youth labour market transitions style Jacqueline O’Reilly University of Brighton, CROME, Brighton Business School 06/12/2018

2. Five Characteristics of Youth Unemployment 1. STYLE project 2. Five Characteristics of Youth Unemployment 3. NEETs, Ethnicity, Gender and Households 4. Policy Implications 06/12/2018

Expected outcomes 1. The project 1. Achieve a critical mass of resources in collaboration with stakeholder communities   2. To advance the knowledge base Achieved through Website Media Center: www.style-research.eu working papers, blogs & newsletters And publication of an ‘International Handbook on Strategic Transitions for Youth Labour in Europe’ 06/12/2018

Project structure Management, Scientific Coordination and Dissemination WP 1, 2 & 12 WP 5 Mismatch Education and Skills WP 6 Mismatch: Migration & Mobility WP 7 Self-employment WP 8 Family & Cultural Drivers WP9 Vulnerable Voices & Cultural Barriers WP 10 Flexicurity Policy Performance & Evaluation Methodologies WP 3 Policy Transfer and Comparative Frameworks WP4 International Handbook of Strategic Transitions for Youth Labour in Europe WP11

Local Advisory Boards (LABS) Communicating across communities Consortium Advisory Network (CAN): EurActiv, European Youth Forum, Business Europe, ILO, OECD, European Trade Union Institute, SOLIDAR, OSE, Eurofound, DG-EMPL style-research.eu Research Workpackages Local Advisory Boards (LABS) Employers, Unions, NGOs and Government Agencies in 19 countries

Conceptual basis of the project: Economic Production & Social Reproduction Comparing Performance &Transitions Policy Learning and Transfer 06/12/2018

What role for the ‘state’ and policy intervention? Humphries and Rubery 1984 Social reproduction Economic Production Social reproduction Economic Production Firms VET Skills Family Attitudes 06/12/2018 What role for the ‘state’ and policy intervention?

2. Preliminary Results Journal publication ‘Five Characteristics of Youth Unemployment: Flexibility, Migration, Family Legacies & EU Policy’ SAGE OPEN March 2015 Open Access http://sgo.sagepub.com/content/5/1/2158244015574962 06/12/2018

What distinguishes the current phase of youth unemployment today compared to earlier periods from the 1980s? 06/12/2018

But 20% of 16-24 unemployed for more than 12 months 06/12/2018 Source: Eurostat

What is the problem? Rates Ratios NEETS Different problems 06/12/2018

Youth Unemployment Rates, Ratios & NEET Rates 2013 (15-24) 06/12/2018

Five Characteristics of Youth Unemployment ‘Flexible’ labour markets Structure of skills & education Patterns of migration Family legacies from previous recessions Increased role for EU policy 06/12/2018

Recent deliverables www.style-research.eu/publications/working-papers/ Preliminary results www.style-research.eu/publications/working-papers/ 06/12/2018

06/12/2018

Seamus McGuiness ESRI, Dublin. “Over education is more common in graduates from poorer backgrounds because they do not have the same “connections” as those from wealthy families or those who can afford to take up unpaid internships.” Seamus McGuiness ESRI, Dublin. 06/12/2018

06/12/2018

3. Ethnicity, households and gender effects on becoming NEET: An intersectional analysis Carolina Zuccotti and Jacqueline O’Reilly University of Brighton The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 613256.

STYLE WP8: Family Drivers Task 8.1 – Work-poor families How do parental characteristic influence the probability/length in unemployment of their children (i.e. offering resources, income support, or prolonged investment in children’s education)? Comparative study for different European countries, using EU-SILC and SHARE data (Universities of Turin and Trento) Study for the UK, using UKHLS data (University of Brighton): focus on ethnic groups www.style-research.eu

Work-rich and work-poor households in the UK Increase in work-rich and work-poor households Decrease of classical male-breadwinner households Increase of single-parent households Source: (Office of National Statistics 2014b) and several papers by Gregg and others (Gregg, Hansen, and Wadsworth 1999, Gregg, Scutella, and Wadsworth 2010, Gregg and Wadsworth 2001, 2003) www.style-research.eu

Intergenerational transmission Having been raised in a work-poor family leads to higher chance of being unemployed and of spending more time out of work or as a NEET, that is, not in employment, education or training (Macmillan 2014, O'Neill and Sweetman 1998, Macmillan 2010, 2011, Schoon 2014, Gregg, Macmillan, and Nasim 2012). This relationship is in part mediated by other factors such as education, cognitive and non-cognitive skills, behavioral characteristics and educational expectations (Macmillan 2013, Cusworth 2006, Ermisch, Francesconi, and Pevalin 2004, Schoon 2014). Mother’s employment has a positive effect on daughter’s employment probabilities (Berloffa, et al. 2015). Evidence of negative effect of having been raised in single-parent households (Schoon 2014, but this varies by country, Berloffa et al. 2015) www.style-research.eu

What do we know about ethnic minorities? Although ethnic minorities do quite well in terms of education and occupational outcomes, and are upwardly mobile (Heath & McMahon 2005 ; Platt 2005, 2007; Zuccotti 2015) some groups - Pakistani, Bangladeshi and Caribbean - have lower employment probabilities, compared to the white British (Heath and Cheung 2007) Pakistani and Bangladeshi women have particularly high rates of inactivity and unemployment (Zuccotti 2015) They are more likely to have workless parents: especially the Pakistanis, Bangladeshis and Black populations (Indian are an exception) There is evidence that the probability of remaining in/moving to workless households among children is higher for Pakistani/Bangladeshi and Caribbean than for the white British (Platt 2010) Asians (especially Pakistanis and Bangladeshis) are very much concentrated in the traditional male-breadwinner households; while Caribbean and African have high proportions of single-parent households www.style-research.eu

Summing up these different dimensions Origin household: Composition: single-parent, 2-parents Parental employment Ethnicity Gender Intersectional approach (Cho et al., 2013; Collins, 2015; Crenshaw, 1991) 6/29/15 www.style-research.eu

Research questions & expectations What association is there between household origins and young people’s NEET probabilities? How does ethnicity and gender affect the probability for young people coming from different family backgrounds of becoming NEET? How does an intersectionality approach shed light on the effects of multiple inequalities for youth? Intersectionality because... Processes of intergenerational transmission are ethnically specific Pakistani and Bangladeshi women are more negatively affected if raised in male-bread winner families (strong gender role models) Black mothers are more likely to combine activity and children care: positive signal for the children Ethnic minorities more negatively affected if raised in workless families: lack networks/labour market knowledge  areas of neighbourhood deprivation Parental motivation leads to higher social mobility, less dependence on origins (Platt 2005, 2007; Zuccotti 2015) www.style-research.eu

Data, population & variables Data = UKHLS, Wave 1 (2009-2010) Population = Young individuals between 16 and 29 years old; UK-born or arrived at a young age; Men & women; main ethnic groups (Asian and Black oversampled) Main explanatory variable = Origin household (HH) Workless: both parents (one parent, in case of single parent households) were out of the labour market when the individual was 14 years old. 1BH: only one parent (usually the father) worked when the individual was 14 years old SPH: single parent household where the parent (usually the mother) worked when the individual was 14 years old 2BH: both parents worked when the individual was 14 years old Dependent variable: NEET: Being unemployed, doing housework or sick/disabled (vs. employed + in education) www.style-research.eu

Origin households by ethnicity, 16-29 yrs old (row %) workless 1 bread-winner single-working 2 bread-winner N white British 7.7 21.0 6.5 64.8 5579 Indian 8.5 32.6 2.8 56.1 298 Pakistani 28.8 60.6 0.6 10.1 384 Bangladeshi 47.0 43.6 3.0 6.4 308 Caribbean 11.3 20.8 14.8 53.1 159 African 18.9 19.4 13.0 48.8 180 www.style-research.eu

NEETs by origin household, gender and ethnicity; 16-29 yrs (%) workless 1 bread-winner single-working 2 bread-winner Total N Men White British 41.0 20.6 31.5 11.7 16.9 2407 Indian 8.5 34.0 13.5 12.0 17.9 143 Pakistani 23.9 19.2 0.0 18.4 161 Bangladeshi 10.3 5.5 7.8 132 Caribbean 18.3 23.1 26.9 39.3 32.4 71 African 8.3 15.3 7.1 12.6 80 Women 48.4 26.7 29.1 14.1 20.7 3083 27.9 25.8 9.8 17.2 153 43.7 29.8 14.3 32.7 215 22.5 28.6 11.4 23.8 175 58.7 5.3 37.0 15.7 85 18.7 31.1 11.3 98 www.style-research.eu

NEET probabilities (men) Advantage: Indian and Bangladeshi in workless hh African in SPH Disadvantage Caribbean in 2BH Controls: education, age, neighbourhood unemployment, parental social class www.style-research.eu

NEET probabilities (women) Advantage: Bangladeshi in workless hh Caribbean in 1EH Controls: education, age, neighbourhood unemployment, parental social class www.style-research.eu

Summary For the white British, workless and SPH origins has a more negative effect than other origin household types But, this varies by ethnicity, which leads to some interesting comparisons: No clear penalty for Pakistani and Bangladeshi women raised in male bread-winner households No clear advantage of black individuals raised in SPH (only African men) Indian men and Bangladeshi men & women: less likely to be NEET among those with workless origins. Motivation? Networks? Similar findings with LS data for the entire population (in terms of occupational mobility) Caribbean men: more likely to be NEET than white British among those raised in 2EH. Pattern also observed when studying social mobility for the entire population with LS data. Incapacity of transferring advantage? www.style-research.eu

4. Policy Discussions Eurofound (2012a), successful policies to address youth unemployment target hard to reach groups; involve young people, Program reputation, and relevant community groups involved in delivery. minor and complex needs of young people, well-trained support staff, holistic guidance, personalized advice “rather than low-quality quick fixes” (Eurofound, 2012a). Flexible responses adapted to the economic cycle together with inter-agency collaboration, the buy-in of employers, and their representatives can provide “cost-effective ways to implement policies” (Eurofound, 2012a). But these measures require systematic and robust monitoring and evaluation if we are to know what really works and why. 06/12/2018