hosted by: volunteering among young people in the uk the state of social capital in britain, 11 th...
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![Page 1: Hosted by: Volunteering among young people in the UK The State of Social Capital in Britain, 11 th November 2015 Matthew R. Bennett, Third Sector Research](https://reader034.vdocuments.us/reader034/viewer/2022051621/5697bf951a28abf838c90e6a/html5/thumbnails/1.jpg)
Volunteering among young people in the UK
The State of Social Capital in Britain, 11th November 2015
Matthew R. Bennett, Third Sector Research Centre, University of Birmingham [email protected]
Meenakshi Parameshwaran, FFT Education [email protected]
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Research goals
• Describe rates of volunteering among youths (10-15 year olds) over time (2010 and 2012).
• Describe transitions into and out of volunteering
• What explains youth volunteering in the UK?– Human capital– Social capital– Cultural capital
• What explains changes in volunteering over time?
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Relevance
• Alienated/disengaged youths today detrimental to a flourishing civil society tomorrow
• Limited representative work exists in the UK– Policy reports/initiatives
• Millennium Volunteers, Active Citizens in Schools, Giving Campaign and Giving Nation, Young Volunteers Challenge, CountinYou, National Citizen Service, Crick Report, Russell Commission, Morgan Inquiry, Manifesto for Change.
– Outcomes• Reduced problem behaviours, higher wellbeing, higher
academic/career aspirations and achievement, sense of community and cohesion.
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Integrated Framework (Wilson and Musick 1997)
• Resources available to engage in volunteering– Human capital
• Skills, knowledge, and experience • Finances, education, and health
– Social capital • Networks – increase information and opportunities
– Cultural capital• Ethical and moral ideals - religion • “Attitudes, knowledge and preferences” - tangible rewards such as
prestige and social status. – Differentiation via these investments– Aesthetic tastes are a marker of status, while others argue that
being a good citizen should also signal status
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Methods
• Understanding Society – Waves 2 and 4 (2010 and 2012)
• 1,912 youths aged 10-15 years old
• Balanced panel (complete cases at both waves)
• Complex panel survey design
• Unweighted analyses (could not find longitudinal weights in youth survey)
• Random effects estimation: average effects of background characteristics between youths AND over time.
• “How often do you do voluntary or community work (including doing this as part of school)?”
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Methods (continued)
Human Capital•Parents’ Class (Goldthorpe schema)•Health•Self esteem
Social Capital•Number of friends
Cultural Capital•Religious classes•Aesthetic tastes (4 items - factor analysis – 1 component)
– Frequency of theatre, dance performances, or classical music concerts– Frequency of live sports events– Frequency of museums or art galleries– Frequency of discussing books at home.
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Descriptive picture of youth volunteering
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Volunteering transitions
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Results
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Conclusions• Descriptive picture of volunteering
– Volunteer: 55% at least yearly, 18% at least monthly, 10% at least weekly– Descriptive picture is relatively stable across time
• Transitions– 26% are disengaged across time points– 37% are stable volunteers and 12% of these increase their engagement
• 13% decrease their volunteering, while 18% stop altogether
– 19% become volunteers having not volunteered previously.
• Explanations of volunteering (paints a picture of an engaged youth)– Females are more likely to volunteer compared to males– Social capital
• Number of friends– Cultural capital
• Religious classes (norms or networks?)• Aesthetic tastes
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Future directions• Link with BHPS data for longer time trends• What benefits do BHPS youth volunteers enjoy as adults?• Is volunteering associated with positive outcomes?
– Health and self-esteem (early analyses suggest there is no relationship)• Contextual effects – school /area characteristics in UKHLS• Latent class analysis / sequence analysis
– Why are some youths always disengaged?– Why do some youths volunteer regularly?– What explains higher/lower transitions?
• Match with parental data over time– Role modeling – Change in SES, etc.