dollé Egadou Sallah 1 Sparse herbaceous/ shrub cover Tsafarfari 11 Sparse herbaceous/ shrub cover Goringo Haussa 5 Herbaceous Cover, closed - open Danfan 1 Herbaceous Cover, closed - open Bayan toudou Danfane 9 Sparse herbaceous/ shrub cover Hayyi 8 Sparse herbaceous/ shrub cover Afalaoulaou 7 Sparse herbaceous/ shrub cover Infen Taharet 13 Sparse herbaceous/ shrub cover Karadji Nord 9 Herbaceous Cover, closed - open Fagima 11 Sparse herbaceous/ shrub cover Wazawaza 7 Herbaceous Cover, closed - open Gatarawa 3 Herbaceous Cover, closed - open Aléla 0 Sparse herbaceous/ shrub cover Ourhamizan 14 Sparse herbaceous/ shrub cover Latchiwa 12 Sparse herbaceous/ shrub cover Sarayé 11 Sparse herbaceous/ shrub cover Imbalgam 12 Herbaceous Cover, closed - open Makanzata Akouloutan 14 Herbaceous Cover, closed - open Chiguinawan 15 Sparse herbaceous/ shrub cover Adernagatt 13 Herbaceous Cover, closed - open Toro Gado 10 Herbaceous Cover, closed - open Doutchi Fara 11 Herbaceous Cover, closed - open Izerwane 7 Sparse herbaceous/ shrub cover Nassam 4 Sparse herbaceous/ shrub cover Tabagat 5 Herbaceous Cover, closed - open Toudouni Farfarou 17 Sparse herbaceous/ shrub cover Ikakan 4 Herbaceous Cover, closed - open Arraweye 0 Herbaceous Cover, closed - open Koukakamé 14 Sparse herbaceous/ shrub cover Anekar 10 Sparse herbaceous/ shrub cover Amaloul 0 Sparse herbaceous/ shrub cover Maifarinkaye haoussa 4 Herbaceous Cover, closed - open Imboram 8 Sparse herbaceous/ shrub cover Affagar I 7 Herbaceous Cover, closed - open Data Sources:

Chimo 12 Herbaceous Cover, closed - open Toro Basso 10 Herbaceous Cover, closed - open Ketere 15 Herbaceous Cover, closed - open Geospatial data on cash transfer villages and nearby markets were sourced from Dr. Jenny Aker’s Abala 1 Sparse herbaceous/ shrub cover Gadiyao 9 Sparse herbaceous/ shrub cover Toro Issak 10 Herbaceous Cover, closed - open research, “Zap it to Me: The Short-Term Impacts of a Mobile Cash Transfer Program,” 2011. Miss- Guigani 6 Herbaceous Cover, closed - open BT Takanamatt 0 Sparse herbaceous/ shrub cover Garanga Marke 13 Herbaceous Cover, closed - open Zongo Arawaye 2 Herbaceous Cover, closed - open Ahandrass 17 Sparse herbaceous/ shrub cover Guidoma 16 Sparse herbaceous/ shrub cover ing gaps in geo-coordinates were filled in using Falling Rain Global Gazetteer Version 2.2 and Laham 16 Sparse herbaceous/ shrub cover Inoussoukane 20 Sparse herbaceous/ shrub cover Tounga koussou 15 Herbaceous Cover, closed - open GeoNames. The basemap is the Physical and Ocean Layer from ArcGIS, roads and commune Taza 1 1 Sparse herbaceous/ shrub cover Agoulmawa 13 Sparse herbaceous/ shrub cover Garin Anissar 17 Herbaceous Cover, closed - open boundaries are from Tufts University GIS Center. Food Aid data is from USAID, 2005. All layers Innawarra 0 Sparse herbaceous/ shrub cover Barmou III 1 Sparse herbaceous/ shrub cover Barmou Moussa 1 Sparse herbaceous/ shrub cover were projected into Projected Coordinate System: WGS 1984 UTM Zone 31N.

Number of Zap Villages 25 Number of Placebo Villages 33 Number of Cash Villages 27 Cartographer: Amanda Meng, Fletcher School, Tufts University, May 6 2012 Treatment Average 10.24 13.68 Treatment Average 8.36 Sparse herbaceous/ shrub cover Treatment Average 8.54 Evenly distributed between the two