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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.

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Presentation on theme: "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."— Presentation transcript:

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 Centre, University of Birmingham m.r.bennett@bham.ac.uk Meenakshi Parameshwaran, FFT Education Datalab Meenakshi.Parameshwaran@fft.org.uk

2 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?

3 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.

4 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

5 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)?”

6 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.

7 Descriptive picture of youth volunteering

8 Volunteering transitions

9 Results

10 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

11 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.


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