Welfare Effects of Unconditional Cash Transfers
Last registered on July 06, 2016

Pre-Trial

Trial Information
General Information
Title
Welfare Effects of Unconditional Cash Transfers
RCT ID
AEARCTR-0000019
Initial registration date
June 28, 2013
Last updated
July 06, 2016 3:21 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Princeton Unviersity
Other Primary Investigator(s)
PI Affiliation
McKinsey & Co.
Additional Trial Information
Status
Completed
Start date
2011-05-01
End date
2013-02-28
Secondary IDs
Abstract
This randomized controlled trial (RCT) evaluates the Unconditional Cash Transfer (UCT) of GiveDirectly, Inc in Western Kenya. Between June 2011 and January 2013, GiveDirectly distributed unconditional cash transfers to 500 poor rural households. The transfers were sent to recipients' mobile phones using the M-Pesa technology. The present RCT includes three treatments: first, the transfers were randomly chosen to be sent to either the primary female or the primary male member of the household. Second, the transfers were randomly assigned to be sent as either a large lump-sum payment, or a series of nine monthly installments of the same total amount. Third, the magnitude of the total transfer to each treatment household was randomly chosen to be either $300 or $1,100. The outcome variables include expenditure, food security, assets, income and enterprise activity, intrahousehold bargaining, domestic violence, education, health, and preferences, as well as psychological well-being and neurobiological measures of stress.
External Link(s)
Registration Citation
Citation
Haushofer, Johannes and Jeremy Shapiro. 2016. "Welfare Effects of Unconditional Cash Transfers." AEA RCT Registry. July 06. https://www.socialscienceregistry.org/trials/19/history/9303
Experimental Details
Interventions
Intervention(s)
The UCT Program implemented by GiveDirectly Inc. (GD) targets impoverished households in Kenya. The study evaluates GD’s intervention in the Rarieda District, in Western Kenya. GD’s intended beneficiaries are especially disadvantaged households, with per capita incomes below $1 per day. Households are identified as eligible using objective and transparent criteria that are highly correlated with poverty: dwellings lacking solid walls, floors, or roofs.

GD’s goal is to provide flexible financial assistance to those in greatest need, while maximizing cost efficiency by transferring cash electronically using M-Pesa mobile money technology, a key innovative feature of the program. M-Pesa is a mobile money system offered by Safaricom, the largest Kenyan mobile phone operator. GD transfers the money from GD’s M-Pesa account to that of the recipient. To facilitate the transfers, GD distributes a SIM card and asks the recipient to sign up for M-Pesa; then, money is transferred to the SIM card, and the recipient can withdraw the balance at an M-Pesa agent by putting the SIM card into the agent's cell phone, or using their own phone.

This delivery method drastically cuts the costs of reaching the recipient: GD transfers 90% of the program’s total budget directly to a poor household, with the remainder covering recipient identification, including staff costs (7%), and mobile transfer fees for both GD and recipients (3%). Not only is the intervention cheap; the insights from the study can also be applied broadly, as the program can be implemented in any area with access to mobile money technology. As such technology spreads throughout the developing world, the program will become increasingly scalable.
Intervention Start Date
2011-06-01
Intervention End Date
2013-01-31
Primary Outcomes
Primary Outcomes (end points)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Evaluation questions

Our main questions are: (i) What are the overall impacts of UCTs on various dimensions of household welfare? (ii) How should UCTs be structured to maximize impact? In particular, how do outcomes vary with three key, yet understudied, design parameters: recipient gender, transfer frequency, and transfer size? The intervention therefore contains the following treatment arms:

1. Transfers to the woman vs. the man in the household. First, half of the transfers were made to the woman, while the other half were made to the man. This feature allows us to identify the differential welfare effects of gender-specific cash transfers.

2. Lump-sum transfers vs. monthly installments. Second, half of the transfers were lump sum, and the other half was paid in 9 monthly installments. By randomizing the month in which the lump sum transfer was made, we kept the discounted present value of the lump-sum and installment transfers similar across the overall lump sum and monthly installment groups.

3. Large vs. small transfers. Finally, a proportion (28%) of the transfers were $1,100 in magnitude, while the remainder were $300. This manipulation allows us to estimate the effect of transfer magnitude on welfare outcomes.

These three treatment arms are fully crossed with each other, except that the $1,100 transfers were made to existing recipients of $300 transfers in the form of a $800 top-up that was delivered as a stream of payments after respondents had already been told that they would receive $300 transfers. The pre-analysis plan outlines how this issue is dealt with in the analysis.

Evaluation Design

Sampling and Identification strategy

To establish a causal relationship between the program and changes in outcomes, this study uses a Randomized Control Trial (RCT). We first identified Rarieda as an intervention area because it has (i) high poverty rates according to census data, and (ii) sufficient M-Pesa access to make transfers feasible. We then identified 100 villages based on the overall prevalence of eligible households in the village. In these villages, we identified 1,500 eligible households, with eligibility determined by residing in a home made of mud, grass, and other non-solid materials. These criteria are simple, objective, and transparent, maximizing accountability. The criteria were not pre-announced to avoid “gaming” of the eligibility rules. We then randomized on two levels -- across villages, and within villages. Specifically, 50 villages were randomly assigned to be treatment villages, while the other 50 were pure control villages. In each of the latter, we surveyed 10 households that did not receive a cash transfer. Within treatment villages, we conducted a within-village randomization: 50% of eligible households were randomly assigned a cash transfer; the other 50% received no transfer (GD will seek to make transfers to this group after the study). This strategy allows us to identify spillover effects (detailed in the pre-analysis plan).

Spillover effects

We use three approaches to quantify spillover effects. First, we used pure control villages to quantify within-village spillovers. Comparing control households in treatment villages to those in pure control villages identifies within-village spillover effects. Second, we identify spillover effects across villages. Note that these effects could potentially be even more pronounced than within villages, if, for instance, entire villages are affected by weather shocks. Using GPS data on village location, we can identify cross-village spillovers, under the assumption that these spillovers are geographically correlated. Third, a separate village-level survey elicited general equilibrium effects of the intervention at the level of the local economy; we surveyed residents of the village on prices, labor supply, wages, crime, investment, community relations (e.g. perceived fairness of targeting criteria) and power dynamics.

Data collection methods and instruments

Data was collected at baseline and one year after the intervention. A midline with a subset of questions was administered to a sample of respondents each month after the intervention. Trained interviewers visited the households; both the primary male and the primary female of the household were interviewed (separately). Surveys were administered on Netbooks using the Blaise survey software. Following standard IPA procedure, we performed backchecks consisting of 10% of the survey, with a focus on non-changing information, on 10% of all interviews. This procedure was known to field officers ex ante. Saliva samples were collected using the Salivette (Sarstedt, Germany), which has been used extensively in psychological and medical research, and more recently in randomized trials in developing countries similar to this one. It requires the respondent to chew on a sterile cellulose swab, which is then centrifuged and analyzed for salivary cortisol.

Power calculation

The sample size of 500 individuals in each of the treatment, control, and pure control conditions was chosen based on a power calculation, which showed that a sample of 1,000 individuals is sufficient to detect effect sizes of 0.2 SD for all treatment vs. pure control households with 89% power. Different treatment arms within the treatment groups (male vs. female recipient, lump-sum vs. monthly, large vs. small transfers) can be compared with 60% power.

Risk and treatment of attrition

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). Nevertheless, we used five approaches to control attrition. First, the survey contained a detailed tracking module developed by Innovations for Poverty Action (IPA), the NGO implementing the fieldwork. IPA and GD collaborated closely throughout the study to facilitate tracking. Second, we incentivized survey completion through a small appreciation gift (a jar of cooking fat); in addition, respondents earned money from the economic games in the survey. Third, in our power calculations, an attrition rate of 20% would still result in a power of 80%. In fact, we only observed 3% attrition between the two visits of the baseline. Finally, we control for attrition econometrically in the analysis, as detailed in the pre-analysis plan.
Experimental Design Details
Randomization Method
Computerized randomization in office.
Randomization Unit
household (treatment vs. spillover households) and village (treatment/spillover vs. pure control villages)
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
100 villages
Sample size: planned number of observations
1500 households
Sample size (or number of clusters) by treatment arms
500 households treatment, 500 households spillover, 500 households pure control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
0.2 SD, 89%
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Innovations for Poverty Action Kenya (IPAK) Institutional Review Board
IRB Approval Date
2011-04-30
IRB Approval Number
none
IRB Name
Ethics Committee, Canton of Zurich
IRB Approval Date
2011-01-11
IRB Approval Number
KEK-ZH-Nr. 2010-0477/0
IRB Name
Kenya Medical Research Institute (KEMRI) Ethics Review Committee (ERC)
IRB Approval Date
2011-04-19
IRB Approval Number
NON-SSC NO. 171
IRB Name
University of Zurich, Department of Economics Institutional Review Board
IRB Approval Date
2011-04-21
IRB Approval Number
none
IRB Name
Massachusetts Institute of Technology (MIT) Institutional Review Board
IRB Approval Date
2011-10-20
IRB Approval Number
1109004672
Analysis Plan
Analysis Plan Documents
Additional Analyses for Revise and Resubmit

MD5: 4a9d369d0a2a0772b9fd8ec042333607

SHA1: 2a6e2d6def133af8739e4b47f44ccda310fd1fdc

Uploaded At: November 21, 2015

2nd Endline Analysis

MD5: 11703e8ff968d8e2366d95c8463842c3

SHA1: e8f55a7182fa9aac78829e22f6488698299213b2

Uploaded At: March 15, 2016

Original analysis plan

MD5: 551fd9f0cb7058f95b29311d339c4004

SHA1: 6399ee6fadbcb9932316519a180d2a4248394412

Uploaded At: June 28, 2013

The Income Elasticity for Nutrition

MD5: 12d2a10adecda022cc605250be01c934

SHA1: e3c96282498aadfe36b30062c7363608b8322a7d

Uploaded At: June 16, 2016

Demand Effects Analysis

MD5: 07e3d33b17eac0cf8c55db5e66adf1d8

SHA1: 6d267c77556a4b853b1c5c59966423228c2599ff

Uploaded At: July 06, 2016

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers