Written evidence submitted by Dr Alex Gibson and Professor Sheena Asthana
Evidence submitted to “Left behind white pupils from disadvantaged backgrounds” Inquiry
Dr Gibson is a Senior Research Fellow in the School of Medicine, University of Plymouth. Professor Asthana is the Director of the Plymouth Institute of Health and Care Research (PIHR). We write in our capacity as individuals working in the field of resource allocation for public services, most recently contributing to MHCLG/DfE-funded review of the Children’s Services Funding Formula.
1.1.1. Access to highly granular data, preferably anonymised pupil-level data, is crucial for effective analysis of attainment and progress. Analysis needs to take account of the way in which attainment and progress responds to individual, community, and wider factors, including LA and school-level funding.
1.1.2. The attainment and progress of white disadvantaged children is much strongly affected by the presence of individual (FSM) and community (IDACI) disadvantage/deprivation than seems to be the case with other ethnic groups.
1.1.3. With respect to both KS4 attainment and progress, LA-level influences can be substantial, and these can be partly explained by LA-level variations in per capita funding.
1.1.4. More local variations in attainment/progress relate to various aspects of the communities in which pupils live; including, but not restricted to, the effectiveness of individual schools. For instance, underachievement is much more likely in coastal communities.
1.2.1. Although they have narrowed since 2016-17, the amounts of funding available for free education and childcare vary geographically, the highest allocations being found in London. In the 2019-20 allocations, Camden and Tower Hamlets top the table (with £8.51 and £8.06 per hour), while 46 Local Authorities received the minimum allocation of £4.30 per hour.
1.2.2. There is a distinct north-south divide with respect to early years attainment, all the top ten performing LAs with respect to all children being in the South East, six in Surrey alone.
1.2.3. In some London boroughs (Newham, Greenwich, Hackney, Westminster and Chelsea) disadvantaged five-year olds are, on average, achieving better than non-disadvantaged five-year olds in some Northern LAs.
1.3.1. While socio-economic variations in educational attainment appear to be slightly closing, regional variations remain pronounced. From a low base in the early 2000s, London (particularly Inner London) is the highest performing region with respect to primary and secondary school performance and in closing the gap between disadvantaged and other pupils.
1.3.2. At Local Authority level, coastal areas and small towns and cities have the lowest attainment scores.
1.3.3. Various factors have been linked to London’s extraordinary improvement in performance. We have provided evidence that resource allocation plays a role.
1.3.4. As a world city, London also gives children exposure to a vast array of social, economic and cultural opportunities that are likely to shape knowledge, aspiration and expectations with respect to a wide range of career opportunities and the role educational success plays in seizing those opportunities.
1.3.5. English coastal areas are characterised by poor educational outcomes, particularly for disadvantaged pupils who achieve about three grades lower at GCSE than disadvantaged children living in non-coastal locations.
1.3.6. Children in economically marginal coastal areas face a distinctively adverse socio-psychological environment associated with a limited range of employment opportunities. Low work expectations contribute to low levels of aspiration and “nothing-to-lose” attitudes that in turn influence harmful behaviours in adolescence. The profound economic downturn in coastal areas due to COVID-19 may increase such risks.
1.3.7. While the NHS funding formulae have increasingly responded to the shift in the pattern of deprivation away from major cities and towards peripheral coastal areas, school funding is on average lower in coastal authorities and higher in large cities.
1.4.1. Situations of financial and familial instability presenting risks for ante- and neo-natal stress; negative parenting; exposure to domestic abuse, problems of substance use and mental health problems. All of these factors have been associated with increased risk of adverse neurodevelopment.
1.4.2. Growing rates of deprivation in coastal areas have become key areas for such familial instability and associated impacts on children’s education.
1.4.3. Educational capital also shapes children’s confidence, expectations and aspirations. Many coastal communities have significantly higher than average proportions of working age adults with low or no qualifications.
1.5.1. Mentoring is a proven way of improving a range of academic, vocational and career outcomes.
1.5.2. Low‐income adolescents are likely to have lower access to naturally occurring mentors.
1.5.3. A recent mapping exercise of mentoring organisations in England found that 36% undertook their work in London with all other regions poorly represented.
1.5.4. New initiatives such as the Educational Endowment Fund’s Online Tuition pilot target large cities, as does the Government’s £1 billion Covid-19 “catch-up” package which responds to the strong weighting given to ethnic minority pupils in the National Funding Formula for schools.
1.5.5. Coastal areas may also be ineligible for funding due to individual charity rules (such as expecting percentage of beneficiaries that are BAME to exceed 10%).
1.6.1. To consider whether a commitment to ‘levelling up’ opportunities and outcomes should be understood as a peripheral as opposed to a north-south challenge.
1.6.2. To move away from working in silos and support a ‘Whole Young Person’ approach involving multiple intervention components and partnerships.
1.6.3. Where economic opportunities are limited (such as in coastal areas), mentoring should be complemented with real world opportunities such as specialist online outreach and work experience (perhaps virtually) in sectors that are not typically found in coastal areas. Commercial and charitable organisations should be made aware of the fact that childhood deprivation and poor educational performance is not confined to the capital.
2.1.1. The DfE makes a wide range of data available on pupil’s performance and progress, including with specific reference to ethnicity. But these data are always aggregated in ways that reflect the particular interests/preconceptions of the DfE. As a result, it is difficult to explore aspects of performance and progress which do not align with the DfE’s focus, including with respect to the underachievement of white pupils. This undermines external scrutiny.
2.1.2. Incidental to research undertaken as part of a MHCLG/DfE-funded review of the Children and Young People’s Services Relative Needs Formula (RNF), we analysed pupil-level KS4 attainment and progress data from the 2015-16 National Pupil Dataset (NPD). Having access to disaggregated data which, critically, includes pupil’s Lower Lever Super Output Area (LSOA) of residence, reveals key features of white underperformance. We outline these below. Of interest and policy relevance in their own right, they also illustrate the value of having access to pupil-level data. Access to highly granular data, preferably anonymised pupil-level data, is crucial for effective analysis of attainment and progress.
2.2.1. The relative underachievement of FSM children is well-known from the data/graphs made available by the DfE. Figure 1 illustrates the gap with respect to the 2015-16 data we are using here; namely pupils with Attainment8 scores attending mainstream-maintained schools for whom place of residence (LSOA) is known (n=528,000). Figure 2 illustrates the relative progress of FSM and Non-FSM pupils in each ethnic group (n=500,799 pupils for whom Progress8 scores are available).
2.2.2. The percentage difference in average FSM scores relative to Non-FSM scores is included on Figure 1, whilst on Figure 2 the difference in actual average Progress 8 scores is given. With regard to both attainment and progress, white FSM pupils fall much further behind their non-FSM peers than appears to be the case with other ethnicities. FSM status, which can be taken as an individual-level indicator of socio-economic disadvantage, appears to have a particularly significant detrimental impact on white pupils.
Figure 1: Average Attainment 8 by Ethnicity (England): FSM versus Non-FSM
Figure 2: Average Progress 8 by Ethnicity (England): FSM versus Non-FSM
2.2.3. A similarly high impact on white pupils is associated with community-level deprivation; here proxied by the 2019 IDACI indicator for the LSOA in which they live. As illustrated by Figure 3, white pupils exhibit a greater drop in Attainment 8 scores between IDACI Decile 10 (least income deprived) and IDACI Decile 1 (most income deprived) than with respect to any of the other ethnic groups (which have been collapsed into Asian, Black and Mixed for display purposes).
Figure 3: Average Attainment 8 by Ethnicity (England): IDACI Deciles
2.2.4. Even more pronounced is how KS4 progress varies across IDACI deciles, as illustrated in Figure 4 below. The systematic falling away of the progress of White British, White Irish and Mixed Ethnicity children as one moves from less to more disadvantaged communities – which is for the country as a whole and thus cuts across innumerable schools – strongly hints at a failure to meet the needs of pupils in disadvantaged communities as a whole rather than a failure of particular schools.
Figure 4: Average Progress 8 by Ethnicity (England): IDACI Deciles
2.3.1. The above shows that, at a national level, the attainment and progress of white disadvantaged children is much strongly affected by the presence of individual (FSM) and community (IDACI) disadvantage/deprivation than seems to be the case with other ethnic groups. This is graphically illustrated by Figure 5 below which compares the attainment of FSM and Non-FSM pupils in each major ethnic group / IDACI Decile. The highlighted points relate to White pupils in IDACI deciles 1 (highest income deprivation) to 10 (lowest income deprivation); the performance of white FSM pupils in all IDACI deciles is far worse than other ethnicity/IDACI cohorts.
2.3.2. The interpretative difficulty, of course, is that the various ethnic groups are distributed unevenly across the country. There is likely to be a complex interplay of factors which cannot be readily captured by simple tables and diagrams. A model-based approach is needed to tease out potential geographic factors. For this, the highly aggregated data currently made available by the DfE is of no use.
2.3.3. Linear mixed effects models provide a well-established method for analysing data with a hierarchical structure, i.e. where individuals might be thought as being nested within some higher unit. Such models can be complex, but in this instance (reflecting the data available to us) we are primarily concerned with the performance/attainment of pupils nested in their local communities and Local Authorities.
2.3.4. This analysis is exploratory, and time and available data limit our conclusions, but there are solid grounds to believe (a) that pupils in particular LAs – generally shire counties and smaller towns and cities – are particularly vulnerable to underachievement, and (b) that this partly reflects past patterns of resource allocation.
Figure 5: FSM/Non-FSM Attainment 8 Scores by Ethnicity & IDACI Decile
2.3.5. With respect to the KS4 Attainment 8 Score, the following model was fitted to data on 527,964 children at mainstream-maintained schools in all LAs except the Isles of Scilly and City of London (excluded because of low numbers):
Table 1: Linear Mixed Effects Model: Attainment 8 Score - Fixed Effects Parameter Estimates
Other Ethnicity (Reference=White)
IDACI (as percent)
2.3.6. The parameter estimates are all highly significant and are all as might be expected. For instance; male pupils, allowing for the impact of all other variables, have a lower attainment than female pupils – by 4 points on average (95%CI: -4.11 – -3.95). The ethnicity parameter estimates are all relative to white ethnicity, the reference category, and all (other than ‘Unknown’) have significantly higher attainment scores. Being on FSM is associated with lower Attainment 8 scores, as is the local level of deprivation, as measured by the IDACI score. Each percentage point increase in the percent of children in the child’s LSOA affected by Income Deprivation is associated with a drop in the Attainment 8 score of 0.355; which is cumulatively very substantial.
2.3.7. Interaction effects (between ethnicity and FSM status and between FSM status and IDACI score) have also been included, as has the local authority in which each child lives as a random effect in the model. This captures the extent to which the attainment of pupils is systematically higher or lower than might be expected given their individual characteristics (sex, ethnicity, FSM status) and the level of deprivation (IDACI) in the LSOA in which they live.
Table 2: Attainment 8 Model: LA-level Random Effects (Top/Bottom 30 LAs)
Isle of Wight
Newcastle upon Tyne
Kingston upon Hull, City of
Herefordshire, County of
Kingston upon Thames
Richmond upon Thames
Hammersmith and Fulham
Bristol, City of
Kensington and Chelsea
2.3.8. The model with respect to Progress 8 Scores paints a similar picture, both in terms of its fixed effects (Table 3) and random effects (Table 4), where a not dissimilar set of names appear in the list of top and bottom LAs in terms of how pupils progress.
Table 3: Linear Mixed Effects Model: Progress 8 Score - Fixed Effects Parameter Estimates
Other Ethnicity (Reference=White)
IDACI (as percent)
Table 4: Progress 8 Model: LA-level Random Effects (Top/Bottom 25 LAs)
Barking and Dagenham
Isle of Wight
Kensington and Chelsea
Kingston upon Thames
Kingston upon Hull, City of
2.3.9. The central point is that with respect to both KS4 attainment and progress, the size of LA-level random effects can be substantial; on average, children in the Isle of Wight, Leicester, Oldham and Bradford do very much worse than one might expect given their individual/LSOA characteristics, whilst those in Hackney, Islington, Westminster and Kensington do very much better.
2.3.10. The model tells us that overall variations in average attainment is due to more than the composition of pupils. These factors matter, but so does the LA in which the child lives, and this maps through to the attainment of White British FSM pupils who, in the former have average Attainment 8 scores of 31.8, 34.1, 33.4 and 35.7 respectively, whilst in the latter they are 41.7, 45.1, 51.2 and 45.4 respectively.
2.3.11. But why does the LA in which children have such a substantial effect?
2.4.1. The LA-level random effects for the Attainment 8 and Progress 8 scores provide a measure of unexplained systematic variation in performance of individual pupils. Some random variation is always likely to be present, and some variation may be due to the effectiveness of individual schools; but the magnitude of the LA-level random effect and the fact that almost all LAs will be served by multiple schools suggests that the LA-level effect originates elsewhere.
2.4.2. A substantial amount of the variation in random effects can, in fact, be explained by LA-level variations in per capita funding. Using (pre-NFF) funding data for 2016-17, and including DSG, High Needs and pupil premium allocations, there is a significant correlation coefficient of 0.622 (r2=0.386; sig<0.001) between per capita allocations and the LA-level random effect.
Figure 6: Per capita funding and LA-level Random Effects: Attainment 8 Model
2.4.3. A distinctive characteristic in Figure 6 above is the cluster of high per capita funded LAs which also exhibit strongly positive LA-level random effects. These are all London authorities; Kensington & Chelsea, Westminster, Islington, Hackney, Southwark, Hammersmith & Fulham, Wandsworth, Lambeth, Tower Hamlets, and Camden. This points towards the positive impact of many years of high per capita funding for schools in London, and particularly Inner London.
2.4.4. The converse, of course, is the predominance of rural shires and smaller towns and cities in the list of authorities with the lowest levels of attainment for White British children (Table 5 below). Many of these authorities also appear in Tables 2 and 4 as having strongly negative Random Effects in the Attainment 8 and Progress 8 models and they are, we would suggest, areas which are likely to have experienced relatively low levels of funding over many years.
Table 5: 20 LAs with the lowest average Attainment 8 scores for White FSM pupils
Isle of Wight
2.4.5. A detailed analysis of overall performance, and that of particular cohorts such as disadvantaged white pupils, will be crucial to the evaluation of the impact of the new NFF for schools. Thus, although the DfE has been clear about its intention to use the new NFF to ‘improve outcomes for pupils and promote social mobility in areas and schools that are currently underfunded’, it will require far more detailed data than are currently available to test whether the DfE is successful. This will become all the more pressing as the allocation ‘hardens’ and is passported directly to schools, not least because of the complex interplay of school effectiveness, locality and funding.
2.5.1. We have focussed above on the use of mixed-effects models using individual-level data and LA-level random effects as a means of, ultimately, exposing the likely impact of funding on the attainment and progress of pupils. But it is important to recognise that there are likely to be more local variations in attainment/progress which relate to various aspects of the communities in which pupils live; including, but not restricted to, the effectiveness of individual schools.
2.5.2. A model of Attainment 8 scores using Middle Layer Super Output Areas (MSOAs) as the random effect illustrates something of the nature of this more local variation (Figure 7). There are innumerable factors or issues of potential interest that can only be explored at this level of analysis. For instance, a matter of some concern (see below) is the extent to which pupils in small coastal towns are disadvantaged by living in communities largely dependent on tourism and the hospitality sectors and from which wider higher education and employment opportunities feel very distant.
Figure 7: Attainment 8 Linear Mixed Effects Model - MSOA Random Effects
2.5.3. Focussing on the relative distribution of Attainment 8 scores achieved by white FSM pupils in coastal communities (as defined by the ONS in 2014) as opposed to pupils living elsewhere, Figure 8 illustrates the fact that under-achievement is much more likely in coastal communities. The difference is not particularly large, but it likely subsumes a variety of outcomes in different contexts; from large coastal cities (such as Plymouth) through to relatively affluent retirement towns (such as Christchurch on the Dorset coast).
Figure 8: Distribution of White FSM Pupils by Attainment 8 Score Deciles
2.5.4. Our exploratory analysis of pupil-level data provides clear evidence of significant LA and more local variations in attainment and progress, and prima facie evidence of the impact of funding on pupil outcomes in different areas. These insights are only possible using data that has not been aggregated into the relatively coarse cross-tabulations currently made available. We believe there is a compelling case to provide anonymised pupil-level data, subject to any necessary, but minimal, aggregation to prevent inadvertent disclosure.
3.1.1. Adversity during early childhood can produce a cascade of changes in brain development and gene expression that may in turn trigger a series of negative influences on educational outcomes and subsequent life chances,,. Against this background, there is a strong case for promoting greater access to high quality pre-school provision, particularly for disadvantaged children.
3.1.2. The expansion of free childcare has been a policy commitment for the past two decades, expenditure having risen from almost nothing in the 1990s to £3.7 billion in 2019. As Ofsted inspections at 31 August 2019 found that nine out of ten registered childcare providers were either good or outstanding, it is reasonable to assume that this policy has had positive impacts on child development. The large-scale Effective Pre-School, Primary and Secondary Education project estimated that children who received the average pre-school experience would go on to earn, on average, around £27,000 more over their working lives than children who received little or no pre-school experience. Moreover, benefits of pre-school attendance should extend beyond retirement age, have intergenerational effects and affect other important outcomes such as improved health or pensions or reduced criminal behaviour. Overall, however, empirical evidence of the impact of early years education on child development in England is mixed. It is worth noting that international evidence finds stronger positive effects.
3.1.3. The lack of strong evidence of impact may reflect a range of factors, including funding. While salary costs have gradually risen with National Minimum Wage/Living Wage increases, average hourly funding rates for free childcare were set in 2015 at £4.49 per three-four-year old and have been frozen since. Individual Local Authorities (LAs) have received varying amounts around this average (see below), many well under £4.49. If this has contributed to variations in performance, good early years outcomes in some LAs may be cancelled out by poor outcomes in others.
3.2.1. Geographical variations in the amounts of funding available for free education and childcare have narrowed since 2016-17 when allocations were based in historic funding levels and ranged from £9.46 and £8.96 in Camden and Tower Hamlets per three or four-year old child per hour to £3.31 and £3.20 in Worcestershire and Solihull. Since the introduction of a new early years formula, Inner London has seen a drop in funding levels and Outer London and the South East a rise. However, in the 2019-20 allocations, Camden and Tower Hamlets still topped the table (with £8.51 and £8.06 per hour), while 46 Local Authorities received the minimum allocation of £4.30 per hour.
3.2.2. Wages and premises costs are certainly higher in London and the South East. However, regional variations in early years attainment do raise questions about whether the distribution of funding has contributed to an inequality in opportunities for young children depending on where they live. There is a distinct north-south divide with respect to early years attainment, all the top ten performing LAs with respect to all children being in the South East, six in Surrey alone. Only one of the bottom ten performers (Thanet) is in the South East. Six are in the North West region. The differences in overall performance are striking, 81% of children achieving a good level of development in 2019 in Hart, Surrey Heath and Tandridge; 57%, 59% and 61% respectively in Pendle, Burnley and Middlesborough.
3.2.3. Such variation with respect to overall performance is largely explained by the different socio-economic composition of local authorities. However, when one looks at attainment by sub-group, intriguing patterns emerge. While, overall, 55% of FSM children achieved a good level of development in 2019, this proportion ranged from 69% in Newham and Greenwich, 68% in Hackney and 67% in Westminster and Chelsea to 35% or less in Ryedale, Staffordshire Moorlands and Chiltern. In the above London boroughs, disadvantaged five-year olds are, on average, achieving better than non-disadvantaged five-year olds in Pendle (59%), Burnley (62%), Middlesbrough (65% and Thanet, Boston, Wellingborough, Kingston upon Hull, Fenland, South Holland and Manchester (66%). Is it a coincidence that Westminster & Chelsea and Greenwich receive significantly higher hourly allocations per three-four-year old (£7.86 and £6.17 respectively) than Pendle, Burnley, Boston, Kingston upon Hull and South Holland, where local authority allocations are set at the minimum £4.30 per hour?
3.2.4. The differential distribution of early years funding reflects a general trend in which education allocations have become more beneficial to lower-income children (particularly those living in the capital) relative to the better off. Belfield et al imply that this is a good thing insofar as education is “middle-class welfare no more”. Because middle class families tend to have higher levels of parental education and greater material resources (both of which impact on early learning environments), the strong targeting of education to address inequalities in outcomes may be supported from a social justice perspective. However, we suspect that we are not alone in feeling uncomfortable about the idea that very young children in London (regardless of their socio-economic status) are effectively being treated as ‘more deserving’ than very young – and disadvantaged - children living elsewhere.
4.1.1. While an attainment gap – by both socio-economic status and geography - is already evident by the time children begin school aged five, it is important to remember that this gap widens during primary and particularly secondary education. The Education Policy Institute estimates that, in 2018, deprived children were around 4.5 months behind their more advantaged counterparts at the end of the early years. By the end of primary school, deprived children were 9.2 months behind, the gap widening to 18.4 months with respect to average GCSE grades. Over time, however, there is evidence that the gap between free-school meal (FSM) children and others has slightly narrowed.
4.1.2. Interestingly, while socio-economic variations in educational attainment appear to be slightly closing, regional variations remain pronounced. From a low base in the early 2000s, London (particularly Inner London) is the highest performing region with respect to primary and secondary school performance and in closing the gap between disadvantaged and other pupils. In line with our findings using data from the NPD (as discussed above), in 2017-18, London topped the regional league table, with an average Attainment 8 score (a measure of a pupil's average grade across a set suite of GCSE eight subjects with a maximum score of 90) of 49.4. Next came the South East (47.8) and East of England (47.0). The North East (44.9) was the poorest performer, followed by Yorkshire and Humber (45.1) and the West Midlands (45.2). At Local Authority level, coastal areas and small towns and cities have the lowest attainment scores, the bottom ten performers including Knowsley (35.3) and Blackpool (38.5) together with Sandwell, Isle of Wight, Salford, Portsmouth, Nottingham, Stoke-on-Trent, Peterborough and Hartlepool. By contrast, six of the top ten LAs are in London, including Sutton (58.1) and Kingston upon Thames (57.8).
4.1.3. Once students enter into Level 3 pathways, their outcomes continue to vary geographically. In 2019, 13.4% percentage of students educated in the state-funded sector achieved AAB or better at A Level. This ranged from 29.0% in Trafford, 28.6% in Kingston-Upon-Thames and 26.4% in Newham to 0% in Knowsley, 3.2% in Sandwell, 3.4% in Reading and 3.8% in Salford. Children in London are most likely (64%) to go into higher education (universities); children in the South West (48%) least likely.
4.2.1. London’s extraordinary improvement in performance (the ‘London Effect’) has been attributed to a range of local factors. These include system reform, key interventions including London Challenge, the academies Programmes and Teach First; system and school leadership; support from local leaders; London’s ethnic composition, the children of immigrants being considered to have particularly high aspirations and ambitions; and the range of opportunities that were and continue to be available outside of the school gates. As a world city, the capital gives children exposure to a vast array of social, economic and cultural opportunities that are likely to shape knowledge, aspiration and expectations with respect to a wide range of career opportunities and the role educational success plays in seizing those opportunities.
4.2.2. Attempts to replicate elements of the London Effect in other parts of the country (e.g. Somerset, Norwich and Hastings) have not resulted in similar success. This may be due to the limited financial resources available for such initiatives, the reasoning being that this made it difficult for local leaders to maximise levers for local change, including getting buy-in from key stakeholders. A more prosaic explanation might lie in the pronounced variation in per capita school funding, the likely significance of which with respect to variations in attainment and progress has already been detailed above. Although the gap is closing, provisional school funding allocations for 2020-21 remain the highest in London (£5,519 per pupil) compared to other regions. Thus, rolling out the ‘London Challenge’ to areas such as Somerset, Kent and Norfolk (£4,602, £4,606 and £4,720 per pupil respectively) rests on a significantly lower pot of money (and thus an ability to invest in additional teachers and teaching assistants) than enjoyed by e.g. Tower Hamlets, Hackney, Southwark and Newham (£6,947, £6,879 and £6,584 and £6,192).
4.3.1. As noted above, English coastal areas are characterised by poor educational outcomes, particularly for disadvantaged pupils who achieve about three grades lower at GCSE than disadvantaged children living in non-coastal locations. This has important consequences for coastal children’s future life trajectories, including their risks of poor health. Because education predicts employment, income and access to material resources as well as psychosocial well-being (and related stress-induced immune changes) and health behaviours, it is arguably the single most important modifiable social determinant of health inequality. Thus, the educational underachievement of coastal children should be concern to the Health as well as Education Committees.
4.3.2. In contrast to London, which now also has the lowest rates of adverse health outcomes among children and young people, health outcomes for children and young people (e.g. mental health, alcohol-specific use, substance-use and self-harm) are poor in coastal counties and Unitary Authorities. E.g. Eight out of the ten areas with the highest rates of hospital admissions as a result of self-harm in 2018-19 (10-24 years) in 2018/19 were coastal. A 2018 ONS report found that six of the 10 towns in England and Wales with the highest rates of death from misuse of heroin/morphine were coastal resorts. In 2016-18, Blackpool had the highest alcohol-specific mortality rate, three times the national average.
4.3.3. The causes of lower educational attainment in coastal areas are complex. As elsewhere, factors such as financial and familial instability and a lack of educational capital in households play an important role (see the home learning environment below). However, children in economically marginal coastal areas, often distant from large urban centres, face a distinctively adverse socio-psychological environment associated with a limited range of employment opportunities. In contrast to the many visible opportunities in London, children in coastal areas are often unable to see or experience opportunities beyond the low-paid hospitality and care sectors. Indeed, the full spectrum of work opportunities may be a rather abstract concept. Low work expectations and poor social mobility contribute to low levels of aspiration. “Nothing-to-lose” attitudes in turn influence harmful behaviours in adolescence and subsequent trajectories in health. The profound economic downturn in coastal areas due to COVID-19 may increase such risks.
4.3.4. It is also worth noting that, in contract to the NHS funding formulae, which have increasingly responded to the shift in the pattern of deprivation away from major cities and towards peripheral coastal areas (Blackpool has the highest per capita funding for Hospital and Community Services), school funding is on average lower in coastal authorities and higher in large cities. This is likely to reflect the strong weighting given to ethnic minority pupils in the National Funding Formula.
5.1.1. Parental background plays a key role in shaping educational outcomes, situations of financial and familial instability presenting risks for ante- and neo-natal stress; negative parenting; exposure to domestic abuse, problems of substance use and mental health problems. All of these factors have been associated with increased risk of adverse neurodevelopment,. A lack of money can also make it difficult to pay for basic items like nutritious food (which is important for healthy brain development) and toys, books and internet access that promote cognitive stimulation.
5.1.2. There are likely to be regional variations in these risk factors. Coastal communities, subject to economic and industrial decline, poor pay and low-skilled seasonal work, high rates of re-housed vulnerable people and, according to the 2019 Index of Multiple Deprivation, growing rates of deprivation have become key areas for such familial instability and associated impacts on children’s education.
Psychosocial factors such as confidence, entitlement in relation to education and expectations with respect to the home learning environment also shape children’s experiences,. Families’ knowledge and information about the school system, their social networks, whether children come to school with a family history of educational success and recognition or with a sense that education is not something they and their families are good at play an important role in determining educational capital. Many coastal communities have significantly higher than average proportions of working age adults with low or no qualifications. These include coastal cities (e.g. Liverpool, Southampton, Plymouth); coastal resorts (e.g. Blackpool, Scarborough, Torbay, Great Yarmouth, Eastbourne); and coastal districts (e.g. Thanet, East Lindsay, North East Lincolnshire, East Riding of Yorkshire and West Lancashire).
6.1.1. Where educational capital is lacking, young people may usefully receive guidance and support from a mentoring relationship with an older and more experienced individual. Although most available evidence on mentoring hails from the US, mentoring is a proven way of improving a range of academic, vocational and career outcomes, such as high school completion, school attendance, academic engagement and higher grades, participation in higher education, gaining employment, developing the skills needed to progress in future careers and increased earnings. Meta-analyses also find modest but significant effects of mentoring on the psychological, emotional and behavioural functioning of participating youth.
6.1.2. Available literature also provides evidence on the how to with respect to mentoring. Several commentators suggest that natural or informal mentoring (within the same social network) is superior to formal in terms of satisfaction and career outcomes for mentees,. However, there are concerns that low‐income adolescents have lower access to naturally occurring mentors. This may be a particular problem in geographically isolated coastal areas due to the practical barriers of distance (geography making it harder to meet up) or a smaller pool of suitable mentors. Pragmatic solutions could be to ensure that formal mentoring arrangements have the qualities of informal relationships (long duration, frequent but shorter meetings, focus on relationship building, realistic expectations),; to draw on particular assets of coastal communities (such as retired professionals); and to deploy digital technologies.
6.1.3. To date, however, mentoring programmes have been targeted at large English cities. Indeed, a recent mapping exercise of mentoring organisations in England found that 36% undertook their work in London with all other regions poorly represented. Similarly, several new initiatives in response to the recognition that the impacts of the COVID-19 pandemic will fall disproportionately on the most disadvantaged young people through their time in education and into the workplace do not benefit coastal communities. For example, the Educational Endowment Fund’s Online Tuition pilot specifically support pupils in Merseyside, Greater Manchester and Leeds while the Government’s £1 billion Covid-19 “catch-up” package responds to the strong weighting given to ethnic minority pupils in the National Funding Formula for schools. We have also found that major educational charitable funders use percentage of beneficiaries that are BAME as basic eligibility criteria. This is hard to achieve in e.g. Cornwall, where, in the 2011 census, only 2% of the population were of ethnic minority status.
7.1.1. We have made a number of references to coastal communities in this submission. Despite evidence of a significant shift in the pattern of deprivation away from major cities and towards peripheral coastal areas, the many problems facing coastal communities continue to be overlooked in media, academic and policy accounts of deprivation in modern Britain. An important question for a national policy that is committed to ‘levelling up’ opportunities and outcomes is whether the challenge should be understood as a peripheral as opposed to a north-south problem.
7.1.2. There is also a need to move away from working in silos. Educational interventions such as providing access to academic and study skills support, wider learning opportunities, out-of-school activities and widening participation initiatives can play an important role in improving the educational attainment of disadvantaged pupils. However, there is growing evidence that a more holistic focus on the healthy development of adolescents, e.g. through family support, building resilience skills and mentoring leads to lasting beneficial effects on a range of educational, social, economic and health outcomes.
7.1.3. A ‘Whole Young Person’ approach can only be achieved through partnership. The Lancet Commission on Adolescence recommends community interventions involving local government, families, voluntary organisations and schools, that seek to promote life skills and positive attitudes including self-confidence and empowerment, social and emotional skills and good problem solving, and that adopt a multi-component strategy. National government has a role to play in supporting such an approach. However, with exceptions such as the Troubled Families programme, policy innovations that recognise that inter-related problems require joined up solutions appear to be rare. The lack of alignment in funding formulae between different departments also suggests a lack of joined up thinking.
7.1.4. Finally, while interventions such as mentoring may increase the aspirations of disadvantaged coastal children, the majority of whom are ethnically white, they do not necessarily transform their expectations. Where economic opportunities are limited and the aim is not only to promote better educational attainment but greater access to graduate and non-graduate career opportunities, mentoring should be complemented with real world opportunities. These might include specialist online outreach, engagement with local, regional and national enterprises and work experience (perhaps virtually) in sectors that are not typically found in coastal areas (e.g. commercial law, technology, investment banking). Commercial and charitable organisations could be usefully made aware of the fact that childhood deprivation and poor educational performance is not confined to the capital.
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