CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU REVISING THE CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU SCOTTISH AREA CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU DEPRIVATION CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU INDEX CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU Volume 1 CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU by CRU CRU CRU CRU CRU CRU CRU Kenneth Gibb, Ade Kearns, CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU Margaret Keoghan, Daniel Mackay CRU CRU CRU CRU CRU CRU CRU and Ivan Turok CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU Department of Urban Studies CRU CRU CRU CRU CRU CRU CRU University of Glasgow CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU The Scottish Office Central Research CRU CRU CRU CRU CRU CRU CRU Unit 1998 CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU CRU Further copies of this report are available priced £5.00. Cheques should be made payable to The Stationery Office and addressed to:

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© Crown Copyright 1998 Limited extracts from the text may be produced provided the source is acknowledged. For more extensive reproduction, please write to the Chief Research Officer at the Central Research Unit, Saughton House, Broomhouse Drive, Edinburgh EH11 3XA. ACKNOWLEDGEMENTS

Acknowledgements are due to many people who helped with the development of this project. Without the data that was kindly provided it would not have been possible to create the index. Many people and organisations helped with the research, in particular, statisticians at the DSS, The Scottish Office, ISD, the Central Research Unit, SHEFC, Karen Hancock and many others. Our steering committee provided useful advice and encouragement throughout, particularly, Elaine Docherty, Jamie Hamilton, Karen MacNee and Christie Smith. Stuart Gardener at The Scottish Office GIS unit gave very useful advice with the mapping element of the study as well as providing the most recent ULA boundary data. Thanks are also due to Glen Bramley, Moira Munro, several colleagues within our Department, Bryan Robson, and at Scottish Homes, Pamela Woodburn and Gillian Young. EXECUTIVE SUMMARY

REVISING THE SCOTTISH AREA DEPRIVATION INDEX A Report to The Scottish Office

Kenneth Gibb, Ade Kearns, Margaret Keoghan, Daniel Mackay and Ivan Turok Department of Urban Studies University of Glasgow

September 1998

1. BACKGROUND

1.1 In April 1998, The Scottish Office commissioned a research team from the Department of Urban Studies at the University of Glasgow to carry out a feasibility study into the construction of a revised index of area deprivation and then to develop such an index for Scotland. The interim report was completed in draft form at the end of May 1998 and the team then proceeded with the development and analysis of the index.

1.2 The key distinguishing features of the new index are that it combines 1991 Census indicators with more recent non-Census indicators of deprivation (concerning deprivation dimensions of health, crime, education, unemployment, etc.) and that necessarily the index has been constructed at the level of Post Code Sector which is the smallest consistent basis possible for the data as a whole. It is acknowledged that Post Code Sectors are far from an ideal basis from which to measure deprivation but they are the best available level of analysis.

1.3 The existing index (Duguid, 1995) was derived wholly from the 1991 Census. Three groupings of indicators were identified (socio-demographic, housing and economic factors) and analysed at the small area level of the enumeration district (ED). The study produced a set of six indicators (dependent households, overcrowding, the permanent sick, unemployment, youth unemployment and single parent families). The index was then presented in terms of the worst 10% of EDs and distributed across (the old District) Scottish local authorities.

1.4 There are two main reasons for wanting to now revise the index. First, there is evidence of significant social, economic and physical change in many of these small areas in the seven years since 1991.

1.5 Second, the research team consider that there are flaws, arguably, in the way the existing index was constructed (although no index is pure or ideal). For instance:

· The index relies on proxies for specific dimensions of poverty or deprivation. · The index employs several indirect measures of deprivation. Indirect measures include the focusing on specific groups who tend to be deprived, for instance, lone parents or the elderly. Not only may this not turn out empirically to be reliable, it may stigmatise and is not as effective as a more direct measure.

iii · It is not evident from the report how robust the statistical models developed were, nor is there evidence on bi-variate relationships between indicators (are specific indicators correlated?). There is also a lack of clarity about the underlying definition or approach to multiple deprivation. The model arguably, therefore, lacks conceptual coherence and transparency.

1.6 The underlying philosophy of the new index is based on the growing literature on area deprivation indicators and indices and in particular, draws heavily from the approach used by the Department of Environment’s Index of Local Conditions (hereafter ILC). Our approach is, however, amended in the light of Scotland’s unique circumstances and the specific objectives of the present research project.

1.7 Deprivation is conceptualised in as broad and inclusive a way as possible, and this is done by developing a comprehensive set of domains of deprivation (reflecting the multiple nature of deprivation, the domains explored include: housing ,health, education, crime, labour market and material poverty).

1.8 The next stage in the index’s development was to develop indicators for each of these domains, in principle from contemporaneous non-census sources but going back to the 1991 Census if that is the only source of information. These indicators are then analysed as a group for inter-correlations and through factor analysis, utilising the results to draw up a final, smaller, set of indicators. The indicators are then measured in terms of their signed Chi-square value (a statistical method for minimising the impact of small and variable denominators in small area statistics such as the population base of an enumeration district or Post Code Sector). The resulting values are then standardised using logs and then summed together (equally weighted), creating a distribution of scores of deprivation for each Post Code Sector in Scotland. However, rather than simply ranking the areas in terms of where the worst are located, the study also seeks to go further and identify the extent and intensity of deprivation.

1.9 In total, a set of 12 indicators are drawn on to construct both the final updated index and a Census 1991 benchmark. The Census variables used in the final set of indicators are the following:

· Overcrowding (households in permanent buildings who are below the occupancy norm relative to all households in permanent dwellings) · Lack of amenities (households in permanent buildings lacking exclusive use of bath/shower/insider WC relative to all households in permanent dwellings) · Vacant dwellings (household spaces classified as vacant accommodation or other, relative to all household spaces) · Participation at school (students in full-time education at age 17+ relative to all 17-18 year olds) · No-car households (households with no car relative to all households) · Children in dependent-only households (dependent children in households which contain no adult in employment relative to all children).

iv 1.10 The non-Census variables chosen for the final set of indicators are the following:

· Standardised mortality ratios (0-64 all causes summed for five years 1992-96 relative to the adjusted 1996 CHI small area population forecast) · Low birth weights (1992-96 summed index of the population of low birth weights relative to all Scotland) · Unemployment rate (claimant count [NOMIS], 1996-97, relative to the adjusted 1996 CHI small area population forecast (age and sex adjusted) · Insurance weightings index (three firm average weighting index of Post Code Sector home contents insurance premiums) · Students in higher education (the number of full time students in higher education at their permanent address relative to the Scottish average) · Income Support claimants (the number of claimants by Post Code Sector based on an August 1996 100% scan of the population).

1.11 Table 1 sets out the indicators selected for the area deprivation index, drawn from Census and non-Census sources. Each domain is made up of indicators coming from either column 2 (1991 Census) or column 3 (a more up to date non-Census variable). Column 4 indicates some Census proxies that could stand in for the non-Census variables.

Table 1 Area Deprivation Indicators (1) (2) (3) (4) Domain of Proposed Census Proposed non- Proposed Census deprivation indicator Census indicator proxy for (3) Housing Overcrowding Lack of basic amenities Vacant dwellings Health (0-65) standardised Permanent sick mortality ratios (singleton) low birth Long-term illness weight ratios Labour market Claimant Claimant unemployment rate unemployment rate Crime/wider Home contents (Vacant dwellings) environment insurance weights Education Participation at school Participation in full- time higher education Poverty No car households Income support recipients Children in non- earner households

v 1.12 There are a number of caveats and qualifications that need to be made when discussing the development of this type of index, the data used and the gap between theory and what is practically possible. Among the main concerns elaborated in the full report were:

· Mismatch between administrative (e.g. Post Code Sectors) and functional (generic area deprivation) geographies. · The vintage of the data that is used and the different ways it is classified. For example, the data has to be analysed using an earlier version of the Post Code Sector series. There are also further problems with data that arise from local government re-organisation. · Other data and interpretation problems relate to the under-enumeration of the Census and the non-rural focus of the present study. Updating denominators (e.g. small area populations) has posed considerable problems for the team. · The team could find no basis for following any weighting strategy other than applying equal weights.

1.13 Table 2 presents an exhaustive description of the indicators that the research team identified for the development of a new index of area deprivation in Scotland. Such a comprehensive list allowed more scope in the development and testing of various forms of indicators, including 1991 benchmarks for the new index (and a replication of the indicators required for the Duguid approach).

Table 2 List of Potential Indicators Indicator Definition Logschl Below expected school participation of 17 year olds: (1991 Census) Logkids Children in dependent-only households (1991 Census) Logoverc Households who are living below the occupancy norm (1991 Census variable) Logsing Single parents (1991 Census variable) Logvacdw Household spaces classified as vacant accommodation (1991 Census variable) Logpermc Those classified as permanently ill (1991 Census variable) Logill Those suffering from a long term illness (1991 Census variable) Loghe Below expected Higher Education participation (Scottish Office) Logcc Unemployment Rate (NOMIS claimant count adjusted by CHI population forecast) Logis Income support claimants (Obtained from Department of Social Security, Newcastle-upon-Tyne) Logeld Elderly households (1991 Census variable) Lognocar No-car households (1991 Census variable) Logamen Lack of basic amenities (1991 Census variable) Logyouth Youth unemployment (1991 Census) Logbirth Low birth weight ratios (External source: ISD, The Scottish Office) Logsmr64 Standardised mortality rates (External source: ISD, The Scottish Office) Loginsur Index of home contents insurance companies premia (based on average of three high street insurers) Logdep Dependency (based on an OECD definition used in Duguid, 1995

vi 1.14 From the initial eight indicators selected as a basis for the index, the factor analysis reduced the final number to six. The new index is based on the summation of the following signed Chi-square indicators: logis, logcc, loghe, logoverc, loginsur and logsmr64.

2. RESULTS

2.1 The index has a mean index score equal to 6.41 and a median score of 4.96 (reflecting the large positive skew in the distribution of the index across Post Code Sectors). For reference, the threshold score to be counted in the 10% worst areas (ranked 1-90) was 11.23. The highest overall score (G22.5) reached was 16.97.

2.2 Table 4.7 in the main report presents in descending order, the ranking of the worst 20% of post-code sectors on the basis of the new updated index (Rank 1998). The table also provides a comparison with the equivalent ranking of the same Post Code Sector in 1991, using the Census variables in 1991 as a comparator benchmark (Rank 1991) and also the remodelled Duguid index at Post Code Sector level (Duguid 1991). Each Post Code Sector has been assigned to a unitary local authority (although this is not always straightforward).

2.3 Making comparison between the 1998 and 1991 versions of the new index (Rank 1998 and Rank 1991), several points stand out. Most significant is the relative stability of bad areas, particularly among the very worst areas. Only 10 out of the worst 10% have managed to improve sufficiently by moving down into the next decile (20%). Thus 89% of the worst 10% of areas in 1991 remain within the lowest decile in 1998. Eleven Post Code Sectors have joined the worst 10%. The top one per cent in the new index all had rankings between one and fourteen in 1991. The top 5% (the 45 highest scores) had only 6 Post Code Sectors which had been out with the top 5% in 1991 (all of which were between the 6-10% highest index scores). Thus, as a proportion of all Post Code Sectors, approximately 1% have moved in or out of the most deprived areas.

2.4 The geography of deprivation is stark with the worst areas wholly dominated by Glasgow, which has no fewer than 52 of the worst 90 post-code sectors. North Lanarkshire and Renfrew have 6 Post Code Sectors in the worst 10% and several, primarily large urban authorities, have between three and give (Edinburgh, Dundee, South Lanarkshire, West Dunbartonshire and Inverclyde). Stirling, Fife, Perth & Kinross and East and South Ayr have one. It was pointed out above that the index would be largely urban-centred and this is clearly borne out by the results. Widening the scale to the worst 20% brings in more local authorities (e.g. North Ayr) but typically reinforces the basic deprivation pattern.

2.5 The intensity of deprivation can be measured on the basis of the average worst three scores for a local authority (who had at least one pc sector within the worst 10%). Glasgow has the highest score of all the ULAs illustrated in the table and is some 17% greater than the next highest score (Edinburgh). Dundee and Renfrew follow some way behind; the two Lanarkshire councils and West Dunbartonshire are then next, with broadly similar scores.

vii 2.6 The extent of deprivation is defined as the share of a local authority’s total population belonging to that local authority’s Post Code Sectors which are in the worst 10% of all Post Code Sectors. This measure is somewhat crude and is subject to a margin of error. Nonetheless, it is quite striking in its distribution. Glasgow has 60% of its population living in areas of multiple deprivation. Just under a third are located in the worst areas of Dundee; are either side of a fifth of the population of Inverclyde, North Lanarkshire, West Dumbarton and Renfrew. This makes an interesting comparison with the number of Post Code Sectors within a local authority that fall into the worst category and suggests that the extent is narrower than number of bad areas for several local authorities. In other words, an authority might be ranked highly in terms of having a large number of bad Post Code Sectors; yet this may not impact on a similar proportion of its population. Note, however, that not all of the population in one of the worst Post Code Sectors may be deprived – this is a somewhat crude measure of the problem.

2.7 Some wider conclusions are possible. Part of the brief required discussion of future developments with the index and its successors. The main points that stand out are as follows:

* There needs to be a separate study of rural deprivation. * There is merit in a multi-level and multi-measure analysis of area deprivation. * Direct measures of deprivation are preferable to indirect or proxy measures (i.e. the Census should not be used if there is better non-Census data, for instance, social security data). * There needs to be continued progress with integration and joint working between government data holders in order to maximise the usefulness of geo- referenced socio-economic data. * Future indexes should continue with the philosophy of multiple domains measured through techniques in similar vein to the signed logged Chi-square. * All indexes are flawed in that there is no pure or wholly correct way to model functional geographies of disadvantage (because spatial data is administratively based). All decisions on levels of analysis therefore require some judgement.

viii 1. INTRODUCTION

1.1 In April 1998, The Scottish Office commissioned a research team from the Department of Urban Studies at the University of Glasgow to carry out a feasibility study into the construction of a revised index of area deprivation and then to develop such an index for Scotland. The interim report was completed in draft form at the end of May 1998 and the team then proceeded with the development and analysis of the index which is reported in two volumes. Volume I (this document) sets out the case for revising the existing index, the methodology adopted to construct a new index, and presents the main results. Volume II provides a more detailed analysis of the index and the background literature as well as providing more quantitative information and appendices.

1.2 The key distinguishing features of the new index are that it combines 1991 Census indicators with more recent non-Census indicators of deprivation (concerning deprivation dimensions of health, crime, poverty, housing, education and unemployment) and that necessarily the index has been constructed at the level of Post Code Sector which is the smallest consistent basis possible for the data as a whole. It is acknowledged that Post Code Sectors are far from an ideal basis from which to measure deprivation but they are the best available level of analysis open to researchers (Section 3 of this report discusses this point further).

1.3 The structure of the present report is as follows. After the introduction, Section 2 outlines the background to the research. Section 3 summarises the new index, its antecedents, the selection of specific indicators and the specific technical construction of the index. The Section also discusses important caveats and qualifications concerning the approach taken. Section 4 presents the main results of the index, starting with descriptive statistics relating to the index’s construction (correlations between potential indicators, factor analysis and the final construction of the index) and its basic structure. It then goes on to present an overview of area deprivation in Scotland, looking at the scale, extent and intensity of deprivation, making some comparisons with other studies and setting out the main implications of the research findings. Section 5 summarises and concludes.

1.4 This report represents a new approach to a Scottish index of deprivation. While the team has received much invaluable help from the project Scottish Office steering committee and other statisticians within Scotland and beyond, we would welcome comments and feedback on the model of deprivation and its application. This is an essential part of the external validation of the model and the future development of models of area deprivation to be used by Government and other interested parties. No index is above criticism and we recognise the inevitable compromises and judgmental decisions made in the process may not be acceptable to all readers. Apart from providing a new index, this report represents part of the process of refining and improving our understanding of area deprivation which will inform future studies.

1 2. BACKGROUND

2.1 The Scottish Office requires a new index of area deprivation to help it prioritise urban regeneration spending. The existing index (Duguid, 1995) is derived wholly from the 1991 Census. The approach taken was to identify three groupings of indicators (socio- demographic, housing and economic factors) analysed at the small area level of the enumeration district (ED), the basic unit of analysis used in the 1981 census, replicated for the 1991 data. From the 12 indicators originally examined, final indicator selection, standardisation and weighting according to Principal-Components Analysis (PCA) followed.

2.2 The Census-based study of 1995 then yielded a final set of six indicators (dependency households, overcrowding, the permanent sick, unemployment, youth unemployment and single parent families) which, when multiplying the ED value for each indicator multiplied by its PCA weighting and then summing the six weighted indicators, produced an index score for each ED in Scotland. The index was then presented in terms of the worst 10% of EDs and distributed across (the old District) Scottish local authorities.

2.3 There are two main reasons for wanting to revise the index at this time. First, there is evidence of significant social, economic and physical change in many of these small areas in the seven years since 1991. Comparison of the two Scottish House Condition Surveys suggest that inequality of incomes has worsened for the poorest in Scotland. Yet, at the same time, there has been considerable investment in the worst areas as a result of the targeting of spending that the index informed. Also, natural migration by households, the development of new housing estates and demolition of older properties, will have impacted on the population base and social composition of many small areas.

2.4 Second, the research team consider that there are flaws, arguably, in the way the existing index was constructed (although no index is pure or ideal):

· It is wholly based on census data and thus the index relies on proxies for specific dimensions of poverty or deprivation, some of which are ambiguous or raise possible double-counting questions concerning specific features of deprivation. · By ranking deprivation solely in terms of EDs, the index fails to take account of possible mismatches of over- or under-bounding of the spatial scale of deprivation (i.e. the precision of the mapped boundaries of an area is unlikely to ‘fit’ the actual spatial scale of experienced deprivation). There is no complete solution to this problem but some approaches have tried to address it by examining deprivation at more than one spatial scale. The English Index of Local Conditions does this and also includes a measure of deprivation intensity (see below and Robson in Room (ed.), 1995). · The index employs a combination of direct and indirect measures of deprivation. Indirect measures include the focusing on specific groups who tend to be deprived, for instance, lone parents or the elderly. Not only may this not turn out empirically to be always valid or reliable, it stigmatises specific groups and is not as effective as a more direct measure of deprivation (for instance, unemployment). It is, however, recognised that sometimes the direct

2 measure of the dimension of deprivation does not exist. However, a Census proxy may be obtainable which does not require using a vulnerable group as the indicator. · It is not evident from the report how robust the statistical models developed were, nor is there evidence on bi-variate relationships between indicators (are specific indicators correlated?). · There is a lack of clarity about the underlying definition or approach to multiple deprivation. Some of the indicators relate to direct measures of deprivation, others to indirect measures and some appear to overlap. The model arguably, therefore, lacks conceptual coherence and transparency.

2.5 The Census is a ten yearly event. This means that any revising of the index must make use of non-census indicators. This affords both problems and opportunities. Non-Census indicators are drawn from different sources where data is collected in different ways for different purposes. All the indicators have to be matched (by Post Code Sector). More fundamentally, the composite index contains indicators drawn from different time periods. Clearly, assumptions must be made that this is justifiable (normally arguments are made that some indicators change relatively slowly and it is therefore less of a problem to use more historic data). Equally, there are potential benefits from non-Census indicators. In particular, one can examine direct measures of deprivation that previously relied on Census proxies (for instance, standardised mortality ratios compared with Census-based measures of the permanent sick). It also allows more up-to-date and meaningful indicators to be chosen.

2.6 Note also that this is an area deprivation index for urban areas. Rural conditions are different from urban ones (see: MVA, 1997) and require a separate analysis taking account of uniquely rural forms of deprivation that arise from remoteness, transport costs, accessibility and low income, in-employment, poverty. Criticisms of urban bias in this report and in the construction of the index (for instance, the use of the Census variable no-car households which is clearly biased to urban areas) should, therefore, be qualified by the fact that it is urban deprivation that is being examined. A separate study of rural deprivation could follow as a worthwhile complement to this research (this is further discussed in the conclusions).

2.7 The underlying philosophy of the new index draws on the growing literature on area deprivation indicators and indices (for instance, see: Room (editor) 1995; Lee, Murie and Gordon, 1995; Robson, Bradford and Tomlinson, 1997) and in particular, draws heavily from the approach used by the Department of Environment’s Index of Local Conditions associated with the latter authors (hereafter ILC). Our approach is, however, amended in the light of Scotland’s unique circumstances and the specific objectives of the present research project.

2.8 Our methodology is set out in detail and critically assessed in the next section but the underlying approach is as follows. Deprivation is conceptualised in as broad and inclusive a way as possible, and this is done by developing a comprehensive set of domains of deprivation (reflecting the multiple nature of deprivation, the domains explored include: housing, health, education, crime, labour market and material poverty).

3 2.9 The next stage in the index’s development is to develop indicators for each of these domains, in principle from contemporaneous non-census sources but going back to the 1991 Census if that is the only source of information. These indicators are then analysed as a group for inter-correlations and factor analysis, utilising the results to draw up a final, smaller, set of indicators. The indicators are then measured in terms of their signed Chi-square value (a statistical method for minimising the impact of small and variable denominators in small area statistics such as the population base of an enumeration district or Post Code Sector – see appendix). The resulting values are then standardised using logs and then summed together (the indicators are equally weighted) creating a distribution of scores of deprivation for each Post Code Sector in Scotland. However, rather than simply rank the areas in terms of where the worst are located, the study seeks to go further and identify the extent and intensity of deprivation.

4 3. THE APPROACH TO THE NEW INDEX

3.1 This section expands on the above brief description of the process by which the index was constructed and also sets out the main qualifications and likely criticisms of the approach taken. It begins by examining the concept of deprivation and the literature on deprivation indexes.

Conceptualising Deprivation

3.2 The definition of deprivation underlying the index should be an inclusive one. Furthermore, there has been an evolution in the conceptualisation of deprivation, moving from a narrow focus on poverty (absolute and now relative dimensions of poverty) toward a wider perspective on the multi-faceted nature of deprivation, alongside the current focus on the relational disadvantage associated with social exclusion.

3.3 Poverty was originally conceived in absolute terms such that if households fell below a minimum level or standard they were deemed to be in poverty. Relative poverty reflects the changing nature of poverty: the life-cycle, changing standards of what is acceptable (and generally materially available). Today, the consensus in the literature that the focus should be on relative poverty, reflects the social nature of human beings (as well as the material requirements, income, which allows individuals and households to participate in society).

3.4 Deprivation is a similar concept but distinct. Townsend (1987) considered that deprivation consisted of ‘observable and demonstrable disadvantage’ relative to the typical experience or benchmark that an individual, household or group would compare with. Deprivation covers various conditions, independent of income, experienced by people who are materially poor. Lee, et al (1995) argue (p.16) that the concept of deprivation is the appropriate starting place to examine area-based poverty – using census-based indicators – even though the census is poor at dealing with concepts like poverty because it does not collect information on income or resources.

3.5 The concept of social exclusion has, recently, taken on a pre-eminence in these debates. Whereas poverty might be considered to be about distributional issues and the lack of resources, Room (1995) argues that social exclusion is about relational issues: inadequate social participation, lack of social integration and lack of power (p.105). Social exclusion looks at the processes which disadvantage individuals and their communities. Lee et al (1995) argue that social exclusion compounds disadvantage, increases the probability of the persistence of disadvantage, as well as its spatial concentration and its resistance to traditional policy solutions (p.15).

3.6 Hirschfield (1993, p.1) argues: ‘Unlike traditional notions of poverty, deprivation cannot be studied adequately through a unitary focus on income. A broad range of indicators is required to identify and measure its various dimensions which reflect actual experiences or ‘outputs’ as well as ‘inputs’. These would include measures of provision, attainment and opportunity in key ‘life chance’ markets such as employment, housing and education and in major quality of life fields such as health’. This seems to be the appropriate way forward for the study, allowing the index to

5 identify people/areas experiencing deprivation on different scales simultaneously. Hirschfield identifies the following scales: individuals, in the home, the neighbourhood, in the workplace, at school and in the provision of health and social services (p.3). Domains or dimensions of deprivation are examined later on in this section of the report.

Modelling Deprivation

3.7 In recent years, a large number of deprivation indexes have been constructed for various national and local studies. Lee, et al, (1995) examine ten separate studies. These studies represent a range of objectives, techniques and methods of validation. An overview of these different approaches helps inform the preferred approach for the updated Scottish index.

3.8 The main studies developed include:

· The 1981 DoE Z-score index, · Jarman’s Underprivileged area score used for targeting resources for primary health care, · Townsend’s deprivation index focusing on health inequalities and poverty, · Carstairs’ area-based index of deprivation and health inequalities, · Separate social and material deprivation indexes created by Forrest and Gordon – separating out social (e.g. relational) from material (economic) deprivation, following on from a similar distinction made by Townsend, · The 1991 and 1997 DoE Index of Local Conditions (discussed in detail below), and; · The Breadline Britain Poverty index – census proxies based on poverty survey responses.

3.9 Looking at the these studies, the most common four indicators are also the most significant: unemployment, over-crowding, lone parent households and the proxy for low income - households with no car. At the other end of the spectrum, only single studies used indicators concerned with educational participation, mobility or the economically inactive.

3.10 A range of statistical and weighting techniques were employed in these studies. Indicators were standardised in a number of ways, primarily through Z-scores, Ranges or Chi-squares (see the appendix at the end of this report). In three of the ten studies, the indicators were transformed through logs or some other process in order to reduce skewness and dampen the effect of outliers in the data. Only three of the ten studies made use of external validation techniques (comparing results with specific surveys or administrative data that was available).

3.11 Lee, et al (1995) perform an exhaustive comparative analysis of their ten different models both on a priori grounds and by testing them on census data (even though not all of them were originally designed to be used in this way). They evaluate them according to basic questions such as weighting, validation, standardisation and transformation. They also examine them for their conceptual underpinnings (i.e. is there an explicit model of poverty or something similar, externally derived, which is

6 driving indicator selection?). They conclude that in order to study area deprivation across UK regions, the Breadline Britain approach is the best of the ten. However, if the objective is to study urban deprivation, then the DoE’s Index of Local Conditions (ILC) is the best approach (p.81). This approach underlies the new Scottish study.

3.12 The philosophy and the methodology adopted by Brian Robson and his colleagues in the 1991 ILC and its 1997 update for the DETR (DoE, 1994; DoE 1997; Robson, et al, 1994; 1995; 1997) appears to be the most appropriate route, once it has been tailored to the Scottish context, research objectives and available data. In order to explain the preferred approach we first outline the characteristics of the ILC and then set out how it would be applied to the Scottish situation, given different objectives and data availability.

3.13 The ILC is a multi-level index using Census and non-Census indicators. It was developed from the 1991 Census and then updated in 1997. Reflecting the fact that it has been used for a range of purposes, it provides a range or matrix of deprivation indexes which operate at ED level (Census-only data), at Ward level (Census-only) and at local authority district level (incorporating a range of additional non-Census indicators). The indexes nest upwards so that Census data collected at ED and Ward can be used at higher levels of aggregation, increasing the number of indicators used at each larger level of spatial analysis. The other distinctive feature of the ILC is that it does not simply provide a rank score which would enable one to identify, for example, the worst ten percent of areas (as with the present Scottish index), it provides a number of measures concerned with rank, spatial extent and intensity of deprivation:

· The intensity of deprivation (taken as the average value of the three most deprived wards in an authority) · The ward-based spatial extent of deprivation (taken as the proportion of the total population living in wards which fall within the most deprived 10% of all wards in England) · The ED-based spatial extent of deprivation (taken as the number of EDs which fall within the most deprived 7% of all English EDs expressed as a percentage of all EDs in the relevant authority).

3.14 The methodological decisions in calculating the index involve three key issues:

· Standardisation – Chi-Squares were used to standardise (see appendix 1). Chi- squares are used because they take account of the robustness of data where small numbers are involved. Positive signed indicator scores represent below average or deprived outcomes (relative to the national average). In 1997, the ILC team decided to set all negative scores (above average scores) to zero, thus focusing on deprivation and not well-being. · Transformation –data transformation into logs was used in order to dampen possible outlier effects. · Summation/weighting - the values of each indicator are then added together to give an overall deprivation score for an area – indicators, therefore have an equal weight of one.

7 3.15 In the 1991 index, the Robson team identified three main domains of deprivation:

· Environment - which included health, shelter, security and dereliction, · skills/socialisation - which involved education and the family; and · the resource base - which focused on employment and income.

The intention was to provide an inclusive view of deprivation, focusing on the multi- dimensional nature of urban regeneration. In other words, a multi-dimensional index is deemed appropriate for prioritising expenditures which themselves seek to tackle multiple problems (although Robson recognises that there are multiple objectives for his index, including prioritising local authorities as a whole). Table 3.1 indicates how the index was then constructed at different levels (note that lower level indicators are also used at those higher levels).

Table 3.1: 1991 ILC Domain of Enumeration Ward Local Authority deprivation District District Environment Health Standardised mortality Shelter Overcrowding Lacking amenities Children in unsuitable housing Dereliction Derelict land Security Insurance weightings Skills/Socialisation Education Low education participation Family Low attainment Resource Base Income No car Children in low earner households Income support Employment Unemployment Long-term unemployed

3.16 In 1997, the updating of the index led to the elimination of some indicators and the updating of others using non-Census indicators. Most importantly, the indicator ‘children in unsuitable housing’ (i.e. flats) was left out, reflecting conceptual criticism in the literature (e.g. Lee, Murie and Gordon, 1997).

3.17 Table 3.2 shows the breakdown of the 1997 index. Comparison of the two tables indicate the main changes where non-census data could be applied:

8 · updated standardised mortality rates, · updated derelict land, · updated insurance weightings, · updated educational attainment, · updated income support, · new housing benefit and council tax benefits (housing assistance), · new free school meals, · updated unemployment based on NOMIS and forecasts of population change at the LA district level and; · updated long term unemployed to all unemployed ratio.

3.18 All of these updated or new indicators apply at the local authority scale (and use local authority level forecasts of change for the denominators based on applying the economically active proportions of the 1991 census to 1995 ONS population estimates) – none of them would be useable at the levels of post code district or sector.

Table 3.2 The 1997 Updated ILC Domains of deprivation Census-based indicators Non-census-based Environment Health Standardised mortality rates 1995 Shelter Overcrowding 1991 Lacking amenities 1991 Security Insurance weightings 1997 Dereliction Derelict land 1993 Skills/socialisation Education Low education participation 1991 Low attainment 1996 Resource base Income Free school meals 1996 Housing assistance 1996 Income support 1995 Employment NOMIS unemployment 1997 Long term unemployed 1997

3.19 Several aspects of the ILC could be readily applied to Scotland. However, the Scottish approach would have to differ in a number of respects. Most importantly, the focus would be at sub local authority levels of geography. The policy interest in the index is to identify small, deprived areas, not to rank local authority districts. This is a bigger challenge than the ILC, since data is generally poorer the smaller the area. Second, we are seeking to identify non-census indicators which are measured at sub local authority level. In practice, this means postcode sectors (these can be contrasted with the use in the ILC of wards).

9 3.20 While the focus of the new index in Scotland is with small areas (there is not the same rationale to analyse the index and its indicators at local authority level), it does make sense to apply the multiple measures of deprivation that were used in the ILC study. There is considerable utility in developing the same measures of rank, extent and intensity that have been so usefully set out by the ILC team.

Domains and Indicators of Deprivation

3.21 The ILC team used three broad domains of deprivation. For our purposes, the Scottish study uses six separate domains. The six domains have been selected on several grounds. First, they follow the ILC approach of inclusiveness and as a result include the main dimensions of deprivation found in the literature and in data accessible to the team. Second, they are chosen on a priori grounds, subject to the outcomes of statistical analysis which could, in principle, determine a different final configuration of indicators (for example, dropping one or more of the indicators). Third, the indicator domains reflect the ways in which commentators believe different forms of deprivation combine and impact on each other (e. g. health and housing; poverty and educational attainment).

3.22 In terms of addressing (1) the non-Census indicators and (2) the integration of Census and non-Census indicators, the domains adopted were as follows:

· The labour market. Potential indicators investigated included unemployment, youth unemployment, long-term unemployment, measures of so-called ‘real’ unemployment (that is, including hidden unemployment and transfers on to sickness benefit) and non-employment. The difficulty in measuring wider forms of non-working led to the adoption of the up to date, narrow, claimant count.

· Benefits dependency. Means–tested benefits, as direct measures of the socio- economic position of households may be viewed as more comprehensive outcome measures of poverty as compared to measures like single parent families (MVA, 1997). Income support caseloads would only pick up the very poor but other benefits might help to identify the working poor (family credit, housing benefit, council tax benefit, etc.). Given available data, and for the purposes of this study, income support claimants denominated by adult population is used as the measure of this domain. Census proxies of low material resources have also been collected (no car households; benefit– dependent families).

· Health indicators. Standardised mortality rates (SMRs), low birth weights, and Census variables such as limiting long term illness, are the standard dimensions of health disadvantage that are typically analysed. The present study adopts both non-Census indicators and the Census proxies.

· Education. The focus on educational indicators has been on non-participation: 17 year olds no longer still at school and the spatial distribution of young people who are not full-time students in further and higher education. Other educational data (for instance, attainment records linked to the exam

10 performance league tables, data linked to poverty based on free school meals and clothing and footwear grants and, disciplinary data relating both to truancy and to exclusions) operates at local authority and school-level – which does not match up to Post Code Sector level in any meaningful way. Data relating to participation in Further Education was also supplied by SHEFC. However, the data proved to be unreliable owing to some mismatching of Post Code Sectors, as well as concerns regarding both missing data and definitions. As a result, the study uses non-participation in higher education (non-Census) and the Census indicator for continuing school participation of 17 year old plus students.

· Crime. One useful proxy, used by the ILC, is the spatial variation in home contents insurance premia. To the extent that high rated areas are associated with vulnerability and insecurity, this indicator could be a good measure of this specific dimension of deprivation. Other sources of crime–related data do exist but are all weakened by problems of incomplete coverage, the wrong spatial scale or non-comprehensiveness.

· Housing. Many of the key housing variables operate at Census level: housing tenure, overcrowding, lack of amenities and children in unsuitable accommodation. There is no comprehensive non-Census housing data collected at sub-local authority level. The study, therefore, uses the three Census indicators to complete its set of non-Census and Census indicators that are used to construct a new updated index and a Census 1991 benchmark (see Table 3.3).

3.23 Table 3.3 sets out the indicators selected for the area deprivation index, drawn from Census and non-Census sources. Basically, each domain is made up of indicators from either column 2 (1991 Census) or column 3 (a more up to date non-Census variable). Column 4 indicates some Census proxies that could mimic or replace the non-Census variables. Note that the benefits indicator focuses on income support only. No other means-tested benefit was available for use in the index. Family Credit or Council Tax Benefit would have been preferable because they would also include those qualifying for means-tested benefits but above the IS threshold.

11 Table 3.3 Area Deprivation Indicators (1) (2) (3) (4) Domain of Proposed Census Proposed non- Proposed Census deprivation indicator Census indicator proxy for (3) Housing Overcrowding Lack of basic amenities Vacant dwellings Health (0-65) standardised Permanent sick mortality ratios (singleton) low Long-term illness birth weight ratios Labour market Claimant Claimant unemployment rate unemployment rate Crime/wider Home contents (Vacant dwellings) environment insurance weights Education Participation at school Participation in full- time higher education Poverty No car households Income support recipients Children in non- earner households

3.24 In total, a set of 12 indicators are drawn on to construct both the final updated index and a Census 1991 benchmark. The Census variables used in the final set of indicators are the following:

· Overcrowding (households in permanent buildings who are below the occupancy norm relative to all households in permanent dwellings) · Lack of amenities (households in permanent buildings lacking exclusive use of bath/shower/inside WC relative to all households in permanent dwellings) · Vacant dwellings (household spaces classified as vacant accommodation or other, relative to all household spaces) · Non-participation at school (young people not in full-time education at age 17+ relative to all 17-18 year olds) · No-car households (households with no car relative to all households) · Children in dependent-only households (dependent children in households which contain no adult in employment relative to all children).

3.25 The non-Census variables chosen for the final set of indicators are the following:

· Standardised mortality ratios (0-64 all causes summed for five years 1992- 96 relative to the adjusted 1996 CHI small area population forecast)

12 · Low birth weights (1992-96 summed index of the proportion of low birth weights relative to all Scotland) · Unemployment rate (claimant count [NOMIS], 1996-97, relative to the adjusted 1996 CHI small area population forecast adjusted for age and sex) · Insurance weightings index (three firm average weighting index of Post Code Sector home contents insurance premiums) · Non-participation in higher education (the number of young people not in full time higher education at their permanent home address relative to the Scottish average) · Income Support claimants (the number of claimants by Post Code Sector based on an August 1996 100% scan of the population).

3.26 The rationale for the selection of these indicators and possible caveats for such indicators are set out in the following paragraphs. Note that this set of indicators will be rationalised to a smaller set of indicators which will form the final index (this is set out in the first part of section 4).

3.27 Housing deprivation has traditionally been viewed as a key determinant of wider deprivation. The housing indicators, however, are wholly derived from the Census because there is no comprehensive sub-local authority housing condition data collected. The Scottish House Condition Survey, for instance, is not meaningful statistically at Post Code Sector level. This leaves the researcher with limited options for housing indicators, especially as there are recognised problems with individual indicators (for instance, children in unsuitable accommodation) and one justification for revising the index was to take account of housing and neighbourhood change since 1991. In the light of this, the three indicators chosen reflect the stress of over- crowding, poor house conditions (lack of basic amenities) and a proxy for poor neighbourhoods (large relative numbers of vacant dwellings). It is recognised that the basic amenity variable is increasingly less relevant at the end of the 1990s but its inclusion in the preliminary analysis follows other recent studies.

3.28 In addition to collecting the Income Support claimant data, the study team returned to standard Census-based proxies of material poverty: no-car households and children in dependent-only households. It is recognised that the no-car households variable displays a well-known urban bias. The counter-argument is simply that this is an urban deprivation index which is to be used to allocate spending in urban priority areas. A separate index needs to be designed and constructed for rural deprivation (this is conceptually well recognised in the literature, for example, MVA, 1997). The Census proxies reflect shortage of resources and benefit-dependent families. The Income Support indicator is a more direct measure of material poverty.

3.29 The education indicators relate to participation in education. The argument here is that deprived areas will produce lower proportions of continuing students at age 17+ at school and in higher education. The first indicator comes from the Census, the latter from official statistics on the population of students in further and higher education in Scotland. The denominator for the students in higher education is the Census number of 16-24 year olds in the Post Code Sector in question (and for 17+ staying at school, the total of 17 and 18 year olds in the relevant Post Code Sector). Problems with denominators are discussed further below. Data was also collected for full-time

13 students in further education, as a measure of both vocational and academic education – however, this was not clean data and proved unsatisfactory on practical grounds.

3.30 The health module yielded two indicators: a standardised mortality ratio and a low birth weight index. The mortality ratio is for those aged 0-64 and for all causes. It is the summation of five years’ data in each Post Code Sector (in an attempt to overcome the small numbers problem) and is denominated by the Census population for the relevant small area. The low birth weight indicator is an index of the relative number of low birth weight singleton babies (i.e. less than 2500g) relative to the average proportion of low birth weight babies across Scotland. Again, this is summed over five years.

3.31 Data in the form of insurance premiums and their variation across Scotland was kindly provided by three major general financial institutions (Lloyds TSB, Royal Sun Alliance and the Prudential). This was turned into an index for each company and then averaged. This is a crude but arguably, informative summary of the perceived risks the major insurers apply to different neighbourhoods across Scotland.

3.32 All studies of multiple deprivation have some form of unemployment indicator. In producing an updated index, this is the variable that has caused the most conceptual and practical difficulties. The problem applies both to the numerator and to the denominator of an unemployment rate. On the numerator side, there is the well- known but unresolved controversy about which measure of unemployment to choose (narrow claimant count, labour force survey, or wider measures). Conceptually, a wider measure incorporating non-activity which arises from discouragement from work would be a useful indicator, especially as there is a growing belief that ‘official’ measures seriously under-represent the real level of involuntary non-working. In the absence of alternative reliable data, however, it is necessary to return to the accurate but narrowly-defined claimant count.

3.33 The unemployment denominator raises a general problem that there has been an absence of reliable sub-local authority population data. However, to overcome this, it has been possible to make use of the Community Health Index, used elsewhere by The Scottish Office, which provides an estimate of small area population change (Post Code Sector level) based on a standardisation of changes to GP registers. The research team has used this index to re-compute the denominators for both the unemployment (the economic active) and the benefits (adult population) indicators.

3.34 It is recognised that there are some practical difficulties with the CHI approach – anecdotal evidence from research users, for instance, suggests that there is a significant lag before migrants found their way onto new registers and are removed from their previous GP’s list. At the same time, certain groups use their GP less frequently and this may compound lags in the data. We do not, however, believe these problems undermine the wider advantages that CHI brings by allowing us to develop an index with more contemporary measures of both unemployment and material poverty.

14 From Indicators to Index

3.35 The valid set of indicators can now be analysed in order to create the actual index of area deprivation. The step from a set of valid indicators to an index of deprivation requires a series of statistical approaches. First, the variables are re-formulated in terms of their signed Chi-square value which is also logged (except for those indicators, like the insurance index and the mortality ratio, which are already standardised and cannot be converted to the Chi-square score).

3.36 The Chi-square approach is a statistical method by which to standardise indicators across small areas where one expects large variations in the population base and thus where comparing percentages would be unreliable (for instance if the average incidence of say unemployment was 10% and two small areas both had 20% unemployment, it would be misleading to treat them equivalently if the first area had a population of 100 and the second 5,000). The Chi-square approach works by comparing the expected value of, for example, unemployment, with the observed value in a small area (the expected value is usually the national average percentage converted into expected numbers within the small area). If unemployment is expected to be 10% and the small area observation was higher, the larger the absolute numbers involved, the more confidence we would have that the level of unemployment in that specific small area was significant. The larger the calculated Chi-square statistic, the more statistically meaningful is the divergence between the expected and observed values. The Chi-square can be thought of as an index of the reliability of the area’s score relative to the typical case (see the Chi-square appendix for more details).

3.37 The indicator is a measure of hypothesised deprivation (for instance, of unemployment relative to the average rate of unemployment rather than employment relative to the average employment level) and the index is a measure, in general, of deprivation not well-being. Rather than have negative scores (i.e. implying a level of deprivation below the average on any one indicator) pushing down the overall index score and obscuring the impact of other indicators, the ILC team set negative indicator scores to zero, in order to focus only on deprivation.

3.38 This technique is known as the signed logged Chi-square approach, such that the index is the sum of equally weighted positive (or zero) scores for each indicator, logged to the base 10 to standardise between the indicators. This approach is adopted in the present study. One implication is that low index scores (relative to the worst areas) do not convey as much meaning as is the case for those areas which are deprived and have positive scores on the majority of indicators. To repeat the point, the index is of deprivation only; it cannot tell us as much about the least deprived small areas in Scotland.

3.39 The team could find no compelling reason to develop an explicit weighting strategy. CURDS (1993) suggested a range of possible weighting alternatives, ranging from, among others, null or equal weights, statistical modelling, expert advice and managerial decisions by the index sponsor (this is discussed in more detail in the next part of this section). The research team, therefore, decided to follow the ILC team and apply null or equal weights. In effect, this is an implicit weighting strategy: each domain is assumed to contribute equally. This may or may not be acceptable to readers but we could find no robust reason to weight any one indicator

15 disproportionately. Standardisation through the taking of logs reduces de facto weighting that arises from scaling differences.1

3.40 Once the indicators have been appropriately measured and transformed, the final task is to evaluate which indicators from the valid set should be included in the actual index. Section 4 reports the process by which this was carried out but basically it involves a combination of a priori views as to preference between alternative indicators, looking at their scores and inter-correlations, and using approaches such as factor analysis to aid the final decision-making. It should be recognised that this is not a wholly deterministic statistical exercise but that judgement also plays a significant role.

Caveats and qualifications

3.41 Before turning to the empirical construction of the index and the main results, it is important to re-assert just what the index is and is not. In particular, the main qualifications and problems with the index (as can be found, to different degrees, in any index of this kind) need to be set out. These questions relate to both conceptual and practical issues involving the philosophy of the underlying index, the treatment of the different domains, the suitability of specific indicators, weighting questions and other matters. Some of these problems have been touched on already but this section consolidates the main issues.

3.42 Post-code sectors are the smallest consistent geography that can match Census and non-Census data consistently on a tractable basis. However, working at post-code sector level can lead to problems of mis-identification of the geography of deprivation. Duguid (1995) used Enumeration Districts (EDs) which are smaller than post-code sectors. It is conceivable that a deprived Post Code Sector will contain a combination of deprived and non-deprived EDs. In such a scenario an ED in the worst 10% of EDs may not be included in the worst 10% of post-code sectors. This is the inevitable result of having to work with post-code sector-level data. However, as we have seen, there are problems with some of the Census indicators that can be used at ED level (i.e. it is the case that good deprivation indicators do not exist for all of the domains we are interested in). Second, and more importantly, EDs are unlikely to be a consistent measure of homogeneous communities.

3.43 The second point needs some elaboration. There is a key distinction to make between administrative and functional geographies. Fundamentally, this project wants to identify functional geographies of multiple deprivation but it has to work within existing administrative geographies because that is how the data is collected and organised. However, there is no necessary relationship between the functional geography or spatial distribution of genuine deprivation and the administrative manner that space is organised for statistical purposes. This is equally true for EDs, even though they are smaller and more fine-grain, than it is for post-code sectors. Deprivation may be under or over-bounded by the spatial units we measure phenomenon with (Robson, 1995). In order to address this fundamental problem, the

1 The insurance index is scaled by logs but is measured so that the minimum score is 100 or 2.0 in logs – this has no effect on the index because all Post Code Sectors start with a minimum value of 2.0.

16 research team have adopted a twin-track approach: first, to capture several measures of deprivation (rank, intensity and extent – which rely on different both Post Code Sector and local authority spatial scales) and allow the reader to infer a wider sense of deprivation in the areas examined; and, second, the relevant enumeration districts are available on request.

3.44 A second problem arising from the use of post codes relates to their vintage. In the course of the 1990s, there have been changes made by the Post Office to the definitions used in the post code maps of Scotland. This particularly applies to the Western Isles and Aberdeen. The data collected has involved the previous definitions of post codes that applied at the time of the 1991 Census. The new post code map of Aberdeen involve smaller and a greater number of Post Code Sectors than the 1991 definitions which form the mapping used throughout the present study. This makes it impossible to infer directly the indicator scores using the new Post Code Sector definitions. Readers should bear in mind, therefore, that the results set out in Section 4 are based on the old Post Code definitions.

3.45 A related problem arises because of the 1996-97 re-organisation of Scottish local authorities into a unitary form of local government. Not only were administrative boundaries changed and new authorities formed but, consequently, many Post Code Sectors now cross local authority boundaries, presenting considerable practical problems for the analysis of this index. There are specific difficulties arising around the boundaries of Glasgow City council (with East Renfrew, Renfrew, West Dunbartonshire, South and North Lanarkshire). Again, the researchers have implemented a multi-track approach to the problem. First, to some extent, there is not really a problem in that our focus is with the Post Code Sector that is deprived – if this requires joint responses from local authorities, so be it. Second, for analysis that requires grouping Post Code Sectors into their local authorities (i.e. intensity and extent measures) a mixture of judgement and common sense can be applied. A key question to ask is where does the deprivation chiefly lie and this is a more meaningful indicator than the balance of population. If that cannot be answered in a specific instance, the deprivation score can be attributed to the relevant local authorities on the basis of population.

3.46 The index is designed to pick up urban concentrations of multiple deprivation. It is not meant to be a rural deprivation index. One should not be surprised, therefore, that the areas dominating the index scores are from mainly urban local authorities. As was suggested above, a separate exercise needs to be conducted, specifying and scoping the nature of rural deprivation before going on to construct a bespoke rural deprivation index.

3.47 The rural/urban issue also has consequences for the use of an insurance indicator in the new 1998 index. It is well known that insurance ratings tend to be higher in cities imparting a possible bias to such an indicator. As this index is constructed with urban deprivation specifically in mind we are of the opinion that its inclusion is not necessarily a disadvantage and in fact to exclude such an indicator could result in some useful information being omitted. More serious however, is the criticism that not all areas where there is a significant degree of deprivation have high crime rates. Nonetheless, this is an identical indicator to that used by the ILC over a long period of time. It is undeniably a crude approximation of the variation in crime and perceived

17 insecurity – but there is little else to replace it to deal with what is a highly significant domain of deprivation. Thus, the research team opted to use an insurance-based indicator, for on balance, its benefits were felt to outweigh its disadvantages. In fact, the statistical modelling in Section 4 suggests that it contributes significantly to the overall index. However, given the contentious nature of this particular indicator the research team agreed to carry out a sensitivity analysis (examination of the ranking of index scores with and without the insurance indicator) and to seek the advice of the ILC team. The results of these investigations are outlined at the end of chapter 4.

3.48 Some of the indicators are directly or indirectly based on data drawn from the 1991 Census (including reliance on the Census for benchmark indexes used to contrast with the new largely non-Census index). While the 1991 Census marked considerable improvement on prior population surveys (as a result of advances in computerisation and small area statistics, for example), it was beset by problems of under- enumeration, known in the literature as the ‘missing million’ – a non-random distribution of unrecorded people. ‘Poll Tax’ non-registration is widely recognised to have led to a substantial under-enumeration (and was likely to have been particularly significant in those areas under investigation in the present research). Diamond (1993) suggested that, for England and Wales, there was a total under-enumeration of 1.2 million.

3.49 It is also worth stressing that this is an interim index in the sense that it revises and updates knowledge from the 1991 Census and can inform the type of index that will be constructed from the 2001 Census (one likely, for instance, to include a banded income variable). This may raise the scope for developing a multi-level index which continues to use Census and non-Census variables. However, what should be stressed is that the present study is a beginning rather the culmination of index construction and analysis in this field and should be viewed in such a light.

3.50 One of the key difficulties with revising or updating the index of area deprivation (or, in fact, devising a wholly new index) is the resolving of problems concerning the appropriateness of the denominators used for the indicators. There are two problems. First, different indicators are based in different time periods or require to be longer term in their construction. Thus, a census indicator is derived from 1991, but mortality and birth weight indicators need five years of data to possess sufficient observations to be statistically meaningful. This variation is acceptable if one does not believe indicators change rapidly. Where they do change rapidly, for instance, claimant counts and the wider population they come from, the research team have tried to secure the most up to date information available. The second type of problem refers to the absence of critical denominators. Small area population change and the use of the Community Health Index were discussed above as the strategy adopted by the research team.

3.51 Weighting is a major concern for deprivation index construction. The present study has rejected explicit weighting derived either from external sources (advice, the wider literature, or from sponsor priorities [although no such prioritisation was advanced]) or from internal sources through applying empirical weights (the approach taken by Duguid (1995) using coefficients that emerged from the Principal Components Analysis that selected the indicators then used). Equal or null weighting is the study team’s preferred approach but it is recognised that it does have an arbitrary dimension

18 (as all weighting schemes have) but it is the preferred weighting approach to a deprivation index (Robson, 1995) and it highlights the importance of indicator selection (effectively weighting the index towards the domains examined) and their appropriate transformation.

3.52 The index has to be externally validated. This should occur on several levels. One would want to compare results with known stylised facts (does the finding match up with our empirical expectations; are their outliers and can they be explained; can one explain why certain areas have changed since 1991?). Existing surveys or other official sources of data may help this component of validation. Second, one would also ideally seek local validation – do the identified ‘worst’ areas accord with local understanding? Third, the index would be contrasted with other Scottish indexes of deprivation (including those that can be simulated by the research team and can thereby serve as benchmarks).

3.53 The above paragraphs indicate some of the major judgements and decisions adopted by the research team while developing and constructing the new area deprivation index. These problems will arise in the construction of any index and suggest that there will always be a considerable element of the discretionary and what economists call the ‘second best’ in the process of index construction. However, as the next section demonstrates, the research team have tried to follow best practice in the process of producing the new index and sought to do so in as transparent a way as possible.

19 4. MAIN RESULTS

Creating the Index

4.1 The index was created by selecting the most suitable sub-set of indicators that would satisfy the working definition of deprivation outlined earlier. This was in turn informed by three stages of index development with ‘weaker’ potential indicators dropping out at each stage, leaving a smaller final set for the new index. First, the research team selected a sub-set of suitably transformed indicators from the list set out in table 4.1 below. This was based on our views of what would be acceptable given the conceptual approach taken on deprivation. From this shorter list, certain specific indicators were preferred to other substitutes/proxies. For present purposes, more recent indicators of similar explanatory power were preferred to older 1991 Census- based indicators. Second, a correlation matrix of the indicators’ inter-correlation was inspected, both independently, and as a precursor to the third stage, factor analysis. All three elements were used in developing the new index reported below.

(i) Potential Indicators

4.2 Table 4.1 presents an exhaustive description of the potential indicators that the research team identified for the development of a new index of area deprivation in Scotland. These were derived from the indicators described in paragraphs 3.24 and 3.25. As can be seen, the indicators are a mix of both 1991 Census variables and variables constructed with the use of non-Census data from the following sources:

(i) Information & Statistics Division of the NHS in Scotland, (ii) NOMIS, (iii) DSS, (iv) Lloyds-TSB Bank, the Royal Sun Alliance and Prudential Insurance Companies and (v) The Scottish Office/SHEFC.

4.3 Such a comprehensive list allowed more scope in the development and testing of various forms of indicators. Not only were there Census proxies for the non-Census indicators (which allowed for the creation of a Census-based 1991 version of the new index), but it was also possible to replicate the Duguid (1995) index at Post Code Sector level using signed logged Chi-squares. Thus, the research team was able eventually to develop a new index using Census and non-Census indicators, as well as two benchmarks defined by the same geography.

20 Table 4.1 List of Potential Indicators Indicator Definition Logschl Non-school participation of 17 year olds: (1991 Census variable) Logkids Children in dependent-only households (1991 Census variable) Logoverc Households who are living below the occupancy norm (1991 Census variable) Logsing Single parents (1991 Census variable) Logvacdw Household spaces classified as vacant accommodation (1991 Census variable) Logpermc Those classified as permanently ill (1991 Census variable) Logill Those suffering from a long term illness (1991 Census variable) Loghe Non-Higher Education participation (External Source: SHEFC) Logcc Unemployment Rate (NOMIS claimant count adjusted by CHI population forecast) Logis Income support claimants (Obtained from Department of Social Security, Newcastle-upon Tyne) Logeld Elderly households (1991 Census variable) Lognocar No-car households (1991 Census variable) Logamen Lack of basic amenities (1991 Census variable) Logyouth Youth unemployment (1991 Census variable) Logbirth Low birth weight ratios (External source: ISD, The Scottish Office) Logsmr64 Standardised mortality rates (External source: ISD, The Scottish Office) Loginsur Index of home contents insurance companies premia (based on average of three high street insurers) Logdep Dependency (based on an OECD definition used in Duguid, 1995)

(ii) Indicator Correlations

4.4 Table 4.2 describes the basic correlation matrix between the potential indicators chosen for the development of the new index. Below the diagonal, one can read off individual correlations between indicators, for example, there is a 0.63 correlation between the log of overcrowding and the log of the insurance index. It is clear from the table that several of the indicators are highly correlated, but equally that some are less correlated; the low birth weight indicator is a good example. According to best practice indicators that have correlations mainly between 0.3 and 0.8 should be selected for further analysis.

21 Table 4.2 Correlation Matrix for Potential Indicators Log- Log- Log- Logill Log- Log- Log- Log- Log- Log- Log- Loghe Log- Logcc Logis over amen permc ump schl nocar kids insur smr64 birth vacdw Log- 1.0 over Log- 0.25 1.0 amen Log- 0.55 0.05 1.0 permc Logill 0.54 0.05 0.83 1.0 Log- 0.66 0.14 0.79 0.80 1.0 ump Log- 0.45 0.10 0.55 0.62 0.63 1.0 schl Log- 0.76 0.21 0.67 0.79 0.81 0.59 1.0 nocar Log- 0.67 0.15 0.70 0.70 0.77 0.52 0.68 1.0 kids Log- 0.63 0.19 0.59 0.58 0.62 0.35 0.66 0.60 1.0 insur Log- 0.36 0.12 0.41 0.42 0.42 0.35 0.43 0.33 0.32 1.0 smr64 Log- 0.24 0.13 0.24 0.27 0.24 0.24 0.29 0.23 0.26 0.39 1.0 birth Loghe 0.39 0.10 0.56 0.61 0.61 0.61 0.54 0.53 0.29 0.33 0.26 1.0 Log- 0.55 0.24 0.19 0.19 0.39 0.39 0.44 0.35 0.24 0.19 0.13 0.22 1.0 vacdw Logcc 0.56 0.13 0.58 0.69 0.80 0.80 0.77 0.60 0.46 0.39 0.21 0.50 0.41 1.0 Logis 0.68 0.14 0.76 0.81 0.91 0.91 0.88 0.73 0.64 0.45 0.27 0.58 0.39 0.85 1.0

22 4.5 As was suggested above, several of the potential indicators can be dropped on the grounds that better alternatives are available. On this basis, the substitute indicators: the log of amenities, the log of vacant dwellings, the two census health proxies, the census unemployment claimant count and the two census proxies for poverty were all dropped from further analysis. This left the eight variables summarised in table 4.3 that formed the set of indicators used in the factor analysis.

Table 4.3 Mean Scores of Indicators Selected for Factor Analysis Indicator Mean Standard Minimum Maximum Cases deviation Logschl 0.31 0.46 0.00 1.73 895 Logoverc 0.36 0.75 0.00 3.17 895 Loghe 0.58 0.77 0.00 2.51 895 Logcc 0.61 0.86 0.00 3.00 895 Logis 0.71 1.08 0.00 3.63 895 Logbirth 1.85 0.56 0.00 2.93 895 Logsmr64 1.94 0.29 0.00 2.48 895 Loginsur 2.21 0.18 2.00 2.70 895

4.6 Table 4.3 above presents some basic descriptive statistics for the remaining indicators. Most of the mean scores are less than one which is the result of taking logs in the transformation process. The mean of logbirth and logsmr64 are however, greater than one due to the fact that the data provided was already standardised and therefore Chi-square standardisation could not be applied. The same reason also holds for the loginsur indicator.

(iii) Factor Analysis

4.7 Factor analysis is a statistical process that examines the inter-relationship between a set of variables (the indicators) in order to uncover an underlying relationship (factor) between a sub-set of the indicators. One way of conceptualising factor analysis is to consider it as a measure of the extent to which several variables are measuring the same underlying ‘thing’ (the factor). The process has three stages. First, correlation coefficients are derived for all the variables to be used (as in Table 4.2). Second, factors are extracted from the correlation matrix using Principal Components Analysis (a mathematical representation of the underlying similarity between variables expressed in high inter-correlations) and the factors rotated. Third, a series of diagnostic tests are evaluated in order to assess the quality of the resulting factor.

4.8 It is worth stressing at this point, prior to examining the final factor analysis results, that this procedure can only inform the selection of indicators for the final index. It is a suggestive rather than a definitive process.

23 4.9 From the initial eight indicators selected as a basis for the index, the factor analysis reduced the final number to six. Table 4.4 illustrates the final six indicators and the results of the factor analysis with diagnostic statistics. Essentially, the output of the factor analysis was to ‘eliminate’ two further variables from the list of eight chosen in the previous stage of analysis. Thus, the low school participation indicator was dropped in preference for non-participation in higher education, and the low birth weight indicator was dropped because of insufficient correlation with the other indicators (see correlation matrix, Table 4.2).

4.10 Table 4.4 suggests that a combined index of all six remaining indicators would provide a sensible updated index at the postcode sector level. Moreover, the diagnostic statistics suggest that this factor is of a high ‘quality’ with all factor loadings on each indicator, (a measure of an indicators contribution to the overall inter-correlation of the factor) above or close to 0.6 as is normally required. The correlation matrix determinant can be interpreted as a test for multicollinearity or singularity among the indicators. Provided its value is greater than 0.00001, then we can be confident that each indicator is fulfilling its specific purpose as an indicator of deprivation for its domain only.

4.11 Provided the factors meet, or are in excess of, a threshold Eigenvalue (here set at 1.0) there can be several valid underlying factors which explain deprivation, as was the case with Duguid (1995). Then, Duguid found three among the twelve indicators used. In the present factor analysis reported in Table 4.4, there was only one factor that reached (or exceeded) the critical value.

Table 4.4 Final Factor Analysis Results Indicator Loading Logis 0.93 Logcc 0.84 Logoverc 0.80 Loginsur 0.74 Loghe 0.66 Logsmr64 0.59 Determinant of 0.04 Correlation Matrix Eigenvalue 3.55

Index Results

4.12 The new index is based on the summation of the following signed Chi-square indicators: logis, logcc, logoverc, loginsur, loghe and logsmr64. Of those indicators only logoverc is from the 1991 Census; all other indicators are constructed from more up to date data sources. The index has a mean index score equal to 6.41 and a median score of 4.96 (reflecting the large positive skew in the distribution of the index across Post Code Sectors). For reference, the threshold score to be counted in the 10% worst areas (ranked 1-90) was 11.23. The highest overall score (G22.5) reached was 16.97. The percentile scores above 90% are listed in Table 4.5. There is an important question to consider as to the appropriate threshold to use for the worst Post Code

24 Sectors. Duguid (1995) used the worst 10%; Robson (1995) on the other hand has viewed this as an empirical question, searching for breaks in the distribution. The choice is, to some extent arbitrary, but looking at the high percentile scores and the distribution overall, it was not at all obvious which threshold to use. Figure 4.1 illustrates the distribution of the new index. For simplicity, therefore, this study remains with the 10% criteria adopted by most previous research in this area.

Figure 4.1 Distribution of 1998 Index

400

y 300 c n e u q e r F

200

100

0 2 3 4 5 6 7 8 9 1 1 1 1 1 1 1 1 .0 .0 .0 .0 .0 .0 .0 .0 0 1 2 3 4 5 6 7 0 0 0 0 0 0 0 0 .0 .0 .0 .0 .0 .0 .0 .0 0 0 0 0 0 0 0 0

New Index Score

Table 4.5 High Percentile Index Scores Percentile Value 91 11.55 92 11.92 93 12.28 94 12.75 95 13.45 96 14.01 97 14.70 98 15.07 99 15.61

4.13 In order to get a deeper insight into the new index and to compare it with earlier periods or index models, it is helpful to have a benchmark as a point of comparison. This did not turn out to be straightforward for the preferred benchmark, the Duguid 1991 Index (Duguid, 1995) was not useable in its original form. The 1991 index of

25 area deprivation was based on enumeration districts and a different methodology (weighting by Principal Components Analysis and used percentage scores rather than absolute values). Using the text of the 1995 report as the basis, the present research team constructed a surrogate index using the 6 indicators calculated by Duguid (1995) and converted them into a logged signed Chi-Square.

4.14 The comparison, then, is between indicators with identical definitions but constructed using different methodologies; i.e. the current use of logged signed Chi-square as opposed to the earlier study’s use of percentages and Principal-Component Analysis co-efficients. A second and arguably more useful comparison of ‘then and now’ is provided by an alternative benchmark which sets the new 1998 index in terms of Census proxy variables only. This is also based on logged and signed Chi-Squares and provides a more robust, consistent, comparison of the extent of deprivation in 1991 with the new, updated index.

Table 4.6 Indicator Composition of the New 1988 Index and its two Benchmarks 1998 index Duguid model using the 1991 Census version of Chi-Square method the new (1998) model Logoverc Logoverc Logoverc Logis Logsing Logkids Logcc Logyouth Lognocar Logsmr64 Logpermc Logpermc Loghe Logump Logump Loginsur Dependency Logschl Logvacdw

4.15 Table 4.6, above, summarises the different indicators used in each index. The main difference is that Duguid (1995) used indicators based on youth unemployment and single parents. These client-based indicators were dropped from the new index on the grounds that they are imperfectly related to more direct measures of deprivation and they may also be stigmatising. In the new model’s 1991 version, permanent illness is used as a proxy for the mortality ratio, the Census unemployment claimant count proxies the updated claimant count, low school participation proxies low higher education participation, and high levels of vacant dwellings proxies the insurance index of crime.

4.16 Table 4.7 presents in descending order, the ranking of the worst 20% of post-code sectors on the basis of the new updated index (Rank 1998). The table also provides a comparison with the equivalent ranking of the same Post Code Sector in 1991, using the Census variables in 1991 as a comparator benchmark (Rank 1991) and also the remodelled Duguid index at Post Code Sector level (Duguid 1991). Each Post Code Sector has been assigned to a unitary local authority. However, in some cases this matching proved difficult because ‘look-up’ tables for this purpose either split a Post Code Sector across local authorities or turned out to be incorrect. Any remaining errors in the local authority coding should have been removed. Ultimately, it is the identity of the Post Code Sector that matters, not the wider local authority to which it is assigned (which is simply a means of classifying the Post Code Sectors). Nonetheless, it is recognised that small errors in local authority attribution may distort

26 the measures of intensity and extent used below – but this should be marginal at worst.

Table 4.7 Area Deprivation Index Rank of Worst 20% Areas by PC Sector Rank 1998 Duguid 1991 Rank 1991 PC Sector Local Authority

1 1 1 G22 .5 Glasgow 2 4 5 G34 .9 Glasgow 3 2 3 G33 .4 Glasgow 4 9 7 G21 .2 Glasgow 5 3 2 G15 .7 Glasgow 6 10 10 G34 .0 Glasgow 7 6 11 G31 .4 Glasgow 8 17 14 G51 .2 Glasgow 9 5 4 G45 .9 Glasgow 10 8 12 G5 .0 Glasgow 11 7 6 G45 .0 Glasgow 12 20 20 G21 .4 Glasgow 13 23 26 G33 .5 Glasgow 14 13 18 G53 .6 Glasgow 15 24 21 G31 .3 Glasgow 16 16 15 PA3 .1 Renfrew 17 22 16 G40 .4 Glasgow 18 29 23 G21 .1 Glasgow 19 26 25 G51 .3 Glasgow 20 25 37 G22 .6 Glasgow 21 15 13 G15 .8 Glasgow 22 21 19 G53 .5 Glasgow 23 14 32 G22 .7 Glasgow 24 11 8 EH16.4 Edinburgh 25 18 17 G20 .9 Glasgow 26 42 38 G32 .6 Glasgow 27 12 9 G33 .3 Glasgow 28 32 52 G51 .4 Glasgow 29 38 22 EH4 .4 Edinburgh 30 31 24 G14 .0 Glasgow 31 19 35 G40 .3 Glasgow 32 30 31 G46 .8 Glasgow/East Renfrew 33 37 44 G32 .7 Glasgow 34 52 43 G42 .7 Glasgow 35 43 27 G5 .9 Glasgow 36 33 21 G21 .3 Glasgow 37 51 39 G53 .7 Glasgow/East Renfrew 38 34 51 DD2 .3 Dundee 39 49 55 G31 .5 Glasgow 40 40 34 G72 .0 South Lanarkshire 41 28 30 ML5 .5 North Lanarkshire 42 110 49 G51 .1 Glasgow 43 46 68 G42 .0 Glasgow 44 63 40 G40 .2 Glasgow 45 47 50 G31 .1 Glasgow 46 27 33 DD4 .8 Dundee 47 35 54 G20 .0 Glasgow 48 58 71 EH5 .1 Edinburgh 49 50 29 DD3 .7 Dundee 50 48 36 G33 .1 Glasgow 51 44 28 G20 .8 Glasgow 52 85 48 DD4 .0 Dundee/Angus

27 53 70 76 G73 .1 South Lanark 54 101 42 G42 .8 Glasgow 55 68 69 G13 .4 Glasgow 56 72 72 PA3 .2 Renfrew 57 62 70 G52 .4 Glasgow 58 57 46 FK8 .1 Stirling 59 39 45 PA15.2 Inverclyde 60 155 102 PA1 .1 Renfrew 61 61 78 ML3 .0 South Lanark 62 45 66 G71 .5 North Lanark 63 74 67 G13 .2 Glasgow 64 84 105 G81 .4 West Dumbarton 65 75 131 G81 .5 West Dumbarton 66 69 90 G81 .1 West Dumbarton 67 105 118 KA3 .1 East Ayr 68 59 62 G32 .8 Glasgow 69 76 60 G4 .0 Glasgow 70 79 117 DD4 .9 Dundee 71 77 80 ML2 .7 North Lanark 72 141 101 G40 .1 Glasgow 73 78 59 PA15.4 Inverclyde 74 108 83 G13 .3 Glasgow 75 80 124 KA8 .9 South Ayr 76 159 91 EH6 .6 Edinburgh 77 67 75 G72 .7 South Lanark 78 55 77 ML4 .2 North Lanark 79 56 61 PA15.3 Inverclyde 80 237 196 PH1 .5 Perth & Kinross 81 66 88 KY5 .8 Fife 82 36 41 G20 .7 Glasgow 83 83 58 PA2 .0 Renfrew 84 114 74 PA1 .2 Renfrew 85 60 63 ML5 .4 North Lanark 86 86 89 ML6 .7 North Lanark 87 82 109 PA3 .4 Renfrew 88 100 85 G52 .1 Glasgow 89 116 145 EH11.3 Edinburgh 90 142 65 G31 .2 Glasgow 91 71 93 ML6 .6 North Lanark 92 65 64 ML5 .2 North Lanark 93 41 57 PA14.6 Inverclyde/Renfrew 94 88 94 KA6 .7 East Ayr 95 89 99 ML2 .0 North Lanark 96 94 100 DD3 .0 Dundee 97 64 84 DD2 .4 Dundee 98 103 113 ML6 .0 North Lanark 99 162 184 DD11.1 Dundee 100 104 110 FK2 .7 Falkirk 101 169 115 DD4 .6 Dundee 102 140 126 G11 .6 Glasgow 103 121 136 FK10.1 Clackmannan 104 197 138 KA7 .1 South Ayr 105 122 112 PA4 .8 Renfrew 106 117 79 DD1 .5 Dundee 107 185 197 KY1 .2 Fife 108 106 132 G81 .2 West Dumbarton 109 81 56 G3 .8 Glasgow 110 96 127 KY8 .2 Fife 111 101 143 G42 .9 Glasgow 112 144 166 KY8 .3 Fife 113 93 95 ML2 .9 North Lanark 114 102 155 KA1 .4 East Ayr

28 115 87 86 G43 .1 Glasgow 116 178 114 AB2 .2 Aberdeen 117 92 87 ML1 .4 North Lanark 118 53 92 PA16.0 Inverclyde 119 148 209 KA18.4 East Ayr 120 146 175 G66 .2 East Dumbarton 121 229 119 AB1 .3 Aberdeen 122 115 122 KA8 .0 South Ayr 123 165 130 G23 .5 Glasgow 124 135 178 DG9 .7 Dumfries and Galloway 125 175 158 G1 .5 Glasgow 126 266 206 EH8 .8 Edinburgh 127 215 188 AB2 .1 Aberdeen 128 126 133 KA18.3 East Ayr 129 326 289 DG1 .2 Dumfries and Galloway 130 190 233 KY1 .3 Fife 131 209 285 KA3 .2 East Ayr/North Ayr 132 133 97 G41 .1 Glasgow 133 283 151 EH3 .8 Edinburgh 134 227 190 EH54.5 West Lothian 135 228 156 EH14.2 Edinburgh 136 138 125 G33 .2 Glasgow 137 111 96 G69 .7 North Lanark/Glasgow 138 201 237 DG2 .0 Dumfries and Galloway 139 95 144 ML1 .5 North Lanark 140 286 168 EH6 .8 Edinburgh 141 139 159 FK1 .4 Falkirk 142 125 73 G12 .8 Glasgow 143 107 134 PA14.5 Inverclyde 144 118 169 KY8 .1 Fife 145 129 121 PA15.1 Inverclyde 146 130 221 G81 .3 West Dumbarton 147 173 200 KA12.9 North Ayr 148 99 103 PA5 .0 Renfrew 149 73 106 ML3 .9 South Lanark 150 255 161 G11 .5 Glasgow 151 156 82 EH8 .9 Edinburgh 152 54 128 PA16.7 Inverclyde 153 112 137 KA20.4 North Ayr 154 291 228 EH11.4 Edinburgh 155 261 141 EH7 .5 Edinburgh 156 277 171 EH11.2 Edinburgh 157 127 148 ML1 .1 North Lanark 158 164 281 IV17.0 Highland 159 147 139 KA20.3 North Ayr 160 329 260 EH17.7 Edinburgh 161 191 107 G41 .2 Glasgow 162 151 199 G15 .6 Glasgow 163 91 91 KA21.6 North Ayr 164 128 153 G73 .4 South Lanark 165 236 195 EH17.8 Edinburgh/Midlothian 166 214 246 FK6 .6 Falkirk 167 210 265 G82 .3 West Dumbarton 168 149 177 KA12.0 North Ayr 169 220 216 EH7 .6 Edinburgh 170 186 183 EH47.9 West Lothian/North Lanark 171 373 327 IV3 .5 Highland 172 143 218 KY11.4 Fife

29 173 113 192 KA18.2 East Ayr 174 232 250 PA2 .6 Renfrew 175 97 53 G3 .6 Glasgow 176 124 202 G73 .5 South Lanark 177 180 181 KA12.8 North Ayr 178 235 142 DD1 .2 Dundee 179 120 154 PA2 .8 Renfrew 180 198 261 ML11.0 South Lanark Notes: 1 Re-estimation of Duguid’s 1991 Census-based index using logged Chi-squares at Post Code Sector level. 2 1991 Census variables only version of the 1998 index (using Census proxies for the 1998 non-Census indicators). 3 Rank 90 (highlighted) is the 10% percentile 4 Several Post Code Sectors were difficult to attribute to specific unitary local authorities – these have been shown in the table sharing more than one ULA. Local experts will know the specific position better than the research team. Throughout the assigning process we have followed judgement where we are confident of the spatial source of deprivation and left the Post Code Sector split where we are not. The maps at the end of the report also proved useful in placing Post Code Sectors within ULAs.

4.17 Making comparison between the 1998 and 1991 versions of the new index (Rank 1998 and Rank 1991), several points stand out. Most significant is the relative stability of bad areas, particularly among the very worst areas. Only 10 out of the worst 10% have managed to improve sufficiently by moving down into the next decile (20%). They are; PA14.6, ML5.2, ML1.4, DD2.4, DD1.5, G3.8, G43.1, G12.8, G3.6 and EH8.9. Thus 89% of the worst 10% of areas in 1991 remain within the lowest decile in 1998.

4.18 Eleven Post Code Sectors have joined the worst 10%. They are: PA1.1, PA3.4, G40.1, G81.4, G81.5, KA3.1, KA8.9, DD4.9, PH1.5 and EH11.3. PH1.5 was the only Post Code Sector that came from a lower decile than the second decile. The top one per cent in the new index all had rankings between one and fourteen in 1991. The top 5% (the 45 highest scores) had only 6 Post Code Sectors which had been out with the top 5% in 1991 (all of which were between the 6-10% highest index scores). Thus as a proportion of all Post Code Sectors, approximately 1% have moved in or out of the most deprived areas.

4.19 There is a limited number of large moves (defined as a jump in excess of 25 places) up or down the rankings. However, some large moves are worth highlighting particularly the large moves up the index (a worsening of relative deprivation) of PH1.5, three West Dumbarton Post Code Sectors (G81.1, G81.4 and G81.5), DD4.9, G40.1, KA8.9, EH11.3 and PA1.1 and the large move down the index (an improvement in relative deprivation) of PA14.6, G31.2, PA2.0, DD3.7, G20.8 and G20.7. However, all of these large moves did take place in the 6-10% part of the first decile (with the exception of PH1.5); moves in the 1-5% band were uniformly smaller.

4.20 For the reasons outlined above, it is difficult to make meaningful comparisons with the 1991 Duguid index results reported in Duguid (1995) because of the different methodology and spatial scale. In that report, four sets of comparative results were considered: the identification of the worst 10% of EDs and their location, comparisons with 1971 and 1981, tabulations of the worst areas by concentrations of housing tenure and by urban-rural divisions. Housing tenure was based on the Census figures, which are not very meaningful for the new index. Neither are urban-rural comparisons, given the specifically urban focus of the new index. Also, the local

30 authority comparison was made on the old district boundaries. Nonetheless, some valid points of comparison do emerge.

4.21 Duguid (1995) reported that 49.5% of EDs in the worst 10% were in Glasgow (old boundaries), whereas the new index suggests that 57.7% of the worst Post Code Sectors are within Glasgow’s (new) boundaries. Aberdeen had less than one per cent; in the new index it has zero. Edinburgh had 6.6% of EDs in the worst 10%; in the new index it has 5.6% of Post Code Sectors. Summing the old North Lanarkshire Districts, the Duguid report suggests that just under 8% of the worst EDs were within North Lanarkshire’s boundaries, compared with 6.7% of Post Code Sectors in the new index. Some of these changes can be explained by the geographic effect of the shift to Post Code Sectors; some of it is due to changing deprivation in the same places. Working out the relative shares will require further analysis of enumeration districts which is beyond the scope of the present research.

Table 4.8 Proportion of worst 10% and 20% areas in each ULA Worst 10% Worst 20% ULA PC Sectors % of total PC Sectors % of total Glasgow 52 57.7 65.5 36.4 Renfrew 6 6.7 10.5 5.8 Edinburgh 5 5.6 15.5 8.6 East Renfrew 1 1.1 1 0.55 Dundee 5 5.6 10 5.55 South Lanark 4 4.4 7 3.88 North Lanark 6 6.7 14.5 8.05 West Dumbarton 3 3.3 6.5 3.6 Stirling 1 1.1 1 0.55 Inverclyde 3 3.3 7.5 4.2 East Ayr 1 1.1 6.5 3.6 South Ayr 1 1.1 3 1.7 Perth & Kinross 1 1.1 1 0.55 Fife 1 1.1 7 3.9 Angus 0.5 0.56 1.5 0.83 North Ayr 0 0.0 6.5 3.6 Falkirk 0 0.0 3 1.7 Clackmannan 0 0.0 1 0.55 Aberdeen 0 0.0 3 1.7 East Dumbarton 0 0.0 1 0.55 Dumfries & 0 0.0 3 1.7 Galloway West Lothian 0 0.0 1.5 0.83 Highland 0 0.0 2 1.11 Midlothian 0 0.0 0.5 0.28

4.22 The geography of deprivation is stark with the worst areas wholly dominated by Glasgow, which has no fewer than 52 of the worst 90 post-code sectors. Table 4.7 indicates that North Lanarkshire and Renfrew have 6 Post Code Sectors in the worst 10% and several, primarily large urban authorities, have between three and five (Edinburgh, Dundee, South Lanarkshire, West Dunbartonshire and Inverclyde). Stirling, Fife, Perth & Kinross and East and South Ayr have one. It was pointed out above that the index would be largely urban-centred and this is clearly borne out by the results. Widening the scale to the worst 20% brings in more local authorities (e.g.

31 Aberdeen, Falkirk and North Ayr) but typically reinforces the basic deprivation pattern. Glasgow does not dominate the second decile but that must be partly because such a large share of the city has already been explained in the first decile! The maps of Glasgow and North Glasgow in particular reinforce the concentration of deprivation in Scotland (see below).

Intensity of Deprivation

4.23 Table 4.9, presents intensity scores for those unitary local authorities where at least three valid Post Code Sector scores could be attributed across the worst 250 Post Code Sectors. The highest three scores for each local authority were summed and divided by three to obtain an average measure of intensity. The shaded area of Table 4.9 indicates those ULAs whose three scores all occurred within the worst 10% of post-code sectors.

Table 4.9 Intensity Measure of Deprivation Unitary Local Average of three highest pc sector scores Authority Glasgow 16.60 Edinburgh 14.24 Dundee 13.50 Renfrew 13.40 South Lanark 12.95 North Lanark 12.62 West Dunbartonshire 12.25 Inverclyde 11.98 East Ayr 11.32 Fife 10.99 South Ayr 10.98 Perth & Kinross 8.15

4.24 As can be seen from Table 4.9, Glasgow has the highest score of all the ULAs illustrated in the table and is some 17% greater than the next highest score (Edinburgh). Dundee and Renfrew follow some way behind; the two Lanarkshire councils and West Dunbartonshire are then next, with broadly similar scores. It should be borne in mind that this is a crude measure of intensity, particularly so as it was necessary to divide some Post Code Sectors between unitary local authorities. Measures of intensity and, to a lesser degree, the extent of deprivation, should be viewed as wider perspectives on the simple ranking score. Moreover, looking at all three measures together does impart more information than just one measure of deprivation. The latter two measures do, however, need to be treated cautiously.

32 Extent of Deprivation

Table 4.10 Extent of Deprivation: Share of ULA Population in Worst 10% of PC Sectors1 ULA ULA Population Population in worst 10% Extent of Post Code Sectors Glasgow 618,430 369,313 0.60 Dundee 150,250 46,977 0.31 West Dumbarton 97,790 21,001 0.21 Inverclyde 87,268 17,947 0.21 North Lanark 325,940 63,343 0.19 Renfrew 178,550 31,715 0.18 Stirling 82,750 8,611 0.10 Edinburgh 443,600 36,401 0.08 South Lanark 307,450 25,346 0.08 South Ayr 114,360 8,440 0.07 East Ayr 123,820 6,114 0.05 Perth & Kinross 132,570 6,860 0.05 East Renfrew 89,383 2,396 0.03 Fife 349,300 6,456 0.02 Angus 111,020 3,799 0.03 Sources: COSLA, 1998 Directory of Scottish Local Government and Dept. of Urban Studies, Glasgow University.

Note: 1. Where Post Code Sectors are split between unitary local authorities, and in the absence of any more refined knowledge, the extent measure has been distributed from the Post Code Sector population according to the percentage share of the population of the two local authorities in question. In other words if Fife had 80% of the combined population of two ULAs then 80% of the population of the Post Code Sector in question was assigned to Fife.

4.25 Table 4.10 illustrates the extent of deprivation which is defined as the share of a local authority’s total population which is located in that local authority’s Post Code Sectors belonging to the worst 10% of all Post Code Sectors. This measure is derived from the CHI index and is subject to a margin of error. It is also the case that there is measurement error in the sense that not everyone living within one of the worst Post Code Sectors can be presumed to be deprived (smaller spatial scales, such as enumeration districts, would be better, though not perfect, at measuring extent). Nonetheless, it is quite striking in its distribution. Glasgow has 60% of its population living in areas of multiple deprivation. Just under a third are located in the worst areas of Dundee; and either side of a fifth of the population of Inverclyde, North Lanarkshire, West Dunbartonshire and Renfrew. This table makes an interesting comparison with Table 4.8 and suggests that extent is narrower than number of bad areas for several unitary local authorities. In other words, an authority might be ranked highly in terms of having a large number of bad Post Code Sectors; yet this may not impact on a similar proportion of its population.

33 4.26 The above paragraphs suggest that all three measures of deprivation be considered along side each other, for looking at any one measure only provides one aspect of the wider picture. For example, Edinburgh scores very highly on intensity of deprivation but in terms of population ‘affected’ this only amounts to 8% suggesting, at first glance, that deprivation is not concentrated or extensive in the city. However, the City of Edinburgh does contain Post Code Sectors which are ranked quite highly up the new index and, one would conclude that serious pockets of deprivation exist there.

4.27 At the other extreme, Perth & Kinross scores as the lowest intensity measure as well as one of the lowest extent measures. However, this hides the considerable jump that PH1.5 made up the ranking table (relative to 1991) suggesting that deprivation has worsened in certain parts of Perth & Kinross. Fife provides another good example. Scoring very low on both intensity and extent again leads us to conclude that deprivation is not a serious problem in Fife. However, a glance at the ranking table reveals that Fife has 7 of its Post Code Sectors in the worst 20% of Post Code Sectors in Scotland.

4.28 It is thus clear, that each measure imparts its own specific ‘angle’ on deprivation in Scotland. One should weigh up all the evidence before arriving at a judgement regarding the seriousness of the problem in any one Post Code Sector or ULA.

Mapping of Deprivation

4.29 The maps on the following pages present graphically the results for the worst 10% of post-code sectors (and in some cases, the worst 20%). With qualifications, the maps demonstrate vividly the spatial concentration of deprivation in Scotland.

4.30 The maps are a crude representation of the spatial pattern of deprivation. They start with the Scottish and central Scottish levels of aggregation before examining individual local authority areas which encompass the worst 10% of Post Code Sectors (see Table 4.8). The tables distinguish between the worst 10% and worst 20% of Post Code Sectors. The worst 10% are represented by a shaded circle; the next 10% (the second decile) by a shaded triangle. The maps are ordered as follows:

· figure 4.2 Scotland worst 10% · figure 4.3 Central Scotland worst 10% and 20% · figure 4.4 West Central Scotland (including Stirling) worst 10% and 20% · figure 4.5 East Central Scotland, Perth and Dundee (10% and 20%) · figure 4.6 Glasgow (10% and 20%) · figure 4.7 Dundee (10% and 20%) · figure 4.8 Edinburgh (10% and 20%)

34 5. CONCLUSIONS

5.1 The concluding section of the report summarises the research, sets out the aims and contents of the accompanying Volume II of findings and makes some suggestions both for future research and for future index revisions.

Summary

5.2 The Scottish Office commissioned the University of Glasgow’s Department of Urban Studies to develop and analyse a new index of area deprivation, thus updating the present index based on 1991 Census variables. A scoping study was completed which identified a method to develop such an index and also set out the desired indicators and the availability of data to create these indicators. This led to an interim index based on a combination of Census and more recent non-Census variables. Subsequently, this index was revised to include more non-Census indicators and further analysis was then carried out and reported in the sections above. The research was carried out between May and September 1998.

5.3 Through a detailed literature review, the research team decided to customise the English Index of Local Conditions (ILC) for the specific Scottish circumstances of the area deprivation index. Basically, the new index operates by summing together suitably transformed indicators of deprivation, with each indicator based on the signed logged Chi-square of the deprivation indicator in question. For instance, the Chi- square for education in the index measures the difference between the actual or observed number of non-participants in higher education in a particular post code sector and what would be expected given national averages. The larger this number, the more meaningful statistically is the gap between the observed and the expected level of non-participation. In this context, ‘signed’ refers to the fact that any negative indicator scores (literally a measure of non-deprivation relative to the average) is set at zero such that the index is exclusively concerned with deprivation and not well- being.

5.4 The ILC index had three other distinctive features: it operated at different geographic scales (enumeration district, ward and local authority); it used Census and non-Census indicators to capture the different domains of deprivation; and, it adopted several different measures of the distribution of deprivation (rank order, intensity and extent).

5.5 Due to data limitations, the new Scottish index only operates at one spatial scale – Post Code Sector. This is the smallest level of geography consistently applicable across all the data. It is less fine-grain than enumeration districts, but it does allow non-Census data to be used at a smaller level of geography than is possible in England. The Scottish index does use the principle of domains of deprivation – six domains are applied, five of which yield non-Census indicators. The Scottish index is also able to replicate the three different measures of deprivation distribution.

35 5.6 The six domains of deprivation and their derived indicators were:

* Labour market disadvantage (unemployment claimant count divided by the economically active), * Material poverty (income support claimants/adult population), * Inadequate housing (overcrowding/permanent households), * Health disadvantage (standardised mortality ratios, all deaths 0-64), * Educational disadvantage (non-participation in higher education/modal age reference group); and * Crime/insecurity (index of variation in insurance home contents from three major insurance providers).

5.7 These indicators were identified separately before factor analysis was applied which suggested that the above six indicators, from a wider set of potential indicators, should make up the final index. Each indicator was therefore based on the signed logged Chi-square and summed, producing an ordering of the 895 Post Code Sectors. Although there were some difficulties resulting from a handful of Post Code Sectors crossing the boundaries of two local authorities, it was then possible to proceed with the analysis.

5.8 The analysis took the form of analysing the ranking of the new index with reference to a benchmark (in practice two benchmarks were used – a surrogate of the new index based wholly on 1991 Census variables and a replication of the earlier Scottish Office index constructed by Duguid but set out in terms of signed logged Chi-squares operating at Post Code Sector level). The analysis also looked at the intensity and extent of deprivation and carried out some preliminary mapping of the results. The main findings are summarised in paragraph 5.9.

5.9 Section 4 describes the main findings from the index and these are presented in Tables 4.3 to 4.10 and the maps and figures also set out in that section. The main findings could be summarised as follows:

· Glasgow dominates the worst 10% of areas across all the measurement dimensions. · Among the other authorities, Dundee, Renfrew, Edinburgh, North and South Lanarkshire, Inverclyde, North and East Ayr score more heavily. · Overall, comparing with the 1991 benchmark, there is comparatively little movement, with only 10 Post Code Sectors moving between the worst 10% and the second 10% (plus one rapidly declining Post Code Sector in Perth and Kinross). · The top 1% of Post Code Sectors remained virtually unchanged (in terms of composition) and there was considerable stability in the worst 5%, too. Overall, as a proportion of all Post Code Sectors, approximately one per cent have moved in or out of the most deprived areas. · The crude measure of intensity of deprivation confirmed the urban concentration of deprivation, showing a close relationship between ranking and intensity. · The extent measure of deprivation indicated the proportion of a local authority’s population living in the worst 10% of Post Code Sectors – Glasgow

36 again dominated with a clutch of urban local authorities well below the Glasgow score.

Volume II and Research Extensions

5.10 The new index contains a wealth of useful information and potential analysis. At the same time, it was recognised that the basic findings of the index had to be entered into the public domain as soon as they became ready. As a result, this volume of the report will be followed by a second volume which will cover the construction of the index, the academic debates, the analysis of the domains of deprivation and a wider examination of the results and the philosophy of the index.

5.11 Research of this type needs to be validated. What this means in practice is that the index needs to be examined by other experts, contrasted with other studies of deprivation and analysed by research users (in the broadest sense, for instance, those citizens who live in the areas at the upper end of the index ranking). In order to facilitate this process, the research has to be disseminated to all those concerned.

5.12 Part of the brief required discussion of future developments with the index and its successors. The main points that stand out are as follows:

* There needs to be a separate study of rural deprivation, * There is merit in a multi-level and multi-measure analysis of area deprivation, * Direct measures of deprivation are preferable to indirect or proxy measures (i.e. the Census should not be used if there is better non-Census data, for instance, social security data), * There needs to be continued progress with integration and joint working between government data holders in order to maximise the usefulness of geo- referenced socio-economic data, * Future indexes should continue with the philosophy of multiple domains measured through techniques in similar vein to the signed logged Chi-square; and * All indexes are flawed in that there is no pure or wholly correct way to model functional geographies of disadvantage (because spatial data is administratively based). All decisions on levels of analysis therefore require some judgement.

5.13 Apart from the immediate purpose of using the new index to prioritise areas for urban regeneration purposes, there are several wider applied uses that could be made of the index. One example of this would be to use the 1991 proxy and the new index to examine the impact of urban regeneration projects – in other words, to try to measure the impact of projects over a period of time. At a more academic level, the new index increases the number of deprivation indexes potentially available to researchers, widening the scope to test the robustness and sensitivity of specific indicators and indexes, both in general and for specific applied purposes.

37 6. REFERENCES

Centre for Urban and Regional Development Studies (CURDS) (1993) Indicators of Disadvantage and the Selection of Areas for Regeneration: A Report to Scottish Homes. Scottish Homes: Edinburgh.

Department of the Environment (1994) A 1991 Index of Local Conditions. HMSO: London.

Department of the Environment (1995) 1991 Deprivation Index: A Review of Approaches and a Matrix of Results. HMSO: London.

Diamond, I (1993) Who and Where are the Missing Millions? Paper prepared for the British Society of Population Studies Conference, Southampton, September.

Duguid, G (1995) Deprived Areas in Scotland. Central Research Unit, The Scottish Office: Edinburgh.

Gibb, K (1997) Housing Investment to Tackle Social and Economic Exclusion. Report to Scottish Homes. University of Glasgow, Centre for Housing Research and Urban Studies.

Hirschfield, A (1993) Using the Population Census to Study Deprivation. Conference Paper, Joint IBG, RSA, BSPS Conference on the 1991 Census, Newcastle-upon-Tyne , September.

Lee, P, Murie, A and Gordon, D (1995) Area Measures of Deprivation: A study of current methods and best practices in the identification of poor areas in Great Britain. Centre for Urban and Regional Studies: University of Birmingham.

MVA (1997) Review of Grant Aided Expenditure Assessments: the Effects of Deprivation on the Need for Expenditure by Local Authorities – Final Report, prepared for The Scottish Office, Central Research Unit.

Morphet, C (1992) ‘The Interpretation of Small Area Census Data’, Area, Vol. 24, pp. 63-72.

Robson, B, Bradford, M and Deas, I (1994) Relative Deprivation in Northern Ireland. Policy Planning and Research Unit, Occasional Paper 28.

Robson, B, Bradford, M and Tomlinson, R (1997) Updating the Index of Deprivation: Consultation Draft: Report to DETR. Centre for Urban Policy Studies: University of Manchester.

Robson, B, Bradford, M and Tye, R (1995) ‘The Development of the 1991 Local Conditions Index’ in Room, G (Ed) Beyond the Threshold: The Measurement and Analysis of Social Exclusion. The Polity Press: Bristol.

Room, G (ed.) (1995) Beyond the Threshold. The Measurement and Analysis of Social Exclusion. The Polity Press: Bristol.

38 Townsend, P (1987) Life and Labour in London. CPAG: London.

Walker, R (1995) ‘The Dynamics of Poverty and Social Exclusion’, in Room, G. (ed.) (op.cit.).

39 APPENDICES

1. Using Signed Chi-Squares The ILC approach is only one of several ways of standardising and arriving at an index made up of multiple indicators. Standardisation is used in index-building to overcome difficulties where variables are measured on different scales or where variables have a differential impact on the population (Lee, Murie and Gordon, 1997, p.19). Lee et al argue that three main techniques have been used to standardise: · Z-scores. This is the extent to which the percentage for a ward differs from the average for all wards (or some other small area). It is measured in terms of standard deviations from the national average and gives a score based on the proportional and relative size of the deprivation indicator when compared to the mean. The index would sum the Z-scores. · Chi-square. Based on absolute values, this takes account of the absolute and relative size of the deprivation indicator when compared with the expected value (national average for the ward or specific small area measured). Lee et al argue that only where small area statistical cells are larger than 17 can this approach be used because of the confidentiality adjustments made to small area statistics (see below). · Range. Each indicator is divided by its range (the maximum value found in all wards or similar small areas). Thus if unemployment in the ward in question was only 10% and the highest national ward score was 20%, its standardised score would be 10/20 or 0.5. As Lee et al say (p.19) ‘This may have simplicity but is poor at reflecting the relative and absolute sizes of the phenomena’.

The Chi-square was chosen for the ILC because, primarily, it reduces the weighting of values where the numbers counted are small (and therefore more likely to be unreliable) – which is typical of the ED scale of analysis. They argue that standardisation methods based on percentages (e.g. the 1991 Scottish index). For example, 3 out of 10 unemployed is cited as a less reliable figure than 30 out of 100. Furthermore, the ILC team argue that the problem is made worse by the anonymising of Census output at small area levels (this is achieved by the random adding of -1, 0 or +1 to values – known as Barnardisation).

The approach taken by the ILC team was as follows:

1. The Chi-square is based on raw values (the actual numbers with a characteristic, not the proportion, and this is compared with the average value for the country as a whole). It compares the observed value (O) with the average or expected value (E) for both categories (i.e. an event occurring such as overcrowding and it not occurring, comparing the specific small area and a expected or average case):

2 2 Chi-square = (Oi – Ei) /Ei + (Oj – Ej) /Ej where

O is the observed value with a characteristic (e.g. unemployed) E is the expected value with a characteristic i is the score where the event, e.g, overcrowding, occurred j is the score where the event, e.g. overcrowding, did not occur

40 2. one is then added to the Chi-square values to avoid values between zero and one becoming negative on transformation.

3. Transformation to eliminate extreme values is carried out by taking logs.

4. A negative is applied to all Chi-squares values where Oi – Ei was negative (given that it is lost through squaring). This means that all scores which were positive indicate above average deprivation and all scores that are negative indicate below average deprivation.

In the 1997 update, it was determined that individual indicators which scored negative acted to pull down overall index scores for areas. Since the index attempts to measure deprivation and not well-being, the ILC team decided to set all negative scores on individual indicators (i.e. indicators with below average deprivation) to zero in order to nullify their effects on the index. A second reason for doing this was statistical. The intuition behind using the Chi- square measure was that as a measure of relative and absolute deprivation, positive values are bigger for any larger absolute number of deprived. However, this phenomenon also applies to negative scores, since the larger is the number deprived in an area below the national average, the greater will be the negative Chi-square and hence the lower will be the apparent level of deprivation. In other words, the Chi-square approach is a sensitive measure of differences among deprived areas but does not discriminate well between areas that are not deprived. However, their (and our focus) is with deprivation, so setting negative scores to zero helps focus on deprived areas.

Further discussion and evaluation of this approach is well set out in a paper in the journal Area, by Morphet, (1992).

41 2. PC Sector Ranked by Individual Indicator Score1 (Worst 10%)

PC LOGHE NEWINDEX PC LOGIS NEWINDEX PC LOGSMR64 NEWINDEX PC LOGCC NEWINDEX PC LOGINSUR NEWINDEX PC LOGOVERC NEWINDEX KA3 .7 2.51 6.69 G15 .7 3.63 15.87 DD1 .3 2.48 7.30 G34 .9 3.00 16.60 G42 .7 2.70 14.16 G22 .5 3.17 16.97 AB2 .7 2.43 8.87 G22 .5 3.60 16.97 G22 .5 2.48 16.97 TD15.1 3.00 8.19 G42 .8 2.70 12.73 G31 .3 3.04 15.14 AB2 .5 2.40 7.02 G21 .2 3.58 16.24 G1 .5 2.47 10.41 G21 .2 2.98 16.24 G42 .9 2.70 10.69 G42 .8 2.95 12.73 G15 .7 2.40 15.87 EH16.4 3.56 14.82 G5 .8 2.43 8.14 G22 .5 2.91 16.97 G51 .2 2.69 15.66 G51 .1 2.87 13.56 KA3 .2 2.40 10.33 G34 .9 3.55 16.60 G5 .0 2.41 15.48 G21 .1 2.91 15.07 G51 .3 2.69 15.02 G34 .9 2.86 16.60 AB2 .2 2.39 10.60 G33 .3 3.51 14.69 PA66.6 2.40 4.47 G21 .4 2.87 15.33 G51 .4 2.69 14.65 G31 .4 2.82 15.85 AB51.9 2.37 6.23 G33 .4 3.50 16.24 PA15.1 2.40 10.05 G15 .7 2.82 15.87 G53 .6 2.69 15.20 EH11.1 2.80 7.14 G33 .4 2.35 16.24 G31 .4 3.49 15.85 G40 .3 2.39 14.43 G33 .4 2.82 16.24 G53 .5 2.69 14.88 EH11.2 2.78 9.68 EH4 .4 2.33 14.65 G51 .2 3.44 15.66 G40 .2 2.39 13.55 DD3 .7 2.78 13.22 G53 .7 2.69 13.90 EH7 .5 2.78 9.78 G45 .9 2.33 15.61 G45 .9 3.44 15.61 G31 .4 2.38 15.85 DD11.1 2.76 10.96 G42 .0 2.68 13.55 G42 .7 2.76 14.16 AB42.6 2.33 6.37 G5 .0 3.44 15.48 G20 .9 2.38 14.77 G51 .2 2.75 15.66 G31 .4 2.67 15.85 G33 .4 2.73 16.24 AB1 .3 2.31 10.50 G53 .6 3.37 15.20 G51 .2 2.37 15.66 DD2 .3 2.73 13.85 G31 .5 2.67 13.75 G32 .7 2.66 14.33 G53 .5 2.31 14.88 G40 .4 3.37 15.09 G40 .4 2.36 15.09 G40 .4 2.71 15.09 G31 .3 2.67 15.14 G21 .2 2.65 16.24 AB43.5 2.30 6.37 PA3 .1 3.37 15.10 PA3 .1 2.36 15.10 G34 .0 2.71 15.86 G31 .1 2.67 13.43 G34 .0 2.65 15.86 G33 .3 2.29 14.69 G15 .8 3.35 14.88 G31 .1 2.35 13.43 PA3 .1 2.70 15.10 G31 .2 2.67 11.23 G45 .9 2.64 15.61 G45 .0 2.27 15.39 G34 .0 3.35 15.86 G5 .9 2.35 14.03 EH4 .4 2.68 14.65 G51 .1 2.67 13.56 G45 .0 2.64 15.39 ML5 .5 2.27 13.56 G45 .0 3.34 15.39 G22 .6 2.35 14.90 G5 .0 2.65 15.48 G33 .4 2.65 16.24 G20 .9 2.61 14.77 G34 .9 2.27 16.60 EH4 .4 3.34 14.65 KA14.3 2.33 6.69 G21 .3 2.65 14.01 G33 .5 2.65 15.33 G33 .5 2.60 15.33 AB2 .6 2.26 7.30 G51 .3 3.33 15.02 G2 .6 2.32 6.57 DD4 .8 2.64 13.43 G33 .3 2.65 14.69 G51 .3 2.56 15.02 G34 .0 2.24 15.86 G53 .5 3.29 14.88 G51 .3 2.32 15.02 G22 .6 2.63 14.90 G33 .1 2.65 13.07 G41 .2 2.55 9.66 AB2 .3 2.24 8.47 DD2 .3 3.28 13.85 G31 .5 2.32 13.75 KY1 .2 2.62 10.75 G33 .2 2.65 10.22 G14 .0 2.55 14.46 G53 .6 2.24 15.20 G21 .3 3.27 14.01 G15 .8 2.31 14.88 G42 .0 2.61 13.55 G33 .6 2.65 4.54 PA1 .1 2.54 12.47 G21 .3 2.20 14.01 G40 .3 3.26 14.43 G65 .7 2.31 6.47 G14 .0 2.60 14.46 G34 .9 2.63 16.60 G32 .6 2.50 14.76 KA3 .1 2.19 12.24 G21 .1 3.23 15.07 PH35.4 2.31 4.37 KY5 .8 2.59 11.54 G34 .0 2.63 15.86 G51 .2 2.50 15.66 G22 .7 2.19 14.83 G40 .2 3.22 13.55 G45 .0 2.30 15.39 G33 .3 2.59 14.69 G22 .5 2.63 16.97 G5 .0 2.47 15.48 G22 .5 2.18 16.97 G33 .5 3.21 15.33 G33 .2 2.30 10.22 G15 .8 2.59 14.88 G22 .6 2.63 14.90 AB1 .3 2.47 10.50 AB2 .1 2.17 10.39 G32 .6 3.20 14.76 G21 .2 2.30 16.24 G20 .8 2.57 13.03 G22 .7 2.63 14.83 G51 .4 2.46 14.65 AB2 .9 2.14 6.18 G20 .9 3.20 14.77 G53 .6 2.30 15.20 G1 .5 2.56 10.41 G5 .0 2.62 15.48 G33 .1 2.44 13.07 G33 .5 2.13 15.33 G22 .7 3.20 14.83 G34 .9 2.29 16.60 KA7 .1 2.56 10.77 G40 .4 2.62 15.09 G21 .4 2.44 15.33 G21 .2 2.12 16.24 G21 .4 3.20 15.33 G31 .3 2.29 15.14 G81 .4 2.54 12.27 G40 .3 2.62 14.43 G20 .8 2.41 13.03 G15 .8 2.10 14.88 G20 .8 3.19 13.03 G52 .1 2.29 11.36 G33 .5 2.53 15.33 G5 .9 2.62 14.03 G31 .2 2.41 11.23 KA6 .7 2.10 11.07 G46 .8 3.19 14.37 G34 .0 2.29 15.86 G5 .9 2.53 14.03 G40 .2 2.62 13.55 G42 .9 2.38 10.69 FK1 .4 2.09 10.11 DD3 .7 3.19 13.22 G21 .1 2.29 15.07 G22 .7 2.53 14.83 G40 .1 2.62 11.88 PA3 .1 2.38 15.10 ML4 .2 2.08 11.64 G42 .0 3.19 13.55 G13 .3 2.29 11.82 G53 .5 2.52 14.88 G3 .8 2.62 10.72 DD4 .6 2.38 10.90 G32 .6 2.08 14.76 G31 .3 3.17 15.14 EH16.4 2.28 14.82 DD4 .0 2.51 12.83 G1 .5 2.62 10.41 G31 .5 2.37 13.75 PH3 .1 2.07 6.19 G32 .7 3.13 14.33 G32 .7 2.28 14.33 G40 .2 2.49 13.55 G3 .6 2.62 9.35 G22 .6 2.35 14.90 ML5 .2 2.07 11.14 G14 .0 3.11 14.46 G40 .1 2.28 11.88 KA3 .1 2.47 12.24 G3 .7 2.62 8.63 G3 .6 2.35 9.35 G31 .4 2.06 15.85 G31 .5 3.11 13.75 PA78.6 2.28 5.20 EH16.4 2.45 14.82 G5 .8 2.62 8.14 G40 .4 2.31 15.09 KA3 .5 2.05 6.22 DD4 .8 3.11 13.43 G33 .3 2.28 14.69 G31 .4 2.41 15.85 G1 .2 2.62 6.89 EH6 .8 2.30 10.13 AB1 .5 2.04 6.04 G31 .1 3.11 13.43 G69 .8 2.27 8.71 KY8 .3 2.39 10.67 G1 .1 2.62 5.71 AB2 .1 2.28 10.39 EH54.5 2.03 10.24 G51 .4 3.10 14.65 G14 .0 2.27 14.46 G46 .8 2.38 14.37 G1 .4 2.62 4.39 G20 .7 2.28 11.54

42 PC LOGHE NEWINDEX PC LOGIS NEWINDEX PC LOGSMR64 NEWINDEX PC LOGCC NEWINDEX PC LOGINSUR NEWINDEX PC LOGOVERC NEWINDEX G72 .0 2.03 13.59 G22 .6 3.07 14.90 G73 .1 2.27 12.83 G51 .3 2.38 15.02 G21 .2 2.61 16.24 G11 .6 2.27 10.90 EH16.4 2.02 14.82 G5 .9 3.03 14.03 G22 .7 2.27 14.83 G53 .6 2.37 15.20 G21 .4 2.61 15.33 PA15.2 2.27 12.47 G46 .8 2.00 14.37 EH5 .1 3.02 13.25 G15 .7 2.26 15.87 G13 .3 2.36 11.82 G21 .1 2.61 15.07 G3 .8 2.25 10.72 PA14.6 2.00 11.13 DD4 .0 3.02 12.83 PA15.2 2.26 12.47 G45 .9 2.36 15.61 G21 .3 2.61 14.01 G53 .6 2.23 15.20 AB1 .4 2.00 5.83 G81 .4 3.01 12.27 G32 .6 2.26 14.76 PA3 .2 2.34 12.63 G32 .7 2.60 14.33 G20 .0 2.23 13.29 G71 .5 1.99 12.29 G20 .0 2.95 13.29 EH5 .1 2.26 13.25 FK8 .1 2.34 12.53 G32 .8 2.60 12.15 G12 .8 2.22 10.11 EH11.4 1.99 9.82 G43 .1 2.95 10.63 G42 .0 2.26 13.55 KY1 .3 2.31 10.34 G15 .7 2.60 15.87 EH7 .4 2.22 7.36 KY8 .2 1.99 10.70 G42 .7 2.93 14.16 G52 .4 2.26 12.53 PA1 .2 2.29 11.44 G15 .8 2.60 14.88 G11 .7 2.18 6.97 EH22.5 1.98 7.16 G72 .0 2.93 13.59 G20 .8 2.25 13.03 G81 .5 2.29 12.25 G52 .4 2.60 12.53 DD3 .7 2.16 13.22 G21 .4 1.97 15.33 G53 .7 2.90 13.90 EH4 .4 2.25 14.65 KA18.4 2.28 10.52 G52 .1 2.60 11.36 ML5 .5 2.16 13.56 ML7 .5 1.97 8.50 G81 .1 2.89 12.24 G21 .4 2.25 15.33 KY8 .1 2.27 10.06 G15 .6 2.60 9.65 EH16.4 2.16 14.82 PA86.0 1.96 5.96 G13 .4 2.88 12.65 G4 .0 2.24 12.07 G42 .8 2.27 12.73 G52 .2 2.60 9.05 DD1 .5 2.16 10.76 G51 .4 1.96 14.65 G32 .8 2.86 12.15 G46 .8 2.24 14.37 KY8 .2 2.26 10.70 G52 .3 2.60 5.89 G44 .4 2.15 6.60 FK2 .9 1.96 8.38 G13 .2 2.83 12.28 G45 .9 2.24 15.61 EH5 .1 2.25 13.25 G45 .9 2.59 15.61 G4 .9 2.15 9.12 EH21.8 1.96 8.93 FK8 .1 2.83 12.53 EH6 .6 2.24 11.71 G52 .4 2.25 12.53 G45 .0 2.59 15.39 G15 .7 2.15 15.87 AB1 .1 1.95 7.74 G51 .1 2.82 13.56 G13 .4 2.24 12.65 G51 .4 2.24 14.65 G32 .6 2.59 14.76 AB2 .3 2.15 8.47 ML2 .9 1.95 10.64 EH6 .6 2.80 11.71 PA15.3 2.24 11.63 G45 .0 2.24 15.39 G32 .0 2.59 8.98 G53 .7 2.12 13.90 AB2 .0 1.95 5.85 G13 .3 2.80 11.82 FK8 .1 2.24 12.53 G42 .7 2.24 14.16 G32 .9 2.59 7.14 G21 .1 2.11 15.07 CA6 .5 1.93 5.67 ML3 .0 2.79 12.42 PA43.7 2.23 5.33 G12 .8 2.22 10.11 G41 .1 2.59 10.29 PA15.4 2.08 11.83 EH32.9 1.93 6.67 G40 .1 2.78 11.88 G43 .1 2.23 10.63 KA21.5 2.22 9.10 G41 .2 2.59 9.66 G40 .3 2.08 14.43 PA87.2 1.93 5.93 G73 .1 2.77 12.83 G53 .7 2.22 13.90 DG2 .0 2.21 10.17 G41 .5 2.59 6.97 ML5 .4 2.08 11.43 AB43.4 1.92 5.88 DD11.1 2.77 10.96 DD2 .3 2.22 13.85 G53 .7 2.21 13.90 G41 .3 2.59 6.04 EH8 .8 2.07 10.40 ML6 .8 1.92 8.81 G1 .5 2.76 10.41 PA61.7 2.22 4.29 G81 .1 2.20 12.24 G41 .4 2.59 4.45 G32 .8 2.05 12.15 ML3 .8 1.92 7.48 ML5 .5 2.73 13.56 PA15.4 2.22 11.83 DG1 .2 2.20 10.37 G20 .9 2.59 14.77 G40 .1 2.05 11.88 G51 .2 1.92 15.66 G20 .7 2.72 11.54 EH8 .8 2.22 10.40 KY1 .1 2.20 8.69 G20 .0 2.59 13.29 PA1 .2 2.05 11.44 ML6 .6 1.92 11.17 G33 .2 2.72 10.22 FK4 .2 2.22 7.10 G4 .0 2.19 12.07 G20 .8 2.59 13.03 G22 .7 2.03 14.83 DD2 .3 1.92 13.85 G52 .4 2.72 12.53 G32 .8 2.21 12.15 G20 .9 2.19 14.77 G4 .0 2.59 12.07 DD2 .4 2.02 11.00 G21 .1 1.92 15.07 KA3 .1 2.71 12.24 PA81.5 2.21 5.66 KA1 .4 2.17 10.64 G20 .7 2.59 11.54 EH3 .9 2.02 7.46 G67 .2 1.91 7.88 G42 .8 2.70 12.73 G21 .3 2.21 14.01 G40 .3 2.17 14.43 G4 .9 2.59 9.12 PA3 .2 2.02 12.63 G40 .3 1.91 14.43 G81 .5 2.69 12.25 PA3 .2 2.21 12.63 IV3 .5 2.17 9.45 G20 .6 2.59 9.04 G5 .9 2.00 14.03 KA3 .3 1.91 6.19 PA15.2 2.69 12.47 G53 .5 2.21 14.88 EH6 .6 2.16 11.71 G2 .7 2.59 8.51 G46 .8 2.00 14.37 AB2 .4 1.90 6.67 KY5 .8 2.68 11.54 G33 .4 2.21 16.24 KA22.8 2.16 8.77 G2 .4 2.59 7.51 G3 .7 1.99 8.63 PA3 .1 1.90 15.10 KA6 .7 2.68 11.07 G20 .7 2.20 11.54 KA18.3 2.15 10.37 G2 .6 2.59 6.57 G72 .0 1.97 13.59 ML7 .4 1.89 9.18 G81 .2 2.64 10.73 G33 .5 2.20 15.33 G31 .3 2.15 15.14 G2 .3 2.59 4.93 G40 .2 1.96 13.55 G5 .0 1.89 15.48 FK10.1 2.64 10.83 PA16.7 2.20 9.84 G31 .1 2.14 13.43 G2 .8 2.59 4.27 PA16.0 1.95 10.53 G42 .0 1.88 13.55 G33 .1 2.63 13.07 G3 .8 2.19 10.72 DG9 .7 2.14 10.42 G14 .0 2.58 14.46 G71 .5 1.93 12.29 DD4 .9 1.88 12.02 PA15.1 2.62 10.05 G51 .4 2.19 14.65 DD3 .6 2.13 9.07 G14 .9 2.57 8.32 G15 .8 1.92 14.88 KY5 .8 1.88 11.54 G4 .0 2.62 12.07 EH1 .2 2.19 8.01 G32 .6 2.12 14.76 G23 .5 2.56 10.45 G53 .5 1.87 14.88 G22 .6 1.87 14.90 ML6 .6 2.61 11.17 PH1 .5 2.19 11.56 KA8 .0 2.11 10.48 G11 .6 2.55 10.90 PA14.5 1.85 10.07 G66 .2 1.87 10.50 PA3 .2 2.59 12.63 G13 .2 2.19 12.28 FK2 .7 2.11 10.93 G11 .5 2.55 9.89 EH8 .9 1.85 9.88 ML1 .4 1.87 10.60 EH14.2 2.58 10.23 G81 .4 2.18 12.27 FK10.1 2.11 10.83 G46 .8 2.55 14.37 ML6 .0 1.84 10.96 ML2 .0 1.87 11.07 DD4 .9 2.56 12.02 ML7 .4 2.18 9.18 PA20.0 2.09 8.98 G46 .7 2.55 4.42 G11 .5 1.84 9.89 PA16.0 1.86 10.53 G31 .2 2.56 11.23 FK18.8 2.18 4.28 DD2 .4 2.09 11.00 G46 .6 2.55 4.28 EH5 .1 1.84 13.25 G72 .7 1.86 11.64 KA1 .4 2.56 10.64 G81 .1 2.18 12.24 G43 .1 2.08 10.63 G12 .8 2.54 10.11 G13 .4 1.83 12.65 PH1 .2 1.85 8.74 KA8 .0 2.55 10.48 DG1 .2 2.18 10.37 IV11.8 2.08 7.79 G12 .9 2.54 5.66 G73 .1 1.82 12.83

43 PC LOGHE NEWINDEX PC LOGIS NEWINDEX PC LOGSMR64 NEWINDEX PC LOGCC NEWINDEX PC LOGINSUR NEWINDEX PC LOGOVERC NEWINDEX ML11.0 1.84 9.20 KA8 .9 2.54 11.71 DD3 .7 2.18 13.22 KY11.4 2.08 9.37 G12 .0 2.54 4.42 G41 .1 1.82 10.29 FK3 .0 1.84 7.34 DD2 .4 2.53 11.00 KW8 .6 2.18 7.29 G52 .1 2.07 11.36 G13 .4 2.54 12.65 EH1 .1 1.82 8.50 ML9 .1 1.84 9.11 ML2 .7 2.53 12.00 G42 .7 2.18 14.16 DD4 .9 2.07 12.02 G13 .2 2.54 12.28 EH3 .8 1.82 10.27 PA5 .0 1.84 9.90 PA1 .1 2.52 12.47 PA42.7 2.18 8.32 KY4 .0 2.06 9.06 G13 .3 2.54 11.82 G69 .7 1.77 10.18

1. The ranking for TD15 .1 in the logcc column should be read with caution. We have a strong suspicion that the claimant count (as well as Income Support) data encompass the town of Berwick which is NOT in Scotland. The denominator on the other hand refers to Scotland only.

44 Appendix 3 Listing of EDs in Worst 10% of Post Code Sectors

POST CODE SECTOR ED NAME(S) CITY OF GLASGOW G22.5 Greater Possil - Possil Park, Keppoch, Hamilton Hill. G34.9 Easterhouse-Easthall, Kildermorie, Blairtumnock, Provanhall. G33.4 Easterhouse- Easthall, Wellhouse; Barlanark, Queenslie. G21.2 Roystonhill, Germiston G15.7 Drumchapel- Langfauld, Broadholm, Pinewood, Waverley G34.0 Easterhouse- Rogerfield, Lochend, Bishoploch G31.4 Parkhead, Barrowfield G51.2 Govan/Kinning Park G45.9 Castlemilk- Howgill, Templecross/Glenwood, Westcastle, Braeside G5 .0 Gorbals/Oatlands G45.0 Castlemilk- Templecross/Glenwood, Braeside, Bowhouse G21.4 Springburn, Balornock G33.5 Easterhouse- Easthall; Garthamlock, G53.6 South Nitshill G31.3 Haghill G40.4 Bridgeton, Dalmarnock G21.1 Keppochhill, Sighthill G51.3 Govan (Drumoyne), Kinning Park G22.6 Greater Possil- Possil Park, Keppoch, Hamilton Hill, Springburn, Milton G15.8 Drumchapel- Cairnsmore, Kingsridge/Cleddans, Waverley G53.5 Pollok G22.7 Milton G20.9 Garbraid/Botany/Wynford, Ruchill; Greater Possil- Possil Park, Keppoch, Hamilton Hill, Springburn, Milton. G32.6 Wester Carntyne, Carntyne, Greenfield G33.3 Blackhill, Cranhill, Ruchazie, G51.4 Govan G14.0 Yoker-Langholm Street, Scotstoun G40.3 Dalmarnock, Parkhead, Barrowfield, Bridgeton G46.8 Carnwadrick/Arden/Kinnishead G32.7 Tolcross, Shettleston G42.7 East Pollockshields, Govanhill G5 .9 East Pollockshields, Gorbals/Oatlands G21.3 Barmulloch, Springburn, Balornock/Robroyston G53.7 Priesthill/Nitshill, Darnley, South Nitshill, Pollok G31.5 Parkhead G51.1 Govan/Kinning Park G42.0 Toryglen, Govanhill G40.2 Bridgeton, G31.1 Reidvale G20.0 Acre, Garbraid/Botany/Wynford G33.1 Blackhill G20.8 Gairbraid/Botany/Wynford G42.8 Govanhill G13.4 Yoker, Archerhill G52.4 Penilee G13.2 Clobberhill/Shafton

45 G32.8 Tolcross, Fullarton, Carmyle- Buckingham Drive/Montrose Avenue G4.0 Calton, Townhead G40.1 Dalmarnock, Calton G13.3 Knightswood- Lincoln Avenue G20.7 Greater Possil- Possil Park, Keppoch, Hamilton Hill; East Woodlands, Woodside/Firhill, G52.1 Pollok G31.2 Townhead CITY OF EDINBURGH EH16.4 Craigmillar- Craigmillar Castle, Thistle Foundation, Greendykes, Niddrie, Niddrie Mains EH4 .4 Greater Pilton- Muirhouse, West Pilton, West Granton, Granton EH5.1 Greater Pilton- Granton EH6.6 Leith EH11.3 Broomhouse, Broomhouse/Sighthill CITY OF DUNDEE DD2.3 Ardler, Lochee, Beechwood DD4.8 Finlathen, Mid Craigie, Douglas DD3.7 Lower Hilltown, Maxwelltown, Fleming Gardens, Dundonald Street/Dura Street/Arthurstone Terrace DD4.0 Whitfield DD4.9 Mill O’ Mains, Finlathen, Fintry North, Kirkton/West March STIRLING FK8.1 Raploch, Town Centre/Merkat Cross PERTH & KINROSS PH1.5 Muirton RENFREW PA3.1 Paisley-Ferguslie Park PA3.2 Paisley- Love Street, Shortroods PA1.1 Paisley- East End PA2.0 Paisley- Foxbar (Mannering Road, Heriot, Rivers) PA1.2 Paisley- George Street, Millarston PA3.4 Paisley- Gallowhill NORTH LANARK ML5.5 Coatbridge- Kirkshaws, Kirkwood/Old Monklands G71.5 Viewpark-Fallside ML2.7 Wishaw- Craigneuk/Wishawhill ML4.2 Bellshill- Orbiston ML5.4 Coatbridge- Greenend/Skyeside, Shawhead, ML6.7 Plains, Caldercruix SOUTH LANARK G72.0 Springwells, Blantyre- Auchinraith/High Blantyre G73.1 West Rutherglen- Burnhill; Shawfield ML3.0 Hamilton- Whitehill G72.7 North West Cambuslang, Cambuslang- Westburn, Halfway

46 INVERCLYDE PA15.2 Greenock- Sinclair Street/Craigieknowes/Bridgend, Gibshill PA15.4 Greenock- Central PA15.3 Greenock- Strone/Mauckinhill WEST DUMBARTON G81.4 Clydebank- Dalmuir G81.5 Clydebank-Faifley G81.1 Clydebank- Glasgow Road, Whitecrook FIFE KY5.8 Crosshill- Inchgall Avenue Lochore-Rosewell Drive/Loanhead Avenue, Balbedie Avenue Ballingry- Kirkland Gardens/Avenue, Kildownie Crescent EAST AYR KA3.1 North West Kilmarnock SOUTH AYR KA8.9 Ayr- Braehead, Whitletts/Dalmalling

47 4. Insurance Indicator Sensitivity Analysis and Validation

Given the uniqueness of the insurance indicator and the potential difficulties associated with its inclusion, the research team sought the advice of Professor Brian Robson at the University of Manchester who was principally responsible for the construction of the ILC index. He intimated that there are no independent justifications of the use of insurance weightings. However, his confidence in their use stems from the extensive conversations he has had with the insurance companies and analysis of insurance weighting changes over 1991 to 1997. The evidence indicates that insurance weightings (based on reported crime and those individuals who are insured) have spread out of the big urban areas as a result of the dramatic changes in recorded burglaries during the 1990’s. As a result he concludes that insurance weightings are a better indicator to use in the construction of an index of deprivation than police reported crime. The only alternative source of crime data, the British Crime Survey, cannot be disaggregated to Post Code Sector level.

There is little doubt from the foregoing analysis of the construction of the index and its testing, that the insurance indicator plays a significant role. However, in order to investigate the possibility that the indicator may distort the results due to its possible bias toward urban areas, the research team decided to construct another index excluding the insurance indicator. Thus this ‘test index’ consisted of the following five indicators: logcc, logis, logoverc, loghe and logsmr64. The index was calculated and the postcode sectors sorted by their absolute score. On inspection of the new ranking we found that there was little difference between the test ranking (which excludes the insurance indicator) and the new index ranking (which includes the insurance indicator). In fact, on closer inspection of post-code sectors (in the bottom two deciles) in obviously rural areas, we found that the position of many of these areas stayed the same and in some cases actually improved. In summary then, there were no large jumps observed in the ‘test index’ rankings. Most jumps were very low and could be confined to movement of less than five places.

We believe that these results mitigate the criticisms levelled at the insurance indicator. These ‘sensitivity results’ combined with the results of the factor analysis convince us of the efficacy of the indicator and its robustness. Further details of the ‘test index’ ranking are available from the authors.

48