Challenges for secondary analysis of existing large scale quantitative datasets for researching older migrants

https://cyuhat.github.io/presentation_valencia/#/title-slide

Hategekimana Vestin

vestin.hategekimana@unige.ch

University of Geneva

Ravazzini Laura

laura.ravazzini@unine.ch

University of Neuchâtel

Analysis of 3 secondary datasets

Why older migrants?

Introduction

Migration Studies

• Field continuously expanding with extensive data and advanced analytical methods.
• Focus predominantly on working-age migrants.

Gap in Research

• Elderly migrants are an underrepresented group in migration studies.
• Current research overlooks important dynamics specific to older age migration.

Why eldery migrants?

Changing Dynamics in Retirement

• Increased life expectancies and advancements in technology and transportation.
  • Greater opportunities for international mobility among retirees.

Implications of Mobility in Older Age

• Potential for more geographically dispersed family units.
• Enhanced global connectivity and extensive travel opportunities.

Goal

Compare three datasets from Switzerland:

1. Migration Mobility Survey (MMS)
2. Transnational Ageing Survey (TAS)
3. TransAge

Seven topics:

1. Life course
2. Vulnerability
3. Integration
4. Mobility
5. Family
6. Transnationalism
7. Health
8. COVID-19

Why Switzerland?

Switzerland offers an interesting context:

• High proportion of the migrant population
  • 31% in 2022 (OFS, 2023)
• High proportion of older individual among migrants
  • 28.7% aged 55 y/o and over in 2021 (OFS, 2022)
• High proportion of older Swiss living abroad
  • 22.5% aged 65 y/o and over in 2020 (OFS, 2023)
• Availability of rich datasets
  • MMS / TAS / TransAge
  • Other longitudinal studies / Register

Migration Mobility Survey (MMS)

• Dataset Overview: National Center of Competence in Research (NCCR) conducts the Migration and Mobility Survey (MMS) biennially to study migration flows and conditions of foreign residents in Switzerland.
  • Size: ~7,000/wave
• Survey Details:
  • Currently in its fourth wave.
  • Targets immigrants aged 18+ at immigration and 24 to 64 at survey time.
  • Excludes under 18s and holders of N (asylum) or F (temporary) permits.
  • Second wave included ages 26 to 70.
• Sampling Technique:
  • Stratified sampling by gender and geopolitical criteria.
  • 36 groups created, with ~190 interviews per stratum.
  • Total target of 6,800 participants from an initial sample of 11,053 (33% participation anticipated).
  • Refresh sample targets underrepresented nationalities, with 67% response rate and 20% reserve sample for sufficiency.
• Data Collection:
  • Mixed modes: 1% via Computer Assisted Telephone Interviews (CATI) and 99% through Online Web Interviews (CAWI).
  • Questionnaires standardized, with minimal changes from 2020 to 2022.
  • Feedback from internal workshops and tests with Master’s students to ensure question clarity and neutrality.
• Purpose: Improve reception and integration of immigrants, contributing to societal and economic development in Switzerland.

Transnational Ageing Survey (TAS)

• Transnational-Ageing Surveys by NCCR – on the move:
  • Purpose: To gather data to improve the quality of life for retirees, particularly those spending part of their year in Switzerland.
  • Participants: Swiss and foreign nationals aged 55 and older.
  • Size: ~5,000/wave
  • Focus Areas:
    • Life in Switzerland.
    • Past and current international movements.
    • Retirement plans (current or future).
    • Family setups in Switzerland and overseas.
    • Health, well-being, and overall living conditions.
• Survey Execution and Details:
  • Initial Survey (2020):
    • Conducted in the first half of 2020.
    • Covered national residents of Switzerland, all nationalities, aged 55+.
    • Received responses from 3,772 participants.
    • Managed by M.I.S Trend survey institute.
  • Subsequent Global Survey (2020):
    • Started in November 2020.
    • Focused on Swiss nationals aged 55+ residing overseas.
    • Aimed to explore living conditions abroad.
    • Collaborated with the Federal Department of Foreign Affairs and the Federal Office of Constructions and Logistics.
• Research Goals:
  • Enhance insights into new international mobility trends among retirees.
  • Inform policies and services to support retirees in both domestic and international contexts.

TransAge

• Dataset Overview: “Transnational Ageing Project: Post-Retirement Mobilities, Transnational Lifestyles and Care Configurations”
  • Focus: Older adults (aged 65 and above) from four groups: Swiss natives, Italian international migrants in Switzerland, Italian stayers in Italy, and internally mobile Italians.
  • Size: ~3,000
• Data Collection and Study Design:
  • Research Institutes: MIS Trend (Switzerland) and Demetra Opinioni (Italy).
  • Collection Methods: Online questionnaires, paper questionnaires, telephone interviews, and face-to-face interactions.
  • Period: June to November 2020, during the first and second COVID-19 waves.
  • Participants: Total of 2,354 older adults.
  • Sampling Strategy:
    • Swiss sample stratified by age and gender.
    • Focused on six cantons (Zurich, Bern, Aargau, Vaud, Geneva, Ticino) hosting 70% of the Italian resident population in Switzerland.
    • Respondents offered a choice between online and paper questionnaires in German, Italian, and French.
• Study Objectives and Context:
  • Aim: Compare groups to understand dynamics due to significant older Italian population in Switzerland, many of whom migrated from Southern Italy between the 1950s and 1970s.
  • Social Desirability Bias Test: Compared Swiss Household Panel data (conducted by telephone) with data from paper/web methods for the Swiss sample. Found no evidence of bias in subjective well-being indicators.
• Significance:
  • Provides insights into post-retirement mobilities, transnational lifestyles, and care configurations among older adults in transnational settings.

Limitation of using secondary datasets for studying older migrant population

When studying older migrant populations using secondary data, there are several key limitations to be aware of. To address these limitations, researchers often need to complement secondary data analysis with primary data collection, including qualitative studies, to gain a more comprehensive understanding of older migrant populations.

Main challenges:

• Methodological
• Ethical
• Data

Literature

Methodological Challenges

1. Defining the Population of Interest

• Complexity: Older migrants are a heterogeneous group, varying in age, origin, migration history, and legal status.
• Migration Status: Deciding whether to include second-generation immigrants, return migrants, or those with dual citizenship.
• Cultural Considerations: Definitions of “older” may differ across cultures and affect self-identification.

Methodological Challenges

2. Sampling Difficulties

• Hard-to-Reach Populations: Older migrants may be socially isolated or live in areas not easily accessible to researchers.
• Language Barriers: May limit participation in surveys or interviews.
• Trust Issues: Some migrants may be hesitant to participate due to fears related to their legal status or past experiences.
• Representativeness: Ensuring the sample accurately reflects the diversity within the older migrant population.
• Sample Size: Obtaining a large enough sample for statistical significance, especially for subgroups.

Methodological Challenges

3. Selection of Appropriate Questions

• Cultural Sensitivity: Ensuring questions are culturally appropriate and not offensive.
• Relevance: Developing questions that capture the unique experiences of older migrants.
• Comparability: Balancing the need for context-specific questions with the ability to compare across different migrant groups or with non-migrant populations.
• Comprehension: Ensuring questions are understood similarly across different linguistic and cultural groups.

Methodological Challenges

4. Ensuring Validity of Answers

• Translation Issues: Ensuring accurate translation of questions and responses across multiple languages.
• Social Desirability Bias: Respondents may answer in ways they think are socially acceptable rather than truthfully.
• Recall Bias: Older participants may have difficulty accurately remembering past events or experiences.
• Proxy Responses: When family members answer on behalf of older migrants, potentially introducing bias.
• Cultural Interpretation: Different cultures may interpret questions or concepts differently.

Methodological Challenges

5. Composition of the Research Team

• Cultural Competence: Ensuring researchers understand the cultural contexts of the populations they’re studying.
• Language Skills: Having team members who speak the languages of the migrant groups being studied.
• Interdisciplinary Approach: Incorporating experts from various fields (e.g., gerontology, migration studies, public health) for a comprehensive perspective.
• Diversity: Including researchers from migrant backgrounds to bring insider perspectives and enhance trust with participants.
• Training: Providing specialized training for researchers on working with older migrant populations.

Ethical challenges

1. Obtaining informed consent:

• Language barriers may make it difficult to fully explain the research and obtain truly informed consent.
• Cultural differences in understanding concepts like research participation and consent.
• Cognitive impairments in some older participants may impact their ability to provide informed consent.

Ethical challenges

2. Discussing sensitive topics:

• Migration experiences, especially for refugees, may involve trauma that is difficult to discuss.
• Cultural taboos around certain topics (e.g. mental health, family relationships) may make participants uncomfortable.
• Researchers need to balance gathering important data with respecting participants’ boundaries.

Ethical challenges

3. Addressing the impact of COVID-19:

• Older migrants are a high-risk group, requiring extra precautions for in-person research.
• Shift to remote/online methods may exclude participants without technology access/litteracy.
• Pandemic-related stressors may impact participants’ wellbeing and willingness to engage in research.
• Need to consider how the pandemic context affects research questions and data interpretation.

Ethical challenges

Additional ethical considerations:

• Ensuring privacy and confidentiality, especially for undocumented migrants.
• Providing appropriate compensation without being coercive.
• Addressing power imbalances between researchers and participants.
• Considering the impact of the research on the broader migrant community.
• Ethical data sharing practices, given the sensitive nature of the information.

Data Limitations in Older Migrant Research

• Lack of comprehensive age-disaggregated data
• Question adapted to the elderly population
  • Life course
  • Vulnerability
  • Integration
  • Mobility
  • Family
  • Transnationalism
  • Health
  • COVID-19

Selected criterias

Criterias for Datasets (1)

Categories:

• Where in the process?
  1. Data planification
  2. Data collection
  3. Final datasets
• Type of challenge?
  1. Methodological
  2. Ethical
  3. Data

Criteria for Datasets (2)

Data planification / Methodological challenges:

1. Wide geographic coverage: Many nationality
2. Diverse migrant categories: Cover different types of migration (e.g., labor migration, family reunification, refugee/asylum)
3. Longitudinal data: Track changes over time to understand integration processes and long-term outcomes
4. Migration history: Capture information on circular and return migration
5. Qualitative data integration: Include or link to qualitative data to provide context and deeper insights
6. Standardized definitions and measures: Employ standardized measures of key variables to allow for comparisons

Criteria for Datasets (3)

Data collection / Ethical challenges:

1. Privacy and confidentiality: Protect personnal data and include information on informed consent procedures
2. Methodological transparency: Provide clear documentation of data collection methods, sampling strategies, and limitations
3. Accessibility: Make data accessible to researchers while protecting privacy

Criteria for Datasets (4)

Dataset:

1. Age disaggregation: Detailed age categories, especially for those 65 and older (e.g., 65-69, 70-74, 75+)
2. Demographic information: Sex, country of origin, duration of stay
3. Socioeconomic factors: Labor market outcomes, educational attainment
4. Health status: Physical and mental health indicators
5. Migration history: Type of migration (e.g., former guest workers, postcolonial migrants, refugees)
6. Destination country data: To facilitate cross-country comparisons
7. Longitudinal data: To allow for trend analysis over time
8. Qualitative data: To supplement quantitative information and provide context
9. Cultural and linguistic factors: To understand integration challenges
10. Access to services: Information on healthcare, social services, and support systems

Contribution

Thes datasets…

• Gives examples of good practices and challenges
• Shows what can and has been done
• Gives insights to a particular migrant population
• Presents the interesting context of Switzerland

Next steps

• Get access to all codebooks
• Analysing the datasets through this approach
• Collect research in the field of older migration that use these datasets
• Discussion ways to improve them

Thank you for your attention!

Questions?

References

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Ekoh, Prince Chiagozie, and Christine A Walsh. 2024. “Valuable Beyond Vulnerable: A Scoping Review on the Contributions of Older Forced Migrants in Post-Migration Recovery.” International Journal of Disaster Risk Science, 1–12.

Ludwig-Dehm, Sarah M, Iuna Dones, and Ruxandra Oana Ciobanu. 2023. “Between Here and There: Comparing the Worry about the Pandemic Between Older Italian International Migrants and Natives in Switzerland.” Comparative Migration Studies 11 (1): 8.

Martineau, Aurélien, and Mathilde Plard. 2019. “Are Elderly Immigrants Meeting the Challenges of Successful Aging? Review of Literature on the Aging of Elderly Migrants in France.” Cybergeo: European Journal of Geography.

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Nedelcu, Mihaela, Laura Ravazzini, and Livia Tomás. 2021. “Transnational Ageing Survey 2020.”

Rampazzo, Francesco, Marzia Rango, and Ingmar Weber. 2023. “New Migration Data: Challenges and Opportunities.” Handbook of Computational Social Science for Policy 345.

Ruspini, Paolo. 2009. “Elderly Migrants in Europe: An Overview of Trends, Policies and Practices.” European Committee of Migration of the Council of Europe, 1–32.

Steiner, Ilka, and Philippe Wanner. 2019. Migrants and Expats: The Swiss Migration and Mobility Nexus. Springer Nature.