General Equilibrium Effects of Cash Transfers in Kenya

Last registered on August 17, 2024

View Trial History

Pre-Trial

Trial Information

General Information

Title

General Equilibrium Effects of Cash Transfers in Kenya

RCT ID

AEARCTR-0000505

Initial registration date

November 03, 2014

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

November 03, 2014, 5:09 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

August 17, 2024, 12:34 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name

Edward Miguel

Affiliation

University of California, Berkeley

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Paul Niehaus

PI Affiliation

UC San Diego

Contact Investigator

PI Name

Michael Walker

PI Affiliation

UC Berkeley

Contact Investigator

PI Name

Johannes Haushofer

PI Affiliation

Princeton University

Contact Investigator

PI Name

Dennis Egger

PI Affiliation

Oxford University

Contact Investigator

Additional Trial Information

Status

On going

Start date

2014-08-18

End date

2025-12-31

Keywords

Welfare

Additional Keywords

Cash transfers, spillovers

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

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.

External Link(s)

Registration Citation

Citation

Egger, Dennis et al. 2024. "General Equilibrium Effects of Cash Transfers in Kenya." AEA RCT Registry. August 17. https://doi.org/10.1257/rct.505-25.0

Former Citation

Egger, Dennis et al. 2024. "General Equilibrium Effects of Cash Transfers in Kenya." AEA RCT Registry. August 17. https://www.socialscienceregistry.org/trials/505/history/232539

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

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

2014-09-15

Intervention End Date

2016-07-15

Primary Outcomes

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

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

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

Not available

Randomization Method

Randomization done in office by a computer.

Randomization Unit

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?

Yes

Experiment Characteristics

Sample size: planned number of clusters

653 villages

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)

Supporting Documents and Materials

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

IRB