Informational Frictions and the Take-up of Earned Income Subsidies

Last registered on June 27, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

Informational Frictions and the Take-up of Earned Income Subsidies

RCT ID

AEARCTR-0015010

Initial registration date

June 25, 2025

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

June 27, 2025, 2:20 PM EDT

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

Locations

Country

Chile

Region

Primary Investigator

Name

Jorge Rodriguez

Affiliation

Universidad de los Andes

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Tomás Rau

PI Affiliation

Pontificia Universidad Católica

Contact Investigator

PI Name

Antonia Aguilera

PI Affiliation

DIPRES, Chile

Contact Investigator

Additional Trial Information

Status

In development

Start date

2025-07-27

End date

2025-09-01

Keywords

Labor

Additional Keywords

Labor supply, earned-income subsidies, information

JEL code(s)

J22, J16, J38, D83

Secondary IDs

Prior work

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

Abstract

This project studies the labor supply effects of an earned income subsidy in Chile and explores the mechanisms mediating its impact. We focus on the Bono al Trabajo de la Mujer (BTM), a targeted wage subsidy aimed at low-income women. Eligibility for the BTM is determined by a sharp threshold in a pre-assigned socioeconomic score, which we exploit using a regression discontinuity design (RDD) to estimate the causal effect of the program on labor force participation at the extensive margin. To shed lights on the mechanisms explaining the reduced-form evidence, we complement the RDD with two additional components. First, we field an original survey to measure program awareness and perceptions of eligibility. Embedded in the survey is a randomized informational experiment designed to test whether informational frictions constrain program take-up. Second, we develop a structural model of labor supply and program participation under incomplete information. This framework allows us to isolate the roles of the subsidy’s generosity—relative to the value of alternative income sources such as welfare—and informational frictions in driving observed responses. The model will also be used to simulate counterfactual policy scenarios, providing insights into the design of more effective transfer programs.

External Link(s)

Registration Citation

Citation

Aguilera, Antonia, Tomás Rau and Jorge Rodriguez. 2025. "Informational Frictions and the Take-up of Earned Income Subsidies." AEA RCT Registry. June 27. https://doi.org/10.1257/rct.15010-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Respondents in the treatment group who report being unaware of BTM or having never applied will be shown an informational message highlighting the program's key features and application process.

Intervention (Hidden)

Respondents in the treatment group who report being unaware of BTM or having never applied will be shown an informational message highlighting the program's key features and application process.

Intervention Start Date

2025-07-27

Intervention End Date

2025-09-01

Primary Outcomes

Primary Outcomes (end points)

BTM awareness.
BTM application and take-up.
Formal employment.

Primary Outcomes (explanation)

BTM awareness: in the follow-up survey, we will ask women if they know about a series of social benefits (among them, the BTM), where alternatives are randomly allocated in a list. We will measure BTM awareness if the woman check "yes" to the question of whether she have heard about the BTM.

BTM application and take-up. In the same follow-up survey, we will ask women if they have applied (or consider applying in the future) and if she has received BTM benefits during the last months.

Formal employment: measured as if the woman presents an earnings registry in the Unemployment Insurance system.

Secondary Outcomes

Secondary Outcomes (end points)

Hours worked.
Earnings.
Reasons for not applying.
Visits to the BTM website.

Secondary Outcomes (explanation)

Hours worked: current (or jobs in the last few months) weekly hours worked. Self-reported in the follow-up survey
Earnings: current (or from jobs in the last few months) net salary. Self-reported in the follow-up survey
Reasons for not applying: the woman checks all reasons from a list in the follow-up survey. self-reported in the follow-up survey
Visits to the BTM website: whether the woman have visited the BTM website in the last few months.

Experimental Design

All participants in the baseline survey will be randomly assigned into an informational experiment embedded within the survey. Upon completing the demographic module, respondents will be randomly allocated—with 50% probability—into either a control or treatment group. The control group will exit the survey at that point, while the treatment group will proceed to a module that elicits familiarity with and perceptions of the BTM. Respondents in the treatment group who report not having heard of BTM or not having applied will then be shown a brief informational message summarizing the program’s key features, eligibility criteria, and how to apply.

Approximately five months after the baseline, all participants—regardless of treatment status—will be re-contacted via email and invited to complete a follow-up survey. The follow-up will measure updated awareness of the BTM, self-reported application behavior, and labor market outcomes, including employment status and hours worked.

Experimental Design Details

Participant recruitment will be conducted via Instagram, leveraging the platform's extensive reach among urban women in Chile. Recruitment advertisements will take the form of a short video reel depicting a relatable woman navigating a university campus, designed to capture attention and build credibility. Interested viewers will be invited to click through to a secure online survey hosted on REDCap, a widely used platform for managing surveys and research data securely. To encourage participation, respondents will be automatically entered into a lottery for grocery store gift cards.

The baseline survey gathers rich demographic and socioeconomic data, with a particular emphasis on awareness of and interaction with the Bono al Trabajo de la Mujer (BTM) program. All participants will be randomly assigned into an informational experiment embedded within the survey. Upon completing the demographic module, respondents will be randomly allocated—with 50% probability—into either a control or treatment group. The control group will exit the survey at that point, while the treatment group will proceed to a module that elicits familiarity with and perceptions of the BTM.

This module collects detailed information on whether respondents have heard of the program, understand how to apply, are aware of the potential benefit amount, and what barriers—if any—have prevented them from applying. To minimize demand or priming effects, the BTM awareness item is placed randomly within a list of questions about other social programs; Each respondent sees a list of $M$ social programs presented in random order; BTM appears with equal probability in any position, ensuring uniform exposure. Respondents in the treatment group who report not having heard of BTM or not having applied will then be shown a brief informational message summarizing the program’s key features, eligibility criteria, and how to apply.

Approximately five months after the baseline, all participants—regardless of treatment status—will be re-contacted via email and invited to complete a follow-up survey. The follow-up will measure updated awareness of the BTM, self-reported application behavior, and labor market outcomes, including employment status and hours worked.

Randomization Method

Randomization done automatically using the REDCap software.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

20,000 women

Sample size: planned number of observations

20,000

Sample size (or number of clusters) by treatment arms

10,000 treatment group
10,000 control group

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

Employment. Unit: percentage points. SD: 0.5 (assuming a 50% baseline employment probability) Minimum detectable effect size: 2 percentage points Awareness. Unit: percentage points SD: 0.4 (assuming 20% of women have heard about the BTM) Minimum detectable effect size: 1.6 percentage points Application. Unit: percentage points SD: 0.218 (assuming 5% of women have heard about the BTM) Minimum detectable effect size: 1 percentage points

Supporting Documents and Materials

IRB