1. LOOKING BACK: ARE CAREER TALKS WITH OUTSIDE SPEAKERS ASSOCIATED WITH HIGHER WAGES? AN ANALYSIS OF THE BRITISH COHORT STUDY Gathered by: Elnaz T. Kashefpakdel, Christian Percy

2. We’re testing a principle from the literature, that there should be a link from employer contacts to labour market outcomes School-mediated employer contact … Employer mentoring Enterprise education Career talks from external speakers Work experience / shadowing Work-place visits … through several possible channels Better understanding o Which career you might want to do o Career pathways; how to succeed o What you are well- suited to Better networks to aid labour market entry + signalling More work-focused attitude … can support labour market outcomes … Less time unemployed Higher wage outcomes More job satisfaction Making the right career choices earlier (balancing achievability, ambition, flexibility etc.)

3. Two studies quantify this theorised link to wage outcomes, but with uncertainties for the broader UK policy context Study Mann & Percy, 2013, Journal of Education and Work 169 full-time 19–24-year-old UK workers on annual salaries YouGov survey in 2010 of 1,002 respondents Result 4.5% wage increase link to each additional school-mediated employer contact from 0 to “4 or more” Controls used: Highest academic qualification achieved, gender, ethnicity, school type and region Limitations in the UK context Small-scale  limited analytical options, limited control variables Relies on recall of school days Student choice to participate in some activities means wage premium may be driven by proactive personality type not added-value of activity Career Academies programme evaluation (Kemple 2008) 1,764 of 15-18 years olds young people in 2008 11% potential wage uplift for the alumni Experimental design used enabling robust controls US context Highly specialized, intense, curriculum-linked employer contact programmes is not current UK policy

4. The 1970 British Cohort Study provides a large, robust longitudinal dataset to explore pathways to labour market Survey background Follows ~17,000 people born in England, Scotland and Wales in a single week of 1970 Data is available at Birth, age 5, 10, 16, 26, 30, 34, 38 and 42 - most recent data from 2012 We gather data for background variables at birth to age 16 Survey attrition means ~48% loss of respondents from birth to age 26

5. Exploring Variables of Interest Full-time weekly income in 1996 (age 26) [nominal £] Following Mann & Percy part-time wage earners were excluded (n=710) in order to identify a more internally comparable set of labour market participants Approximately 38% of full time employed individuals reported a weekly income between £200 and £300; Male earns more than female on average (£238 vs. £192 per week) Academic ability at age 16 is strongly associated with earning at age 26; the high achievers on average earn more. (on average 7%) Young people ‘s wage from more privileged families is higher than families with lower social classes ( on average 4.3%)

6. Career talks were the most common employer activity and afford the best opportunity to add to the UK research base # Career talks with outside speakers aged 15-16 66% did at least one, with many doing two or more, allowing us to test the idea: “more is more” Typically held during school day, decided by school – less individual agency # Work experience placements 33% participated but few did 2+. Limited variation is a restriction on detailed regression analysis Prior to policy decisions widening work experience participation, so individuals had more choice about participation Workplace visits with school Simplistic binary variable BCS questions do not allow us to unpack different types of visit, e.g. vocational course-linked, careers linked or research field trips, which could obscure the theorised link between employer contact and income Employer engagement activities measured in BCS

7. Exploring career talks at year 10 & 11 On average young people attended more talks at year 10 comparing to year 11 The most frequent number of talks is 1 and 2 but in year 10 an 11 there are participants who did more than 6 talks

8. Each extra career talk in Year 10 is associated, on average, with a 0.8% increase in wage at age 26 - no controls in place Direct relationship – Career talks in Year 10 vs weekly income age 26 On average, benefits associated with careers talks begin to manifest at 5 talks or more Regression analysis on log wage (ln) Co-eff:0.008 Stnd-error:0.003 P-value:0.004 R^20.6% N:1,409 Bubble size indicates sample size at that point. Linear trendline applied by Excel bubble chart algorithm

9. With control variables, the average effect associated with an extra career talk increases to 1.2%, significant at 5% level Control variables reviews (Bold = Included in final analysis) Results Academic attainment Socio-economic status Early home learning environment Demo-graphics Teacher assessment of academic ability at 16 Maths - CSE/O-level results Highest level of qualification at 26 Frequency of parents reading to child age 5 Mother’s interest in child’s education age 5 Cognitive assessment age 5 (human fig. drawing) Amount of TV watched age 10 Father socio-economic status Mother socio-economic status In receipt of council housing (or benefits) Gender Whether has a UK parent Regression : Number of talks at year 10 vs. Ln(wage) at age 26 Co-eff:0.012 Stnd-error: 0.003 P-value:0.000 R^220.6% N740

10. At year 11, the direct relationship is still present at 0.3% on average, but much weaker and less statistically significant Direct relationship – Career talks in Year 11 vs weekly income age 26 Regression: Analysis on log wage (ln) Co-eff:0.003 Stnd-error:0.002 P-value:0.145 R^20.1% N:2,854

11. With control variables, the average effect associated with an extra career talk at increases to 0.5%, significant at 10% level Control variables reviews (Bold = Included in final analysis 1) ) Results Academic attainment Socio-economic status Early home learning environment Demo-graphics Teacher assessment of academic ability at 16 Maths - CSE/O-level results Highest level of qualification at 26 Frequency of parents reading to child age 5 Mother’s interest in child’s education age 5 Cognitive assessment age 5 (human fig. drawing) Amount of TV watched age 10 Father socio-economic status Mother socio-economic status In receipt of council housing (or benefits) Gender Ethnicity Regression : Number of talks at year 11 vs. Ln(wage) at age 26 Co-eff:0.005 Stnd-error: 0.002 P-value:0.054 R^220.6% N740

12. Individuals who found the career talks very useful display a stronger wage premium, but sample sizes are small Did you find the careers talks useful?Regression output, Co eff 1) Model Did not find helpful Found very helpful Yr 10 – No controls 1.7%** N = 144 1.1% * N = 231 Yr 10 – With Controls 1.0% N = 85 1.5%** N = 117 Yr 11 – No controls 0.4% N = 423 0.6% N=501 Yr 11 – With Controls 0.3% N = 234 1.1% ** N=243 1)If we include Quite Helpful alongside Very Helpful answers as positive experiences, the slight difference between the two cohorts shown in this table disappears. Both analyses are indicative only due to the small sample sizes. * significant at 10% and ** significant at 5%

13. Concluding remarks  We used the large-scale, British longitudinal dataset (BCS70) to explore whether careers talks with outside speakers in the mid 1980s have a positive association with the wages of full-time employees in 1996  We found a strong association between the number of career talks and wage premium. Results are statistically significant at the 5% or 10% level  Earlier is better, with a 1.2% wage premium for each talk in Year 10 vs 0.5% for each talk in Year 11  Analysis of the direct relationship suggests that the beneficial association begins to manifest from around 5 or more career talks on average. This may relate to the general need for repetition in education for learning and attitudinal shift or the need to hear about several pathways before one happens to gel

14. Concluding remarks  Individuals who found the career talks ‘very helpful‘ overall were more likely to earn more than those who did not find them useful, given the decreased sample size  Comparison with the literature adds weight to the intepretation via social capital and via weak ties, in particular the value of trusted, non-redundant information that can be obtained through multiple, diverse channels, even in brief encounters: If done right, a lot of a little can make a big difference.  Given how careers talks with outside speakers, particularly in significant numbers, are coordinated within schools, our findings are likely to reflect the influence of talks taking place in the context of overall school-based and professional careers provision.

15. What Next...  Test the role of careers lessons/meetings directly, in order to better isolate the potential added value of outside speakers (using BCS)  Extend the analysis with new control variables such as region and local education authority to account for differences in employment by area  Conduct the analysis with a more recent dataset such as Longitudinal Survey of Young People in England, when the new cohort data are released  How does the combination of these engagement activities affect young people‘s position in labour market in terms of wage premium, both at 26 and at later stages  Other labour market outcomes to be tested include longest period unemployed, job satisfaction and job status

16. We welcome your comments and suggestions

17. Control variables descriptive Appendix

18. Control variable descriptive ScorePercentage O level A11 O level B14.6 O level C/ CSE 122 O level D/ CSE 215.5 O level E/ CSE 317.7 CSE 412.8 CSE 55.8 Fail0.5 Academic ability assessed by teacher at 16 Standardised Math Score age 16 AssessmentPercentage Top 5%4.8 Well above average12.7 Above average26.9 Average34.6 Bellow average13.5 Well below average5.4 Bottom 5%1.7 *N=3716 *N=5428

19. Control variable descriptive ScorePercentage I7.6 II28 III non-manual9.7 III manual38.1 IV8.8 V2.3 Student2 Dead3.5 Highest level of qualification at age 26 Father social class *N=6735 *N=8399

20. Control variable descriptive SexPercentage Female49.9 Male50.1 In receipt of government benefit? Gender *N=11615 *N=9358