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Sensitivity analysis in unconditional quantile effects

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  • Julián Martínez-Iriarte

    (UC San Diego)

Abstract

This paper proposes a framework to analyze the effects of counterfactual policies on the unconditional quantiles of an outcome variable. For a given counterfactual policy, we obtain identified sets for the effect of both marginal and global changes in the proportion of treated individuals. To conduct a sensitivity analysis, we introduce the quantile breakdown frontier, a curve that quantifies the maximum amount of selection bias consistent with a given conclu- sion of interest across different quantiles. We obtain a v n-consistent estimator of the curve, and propose a bootstrap-based inference procedure. To illustrate our method, we perform a sensitivity analysis on the effect of unionizing low income workers on the quantiles of the distribution of (log) wages.

Suggested Citation

  • Julián Martínez-Iriarte, 2021. "Sensitivity analysis in unconditional quantile effects," Working Papers 52, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:52
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    Cited by:

    1. Javier Alejo & Antonio F. Galvao & Julián Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Working Papers 217, Red Nacional de Investigadores en Economía (RedNIE).

    More about this item

    Keywords

    unconditional quantile effects partial identification sensitivity analysis directional differentiability;

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