Kling Summary Index Aggregation
Observational StudyReview
Testing multiple correlated outcomes individually increases the risk of false positives (Type I error) and can produce fragmented conclusions across individual statistical measures. A composite index is required to capture the overall directional impact across related financial dimensions.
Picture this
Think of evaluated student performance across math, reading, science, and history tests; rather than debating separate scores across all four subjects, you average the standardized grades into a single overall GPA to evaluate total academic standing.
What the evidence says
The Kling labeling index demonstrated a statistically significant treatment take-up effect (mean index = 1.233, t = 44.445, p < 0.001) and a statistically significant overall remittance increase in the low-baseline subsample (+0.027 index units, p < 0.01).
- Who was studied
- 137,927 weekly panel observations across 4,451 analyzed migrant study participants in the UAE.
- How
- Application of the Kling, Liebman, and Katz (2007) standardizing procedure combining binary participation, transfer counts, raw amounts, and log amounts into a unified z-score index.
What to do
Construct composite Kling z-score summary indexes when evaluating multi-variable treatment impacts to prevent false positive risks from multiple hypothesis testing.
From the source
"The last column of the table examines a 'labeling index', a Kling et al (2007) index of the four other outcomes in the table. This labeling index is also positive and statistically significantly different from zero..."
A Field Experiment among Filipino Migrant Workers in the UAE