Endogenous Stratification and Equity Gains
Job-seekers with disadvantaged backgrounds—such as lower education, lack of permanent work experience, or lower predicted earnings—face disproportionate labor market exclusion due to negative employer stereotypes.
Picture this
Imagine a footrace where runner speed is obscured by heavy backpacks given to certain runners based on their background. Removing the opacity allows judges to see that those wearing the heaviest backpacks are actually fast runners, disproportionately lifting the slowest baseline performers to the front of the pack and closing the finish-line gap.
What the evidence says
The application workshop generated a **467.1 ETB monthly wage increase** for the low-predicted-earnings group (50% of control mean, p < 0.01) compared to a -99.0 ETB non-significant effect for the high-predicted-earnings group (p = 0.0696 for equality), **reducing the high-to-low earnings gap from 142% to 54%**.
- Who
- Subgroup analysis of **N = 3,052 youth sample** in Addis Ababa, Ethiopia [5, 23].
- How
- Machine-learning based split-sample **endogenous stratification** (Abadie et al., 2017) categorizing participants by predicted endline earnings using baseline covariates [23].
What to do
Stratify target populations by predicted baseline earnings to deliver skill-signaling tools specifically to low-predicted-earning youth.
From the source
"The estimated effect size for the low-predicted-earnings group is about 50% of the control mean. This causes a large reduction in earning inequality: the earning gap between the low and the high earnings group drops from 142 percent to 54 percent..."
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