ELIPA-2 Survey Data on Social Integration of New Immigrants in France

ELIPA-2 is a government-led longitudinal survey dataset of 4,053 non-EU immigrants in France, collected across three waves from 2019 to 2022. The dataset includes 93 predictor variables and an outcome variable measuring social integration, analyzed using machine learning methods. It was authored by Germano Vera Cruz and published on figshare under a CC-BY-4.0 license.

Use Cases

• Predicting social integration scores based on personal characteristics, income, and social capital.
• Analyzing the impact of immigration policies and pre-migration trauma on integration outcomes.
• Training machine learning models to identify key predictors of immigrant life satisfaction and sense of belonging.

Strengths

• Dataset contains 4,053 survey responses from a government-led longitudinal study.
• Analysis includes 93 predictor variables, allowing for a multi-faceted examination of integration.
• Data covers three survey waves (2019, 2020, 2022), providing a longitudinal perspective.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic bias, as 71% of respondents originate from Africa.

Provenance

Source

ELIPA-2 longitudinal survey conducted by the French government.

Collection Method

Survey data collected from a random sample of non-EU immigrants legally living in France.

Time Range

2019, 2020, 2022 survey waves.

Freshness

Last updated 2026-05-04 05:32:44; freshness should be verified.

Geography

France.