# Against the odds Re-engaging young people in education, employment and training

(1)

## odds

(2)

(3)

### Contents

Introduction 4

The predictive model 5

(4)

Introduction

### Introduction

1 This analysis quantifies the relationship between the characteristics of young people and their risk of being out of education, employment or training (NEET) for six months or more.

2 Nine Connexions teams provided data on each of the young people recorded on their database. The analysis focused on the population of around 24,000 young people aged 16 on 1 September 2007. It includes data about the activities undertaken by each young person between 1 September 2007 and 31 August 2009. The analysis excludes the cases of any young people involved with Connexions teams for less than

six months. Of the 24,000 young people included in the database, 10.3 per cent were NEET for six months or more in the period.

3 The analytical approach uses binary logistic regression to quantify the extent to which information about the characteristics of a young person predicts whether he or she would be NEET for six months or more in the period between 1 September 2007 and 31 August 2009. The data held by Connexions teams includes information about the personal characteristics of each young person, for example their gender or ethnic group.

4 Binary logistic regression is a method estimating how likely it is that a particular outcome will take place, based on one or more explanatory factors. The outcome in this analysis is whether a young person is NEET for six months or more. The

explanatory factors are information about each young person and the activities they have been involved in.

(5)

Limitations

### The predictive model

5 Table 1 lists the explanatory factors used in this analysis. This list of explanatory factors was selected from a longer list by testing whether there was evidence that young people with a certain characteristic were more likely to be NEET for more than six months than those without the characteristic. Each variable in the table was converted into a 'Yes/No' format where ‘1’ equals ‘Yes’ and ‘0’ equals ‘No’. For example, young people recorded as having a special educational need (SEN) were labelled with a value of ‘1’ for the variable 'SEN identified'.

### Factors used in the analysis

Nine factors were selected for use in this analysis.

Explanatory factor Description Percentage of young people

Carer Young person is recorded as a carer between 1 September 2007 and 31 August 2009.

0.3 Education_3_month Young person was involved in education for

less than 3 months between 1 September 2007 and 31 August 2009.

3.1

LLD Young person has a limiting learning difficulty. 7.0 NEET Young person is NEET one or more times

between 1 September 2007 and 31 August 2009.

25.9

Pregnant_parent Young person is recorded as pregnant or a parent between 1 September 2007 and 31 August 2009.

3.0

SEN_identified Young person has one or more special educational needs recorded.

2.9 Substance_misuse Young person has disclosed substance misuse

one or more times between

1 September 2007 and 31 August 2009.

0.7

Supervised_by_YoT Young person is recorded as supervised by a youth offending team between

1 September 2007 and 31 August 2009.

3.5

White_British Young person's ethnic group is White British. 70.1 Audit Commission

(6)

The predictive model

6 The model uses information about the explanatory factors for each young person in the Connexions data to predict whether the young person will be NEET for six months or more between 1 September 2007 and 31 August 2009. Table 2 describes the model.

### Predictive model

Nine indicators predict the NEET status of 91 per cent of young people.

Explanatory factor Direction of relationship

Increase in likelihood of being NEET for six months or more

NEET Positive 7.877 times more likely

Pregnant_parent Positive 2.816 times more likely Supervised_by_YoT Positive 2.575 times more likely Education_3_month Positive 2.281 times more likely Substance_misuse Positive 2.084 times more likely

Carer Positive 2.024 times more likely

SEN_identified Positive 1.483 times more likely

LLD Positive 1.319 times more likely

White_British Positive 1.221 times more likely Audit Commission

7 Each of the explanatory factors increases the chances of a young person being NEET for more than six months. The third column in Table 2 shows how much each factor affects the likelihood of a young person being NEET for six months or more. For example, if a young person has disclosed substance misuse then they are twice as likely (2.084) to be NEET for six months or more, compared to someone who has not.

8 The model correctly predicts the NEET status of 91 per cent of the young people included in the analysis. It is most effective at predicting the status of young people who are not NEET, with 97 per cent predicted correctly. The model is less effective at predicting the status of young people that are already NEET. The model correctly predicts the status of 39 per cent of young people who were NEET for more than six months.

(7)

Limitations

### Limitations

9 The model seeks to use information from nine teams to predict the likelihood that specific young people will be NEET for six months or more based on their

characteristics. Although we are using data for a substantial number of young people, they were from just nine areas. It is reasonable to expect that some of the variation in the likelihood of being NEET will depend on the area in which a young person lives, over and above their personal characteristics. We cannot model this variation without data from a wider selection of team areas.

10 The model is less effective at predicting the NEET status of young people who are NEET for more than six months than for those who are not. This means that the factors listed in Table 2 are only part of the prediction of the NEET status of young people. It is the case that young people that possess one or more of the attributes listed in Table 2 are substantially more likely to be NEET than those who have none of these attributes. However, it is also the case that there are young people with none of these attributes who go on to be NEET for six months or more.

11 We can mitigate the risk posed by these limitations in three ways:

• focus on quantifying the broad differences in the impact of the explanatory factors;

• acknowledge that many young people with none of the explanatory factors listed in Table 2 will be NEET for six months or more; and

• take any opportunities to extend the geographic coverage of the dataset.

(8)

### 0844 798 7070

If you require a printed copy of this document, please call:

0800 50 20 30 or email: ac-orders@audit-commission.gov.uk This document is available on our website.

We welcome your feedback. If you have any comments on this report, are intending to implement any of the recommendations, or are

planning to follow up any of the case studies, please email: nationalstudies@audit-commission.gov.uk

Printed in England by AccessPlus with ISO 14001 environmental accreditation on Revive FSC and ECF 100% recycled paper. Design and production by the Audit Commission Publishing Team. Image copyright © Audit Commission

### 0844 798 7070

If you require a printed copy of this document, please call: 0800 50 20 30 or email: ac-orders@audit-commission.gov.uk This document is available on our website.

We welcome your feedback. If you have any comments on this report, are intending to implement any of the recommendations, or are

planning to follow up any of the case studies, please email: nationalstudies@audit-commission.gov.uk

Design and production by the Audit Commission Publishing Team. Image copyright © Big Stick

(9)

Audit Commission 1st Floor Millbank Tower Millbank London SW1P 4HQ Telephone: 0844 798 3131 Fax: 0844 798 2945 Textphone (minicom): 0844 798 2946 www.audit-commission.gov.uk 10_0182

Updating...

## References

Updating...

Related subjects :