Attanasio Selection-Bias Decomposition Framework
RCTReview
Measuring treatment effects on output quality strictly among program completers introduces selection bias because peer interaction changes the baseline propensity to finish or submit work.
Picture this
Think of testing a new coaching program on marathon runners. If the coaching motivates slower runners to finish who would otherwise drop out, the average finishing time of all completers might look worse even if the coaching actually made every individual runner faster.
What the evidence says
Virtual-Within interaction yields a statistically significant intensive quality increase among motivated Milestone 0 completers ($\beta = 0.138, p < 0.05$ in Large-Country Sample; $\beta = 0.427, p < 0.05$ in Uganda Sample). Under the BAS assumption, this observed conditional effect serves as a strict lower bound for the true unconditional quality impact.
- Who was studied
- N = 3,333 entrepreneurs in the Large-Country Sample (N = 1,322 submitted proposals evaluated by 15 professional judges).
- How
- Non-parametric selection-bias decomposition assuming No Defiers (ND) and Better Always Submitters (BAS), separating participants into always-submitters, never-submitters, compliers, and defiers.
What to do
Decompose conditional quality metrics using the Better Always Submitters framework when program treatments induce differential submission or completion rates between treated and control groups.
From the source
"Therefore, if the estimated treatment effect on quality conditional, of course, on submission is positive and significant, this is in effect an unambiguous indication of a positive unconditional effect as well."
Peer Networks and Entrepreneurship- a Pan-African RCT.pdf