Addressing human-capital poverty traps in Bangladesh

Last registered on October 01, 2021
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Pre-Trial

Trial Information

General Information

Title
Addressing human-capital poverty traps in Bangladesh
RCT ID
AEARCTR-0007411
Initial registration date
September 28, 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
October 01, 2021, 2:25 PM EDT

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

Locations

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Primary Investigator

Name
Stefano Caria
Affiliation
University of Warwick
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Other Primary Investigator(s)

PI Name
Oriana Bandiera
PI Affiliation
London School of Economics
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PI Name
Robin Burgess
PI Affiliation
London School of Economics
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Additional Trial Information

Status
On going
Start date
2020-12-02
End date
2024-12-31
Keywords
Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We plan to study the labour market impacts of a training intervention developed by the NGO BRAC to help young Bangladeshis acquire skills and find employment. We will randomly vary both the offer of the training and the conditions at which the training is offered: full fee, discounted fee, or a fee payable only when trainees find work. This will enable us to estimate the impacts of the training program for the different groups of young people that are selected at the different payment conditions. In particular, we will explore whether the standard training package fails to reach a group of high-return, liquidity-constrained individuals who cannot muster the resources to optimally invest in human capital. We are also interested to understand whether the NGO can attract this group most cost-effectively by offering up-front discounts or by making fees conditional on subsequent employment.
External Link(s)

Registration Citation

Citation
Bandiera, Oriana, Robin Burgess and Stefano Caria. 2021. "Addressing human-capital poverty traps in Bangladesh." AEA RCT Registry. October 01. https://doi.org/10.1257/rct.7411-1.0
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Experimental Details

Interventions

Intervention(s)
We study the impacts of a training intervention developed by the NGO BRAC in Bangladesh. The training covers one of four potential trades and is delivered through a placement in a local firm. We will offer this training under four different contractual conditions: full fee, 30 percent discount, 70 percent discount, and “pay-if-employed”. Under “pay-if-employed”, participants are charged the full fee, however they only start paying this fee when they find employment or after 12 months, if they are still unemployed at that point.
Intervention Start Date
2021-08-22
Intervention End Date
2022-02-28

Primary Outcomes

Primary Outcomes (end points)
Demand for training;
Employment;
Earnings.
Primary Outcomes (explanation)
We will measure employment with a dummy capturing whether the individual is employed. We will calculate earnings by imputing a 0 for individuals not in employment. We will measure demand for training by using respondents' baseline expression of interest as well as final training completion.

We will explore impacts on our primary outcomes using (i) the full experimental sample and (ii) subgroups defined on the basis of:
- Demand for training
- Access to liquidity
- Expected benefits from training
- Gender, fertility and marital status
- Parent’s occupation

Secondary Outcomes

Secondary Outcomes (end points)
Employment quality and type;
Fee payment;
Psychological well-being.
Secondary Outcomes (explanation)
We will measure employment quality and type through a battery of questions covering occupation and contract type, whether the occupation matches the desired occupation, firm size.

We will measure fee payment by calculating the share of the training fee that was repaid and by creating a dummy capturing whether an individual has paid the full fee.

We will measure well-being using questions on life satisfaction and affect.

Experimental Design

Experimental Design
We randomise the conditions of the training (full fee, 30 percent discount, 70 percent discount, or pay-if-employed) at the branch X trade level. We then use baseline information on training demand to generate lists of individuals interested in being trained in a given trade at the conditions offered by their local BRAC branch. We randomise the invitation to the training among this sample of interested individuals.
Experimental Design Details
Not available
Randomization Method
By computer.
Randomization Unit
There are two levels of randomization:
(i) We randomly allocate the training conditions at the branch X trade level;
(ii) Within a given branch, we randomly invite individuals for training in their favorite trade. For this randomization, we only consider individuals who have expressed an interested to be trained in their favorite trade at the conditions randomly allocated to their local branch.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
120 clusters
Sample size: planned number of observations
We plan study demand for training (measured through the baseline expression of interest) with a sample of 18,000 individuals who are potentially interested in at least one trade. We plan to study impacts on primary and secondary outcomes with a sample of 3,900 individuals who will complete the endline interviews.
Sample size (or number of clusters) by treatment arms
30 clusters full fee
30 clusters 30pct discount
30 clusters 70pct discount
30 clusters pay-if-employed
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Treatment effects on employment. We have a MDE of 2.8 percentage points (0.057 standard deviations for a control employment rate of 60 percent) for an omnibus comparison of all individuals invited for training against all individuals not invited for training. For the comparison of all individuals invited at full-fee vs all individuals invited at the pay-if-employed fee, we have a MDE of 4.5 percentage points (0.092 of a standard deviation). Treatment effects on earnings. We have a MDE of 0.130 and 0.177 for the two comparisons discussed above.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
London School of Economics
IRB Approval Date
2020-02-24
IRB Approval Number
N/A
Analysis Plan