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Taking an Extra Moment to Consider Treatment Effects on Distributions

Heckley, Gawain LU orcid and Petrie, Dennis (2025) In Working Papers
Abstract
This paper introduces Parameter Estimation by Raw Moments (PERM), a flexible method for evaluating a policy’s impact on the parameters of an outcome distribution. Such parameters include the variance (E[Y2]−E[Y]2), skewness and covariance of two outcomes. PERM simplifies distributional analysis by first separately estimating higher-order moment treatment effects (e.g., E[Y2]), then combining these to derive distribution parameter treatment effects. Two implementations are discussed: regression with controls and DiD with staggered roll-out. Applying PERM DiD to a Swedish school reform finds it reduced education inequality but increased earnings variance resulting in a lower covariance between education and earnings.
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
Causal Inference, Policy Evaluation, Distribution Impacts, Income Inequality, Education Inequality, I24, I26, C10
in
Working Papers
issue
2025:4
pages
67 pages
language
English
LU publication?
yes
id
b3b21374-188c-4890-a2c6-253c3e31bd1c
date added to LUP
2025-04-01 10:26:45
date last changed
2025-04-04 14:05:31
@misc{b3b21374-188c-4890-a2c6-253c3e31bd1c,
  abstract     = {{This paper introduces Parameter Estimation by Raw Moments (PERM), a flexible method for evaluating a policy’s impact on the parameters of an outcome distribution. Such parameters include the variance (E[Y2]−E[Y]2), skewness and covariance of two outcomes. PERM simplifies distributional analysis by first separately estimating higher-order moment treatment effects (e.g., E[Y2]), then combining these to derive distribution parameter treatment effects. Two implementations are discussed: regression with controls and DiD with staggered roll-out. Applying PERM DiD to a Swedish school reform finds it reduced education inequality but increased earnings variance resulting in a lower covariance between education and earnings.}},
  author       = {{Heckley, Gawain and Petrie, Dennis}},
  keywords     = {{Causal Inference; Policy Evaluation; Distribution Impacts; Income Inequality; Education Inequality; I24; I26; C10}},
  language     = {{eng}},
  note         = {{Working Paper}},
  number       = {{2025:4}},
  series       = {{Working Papers}},
  title        = {{Taking an Extra Moment to Consider Treatment Effects on Distributions}},
  url          = {{https://lup.lub.lu.se/search/files/212794496/WP25_4.pdf}},
  year         = {{2025}},
}