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© Institute for Fiscal Studies Drivers and barriers to educational success Haroon Chowdry, Claire Crawford, Alissa Goodman Institute for Fiscal Studies.

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Presentation on theme: "© Institute for Fiscal Studies Drivers and barriers to educational success Haroon Chowdry, Claire Crawford, Alissa Goodman Institute for Fiscal Studies."— Presentation transcript:

1 © Institute for Fiscal Studies Drivers and barriers to educational success Haroon Chowdry, Claire Crawford, Alissa Goodman Institute for Fiscal Studies

2 © Institute for Fiscal Studies Background and Motivation Why do children from poor backgrounds do worse at school (and in later life) compared to children from better off backgrounds? Related issue: concern about lack of social mobility in UK –Strong correlation between parental socio-economic status (SES) as a child, and SES as an adult

3 Background and Motivation © Institute for Fiscal Studies

4 Background and Motivation Educational attainment plays key role in transmission of (dis)advantage across generations –35-40% of correlation between parents and sons income (Blanden et al., 2005) Educational inequalities matter more generally: –Socioeconomic inequality in HE participation –NEETs (especially in current economic climate) –Research has shown that attainment in school plays a crucial role –Improving attainment early on may have compound impact © Institute for Fiscal Studies

5 Background and Motivation This presentation focus on low achievement in secondary school –Routes through which children from poor backgrounds fare badly as teenagers Complex set of influences throughout childhood –Early years: home learning environments, parenting styles, health- related behaviours –Primary school: lasting influence of early years, maternal aspirations, childs own ability beliefs Teenage years: young persons own attitudes and behaviours; lasting influence of parents; material resources in the home Important caveat: none of this is a causal analysis –Does not tell us if we increased resources/social position, whether outcomes would change –Cannot make specific policy recommendations © Institute for Fiscal Studies

6 The teenage attainment gap © Institute for Fiscal Studies

7 Attainment gap: intuitive examples Average outcome by SEP quintile Poorest2Middle4Richest Key Stage 3 (age 14) % reaching expected level51.9%66.1%77.4%84.7%92.7% Key Stage 4 (age 16) % attaining 5 or more GCSEs A*-C 33.2%46.4%59.3%70.6%84.0% % attaining 5 or more GCSEs A*-C (incl. English & Maths) 21.4%33.6%46.4%57.9%74.3% © Institute for Fiscal Studies

8 Methodology Define a set of possible pathways (transmission mechanisms) between family background and attainment Family background –Socioeconomic position (SEP) –Parental education –Demographics, family structure, etc. Possible transmission mechanisms 1.Schools (quality and composition) 2.Neighbourhoods (composition) 3.Material resources on educational items 4.Parental attitudes and behaviours 5.Child attitudes and behaviours © Institute for Fiscal Studies

9 Model of attainment at secondary school Parental socio- economic status Parental education Other family background and demographics 1. Schools 2. Neighbourhoods 3. Material resources diverted to education 4. Parental attitudes and behaviours (As and Bs) 5. Young peoples attitudes, and behaviours (As and Bs) 5. Young peoples attitudes, and behaviours (As and Bs) Key Stage 3 results Changes in family background 5. Changes in YP attitudes, and behaviours Key Stage 4 results Unobservables FAMILY BACKGROUND TRANSMISSION MECHANISMS OUTCOMES @ 14 CHANGES IN TRANSMISSION MECHANISMS OUTCOMES @ 16

10 Empirical analysis Estimate series of simple equations, starting with – Y t = α + βSEP + ε (levels) – Y t = α + γY t –1 + βSEP + ε (value-added) These give the SEP gradient we are trying to explain Add in our transmission mechanisms and observe size of SEP gradient with inclusion of each one: – Y t = α + βSEP + δPED + ηFAM + ε – Y t = α + βSEP + δPED + ηFAM + λSCH + μNBD + ρMATRES + κMPABS + σ YPABS + ε These suggest how much the SEP gradient (β) can be explained by controlling for differences in each of these sets of factors But this is NOT a causal analysis (reverse causation/unobservab les) © Institute for Fiscal Studies

11 Data Longitudinal Study of Young People in England (LSYPE) –Single academic year cohort born in 1989/90 –Study started in Year 9 (age 13), we have data up to Year 11 –Detailed questions from young person and parents –Linked to administrative attainment records at age 11, 14, and 16 –Our sample: with complete administrative records (state school only), approximately 15,770 © Institute for Fiscal Studies

12 Variables derived from LSYPE SEP –Income (averaged across waves) –Occupation of both parents –Housing tenure –Financial difficulties –Take principal component to derive SEP index, divide into quintiles Parental education Matched information from administrative school data –School quality and composition –Neighbourhood composition © Institute for Fiscal Studies

13 Variables derived from LSYPE Material resources devoted to education –Private lessons –Computer access –Internet access Parental attitudes and beliefs –Education values (getting a good education is important) –Aspirations for age 16 –Expectations for HE –Education interactions (help with homework, talking about reports) –Family interactions (sharing meals, arguing) –Involvement in school activities (parents evenings etc.) © Institute for Fiscal Studies

14 Variables derived from LSYPE Young person attitudes and behaviours –Ability beliefs (I get good marks) –Locus of control (In control of destiny) –Likes school (I like school) –School valuable (School is a waste of time ) –Aspirations for age 16 (Wants to stay on in FTE) –Expectations for HE (Likely to apply to HE) –Job/career values (Having a job that leads somewhere is important) –Experiences of bullying –Anti-social behaviour (fighting, trouble with police, shoplifting) –Truancy, suspension, exclusion –Substance use (smoking, drinking, cannabis) –Teacher child relations (I like my teachers) –Positive activities (sport, reading etc.) © Institute for Fiscal Studies

15 Descriptives: parents © Institute for Fiscal Studies

16 Descriptives: young people © Institute for Fiscal Studies

17 Results How important are these transmission mechanisms for understanding the inequalities in school attainment? Answer in two stages: 1.Do transmission mechanisms have an impact upon attainment? 2.Do transmission mechanisms help to explain the socioeconomic gap in attainment? © Institute for Fiscal Studies

18 1) Impact of TMs on KS4 scores (std. devs.) Parental education, schools, material resources and attitudes Key Stage 4Key Stage 4 value added Mothers highest qualification NVQ Level 4/5 0.127**0.037 Outstanding Ofsted report 0.137**0.132** Grammar school 0.300**0.045 YP thinks most friends will stay on post 16 0.111**0.044** Computer access 0.132**0.090** Internet access 0.146**0.062** Parent thinks v/fairly likely YP will go to HE 0.232**0.029 Family child interactions (scale) 0.037**0.041** © Institute for Fiscal Studies

19 Young person attitudes and behaviours Key Stage 4Key Stage 4 value added Ability beliefs (scale) 0.244**0.030* Locus of control (scale) 0.084**0.035** Likely to apply to HE, and likely to get in 0.273**0.117** Experience of bullying (scale) -0.132**-0.058** Education behavioural difficulties (scale) -0.123**-0.073** Anti-social behaviour (scale) -0.057**-0.045** Frequent smoker -0.292**-0.233** Experience of bullying (scale) -0.132**-0.058** Education behavioural difficulties (scale) -0.123**-0.073** 1) Impact of TMs on KS4 scores (std. devs.)

20 Changes between 14 and 16 in attitudes and behaviours Key Stage 4Key Stage 4 value added Stops thinking gets good marks -0.217**-0.109** Stops liking school -0.057**-0.052** Stops finding school valuable -0.083**-0.050** Starts thinking it likely that they will apply to HE 0.216**0.103** Stops thinking it likely that they will apply to HE -0.302**-0.161** Starts playing truant -0.063**-0.057** Starts being suspended from school -0.168**-0.122** Starts being expelled from school -0.274**-0.320** Starts smoking cannabis -0.045*-0.093** Starts smoking cigarettes frequently -0.169**-0.146** Starts drinking regularly 0.053*0.051** Starts liking their teachers 0.071*0.066** 1) Impact of TMs on KS4 scores (std. devs.)

21 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344** 3rd0.636** 4th0.848** Top1.151** © Institute for Fiscal Studies

22 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55% 3rd0.636**82%57% 4th0.848**79%55% Top1.151**75%55% © Institute for Fiscal Studies

23 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55%56% 3rd0.636**82%57%50% 4th0.848**79%55%46% Top1.151**75%55%44% © Institute for Fiscal Studies

24 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55%56%52% 3rd0.636**82%57%50%54% 4th0.848**79%55%46%50% Top1.151**75%55%44%51% © Institute for Fiscal Studies

25 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55%56%52%49% 3rd0.636**82%57%50%54%47% 4th0.848**79%55%46%50%40% Top1.151**75%55%44%51%36% © Institute for Fiscal Studies

26 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55%56%52%49%33% 3rd0.636**82%57%50%54%47%38% 4th0.848**79%55%46%50%40%37% Top1.151**75%55%44%51%36%38% © Institute for Fiscal Studies

27 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55%56%52%49%33%38% 3rd0.636**82%57%50%54%47%38%34% 4th0.848**79%55%46%50%40%37%30% Top1.151**75%55%44%51%36%38%27% © Institute for Fiscal Studies

28 2) How much of KS4 gap can be explained by TMs (1)(2)(3)(4)(5)(6)(7)(8)(9) SEP NoneP EduFamSchNeiMPM ResYPAll Gap as a % of raw SEP gradient 2nd0.344**83%55%56%52%49%33%38%26% 3rd0.636**82%57%50%54%47%38%34%21% 4th0.848**79%55%46%50%40%37%30%17% Top1.151**75%55%44%51%36%38%27%13% © Institute for Fiscal Studies

29 Conclusions Large SEP gap in education outcomes Correlations of particular note: –Maternal education (causal analysis supports this too) –Parents and young peoples educational aspirations –Family child interactions –Computer and internet access in the home Changing attitudes and aspirations – the answer? –Aspirations are high across the board at age 14 and still to an extent at age 16: just raising them may not be enough –Ability beliefs: kids from poor backgrounds more likely to think that they are good at school than young people from better off backgrounds after taking Key Stage 2 into account: no evidence of systematic under-estimation here This analysis identifies some key areas –...but not policy answers! © Institute for Fiscal Studies


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