Non-Existent Outcomes in Research on Inequality: A Causal Approach

TL;DR

This paper introduces a causal framework to analyze outcomes that only exist for some individuals, addressing biases from traditional methods and providing new estimands and adjustment techniques.

Contribution

It develops a novel principal stratification approach for outcomes with non-existence issues, extending to confounded observational data with regression and simulation methods.

Findings

Adjusted estimates reveal larger effects than traditional methods.

Framework successfully applied to labor market outcomes.

Provides tools for causal inference with non-existent outcomes.

Abstract

Scholars of social stratification often study exposures that shape life outcomes. But some outcomes (such as wage) only exist for some people (such as those who are employed). We show how a common practice -- dropping cases with non-existent outcomes -- can obscure causal effects when a treatment affects both outcome existence and outcome values. The effects of both beneficial and harmful treatments can be underestimated. Drawing on existing approaches for principal stratification, we show how to study (1) the average effect on whether an outcome exists and (2) the average effect on the outcome among the latent subgroup whose outcome would exist in either treatment condition. To extend our approach to the selection-on-observables settings common in applied research, we develop a framework involving regression and simulation to enable principal stratification estimates that adjust for measured confounders. We illustrate through an empirical example about the effects of parenthood on labor market outcomes.