Guidance

Youth Investment Fund (YIF) - places selection methodology

Updated 19 April 2023

Applies to England

Introduction

DCMS is launching the Youth Investment Fund (YIF) for England which seeks to improve the effectiveness of youth facilities and services to drive better outcomes:

• improve health and wellbeing of young people

• equip young people with skills for work and life

• empower young people to be active members of their communities and society

• Levelling up of provision in ‘left behind’ areas

The aim of this document is to outline the metrics used and underlying methodology to select the shortlist of geographical areas YIF will be targeted to.

There are two broad types of areas this fund is designed to support. Firstly upper tier local authority areas that can demonstrate high levels of need and gaps in provision across a whole borough.

Secondly, the government is keen for YIF funding to reach more isolated pockets of need, at ward level (where a two tier system of local government is in place, we are using district wards, not county divisions). These smaller areas of need are often overlooked because they are masked at local government level by other, relatively less deprived, areas.

Although we have defined the areas by local government boundaries, this fund is not restricted to local authorities to bid into. We welcome and encourage applications from any organisation that meets the criteria of the fund and deliver for young people in the eligible areas.

• List 1: This will be made up of 45 upper tier local authorities, who have been picked using a mix of youth needs and provision of youth services data.

• List 2: This will be made up of 674 wards in upper tier local authorities that are not major/minor urban conurbations and where youth need/deprivation is the highest.

Please note, this list of areas eligible for YIF are fixed (as published January 2022) and will not change during the lifetime of the fund. To determine which areas are eligible via postcode, please use the Youth Investment Fund postcode checker.

A mixed approach has been chosen for a number of reasons:

• to make sure we have a mix of geographies across England and at a range of levels

• targeting wards means that areas that are ‘left behind’ but are often masked by wealthy areas are identified and able to apply

• large geographical areas identified in list 1 are needed to create partnerships and access large youth populations in order to deliver ambitious proposals for the main fund when it is launched

• having a mix of areas also allows a range of types and sizes of organisations to apply

List 1

Selected metrics

The metrics were chosen based on their links to the objectives of the fund, transparency and quality of the data available. One of the key objectives of the fund is to ‘level up’ youth provision across the UK. There are two key aspects to this:

• the distribution of disadvantaged young people across the UK (the ‘need’)

• the distribution of youth services (the ‘provision’)

While there are many ways to define the ‘need’ of young people, this selection criteria was chosen to be transparent and reflect some of the key outcomes DCMS would like to achieve with young people. A range of data was explored, particularly around deprivation, education, employment and skills for life.

To address need 2 metrics from published data sources have been chosen due to being the best fit:

• Income Deprivation Affecting Children Index (IDACI) for 2019. This is subset of the English Index of Multiple Deprivation (IMD), published by the Ministry of Housing, Communities and Local Government (now the Department for Levelling Up, Housing & Communities, DLUHC)

• Not in Education, Employment and Training (NEET). This data is published by the Department of Education.

To represent the ‘provision’ of youth services, we explored a range of data but the best quality official data we could find was around the supply of statutory youth provision by local authorities. The data used is:

• Section 251, which is published by DfE as collated Local Authority out-turn submissions.

Methodology

The following methodology was used to extract and convert the data sources, and combine them into a single metric for assessing upper tier local authorities.

• NEET data is taken from DfE 2020 records. The figure excludes those “unknown” to the local authority to avoid issues arising from administrative changes and errors.

• IDACI data is taken from MHCLG 2019 records. The score taken is the IDACI average scores.

• Section 251 data for ‘services for young people’ is taken from the years 2017/18 to 2019/20. Previous years are inflated into 2019/20 price years, and then an average is taken across the three years to control for volatility.

This spend is then divided by the number of young people (aged 11-18) in each upper-tier local authority, to control for differences in youth populations. Each of the three variables are then standardised^{[footnote 1]} in order to put them in comparable terms whilst keeping the magnitude of the respective distances from the mean. The index combines these metrics by adding NEET + IDACI together (as they are both negative indicators) and then subtracting Spend per YP (as it is a positive indicator), with equal weighting given to each variable:

YIF Selection Metric = IDACI + NEET - Section.251

Our measurement of spend only captures the direct statutory duty by local governments. This does not measure the wider youth sector, including other private, public and charitable funding of similar services.

List 2

Selected metrics

The aim of list 2 is to ensure that YIF funding is allocated to smaller areas of acute need that are present within more affluent local authority areas that would be out of scope of list 1.

A range of different geographical levels were explored but it was decided that wards would be the best fit, as they provide a much lower geographical level without population sizes becoming too small. Electoral wards were used, as this is the standard definition of wards, as used ONS.

Due to the lack of data available at a lower geographical level, there were limited options in data that linked to the objectives of the YIF, was transparent and of reasonable quality. Because of this and to maintain consistency with the metrics used for list 1, the Income Deprivation Affecting Children Index (IDACI) data was selected.

NEET data is not available at ward level meaning this metric could not be used. Similarly, the data on the supply of statutory youth provision by local authorities (section 251) is only available at upper tier local authority level, so could not be included. As a result, the areas in list 2 are selected based on young people’s ‘need’ only.

Methodology

The following methodology was used to create list 2, which is made up of 674 wards in England:

• Firstly all wards from the 45 upper tier local authorities identified in list 1 were excluded

• Then all wards in England were excluded that were in the large urban areas to that often overlooked areas are included. We did this by linking each ward to the DEFRA urban-rural classification and excluding those wards that were classified as either “Urban: Major Conurbation” or “Urban: Minor Conurbation”.^{[footnote 2]}

• For the remaining wards, the IDACI data was used to rank the wards based on their level of deprivation.

• The IDACI data is taken from the DLUHC 2019 records and the score taken is the IDACI average scores.

• IDACI scores are published at Lower Super Output Areas (LSOAs) but not at ward level.

• DLUHC provides a published methodology for creating IDACI scores for other types of geographies^{[footnote 3]}. There were 40 wards which LSOAs could not be assigned so a score^{[footnote 4]} was created for these wards based on an average score from neighbouring wards.

• We followed this methodology to create IDACI scores for each ward. These scores have been quality assured by DLUHC.

• From this list the 15% of wards with the highest IDACI scores were chosen. This produces a list of 674 wards.

Limitations

There are a number of limitations with the lists that have been created for YIF. The main limitation being the lack of available high quality data - there is limited data available on young people, especially data that doesn’t focus on education. This meant the pool of data to choose from that related to the objectives of YIF was small. Even the data that was chosen, has some limitations:

• Different data sources cover different time periods with different lags on the availability of updated data, this is case for:

  • the IDACI which is from 2019

  • the Section 251 covers a 3 year period from 17/18 to 19/20

  • the DEFRA urban-rural classification is taken from 2011 as it is based on data from the 2011 census

• NEETs data: this data is only for 16-18 year olds, meaning it is young people at the older age range of DCMS’ remit.

• IDACI data: The creation of IDACI scores at a higher level (ward and upper tier local authorities) should be treated with caution as higher-area measures are summaries as the Indices of Deprivation are designed primarily to be small-area or neighbourhood measures of relative deprivation.

• Section 251 data: There are differences in how data is filled in by local authorities.

• Related to this, the Section 251 data does not cover all spend in the youth sector, as much of the funding comes from a mix of sources - for example charities, central government and Arms Length Bodies, philanthropic organisations, private/corporate organisations etc. This means a large proportion of funding is not being taken into account as there is no reliable and easily identifiable data to do this.

It is also worth noting that the metrics chosen do not cover some of the key objectives of YIF, particularly around improving young people’s health and mental wellbeing, this is due to there being no high quality data at a higher geographical level around young people’s wellbeing.

1. In statistics, standardised variables are variables that have been standardised to have a mean of 0 and a standard deviation of 1. The variables are rescaled using the z-score formula. Standardising makes it easier to compare scores, even if those scores were measured on different scales. ↩

2. DEFRA (2011), Urban Rural Classification ↩

3. Indices of Multiple Deprivation FAQs, p15 ↩

4. See ONS best fitting guidance ↩