The Happy Migrant? Emigration and its Impact on Subjective Well-Being

Auditor, Jean Guedes; Erlinghagen, Marcel

doi:10.1007/978-3-030-67498-4_11

Cited by 2 publications

(1 citation statement)

References 61 publications

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“…From the aspect of migration timing, Guedes Auditor and Erlinghagen (2021) found some evidence for an increase in individual SWB in the course of migration and a significant decrease in SWB 1–2 years before migration ( Erlinghagen, 2016 ). Lower life satisfaction and higher perceived isolation reported from those couples which men immigrate in advance of their female partners ( Erlinghagen, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting happiness levels of European immigrants and natives: An application of Artificial Neural Network and Ordinal Logistic Regression

2022

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The main purpose of this paper is to investigate the happiness factors and assess the performance of machine learning techniques on predicting the happiness levels of European immigrants and natives. Two types of machine learning methods, Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN), are employed for analytical modeling. Our results with a total sample size of 196,724 respondents from nine rounds of the European Social Survey (ESS) indicate that the determinants of happiness for immigrants and natives are significantly inconsistent. Therefore, variables should be specifically selected to predict the happiness levels of these two different groups. The sensitivity analysis shows that satisfaction with life, subjective general health, and the highest level of education are the three most prominent determinants that contribute to the happiness of immigrants and natives. The overall accuracies of OLR and ANN baseline models are >80%. This can be further improved by building models for each individual country. The application of OLR and ANN implies that machine learning algorithms can be a useful tool for predicting happiness levels. The greater knowledge of migration and happiness will allow us to better understand the decision-making processes and construct more effective policies.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting happiness levels of European immigrants and natives: An application of Artificial Neural Network and Ordinal Logistic Regression

2022

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International migration and shifts in subjective well-being: A longitudinal study using German panel data

Genoni,

Stawarz,

Ette

et al. 2024

Migration Studies

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In many economically advanced countries, a relevant proportion of the population migrates across national boundaries every year. For these individuals, migrating abroad brings benefits (e.g. monetary returns) but also potential pitfalls, as migration is accompanied by profound changes in everyday life. However, there are mainly cross-sectional studies of individuals’ migration experiences that analyse the effects of migration on these individuals’ quality of life. This study fills this gap by examining shifts in the subjective well-being (SWB) of internationally mobile German citizens from a longitudinal perspective using data from the German Emigration and Remigration Panel Study and fixed effects panel regressions. To observe SWB over the course of actual migration events, we drew on a sample of repeat migrants: Germans who returned to Germany shortly before the panel and emigrated again during the panel. Consistent with methodologically comparable studies on internal migration, the analyses show a happiness dip before migration and an increase in SWB with the migration event. The increase is consistent, tends to be more pronounced than that observed in internal migration studies, and to some extent persists after the first few years of migration. Moreover, the results suggest that the increase in SWB systematically depends on the influence that individuals can exert on their migration decisions. Individuals without a partner show the highest increases in SWB, followed by so-called leading partners, partners of couples with a mutual migration decision, and trailing partners.

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The Happy Migrant? Emigration and its Impact on Subjective Well-Being

Cited by 2 publications

References 61 publications

Predicting happiness levels of European immigrants and natives: An application of Artificial Neural Network and Ordinal Logistic Regression

Predicting happiness levels of European immigrants and natives: An application of Artificial Neural Network and Ordinal Logistic Regression

International migration and shifts in subjective well-being: A longitudinal study using German panel data

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