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Inclusive Growth Monitor: Technical Notes Authors: Christina Beatty Tony Gore Richard Crisp May 2016

Contents Introduction... i 1. Composition and Derivation of Inclusive Growth Indicators... 1 2. Converting Indicators to a LEP Basis... 12 3. Normalised Scores... 13

Introduction This technical note accompanies the report entitled An Inclusive growth monitor for measuring the relationship between poverty and growth which was funded by the Joseph Rowntree Foundation. The report was published in May 2016 and can be found at https://www.jrf.org.uk/report/inclusivegrowth-monitor This note contains details of all the data sources and methods used to construct and analyse the full set of 18 indicators used in the report to measure the relationship between poverty and growth at city regional level. Future versions of the indicator set will be produced by the Inclusive Growth Analysis Unit (IGAU) and found at www.manchester.ac.uk/inclusivegrowth Centre for Regional Economic and Social Research i

1. Composition and Derivation of Inclusive Growth Indicators 1 1.1. INCLUSION THEME 1.1.1. Income Dimension I1.1: Out-of-work benefits % of working-age population receiving out-of-work benefits Percentage DWP Work and Pensions Longitudinal Study (benefit claimants - working-age client group) Further data derivation nstr uct&version=0&dataset=105 Place of residence; LEPs, regions and countries Resident population aged 16-64 estimates (built into source data set) Out-of-work benefit recipients comprise all individuals whose entitlement is based on their lack of employment. Welfare benefits included under this heading are as follows: Jobseekers Allowance Incapacity Benefit/Employment Support Allowance Income Support Other income-related benefits. Centre for Regional Economic and Social Research 1

I1.2: In-work tax credits % in-work households with and without children receiving Child and/or Working Tax Credits Percentage HMRC Child and Working Tax Credit Finalized Award Statistics - Geographical Statistics Via GOV.UK: https://www.gov.uk/government/collections/personal-tax-creditsstatistics Place of residence; LADs, regions and countries Figures produced from source data set by subtracting 'out-of-work families' from 'total in receipt' to give numbers in work receiving tax credits Estimates of number of working households by LEP area, from Annual Population Survey - combined economic activity status of households data set. Accessed via NOMIS: https://www.nomisweb.co.uk/query/construct/summary.asp?mode=c onstruct&version=0&dataset=136 Absolute figures for recipients for constituent LADs aggregated for each LEP, and the resultant values combined with the denominator to generate a new overall LEP percentage. Based on annual figures provided by HMRC series "Personal tax credits: Finalised award statistics - geographical statistics" I1.3: Low earnings 20 th percentile of gross weekly earnings (Twenty per cent of full-time workers receive earnings equal to or below this threshold) Monetary value in Annual Survey of Hours and Earnings (ASHE) resident analysis nstruct&version=0&dataset=30 Place of residence; LADs, regions and countries (but see below) ASHE earnings figures for each LAD multiplied by number of full-time workers in quintile to produce aggregate earnings for each year. This and number of full-time workers then aggregated separately for each LEP area, with the result of the former divided by the result of the latter to produce a new overall LEP threshold. ASHE is based on a sample of employee jobs taken from HM Revenue & Customs PAYE records. Information on earnings and hours is obtained in confidence from employers. ASHE does not cover the self-employed nor does it cover employees not paid during the reference period. All source figures are estimates based on relatively small samples for individual LADs. Standard errors for both earnings and full-time workers may be as high as 12 to 14 per cent. Where estimates for small areas are missing because of unreliability, these have been treated as zero. Centre for Regional Economic and Social Research 2

1.1.2. Living Costs Dimension I2.1: Housing affordability Ratio of lower quartile house price to lower quartile earnings Ratio (to two decimal places) Department for Communities and Local Government Housing Statistics, Table 576: Ratio of Lower Quartile House Price to Lower Quartile Earnings by District, from 1997. Via GOV.UK: https://www.gov.uk/government/statistical-data-sets/live-tables-onhousing-market-and-house-prices Place of residence; LADs, regions and England Figures for 2014 derived separately from two data sets: Office for National Statistics House Price Statistics for Small Areas (HPSSA) Dataset 15: Lower quartile house price for national and subnational geographies, quarterly rolling year. Table 6a: figures for local enterprise partnerships, Q4 1995 to Q2 2015. Values for Q2 2014 used in these calculations. Accessed via ONS Housing page: http://www.ons.gov.uk/peoplepopulationandcommunity/housing/ articles/housepricestatisticsforsmallareas/yearendingquarter419 95toyearendingquarter22015/relateddata?page=3 Annual Survey of Hours and Earnings (ASHE) resident analysis, lower quartile gross annual pay of full-time workers, 2014. Accessed via NOMIS (see I1.3 above). ASHE annual earnings figures for each constituent LAD then multiplied by number of full-time workers in quartile to produce aggregate earnings for each year. This and number of full-time workers then aggregated separately for each LEP area, with the result of the former divided by the result of the latter to produce a new overall LEP lower quartile earnings figure. The lower quartile house price for each LEP divided by this lower quartile annual earnings figure then generated the ratio for 2014. Lower quartile house price/earnings ratios for each constituent LAD weighted by total number of households in each area (taken from the Annual Population Survey estimates). Number of households and weighted values then aggregated separately for each LEP area, with the results of the latter divided by the results of the former to produce a new overall ratio. Note that original ratios from the CLG data set are used where LEP boundaries coincide with whole administrative areas such as counties. Drawn directly from time series data as part of CLG's "Live tables on the housing market and house prices". These are based on data extracted from ASHE gross annual earnings on the one hand, and Land Registry house sale price records on the other. Current CLG data set only runs to 2013; 2014 values have been calculated from alternative sources (see above). Centre for Regional Economic and Social Research 3

I2.2: Rented housing costs Median monthly rents for private sector two bedroom properties Rental charge in Valuation Office Agency Private Rental Sector Market Statistics: Summary of property type '2 bedrooms' gross monthly rents by region and administrative area for England (Tables 1.4 and 2.4: includes average, median, upper and lower quartile values) Via GOV.UK: Place of residence; LADs, regions and England (but see below) Median rental figures for each constituent LAD multiplied by number of households in that area living in the private rented sector (figures extracted from the 2011 Census of Population, via NOMIS: Table QS405EW - Tenure - Households). These aggregate rental figures and PRS households then summed separately for each LEP, with the result of the former divided by the result of the latter to provide median rent figures for LEPs. Values are for the 12 months ending in March each financial year. Thus, figures listed for 2014 are strictly speaking for 2013/14. Also note that the figures are not based on comprehensive coverage of all privately rented properties, but just those entered into the lettings administrative information database. This means that there may be variations in sample sizes underpinning the source data, both between years and also between areas in the same year. Comparisons over time and across geographical space therefore need to be treated with some caution. I2.3: Fuel poverty % of households classed as being 'fuel poor' (using Low Income-High Costs model) Percentage DECC Fuel poverty sub-regional statistics Via GOV.UK: https://www.gov.uk/government/statistics/private-rental-marketstatistics-england-only https://www.gov.uk/government/collections/fuel-poverty-sub-regionalstatistics Place of residence; LADs, regions and England Number of households (built into source data set) Estimates for both 'fuel poor' households and total number of households for constituent LADs aggregated separately for each LEP area. Results of the two calculations then combined to produce a new overall percentage figure for each LEP. Current data set only runs to 2013; next release of series (with 2014 data) is due in July 2016. In the current version 2013 values have been replicated in the derivation of the overall normalised scores for 2014. Centre for Regional Economic and Social Research 4

1.1.3. Labour Market Exclusion Dimension I3.1: Unemployment % of working-age population not in employment but actively seeking and available to start work Percentage (rate) Annual Population Survey (APS) - combined economic activity status data set. nstruct&version=0&dataset=17 Place of residence; LEPs, regions and countries Economically active resident population aged 16-64 (built into source data set) The unemployment variable in the APS data set uses the International Labour Office (ILO) definition, which states that "the main criteria for identifying a person as unemployed are that a) he/she has been actively looking for a job in the past 4 weeks, and b) he/she is available to start work within 2 weeks." See: https://www.nomisweb.co.uk/published/stories/story.asp?id=10 I3.2: Economic inactivity % of working-age population who are economically inactive Percentage (rate) Annual Population Survey (APS) - combined economic activity status data set. nstruct&version=0&dataset=17 Place of residence; LEPs, regions and countries Resident population aged 16-64 (built into source data set) According to Leaker (2009), "the economically inactive include those who want a job but have not been seeking work in the last four weeks, those who want a job and are seeking work but are not available to start work, and those who do not want a job." This group is usually subdivided into the following categories: students those looking after home or family the temporarily sick the long-term sick discouraged workers the retired others (e.g., those who do not need a paid job, and those who provided no reason). Centre for Regional Economic and Social Research 5

I3.3: Workless households % of working age households with no one in work Percentage (rate) Annual Population Survey (APS) - households by combined economic activity status nstruct&version=0&dataset=136 Place of residence; LEPs, regions and countries Working age households (i.e., those that include at least one person aged 16 to 64) (built into source data set). A workless household is defined as one in which no individuals aged 16 and over are in employment. Centre for Regional Economic and Social Research 6

1.2. PROSPERITY THEME 1.2.1. Output Growth Dimension P1.1: Output (GVA) Gross Value Added (GVA) per capita Monetary value in at current basic prices ONS Regional GVA (Income Approach) Statistics Via ONS section of National Archives website: http://webarchive.nationalarchives.gov.uk/20160105160709/http://ww w.ons.gov.uk/ons/rel/regional-accounts/regional-gross-value-added-- income-approach-/december-2015/stb-regional-gva-dec-2015.html Place of work; NUTS2 and 3, regions and countries Population bases for each LAD reconstituted by dividing total GVA by per capita figures for each area. Total resident population (built into source data set - see above) Total GVA and reconstituted population bases for each constituent NUTS area aggregated separately for each LEP. The result of the former then divided by the result of the latter to produce a new overall per capita GVA figure for each LEP area. Note that original per capita figures from the ONS data set are used where LEP boundaries coincide with whole administrative areas such as counties. GVA (Income Approach) (GVA(I)) figures are made up of a number of components, as follows: Compensation of employees: the total remuneration payable to employees in cash or in kind, including the value of social contributions payable by the employer. Gross operating surplus: comprises gross trading profits and surpluses, non-market capital consumption and rental income, less holding gains. Mixed income: the income generated by sole traders (selfemployed people not registered as partners). Taxes on production: compulsory taxes levied by the government relating to the production and import of goods and services, the employment of labour, or the ownership or use of land, buildings or other assets in production. Subsides on production: payments made by government or the European Union to enterprises, including subsidies to farmers for land set-aside, as well as government incentives to promote research and development. Centre for Regional Economic and Social Research 7

P1.2: Private sector businesses Number of private sector workplaces per 1,000 resident population Rate per 1,000 ONS UK Business Counts - Local Units nstruct&version=0&dataset=141 Place of work; LEPs, regions and countries Resident population, taken from the ONS Mid-Year Population Estimates. Accessed via NOMIS: nstruct&version=0&dataset=31 A local unit refers to an individual site (for example a factory or shop) associated with an enterprise (also known as a workplace). Figures are extracted from the Inter Departmental Business Register (IDBR), using records of local units that were live in March of each year. P1.3: Wages/earnings Median gross weekly pay for full-time workers Monetary value in Annual Survey of Hours and Earnings (ASHE) workplace analysis nstruct&version=0&dataset=99 Place of work; LADs, regions and countries (but see below) ASHE earnings figures for each LAD multiplied by number of full-time workers listed in source data set to produce aggregate earnings for each year. This and number of full-time workers then aggregated separately for each LEP area, with the result of the former divided by the result of the latter to produce a new overall LEP earnings figure. ASHE is based on a sample of employee jobs taken from HM Revenue & Customs PAYE records. Information on earnings and hours is obtained in confidence from employers. ASHE does not cover the self-employed nor does it cover employees not paid during the reference period. All source figures are estimates based on relatively small samples for individual LADs. Standard errors for both earnings and full-time workers may be as high as 14 to 16 per cent. Where estimates for small areas are missing because of unreliability, these have been treated as zero. Centre for Regional Economic and Social Research 8

1.2.2. Employment Dimension P2.1: Workplace jobs Employee jobs by working-age population (jobs density) Ratio ONS Jobs Density series nstruct&version=0&dataset=57 Place of work and place of residence; LEPs, regions and countries Figures for 2014 were generated firstly by comparing BRES total workplace employment figures for each LEP by year 2010-2013 with the equivalents used in the ONS Jobs Density series (see below). Discrepancies were expressed as a multiplier which was then averaged across the four years to provide a scaling up factor for each LEP area for 2014. This was then applied to 2014 BRES employment figures for each LEP to generate estimates in line with previous years. These were then combined with figures for resident working age population (taken from the Mid-Year Population Estimates - see under G1.2 above) to provide an overall jobs density figure for each LEP for 2014. Resident population aged 16-64 (built into source data set for 2010-2013; from Mid-Year Population Estimates for 2014) Current ONS data set is only available to 2013. Employment figures used in the ONS Jobs Density series are special estimates which effectively scale up employment figures from BRES to account for those excluded (e.g., people working in agriculture and in various forms of self-employment). Access to BRES data is restricted to those with Ministerial authorisation (available on application, subject to status). P2.2: Employment rate % of working age population in employment (employment rate) Percentage (rate) Annual Population Survey (APS) nstruct&version=0&dataset=17 Place of residence; LEPs, regions and countries Resident population aged 16-64 (built into source data set) All source figures are estimates based on samples for individual LEPs. Standard errors for employment rates tend to be fairly low at this scale (typically between 1 and 3 per cent). Centre for Regional Economic and Social Research 9

P2.3: Employment in low pay sectors % employed in administrative and support services, wholesale and retail trade, accommodation and food services, and residential social care Percentage (rate) Business Register Employee Survey (BRES) https://www.nomisweb.co.uk/home/notice_info.asp Place of work; LEPs, regions and countries Totals generated by summing figures across all component sectors (see below for further details). Total workplace employment (also from BRES, built into source data set) Component sectors (following the 2007 Standard Industrial Classification) are as follows: G: Wholesale and retail trade; repair of motor vehicles and motorcycles I: Accommodation and food service activities N: Administrative and support service activities 87: Residential care activities Please note that access to BRES data is restricted to those with Ministerial authorisation (available on application, subject to status). See link to relevant NOMIS page above. 1.2.3. Human Capital Dimension P3.1: Higher level occupations % workers in managerial, professional and technical/ scientific occupations (SOCs 1, 2 and 3) Percentage Annual Population Survey (APS) nstruct&version=0&dataset=17 Place of residence; LEPs, regions and countries Totals generated by summing figures across all component categories (see below for further details). Resident population aged 16-64 in employment (from APS via NOMIS) Component categories (following the 2010 Standard Occupational Classification) are as follows: 1. Managers, Directors and Senior Officials 2. Professional Occupations 3. Associate Professional and Technical Occupations Centre for Regional Economic and Social Research 10

P3.2: Intermediate and higher level skills % working-age population qualified at NVQ Level 2 and above Percentage Annual Population Survey (APS) nstruct&version=0&dataset=17 Place of residence; LEPs, regions and countries Totals generated by summing figures across NVQ2, NVQ3, NVQ4+ and Trade Apprenticeship categories Resident population aged 16-64 (built into source data set) Trade Apprenticeship qualifications are generally allocated equally between NVQ Levels 2 and 3 P3.3: Educational attainment % of pupils at the end of Key Stage 4 achieving 5 or more GCSEs or equivalent at grades A* to C (including English and Mathematics) Percentage Department for Education GCSE (Key Stage 4) Statistics Via GOV.UK: https://www.gov.uk/government/collections/statistics-gcses-key-stage- 4 Place of residence; LEAs, regions and England Number of pupils achieving specified grades reconstituted for each LEA by combining total number of pupils and percentage achieving these grades. Number of end of Key Stage 4 pupils (built into source data set) Figures for number of pupils at end of KS4 and for the number achieving the specified grades for constituent LEAs both aggregated separately for each LEP area. The two results then combined to produce a new overall percentage for each LEP. Note that original source figures from the DfE data set are used where LEP boundaries coincide with a single LEA area such as a county. Note also that in LEPs containing some Shire (lower tier) LADs but not the whole County LEA, the figures for the latter have been included in the calculation. The reduction in the percentage of pupils achieving the specified grades in 2013/14 appears to be related to three main factors: a sharp rise in the number of pupils aged 15 or younger taking GCSE exams; tougher science papers; and more students taking subjects multiple times. Centre for Regional Economic and Social Research 11

2. Converting Indicators to a LEP Basis 2 As the previous section illustrates eight of the core data sets underpinning the eighteen indicators contain sub-national geographical breakdowns which do not correspond to LEPs. In order to convert the figures to a LEP basis it was first necessary to define each LEP in terms of their constituent areas. The result was a series of look-up tables which could be used to extract the relevant figures for these constituent areas, then allowing re-aggregation of these figures in order to provide a new overall figure for each LEP. The types of area for which these look-up tables were compiled are as follows: Local Authority Districts (LADs) (including Metropolitan Boroughs and Unitary Authorities) Local Education Authorities (County Councils, Metropolitan Boroughs and Unitary Authorities) NUTS2 and NUTS3 areas. The look-up tables are held as a series of worksheets, and are available on request. Centre for Regional Economic and Social Research 12

3. Normalised Scores 3 3.1. Introduction The technical specifications set out in the previous section highlight the fact that the different 'inclusive growth' indicators have not been calculated on the same basis, with some related to the total resident population, others to the working-age resident population, some to the number of employees working in the area, and the rest to a range of other appropriate 'population' denominators. These variations occur within each dimension, as well as between them. Although all initial indicator data has already been subject to simple standardisation in the form of percentages or rates per thousand, the use of different denominators still makes comparisons between them difficult. This in turn makes it difficult to combine them into a single 'score' for each dimension or theme in a way which allows direct comparison to be made between prosperity and inclusion. In order to construct this scoring system it was necessary to transform or rescale each figure using a recognised statistical technique, so that values for all indicators are on an equivalent scale. For this it was decided that the statistical calculation known as normalisation was most appropriate. 3.2. Normalisation This involves the adjustment of data values calculated on different bases to a common scale. In doing so it brings the probability distribution for each into alignment - in other words, it eliminates the effect of outliers in the data range to produce a normal distribution. In the simplest form ('unity-based' normalisation) the resulting data values run on a scale of 0 to 1. The formula for calculating each value is as follows: i - min j = max - min where j = new value on a scale of 0 to 1; i = the original data value; min = minimum value in the data range; max = maximum value in the data range. Centre for Regional Economic and Social Research 13

In simple terms, 0 denotes worst performance, and 1 best. This normalisation technique is most effective with ratio measurements: it cannot be applied to interval scale data unless the values are first converted to ordinal rankings (which would itself be a form of normalisation). All the indicators fall into this ratio category. For those indicators where a decrease in value signifies 'improvement' (e.g., unemployment, in-work poverty), the same calculations are applicable, but with an additional step for unity-based normalisation in which the rescaled value is subtracted from 1. This has the effect of reversing the value range order. This has been applied as required with cross-sectional data (annual levels) and change over time rates for each indicator (see table below). This ensures that all final indicators move in the same direction i.e. a positive score indicates an improvement in the underlying indicator. The same normalisation technique has also been applied to percentage rates of change, both between years and over the whole period under study (2010-2014). Of course such rates of change may be positive or negative; the direction of movement can thus be masked by applying normalisation techniques which by definition return a positive value. Relative performance between areas might also fluctuate more wildly too, particularly when looking at year-on-year change, thus possibly giving a false impression of the progress being made. There is also the 'small number' problem, where a high rate of change in a small area might be of less impact than a low to medium rate for larger populations. 3.3. Benchmarking Essentially the procedure of ascertaining the normalised figure for a given LEP area such as Leeds City Region was an exercise in benchmarking against other equivalent areas. Geographical benchmarking (i.e., values for all LEPs at a given point in time) was adopted as the approach which would be most easily understood. This required the full range of values for all LEPs for each indicator. Calculating these scores for each year and then comparing between them provides one way in which relative change over time may be tracked. The second approach used involved the same method for calculating normalised scores, but applied instead to percentage rates of change. Centre for Regional Economic and Social Research 14

Normalisation approach used with each inclusive growth indicator Dimension Indicator Approach I1.1: Out-of-work benefits Reversed Income I1.2: In-work tax credits I1.3: Low earnings Reversed Standard Living costs Labour market exclusion Output growth Employment Human capital I2.1: Housing affordability I2.2: Rented housing costs I2.3: Fuel poverty I3.1: Unemployment I3.2: Economic inactivity I3.3: Workless households P1.1: Output (GVA) P1.2: Private sector businesses P1.3: Wages/earnings P2.1: Workplace jobs P2.2: Employment rate P2.3: Employment in low pay sectors P3.1: Higher level occupations P3.2: Intermediate and higher level skills P3.3: Educational attainment Reversed Reversed Reversed Reversed Reversed Reversed Standard Standard Standard Standard Standard Reversed Standard Standard Standard Centre for Regional Economic and Social Research 15

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