Liquidity constraints: Allocating clean water to the rural poor

Last registered on September 08, 2023

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Pre-Trial

Trial Information

General Information

Title

Liquidity constraints: Allocating clean water to the rural poor

RCT ID

AEARCTR-0010545

Initial registration date

December 05, 2022

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

December 13, 2022, 9:47 PM EST

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

Last updated

September 08, 2023, 10:09 AM EDT

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

Locations

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Primary Investigator

Name

Fiona Burlig

Affiliation

University of Chicago

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Other Primary Investigator(s)

PI Name

Amir Jina

PI Affiliation

University of Chicago

Contact Investigator

PI Name

Anant Sudarshan

PI Affiliation

University of Warwick

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Additional Trial Information

Status

On going

Start date

2022-05-20

End date

2025-05-01

Keywords

Environment & Energy, Health

Additional Keywords

water, resource allocation, quotas

JEL code(s)

D45, O13, Q25, Q53

Secondary IDs

Prior work

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

Abstract

How should scarce resources be allocated in developing countries? Weitzman (1977) highlights a trade-off between prices, which generate allocative efficiency, and quotas, which might have desirable distributional consequences. In partnership with a private company supplying clean water to rural Odisha, India, we plan to run an experiment to measure the relative effectiveness of different allocation mechanisms. We will measure the price elasticity of demand for clean water, health effects from consuming clean water, and the extent to which liquidity constraints and intra-household inefficiencies reduce consumption. To do so, we implement a cluster-randomized trial, where 160 villages are randomized into a pure control group and multiple treatment arms: (i) discounts; (ii) a monthly quota; and (iii) an exchangeable quota, where unused allocation can be exchanged for cash. We subsequently randomize which households within each treatment village will receive treatment. We plan to measure effects of treatment on water consumption and health outcomes using a combination of survey and administrative data.

External Link(s)

Registration Citation

Citation

Burlig, Fiona, Amir Jina and Anant Sudarshan. 2023. "Liquidity constraints: Allocating clean water to the rural poor." AEA RCT Registry. September 08. https://doi.org/10.1257/rct.10545-2.0

Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)

We implement a cluster-randomized trial in which villages are randomized into one of four conditions: pure control, discount, pure quota, and exchangeable quota. In each treatment (i.e. not pure control) village, households are subsequently randomized to receive clean water through one of the corresponding allocation mechanisms. The discount and exchangeable quotas have sub-arms that vary the level of the marginal incentive.

Intervention Start Date

2023-05-20

Intervention End Date

2023-05-31

Primary Outcomes

Primary Outcomes (end points)

Quantity of water purchased; health outcomes.

Primary Outcomes (explanation)

Please see our PDF PAP for more details.

Secondary Outcomes

Secondary Outcomes (end points)

Please see our PDF PAP for more detailed information on outcomes.

Secondary Outcomes (explanation)

Please see our PDF PAP for more details.

Experimental Design

We conduct a cluster-randomized trial that assigns each of 160 villages to one of four groups, and then allocates approximately 40 households per treatment village to treatment and the remainder to control. We also sample approximately 15 households to survey in each village.

Experimental Design Details

Not available

Randomization Method

Randomization done in office by a computer

Randomization Unit

First level: village. Second level: household.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

160 villages

Sample size: planned number of observations

160 villages x approximately 500 households per village: approx 80,000 households. Approx 1,800 households in the survey.

Sample size (or number of clusters) by treatment arms

40 villages pure control; 40 villages discount; 40 villages pure quota; 40 villages exchangeable quota. Approximately 40 treatment households per treated village, and all remaining households are control.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB