Maps and Intelligence to Support the Targeting of Aimhigher Programme Activities in the North East

CURDS Newcastle University Mike Coombes Emmanouil Tranos Simon Raybould

Maps and intelligence to support the targeting of Aimhigher programme activities in the North East

Report to the Aimhigher team in the North East

November 2007

Summary This is the Final Report by Newcastle University‟s Centre for Urban and Regional Development Studies (CURDS) to the Aimhigher programme in the North East region (Ah_NE). It is one of two key outputs from a research study: central to the work has been the production of maps to help Ah_NE target Aimhigher activities on the priority groups identified by the recent Guidance (2007) (HEFCE/DfES/LSC 2007). Alongside the maps – supplied on CD – this report provides supporting intelligence. In particular, the report documents the distribution of priority groups across the sub-regions through which the Ah_NE programme is delivered. It also compares different ways of identifying priority groups. The report ends by outlining some approaches to targeting which maps and/or the supporting intelligence could be used for.

Acknowledgements

The research team are grateful for the Guidance (2007) of Richard Dodgson (Ah_NE) and for the comments of potential users on early versions of the maps. Census output is Crown copyright and is reproduced with permission of the Controller of HMSO and the Queen's Printer for Scotland.

Glossary of abbreviations used in the report

Ah_NE Aimhigher programme in the North East

Guidance (2007) Good Practice Guidance 2007/12 by HEFCE/DfES/LSC

IMD Index of Multiple Deprivation 2004

LA local authority area

LSOA Lower-level Super Output Area

NS-SEC National Statistics Socio-Economic Classification

OA Output Area

PLASC pupil level annual schools census

2 Introduction

1. As one of the outcomes of the Comprehensive Spending Review (2007), government minister Bill Rammell announced that there would be continuing funding until at least 2011 for the Aimhigher programme to widen participation in higher education. This news should be seen in the context of the research upon current practice in widening participation, reported by HEFCE (2006), which concluded that “[b]etter targeting is required” (p. 43).

2. The recently published policy Guidance (2007) document “Higher education outreach: targeting disadvantaged learners” (HEFCE/DfES/LSC 2007) – which will from hereon be referred to as “Guidance (2007)” – sets out a three stage process for Aimhigher partnerships to follow. In the Guidance (2007) there are key principles, but there is also flexibility over how these principles should be implemented. This report aims to support the Aimhigher programme in the North East (Ah_NE) by providing intelligence needed to implement the first stage of targeting. To be specific, the Guidance (2007 page 11 para 39) calls for all Aimhigher partnerships to identify “the schools, colleges and communities where disadvantage is concentrated and where effort and resources should be concentrated.”

3. The research which produced this report also generated a large set of maps which are detailed in the Annex and supplied on a CD in parallel to this report. The principal aim for the report itself is to provide the supporting intelligence so that the maps can be used as effectively and appropriately as possible. There are four requirements:  explaining the nature of the prioritisation depicted on the maps provide on the CD accompanying this report,  summarising the patterns shown in the mapped distribution of priority groups across the region,  commenting on the difference between alternative ways of identifying priority groups, and then  discussing a number of options for targeting both within and beyond the first stage of the approach outlined in the Guidance (2007). These tasks are taken up in turn in the main sections of the report that follow.

3 Mapping Priority Groups

4. The maps are presented in a format that is familiar in the field of public policy: every area is shaded according to its level of priority, with a selection of other features also shown to help the user to find the areas of most interest to them. As well as selecting the features to help users orientate, it is essential to make three critical decisions in the data analysis lying behind the shading of areas by level of priority. First a decision needs to be taken on which statistical measure to use to assess each area‟s level of priority. Second the set of geographical areas to be analysed must be chosen: for example they could be local authority areas (LAs) although, in fact, much smaller areas will be needed here. Third a decision is needed on the way the measured values are divided up (eg. it may or may not be necessary to split up the areas which are not high priority, so as to identify those which are the lowest priority of all). Although there are „pointers‟ in the guidelines to help with all these decisions, it is important to recognise that decisions did have to be made. The remainder of this part of the report describes the decisions made and the alternatives which were considered by Ah_NE before the final set of maps was produced.

Which statistical measure would best identify areas of priority?

5. In the Guidance (2007) it is clear that targeting at this initial stage should identify the areas where the potential Aimhigher programme participants are most likely to need encouragement and/or support before they will actively consider participating in further or – perhaps especially – higher education. It is worth understanding the underlying process here: targeting can be done by area because the social and economic „drivers‟ of housing markets tend to create neighbourhoods which mostly include people with similar characteristics, especially in terms of their level of affluence or poverty, and the results is that some areas house concentrations of young people who do less well in terms of education (Raffo et al 2007).

6. Robertson and Hillman (1997) reviewed mounting evidence of strong variation between neighbourhoods in their residents‟ participation rates in education, with this variation found to be related to areas‟ level of poverty. Within the

4 North East itself, Conway et al (2002) and Coombes and Raybould (2003) showed the strength of the association between neighbourhood deprivation and higher education participation across the adult age range. Although the Guidance (2007) locates the root of low participation at the household scale – that is, participation is least likely by young people from households where neither parent had experienced higher education – it then recognises that their lower levels of qualification mean that such parents are unlikely to have high-status or highly paying jobs. Thus the neighbourhoods to prioritise for Aimhigher will be those where there are  few parents with degrees or similar qualifications,  few parents with high-status jobs, and there is also  a high incidence of poverty or deprivation.

7. In relation to the first of these factors, Cassen and Kingdon (2007) have found an association between children‟s educational attainment at school and the proportion of parents in the neighbourhood with low qualifications. In fact the Guidance (2007) focusses on the other two factors, probably because they are more readily measured with available data. Of these two, the Guidance (2007) places greater emphasis on the deprivation indicator; it may be that one reason for emphasising deprivation is that targeting analyses can then use the officially approved Index of Multiple Deprivation (IMD). Although the IMD offers a range of different sub-indexes (including one which specifically relates to education in fact), the Guidance (2007) is clear that the overall IMD scores is to be used for targeting analyses.

Which areas should be used for analyses in support of targeting?

8. Given the assumption in the Guidance (2007) that low participation is primarily rooted at the household scale, it follows that targeting by area is at least in part acting as a „proxy‟ for the targeting by household. As is usual with targeting, the main reason for this strategy is that there is a lack of readily available data at the household scale. One consequence is that it is usually assumed that targeting is best carried out using the smallest possible areas; in this way, the analysis is as close as possible to the ideal scale of the individual household, and also it minimises the mixing of more and less deprived households which will tend to be found with larger areas. In practice, this means using what are termed Lower-level Super Output Areas (LSOAs) because they are the smallest areas for which IMD data can be obtained.

9. As will be discussed in more detail in a later section of this report, it is possible to get some data relevant to targeting Aimhigher activities for smaller areas. These smaller areas are Census 2001 Output Areas (OAs): each OA houses about 200 households on average, whereas the average LSOA is roughly seven times larger. Even so, OAs will not necessarily be more appropriate for targeting by area. Many studies have shown educational participation and outcomes to be shaped by neighbourhood „ethos‟ as well as by characteristics of students and their households (Coombes & Raybould 1997). What remains uncertain is the exact scale over which the neighbourhood influences operate (cf. Bolster et al 2007). It remains possible that, although they are larger, LSOAs are a closer match to the scale at which socio-cultural factors operate to produce the neighbourhood effects observed in much educational research (eg. Universities of the West of England and Nottingham 2007).

Which sets of IMD values should be identified on the maps?

10. In the Guidance (2007) it is suggested that Aimhigher efforts be targeted on residents of those LSOAs which are among the country‟s most deprived 13,000 LSOAs; these areas include roughly 40% of the population. It would be possible to use only the North East LSOAs when identifying the top 40% but, so as to follow the Guidance (2007) as closely as possible, the national rankings were used here. The region has a disproportionately high share of England‟s deprived areas, so more than 40% of LSOAs in the North East are among the most deprived 40% in the country.

11. In order to provide rather more information than was the minimum necessary, the maps colour all LSOAs according to where they come in a ranking of all English LSOAs by IMD quintile (ie. groups of 20%). The two categories shaded red cover the North East LSOAs which fall into the top two quintiles nationally in terms of their IMD values: thus the basic idea would be that Ah_NE programmes should be targeted at potential participants who are living in LSOAs shaded red.

12. In the Annex there are further details of the maps on the CD accompanying this report. A final set of background information presented here is a summary of the steps required to produce the maps. 1: check school/college grid references; create short-hand names for maps

6 2: create a database of all English LSOAs, identify those in LAs in the region 3; collate LSOA IMD scores plus Census data for other prioritisation measures 4: rank English LSOAs and allocate by quintiles on each measure separately 5: devise suitable LA groupings within the Shires (as agreed with Ah_NE) 6: map results of the above, adding extra information for orientation purposes

7 Results of the mapping analysis

13. Figure 1 presents a regional view of the results from mapping the IMD values of LSOAs, together with the location of schools and colleges for 17 year olds.

Figure 1 Distribution of LSOAs by quintile of IMD value

8 The schools and colleges are listed, along with other supporting information for the maps, in an Annex to this report. The maps provided separately on the parallel CD to this report allow detailed scrutiny of the patterns, within the more urbanised areas especially (nb. Figure 1 indirectly shows where the more urbanised areas are, in the form of „clusters‟ of schools or colleges). Figure 1 is necessarily at a rather small scale, so as to show the whole region, but some clear patterns can be seen nevertheless.

14. Together the two quintiles shaded red cover LSOAs whose values rank them in the highest 40% of LSOAs nationally which the targeting is to focus upon. As would be expected, there is a much higher proportion of red-shaded areas in the urbanised coastal area than in the region‟s more rural west. The nearest to exceptions to this pattern are the relatively high IMD values in parts of the northernmost LA of the region (Berwick), even though this is a very rural area, and the low IMD values – shaded blue – along the coast in the neighbouring LA to the south (Alnwick). The southernmost coastal part of Alnwick LA is the location of Amble which is that LA‟s one deprived area. This is the northern edge of the coalfield and there is a succession of high IMD values extending from there in a near-unbroken chain to Teesside in the south of the region, mirroring the distribution of former mining communities.

15. Figure 2 displays the composition of each LA by showing the proportion of its LSOAs which fall into each quintile of the ranking of IMD scores nationally. For example, the top row shows that just over 40% Gateshead‟s LSOAs are among the most deprived 20% in the country (as measured by IMD scores). As on the map, the two quintiles coloured red cover those LSOAs which have IMD scores among the highest 40% nationally which the Guidance (2007) indicates should be the focus for targeting. There are only 5 LAs in the region with fewer than 40% of their LSOAs in this priority category (Figure 2 has a purple line at the 40% level to highlight this „expected‟ level of high IMD values). Every one of these five LAs is a Shire District: Figure 2 has all the ten unitary LAs at the top, and it can be seen that their high level of urbanisation goes with an above-average level of deprivation. That said, the LA with more of its LSOAs among the most deprived 40% than any other LA in the region is the Shire District of Easington where the lack of a very large urban areas is the legacy of its coalfield history as a collection of mining villages.

16. Figure 2 shows at its foot the values for the sub-regions and the whole region. Of these, just Northumberland is near to having only 40% of its LSOAs among

9 England‟s most deprived 40% which are to be targeted, whereas all the other three sub-regions have similar proportions of their LSOAs (60%-70%) in this priority category. The proportion of areas‟ population whose neighbourhoods fall in the category of the most deprived 40% of LSOAs nationally provides the basic element of a targeting process which is in keeping with the Guidance (2007). Later in this report there is brief look at some ways to implement targeting, along with indications of the funding „shares‟ for each area which the results here imply,

10 Figure 2 Composition of LAs in terms of LSOA values of IMD

highest 20% next middle 20% next lowest 20% 0% 20% 40% 60% 80% 100%

Gateshead Newcastle upon Tyne North Tyneside South Tyneside Sunderland Hartlepool Middlesbrough Redcar and Cleveland Stockton-on-Tees Darlington Chester-le-Street Derwentside Durham Easington Sedgefield Teesdale Wear Valley Alnwick Berwick-upon-Tweed Blyth Valley Castle Morpeth Tynedale Wansbeck Tyne & Wear Tees Valley Co. Durham Northumberland North East

11 Alternative ways of identifying priority groups

17. It is appropriate to briefly set the mapping analyses here in a wider framework, not least by recognising alternative categories of people who might have been the subjects of targeting. This might have been done by referring to the final report of the Equalities Review Panel (2007) where numerous disadvantaging factors are considered. Rather surprisingly, the report does not present a listing of disadvantaging factors to consider when deciding whether to target support such as that which the Aimhigher programme provides. As a result, this section of the report begins by just recognising some potentially disadvantaging issues – such as ethnicity and gender – which the Guidance (2007) mentions as part of the context it sets for the decision it finally makes to focus on socio-economic class and deprivation.

Categories not considered in the analyses here

18. The guidelines assert that ethnic minorities do not need to be explicitly targeted by Aimhigher because they are reasonably well represented among higher education students already. One concern could be that, although in aggregate ethnic minorities are well represented in higher education, it is still possible that some specific groups are not participating to the same level as the majority are. If so, then it may well be that these low participation groups are prominent among the few minority groups who have a significant presence in this region. Testing whether or not this is true could be done by a comparison of national data on university admissions – which now have ethnicity codings on them – against Census data on the age and ethnicity profile of each area.

19. The possibility of targeting by gender is not even mentioned in the Guidance (2007), probably because women began to outnumber men among undergraduates some years ago. The Guidance (2007) does mention work- based learners, along with gifted/talented young people, but the targeting here is not concerned with these groups. Disability is mentioned as one relevant criterion for targeting, but for stage 1 – targeting by area – this is not relevant because disabled people are not very clustered spatially, so they could not be targeted by where they live. What the Guidance (2007) does not consider is that there may be a need for targeting by combining factors other than socio- economic status: for example, there may be low participation by women from some ethnic or religious backgrounds.

12 Alternative ways of analysing the primary concerns here

20. The primary concern identified in the Guidance (2007) is with young people whose parents had not experienced higher education. This puts households at centre stage and means that analyses at the neighbourhood scale are essentially „proxies‟ for a form of targeting which would identify individual young people according to the characteristics of their parents, or households. In fact the Guidance (2007) also considers a possible „hybrid‟ approach which targets at the neighbourhood level, but focusses on the profile of parents in terms of their socio-economic status (instead of using the IMD measure of the deprivation level in the neighbourhood).

21. The key dataset here is the Census 2001 which reports socio-economic status by National Statistics Socio-Economic Classification (NS-SEC). In practice, the same NS-SEC status is applied to all members of a household based upon the characteristics of the Household Reference Person (who is the household member likely to earn the most). The Guidance (2007) suggests that targeting should focus on all in NS-SECs 4to8 which, in practice, will include all households apart from those with at least one person who is in a managerial, professional or “intermediate” occupation (NS-SECs 1to3). This measure of the percentage of an area‟s households which are in NS-SECs 4to8 then offers an alternative to the IMD deprivation score as a tergeting „index‟ at the neighbourhood scale.

22. When implementing a targeting process in practice, it is not necessarily helpful to have several alternative measures which could be used as the key measure of need. In the Guidance (2007) the presumption is towards using the IMD for targeting, but the household focus of the NS-SEC approach seems closer to the ideal form of targeting which is to focus down to the household scale. What then becomes important in practice is to examine results from the two approaches and see how different they are: to put it crudely, would some areas „score‟ much more highly on one that the other?

23. Figure 3 presents the first step – the LSOA scale – in comparing the two targeting indexes. The horizontal axis uses the IMD score, the vertical shows the proportion of the LSOA‟s households with dependent children which are classified in NS-SEC 4to8 according to the 2001 Census data (Table CS044). There is a clear similarity between the values on the two alternative indexes for most LSOAs, as shown by most falling close to a diagonal from bottom left to

13 top right. There is too a fair degree of „scatter‟ around the diagonal and this shows the two indexes rate some LSOAs rather differently. Figure 3 outlines in red the part of the chart which includes LSOAs where the choice between the two indexes makes most difference for targeting: these are LSOAs with NS- SEC 4to8 values that put them in the top 40% nationally, but IMD values which leave them out of that priority range. It is encouraging to see that they are not very numerous, and also that they are to be found across the region.

Figure 3 Relationship between LSOA values of IMD and % NS-SEC 4-8

Tyne & Wear Tees Valley Co. Durham Northumberland

32500 low 26000

19500

13000

6500

% NS-SECrank on 4-8 in LSOA high high 0 0 6500 13000 19500 26000 32500 high deprivation rank on IMD of LSOA low deprivation

Figure 4 presents the breakdown of each LA in terms of the NS-SEC index values of its LSOAs, using the same format as was used for the IMD values Figure 2). There is much common ground between the two sets of results, especially in relation to the relativities between areas which are critical to most targeting systems. In fact, the commentary in the previous section of this report on the distribution of IMD values (Figure 2) could be reproduced with very little change and it would apply equally well to the distribution of NS-SEC values (Figure 4). The way the two sets of index values do differ notably is in the level of need they identify in this region: on the IMD measure the region overall had over 60% of its LSOAs in the top 40% nationally, whereas on the NS-SEC index only just over 50% of the region‟s LSOAs fall into the priority category to target. Whether this makes a difference to the level of funding for Ah_NE will depend on processes at national level, but in case it would be disadvantaging the region

14 to favour the NS-SEC index, the IMD may be the better „default‟ option here.

15 Figure 4 Composition of LAs in terms of LSOA values of % NS-SEC 4-8

highest 20% next middle 20% next lowest 20% 0% 20% 40% 60% 80% 100%

16 24. Figure 5 provides a part of the answer to the question of whether some areas in the region would benefit from switching to the NS-SEC index for targeting (even though this switch might not help the region overall). For each of the index measures separately, the LSOAs in the top 40% nationally have been identified and used to „share‟ out 100% of the funding (say) for targeting within the region. Thus an LA is here allocated a 7.5% share of this nominal funding if it includes 7.5% of all LSOAs in this region that are in the top 40% nationally on the index concerned. For example, the LA which is furthest towards the top right of the chart (which is Sunderland in fact) merits a share of between 12.5% and 15% of the regional targeting „funding‟ whichever of the two indexes is used. A trend line is superimposed on the chart, and this helps establish the fact that for most LAs it would make very little difference to their share if the IMD was replaced by the NS-SEC index as the basis for targeting.

Figure 5 Targeting ‘shares’ for LAs using IMD or % NS-SEC 4-8

15.0

12.5

10.0

7.5

5.0 shareby NS-SEC

2.5

0.0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 share by IMD

25. A final step in assessing the difference it would make if the IMD was replaced by the NS-SEC index involves shifting to a sub-regional scale of analysis. Figure 6 presents for each sub-region the sum of the shares (as they have just been defined) which were calculated for its constituent LAs. Here the strong degree of similarity in the results from the two alternative indexes can be readily appreciated. So far as it would make a difference, shifting to the NS-SEC measure would further concentrate funding in the two conurbation sub-regions of Tyne & Wear and Tees Valley and it seems unlikely that this outcome would attract widespread support within the region.

17 Figure 6 Targeting ‘shares’ for sub-regions using IMD or % NS-SEC 4-8

IMD NS-SEC

0 10 20 30 40 50

Tyne & Wear

Tees Valley

Co. Durham

Northumberland

An alternative geographical scale of analysis

26. Figures 4 to 6 have presented results at a range of scales – varying from the LSOA through the LA to the sub-region – but they were all derived from basic analyses carried out at the LSOA scale (whether they used the IMD or the NS-SEC index). A remaining question concerns the extent to which the results would be different if the analyses used a different set of areas. It has already been mentioned that there is a widespread assumption that targeting analyses will be more accurate if the smallest possible areas area used. For analyses based on IMD scores then LSOAs are indeed the smallest possible areas because the IMD is not available for any smaller areas. However for analyses based on NS-SEC data, it is possible to use Output Areas (OAs) which are considerably smaller than LSOAs and so could offer a significant improvement in targeting precision. For this reason, it is appropriate here to examine how different the results would be if OAs were used.

27. Figure 7 offers an insight into the difference which scale of analysis makes. Figure 4 had shown (on its vertical axis) the values of % NS-SEC 4to8 for each LSOA and here each of those LSOA values has been assigned to every OA which that LSOA was constructed from. (Taking a statistical value which refers to a Shire County and attributing it to all its constituent Districts would be an equivalent procedure.) Figure 7 has these LSOA-based values on its

18 vertical axis. They are then compared with the „true‟ values of the OAs for the % NS-SEC 4to8 index, which are shown on the horizontal axis. There is broad agreement between the two measures, as revealed by many of the cases lying close to the diagonal across the chart, but there is also a considerable scatter of more outlying cases. The cases of most concern are those within that part of the chart outlined in red: these OAs have OA-level values which put them among the most deprived 40% of OAs nationally, but they form part of LSOAs whose NS-SEC index values do not rank them among the highest 40% of LSOAs in England and so they do not qualify for priority status.

28. Considering the chart as a whole, a fair conclusion would be that the proportion of the region‟s OAs in this part of the chart is not very large. Looking at where these OAs are located, Northumberland has quite clearly contributed a higher proportion of these „problem‟ OAs – those shown in green – than would be expected given the county‟s share of regional population. Much past research on socio-economic indexes can offer an explanation here. More rural areas have a smaller „grain‟ of settlement: for example, there are no large estates housing people with similar characteristics, but instead there may be „pockets‟ of deprivation. The result is that in analyses based on larger areas a lower proportion of the „extreme‟ values will be in more rural areas, because the extreme values which could be seen when using a very detailed set of areas are more likely to be „averaged away‟ in rural areas than they are in more urban areas. Thus of the OAs with high index values – towards the left of the chart – it is those in the rural county of Northumberland which are particularly likely to be part of LSOAs with substantially lower index values (and so be found higher up in the chart).

29. How much of a concern is it that rural areas seem slightly disfavoured by the LSOA scale of analysis? The first point is that small pockets of deprivation getting „lost‟ to analyses at broader scales is not a new problem and examples are found everywhere, even the risk is more acute in rural areas. There is also the possibility that LSOAs – which of course are far from being large areas – may be nearer to the true scale of neighbourhood processes such as peer pressure that are relevant to the levels of aspiration among young people. More pragmatically, shifting analysis scale to OAs is only an option if the analysis also shifts from using the IMD to the NS-SEC and it was pointed out earlier that the NS-SEC index does not favour the region as a whole. It was also shown that Northumberland is less favoured by the NS-SEC index than the IMD (Figure 6) so it may lose as much as it gains by shifting to using OAs.

19 Figure 7 Comparison of LSOA-based and OA values of % NS-SEC 4-8

Tyne & Wear Tees Valley Co. Durham Northumberland

166666

133333 low dep.n low dep.n

100000

66667 rank on LSOA value rank on

33334 high dep.n dep.n high 1 1 33334 66667 100000 133333 166666 high deprivation rank on OA value low deprivation

20 Targeting in practice

30. It is not for a research study to prescribe the way in which its findings should be used. Much less ambitiously, this final section of the report considers some options available, both for this first stage of implementing the Guidance (2007) and also for later developments. These options are by no means the only ones open to Ah_NE at the present, and no doubt some other as yet unforseeable options will become available in the future. All the same, it may well be helpful to outline these selected possibilities at this early stage in developing suitable targeting processes.

31. At present, the key information resources for targeting are the IMD scores for LSOAs and the associated analyses summarised in this report. An alternative set of ranking measures, based on NS-SEC data, was discussed but viewed as a less appropriate option for Ah_NE stage 1 targeting processes. By using the IMD data at the LSOA scale, and focussing on the distribution of those LSOAs in the regions which are among the 40% most deprived nationally, each LA or sub-region was notionally allocated a „share‟ of a steam of funding to cover the region. For example, it was seen that around 14% of deprived LSOAs in the region are in Sunderland so this area might expect to receive around 14% of regional funding for the AN_NE widening participation efforts. Other options for using the targeting analysis data at present could include:  weighting the basic „shares‟ approach outlined here so as to give extra help to those areas with the very highest levels of deprivation, and also  adding a „top slice‟ element that might be of a similar value to all areas meeting some requirements (eg. having established a partnership).

32. All the discussion so far has been in terms of geographical areas, both for the measurement of need – where the more ideal scale of targeting was in fact seen as the household – and also here in the targeting of the policy response. Yet much of the response could be focussed on individual schools or colleges, and it is quite possible to profile them in terms of their relative levels of need. One method would be to use PLASC data for all the schools/colleges and derive an annual overall measure of the socio-economic status of their intake of young people. The analysis would be based on the IMD score for each young person‟s home postcode and could yield either an „average‟ IMD score

21 for the school/college intake population, or a proportion of that population living in LSOAs in the 40% most deprived LSOAs in the country.

33. Other options which could be considered in the future include using alternative indicators of the need to be prioritised in Ah_NE widening participation efforts. In fact the Guidance (2007) mentioned some possibilities on this front: free school meal statistics or Fischer Family Trust data (eg. on „value added‟ by school). The logic followed in this research suggests that first there should be a robust rationale for wishing to stop using the IMD which, it should be remembered, was based on much research and wide consultation and not only has official status but also has proved its utility in related fields. If the rationale for change is seen as persuasive by Ah_NE then the next step should be sensitivity testing along similar lines to the latter parts of this report: in this way the results obtained from using an alternative dataset are compared with those derived from using the IMD values, so as to identify the areas which are likely to „gain‟ or „lose‟ and looking for the reasons behind that pattern. Knowing why one approach favours one set of areas over another provides a justification for the choice between staying with, or shifting from, using IMD for targeting.

34. For later stages of development in targeting processes, many other options may become feasible. For example, is it possible for every school/college that is part of the Ah_NE programme to use the data lying behind PLASC datasets to profile its intake, perhaps at the start of their Year 10? The aim here could be to identify the need for Aimhigher support at the individual level. To help this option to get still closer to the ideal approach hinted at in the Guidance (2007), the data available to each school/college would need to include for every young person the occupational status of the highest earner in their household (nb. Coombes & Raybould 1997 have shown the difficulty of collecting consistent data from young people, especially for difficult-to-code information items such as occupational status). If robust practices were put in place along these lines, what could become possible is for the sharing of regional funding to be no longer based on entitlement (ie. „bob a knob‟ based on numbers who are in need) but instead to be as a reimbursement of auditable participation within widening participation programme activities by young people who had already been robustly identified as among those needing to be targeted.

22 References

Bolster A, Burgess S, Johnston R, Jones K, Propper C and Sarker R (2007) “Neighbourhoods, households and income dynamics: a semi-parametric investigation of neighbourhood effects” Journal of Economic Geography 7 1- 38

Cassen R and Kingdon G (2007) Tackling low educational achievement Joseph Rowntree Foundation, York

Conway C, Coombes MG and Raybould S (2002) Participation in further and higher education across the North East region: a bench-mark analysis of recent patterns Universities for the North East, Sunderland

Coombes MG and Raybould S (1997) “Modelling the influence of individual and spatial factors underlying variations in the levels of secondary school examination results” Environment & Planning A 29 641-658

Equalities Review Panel (2007) Fairness and freedom: the final report of the Equalities Review Communities and Local Government, Wetherby

HEFCE (2006) Widening participation: a review HEFCE, Bristol

HEFCE/DfES/LSC (2007) “Higher education outreach: targeting disadvantaged learners” Guidance for Aimhigher partnerships and higher education providers www.hefce.ac.uk/pubs/hefce/2007/07_12/

Raffo C, Dyson A, Gunter H, Hall D, Jones L and Kalambouka A (2007) Education and poverty: a critical review of theory, policy and practice Joseph Rowntree Foundation, York

Universities of the West of England and Nottingham (2007) Young participation in hogher education in the Parliamentary Constituencies of Birmingham Hodge Hill, Bristol South, Nottingham North and Sheffield Brightside HEFCE, Bristol

23 Annex Specification details of the maps on the CD level coverage number boundaries school/colleges targeting data* 1 region 1 Region LAs not shown IMD / NS-SEC 2 sub-region 4 LAs Districts shown (with codes) IMD / NS-SEC 3 locality 17 LAs Districts shown (and named) IMD

* IMD LSOAs shaded in 5 bands, coloured to highligh those in England‟s top 40% NS-SEC LSOAs‟ shaded in 5 bands, coloured to highlight those in England‟s top 40%

Codes used for Schools/Colleges on the sub-region maps

abbreviations used in the school/college names

6th 6th form Bus. Business [or Business& Enterprise] CofE Church of England Col. College Comm. Community Comp. Comprehensive FE Further Education HE Higher Education RC Roman Catholic Sch. School Sec. Secondary Tec. Technology [or Technology & Enterprise]

code name 101 Branksome Sch. 102 Carmel RC Col. 103 Eastbourne Comp.Sch. 104 Haughton Comm.Sch. 105 Hummersknott Sch. 106 Hurworth Sch. 107 Longfield Sch. 108 Belmont Sch. Comm. Arts Col. 109 Bishop Auckland Col. 110 Bishop Barrington Sch. 111 Consett Comm. Sports Col. 112 Dene Comm.Sch. of Tec. 113 Derwentside Col. 114 Durham Comm. Bus. Col. for Tec. 115 Durham Gilesgate Sports Col. & 6th Centre 116 Durham Johnston Comp.Sch.

24 117 Easington Comm.Sch. 118 East Durham & Houghall COMM. COL. 119 Ferryhill Bus. Col. 120 Framwellgate Sch. 121 Fyndoune COMM. COL. 122 Greencroft Sch. 123 Greenfield Sch. Comm. & Arts Col. 124 King James I Comm. Arts Col. 125 Moorside Comm. Tec. Col. 126 New Col. Durham 127 Park View Comm.Sch. 128 Parkside Sch. 129 Roseberry Sports & COMM. COL. 130 Seaham Sch. of Tec. 131 Sedgefield COMM. COL. Sports SC 132 Shotton Hall Sch. 133 Spennymoor Comp.Sch. 134 St Bede's RC Comp.Sch. 135 St Bede's RC Comp.Sch. & 6th Col. 136 St John's RC Tec. Sch. & 6th Centre 137 St Leonard's RC Comp.Sch. 138 Staindrop Sch. 139 Stanley Sch. of Tec. 140 Sunnydale COMM. COL. 141 Tanfield Sch. 142 Teesdale Sch. 143 The Hermitage Sch. 144 Tudhoe Grange Sch. 145 Wellfield Comm.Sch. 146 Wolsingham Sch. & COMM. COL. 147 Woodham Comm. Tec. Col. 148 Emmanuel Col. 149 Heworth Grange Comp.Sch. 150 Hookergate Sch. 151 Joseph Swan Sch. 152 Kingsmeadow Comm. Comp.Sch. 153 Lord Lawson of Beamish Comm.Sch. 154 Ryton Comp.Sch. 155 St Edmund Campion RC Sch. 156 St Thomas More RC Sch. 157 Thomas Hepburn Comm. Comp.Sch. 158 Whickham Sch. 159 Brierton Comm.Sch. 160 Dyke House Comp.Sch. 161 High Tunstall Col. of Science 162 Manor Col. of Tec. 163 St Hild's CofE Sch. 164 English Martyrs Sch. & 6th Col.

25 165 Acklam Grange Sch. 166 Hall Garth Sch. 167 King's Manor Sch. 168 Macmillan Academy 169 Ormesby Sch. 170 St David's RC Tec. Col. 171 King's Academy 172 Newlands RC Sch. 173 Unity City Academy 174 All Saints Col. 175 Benfield Sch. 176 Gosforth HS 177 Gosforth Junior HS 178 Heaton Manor Sch. 179 Kenton Sch. 180 Sacred Heart HS 181 St Cuthbert's HS 182 St Mary's RC Comp.Sch. 183 Walbottle Campus Tec. Col. 184 Walker Tec. Col. 185 West Gate COMM. COL. 186 Burnside Bus. Col. 187 Churchill COMM. COL. 188 George Stephenson Comm. HS 189 John Spence Comm. HS 190 Longbenton COMM. COL. 191 Marden HS 192 Monkseaton Comm. HS 193 Norham Comm. Tec. Col. 194 Seaton Burn Col.. 195 St Thomas More RC HS 196 Whitley Bay HS 197 Ashington Comm. HS 198 Astley Comm. HS 199 Bedlingtonshire Comm. HS 200 Berwick Comm. HS 201 Blyth COMM. COL. 202 Coquet HS 203 Cramlington Comm. HS 204 Haydon Bridge Comm. HS & Sports Col. 205 Hirst HS 206 Ponteland Comm. HS 207 Prudhoe Comm. HS 208 Queen Elizabeth HS 209 St Benet Biscop RC HS 210 The Duchess's Comm. HS 211 King Edward VI Sch. 212 Bydales Sch.

26 213 Eston Park Sch. 214 Freebrough Specialist Engineering Col. 215 Gillbrook Col. 216 Huntcliff Sch. 217 Laurence Jackson Sch. 218 Nunthorpe Sch. 219 Redcar COMM. COL. 220 Rye Hills Sch. 221 Sacred Heart RC Sch. 222 Saint Peter's RC Col. 223 Boldon Sch. 224 Harton Tec. Col. 225 Hebburn Comp.Sch. 226 Jarrow Sch. 227 Mortimer Comp.Sch. 228 South Shields Comm.Sch. 229 St Joseph's RC Comp.Sch. 230 St Wilfrid's RC Col. 231 Whitburn CofE Sch. 232 All Saints CE Sch. 233 Bassleton Sch. 234 Billingham Campus Sch. 235 Bishopsgarth Sch. 236 Blakeston Sch. 237 Conyers Sch. 238 Egglescliffe Sch. 239 Grangefield Sch. & Tec. Col. 240 Ian Ramsey CofE Comp.Sch. 241 Northfield Sch. & Sports Col. 242 Our Lady & St Bede RC Sch. 243 St Michael's RC Comp.Sch. 244 St Patrick's RC Comp.Sch. 245 The Dene Sch. 246 The Norton Sch. 247 Thornaby Comm.Sch. 248 Biddick Sch. Sports Col. 249 Castle View Sch. 250 Farringdon Comm. Sports Col. 251 Hetton Sch. 252 Houghton Kepier Sports Col. 253 Hylton Red House Sch. 254 Monkwearmouth Sch. 255 Oxclose Comm.Sch. 256 Pennywell Sch. 257 Sandhill View Sch. 258 Southmoor Comm.Sch. 259 St Aidan's RC Sch. 260 St Anthony's RC Girls' Sch.

27 261 St Robert of Newminster RC Sch. 262 Thornhill Sch. 263 Usworth Sch. 264 Venerable Bede CofE Sec. Sch. 265 Washington Sch. 266 Bede Col. 267 City of Sunderland Col. 268 Cleveland Col. of Art & Design 269 Darlington Col. 270 Gateshead Col. 271 Hartlepool Col. of FE 272 Hartlepool 6th Col. 273 Middlesbrough Col. 274 Newcastle Col. 275 Northumberl& Col. 276 Prior Pursglove Col. 277 Queen Elizabeth 6th Col. 278 Redcar & Cleveland Col. 279 South Tyneside Col. 280 St Mary's Col. Middlesbrough 281 Stockton Riverside Col. 282 Stockton 6th Col. 283 Tyne Metropolitan Col.

28 level of LSOA shaded by file name on CD supplied mapping IMD or NS-SEC coverage in parallel to this report 1 IMD whole region I1region 1 NS-SEC whole region N1region 2 IMD Co. Durham I2CoDurham 2 IMD Northumberland I2Northumberland 2 IMD Tees Valley I2TeesValley 2 IMD Tyne & Wear I2TyneWear 2 NS-SEC Co. Durham N2CoDurham 2 NS-SEC Northumberland N2Northumberland 2 NS-SEC Tees Valley N2TeesValley 2 NS-SEC Tyne & Wear N2TyneWear 3 IMD Co. Durham [part]: Durham City & Sedgefield I3CoDurhamCentral 3 IMD Co. Durham [part]: Easington I3CoDurhamEasington 3 IMD Co. Durham [part]: Chester-le-Street & Derwentside I3CoDurhamN 3 IMD Co. Durham [part]: Teesdale & Wear Valley I3CoDurhamSW 3 IMD Northumberland [part]: Alnwick & Berwick I3NorthumberlandN 3 IMD Northumberland [part]: Blyth Valley & Castle Morpeth & Wansbeck I3NorthumberlandSE 3 IMD Northumberland [part]: Tynedale I3NorthumberlandTynedale 3 IMD Tees Valley [part]: Darlington I3TeesValleyDarlington 3 IMD Tees Valley [part]: Hartlepool I3TeesValleyHartlepool 3 IMD Tees Valley [part]: Middlesbrough I3TeesValleyMiddlesbrough 3 IMD Tees Valley [part]: Redcar and Cleveland I3TeesValleyRedcar&C 3 IMD Tees Valley [part]: Stockton on Tees I3TeesValleyStockton 3 IMD Tyne & Wear [part]: Gateshead I3TyneWearGateshead 3 IMD Tyne & Wear [part]: Newcastle-upon-Tyne I3TyneWearNewcastle 3 IMD Tyne & Wear [part]: N. Tyneside I3TyneWearNTyneside 3 IMD Tyne & Wear [part]: S. Tyneside I3TyneWearSTyneside 3 IMD Tyne & Wear [part]: Sunderland I3TyneWearSunderland