Volume 47 - Article 7 | Pages 161–198

Going "beyond the mean" in analysing immigrant health disparities

By Gabriella Berloffa, Francesca Paolini

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Date received:21 May 2021
Date published:19 Jul 2022
Word count:6603
Keywords:decomposition analysis, health inequalities, immigrants, Italy, unconditional quantile regression


Background: On arrival, immigrants are on average healthier than Italian natives, but their health advantage tends to dissipate over time. This constitutes a relevant public health issue for the hosting societies, as it implies higher health care costs, lower labor market participation among immigrants, and lower tax revenues.

Objective: This study is the first to take a “beyond the mean” perspective in analyzing health differences between Italians and short-say immigrants, as well as between short- and long-stay immigrants. It highlights whether health differences are concentrated in specific parts of the distributions and which observed or unobserved factors contribute to these differences.

Methods: We use unconditional quantile regressions combined with Oaxaca-Blinder decompositions on data from the Italian Health Condition Survey.

Results: We find that the health advantage of short-stay immigrants over both Italians and long- stay immigrants is concentrated in the lower part of the health distributions. In both cases, this is mainly due to unobserved factors. Observed economic characteristics are actually associated with better health for long-stay immigrants compared to short-stay immigrants. Our results reveal the need of monitoring immigrants’ health, particularly of those with poorer initial health conditions.

Contribution: We examine immigrant health disparities across the entire health distribution. This helps in shaping effective health policies. Policy interventions should be tailored to immigrants with poor health conditions, for example, by improving their access to the health care system.

Author's Affiliation

Gabriella Berloffa - Università degli Studi di Trento, Italy [Email]
Francesca Paolini - Università degli Studi di Trento, Italy [Email]

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