# Treatment effects at the margin: Everyone is marginal

## TL;DR

This paper introduces a new framework for identifying treatment effects at the margin, accounting for policies that influence both participation incentives and outcomes, which standard methods struggle to isolate.

## Contribution

It develops marginality-weighted estimands that recover causal effects among policy-induced entrants, addressing endogenous selection issues in treatment effect estimation.

## Key findings

- Framework applicable to various policy evaluation contexts
- Addresses limitations of traditional selection models
- Enables causal inference at the policy-induced margin

## Abstract

This paper develops a framework for identifying treatment effects when a policy simultaneously alters both the incentive to participate and the outcome of interest -- such as hiring decisions and wages in response to employment subsidies; or working decisions and wages in response to job trainings. This framework was inspired by my PhD project on a Belgian reform that subsidised first-time hiring, inducing entry by marginal firms yet meanwhile changing the wages they pay. Standard methods addressing selection-into-treatment concepts (like Heckman selection equations and local average treatment effects), or before-after comparisons (including simple DiD or RDD), cannot isolate effects at this shifting margin where treatment defines who is observed. I introduce marginality-weighted estimands that recover causal effects among policy-induced entrants, offering a policy-relevant alternative in settings with endogenous selection. This method can thus be applied widely to understanding the economic impacts of public programmes, especially in fields largely relying on reduced-form causal inference estimation (e.g. labour economics, development economics, health economics).