Caste-Stratified Village Identification Strategy
RCTClinical Trial
Estimating general equilibrium market price spillovers requires random variation in product market density across communities without introducing endogenous correlations with village population size or social structure.
Picture this
Think of trying to test how a new vaccine affects an entire town's virus spread by giving it to random families. If you just picked bigger towns to give more vaccines to, you couldn't tell if the results came from the vaccine or town size. Instead, by randomly selecting specific social clubs (castes) of varying sizes within each town and offering vaccines only to members of those chosen clubs, you create random variations in total town coverage without biasing the test by town size.
What the evidence says
Conditioning on eligibility population shares eliminates endogeneity bias, reducing correlations between insurance marketing proportions and village characteristics (such as caste concentration and total households) to statistically insignificant levels (p-values > 0.33).
- Who was studied
- 118 unique castes (jatis) with >50 households across 42 randomized treatment villages out of 63 REDS survey villages in India.
- How
- Two-stage randomized design stratifying insurance offers by sub-caste population thresholds (>50 members), assigning 93 treatment castes and 25 control castes to create exogenous variation in village treatment density (0% to 53% for cultivators; 0% to 100% for laborers).
What to do
Design general equilibrium field experiments by randomizing treatment across social network sub-units of varying sizes to generate exogenous community-level coverage variation.
From the source
"Stratification of the random assignment by caste creates natural variation in the number and fraction of farming households in each village receiving insurance offers."
300_400 Wages General Equilibrium NBER Jan2014.pdf