Effects of Emigration on Rural Labor Markets

Last registered on October 10, 2017

Pre-Trial

Trial Information

General Information

Title
Effects of Emigration on Rural Labor Markets
RCT ID
AEARCTR-0002507
Initial registration date
October 10, 2017

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 10, 2017, 5:03 PM EDT

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

Locations

Primary Investigator

Affiliation
Yale University

Other Primary Investigator(s)

PI Affiliation
The University of Sydney
PI Affiliation

Additional Trial Information

Status
Completed
Start date
2014-11-01
End date
2016-08-15
Secondary IDs
Abstract
Rural to urban migration is an integral part of the development process, but there is little evidence on how out-migration transforms rural labor markets. Emigration could benefit landless village residents by reducing labor competition, or conversely, reduce productivity if skilled workers leave. To study this, we offer to subsidize transport costs for 5,792 potential seasonal migrants in Bangladesh, randomly varying saturation of offers across 133 villages. The transport subsidies increase beneficiaries’ income due to better employment opportunities in the city, and also generate the following spillovers: (a) A higher density of offers increases the individual take-up rate, and induces those connected to offered recipients to also migrate. (b) This increases the male agricultural wage rate in the village, and the available work hours in the village, which combine to increase income earned in the village, (c) There is no intra-household substitution in labor supply, but primary workers within households earn more during weeks in which many of their village co-residents moved away. (d) The wage bill for agricultural employers increases, which reduces their profit, with no significant change in yield. (e) Food prices increase driven by an increase in the price of (fish) protein, and offset by (f) a decrease in the price of non-tradables like prepared food and tea. Seasonal migration subsidies not only generate large direct benefits, but also indirect spillover benefits by creating slack in the village-of-origin labor market during the lean season.
External Link(s)

Registration Citation

Citation
, , Shyamal Chowdhury and Ahmed Mobarak. 2017. "Effects of Emigration on Rural Labor Markets." AEA RCT Registry. October 10. https://doi.org/10.1257/rct.2507
Former Citation
, , Shyamal Chowdhury and Ahmed Mobarak. 2017. "Effects of Emigration on Rural Labor Markets." AEA RCT Registry. October 10. https://www.socialscienceregistry.org/trials/2507/history/22217
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Experimental Details

Interventions

Intervention(s)
The form of our intervention was the offer of a cash grant worth 1,000 taka (13 USD) to rural households in northern Bangladesh to cover the round-trip cost of travel to nearby cities where there are job opportunities during the lean season. This was a conditional transfer, where the subsidy is conditional on one person from the household agreeing to out-migrate during the lean season. As offers were made, we let households know that they may have a better chance of finding work outside of their village, but we did not offer to make any connections to employers. No requirement was imposed on who within the household had to migrate, or what city they had to go to. Migration was carefully and strictly monitored by project staff to ensure adherence to the conditionality.

We first conducted village censuses to identify all households that would be “eligible” to receive this intervention in each of these villages. A household was deemed eligible if (1) it owned less than 0.5 acres of land, and (2) it reported back in 2008 that a member had experienced hunger (i.e., skipped meals) during the 2007 lean season.

We varied the number of eligible households in a village that received a travel grant offer. We randomly assigned 133 villages into three groups:
(a) Low Intensity – 48 villages where we targeted migration subsidies to roughly 14% of the eligible (landless, poor) population.
(b) High Intensity – 47 villages where we targeted roughly 70% of the eligible population with migration subsidy offers.
(c) Control – 38 randomly selected villages where nobody was offered a migration subsidy.

Within each village, eligible households were randomly selected to receive the travel grant offer.
Intervention Start Date
2014-11-01
Intervention End Date
2014-11-30

Primary Outcomes

Primary Outcomes (end points)
- Individual propensity to migrate
- Individual income earned
- Village wage rate
- Employer profit
- Shopkeeper food prices
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Wage Rate in the village
Food prices in the village
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We varied the number of eligible households in a village that received a travel grant offer. We randomly assigned 133 villages into three groups:
(a) Low Intensity – 48 villages where we targeted migration subsidies to roughly 14% of the eligible (landless, poor) population.
(b) High Intensity – 47 villages where we targeted roughly 70% of the eligible population with migration subsidy offers.
(c) Control – 38 randomly selected villages where nobody was offered a migration subsidy.

The high vs. low intensity design was chosen to generate significant variation in the size of the emigration shock.

This experimental variation enabled the PIs to estimate the impact the grant offers had on the probability of migration and labor market participation, including for households not offered the grant (spillover households). The design also allowed PIs to estimate the impact of out-migration on village economies including impacts on local wages, employers and food prices.
Experimental Design Details
The precise target (14% vs 70%) varied a little across villages within treatment arms. This is because our village population estimates were dated (from 2008) for most (100) villages, and imprecise in the 33 other villages, which made it difficult for us to precisely estimate the ratio (offers/eligible population) in each village.
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Village.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
133 villages.
Sample size: planned number of observations
Endline survey: 3,602 households; Employer survey: 1,099 employers (year-1) and 649 (year-2); Shopkeeper survey: 399.
Sample size (or number of clusters) by treatment arms
(a) Low Intensity – 48 villages; 883 households offered grant.
(b) High Intensity – 47 villages; 4,881 households offered grant.
(c) Control – 38 villages; zero households offered grant.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Yale University Institutional Review Board
IRB Approval Date
Details not available
IRB Approval Number
1010007571

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Yes
Data Collection Completion Date
August 15, 2016, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
133 villages
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
Varied across survey instruments. See attached paper
Final Sample Size (or Number of Clusters) by Treatment Arms
133 villages
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Abstract
Citation

Reports & Other Materials