The outcome variable is young person’s main activity at the ages 18 19 and the
main independent variable is the Crime Score of the IMD index. The LSYPE dataset o↵ers detailed histories of individuals’ monthly main economic activity after compul-
sory education, between September 2006 and May 2010 in Wave 7. Monthly main activity files report employment activity of 11,821 respondents. Fourteen di↵erent ac- tivity categories were summarised into four main categories: Education; Employment; Training; and Unemployed / Inactive (NEET). Following previous studies methodol- ogy (Payne [141], 2001; Bynner and Parsons [29], 2002; Yates et al [203] 2011) “NEET status” in this study refers to young people who are unemployed or inactive without participating in any form of education or training for a period of six months.
The main independent variable, neighbourhood deprivation, is measured with the Crime Score of the decomposed IMD index. Neighbourhood disadvantage is associated with a number of correlated variables such as for example ethnicity, parental socio- economic status and parental educational level and individual characteristics which could have an e↵ect on young people becoming NEETs. In area analysis it is diffi- cult to isolate neighbourhood e↵ects and to draw causal conclusions. Therefore, these correlated characteristics will be used as controls in the analysis to investigate the mech- anisms through which neighbourhoods influence educational and employment outcomes of young people.
5.6
Summary and Conclusions
The UK has developed a tradition for producing high quality longitudinal datasets which are used by analysts across the world. Despite the time and cost required to undertake longitudinal studies, these studies make a significant contribution to inform policy and understand the impact of policy interventions. A number of longitudi- nal surveys were considered prior to deciding a dataset for the current analysis. In comparison to the other studies investigated, LSYPE was chosen because it o↵ers the biggest sample at the appropriate age. Additionally, it investigates thoroughly young people and provides insightful information on the factors that are considered to have significant impact on young people’s development and the trajectories they follow after
compulsory education. The LSYPE questionnaires report a broad range of important characteristics and look at the key issues that a↵ect the lives of young people, the pathways through which they move into adulthood and their educational and employ- ment outcomes. The LSYPE covers demographic and household information; young people’s educational attainment, attitudes to schooling, risk factors encountered, am- bitions for the future, friendships, higher education and employment and; parental attitudes, practices and aspirations. Due to the fact that the LSYPE questionnaires take many di↵erent directions and involve a wide range of di↵erent but related infor- mation, they allow the current analysis to focus on the factors associated with and influencing the transitions young people make and the di↵erent paths they follow at
the ages 18 19.
The LSYPE response rates remain relatively high throughout the study and do not di↵er from response rates from other longitudinal studies and in relation to the original sample (Lynn [117], 2005). About 7% of respondents drop out in each Wave. Low drop rates reflect a good representation of respondents in the study. Given the young age of respondents in the study, the danger of attrition was highly likely. For this reason, unconditional incentives were adopted to increase willingness to participate and to minimize levels of attrition.
A strength of the LSYPE data is that it has incorporated data from the National Pupil Database (NPD) and the Pupil Annual School Census (PLASC) which add information on students’ academic achievement and o↵er school level data allowing the investigation of educational outcomes. Additionally, the LSYPE has included geographic information through the linkage with the IMD index of area deprivation. Geographic information is essential for the neighbourhood focus of the present study. The IMD index and its seven constituent domains provide important contextual information with regards to the neighbourhood where the young people live.
clear that the key benefit of using the LSYPE study is that it enables the Compositional Model of Neighbourhood E↵ects to be tested. In addition, the LSYPE allows the application of statistical techniques to reduce selection bias and to identify the e↵ect of area deprivation in combination with di↵erent spheres of young peoples lives to their development.
The next step involves selecting the vector of covariates that depict family, individual, school and peer group characteristics in order to address the first five research questions and to estimate the probability of a young person becoming NEET if they live in a high Crime area. The following Chapter employs multivariate logistic regression analysis to test the Compositional Model of Neighbourhood E↵ects taking into account that the outcome variable, NEET, is a dichotomous variable. The goal will be to estimate the probability that a young person will become NEET based on the values of the set of independent covariates proposed by the Ecological model of Neighbourhood e↵ects that is put forward in this thesis.
Controlling for Family and
Individual Characteristics
6.1
Introduction
This thesis started with defining young people Not in Education, Employment or Train- ing and describing the factors associated with entry to NEET status. Subsequent chapters presented theories that have been used to study neighbourhood e↵ects and theories on individual development to help explain the pathways through which area
deprivation influences young people’s trajectories at the ages 18 19. These theoreti-
cal frameworks have informed the Compositional Framework of Neighbourhood E↵ects that will be tested in this thesis. This chapter, investigates the first five research questions proposed by the Compositional Framework of Neighbourhood E↵ects. Section 6.2 of Chapter 6 presents the research questions that are going to be addressed, the data that will be employed to investigate each question and how attrition and non- response are going to be taken into consideration in the analysis. Section 6.2 also describes the method of data analysis that will be followed throughout the analysis to test the association between NEETs and the set of selected covariates. The analysis
will begin with descriptive statistics and subsequently continue with logistic regression analysis. Binary logistic regression is selected because the outcome of interest is a dichotomous variable, NEET status which is classified as “yes” or “no”. This section identifies the properties of the logistic function and explains how the logistic formula will be applied in this study. Section 6.3 presents descriptively the key measures that will be employed in the analysis. The first key measure that will be employed is the main activity of young people, described by the main activity files in the LSYPE study. The second key measure is the indicator of area deprivation, which is measured by the Index of Multiple Deprivation 2010 (IMD) average score and its seven sub- indices. Finally, a summary of all the measures that will be employed to test the Compositional model of neighbourhood e↵ects is presented. Section 6.4 includes the statistical analysis to address the first four research questions. The analysis will begin with descriptive statistics of the data and the Pearson chi-square measure of association to measure the strength of the relationship between NEET and each covariate. Further analysis will be carried out, using a binary logistic regression model, to estimate the probability that a young person will become NEET or not based on the values of the set of independent variables. Section 6.4 includes the discussion and analysis of results presented in Section 6.3.