NEETs and Refugees in Latin America: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Graham Pluck
,
María Crespo
,
,
María Sol Garcés

NEET refers to young people who are Not in Education, Employment or Training (also known as nini in Latin America). The usual age range for people identified as NEET is 15-24. The International Labor Organization that more than one in five aged people globally can be described as NEET. However, it is important to note that they represent a very diverse group. The socioeconomic factors behind NEET status vary by context and country. Individual risk factors include adverse family environments, low household income or educational levels, disability, living in remote areas, and immigration background. The total number of people who are NEET in Latin America has remained practically constant between the beginning and the end of 1992–2014: it went from 19.0 million in 1992 to 18.7 million in 2014. NEET status in Latin America is particularly associated with the region's sociopolitical issues, relating to labour migration and individuals seeking political refuge or fleeing political violence. As in other parts of the world, NEET status is associated with several psychological factors.

  • NEETS
  • refugees
  • mental health
  • cognition
  • Latin America
  • Labor migration
  • Unemployment
  • Education
  • Training
  • Employment

1. Introduction

According to the International Labor Organization, there is a high rate of young people, more than one in five (22.4%) aged 15–24, neither in employment, education, nor training (NEET). Acknowledging this problem worldwide led to adopting the NEET rate as part of the 2030 Agenda for Sustainable Development, as an indicator of progress towards reducing the proportion of the NEET individuals within a population (O’Higgins 2020; Organisation for Economic Co-Operation and Development et al. 2016; Organisation for Economic Co-Operation and Development 2021).

2. NEETs in Latin America

NEET rates vary widely across countries, showing the highest rates in lower-middle-income countries and with young women outnumbering young men by a factor of two to one (O’Higgins 2020). NEETs are not a homogeneous group; the causes behind this status are diverse (Tamesberger and Bacher 2014). The Eurofound ([1] 2016) suggested subgroups in the NEET population that include a mix of vulnerable and non-vulnerable young people with different characteristics and needs. The individual risk factors include adverse family environments, low household income or educational levels, disability, living in remote areas, and immigration background (Eurofound 2012; Assmann and Broschinski 2021). 
Clearly, stability of the labor market and lack of opportunities are important factors contributing to people in Latin America being unable to find work. In addition, access to quality education and training is an important limiting factor in many countries of Latin America. Mass migration and people seeking political refuge are additional critical social issues facing Latin America. In 2019, 79.5 million people were forcibly displaced worldwide. Four million of them left Venezuela and now live mainly in Latin America and the Caribbean (United Nations High Commissioner for Refugees 2020). In addition, for decades, Colombians were forced to leave their country, escaping the violence of the internal conflict and looking for security in neighboring countries. Similarly, there are many people of Cuban origin seeking refuge or work opportunities in Latin American countries. Youths from a migration background have an increased risk of becoming NEET. Dropout from education or disengagement of the labor market exposes youth to a greater risk of social exclusion, psychological distress, disability, maladaptive behaviors, illness, and disease (O’Dea et al. 2014; Fernández-Suárez et al. 2016; Hjorth et al. 2016).
The total number of people between 15 and 24 years of age who are NEET in Latin America remained practically constant between the beginning and the end of 1992–2014: it went from 19.0 million in 1992 to 18.7 million in 2014 (Tornarolli 2016). Four out of five NEETs in Latin America are females; they more often come from a rural background and belong to the poorest quintile of the wealth distribution (Minujín et al. 2016).

3. Refugees in Latin America

Being a refugee is a risk factor for becoming NEET. According to the United Nations High Commissioner for Refugees (2020), Ecuador has the largest population of recognized refugees in Latin America, with 69,897 recognized refugees arriving between 1989 and 2020, most of them coming from Colombia and Venezuela. For decades, Colombians were forced to leave their country, escaping the violence of the internal conflict and looking for security (United Nations High Commissioner for Refugees 2020). In 2019, 79.5 million people were forcibly displaced worldwide. Four million of them left Venezuela and now live mainly in Latin America and the Caribbean (United Nations High Commissioner for Refugees 2020). In the first quarter of 2019, around 470,095 Venezuelans lived in Ecuador, almost 3% of Ecuador’s population (Olivieri et al. 2020). Young refugees in Latin America may be orphaned or separated from their families and are at risk of becoming 'street children' or else may be raised in foster care homes (Pluck, 2021; Trueba & Pluck, 2021) 
For Colombians to some extent, but especially for Venezuelans, forced migration increased the vulnerability to ‘labor and sexual exploitation, trafficking, violence, discrimination and xenophobia’ (United Nations High Commissioner for Refugees 2021). Refugees and asylum seekers are among the most vulnerable groups ([2] 2016). Their vulnerability is linked to experiences before migration due to conflict, violence, or persecution and to post-migration stressors such as exclusion, unemployment, and discrimination (Hameed et al. 2018). As reported in the literature, having a migration background increases the youth person’s likelihood of becoming NEET by 70% (Eurofound 2012). Therefore, the probability that a significant percentage of young refugees in Latin America are in NEET status is high, becoming a socioeconomic concern.

4. NEET Mental Health and Cognition

Evidence suggests that NEETs struggle more with mental health problems than non-NEET peers. NEETs may be at greater risk of depression and anxiety (Feng et al. 2015) and severe symptoms of these disorders (Basta et al. 2019; O’Dea et al. 2014). The relationship between mental health and NEET status could be bidirectional: the pre-existence of a mental health condition may constitute a risk factor for becoming NEET, and conversely, mental health problems could be a long-term consequence of NEET status. In a British cohort study, nearly 60% of NEETs had suffered from a mental health problem during childhood or adolescence compared to 35% of non-NEETs (Goldman-Mellor et al. 2015). Moreover, an association between NEET status during adolescence and several mental health outcomes in adulthood is established (Gutiérrez-García et al. 2017; Power et al. 2015). The mental health problems experienced by NEETS are thought to have been exacerbated by the effects of the COVID-19 pandemic (Kvieskienė et al. 2021).
Cognitive and non-cognitive skills negatively correlate with the probability of being NEET. Higher scores in cognitive measures and socio-emotional abilities such as self-esteem, self-efficacy, and locus of control are associated with a lower probability of being NEET (Alvarado et al. 2020). Education has been related to better performance on cognitive measures and further engagement with cognitive activities and challenges (Pluck et al. 2020; Guerra-Carrillo et al. 2017). Parisi et al. (2012) found that educational and other cognitively charged experiences may benefit brain structure and function, developing neuroprotective effects to face age-related decline. Additionally, education has economic implications as cognitive and non-cognitive skills have labor-market value and impact workplace performance and individual earnings (Pluck et al. 2020; De Hoyos et al. 2016). Therefore, the lack of opportunities to develop skills is crucial for future NEET status (Gladwell et al. 2015).

5. Mental Health and Cognition of Refugees

The adverse psychosocial factors experienced during or after migration could negatively impact migrant mental-health status and increase the risk of cognitive impairment (Xu et al. 2018). Most of the current research on the mental health of refugees has shown that they are at higher risk for a variety of psychiatric disorders such as post-traumatic stress disorder, anxiety, and depression, due to their experiences before migration (Peterson et al. 2020; Chaplin et al. 2020; Hameed et al. 2018). In addition, post-migration distress, mainly acculturative stress, can worsen mental health symptoms (Hameed et al. 2018).
There is strong evidence that cognitive factors are essential for successful refugee adjustment (Hahn et al. 2019). Xu et al. (2018) identify different pathways that may link migration and cognitive function from current evidence. Socioeconomic status, psychosocial and behavioral traits, and physical and psychological health prior to migration could influence cognitive function (Xu et al. 2018). A study comparing executive cognitive abilities, specifically working memory and inhibitory control, between refugee and non-refugee adolescents, found that poverty was more critical to cognitive function than traumatic experiences (Chen et al. 2019). Therefore, since dropout from education and unemployment are risk factors for poverty, refugees in the NEET situation may also be more susceptible to executive cognitive problems. Executive functions are important considerations because they are both significant predictors of educational and workplace success (Pluck et al. 2019; Pluck et al. 2020) and are also known to be malleable, being sensitive to behavioral interventions (Stamenova and Levine 2018). For instance, researchers found that the Hayling Test that measures verbal response suppression was the best predictor of workplace success, specifically sales (Pluck et al. 2020) as well a predictor of academic performance in college and high school (Pluck et al. 2019). Cognitive switching measured using Trail Making Test of the Delis–Kaplan Executive Function System was a predictor of working hours in populations struggling with mental (McGurk and Mueser 2006) and physical health (Rabkin et al. 2004), and spatial working memory is a predictor of vocational participation (Cairns et al. 2017).

This entry is adapted from the peer-reviewed paper 10.3390/socsci11040155

References

  1. O’Higgins, Niall. 2020. Young People Not in Employment, Education or Training, ILO/SIDA Partnership on Employment. ILO/SIDA Technical Brief no. 3. Geneva: International Labour Organization.
  2. Organisation for Economic Co-Operation and Development, Development Bank of Latin America, and Economic Commission for Latin America and the Caribbean. 2016. Latin American Economic Outlook 2017 Youth, Skills and Entrepreneurship. Available online: https://www.oecd.org/dev/americas/Overview_LEO2017.pdf (accessed on 11 June 2021).
  3. Organisation for Economic Co-Operation and Development. 2021. Youth Not in Employment, Education or Training (NEET) (Indicator). Paris: OECD.
  4. Tamesberger, Dennis, and Johann Bacher. 2014. NEET youth in Austria: A typology including socio-demography, labour market behaviour and permanence. Journal of Youth Studies 17: 1239–59.
  5. Eurofound. 2012. NEETs—Young People Not in Employment, Education or Training: Characteristics, Costs and Policy Responses in Europe. Luxembourg: Publications Office of the European Union.
  6. Eurofound. 2016. Exploring the Diversity of NEETs. Luxembourg: Publications Office of the European Union.
  7. Assmann, Marie-Luise, and Sven Broschinski. 2021. Mapping young NEETs across Europe: Exploring the institutional configurations promoting youth disengagement from education and employment. Journal of Applied Youth Studies 4: 95–117.
  8. O’Dea, Bridianne, Nicholas Glozier, Rosemary Purcell, Patrick D. McGorry, Jan Scott, Kristy-Lee Feilds, Daniel F. Hermens, John Buchanan, Elizabeth M Scott, Alison R Yung, and et al. 2014. A cross-sectional exploration of the clinical characteristics of disengaged (NEET) young people in primary mental healthcare. BMJ Open 4: e006378.
  9. Fernández-Suárez, Asunción, Juan Herrero, Beatriz Pérez, Joel Juarros-Basterretxea, and Francisco J. Rodríguez-Díaz. 2016. Risk factors for school dropout in a sample of juvenile offenders. Frontiers in Psychology 7: 1993.
  10. Hjorth, Cathrine F., Line Bilgrav, Louise Sjørslev Frandsen, Charlotte Overgaard, Christian Torp-Pedersen, Berit Nielsen, and Henrik Bøggild. 2016. Mental health and school dropout across educational levels and genders: A 4.8-year follow-up study. BMC Public Health 16: 976.
  11. Tornarolli, Leopoldo. 2016. El Fenómeno de los Ninis en América Latina. CAF—Documento de Trabajo; N° 2016/18. Buenos Aires: CAF, December 30, Available online: http://scioteca.caf.com:8080/xmluicaf/handle/123456789/988 (accessed on 9 June 2021).
  12. Minujín, Alberto, Diego Born, María Laura Lombardía, and Enrique Delamónica. 2016. Unpacking the NEETs of Latin America and the Caribbean: Methodological challenges and surprising results. In Child Poverty, Youth (Un)Employment, and Social Inclusion. Edited by Maria Petmesidou, Enrique Delamónica, Christos Papatheodorou and Aldrie Henry-Lee. Stuttgart: Ibidem Press, pp. 121–56.
  13. United Nations High Commissioner for Refugees. 2020. Ecuador . Available online: https://www.refworld.org.es/docid/5f691c434.html (accessed on 9 June 2021).
  14. Olivieri, Sergio, Francesc Ortega, Ana Rivadeneira, and Eliana Carranza. 2020. The Labor Market Effects of Venezuelan Migration in Ecuador. Policy Research Working Paper; No. 9336. Washington, DC: World Bank, Available online: https://openknowledge.worldbank.org/handle/10986/34254 (accessed on 8 June 2021).
  15. United Nations High Commissioner for Refugees. 2021. Venezuela Situation. Retrieved May 9. Available online: https://www.unhcr.org/venezuela-emergency.html (accessed on 8 June 2021).
  16. United Nations High Commissioner for Refugees. 2015. Worldwide Displacement Hits All-Time High as War and Persecution Increase. June. Available online: http://www.unhcr.org/558193896.html (accessed on 9 June 2021).
  17. United Nations High Commissioner for Refugees. 2016. Global Trends: Forced Displacement in 2015. Available online: https://www.refworld.org/docid/57678f3d4.html (accessed on 9 June 2021).
  18. Hameed, Sameena, Asad Sadiq, and Amad U. Din. 2018. The increased vulnerability of refugee population to mental health disorders. Kansas Journal of Medicine 11: 1–12.
  19. Feng, Zhiqiang, Dawn Everington, Kevin Ralston, Chris Dibben, Gillian Raab, and Elspeth Graham. 2015. Consequences, Risk Factors, and Geography of Young People Not in Education, Employment or Training (NEET); Edinburgh: Scottish Government, p. 88. Available online: https://dera.ioe.ac.uk/24628/1/00487865.pdf (accessed on 8 June 2021).
  20. Basta, Maria, Stamatis Karakonstantis, Katerina Koutra, Vassilis Dafermos, Antonis Papargiris, Maria Drakaki, Stelios Tzagkarakis, Alexandros Vgontzas, Panagiotis Simos, and Nikos Papadakis. 2019. NEET status among young Greeks: Association with mental health and substance use. Journal of Affective Disorders 253: 210–17.
  21. Goldman-Mellor, Sidra, Avshalom Caspi, Louise Arseneault, Nifemi Ajala, Antony Ambler, Andrea Danese, Helen Fisher, Abigail Hucker, Candice Odgers, Teresa Williams, and et al. 2015. Committed to work but vulnerable: Self-perceptions and mental health in NEET 18-year olds from a contemporary British cohort. Journal of Child Psychology and Psychiatry 57: 196–203.
  22. Gutiérrez-García, Raúl A., Corina Benjet, Guilherme Borges, Enrique Méndez Ríos, and María Elena Medina-Mora. 2017. NEET adolescents grown up: Eight-year longitudinal follow-up of education, employment and mental health from adolescence to early adulthood in Mexico City. European Child & Adolescent Psychiatry 26: 1459–69.
  23. Power, Emmet, Mary Clarke, Ian Kelleher, Helen Coughlan, Fionnuala Lynch, Dearbhla Connor, Carol Fitzpatrick, Michelle Harley, and Mary Cannon. 2015. The association between economic inactivity and mental health among young people: A longitudinal study of young adults who are not in employment, education or training. Irish Journal of Psychological Medicine 32: 155–60.
  24. Kvieskienė, Giedrė, Ilze Ivanova, Karmen Trasberg, Viktorija Stasytytė, and Eglė Celiešienė. 2021. Modelling of social policy and initiatives under COVID-19: Rural NEET youth case study. Social Sciences 10: 393.
  25. Alvarado, Alfredo, Belén Conde, Rafael Novella, and Andrea Repetto. 2020. NEETs in Latin America and the Caribbean: Skills, aspirations, and information. Journal of International Development 32: 1273–307.
  26. Guerra-Carrillo, Belén, Kiefer Katovich, and Silvia A. Bunge. 2017. Does higher education hone cognitive functioning and learning efficacy? findings from a large and diverse sample. PLoS ONE 12: e0182276.
  27. Parisi, Jeanine M., George W. Rebok, Qian-Li Xue, Linda P. Fried, Teresa E. Seeman, Elizabeth K. Tanner, Tara L. Gruenewald, Kevin D. Frick, and Michelle C. Carlson. 2012. The role of education and intellectual activity on cognition. Journal of Aging Research 2012: 416132.
  28. De Hoyos, Rafael, Halsey Rogers, and Miguel Székely. 2016. Out of School and Out of Work: Risk and Opportunities for Latin America’s Ninis. Washington, DC: World Bank.
  29. Gladwell, Daniel, Gurleen Popli, and Aki Tsuchiya. 2015. A Dynamic Analysis of Skill Formation and NEET Status. Working Papers 2015016. Sheffield: Department of Economics, The University of Sheffield, Available online: http://www.sheffield.ac.uk/economics/research/serps/articles/2015_016 (accessed on 9 June 2021).
  30. Xu, Hanzhang, Allison A. Vorderstrasse, Eleanor S. McConnell, Matthew E. Dupre, Truls Østbye, and Bei Wu. 2018. Migration and cognitive function: A conceptual framework for global health research. Global Health Research and Policy 3: 34.
  31. Peterson, Cynthia, Kalpana Poudel-Tandukar, Kirk Sanger, and Cynthia S. Jacelon. 2020. Improving mental health in refugee populations: A review of intervention studies conducted in the United States. Issues in Mental Health Nursing 41: 271–82.
  32. Chaplin, Lucia, Lauren Ng, and Cornelius Katona. 2020. Refugee mental health research: Challenges and policy implications. BJPsych Open 6: e102.
  33. Hahn, Elisabeth, David Richter, Jürgen Schupp, Mitja D. Back, Brent Donnellan, and Beth Visser. 2019. Predictors of refugee adjustment: The importance of cognitive skills and personality. Collabra: Psychology 5: 23.
  34. Chen, Yaojing, Chenlong Lv, Xin Li, Junying Zhang, Kewei Chen, Zhongwan Liu, He Li, Jialing Fan, Ting Qin, Liang Luo, and et al. 2019. The positive impacts of early-life education on cognition, leisure activity, and brain structure in healthy aging. Aging 11: 4923–42.
  35. Pluck, Graham, David Villagomez-Pacheco, María Isabel Karolys, María Emilia Montaño-Córdova, and Pamela Almeida-Meza. 2019. Response suppression, strategy application, and working memory in the prediction of academic performance and classroom misbehavior: A neuropsychological approach. Trends in Neuroscience and Education 17: 100121.
  36. Pluck, Graham, Cristina Crespo-Andrade, Patricia Parreño, Karla I. Haro, María A. Martínez, and Sarahí C. Pontón. 2020b. Executive functions and intelligent goal-directed behavior: A neuropsychological approach to understanding success using professional sales as a real-life measure. Psychology & Neuroscience 13: 158–75.
  37. Stamenova, Vessela, and Brian Levine. 2018. Effectiveness of goal management training® in improving executive functions: A meta-analysis. Neuropsychological Rehabilitation 29: 1569–99.
  38. McGurk, Susan R., and Kim T. Mueser. 2006. Cognitive and clinical predictors of work outcomes in clients with schizophrenia receiving supported employment services: 4-year follow-up. Administration and Policy in Mental Health and Mental Health Services Research 33: 598–606.
  39. Rabkin, Judith G., Martin McElhiney, Stephen J. Ferrando, Wilfred Van Gorp, and Shu Hsing Lin. 2004. Predictors of employment of men with HIV/AIDS: A longitudinal study. Psychosomatic Medicine 66: 72–78.
  40. Cairns, Alice J., David J. Kavanagh, Frances Dark, and Steven M. McPhail. 2017. Prediction of vocational participation and global role functioning in help-seeking young adults, from neurocognitive, demographic and clinical variables. Journal of Affective Disorders 221: 158–64.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback