Guaranteed Basic Income in a Rural Setting: Subsidizing Leisure and Reducing Labor Force Participation or Alleviating Poverty Traps?

Last registered on November 09, 2021
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Pre-Trial

Trial Information

General Information

Title
Guaranteed Basic Income in a Rural Setting: Subsidizing Leisure and Reducing Labor Force Participation or Alleviating Poverty Traps?
RCT ID
AEARCTR-0008509
Initial registration date
November 08, 2021

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 09, 2021, 10:06 AM EST

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

Locations

Country
United States of America
Region
South Carolina

Primary Investigator

Name
Jorge Luis García
Affiliation
Clemson University
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Other Primary Investigator(s)

PI Name
Patrick Warren
PI Affiliation
Clemson University
Contact Investigator
PI Name
Lawrence Reed Watson
PI Affiliation
Clemson University
Contact Investigator

Additional Trial Information

Status
In development
Start date
2022-01-17
End date
2024-03-01
Keywords
Labor
Additional Keywords
guaranteed basic income, unconditional cash transfers, labor force participation
JEL code(s)
J01, J21, J24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Guaranteed Basic Income (GBI) is a policy that grants an unconditional cash grant to households or individuals, usually every month. It received attention recently as the main policy proposal of presidential primary candidate Andrew Yang. The 1968 Poor People’s Campaign proposed GBI for the socioeconomically disadvantaged. Economist Milton Friedman also advocated for GBI.

Basic economic theory predicts that guaranteeing a stable source of income decreases labor force participation: A recipient of such income increases their consumption of all goods, including leisure time. This prediction is the reason why GBI has political opposition. More general economic theories predict that GBI potentially releases individuals from poverty traps—where the day-to-day struggle to meet immediate needs means that a single economic shock, like the Covid-19 PANDEMIC, can lead to job loss, eviction, bankruptcy, or reduced health. In a poverty trap, the long-term investments in skills and competency required to escape poverty are extremely risky. GBI can mitigate the investment risks and increase labor force participation in the long term. The opposing theoretical predictions of GBI’s outcomes make an empirical evaluation fundamental for understanding the policy.

The proposed project is based on a 24-month field randomized controlled trial of GBI in rural South Carolina. The project is a collaboration between Clemson University’s John E. Walker Department of Economics’ and Anderson Interfaith Ministries, a non-profit organization in Anderson, South Carolina devoted to providing community services. Anderson Interfaith Ministries (hereinafter “AIM”) seeks to “connect people with support, resources, and education so they can empower themselves to become self-sufficient,” and conducts several programs specifically aimed at bolstering financial stability and educational attainment within the Anderson community, including in some instances direct cash payments to individuals and families.
External Link(s)

Registration Citation

Citation
García, Jorge Luis, Patrick Warren and Lawrence Reed Watson. 2021. "Guaranteed Basic Income in a Rural Setting: Subsidizing Leisure and Reducing Labor Force Participation or Alleviating Poverty Traps? ." AEA RCT Registry. November 09. https://doi.org/10.1257/rct.8509-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The proposed project is based on a 24-month field randomized controlled trial of GBI in rural South Carolina. The project is a collaboration between Clemson University’s John E. Walker Department of Economics’ and Anderson Interfaith Ministries, a non-profit organization in Anderson, South Carolina devoted to providing community services.
Intervention Start Date
2022-02-01
Intervention End Date
2024-02-01

Primary Outcomes

Primary Outcomes (end points)
labor force participation, household consumption, and education and related investments
Primary Outcomes (explanation)
Our data collection plan includes one comprehensive baseline survey with each participant. The baseline survey will cover the following categories: demographics, education, employment, household composition, living arrangement, household expenditure, food availability, social network, personality traits, mental health, financial stress, economic situation (e.g., assets, rental, credit, insurance), and self-reported health measures.

We will also follow up multiple times with each participant on a subset of the baseline survey categories to analyze the program's short- and longer-term effects. The categories intrinsically characterizing the individuals are obviated. The focus in the follow-ups is on documenting the impact of GBI. The baseline survey contains basic variables for understanding and corroborating the demographic characteristics of the samples of treatment and comparison groups. These variables are fundamental to ensure comparability between treatment and control groups and to ensure that the comparisons between the groups provide direct estimates of the effect of GBI. In all of our data-collections rounds, we will combine survey instruments that have already been piloted and then have successfully been fielded when surveying disadvantaged individuals in different areas of the US. These data collections have already resulted in publications and working papers (García et al. 2018, 2019, 2020, 2021a 2021b). These surveys include permission requests to access administrative data and other information like credit-scores from the experiment participants.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The close relationship of AIM with the pool of potential participants and our conversations with AIM indicate that the take-up when forming our baseline sample will be very high. We aim to have a sample of 100 participants. The randomization approach that we will follow is usual when evaluating the expansion of social policies. For example, it was used in Oregon’s Medicaid health insurance experiment to evaluate Medicaid expansions (Finkelstein et al., 2012). We will stratify the randomization by family (e.g., if a father and son are in the sample of 100 participants, they will both be either in the treatment or control group. We will not have members of the same family with different randomization status).

We will randomize 40 participants to the treatment group and provide them a cash grant of 200 US dollars per month for the duration of the program. The remaining 60 participants are the control or comparison group. They receive 10 dollars per month as an honorarium for answering the baseline and follow-up surveys. Our power calculations across several outcomes indicate that the cash grant is large enough to detect impacts when assuming effects in the confidence-interval lower bound of the estimates reported by SEED (2021). This follows in part because the cash grant will represent a large proportional increase in the annual income of the disadvantaged households that we will target. All of the individuals targeted revolve around the poverty line. The cash grant would thus increase their annual household income by approximately 19%, 14%, and 11% for one-person, two-person, and three-person households, respectively.
Experimental Design Details
Randomization Method
randomization done in an office computer
Randomization Unit
Individuals.

We will stratify the randomization by family (e.g., if a father and son are in the sample of 100 participants, they will both be either in the treatment or control group. We will not have members of the same family with different randomization status).
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
100
Sample size: planned number of observations
100 individuals
Sample size (or number of clusters) by treatment arms
40 treatment and 60 control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For labor force participation, our main outcome of interest, the minimum detectable effect size on the probability of supplying labor is 0.10.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials