GiveDirectly - 2013 Review

We have published a more recent review of this organization. See our most recent report on GiveDirectly.

Published: November 2013

Summary

What do they do? GiveDirectly (www.givedirectly.org) transfers cash to households in developing countries via mobile phone-linked payment services. It targets extremely low-income households (more).

Does it work? We believe that this approach faces an unusually low burden of proof, and that the available evidence supports the idea that unconditional cash transfers significantly help people. It appears that GiveDirectly has been effective at delivering cash to low-income households. GiveDirectly has two randomized controlled trials of its impact (one ongoing), and has taken the unusual step of making the details of these studies public before data is collected (more).

What do you get for your dollar? GiveDirectly aims to deliver about 90% of campaign expenses directly to recipients. Its current campaigns in Kenya hit this target (more).

Is there room for more funding? We believe that GiveDirectly has significant room for more funding and would like to see GiveDirectly receive at least $10 million over the next year (more).

GiveDirectly is recommended because of its:

• Focus on a program with a low burden of proof and a strong track record (more).
• Strong (and evolving) process for ensuring that cash is well-targeted and consistently reaches intended targets (more), and documented success in transferring a high portion of funds raised directly to recipients (more).
• Standout transparency and commitment to self-evaluation (more).
• Room for more funding - we believe that GiveDirectly can use substantial additional funding productively (more).

Major unresolved issues include:

• While GiveDirectly has been accumulating more evidence on this question in addition to a recently released RCT studying its activities in Kenya, there is still limited evidence on the humanitarian impact of the type of transfers (large, one-time transfers) that GiveDirectly provides, particularly the long-term impact of such transfers.
• GiveDirectly has successfully scaled up its activities over the past year (including expanding into a second country) enabling it to transfer a few million dollars in 2013. It now hopes to scale up further. We are optimistic about its ability to do so, but note that there are substantial risks associated with scaling and whether GiveDirectly can continue to scale successfully is an open question.
• The size, schedule, and targeting strategy of GiveDirectly's transfers have not been fully tested against possible alternatives. A major benefit we see in GiveDirectly is the opportunity it has to test different approaches to cash transfers and gather data relevant to answering questions about low-income households' preferences. We have yet to see GiveDirectly execute on this to the extent we hope it eventually will (more).
• GiveDirectly is a relatively young organization and its leadership consists in part of directors who work part-time and unpaid; this may make the organization less likely to reach its maximum potential.

Our full review, below, discusses our full assessment of GiveDirectly, including what we see as its strengths and weaknesses as well as issues we have yet to resolve. This review was initially written in 2012 and has since been updated. All content on GiveDirectly is available here.

Our investigation process

To date, our investigation process has consisted of

• Conversations with GiveDirectly staff: Paul Niehaus (Co-founder, President), Rohit Wanchoo (Co-founder, Director), Michael Faye (Co-founder, Director), and Jeremy Shapiro (Co-founder, former Director), Piali Mukhopadhyay (COO, International), Joy Sun (COO, Domestic), and Carolina Toth (Field Director).
• Reviewing documents GiveDirectly sent in response to our queries.
• In November 2012, we visited GiveDirectly's operations in Kenya where we met with beneficiaries of its work and spoke with its local field staff (including Lydia Tala, now serving as Senior Field Officer, and one of 8 staff members responsible for enrolling new recipients).

All content on GiveDirectly, including updates, blog posts and conversation notes, is available here.

What do they do?

GiveDirectly transfers cash to poor households in the developing world via mobile phone-linked payment services.1 It is currently active in Kenya and Uganda.2

Its standard model involves grants of $1,000 (USD) per household over approximately one year,3 after which recipients become ineligible,4 though it continues to experiment with other grant size/duration approaches (more). GiveDirectly aims to help the poorest households, targeting those that are in "acute poverty."5 Recipient households are currently identified by (a) selecting poor villages based on poverty data; (b) determining eligibility based on pre-set criteria, such as building materials of homes and demographic criteria (more). It aims to deliver about 90 cents directly to recipients of every $1.00 in campaign expenses.6

Below, we

• Discuss the structure of GiveDirectly's transfers
• Report the status of GiveDirectly's transfer campaigns
• Summarize GiveDirectly's expenses to date
• Discuss GiveDirectly's process for identifying recipient households and delivering cash transfers
• Describe GiveDirectly's staff structure

Grant structure

GiveDirectly's standard model involves grants of $1,000 (USD) over approximately one year,7 after which recipients become ineligible.8 This is a very different approach from the approach we've seen in government cash transfer programs. One way of putting the difference (which has been reflected in GiveDirectly's communications with us) is that government programs aim for "income transfers" (small supplements to income over many years), whereas GiveDirectly aims for "wealth transfers" (large, one-off transfers that hopefully give people more flexibility to make large purchases and investments).

Note that household size varies substantially in the data we've reviewed (from the first round of Siaya transfers): while the mean household size is ~4.7 and the median size is 4, 16% of households have 1 or 2 people, ~20% have 6 or more, and the maximum household size is 16.9 We estimate that the average family receives $288 per capita from GiveDirectly, which is 121% of baseline annual consumption per capita.10

Frequency and sizing of transfers

In Kenya, GiveDirectly's standard transfer schedule involves a small initial transfer of about $50 USD, followed by two larger transfers of about $475 USD. These transfers are sent over a period of approximately one year.11

In Uganda, GiveDirectly's planned transfer schedule involves 10 monthly transfers of about $100 USD each.12 GiveDirectly told us that the transfer schedule in Uganda was designed to be more manageable for the mobile money providers than two lump-sums, and also that it would provide more frequent opportunities to evaluate the performance of the mobile money providers.13

GiveDirectly has indicated that it may be interested in experimenting with the frequency and sizing of transfers, possibly by giving recipients the ability to choose their transfer schedules.14

Status of transfer campaigns

As of October 2013, it has provided partial or full cash transfers to more than 4000 households in western Kenya and eastern Uganda, and is continuing to transfer funds to additional households in both places.15

GiveDirectly's work to-date can be grouped into 6 campaigns:

• Rarieda: this campaign includes cash transfers to the treatment groups of a randomized controlled trial (RCT) of the effects of unconditional cash transfers; now that RCT data collection is complete, GiveDirectly is providing "top-up" transfers to all participants who previously received less than $1,000; first transfers were sent in June 2011.
• Siaya: this campaign is in Kenya; first transfers were sent in July 2012.
• Nike: this campaign includes transfers to young women in Siaya district as part of a RCT funded by the Nike Foundation; first transfers were sent in September 2012.
• Google: this campaign is in Kenya, funded by a Google Global Impact Award; first transfers were sent in January 2013.
• Kenya 2M: this campaign includes villages in GiveDirectly's informal study of different targeting strategies (more); first transfers were sent in October 2013.
• Uganda: this campaign includes GiveDirectly's transfers to households in Uganda, funded by the Google Global Impact Award; first transfers were sent in June 2013 as part of the initial pilot.

Details of each of these transfer campaigns is provided in this table:16

We have also tracked the total amounts that GiveDirectly has transferred to recipients or plans to transfer in each month for each campaign.35

Expenses to date

Based on the financial information that GiveDirectly provided, we calculate that in total it has incurred costs of $3,101,810 through the end of FY 2013 (August 31, 2013). This amount includes all spending on transfer campaigns and liabilities (future transfers committed to enrolled recipients and future wages for staff), as well as the costs of initially setting up in each country and of outreach/fundraising efforts.36 GiveDirectly has stated that marginal donations from the public are used to support transfer campaigns, while set-up and outreach costs are funded separately, by designated gifts from large donors.37 The costs that are not included in GiveDirectly's total spending are the value of its unpaid President's time (about 40 hours/week),38 and time spent by other board members (about 45 hours/week).39

Of GiveDirectly's total spending through FY 2013, $2,663,269 (or 85.9%) was transferred directly to recipients.40 Including projected future costs for current campaigns, the ratio of direct grants to total spending rises to 87.1%.41

GiveDirectly calculates its "efficiency ratio" by the amount transferred directly to recipients out of total spending on transfer campaigns. GiveDirectly explains that it excludes set-up and outreach costs from total spending in this calculation because they are one-time costs (e.g., NGO registration) that can be reused indefinitely, as opposed to marginal costs.42 Under this condition, GiveDirectly's efficiency ratio in Kenya is 89.7% based on incurred costs and 90.6% including projected future costs for ongoing campaigns;43 in Uganda, GiveDirectly's efficiency ratio is 92.5% based on incurred costs and 87.6% including projected future costs.44

Below we break down GiveDirectly's total spending through the end of FY 2013 (August 31, 2013).

GiveDirectly's process

The steps of GiveDirectly's process are as follows:

1. Selection of a country. GiveDirectly told us that it chose to work in Kenya due to the robustness of M-Pesa as a mobile banking platform and the large population of people meeting its criteria who have access to mobile technology.53 In choosing a second country in which to work, GiveDirectly said that it considered whether there was a mobile money provider accessible to the very poor, how costly it would be to operate in the country, how politically stable the country is and how common corruption is in government affairs. The ease of moving staff between Kenya and Uganda was also a factor, as GiveDirectly's current COO will be overseeing the work in both places.54
2. Selection of a region. GiveDirectly told us that it initially chose to work in western Kenya and eastern Uganda based on poverty statistics.55 We have reviewed the poverty data for Uganda; we have not reviewed poverty data across districts for Kenya.
3. Selection of counties. For Kenya, GiveDirectly told us that its executive staff56 uses data on poverty, population density, security, and presence of poverty-focused NGOs (with the goal of avoiding overlapping with these) to select counties;57 we have reviewed poverty data for divisions within Siaya district but have not reviewed other data used to select counties in Kenya.58 For Uganda, GiveDirectly told us that it chose a county to target initially based on poverty statistics, logistical factors and security considerations;59 we have reviewed the poverty data for Uganda.
4. Selection of villages. GiveDirectly states that it uses a predictive model of income to select villages.60 GiveDirectly has shared the full details of its village selection process used in Kenya, including data for each village and the method for weighting the different factors used to select villages;61 In Uganda, GiveDirectly relied on publicly available poverty data, which we have reviewed, as well as data that it received from local officials, which we have not yet reviewed. GiveDirectly also sent field staff to test cell phone reception and measure proximity to market centers, and used these as inputs into its village selection (optimizing for good reception and longer distances from market centers). GiveDirectly also considered population size in the villages, so that it could enroll all eligible households in each village and not exceed the budget for this campaign, which was funded by part of the Google Global Impact Award.62 For details on how GiveDirectly has targeted villages historically, see this footnote.63
5. Obtaining permission from local officials. Throughout the process of planning and implementing a campaign, GiveDirectly meets with local officials to obtain permission to conduct the campaign.64
6. Selection of households. GiveDirectly has field staff visit the village to create a census of all households (this step was different in earlier campaigns).65 In conducting the census, field staff collect data such as GPS coordinates and note which households are eligible for transfers66 (the criterion for eligibility in a standard campaign is that the home is made of mud and thatch).67 We have reviewed the census data collected for eligible recipients from all campaigns for which GiveDirectly conducted a census,68 as well as full census data for the Kenya 2M campaign.69
7. Enrollment and eligibility checks. GiveDirectly has a separate set of field staff visit households marked as eligible in the census and enroll them. In Kenya, enrollment involves giving the household member a SIM card (if they do not already have an M-PESA account), which is used to transfer funds through the M-PESA system,70 and collecting other data including GPS coordinates which can be checked against the initial data set (with discrepancies being flagged for audit).71 Recipients are given the option of purchasing a cell phone from GiveDirectly at the time of enrollment, the cost of which is removed from the recipient's transfer.72 In Uganda, a similar enrollment process was carried out, and in addition, GiveDirectly helped recipients obtain ID cards and arranged for mobile money agents to visit villages to register recipients in the mobile money system.73 Enrollment was different in early transfer campaigns.74

   We have reviewed (and made public) data collected during enrollment, with deletions to preserve anonymity.75 GiveDirectly has told us that in the above enrollment and eligibility checks and the further checks and audits described below, it errs on the side of excluding rather than including a recipient if there is reason to believe that the potential recipient does not meet GiveDirectly's eligibility criteria.76

8. Further checks and audits. GiveDirectly performs a series of checks and audits to confirm eligibility:
   1. GiveDirectly staff "check that the data collected in enrollment and the village census match, that GPS coordinates show an eligible house, that we have a valid photograph showing recipients in front of an eligible house, etc."77 Discrepancies are flagged for audit.78 We have reviewed (and made public) data from these checks, with deletions to preserve anonymity.79 Note that in enrollment rounds completed before November 2012, GiveDirectly did not use all of these steps as "hard checks." Recipients were still visited by two independent field teams who verified eligibility, but potential recipients could remain eligible even if they failed one of these steps.80
   2. GiveDirectly retains a third set of staff to conduct "back-checks," re-verifying eligibility and asking whether households were asked to pay a bribe to enroll.81 Discrepancies are flagged for follow-up phone calls or audits.82 We have reviewed (and made public) data from these checks, with deletions to preserve anonymity.83 GiveDirectly aims to identify all households and does not disqualify a household merely because a potential recipient is not home.84
   3. GiveDirectly "audits 15% of recipients including any who were flagged in any of the checks and including some of the work done by each field worker."85 We have reviewed (and made public) data from these checks, with deletions to preserve anonymity.86
9. Communications with recipients and other village members
   1. A village meeting is held (first implemented in the Google campaign87 ) "to answer questions anyone may have about the program, clarify that [GiveDirectly is not] affiliated with a political party, etc." 88
   2. GiveDirectly staff make multiple phone calls to recipients. Before a transfer is sent, staff "call recipients to check that they know what they’re doing and ask if they had any problems / had to pay anyone to enroll."89 This includes verifying that their SIM card number matches the number they provided during enrollment.90 Staff make another call soon after GiveDirectly transfers an initial, small installment of funds (approximately 5-10% of the total transfer) to recipients.91 This is different from early transfer campaigns.92
   3. GiveDirectly staff call or visit recipients again 1-2 weeks after each main installment of the transfer is sent to conduct follow-up surveys that confirm the transfer was received, ask how recipients spent their funds, and ask again whether they experienced any problems.93 In Kenya, recipients are called twice for full follow-up surveys (once after each lump sum). In Uganda, GiveDirectly plans to conduct these follow-up surveys 3 times over the course of the ten monthly transfers, and is experimenting with conducting them in-person rather than over the phone,94 with shorter calls on off months to confirm that the transfer was received and ask if the recipient experienced any problems.95 We have reviewed and made public data from these calls for ongoing transfer campaigns in Kenya.96
   4. GiveDirectly maintains a phone "hotline" for recipients to call if they have any questions about the transfers or issues in obtaining funds.97 We have reviewed the records of calls made to GiveDirectly's Kenya hotline from May 2012 – October 2013.98

Key differences in some past distributions were (a) the lack of a "census" (instead, GiveDirectly asked village officials to take them to eligible households, and thus conducted two in-person checks of each house rather than three); (b) the lack of a village meeting.

In early transfer campaigns, GiveDirectly used a somewhat informal "preponderance of evidence" approach to determining eligibility: while it has conducted all the checks above, it has been open to declaring people eligible despite, e.g., the lack of a photo or GPS match.99 We discuss the details of how the "preponderance of evidence approach" was implemented below. In more recent transfer campaigns, GiveDirectly has more strictly adhered to eligibility checks. If any given check raises a potential issue, the household is flagged for audit.100 We have reviewed data from these checks and audits for all of GiveDirectly's ongoing campaigns.101

GiveDirectly implemented further changes to its process in its most recent campaign in Kenya (referred to in this review as Kenya 2M). Process changes included:102

• Created Senior Field Officer layer of management to improve quality control
• Used Google Earth to generate data on villages that is used for village selection
• Signed memorandums of understanding (MOUs) with local officials to increase buy-in and formalize relationship
• Created a formal mechanism to capture complaints from community at enrollment stage103
• Reduced the time spent on and the cost of photo/satellite image back check by using crowd-sourced labor
• Automated staff timecards and outsourced payroll deductions processing

GiveDirectly said that it plans to implement similar changes in Uganda in future campaigns.104

Staff structure

GiveDirectly delivered its first cash transfers in 2011 (see table above). Starting in January 2011 it had one full-time staff member;105 in early 2013 it hired a second full-time staff member to serve as COO, Domestic;106 and by May 2013 it had added one full-time Field Director with plans to hire another.107

As of October 2013, GiveDirectly was still in the process of expanding its staff to build capacity for current and future transfer rounds. GiveDirectly plans to have the following field staff going forward:108

• Chief Operating Officer (COO): Previously, the COO was directly managing transfers in the field; in the new organizational structure, her time will be spent on oversight and quality control of the entire operation. The COO oversees the Field Directors.

  Piali Mukhopadhyay is continuing on as COO.

• Field Directors (FDs): This is a new role as of mid-2013. FDs will be in charge of conducting audits and back-checks of enrollment data, as well as approving transfer schedules and rosters. FDs oversee Senior Field Officers.

  As of October 2013, GiveDirectly has hired one FD and is in the process of hiring a second.

• Senior Field Officers (SFOs): This is a new role as of mid-2013. SFOs manage the logistics of transfer rounds and oversee Field Officers, providing coaching and feedback to them. Previously, this work has fallen on the COO, who was acting essentially as a FD in Kenya.

  GiveDirectly plans to have 2-3 SFOs per team of 30 Field Officers. It has hired three so far, all of whom were promoted from Field Officers; going forward, GiveDirectly plans to continue to hire from within its staff of Field Officers for this position.

• Field Officers (FOs): FOs implement the steps required on the ground to enroll households and follow up with them. They have the most face-to-face interaction with recipients, and are all hired within the country of the transfers. There is a separate group of FOs for each of the pre-transfer stages: initial census, enrollment, and back checks. FOs are also hired to conduct follow-up surveys with recipients post-transfers.109

  GiveDirectly has hired all of the requisite FOs for its current campaigns.

Does it work?

This section discusses the following questions:

• Generally speaking, are unconditional cash transfers a promising approach to helping people? We believe that this approach faces an unusually low burden of proof, and that the available evidence is consistent with the idea that unconditional cash transfers help people.
• Does GiveDirectly have a reasonable and effective process for selecting recipients and getting cash to them? We believe GiveDirectly's process is a reasonable way to identify low-income people, and it appears that the process has been relatively effective so far.
• Are the people meeting GiveDirectly's criteria low-income? The evidence we have suggests that they are.
• Do the cash transfers cause problems and complications that offset their positive impact? GiveDirectly has taken some measures to address this question, and we believe that the evidence so far suggests that - while the cash transfers do raise some problems - these problems are relatively minor in the scheme of things.
• Are the size and structure of the cash transfers well-thought-through and appropriate? We find GiveDirectly's approach to be defensible, but hope to see more experimentation with different approaches in the future.

Generally speaking, are unconditional cash transfers a promising approach to helping people?

We discuss this question more extensively at our report on cash transfers. In brief:

• The evidence most relevant to GiveDirectly comes from an RCT of a GiveDirectly campaign (available here). We discuss the findings of this RCT in our cash intervention report.
• Cash transfers are amongst the best-studied development interventions, though questions remain. These studies generally show substantial increases in short-term consumption, especially food, and little evidence of negative impacts (e.g. increases in alcohol or tobacco consumption). It is important to note that most of these studies are of income transfers; there is more limited evidence for programs with wealth transfer models like GiveDirectly's. This is a potential cause for concern, and one of the reasons that we are particularly interested in GiveDirectly experimenting with and evaluating different approaches.
• There is also some evidence that recipients are able to invest cash transfers at high rates of return (e.g. ~20% per year), leading to long-term increases in consumption.
• We feel that this intervention faces an unusually low burden of proof, though donors' intuitive reactions to it may vary widely.

Does GiveDirectly have a reasonable and effective process for selecting recipients and getting cash to them?

GiveDirectly's process is described above. We find it to be generally reasonable, in that

• We would guess that most people living in mud and thatch homes are low-income, particularly in villages in sub-Saharan Africa that available data has identified as high-poverty. (Our site visit in late 2012 was consistent with this.110 )
• The process involves multiple visits by different staff to each recipient home, as well as spot-checks and remote checks by higher-level staff, in order to confirm that recipients meet criteria. (The idea being that if someone tries to temporarily occupy a mud and thatch home in order to be enrolled, they are unlikely to be sure of being present for future re-checks.)
• The process also involves some procedures for catching other issues, in particular the surveys administered periodically after transfer installments are sent.

We have examined data collected by GiveDirectly from its enrollment process, back-checks, remote checks and audits for all ongoing transfer campaigns.111 We have also examined the results of its post-cash-transfer phone surveys.112

As an example, the enrollment and back-check file for Siaya lists 226 individuals who began the enrollment process; 27 of them were ultimately deemed ineligible and transfers were made to 199 of them.

GiveDirectly's President told us that ineligibility in this campaign was determined by a "preponderance of the evidence."113 We have not seen documentation of GiveDirectly's process in reaching these conclusions. GiveDirectly told us that in most cases where a household was deemed ineligible, GiveDirectly came to believe during its back-check based on information from other villagers that the ineligible individuals either did not live in the village GiveDirectly was targeting or lived in metal-roof houses and had tried to game the system by pretending to live in thatch-roof houses.114 (GiveDirectly believes many of these ineligible households were introduced because GiveDirectly allowed the village elder to lead staffers to households, enabling the village elder to help his/her friends or family. For this reason, GiveDirectly aims to rely on a village member rather than the village elder as a guide, when possible,115 although this method still seems vulnerable to manipulation.) The file also shows that the 199 eligible households were checked and met GiveDirectly's eligibility criteria.116 GiveDirectly now strictly adheres to all eligibility checks; if any given check raises a potential issue, the household is flagged for audit.117

Are the people meeting GiveDirectly's criteria low-income?

GiveDirectly has collected data on poverty levels of the people receiving cash transfers as part of its studies (these people were selected using the process detailed above). GiveDirectly has shared the full survey form used to interview participants,118 as well as its own summary of the data collected as of March 2012:119

GiveDirectly also provided charts that show a clear difference in the consumption, expenditures, and assets of households in mud and thatch homes compared to those in cement homes, but fairly small differences between those living in mud and thatch homes and those living in mud and iron roof homes.120

The RCT that Innovations for Poverty Action conducted of GiveDirectly's transfers in Rarieda provides end-line data on food consumption among control group recipients.121 This data shows that "20% [sic122 ] of the control group reports that not all household members usually eat until they are content, 23% of respondents report sleeping hungry in the last week, and only 36% report having enough food in the house for the next day."123 Other results related to food consumption are measured as well which are, in our view, consistent with the notion that recipients are extremely poor.

Anecdotal evidence from GiveWell's site visit to Kenya

In November 2012, GiveWell staff visited Kenya to view GiveDirectly's program in the field. See our notes and photographs from the site visit. We visited 5 locations (3 in Siaya and 2 in Rarieda) where GiveDirectly had transferred funds or was in the process of enrolling recipients to receive funds. We visited approximately 15 households across the 5 locations (including 2 non-recipient households that had metal roofs and cement walls and floors and did not qualify for GiveDirectly's program). For details on how homes we visited were selected, see this footnote.124

We would characterize the ~15 households we visited (as well as other households we saw while walking but did not speak with directly) as extremely poor. We summarize characteristics of these households as follows:

• Most homes are made up of three rooms.
• The main room is a sitting area. In the homes we visited, this room varied in size from approximately 10'x12' to 12'x20'. This room has 3 doors: one to the outside; one to what appeared to be a storage room; one to a bedroom. The husband and wife sleep in the bedroom and the children sleep in the storage room or in the kitchen.
• The kitchen is often a separate structure, most often thatched-roof (even for homes that have tin roofs). Some households have no kitchen structure but just cook outside. Others have a small kitchen in place of a storage room.
• There are no doors in between rooms in the house, just hung curtains.
• The living room has many chairs and couches for sitting. They often almost fully cover the wall area (aside from doors). There are also coffee tables in the middle of the room. Poorer homes had less furniture; one home we saw had a single chair and a single broken table.
• People have wall hangings for decoration. The most common hanging we saw was old calendars (e.g., from 2003) that have pictures and can be used for decoration.
• Most houses had 1-2 kerosene lamps that provide light since they don't have electricity. One home (a non-recipient we visited) had two electric lights, which were powered with a solar panel.
• People we spoke with reported walking 5-20 minutes daily to obtain drinking water, which one recipient reported doing 1-2x per day.
• Most households owned a bicycle.
• Some households have radios (both of the non-recipients we saw had radios; one had a TV). Of the others, 3/14 had radios. These were most often powered with what looks like a car battery .
• Most people owned one cell phone pre-GiveDirectly.
• Households tend to own some livestock. Most commonly, we saw 1-2 cows and 4-5 chickens each. They say they use the milk from the cows for themselves and also sell it and many mentioned being able to sell their cows in the future when they need money for kids to go to secondary school.

Note that among the households we visited, many had already received part or all of their transfer from GiveDirectly, so these characterizations are based on a selection of households that include some newly-built or renovated structures in addition to older structures. Given that some of the recipients we met used transfers to build larger houses or buy livestock, our observations would likely over-estimate the assets of each household pre-transfer.

In addition, the homes we saw from afar in villages we visited and homes we passed while driving in the area appeared to be at a similar level of extreme poverty.

How do recipients spend their cash, and how does this spending impact their lives?

Findings from the RCT

Effects of receiving cash transfers

It is difficult to interpret the size of the main treatment effects reported in the RCT of GiveDirectly's program in Rarieda, because embedded within the “main treatment” were two different transfer sizes ($287 and $1,085), which we would expect to have very different impacts and to be spent in very different ways. The mean transfer size was about $513, which is about half of the size of GiveDirectly’s standard transfer.125 We also note that we have reported transfer sizes in exchange-rate adjusted terms, but outcomes are reported in purchasing power parity (PPP) adjusted U.S. dollars.126

Except for where we explicitly state otherwise, all results mentioned in this intervention report are statistically significant at the 95% level.

How were GiveDirectly transfers spent?
Researchers collected data by surveying members of the treatment and control groups about their recent spending. All data that follows comes from participant self-reports. GiveDirectly recipients increased the value of their non-land assets and their monthly consumption.127 Their spending is broken down in more detail below.

• Total non-land assets.128 Receipt of transfers increased households’ non-land assets by an average of $279.129 Households that received large transfers increased their total non-land assets by about $253 more than households that received small transfers.130 The largest categories of asset increases were livestock ($85), durable goods ($53; primarily furniture), and savings ($10).131 Transfers also increased the likelihood that a household had an iron roof by 23 percentage points.132 Recipients of large transfers were 23 percentage points more likely to have iron roofs at end-line than recipients of small transfers.133 The RCT estimated that iron roofs cost about $564 USD PPP and had a return on investment of between 7 and 14%, but the source of this data was unexplained.134
• Business expenses. Treatment households spent about $13 per month more than control households on business expenses, which were primarily made up of non-durable expenses on non-agricultural businesses.135 There was no evidence that recipients of large transfers invested more in business expenses than recipients of small transfers.136
• Health expenditures. Treatment households spent about $3 per month more than control households on health expenditures.137 There was no evidence that recipients of large transfers spent more on healthcare than recipients of small transfers.138 This spending was also included within the estimate of spending on consumption, below.
• Education expenditures. The average treatment household with children spent about $19 more per month on education than the average control household with children although this estimate was imprecisely measured and not statistically different from zero.139 There was no evidence that households receiving large transfers spent more on education than households receiving small transfers.140 This spending was also included within the estimate of spending on consumption, below.
• Consumption. Treatment households consumed about $36 more per month than control households.141 According to point estimates, recipients of large transfers spent about $20 per month more on consumption than recipients of small transfers but this difference was not statistically different from zero.142 Slightly more than half of this additional consumption was on food.143 This additional consumption also included increased spending on social expenditures and various other expenditures.144
• Alcohol and tobacco. Treatment households did not increase their spending on alcohol or on tobacco.145

What were the overall effects of GiveDirectly?

• Food security. At baseline, food security was low among participants.146 Program participants reported a 0.25 standard deviation increase in a food security index over controls.147 Some food security related variables improved more for recipients of large transfers, but the study did not have high enough power to determine whether there was an effect on the overall index.148
• Health and education. The study did not detect an effect on indices of health and educational outcomes.149
• Revenue and profits. Receipt of transfers lead to a $17 per month increase in total revenues but no detectable increase in profits.150 We emphasize that these are very short-run effects and we do not know whether participants’ business investments might lead to profits in the longer run. There is no evidence that large transfers had greater effects on revenue or profits.151

Researchers also considered more subjective measures of impact on recipients' quality of life:

• Psychological well-being. Treatment improved an index of psychological wellbeing by 0.20 standard deviations.152 There was no observable effect on cortisol for the treatment group as a whole although cortisol, an indicator of stress, was slightly lower in the large transfer group than the small transfer group, a difference that was statistically significant at the 10% level when controls were included in the model .153 Recipients of large transfers measure 0.35 standard deviations higher on the well-being index than recipients of small transfers.154
• Female empowerment. Control households in treatment villages measure 0.23 standard deviations higher on an index of female empowerment than control households in control villages.155 This suggests that cash transfers to a village unexpectedly empowered females in both recipient and non-recipient households. The researchers propose potential mechanisms for this effect, but are explicit that these measured results are surprising and warrant further investigation.156 Note that we report this result for the sake of comprehensiveness but would guess that it is more likely to be random than real.

Data from follow-up surveys

GiveDirectly provided us with data from responses recipients gave to surveys conducted by GiveDirectly staff concerning how recipients used their cash transfers. This data was collected at different points in the transfer cycle of each campaign (e.g., Google data represents only the small initial transfer plus one lump sum, while Siaya data represents full transfers).157 We summarize the data below.

Percentage of recipients who reported spending in each category

Percentage of funds reported to be spent in each category

Anecdotal evidence from our site visit

In our site visit to GiveDirectly recipients in Kenya, we asked about the value of items commonly purchased with transfer funds.166 Recipients reported that their thatched-roofs frequently leak when it rains and require replacement every 3-4 months at a cost of 1,000 Kenyan shillings ($11.68 based on the exchange rate as of November 15, 2012167 ) as well as time/labor. One recipient also reported that when it rains, she moves her family and their belongings into other structures to stay dry. Recipients reported buying livestock as an investment/savings device, hoping that they could (a) use the milk from the cow or goat for additional income and (b) sell the cow or goat and any offspring in the future if/when they needed additional funds (for e.g., secondary school fees for their children which are approximately 15,000 Kenyan shillings per year168 ($175.13 based on the exchange rate as of November 15, 2012169 )).

Do the cash transfers cause problems and complications that offset their positive impact?

• Does distribution to some community members and not others result in jealousy, conflict, or related issues?

  The RCT that Innovations for Poverty Action conducted of GiveDirectly's transfers in Rarieda found no significant effects of transfers on the rate of crime in treatment villages170 or on instances of physical, sexual, or emotional violence in treatment households as compared to control households in treatment villages.171

  GiveDirectly surveys recipients (post-transfer) on the following questions:172

  • Have you heard complaints about GD in your community? What complaints are you hearing? Who is upset/complaining? Who are they upset with?
  • Has there been any shouting or angry arguments among people in your village about these transfers? If yes, describe.
  • Has there been any violence, theft, or other crime in your village related to these transfers? If yes, describe.

  GiveDirectly sent us results from the follow-up surveys that have been conducted for four ongoing transfer campaigns. Below, we summarize the survey data for some of the questions included in these surveys. For a full list of follow-up survey questions, see GiveDirectly, Kenya verification template, August 5, 2013. Percentages reported in this table represent the number of recipients who are marked as having responded "yes" (that they had the issue) out of those for whom a response is recorded in the data.

  Question	Rarieda (top-ups only)173	Siaya174	Nike175	Google176
  Did you have any trouble collecting this transfer?	1.7%	4.6%	0.0%	1.3%
  Do you have any regrets about how you used the transfer?	0.3%	2.1%	5.4%	0.4%
  Are you unhappy with the spending decisions someone else made?	0.3%	0.5%	0.0%	0.6%
  Have you heard complaints about GD in your community?	28.5%	63.6%177	21.8%	46.4%
  Has there been any shouting or angry arguments among people in your village about these transfers?	0.4%	5.7%	1.8%	2.0%
  Has there been any violence, theft, or other crime in your village related to these transfers?	0.7%	2.1%	1.8%	1.2%
  Did you and others in your household argue about what to do with the transfer?	0.6%	0.0%	0.0%	1.3%
  Has anyone told you that you need to pay them to collect your transfer?	0.0%	0.0%	3.6%	0.0%
  Have you felt threatened by any one since you were visited by Give Directly?	0.3%	2.6%	3.6%	0.1%
  Has the village elder approached you for any money since you were visited by Give Directly?	5.9%	3.1%	7.3%	0.3%
  Have you heard of any other recipients in your village being asked to pay money?	5.0%	8.8%	7.3%	0.1%
  Did anyone ask you for money to check your M-Pesa balance?	0.0%	-	0.0%	0.1%
  Did the M-Pesa agent ask you for a bribe?	0.0%	1.0%	0.0%	0.3%

  Note that GiveDirectly surveys only cash recipients, not non-recipients.

  We have also reviewed the records of calls made to GiveDirectly's Kenya hotline from May 2012 – October 2013, which provide anecdotal evidence of tension and conflict caused by the cash transfers, according to recipient reports. Reported issues include marital disputes and pressure from village elders to give them some of the funds from the transfer. Of the 78 issues tracked from July – October 2013, 9 involved tension, conflict or theft.178

  In its Kenya 2M campaign, GiveDirectly is experimenting with providing transfers to most of the households in a village, as opposed to targeting only mud and thatch households (more). GiveDirectly is conducting its standard follow-up phone surveys and will use that data to understand the effects of different targeting strategies on tension and conflict in the village.179 As of October 2013, GiveDirectly reported that preliminary survey data from the Kenya 2M campaign did not show a substantial difference in tension and conflict between villages with different targeting strategies.180 We have not yet reviewed this data.

  We have found very limited information about jealousy and conflict related to other cash transfer programs, but one study that found small levels of hostility towards recipients of an unconditional wealth transfer in Uganda is discussed in our cash transfer intervention report.

• Do grants distort incentives and decisionmaking? We have not seen information on the question of whether individuals who live in the areas served by GiveDirectly change their behavior in order to increase their chances of receiving transfers - for example, by spending more time at home to increase their chances of being at home when GiveDirectly staff visit, or by choosing to live in poorer quality housing in hopes of receiving transfers. The one-off nature of transfers (recipients are not eligible for a second round of transfers)181 may help to mitigate these effects among past and current recipients, though there is information to suggest that some recipients believe transfers could be given again in the future.182 We also have seen some very limited evidence (mostly pointing to no distortion) in the broader literature on cash transfers.

  Another way in which grants may distort decisionmaking is if they are promised and not delivered in time (causing people to make plans that cannot be executed). We do not have data directly addressing this issue, but GiveDirectly did provide statistics on the speed with which transfers were received for Rarieda and Siaya.183 In Rarieda, while 67% (359 of 536) of recipients waited less than a month and 84% (448 of 536) waited 3 months or less, 6% (34 of 536) waited 6 months or more. In Siaya (a later group), 188 of 193 recipients waited less than a month, and the remaining 5 waited 2-3 months. GiveDirectly comments:184
  

  We were able to accelerate [the time it took for recipients to register for M-PESA] significantly for two reasons: (a) we gave clearer instructions, and (b) we let recipients designate which household member they wanted to receive the transfers, which gives them flexibility to choose someone who already has a National ID; in the Rarieda round we could not do this as we were randomizing recipient gender. I expect the Nike cohort will take longer to register as that project focuses on 18-19 year old women, many of whom will not yet have IDs.

  GiveDirectly told us that for Kenya, the key factor determining when a recipient receives funds is when he or she registers for M-PESA, and that recipients are told from the beginning that they will not receive transfers until they have registered.185 GiveDirectly's records of calls to its Kenya hotline demonstrate that some recipients have been delayed in registering for M-PESA or collecting transfers due to issues outside of their control (e.g., a recipient's SIM number was already registered to someone else's M-PESA account; another recipient reported that an agent mistakenly claimed that the recipient's account had expired). The hotline records also documents the steps that GiveDirectly has taken to follow up on recipients' concerns; we have reviewed these and find them to be reasonable and appropriate. 186

  In Uganda, the agent networks of GiveDirectly's two mobile money providers are not very robust, which means that recipients must either travel farther, on average, to reach an agent or must withdraw cash only on days when GiveDirectly has arranged for agents to visit the villages (which GiveDirectly announces in advance).187 These challenges and lack of flexibility may hamper recipients' abilities to execute on plans for how and when to use funds. GiveDirectly told us that so far, the vast majority of recipients have been able to collect their transfers, with a few delays of up to a few hours on days when transfers are scheduled due to agents needing to replenish their cash stocks.188

• Do grants distort local markets? It seems possible to us that a large infusion of cash into an area could alter economic opportunities for both recipients and non-recipients. Such effects could be positive (for example by spurring investment and job creation or by increasing the availability of retail goods) or negative (for example, by leading primarily to local inflation). The limited evidence addressing this issue in the RCT of GiveDirectly's program in Rarieda and the broader literature on cash transfers points to no distortion. GiveDirectly's future evaluation plans (discussed below) may include a study that would provide information on the matter.
• Do cash transfers lead to large increases in spending on alcohol and tobacco? The RCT of GiveDirectly's program in Rarieda did not find an increase in spending on alcohol or tobacco. As discussed in our intervention report on cash transfers, RCTs of other programs that report spending on alcohol or tobacco do not find large increases in spending on these goods.

We also spoke with recipients and non-recipients about potential problems during our site visit to GiveDirectly's operations in Kenya in November 2012. For more, see our site visit notes.

Do the funds GiveDirectly transfers fully reach recipients?

GiveDirectly transfers funds to recipients through mobile money providers. In Kenya, the mobile money provider, M-PESA, allows users to receive, send, deposit, and withdraw funds on their mobile phones. When withdrawing funds, recipients must present ID along with their mobile phone number and user-specified M-PESA pin code to the M-PESA agent.189 Users enter the amount they want to withdraw on their own phone, and after each transaction, they can see their remaining balance,190 minimizing the ability of agents to defraud clients of funds.

Nonetheless, Lydia Tala, the Senior Field Officer who was responsible for making post-transfer phone calls to recipients in Kenya, reports that one of the most common client complaints is the belief that M-PESA agents are overcharging or stealing funds. Ms. Tala believes that these reports are incorrect and that approximately 10% of recipients are not fully aware of how to use M-PESA, so they may withdraw funds without checking their balance, ultimately being surprised when they have drawn down their account.191 GiveDirectly told us that it recognizes this issue and maintains a hotline to provide recipients with assistance in navigating the M-PESA system.192

The records of calls to GiveDirectly's Kenya hotline indicate that some recipients have had trouble using the M-PESA system, which has caused a delay in their receipt of funds. In some cases, recipients needed to travel to Kisumu, a nearby city, to get help from customer service at Safaricom (the company that owns M-PESA) before they were able to obtain their cash transfers.193

In Uganda, GiveDirectly is working with two different mobile money providers (745 recipients were assigned to the primary provider, 215 recipients to the secondary provider). The primary mobile money system sends recipients SMS notifications about their transactions, but recipients cannot check their balances or conduct transactions using only their phones; these actions require visiting a mobile money agent. GiveDirectly told us that this mobile money provider plans to build in more mobile functionality over time.194

GiveDirectly has told us that the network of mobile money agents is not as ubiquitous in Uganda as it is in Kenya, especially for the primary provider, so recipients in Uganda have to travel farther to reach an agent.195 In order to address this, GiveDirectly arranged to have agents from the primary provider visit villages to conduct transactions with recipients, rather than have recipients travel to agents.196 GiveDirectly told us that it hopes providing cash transfers will create enough demand for both mobile money services that they will deploy more agents to the areas in which the transfers are being sent, making it more convenient for recipients to cash out on any given day.197

One reason that GiveDirectly designed the transfer schedule in Uganda as monthly transfers instead of lump-sums was to make it more manageable for the mobile money provider.198 GiveDirectly told us that it has also given the mobile money provider advance notice before sending the funds so that agents could be prepared.199

GiveDirectly reports that overall, recipients in Uganda have not had a problem withdrawing funds, but that there have been a few delays of up to a few hours for recipients waiting to withdraw funds, due to agents needing to replenish their cash stock.200

Does GiveDirectly divert skilled labor away from other areas?

If it were to continue moving funds to recipients at the rate of $8 million per year, GiveDirectly has said that it would want a field staff for each country of about 30 Field Officers, two Senior Field Officers, and one Field Director.201

GiveDirectly recruits FOs by referrals from peer organizations, postings at universities, and running job advertisements. The application process involves an interview with the FD and a language competency exam. GiveDirectly reports that it receives approximately 6 times the number of resumes as openings for Field Officer positions.202 Regarding its field staff in Kenya, GiveDirectly explained that successful candidates generally have a college education203 and are paid approximately $12 per day, in addition to expenses for travel and lodging while working.204 GiveDirectly reports that there is greater language heterogeneity in the areas in which it works in Uganda, which made it harder to hire qualified field staff who also had the necessary language skills.205 Based on GiveDirectly's current staffing situation, we do not see diversion of skilled labor as a serious concern.

Are the size and structure of the cash transfers well-thought-through and appropriate?

GiveDirectly’s standard model involves grants of $1,000 (USD) to households over approximately one year,206 after which point recipients become ineligible.207 It gives the following rationale for the size of its transfers:208

We have reservations about the above reasoning:

• Regarding "fair." Pre-cash-transfer wealth/income differences between eligible and ineligible recipients may exist for a number of reasons; we don't believe it's warranted to assume that a "fair" world would see the two groups with the same wealth/income due to an equalizing transfer, and more to the point, we don't believe that the ineligible households are likely to see the situation as "fair." In addition, we are concerned that by aiming to equalize eligible and ineligible households, GiveDirectly takes on a substantial risk of its calculations being off in a way that leads to eligible households becoming systematically better off than ineligible households, which could distort incentives and lead to conflict.

  Strong evidence that the cash transfers do not lead to conflict and jealousy would reduce the weight we put on this concern, though as discussed above, it currently appears that GiveDirectly's cash transfers do cause some tension.

• Regarding "well-understood." GiveDirectly notes that its transfers are similar - in dollar terms - to those of government programs, but that they are likely much larger in "percentage of income" terms. We note that the cash transfer programs that have been studied to date seem to be in the range of 9-27% of recipients' annual consumption; by contrast, if GiveDirectly's clients average $0.65 in daily per capita consumption and receive an average of $288 per person over the course of a year (see above), this implies that people receive an average of 121% of their annual consumption in the year in which they receive the transfer.209 The quote above states that the lower level of initial income is an argument for making the cash transfer larger, but to us, it means that the risks of distorting incentives, causing conflict, etc. are likely to be greater than those of previously-studied programs, since the transfers are a substantially greater percentage of consumption. This issue is somewhat mitigated by the fact that GiveDirectly's transfers are designed as "wealth transfers" rather than as "income transfers": recipients receive funds over the course of a year and then become ineligible, whereas the government programs it alludes to have longer periods of eligibility. GiveDirectly has also told us that its decision to make larger transfers over a shorter period of time is based on recipients' reported preferences.210 But psychologically, the impact of GiveDirectly's transfers may be very different from those of government programs.

Our reservations about GiveDirectly's targeting strategy

We have reservations about the approach of targeting people based on the materials their houses are made of. GiveDirectly stated that it also views this as an open question, which is why it is experimenting actively with other targeting methods (e.g., village saturation).211

• As stated above, it is not clear to us that people in thatched-roof homes (eligible for transfers) are substantially and consistently poorer than people in iron-sheet-roofed homes with mud walls and floors (not eligible for transfers in a standard campaign).
• As discussed above, cash transfers may lead to tension between recipients and non-recipients; making transfers to all people living in a village might mitigate this issue. (GiveDirectly is experimenting with making transfers to most of the households in a village in its most recent campaign in Kenya212 ).
• We think it's possible that the difference between people in thatched-roof homes and people in iron-sheet-roofed homes comes down to fortune/luck (i.e., that the latter people have been more fortunate and thus have been able to afford iron sheets), but we also think it may come down to differences in choices regarding financial management (i.e., people in iron-sheet homes may have demonstrated better financial management and planning, thus allowing them to acquire iron sheets). If the latter is the case, there is a potential risk that GiveDirectly is systematically targeting the people who are less likely to use additional money well. GiveDirectly comments:213
  
  The most informative data available on this point are the differential impacts we’re seeing within the set of eligible households – specifically, poorer families seeing bigger impacts on nutrition while richer households see bigger impacts on tangible investment.

  We expect to learn more about the different spending choices of recipients in households with thatch roofs versus metal roofs from GiveDirectly’s most recent campaign in Kenya (referred to in this review as Kenya 2M).

Evaluation and experimentation

Results of the RCT of GiveDirectly's Rarieda campaign

Innovations for Poverty Action conducted a randomized controlled trial (RCT) of GiveDirectly's program in which eligible households were selected by lottery to receive cash transfers. These transfers were made in Rarieda (more information above) in 2011-12. GiveDirectly publicly provided the plan for collecting and analyzing data to determine the impact of these transfers. The RCT has now been published; we discuss it in detail here.

Other experimentation and evaluation

Cash transfers to girls
GiveDirectly's Nike transfer campaign will also be evaluated in a randomized controlled trial. These transfers target young women aged 18-19 and are being funded by the Nike Foundation. GiveDirectly has shared the survey instrument it plans to use with us.214 GiveDirectly has not yet shared an analysis plan for the Nike study, as it did for the Rarieda RCT.

Extended data collection by phone
Innovations for Poverty Action received a $30,200 grant to extend data collection in a sub-sample of participants from the Rarieda RCT using mobile phone-based data collection techniques. The grant is from USAID via the Policy Design and Evaluation Lab at UCSD. The goals of the project are to generate data on longer-term effects of cash transfers (up to 2 years after completion of the RCT) and how treatment effects unfold over time, as well as to study the potential for using mobile phones as cost-effective, easily adaptable tools for data gathering.215

Targeting strategies
GiveDirectly is also conducting an informal study in which it randomized villages into one of three groups:

1. A control group, in which no households will receive transfers
2. A treatment group with standard targeting (only mud and thatch-roof households will receive transfers)
3. A treatment group in which most households in the village will receive transfers (all households with mud walls and thatch or metal roofs will receive transfers, but households with cement walls and metal roofs will not)216

GiveDirectly plans to collect standard follow-up data via phone surveys and use this data to assess how the different targeting strategies affect levels of tension and conflict in the villages.217 GiveDirectly has stated that with adequate funding it would be interested in expanding the number of villages in this study and using it to assess the comparative impacts of transfers on local economies.218

Behavioral economics
GiveDirectly has stated that it is interested in experimenting with other aspects of its process:

• Frequency, timing, and sizing of transfer installments219
• Messaging to recipients220
• Decision-making supports (e.g., providing information on how other recipients have spent funds, providing a "sounding board" for recipients who are planning how they will use funds).221 GiveDirectly has said that it plans to implement decision-making supports in a campaign beginning in January 2014.222

What do you get for your dollar?

What percentage of GiveDirectly's expenses end up in the hands of recipients?

GiveDirectly aims to deliver a high proportion of campaign expenses directly to recipients: 90% in Kenya, and 88% in Uganda.223 GiveDirectly explains that it excludes set-up and outreach costs from total spending in this "efficiency" calculation because they are one-time costs (e.g., NGO registration) that can be reused indefinitely, as opposed to marginal costs.224 Data from GiveDirectly's distributions imply that it has been hitting this target. When all costs are considered, including set-up and outreach, cash grants make up 87.1% of expenses (more).225

Excluded from GiveDirectly's total costs is the unpaid time spent by its President and Directors, which GiveDirectly estimates at 85 hours per week.226

Does GiveDirectly offer a large amount of humanitarian impact per dollar?

We have not conducted a cost-effectiveness analysis that attempts to quantify the benefits of cash transfers in humanitarian terms. Instead, in comparing cash transfers to the intervention conducted by our two other top charities, we have attempted to monetize some of the benefits of the latter, in particular the “developmental effects” of deworming.

In practice, these calculations are highly sensitive to assumptions, especially regarding:

• the investment returns to cash transfers;
• how much confidence one places in the developmental impacts of deworming based on limited evidence; and
• the subjective assessment of the relative value of averting child mortality and improving incomes.

We guess that in purely programmatic terms, and given our values, deworming is more cost-effective than cash transfers. However, we think there are plausible values for these assumptions that would permit any ordering of the three charities.

We encourage readers who find formal cost-effectiveness analysis important to examine the details of our calculations and assumptions, and to try putting in their own. To the extent that we have intuitive preferences and biases, these could easily be creeping into the assumption- and judgment-call-laden work we’ve done in generating our cost-effectiveness figures, and we’re not entirely confident that the figures themselves are adding substantial information beyond the intuitions we have from examining the details of them.

Our 2013 spreadsheet is available here. See also, our 2012 discussion of the cost-effectiveness of cash transfers and other interventions.

Room for more funding

Projections for 2014

In October 2013, GiveDirectly provided the following statement regarding its room for more funding in FY 2014:227

Note that the above statement refers only to GiveDirectly's room for more funding for transfer campaigns. GiveDirectly fundraises separately to cover costs of fixed investments, including the costs of outreach and technology improvements. Accordingly, GiveDirectly excluded fixed investments from the above projection.

In November 2013, GiveDirectly sent us documents outlining its ideal funding for calendar years 2014-2015 with a general breakdown of proposed uses of funds and potential staff expansion. In these documents, GiveDirectly suggests that it could productively use $14.6 million in 2014 and $26 million in 2015.228 For the purposes of updating our view in 2013, we focused primarily on GiveDirectly's room for more funding in 2014. We did not thoroughly review GiveDirectly's proposals for 2015. We do not know how GiveDirectly would adjust its proposed uses of funds if it were to receive less than its ideal amount of funding.

We have reasons to believe that GiveDirectly's estimate for its room for more funding in 2014 is reasonable:

• GiveDirectly successfully expanded its staff capacity over the past year and expects to hire a second field director imminently (more).
• GiveDirectly incurred costs and liabilities of $2 million over a 3-month period in 2013; it used this as the basis for estimating the pace at which one Field Director could move funds ($8 million/year).229
• The total amounts that GiveDirectly has transferred to recipients each month has increased dramatically since its inception. In 2012, the average total amount transferred to recipients per month was $27,000, in the first ten months of 2013, the average total amount transferred per month was $148,000.230

However, there are also factors that could limit GiveDirectly's capacity in the coming year:

• In Uganda, GiveDirectly has had to be more proactive in working with one of the mobile money providers to arrange registration and cash-out days, as the provider's agent network is currently very limited.231 If the provider does not continue to cooperate with GiveDirectly, this could cause unexpected delays in enrollment or the transferring and withdrawal of funds.
• As of November 2013, GiveDirectly was finalizing new hire arrangements with a second Field Director who would start working in January 2014. When adding new staff, there is some uncertainty as to whether they will be successful in the role and how much training and management time will be required. Though GiveDirectly has expanded its staff this year, it is still a relatively young organization with minimal experience training new staff into the Field Director role. In addition, hiring itself can meet with unexpected delays. In July, GiveDirectly told us that it expected the second FD to be hired by October, and now that timeline has been pushed back to January.232
• In October 2013, GiveDirectly told us that it was currently in conversation with 8 potential donors who were considering gifts of $1 - $5+ million, though GiveDirectly was not able to predict whether or when any of these donations might come through.233 Some of these potential donations would go to support projects in Kenya, for which GiveDirectly would likely re-assign its current staff, thereby taking up some of its future capacity and room for more funding. Other projects being considered for funding by potential donors would be located in countries that are new for GiveDirectly, so it would likely increase its staff capacity for these projects, and its projected room for more funding would remain largely the same.234 However, we do not have a well-grounded estimate of how much unrestricted funding GiveDirectly is likely to receive, and were it to receive more than we anticipate, this would reduce its room for more funding.

Our bottom line

It is our understanding that GiveDirectly expects to use the majority of funds it receives in 2014-2015 for cash transfers that are part of ongoing or future experiments/evaluations, though we do not yet know the specifics of these studies.235

Based on our understanding of GiveDirectly's future plans, we would like to see GiveDirectly receive at least $10 million over FY 2014 (September 1, 2013 - August 31, 2014) to maintain its rapid pace of growth (roughly 2x what it received the year before). GiveDirectly has said it could receive and move up to approximately $40 million during 2014 and 2015.

Given GiveDirectly's track record over the past year of successfully scaling its operations, we believe it would not be unreasonable for donors to provide sufficient funding for GiveDirectly to scale as quickly as it can. Nevertheless, we remain cautious about recommending that GiveDirectly receive funding that would represent a large multiple of what it has previously received.

For follow-up on our projection of GiveDirectly's room for more funding in 2013 and its actual use of funds, see this footnote.236

Unrestricted vs. restricted funds

GiveDirectly has stated to us that by default, it intends to use all donations from individual donors as restricted funds for cash transfers of the same model as is standard in each country (in terms of size, structure and targeting), aiming to spend about 90% of such donations directly on transfers. However, GiveDirectly has also stated that it is interested in providing cash transfers in ways that differ from standard protocols in order to continue experimenting and learning, as discussed above. In the past, GiveDirectly has funded these more experimental projects with grants from larger donors and unrestricted funds from GiveWell donors.

GiveDirectly told us that it is also aiming to build capacity to provide cash transfers at a much larger scale, so that it could act in the future as an implementing organization for government-funded cash transfer programs in developing countries. Towards this goal, GiveDirectly is seeking funding to expand its domestic team, in order to execute a strategy for entering institutional markets, and to improve its technological capabilities, in order to be able to serve these markets.237 GiveDirectly has thus far been seeking funding for its capacity building from larger donors, but it could potentially use unrestricted funds from GiveWell donors for this purpose as well. We have not assessed the likely impacts of GiveDirectly serving institutional markets and have not vetted its plans to build capacity for this work, so we do not have a view on how it compares to GiveDirectly independently delivering cash transfers.

We prefer that GiveDirectly spend funds in the way that it believes will maximize its potential and, accordingly, do not recommend that GiveWell donors restrict their donations in any way. On our "Donate" page, linked from here, we explain to donors that funds given via the link on this page will be treated as unrestricted donations (in which case GiveDirectly may use funds for all purposes, including experimenting with its model and process and organizational capacity building). Any donations made directly via GiveDirectly’s website will continue to be directed to transfers using its standard process.

GiveDirectly as an organization

GiveDirectly is a relatively young organization. It was founded in 2008 when its founders were graduate students in economic development; Paul Niehaus, President and co-founder of GiveDirectly, is also an Assistant Professor of Economics at the University of California, San Diego.238

We believe GiveDirectly to be an exceptionally strong and effective organization:

• Self-evaluation: GiveDirectly has invested heavily in self-evaluation from the start, and furthermore, the study design of its Rarieda RCT was pre-registered for additional accountability and credibility.
• Track record: Although it is relatively young, we feel that GiveDirectly's first two years have gone well: GiveDirectly has successfully accomplished its goal of transferring cash to extremely low-income people at roughly a 90% ratio.
• Communication: GiveDirectly has always communicated extremely clearly and directly with us and given thoughtful answers to our critical questions. Generally, GiveDirectly seems to come to conclusions that we find reasonable on key questions.
• Transparency: GiveDirectly appears to value transparency as much as any organization we’ve encountered. We have not seen it hesitate to share information publicly (unless it had what we consider a good reason).

However, we continue to see potential room for improvement, particularly related to the fact that Paul Niehaus, the President, spends the majority of his (unpaid) hours fundraising for GiveDirectly. We think that there is substantial potential for GiveDirectly to experiment with different approaches to its intervention, and that more of the President's attention would be beneficial for this.

More on how we think about evaluating the leadership of organizations at our 2012 blog post.

Sources

• 1

  In Kenya: "We transfer your donation electronically to a recipient's cell phone

  We move the money from our US bank to our account with Safaricom's M-Pesa mobile payment system using a foreign exchange broker. We then transfer money from our M-Pesa account to the recipient's M-Pesa account. As a security measure we only transfer funds to a recipient if the name in our records matches the name on the national ID document he or she used to register for M-Pesa. The recipient gets an SMS text message reminding him or her of the transfer and then collects the transfer from a local M-Pesa agent, who is typically a shopkeeper in the recipient's village or in the nearest town. The recipient transfers his or her electronic balance to the agent's phone in return for cash." GiveDirectly, How it works

  Note that GiveDirectly's primary mobile money provider in Uganda is a mobile-linked system (recipients receive SMS notifications about their transactions), but recipients cannot check their balances or conduct transactions using only their phones; these actions require visiting a mobile money agent. GiveDirectly told us that this mobile money provider plans to build in more mobile functionality over time. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 2

  GiveDirectly, GD – GW update, July 17, 2013.

• 3

  The amount of time that it takes for total transfers to be sent has varied between campaigns. The Google transfer campaign is likely the best approximation of timing for a standard campaign, because it does not involve an experiment (like Rarieda and Nike), and it is more recent than the Siaya campaign. In the Google campaign, the planned schedule for transfers lasts approximately eight months for any given recipient, not including the time for census and enrollment (GiveDirectly, Google transfer schedule, July 2013).

  "We send each recipient household a total of $1,000 over one to two years, or $200 per household member for the average household. Our analysis suggests that this amount is fair, well-understood, and potentially transformative." GiveDirectly, FAQs 2013: "How much do recipients get?"

• 4

  "GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33. (Note that this comment refers to one completed set of transfers per recipient).

• 5

  "GiveDirectly uses objective criteria to determine which households are eligible for transfers. We target households which do not have solid (cement or iron) walls, floors or roofs in their houses. Our research shows this criterion is highly correlated with acute poverty." GiveDirectly, FAQs 2012

• 6
  • "Our standard: Put at least 90% of every donated dollar in the hands of the poor." GiveDirectly, Values
  • "Based on performance to date we expect to put 93% of your donation into the hands of a recipient." GiveDirectly, Financials
  • "Our 90% target has always been on efficiency as we define it (i.e., transfers as a % of total campaign expenses). It is also specific to Kenya; we are currently planning to set Uganda target at 88% based on performance to date" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 7

  The amount of time that it takes for total transfers to be sent has varied between campaigns. The Google transfer campaign likely gives the best approximation of timing for a standard campaign, because it does not involve an experiment (like Rarieda and Nike), and it is more recent than the Siaya campaign. In the Google campaign, the planned schedule for transfers lasts approximately eight months for any given recipient, not including the time for census and enrollment (GiveDirectly, Google transfer schedule, July 2013).

• 8

  "GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." @GiveDirectly, Offering memorandum (January 2012)@ Pg 33. (Note that this comment refers to one completed set of transfers per recipient).

• 9

  GiveWell Household size analysis

• 10

  GiveWell Household size analysis

  Mean daily per capita consumption among eligible households = $0.65 (GiveDirectly, Offering Memorandum (January 2012) Pg 24.)

• 11

  The amount of time it takes for total transfers to be sent has varied between campaigns. The Google transfer campaign in Kenya likely gives the best approximation of timing for a standard campaign, because it does not involve an experiment (like Rarieda and Nike), and it is more recent than the Siaya campaign. In the Google campaign, the planned schedule for transfers (including the small initial transfer and two larger transfers) lasts approximately eight months for any given recipient, not including the time for census and enrollment. GiveDirectly, Google transfer schedule, July 2013

• 12

  GiveDirectly, Uganda transfer schedule

• 13

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 14

  "GiveDirectly: We are considering experimenting with a few aspects of our transfer model:

  Frequency of payments. The preliminary results from the first randomized controlled trial (RCT) of a GiveDirectly campaign have suggested very slight differences between lump-sum and stream payments. Some recipients of GiveDirectly cash transfers report that they would prefer lump sum payments, while others report that they’d prefer stream payments. There may be a benefit in giving recipients the ability to choose the frequency of their payments."

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013

• 15

  GiveWell, GiveDirectly financials tables Sheet: "Campaign expenses"

• 16

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012.
  Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012.
  

• 17 The transfer sizes presented here are rounded figures - the actual amounts transferred to recipents vary by campaign. For example, a full transfer in the Nike campaign is 81,950 Kenyan Shillings (GiveDirectly, Nike transfer schedule, August 2013) and a full transfer in the Kenya 2M campaign is 86,368 Kenyan Shillings (GiveDirectly, Kenya 2M transfer schedule, November 2013). We expect that some or all of the variance in transfer size could be due to differing exchange rates at the times the amounts were determined for each campaign, but we have not asked GiveDirectly about this.
• 18 GiveDirectly, Rarieda transfer schedule, August 2013
• 19 "To this end, 137 households in the treatment group were randomly chosen and informed in January 2012 that they would receive an additional transfer of KES 70,000 (USD 798, PPP 1,112), paid in seven monthly installments of KES 10,000 (USD 114, PPP 160) each, beginning in February 2012. Thus, the transfers previously assigned to these households, whether monthly or lump-sum, were augmented by KES 10,000 from February 2012 to August 20128, and therefore the total transfer amount received by these households was KES 95,200 (USD 1,085, PPP 1,525). The remaining 348 treatment households constitute the 'small' transfer group, and received transfers totaling KES 25,200 (USD 287, PPP 404) per household" (Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013). According to GiveDirectly, Rarieda transfer schedule, August 2013, there are 333 recipients scheduled to receive 60900 KES (approximately $700) from March 2013 - April 2014, so it may be the case that 15 group A recipients have a different transfer schedule or are not receiving the same amount of top-up.

  Note that there are 53 recipients listed in GiveDirectly, Rarieda transfer schedule, August 2013 who are unaccounted for in groups A or B. According to this document, 41 recipients are scheduled to receive less than $30 total; GiveDirectly told us that these recipients were part of a preliminary trial of transfers in Rarieda, but included in the control group because of the small size of the transfers (Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013). The other 12 recipients excluded from group A and B may be among the 13 recipients scheduled to receive an amount within the range of $250- $440; we do not know why these recipients will not receive full transfers.

  Note that at the time we completed our full review of GiveDirectly, we were told that there were 358 recipients in group A. We do not know the reason for this apparent attrition.

• 20

  GiveDirectly, Rarieda transfer schedule, August 2013 Note: a few recipients appear to have different transfer schedules, but the vast majority of top-ups were sent in 2 installments.

• 21 GiveDirectly, Rarieda transfer schedule, August 2013
• 22 "To this end, 137 households in the treatment group were randomly chosen and informed in January 2012 that they would receive an additional transfer of KES 70,000 (USD 798, PPP 1,112), paid in seven monthly installments of KES 10,000 (USD 114, PPP 160) each, beginning in February 2012. Thus, the transfers previously assigned to these households, whether monthly or lump-sum, were augmented by KES 10,000 from February 2012 to August 20128, and therefore the total transfer amount received by these households was KES 95,200 (USD 1,085, PPP 1,525). The remaining 348 treatment households constitute the 'small' transfer group, and received transfers totaling KES 25,200 (USD 287, PPP 404) per household." (Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013).

  Note that there are 53 recipients listed in GiveDirectly, Rarieda transfer schedule, August 2013 who are unaccounted for in groups A or B. According to this document, 41 recipients are scheduled to receive less than $30 total; GiveDirectly told us that these recipients were part of a preliminary trial of transfers in Rarieda, but included in the control group because of the small size of the transfers (Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013). The other 12 recipients excluded from group A and B may be among the 13 recipients scheduled to receive an amount within the range of $250- $440; we do not know why these recipients will not receive full transfers.

• 23 "This process resulted in 503 treatment households at baseline, and 505 control households in treatment villages" (Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013).
• 24 Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013
• 25 "There were roughly 500 control households in treatment villages; we plan to transfer $1K to all of them but haven’t made a decision yet on timing." GiveDirectly, GW Q&A, April 26, 2013, Pg 2.
• 26 Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013
• 27 GiveDirectly, Siaya transfer schedule, June 2013
• 28 GiveDirectly, Nike transfer schedule, December 2013
• 29 GiveDirectly, Nike transfer schedule, December 2013
• 30 GiveDirectly, Google transfer schedule, December 2013
• 31GiveDirectly, Google verification, September 21, 2013
• 32 GiveDirectly, Kenya 2M transfer schedule, November 2013
• 33 GiveWell, Uganda payments

  GiveDirectly, Uganda monthly forecast, November 18, 2013

• 34 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 35

  GiveWell, Amounts transferred by month by GiveDirectly

• 36

  See GiveWell, GiveDirectly financials tables Sheet: "Total cumulative expenses"

  Note that GiveDirectly did not bear the costs of the Rarieda RCT, and they are therefore excluded from its budget. GiveDirectly has estimated that the experiment budget covered $4,000 worth of costs that GiveDirectly would have paid in the absence of the RCT. GiveDirectly included these costs in the financial documents it sent us in 2012; we assume they have been included again in the 2013 versions.

  "[We have] included on our books our best estimate of the RCT costs that would normally have been incurred by GiveDirectly, i.e. $4,000, primarily for travel expenses. This cost is included in all of our performance figures." @GiveDirectly, GiveWell Clarifications (March 28, 2012)@

• 37

  GiveDirectly expects to spend a total of $0.5 - $1 million on fixed investments in FY 2014, which it is seeking from major donors, not retail donors whose marginal dollars will continue to be directed exclusively to cash transfers. Its total spending on transfer costs will similarly depend on fundraising. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013.

  "We anticipate a FY2014 budget for outreach, international expansion and technology investments in the $0.5M-$1.0M range for which we are fundraising separately." GiveDirectly, GD – GW update, July 17, 2013.

• 38

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 39

  GiveDirectly, FY 2012 Form 990, Pg 7.

• 40

  GiveWell, GiveDirectly financials tables Sheet: "Campaign expenses"

• 41

  Note that this excludes future set-up and outreach costs, for which we do not have estimates.

  GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013

• 42

  "First, as a general principle we'll always aim to publish spending clearly enough so that anyone can calculate efficiency however they want. Second, when we analyze our efficiency for management and forecasting purposes we distinguish between (1) operating costs that are ongoing but shared across campaigns and (2) one-time setup costs. (1) is included in the efficiency calculation and (2) is not. Examples of (1) are Field Director time (which is "fixed" for the duration of his/her contract) and office space (which is "fixed" with respect to number of ongoing campaigns). Examples of (2) are costs of registering as an NGO in a new country and costs of developing code which can then be re-used indefinitely. We accept donations from the public for (1) but not (2)." Email from Joy Sun, COO, Domestic, GiveDirectly, August 9, 2013

  "Set-up costs are excluded because one-time costs (e.g. NGO registration) can be re-used indefinitely, as opposed to marginal costs." Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

  GiveDirectly provided its own efficiency calculations in GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013 and provided a separate budget with set-up and outreach costs in GiveDirectly, Set-up and domestic expenses, October 2, 2013.

• 43

  GiveWell, GiveDirectly financials tables Sheet: "Campaign expenses"

  Note: this is based on spending in all Kenya campaigns except Nike. GiveDirectly explains: "We show the aggregate efficiency for our retail product, i.e., transfers in Kenya excluding Nike for which we do not accept retail funding." Email from Joy Sun, COO, Domestic, GiveDirectly, September 27, 2013

• 44

  GiveWell, GiveDirectly financials tables Sheet: "Campaign expenses"

• 45 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 46 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 47 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 48 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 49 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 50 GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013
• 51 GiveDirectly, Set-up and domestic expenses, October 2, 2013; GiveDirectly, Board spending breakdown
• 52 GiveDirectly, Set-up and domestic expenses, October 2, 2013; GiveDirectly, Board spending breakdown
• 53

  "Kenya was selected due to 1) the robustness of M-Pesa as a mobile banking platform 2) large population of target poor with access to mobile tech" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 54

  Conversation with Paul Niehaus, President, and Rohit Wanchoo, Director, GiveDirectly, March 18, 2013

• 55
  • Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012
  • "GiveDirectly selected a region of the [second] country to set up initial operations in based on poverty data." Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 56

  Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012

• 57

  "County level – we look at data on poverty, population density, presence of poverty-focused NGOs (which we try to avoid), and security."
  @GiveDirectly, Operational Process Overview@ Pg 1.

• 58

  GiveDirectly, Siaya poverty data by location

• 59

  "Factors that informed decision to locate initial campaign in […] County: poverty rate, […] logistical ease for set up activities and cross-country management, […] minimum security for staff" GiveDirectly, Uganda targeting data, July 22, 2013
  

• 60

  "Village level – we collect basic information on villages (e.g. distance to town, number of schools) and plug these into an algorithm that predicts mean income." @GiveDirectly, Operational Process Overview@ Pg 1.

• 61

  See @GiveDirectly, Siaya Village Index@ for the calculations that GiveDirectly did to create "poverty scores" for different villages in Siaya. The weights placed on each indicator (in constructing the index) were determined using the process described in @GiveDirectly, Village Targeting Regression@: the more detailed "poverty scores" from GiveDirectly's Rarieda study were regressed on indicators such as "village population," "number of boreholes," etc.

• 62

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 63
  • In Rarieda (the site of the RCT and the first transfers GiveDirectly provided), GiveDirectly sought poorer districts (based on 2005 census data) that were in places with sufficient accessibility, M-PESA usage, population density to make it more convenient, proximity to Innovations for Poverty Action (the RCT implementer) offices and where there would be a sufficient number of potential recipients in this district (# of thatched roof houses). GiveDirectly chose to work in Rarieda District because it was slightly poorer than the nearby Siaya district. In Rarieda, there are slightly more than 300 villages, and GiveDirectly conducted a census in each village to determine the number of eligible (thatched-roof) and ineligible households in each. It then selected the 100 with the highest proportion of thatched-roof to non-thatched-roof households, and randomly selected 60 of those to serve as the treatment and control groups in its trial. (Faizan Diwan, Innovations for Poverty Project Associate, conversation with GiveWell, November 8, 2012)
  • GiveDirectly chose to work in Siaya District, the location of the three other sets of transfers GiveDirectly has initiated, because it shared local administration with the Rarieda District, making expansion easier; it chose not to remain in Rarieda because it did not want to overlap in areas in which the RCT was being conducted. Using administrative data, it chose 3 locations within Siaya that it believed had the highest poverty levels. It then carried out the process described in the main text to rank the 100 villages in these locations. GiveDirectly was not able to contact all village elders to obtain data (staff estimate they reached 85 out of 100) and it excluded villages whose village elders it was not able to reach. In June 2012, it selected the 7 villages which its model ranked as highest poverty to receive the Siaya transfers.

    For the project funded by the Nike Foundation, GiveDirectly selected the next 36 villages in its ranked list of 100 villages in Siaya District, as this number of villages provided a sample size sufficient to meet their target size.

    In the Google transfers, GiveDirectly is continuing to work down the list of 100 villages to target those not already targeted in the Siaya or Nike Foundation transfers. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012)

• 64

  "When entering a new area, the COO meets with a series of officials to explain the project, obtain permission, and establish a relationship in case any problems arise:

  • District Commissioner
  • Chief
  • Assistant chiefs
  • Village elders"

  @GiveDirectly, Operational Process Overview@ Pg 1.

  In Uganda, GiveDirectly had to spend more time meeting with officials early on in the process, because there is a greater bureaucratic structure than in Kenya; this engagement tapered off after the early stage, though GiveDirectly is still in touch with officials by phone to let them know when GiveDirectly has started conducting field activities. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 65

  In the Rarieda campaign, a census of all households was completed before enrollment. However, in this case, the census process was implemented by Innovations for Poverty Action, the organization conducting the randomized controlled trial of GiveDirectly's program, as opposed to GiveDirectly staff.

  In the Siaya campaign, GiveDirectly did not complete a full census of all households in the village. Instead, staff went to the village elder and asked him or her to take them to each thatched-roof household in the village to verify that it was eligible for the transfer. GiveDirectly discovered that some village elders were assisting friends or family members in pretending that they live in thatched-roof houses so that they could receive transfers, so it aims, when possible, to find a village member who can serve as a guide rather than to rely on the village elder. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012; Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013)

  In the Nike campaign, GiveDirectly staff requested that the village elder lead them to eligible households: women aged 18-19, living in thatched-roof homes. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012)

• 66

  "Enumerators enter villages, engage a local to serve as guide for the day, and
  enumerate all households living in the village, noting which homes are eligible." @GiveDirectly, Operational Process Overview@ Pg 1.

• 67

  "Household level – we enroll households living in mud and thatch homes." @GiveDirectly, Operational Process Overview@ Pg 1.

• 68

  GiveDirectly, Google Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 69

  GiveDirectly, Kenya 2M census results, July 8, 2013

• 70

  "Enrollment. A second, distinct enumerator returns to enroll households identified as eligible, give them a SIM card and instructions on how to register if needed." @GiveDirectly, Operational Process Overview@ Pg 1.

• 71

  See process description in @GiveDirectly, Operational Process Overview@ Pg 3.

• 72

  GiveDirectly gave cash transfer recipients the option of spending some of the money that they receive to buy a phone provided by GiveDirectly. Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, October 6, 2012

• 73

  GiveDirectly told us that in Uganda, it is possible to purchase ID cards that can be formally approved by the signature of one's local councilperson. GiveDirectly helped recipients obtain ID cards by purchasing the cards, sending field staff to villages to take photographs of recipients, printing the photographs for the ID cards at a local printer, working with local councilpeople to approve the cards, and arranging for recipients to collect their cards.

  GiveDirectly told us that the mobile money agents did not charge a fee to visit villages to register recipients, and that GiveDirectly field staff were present to supervise the process.

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 74

  As noted above, in the Siaya campaign, GiveDirectly's staff initially engaged the village elder to lead them to eligible households. Once they had been led to an eligible home, GiveDirectly staff provided the household with a SIM card and enrolled them in the program. Thus, in Siaya, there was one fewer back-check than exists in the current process. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012)

  In the Nike campaign, GiveDirectly staff requested that the village elder lead them to eligible households, so there was one fewer back-check in this process as well. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012)

• 75

  @GiveDirectly, Siaya Enrollment Database@
  GiveDirectly, Google Enrollment DB
  GiveDirectly, Nike Enrollment Database
  GiveDirectly, RCT Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 76

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012. Ms. Mukhopadhyay told us that she had reviewed cases in which some members of the village told GiveDirectly's staff that enrollees were not eligible because they did not live in thatched-roof homes. In these cases, Ms. Mukhopadhyay decided to exclude these potential recipients.

• 77

  @GiveDirectly, Operational Process Overview@ Pg 1.

• 78

  See process description in @GiveDirectly, Operational Process Overview@ Pg 3.

• 79

  @GiveDirectly, Siaya Enrollment Database@
  GiveDirectly, Google Enrollment DB
  GiveDirectly, Nike Enrollment Database
  GiveDirectly, RCT Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 80

  Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012.

• 81

  "Back-checking. Another enumerator, distinct from the census and enrollment workers, revisits each enrolled household to check that they eligible, didn’t have to pay a bribe to enroll, etc." @GiveDirectly, Operational Process Overview@ Pg 2.

• 82

  See process description in @GiveDirectly, Operational Process Overview@ Pg 3.

• 83

  @GiveDirectly, Siaya Enrollment Database@
  GiveDirectly, Google Enrollment DB
  GiveDirectly, Nike Enrollment Database
  GiveDirectly, RCT Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 84

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012

• 85

  @GiveDirectly, Operational Process Overview@ Pg 2.

• 86

  @GiveDirectly, Siaya Enrollment Database@
  GiveDirectly, Google Enrollment DB
  GiveDirectly, Nike Enrollment Database
  GiveDirectly, RCT Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 87

  Village meetings were implemented for the first time as part of the Google campaign. GiveDirectly, GW Q&A, April 26, 2013

• 88

  @GiveDirectly, Operational Process Overview@ Pg 1.

• 89

  @GiveDirectly, Operational Process Overview@ Pg 2.

• 90

  See process description in @GiveDirectly, Operational Process Overview@ Pg 3.

• 91

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012

  In Kenya, the initial transfer amount has been 5% of the total transfers (see GiveDirectly, Google transfer schedule, July 2013). As of September 2013, the initial transfers sent in Uganda have been 10% of the total transfers (GiveDirectly, Uganda transfer schedule).

• 92

  In early transfer campaigns, GiveDirectly transferred a first, full installment to recipients before its first call; in its Google campaign, GiveDirectly implemented the initial small transfer to enable it to identify problems before transferring the larger amount. Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012.

• 93

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012.

• 94

  GiveDirectly explained that it is experimenting with having field staff conduct follow-up surveys in-person rather than over the phone because the travel costs for field staff to visit villages is less than the cost of airtime for phone surveys (field staff live in towns fairly proximate to the villages), and also because face-to-face interactions with recipients will help GiveDirectly to establish a presence on the ground in Uganda. In-person surveys also avoid the issue of dropped calls leading to incomplete survey responses. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 95

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 96
  • GiveDirectly, Rarieda verification stats
  • GiveDirectly, Siaya verification stats
  • GiveDirectly, Disaggregated Siaya Follow-up Data
  • GiveDirectly, Disaggregated Nike Follow-up Data
  • GiveDirectly, Disaggregated Google Follow-up Data (LS - long)
  • @GiveDirectly, Rarieda Top-up verification (short)@
  • GiveDirectly, Rarieda verification (top ups), May 26, 2013
  • GiveDirectly, Siaya verification, June 15, 2013
  • GiveDirectly, Google verification, September 21, 2013
  • GiveDirectly, Nike verification (final), September 24, 2013 (based on full survey given to recipients after whole transfer was sent)
  • GiveDirectly, Nike verification (combined), May 18, 2013 (based on full survey given to recipients early in campaign, which GiveDirectly later stopped administering because it was too time-consuming with monthly transfers Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013)
  • GiveDirectly, Nike verification (short version), June 20, 2013 (based on abbreviated survey given to recipients after each part of the transfer was sent, in place of "combined" survey Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013)

• 97

  Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012.

• 98

  GiveDirectly, Kenya hotline log, July 24, 2013 (May 2012 – July 2013)
  GiveDirectly, Follow-up tracker, July 2013 (July – October 2013)

• 99

  Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012

• 100

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 101

  @GiveDirectly, Siaya Enrollment Database@
  GiveDirectly, Google Enrollment DB
  GiveDirectly, Nike Enrollment Database
  GiveDirectly, RCT Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 102

  GiveDirectly, Update on process changes, August 28, 2013

• 103

  GiveDirectly told us that for the Kenya 2M campaign, its enrollment field staff conducted a short survey with anyone who approached the field staff to complain that they had been unfairly or mistakenly skipped during the census. Though GiveDirectly will not add recipients after the census has been conducted, it intends to continue carrying out the surveys for future campaigns, as a way of tracking complaints, recognizing potential issues with the census, and assessing changes intended to improve GiveDirectly's census process. Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013 We have not reviewed the results of this survey.

• 104

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 105

  "GD has had one person working full-time since January of 2011. Jeremy Shapiro was full time during 2011 and Piali Mukyopadhyay is full-time now." GiveDirectly, clarifications on GiveWell's draft review of GiveDirectly

• 106

  Conversation with Paul Niehaus, President, and Rohit Wanchoo, Director, GiveDirectly, March 18, 2013

• 107

  [GiveDirectly] has hired one Field Director and is in the process of hiring a second.
  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 108

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 [Unpublished]
  GiveDirectly, GD – GW update, July 17, 2013
  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013
  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 109
  Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

  In Uganda, GiveDirectly hired some of the highest performing FOs from the pre-transfer FO staffs to stay on as FOs who would conduct follow-up surveys. (Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013). It is our impression that FOs who conduct follow-up surveys are often hired out of the pre-transfer FO staffs.

• 110

  See GiveWell Site visit notes.

• 111

  @GiveDirectly, Siaya Enrollment Database@
  GiveDirectly, Google Enrollment DB
  GiveDirectly, Nike Enrollment Database
  GiveDirectly, RCT Enrollment DB
  @GiveDirectly, Kenya 2M Enrollment Database, September 23, 2013@
  GiveDirectly, Kosile, Uganda Enrollment database
  GiveDirectly, Kawo, Uganda Enrollment database
  GiveDirectly, Kanyamutamu, Uganda Enrollment database
  GiveDirectly, Akumure, Uganda Enrollment database

• 112

  @GiveDirectly, Siaya Verification Stats@
  GiveDirectly, Rarieda verification stats
  GiveDirectly, Disaggregated Siaya Follow-up Data
  GiveDirectly, Disaggregated Nike Follow-up Data
  GiveDirectly, Disaggregated Google Follow-up Data (LS - long)
  @GiveDirectly, Rarieda Top-up verification (short)@
  GiveDirectly, Rarieda verification (top ups), May 26, 2013
  GiveDirectly, Siaya verification, June 15, 2013
  GiveDirectly, Google verification, September 21, 2013
  GiveDirectly, Nike verification (final), September 24, 2013

• 113

  Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012.

• 114

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012

• 115

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 116

  The back-checks identified two households had cement floors, which normally would disqualify them, but GiveDirectly determined that a charity had cemented these household's floors as part of a campaign to prevent jiggers (a parasite) from infecting barefoot children living in the home. Because the floor had been installed as part of this charity's campaign, GiveDirectly felt that the existence of a cement floor was not an indication of the households' wealth. Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012.
  

• 117

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 118

  @GiveDirectly, Survey for Randomized Controlled Trial@

• 119

  GiveDirectly, Offering Memorandum (January 2012) Pgs 23-24.

• 120

  GiveDirectly, Offering Memorandum (January 2012) Pg 25.
  

• 121

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013

• 122
  The data reported for this variable in Table 4 of Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 32, appears to demonstrate that 21% (not 20%) of the control group reports that not all household members usually eat until they are content.
  
• 123

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 18, corresponding data in Table 4, Pg 32.

• 124

  Several recipient households had been selected by GiveDirectly for our visit as representative of how recipients use funds. 2 locations (and the households within them) were selected as a function of GiveDirectly's activities that day -- an area of Rarieda where the end-line survey for the RCT was being implemented and an area of Siaya where enrollment was being undertaken. In both cases, we don't know whether enrollment and surveying activities were taking place elsewhere that day which would have given GiveDirectly discretion in choosing these areas.

  On the final day of our visit, we asked GiveDirectly whether we could randomly select 3 households to visit. GiveDirectly sent us a list of 15 households in a location in Rarieda where the end-line survey for the RCT was complete and therefore we could question recipients without interfering with the RCT. We don't know whether GiveDirectly had discretion in choosing these 15 households. We selected 5 households from the list using Excel's RAND() function and visited 3 of them. (GiveDirectly made appointments with the households in advance and could not reach 2 of them.)

  We would characterize all the households we visited -- those that GiveDirectly fully selected for us, those over which GiveDirectly had less discretion, and those we selected randomly -- as extremely poor. We did not see any significant differences in wealth between them.

• 125
  • “28% of the treatment group received a transfer of KES 95,200 (USD 1,085, PPP 1,525), while the remaining 72% received KES 25,200 (USD 287, PPP 404); the average transfer was thus KES 45,016 (USD 513, PPP 721).” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 12.
  • We do not know whether the marginal effects of dollars transferred increase, decrease, or stay constant as transfer size increases, but, based on our intuition and on the estimated relative effects of large transfers, we would guess that the effects of GiveDirectly’s standard transfer are substantially larger than the average treatment effects in the evaluation but are slightly less than double those effects. Recipients of large transfers increased their assets by $253 more than recipients of small transfers. Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29. Point estimates suggested that recipients of large transfers increased their consumption by $20/month more than recipients of small transfers but this estimate was not statistically significant. Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29. The average treatment effect was a $279 increase in assets and a $36/month increase in consumption. Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29. The effect of increasing the transfer size by about $800 from a small transfer to a large transfer was therefore positive but smaller than the overall treatment effect for grants averaging $513. This may be evidence in favor of diminishing returns but may also be evidence that recipients of large transfers spend their transfers more slowly.

• 126

  The PPP adjusted values of the small, mean, and large transfer are $404, $721, and $1,525 respectively. “28% of the treatment group received a transfer of KES 95,200 (USD 1,085, PPP 1,525), while the remaining 72% received KES 25,200 (USD 287, PPP 404); the average transfer was thus KES 45,016 (USD 513, PPP 721).” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 12.

• 127

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29.

• 128

  Non-land assets includes “the value, in USD PPP, of all moveable assets the household owns, including savings, plus the value of the roof of the home.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 10.

• 129

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 30.

• 130

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 30.

• 131

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 30.

• 132

  “Cash transfers increase the likelihood of having an iron roof by 23 percentage points relative to a control group mean of 16%.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 16.

• 133

  “Recipients of large transfers are 23 percentage points more likely to have an iron roof than recipients of small transfers.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 17.

• 134

  “The purchase of an iron roof represents an expenditure of approximately KES 35,220 (USD 402, PPP 564), or 75% of the average transfer value. In addition to a store of value (roofs can be resold), an iron roof potentially provides an investment return to households by obviating the need to periodically replace their thatched roofs, which must be done ever 1 to 2 years, costing approximately KES 4,800 (USD 55, PPP 77) per replacement, implying a simple return on the investment in the roof of between 7 and 14%. Reported savings balances double as a result of cash transfers, but from low initial levels (PPP USD 10).” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pgs 16-17.

• 135

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 33.

• 136

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 33.

• 137

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 31.

• 138

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 31.

• 139

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 35.

• 140

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 35.

• 141

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29.

• 142

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29.

• 143

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 31.

• 144

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 31.

• 145

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 31.

• 146

  “Food security is low in this population. Though instance of skipped meals are not extreme, 20% of the control group reports that not all household members usually eat until they are content, 23% of respondents report sleeping hungry in the last week, and only 36% report having enough food in the house for the next day.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 18.

• 147
  • ”With one exception (where the coefficient is small and non-significant), all of the signs of the coefficients are in the direction of increased food security, resulting in a statistically significant 0.25 SD increase in our food security index. This effect is driven by a broad range of individual variables, many of which are individually highly significant. For instance, cash transfers reduce the likelihood of the respondent having gone to bed hungry in the preceding week from 23% to 16% (a 30% decrease), increase the likelihood of having enough food in the house for the next day from 36% to 43% (a 20% increase), and reduce by 42% the number of days children go without food.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 18.
  • This effect was driven by an improvement in a broad range of food-security related variables such as a reduction in “the likelihood of the respondent having gone to bed hungry in the preceding week from 23% to 16% . . . [an] increase [in] the likelihood of having enough food in the house for the next day from 36% to 43% . . . and [a reduction] by 42% [in] the number of days children go without food.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 18.

• 148
  • The point estimate suggests that recipients of large transfers reported 0.16 standard deviations higher on the food security index than recipients of small transfers, but this estimate was statistically insignificant and imprecise. Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 29.
  • ”The test of joint significance, however, rejects equality among large and small transfer recipients in terms of food security.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 18.

• 149
  • “As discussed above, we fail to detect an impact of cash transfers on our index of health outcomes.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 19.
  • “Consistent with the lack of an average impact on our educational index, in Table 7 we do not detect significant impacts on educational outcomes.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 19.

• 150
  • ”These effects translate into an overall increase in monthly revenues for recipient households, PPP USD 15 higher (including revenues from the sale of animals and meat), but we do not observe a significant increase in estimated profits from self-employment.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 19.
  • According to Table 5, total revenue was about $17 per month higher for treatment than for control groups.
  • In particular, the RCT found no increase in imputed or self-reported non-agricultural profits. There was a $1.21 per month decrease in farm profits and a $1.68 per month increase in livestock flow profits. Total profits were estimated to decrease by a statistically insignificant $0.16 per month (with a standard error of $3.74). Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 33.

• 151

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 33.

• 152

  “Treatment effects on psychological and neurobiological outcomes are shown in Table 8. Overall, we find a 0.20 SD increase in the index of psychological well-being in the treatment compared to the spillover group.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 19.

• 153
  • ”We observe no effect on cortisol levels, although several pre-defined subgroups show significant effects; these are discussed below.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 20.
  • Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 36.

• 154

  “The overall index is 0.35 SD higher for households receiving a large transfer compared to those receiving a small transfer . . . .” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 20.

• 155

  “As discussed above, we observe a statistically significant spillover effect on the female empowerment index: the index is 0.23 SD higher for control house- holds in treatment villages than for pure control households.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 21.

• 156

  “As changes in domestic violence were hypothesized to arise through mechanisms directly associated with cash transfers (such as a change in women’s bargaining power, or a reduction in domestic tension over economic hardships), these spillover effects are somewhat surprising. One possible explanation is that the results are simply an artifact of reporting bias, where the spillover sample believed that a different answer was desired from them than the control group. However, given that we do not find spillover effects in other measures that target unobservable outcomes, we find this explanation implausible. Another possibility is that the presence of the cash transfer program in the village motivated the husbands in untreated households to change their behavior in the hope of receiving transfers in the future. For instance, knowing that the primary female in the household was equally likely to receive the transfer as the primary male, men may have shifted their behavior to establish better relationships with their spouse. Alternatively, the spillover effect may operate through changes in attitudes among either or both husbands and wives in non-treated households. Our data does not distinguish between these possibilities; we find these unexpected results intriguing and believe they warrant further investigation.” Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 21.

• 157
  • GiveDirectly, Rarieda verification (top ups), May 26, 2013
  • GiveDirectly, Siaya verification, June 15, 2013
  • GiveDirectly, Google verification, September 21, 2013
  • GiveDirectly, Nike verification (final), September 24, 2013

  Note that in some cases, we have cleaned this data (where it was obvious what the error was in the data entry). These cases have been marked with cell comments. In other cases, when it was not clear what the error was (for example, when an amount of funds was mistakenly placed in a spending category column, and it was not clear whether the amount was intended to be placed in the former or latter column), we deleted the data and commented on the cell to indicate that data had been deleted. Deletions were made no more than 5 times in any given spreadsheet.

  In GiveDirectly, Google verification, September 21, 2013, there were many spending category columns where a "yes" (did spend in this category) was marked with a 1, and a "no" (did not spend in this category) was marked with a 0 or a 2 - most of the "amounts spent" reported for categories marked with 2s in the spending column are $0. However, there are a few 2s in the spending category column that correspond to non-zero amounts spent. We only counted the 1s to calculate the aggregated % of recipients who reported spending in a given category, so this would miss those recipients who were marked as 2s but actually did spend in that category. There were so few apparently mis-labeled 2s that we did not bother to confirm whether this was indeed an error or clean the data.

  We have relied here on GiveDirectly, Nike verification (final), September 24, 2013, which represents the data collected from comprehensive follow-up surveys conducted after recipients received their full transfers. Early in the Nike campaign, GiveDirectly was conducting comprehensive follow-up surveys after each installment of a transfer was sent, but it discontinued these surveys because they were too time consuming (Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013). Data from these earlier follow-up surveys is available in GiveDirectly, Nike verification (combined), May 18, 2013, but was not included in the summary statistics presented in the table above.

• 158 @GiveDirectly, Rarieda Verification (top ups), May 26, 2013@ This data includes responses to a full follow-up survey adminsitered to 338 recipients who received top-ups.
• 159 @GiveDirectly, Siaya Verification, June 15, 2013@ This data includes responses from 198 recipients to full follow-up surveys that had been administered 1-2 times per recipient. In aggregating survey responses, we considered a recipient to have reported spending in a certain category if they reported spending in this category in at least one survey.
• 160 @GiveDirectly, Nike Verification (final), September 24, 2013@ This data includes survey responses from 56 of the 77 recipients in the Nike campaign to a full follow-up survey administered after completed transfers were sent.
• 161 @GiveDirectly, Google Verification, September 21, 2013@ This data includes responses to a full follow-up survey administered to 861 recipients.
• 162 @GiveDirectly, Rarieda Verification (top ups), May 26, 2013@ This data includes responses to a full follow-up survey adminsitered to 338 recipients who received top-ups.
• 163 @GiveDirectly, Siaya Verification, June 15, 2013@ This data includes responses from 198 recipients to full follow-up surveys that had been administered 1-2 times per recipient.
• 164 @GiveDirectly, Nike Verification (final), September 24, 2013@ This data includes survey responses from 56 of the 77 recipients in the Nike campaign to a full follow-up survey administered after completed transfers were sent.
• 165 @GiveDirectly, Google Verification, September 21, 2013@ This data includes responses to a full follow-up survey administered to 861 recipients.
• 166

  For full details of our interviews with recipients, see GiveWell Site visit notes.

• 167

  Provided by Google via Citibank N.A. on November 15, 2012.

• 168

  Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012.

• 169

  Provided by Google via Citibank N.A. on November 15, 2012.

• 170

  Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Table 10, Pg 38.

• 171

  "Additional variables in table 9 show the frequency of any episode of physical, sexual or emotional violence in the last six months, and the percentage of respondents who believe that domestic violence is justified in some instances. The point estimates for these variables suggest a reduction in domestic violence, although none are individually different from zero at conventional significance levels." Johannes Haushofer and Jeremy Shapiro, Policy Brief: Impacts of Unconditional Cash Transfers, October 24, 2013, Pg 21.
  

• 172

  GiveDirectly sent 8 versions of the survey forms it used, as the form was modified over time. For the most recent version we have seen, see @GiveDirectly, Kenya verification Template, August 5, 2013@

  For earlier versions, see @GiveDirectly, Verification Template (October 1, 2012)@ and @GiveDirectly, Verification Template (November 7, 2011)@ (corresponding data for the latter: GiveDirectly, Verification data (November 17, 2011))

• 173 @GiveDirectly, Rarieda Verification (top ups), May 26, 2013@ This data includes responses to a full follow-up survey adminsitered to 338 recipients who received top-ups.
• 174 @GiveDirectly, Siaya Verification, June 15, 2013@ This data includes responses from 198 recipients to full follow-up surveys that had been administered 1-2 times per recipient. In aggregating survey responses, we considered a single "yes" answer sufficient to count the recipient as having answered "yes" to the relevant question.
• 175 @GiveDirectly, Nike Verification (final), September 24, 2013@ This data includes responses from 56 of the 77 recipients in the Nike campaign to a full follow-up survey administered after completed transfers were sent.
• 176 @GiveDirectly, Google Verification, September 21, 2013@ This data includes responses to a full follow-up survey administered to 861 recipients.
• 177 We initially calculated this statistic based on the number of recipients who responded "yes" to this question in any of the surveys out of the number of recipients for whom an answer was recorded for this question in any of the surveys (this came to 96.2%). GiveDirectly pointed out: "the denominator is not the correct one because some staff left column AZ in "original data" tab [the column that reports answers to this question] blank if the answer was "no". As you can tell, the majority of answers that are coded as "1" [or, "yes"] have follow-on data in the next 2 columns indicating who was upset with whom (while the ones that are blank in AZ do not). The appropriate denominator here is the total number of recipients surveyed, as staff were not systematically skipping this question" (Email from Piali Mukhopadhyay, COO, International, GiveDirectly, November 25, 2013). This explanation is consistent with the fact that all other questions had at least a 96.5% response rate, while this question had only a 66.2% response rate, indicating that many "no" answers may have not been recorded. It is possible that survyeors were systematically skipping this question because they expected the answers to report a large number of issues, but we regard this as highly unlikely, as GiveDirectly does not evaluate or compensate its field staff on the basis of how many issues are reported in survey responses, and GiveDirectly does ask its staff to make efforts to collect complete survey responses (e.g., by calling recipients back to complete surveys when calls are dropped). We also regard it as highly unlikely that recipients would have consistently refused to answer this question in particular; GiveDirectly's transfers are in no way contingent on a recipients' survey responses, and even if recipients felt they should not report issues, it seems more likely that they would reply "no" than refuse to answer the question. Accordingly, we changed our calculation of this statistic to rely on the number of recipients surveyed as the denominator (@GiveDirectly, Siaya Verification, June 15, 2013@).
• 178

  GiveDirectly, Kenya hotline log, July 24, 2013 (May 2012 – July 2013)
  GiveDirectly, Follow-up tracker, July 2013 (July – October 2013)

• 179

  “GiveDirectly: As of March 2013, GiveDirectly had received $790,000 from GiveWell donors designated as “flexible funds.” This includes a $500,000 gift from Good Ventures.

  The research question we are most interested in is whether providing cash transfers to all households in a village, rather than targeting the poorest households, could reduce tension and improve social outcomes of the transfer campaigns.

  In order to address this question, we’ve created 3 groups of randomly assigned villages for GiveDirectly’s most recent campaign in Kenya:

  1. Villages in which no households will receive transfers
  2. Villages in which only mud-wall and thatch-roof households will receive transfers
  3. Villages in which nearly all households will receive transfers (all households with mud walls and thatch or metal roofs will receive transfers, only households with cement walls and metal roofs will be excluded)

  We are currently finishing up enrollment for this campaign, so transfers will be sent soon. We plan to collect data by administering our standard phone surveys, which include questions about tension, disagreements with neighbors, etc. We expect to receive the first round of data within the next month or two.

  The “flexible funds” received from GiveWell donors are going to be used for transfers to villages in group 3, including households with mud walls and either thatch or metal roofs.”

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013

• 180

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 181

  "GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33. (Note that this comment refers to one completed set of transfers per recipient).

• 182

  "Attrition was not a significant concern in this study because it became evident early on in GD’s work in Kenya that respondents were highly interested in maintaining relations with GiveDirectly in the hope of receiving future transfers (although these are never promised)." Johannes Haushofer and Jeremy Shapiro, Welfare Effects of Unconditional Cash Transfers: Pre-Analysis Plan, June 27, 2013 Pgs 5-6.

• 183

  GiveDirectly, Enrollment speed of distributions

• 184

  GiveDirectly, Updated data (March 31, 2012)

• 185

  Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012.

  Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 186

  GiveDirectly, Kenya hotline log, July 24, 2013
  GiveDirectly, Follow-up tracker, July 2013

• 187

  "The main downside to both of the mobile money services in [Uganda] as compared to MPESA in Kenya is that there are fewer mobile money agents in the rural areas that GiveDirectly is targeting. In response, GiveDirectly has been more proactive in coordinating with the mobile money service for the transfers that have begun, for example, by giving the mobile money service advanced notice before sending the funds so that agents could be prepared. In some cases, agents traveled to the villages in which recipients live to reduce recipient travel time." Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

  "We do inform recipients in advance when pay-days will be happening" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 188

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 189

  Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012 and GiveWell visit to M-PESA agent, November 8, 2012.

• 190

  GiveWell visit to M-PESA agent, November 8, 2012.

• 191

  Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012.

• 192

  GiveDirectly helps recipients with issues using M-PESA via our hotline. Staff sometimes contact M-PESA agents to notify them one of our clients will be visiting and may need assistance. Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012.

• 193

  GiveDirectly, Kenya hotline log, July 24, 2013
  GiveDirectly, Follow-up tracker, July 2013

• 194

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 195

  "While the first service has more agents nationwide, the second service has been more willing to collaborate with GiveDirectly and customize the service to its needs." Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 196

  "The main downside to both of the mobile money services in Uganda as compared to MPESA in Kenya is that there are fewer mobile money agents in the rural areas that GiveDirectly is targeting. In response, GiveDirectly has been more proactive in coordinating with the mobile money service for the transfers that have begun, for example, by giving the mobile money service advanced notice before sending the funds so that agents could be prepared. In some cases, agents traveled to the villages in which recipients live to reduce recipient travel time." Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 197

  GiveDirectly hopes that providing cash transfers will create enough demand for the second mobile money service that it will deploy more agents to the areas in which the transfers are being sent, increasing the convenience for recipients. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 198

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 199

  The main downside to both of the mobile money services in Uganda as compared to MPESA in Kenya is that there are fewer mobile money agents in the rural areas that GiveDirectly is targeting. In response, GiveDirectly has been more proactive in coordinating with the mobile money service for the transfers that have begun, for example, by giving the mobile money service advanced notice before sending the funds so that agents could be prepared. In some cases, agents traveled to the villages in which recipients live to reduce recipient travel time. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 200

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 201

  GiveDirectly, GD – GW update, July 17, 2013.

  "This is the staff size we would want in order to achieve the 8M/yr throughput" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 202

  GiveDirectly has so far received about six applications for every one FO position it has open, which it sees as an indicator that the necessary talent is available for it to scale its operations. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 [Unpublished]

• 203

  Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012.

• 204

  GiveDirectly, Budget summary, July 22, 2013

• 205

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 206

  The amount of time that it takes for total transfers to be sent has varied between campaigns. The Google transfer campaign likely gives the best approximation of timing for a standard campaign, because it does not involve an experiment (like Rarieda and Nike), and it is more recent than the Siaya campaign. In the Google campaign, the planned schedule for transfers lasts approximately eight months for any given recipient, not including the time for census and enrollment. GiveDirectly, Google transfer schedule, July 2013

• 207

  "GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33. (Note that this comment refers to one completed set of transfers per recipient).

• 208

  @GiveDirectly, Contextualizing Transfer Size@

• 209

  $0.65 in pre-cash-transfer income per person per day implies (365*$0.65) = $237.25 per person per year. If each person receives $288 in a year from GiveDirectly, that's (288/237.25) = 121%.

• 210

  Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012. We have not reviewed the data GiveDirectly used to reach this conclusion.

• 211

  "We also view this as an open question and hence are experimenting actively with other targeting methods (e.g., village saturation)" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 212

  “GiveDirectly: As of March 2013, GiveDirectly had received $790,000 from GiveWell donors designated as “flexible funds.” This includes a $500,000 gift from Good Ventures.

  The research question we are most interested in is whether providing cash transfers to all households in a village, rather than targeting the poorest households, could reduce tension and improve social outcomes of the transfer campaigns.

  In order to address this question, we’ve created 3 groups of randomly assigned villages for GiveDirectly’s most recent campaign in Kenya:

  1. Villages in which no households will receive transfers
  2. Villages in which only mud-wall and thatch-roof households will receive transfers
  3. Villages in which nearly all households will receive transfers (all households with mud walls and thatch or metal roofs will receive transfers, only households with cement walls and metal roofs will be excluded)

  We are currently finishing up enrollment for this campaign, so transfers will be sent soon. We plan to collect data by administering our standard phone surveys, which include questions about tension, disagreements with neighbors, etc. We expect to receive the first round of data within the next month or two.

  The “flexible funds” received from GiveWell donors are going to be used for transfers to villages in group 3, including households with mud walls and either thatch or metal roofs.”

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013

  In GiveDirectly's Kenya 2M campaign, "saturation villages" are villages in which all households with mud walls and thatch or iron roofs will receive transfers. These households make up about 85% of the households in saturation villages. The other 15% of households are built with all man-made materials (e.g., cement walls and iron roof) and will not receive transfers. Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013

• 213

  Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012.

• 214

  GiveDirectly, Nike instrument

• 215

  Johannes Haushofer and Paul Niehaus, DIL Demonstration Proposal

• 216

  GiveDirectly has told us that households with cement walls tend to be substantially less poor than those with thatch or metal roofs and mud walls (Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013). GiveDirectly also provided some data on consumption, expenditures, and assets that demonstrate a much larger gap between thatch-roof houses and cement houses than thatch-roof houses and metal-roof houses:

  GiveDirectly, Offering Memorandum (January 2012) Pg 25.
  

• 217

  “GiveDirectly: As of March 2013, GiveDirectly had received $790,000 from GiveWell donors designated as “flexible funds.” This includes a $500,000 gift from Good Ventures.

  The research question we are most interested in is whether providing cash transfers to all households in a village, rather than targeting the poorest households, could reduce tension and improve social outcomes of the transfer campaigns.

  In order to address this question, we’ve created 3 groups of randomly assigned villages for GiveDirectly’s most recent campaign in Kenya:

  1. Villages in which no households will receive transfers
  2. Villages in which only mud-wall and thatch-roof households will receive transfers
  3. Villages in which nearly all households will receive transfers (all households with mud walls and thatch or metal roofs will receive transfers, only households with cement walls and metal roofs will be excluded)

  We are currently finishing up enrollment for this campaign, so transfers will be sent soon. We plan to collect data by administering our standard phone surveys, which include questions about tension, disagreements with neighbors, etc. We expect to receive the first round of data within the next month or two.

  The “flexible funds” received from GiveWell donors are going to be used for transfers to villages in group 3, including households with mud walls and either thatch or metal roofs.”

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013

• 218

  Impacts of transfers on local economies: GiveDirectly has $2 million that it is planning to use for transfers to randomly selected villages in Kenya. In about half of the villages chosen, GiveDirectly will target mud and thatch-roof households only, while in the other half it will provide cash transfers to every household in the village. As of now, GiveDirectly is planning to conduct its standard follow up surveys by phone, as well as additional in-person follow-up surveys with a limited number of recipients in these villages. GiveDirectly has stated that this research question is high priority, but that more transfers to get to the minimum sample size needed for additional outcome measurements are as yet unfunded.

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 219
  • Frequency of payments. The preliminary results from the first randomized controlled trial (RCT) of a GiveDirectly campaign have suggested very slight differences between lump-sum and stream payments. Some recipients of GiveDirectly cash transfers report that they would prefer lump sum payments, while others report that they’d prefer stream payments. There may be a benefit in giving recipients the ability to choose the frequency of their payments. (Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013)
  • Payments can also be timed such that they are conducive to certain spending behaviors (e.g., arrive before school fees are due). (Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, November 19, 2013)

• 220

  Messaging to recipients. One of the difficulties of running a cash transfer program is ensuring that all recipients are given a consistent and accurate message about the program. Within the last few weeks, GiveDirectly has decided to experiment with compensation for field staff based on how effectively they communicate information to recipients during enrollment. The effectiveness of field staff communications will be evaluated based on the fraction of recipients who can correctly explain information about the transfers on a follow-up phone call, including what the transfers can be used for and if there are conditions attached. Once GiveDirectly is confident that its field staff are delivering a consistent message, it may be interested in varying that message to emphasize different elements, such as encouraging long term investments versus not giving any advice on how funds should be spent.

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013

• 221

  Decision-making supports. GiveDirectly is interested in researching the impact of decision-making supports (e.g., providing information on what other recipients have done with transfer funds, providing a "sounding board" for recipients who are planning how they will use funds), as well as the optimal timing and frequency of transfers based on how and when recipients budget their expenses. Some donors have expressed interest in this type of study, though funding is not yet confirmed.

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

• 222

  Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 223
  • "Our standard: Put at least 90% of every donated dollar in the hands of the poor." GiveDirectly, Values
  • "Based on performance to date we expect to put 93% of your donation into the hands of a recipient." GiveDirectly, Financials
  • "Our 90% target has always been on efficiency as we define it (i.e., transfers as a % of total campaign expenses). It is also specific to Kenya; we are currently planning to set Uganda target at 88% based on performance to date" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 224

  "First, as a general principle we'll always aim to publish spending clearly enough so that anyone can calculate efficiency however they want. Second, when we analyze our efficiency for management and forecasting purposes we distinguish between (1) operating costs that are ongoing but shared across campaigns and (2) one-time setup costs. (1) is included in the efficiency calculation and (2) is not. Examples of (1) are Field Director time (which is "fixed" for the duration of his/her contract) and office space (which is "fixed" with respect to number of ongoing campaigns). Examples of (2) are costs of registering as an NGO in a new country and costs of developing code which can then be re-used indefinitely. We accept donations from the public for (1) but not (2)." Email from Joy Sun, COO, Domestic, GiveDirectly, August 9, 2013

  "Set-up costs are excluded because one-time costs (e.g. NGO registration) can be re-used indefinitely, as opposed to marginal costs." Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 225

  Note that GiveDirectly did not bear the costs of the Rarieda RCT, and they are therefore excluded from its budget. GiveDirectly has estimated that the experiment budget covered $4,000 worth of costs that GiveDirectly would have paid in the absence of the RCT. GiveDirectly included these costs in the financial documents it sent us in 2012; we assume they have been included again in the 2013 versions.

  "[We have] included on our books our best estimate of the RCT costs that would normally have been incurred by GiveDirectly, i.e. $4,000, primarily for travel expenses. This cost is included in all of our performance figures." @GiveDirectly, GiveWell Clarifications (March 28, 2012)@

• 226

  Paul Niehaus told us during our Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013 that he spends approximately 40 hours/week on GiveDirectly. We draw estimates of time spent by other Directors from GiveDirectly, FY 2012 Form 990, Pg 7.
  

• 227

  GiveDirectly, GD – GiveWell update, October 16, 2013, Pg 3

• 228

  GiveDirectly, Cash transfer plan, November 7, 2013
  GiveDirectly, Ideal budget overview, November 7, 2013
  GiveDirectly, Domestic organizational structure, November 7, 2013

• 229

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 230

  GiveWell, Amounts transferred by month by GiveDirectly

• 231

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

  Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 232

  GiveDirectly, GD – GW update, July 17, 2013
  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013
  Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 233

  GiveDirectly, GD – GiveWell update, October 16, 2013, Pg 4

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 234

  Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 235

  GiveDirectly's plans for future experimentation and evaluation include:

  • A study of the effects of cash transfers on village economies. GiveDirectly plans to continue to distribute cash transfers in Kenya to randomly selected villages, so that these villages (and the unselected villages) can serve as treatment (and control) groups in a study of the effects of cash transfers on village economies. For the treatment villages selected so far, GiveDirectly has applied its standard targeting criteria (thatch-roofs only) to some, while for others it has used a "saturation" model, whereby households with mud walls and thatch or iron roofs are given cash transfers. GiveDirectly has said that it may or may not continue to apply the saturation model to additional villages, depending on results from the first round of villages. It is our impression that the future study of effects on village economies may include both types of treatment villages, though we do not know if this has been decided, and we assume that it depends on whether the saturation model is extended to a sufficient number of villages. GiveDirectly told us that in order to have a sufficient sample size for the village economies study, it would need on the order of $15 million for transfers. This is a highly preliminary estimate that will be refined in the coming months with input from Professor Ted Miguel and a graduate student at the University of California, Berkeley, who have agreed to conduct the follow-up data collection for this study and are currently seeking funding (Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, November 19, 2013).
  • Studies of the effects of various treatments based on behavioral economics. GiveDirectly mentioned three types of treatments that it plans to incorporate into cash transfers as part of formal studies: timing transfers to be conducive to certain spending choices, providing information about spending options to recipients (planned for campaign in January 2014), and providing recipients with a message from donors. The research for these studies will be conducted by Paul Niehaus (President of GiveDirectly and assistant professor of economics at University of California, San Diego) and two researchers at Ideas42 (http://www.ideas42.org/). This study will be funded by a grant from a foundation, which covers both the transfers and measurement costs. GiveDirectly is planning to reach ~500 households in the treatment group. (Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, November 19, 2013).
  • Studies of different targeting strategies. In addition to experimenting with "saturation" targeting (described above), GiveDirectly has told us that it is considering expanding its one-village pilot of community-based targeting, wherein it let the village members decide what criteria should be used to identify the poorest residents of the village, who then received transfers. GiveDirectly told us that it has yet to decide whether to extend this pilot and to how many villages (Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, November 19, 2013). It is our impression that this would be an informal study, with no independent data collection.

  GiveDirectly, Cash transfer plan, November 7, 2013 states that GiveDirectly would ideally like to deliver $35.3 million in direct grants to households over 2014-2015, of which $33.8 million is as yet unfunded. It is our impression that the studies described above, in addition to GiveDirectly's experimentation with its operational model as it expands, would involve the majority of those funds.
  

• 236

  Original projections for 2013 and actual use of funds
  In 2012, GiveDirectly provided the following statement regarding its room for more funding:

  Our target for additional revenue in FY 2013 is at least $2.85M and at most $6.85M.

  We think about the first number as follows. With our current staff (one COO) we have, conservatively estimated, the capacity to move $5M per year to new recipients. We also wish to increase transfer commitments to current recipients by a total of $250K. Against this total capacity of $5.25M we have already received a $2.4M grant, leaving room for $2.85M.

  As part of the grant agreement we expect to hire a second full-time field manager. This manager will ultimately provide an additional $5M / year in throughput capacity but will first participate in a two-month apprenticeship, leaving time to manage $4M during his/her first year. This yields a total capacity of $6.85M.

  To move amounts larger than this we would hire additional full-time field managers. We are open to this in principle but would want a frank conversation with interested donors about the costs and benefits of scaling up at that pace, as opposed to staging donations over multiple years.

  (@GiveDirectly, Room For More Funding Summary@)

  • At the end of 2012, we wrote that while these funding goals constituted a significant expansion in its capacity, and there existed some risk that as a relatively young organization GiveDirectly would struggle to transfer these funds, we believed that the risk did not seem too large and the benefit of enabling GiveDirectly to expand quickly was significant. We wrote at that time that we believed it was reasonable for donors to provide GiveDirectly with up to $7 million, the amount that we thought would enable it to maximize its potential for transfers in 2013.
  • Cumulatively through August 31, 2013 since its founding, GiveDirectly received about $6 million in donations, of which it had incurred costs and liabilities (mostly transfers) totaling $3 million (GiveDirectly, GD – GW update, July 17, 2013, GiveWell, GiveDirectly financials tables Sheet: "Total cumulative expenses"), and planned to use $2.4 million for future transfers in ongoing campaigns and $87,000 for domestic expenses. Thus, at the end of FY 2013, GiveDirectly had $0.6 million in uncommitted funds (GiveDirectly, GD – GiveWell update, October 16, 2013, Pg 3).
  • We wrote in November 2012 that given that the primary capacity needs GiveDirectly had were surveyors to conduct the census, enroll recipients and back check eligibility and that GiveDirectly has successfully hired these employees in the past, we would guess that they would not struggle to hire many of these types of employees in the future. We also had the informal impression (from our site visit) that these types of employees are hired in greater quantities by Innovations for Poverty Action, which GiveDirectly has worked with in Kenya, so presumably there is sufficient supply of the types of employees GiveDirectly would hire to scale up. As of October 2013, we feel confident that hiring field staff has not presented a constraint on GiveDirectly's growth: GiveDirectly has successfully hired more Field Officers (see above) and reports receiving six times the number of resumes as open positions. (Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 [Unpublished])
  • In November 2012, we felt that the biggest risk to GiveDirectly's ability to expand was that it would be unable to find additional capacity at the executive-level to manage its expansion. At that time, GiveDirectly's COO was its only executive employee, and she worked mainly in the field. It has now expanded its staff significantly (see above); we no longer see its ability to hire executive-level staff as a major concern.

• 237

  Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, November 19, 2013

• 238

  GiveDirectly, Team
  Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012