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General Equilibrium Effects of Cash Transfers in Kenya
Initial registration date
November 03, 2014
February 18, 2019 4:40 PM EST
University of California, Berkeley
Other Primary Investigator(s)
UC San Diego
Additional Trial Information
How redistribution affects the real economy is one of the central, unanswered questions in development economics. The effect of redistribution on the welfare of non-beneficiary households is theoretically ambiguous: there could be positive spillover effects through increased aggregate demand (a multiplier effect), or negative spillovers from price inflation or crowd-out by business expansion for non-beneficiaries. The NGO GiveDirectly provides large cash transfers to rural households in Kenya. We utilize an RCT to study the spillover effects of cash transfers on household welfare, prices, enterprise creation and local public finance and will make use of spatial variation in treatment density in order to estimate these effects.
The NGO GiveDirectly is responsible for the intervention; GiveDirectly provides large, unconditional cash transfers to poor households in rural Kenya. GiveDirectly identifies villages in which they are willing to work, and in order to facilitate research on cash transfers, these villages are randomly assigned to treatment or control status. Within treatment villages, GiveDirectly then identifies all households that meet their eligibility criteria, enrolls and verifies the eligibility of eligible households, and sends cash transfers to all eligible households via the mobile money system M-Pesa. Eligible households receive a one-time of around USD 1,000 made in a series of three payments.
This intervention will serve as the basis for the current study on general equilibrium effects, as well as a future study investigating long-term effects of cash transfers.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
The following are key outcome variables of interest: 1. Prices: how do prices evolve in response to an influx of cash into local economies? 2. Number of enterprises: how does the supply side change in response to a potential increase in demand due to redistribution? 3. Household welfare: how do household income and assets change in response to the cash transfers, both for direct beneficiaries and for non-beneficiaries in the study area? 4. Local public finance outcomes: are there changes in local fundraising for public goods?
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
The study will take place across 653 villages in Western Kenya. Villages are randomly allocated to treatment or control status. In treatment villages, GiveDirectly enrolls and distributes cash transfers to households that meet its eligibility criteria. In order to generate additional spatial variation in treatment density, groups of villages are assigned to high or low saturation. In high saturation zones, 2/3 of villages are targeted for treatment, while in low saturation zones, 1/3 of villages are targeted for treatment. The randomized assignment to treatment status and the spatial variation in treatment intensity will be used to identify direct and spillover effects of cash transfers.
The research team undertakes independent household censuses of treatment and control villages and makes their own judgment on whether households are eligible based on GiveDirectly's criteria. Households are then randomly selected to be surveyed by eligibility status (roughly 8 eligible households per village and 4 ineligible households). The research team also conducts a census of enterprises, enterprise surveys, market surveys and surveys with local leaders (including school principals) to collect data on prices, enterprise creation and local public finance.
Experimental Design Details
Randomization done in office by a computer.
Randomization to treatment status is conducted at the village level. High versus low saturation is randomly assigned to groups of villages based on their sublocation, an administrative unit above the village.
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
7,836 households (12 per village), 6,530 enterprises (this assumes an average of 10 enterprises per village; the exact number will depend on the number of enterprises in the study area).
Sample size (or number of clusters) by treatment arms
328 treatment, 325 control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
INSTITUTIONAL REVIEW BOARDS (IRBs)
University of California, Berkeley Committee for the Protection of Human Subjects
IRB Approval Date
IRB Approval Number
Maseno University Ethics Review Committee
IRB Approval Date
IRB Approval Number