No Lean Season 2017 Evaluation
Last registered on March 20, 2018


Trial Information
General Information
No Lean Season 2017 Evaluation
Initial registration date
January 18, 2018
Last updated
March 20, 2018 1:29 PM EDT

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Primary Investigator
Evidence Action
Other Primary Investigator(s)
PI Affiliation
University of California Davis
PI Affiliation
Evidence Action/Yale University
PI Affiliation
Yale University
PI Affiliation
London School of Economics
Additional Trial Information
On going
Start date
End date
Secondary IDs
This is a randomized trial evaluating a seasonal migration incentivization program, No Lean Season, implemented in partnership by Evidence Action and RDRS. The program aims to mitigate seasonal hardship in rural agricultural areas but providing loans to rural laborer, incentivizing them to seek temporary employment in nearby cities. The evaluation aims to (a) update findings from previous research studies, and in particular, to investigate whether the program's positive impact will be replicable at scale; and (b) investigate the program’s spillover effects on workers at the migration destination who are not offered migration incentives. Welfare outcomes (expenditure, caloric intake, income, and food security) will be measured in a manner consistent with previous evaluations of this program.
External Link(s)
Registration Citation
Bryan, Gharad et al. 2018. "No Lean Season 2017 Evaluation." AEA RCT Registry. March 20.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Migration, food expenditure, non-food expenditure, caloric intake, income, food security
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
RDRS is organized administratively into branch offices. Each branch has a set of villages in its catchment area defined by the geographic road) distance to the branch. Branch catchment areas are non-overlapping so each village in the experiment can be allocated to a single branch.

Treatment, defined as the offer of a migration subsidy (incentivization), occurs at the village level. Every eligible household in a treated village is offered the migration subsidy. Our randomization strategy places villages into four categories:

1. Incentivized: Villages in which the migration subsidy offer is made.
2. Spillover: An untreated village geographically in the middle of a group of treated villages.
3. Spillover-control: An untreated village that belongs to a branch that includes treated villages, but is surrounded by other untreated villages.
4. Pure-control: An untreated village that belongs to a branch that has no treated villages.

To achieve this classification, we randomize at two levels. First, we randomly divide branches into treated and control. Branches assigned to be control contain only pure-control villages. Branches assigned to be treated contain the other three types of villages.

Within treated branches, our randomization strategy generates a treated sector (designated as incentivized), a single untreated village within the treated sector (designated as spillover), and an untreated sector (designated as spillover-control). In accordance with the RDRS workplan, the treated sector comprises one third of the villages in a treated branch. For assignment, we identify the centroid of the branch catchment area and then project each village onto a circle around the centroid. We randomly select one village on this circle and designate it as spillover. We then define the incentivized sector as the third of the circle surrounding the spillover village. In effect, we create a “pie slice” (designated as the incentivized sector), with one village in the middle left untreated as spillover.

This strategy stems from the fact that incentivization may generate spillovers onto nearby villages. Spillovers come from three main sources. First, we find in previous work that migrants generally travel in groups and migrants from geographically close sources tend to go to geographically similar destinations. Therefore, inducing migration in one village may lower the returns to migration from nearby villages through the destination labor market. Second, labor markets may be locally integrated. Out-migration from an incentivized village may lower labor supply, raise wages, and induce in-migration from nearby villages. Third, household risk sharing networks may extend beyond village boundaries. An incentivized household may share the benefits of migration with others in nearby villages.

Our randomization strategy creates multiple types of non-incentivized villages to evaluate the geographic extent of these spillovers. The spillover village in a treated branch is on average closest to incentivized villages and therefore most exposed to treatment spillovers. At the other extreme, we believe pure-control villages are sufficiently far from treated regions that their workers are no more exposed to treatment spillovers than workers from anywhere else in the country. Spillover-control villages falls between these extremes and allow us to estimate how quickly the spillovers dissipate with distance.

For evaluation, we plan to survey (record) households in only a subset of incentivized, spillover-control, and pure-control villages. Survey villages are selected as follows:

1. Incentivized: One randomly selected village in the incentivized sector per branch.
2. Spillover: The village in the middle of the incentivized sector, designated as spillover in each treated branch.
3. Spillover-control: The village diametrically opposite the spillover village on the circle projection.
4. Pure-control: One randomly selected village in each untreated branch.

The randomization design generates the four experimental categories while ensuring that the status of a village is uncorrelated with other geographic characteristics. In particular, treatment status is orthogonal to the geographic density of villages and their proximity to a branch’s boundary. The survey design preserves orthogonality between likelihood of being surveyed and geographic characteristic as well. Unfortunately,
in maintaining this orthogonality, we cannot guarantee that spillover villages are closer to the incentivized sector than spillover-control in every treated branch. We do not account for proximity to the centroid in randomization, meaning that a very central spillover-control village may be closer to the treated region than a peripheral spillover village. However, on average, spillover villages are closer to incentivized villages
than spillover-control villages. Similarly, an incentivized village in our sample is on average closer to the incentivized sector than a spillover-control village, but slightly father on average than a spillover village.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by computer.
Randomization Unit
Two levels:
1. RDRS branch
2. Village
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
221 villages in 121 branches
Sample size: planned number of observations
5420 households
Sample size (or number of clusters) by treatment arms
50 treated branches and 71 control branches: 3 villages (incentivized, spillover, and spillover-control villages) per treated branch and 1 pure-control village per control branch.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Yale University, Human Investigation Committee
IRB Approval Date
IRB Approval Number
IRB Protocol ID 1010007571
Analysis Plan
Analysis Plan Documents
PAP v1.0

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SHA1: fa73171ea208716600599a519c2f0ae0c33dd89e

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PAP v1.1

MD5: 1cf69227cc4948467cf92328df49bb2d

SHA1: 4befacbd7a03c15509f2ef8104c84a87724ce52a

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Destination Model

MD5: cd2205c24688b94df888e83661b5c8da

SHA1: d894b5176bb8ba94ed2934ff74080b9d90f38470

Uploaded At: March 12, 2018

PAP v1.2

MD5: 0716bb9063495b89b5bb9d7f6183ace5

SHA1: 3d2e4d5ebbcc143496d5aa9572e17078ec05f863

Uploaded At: March 20, 2018