Growing concern regarding the number of young people who are NEET in the UK as opposed to youths who are unemployed is the focus of this research. This thesis tries to make several contributions to the existing literature by extending the use of the NEET concept for different age groups, both youth and adult age groups, using a larger dataset of the 18 waves of the British Household Panel Survey (BHPS) and the first five waves of the Understanding Society survey.
In previous studies, discussion of the labour market for young people is mostly conducted separately from those for adults. Moreover, as also mentioned previously, the use of the NEET concept is mostly applied to young people, and none of the existing literature, to the best of our knowledge, has applied this concept to older age groups. In this regard, most previous studies disaggregated the labour market status into three major standard categories: employment, unemployment, and inactivity. In this study, we try to include another type of labour market category, that of being in education (or training) into our analyses.
Furthermore, in our labour market transition estimations, not only do we estimate the transition out of the education (or training) state (commonly known as the
school-to-work transition), but we also analyse the reverse transition probabilities from other labour market states into the education (or training) state. We are aware that the number of individuals from the adult age group who are in the education state is much lower compared to those from the youth age group. However, unlike the labour market state of being retired that only applies to older individuals, we still find sufficient observations for the adult age group in education (or training) who make transitions in and out of this state.
Another contribution of this thesis is to include estimations for different business cycle phases, both recession and non-recession periods, as illustrated in Figure 1. We include the business cycle indicator into our estimations in the form of time dummy variables. In addition, the trends in both GDP levels and unemployment rates, depicted in Figure 1, are used as our benchmark to generate these dummy variables. Disaggregating the business cycle into several non-overlapping periods allows us to investigate the various impacts of different business cycle periods on the labour market. More specifically, we would be able to observe whether all recession periods have similar adverse impacts on the labour market and whether all non-recession periods have an equal positive effect on the labour market. Since different recessions are different in length and depth, we expect that the effect of each recession period on the labour market will be different. Similarly, different non-recession periods might have different effects on the labour market. In this case, we expect that the non- recession period which follows immediately after a recession will still have an adverse impact on the labour market.
CHAPTER 2
The Determinants of NEET
As in most European countries, the issue of youth unemployment in the UK has received great concern from policymakers particularly during the recent Great Recession in 2008/2009. In the context of youth unemployment, policymakers in the European countries are increasingly using the concept of NEET – Not in Education, Employment or Training – to adequately capture the situation of young people, since many of these young people are students and thus they are regarded as being out of the labour force when the traditional unemployment indicator is used. At the EU level, NEETs are considered to be one of the most problematic groups in the context of youth unemployment (Eurofound, 2012). Moreover, in terms of policy perspective, the main challenges for policymakers are to provide pathways for the young people back into education and training as well as enabling contact for these young people with the labour market.
The usage of the concept of NEET, however, both in the context of policy making and in the existing literature, only applied to young people, and thus this concept is rarely to be found in the discussions of adult age groups. One of the contributions of our study is to apply the concept of NEET not only to the group of young people but also for those in the older age groups. By definition, the classification of NEET in the UK includes those (young people) who are unemployed (‘active’ NEET) and those looking after children or relatives at home, temporarily sick or long-term disabled (‘inactive’ NEET), as well as those putting their efforts into developing artistic or musical talents, or simply taking a break from work or education (Furlong, 2006, pp. 554). Thus, the concept of NEET involves the situation of being unemployed and economically inactive, which could also be applied in the case of adult labour market status.
Furthermore, with respect to the policy context, the concept of NEET has been used by policymakers to measure the disengagement of young people from the labour market and from society in general (Eurofound, 2012; Maguire, 2015). By the same token, this concept could be applied as a measurement of disengagement for adults who are economically inactive and unemployed. It has been well documented in
several studies (see, for example, Haardt, 2005 and Cappellari et al., 2005) that one of the policy concerns in the case of adult labour market, particularly for older individuals, is to re-engage those who are out of the labour force back into employment. Hence, applying the same concept of NEET for adults would enable us to tackle the issue of social exclusion in the case of adults who are currently being disengaged from the labour market due to economic inactivity as well as for those who are in unemployment.
As mentioned in the previous chapter, we are aware of the fact that adults are less likely than youths to attend full-time education or government training. Moreover, the previous chapter has also presented some evidence that young people seemed to take ‘shelter’ in education during the recent Great Recession, as indicated by the increasing participation rates in Higher Education (see Barakat et al., 2010 and Jenkins et al., 2012). In this study, we will try to examine whether similar patterns also exist in the case of adult age groups; that is, whether the adult age groups are also more likely to be in education during bad economic conditions. The results from this analysis might be of interest for policy purposes, since it would enable us to see whether policy to provide pathways for individuals to return to education or training would only be applicable in the case of youths or it would also be effective for the older individuals.
For the above reasons the main objective of this chapter is to analyse the characteristics of individuals in a given labour market state at a given point in time. In other words, we try to estimate the state probabilities associated with being NEET and to see ‘who is in what state.’ Our main focus in this chapter is to analyse the determinants of being in NEET and Non-NEET states, particularly regarding the impacts of recession on different age groups as well as for different regional areas in the UK. For the later analysis, we disaggregated regions in the UK between the northern and southern regions.21
In regard to the effect of recession on different age groups, numerous studies have suggested that the employment of youths is highly vulnerable to the overall
21 The northern regions consist of North East, North West, Yorkshire and Humber, Wales, Scotland,
Northern Ireland and Channel Island; while the southern regions are East Midlands, West Midlands, East, London, South East, and South West.
conditions of the economy. Scarpetta and Manfredi (2010) using the OECD data find that youth unemployment is indeed more sensitive to the business cycle than adult unemployment. Utilizing data from more than 70 countries spanning the period of 1980 – 2005, Choudhry et al. (2012) also shows evidence that the impact from recession is higher for the youth than for adult unemployment rate. Moreover, growing number of studies have concentrated their discussions on those young people who are NEET as opposed to unemployed. Nevertheless, as previously discussed, discussions regarding the importance of NEET on older age groups are rare to be found.
Looking at the trends in unemployment for different regions in the UK during various business cycle periods, Figure 7 provides the pictures of the real per capita Gross Value Added (GVA) and unemployment rates for those regions in the northern and southern parts of the UK.22 For comparison, the level of real GVA and unemployment rate at the national level are also included. It is clear from Figure 7 that output for regions in the southern part of the UK is far higher than output for regions in the northern part. One reason could be that regions in the southern part of the UK, such as London, are endowed with better infrastructure and might have more industry that tends to be high end, such that the value added from these regions are higher compared to regions in the northern part of the UK. Moreover, it is also evident from the Figure that recessions hit both northern and southern regions in the UK, indicated by a fall in the real GVA level, except for the period during the dot.com recession in the early 2000s which had little severe impacts on the UK economy. Later in our analysis, we find that the impacts of recession by region are different depending on the cause of the recession and the difference in industrial structure between the northern and southern region of the UK.
22 The regional income data from the Office for National Statistics (ONS) are only available in the form
of Gross Value Added (GVA). The ONS did recently release the historical Regional GDP data from 1968-1996 upon user request. However, the ONS warns that these data may not be suitable for all analytical purposes, partly because they were compiled as GDP rather than GVA estimates (see ONS, 2016b). The latest ONS Regional GVA estimates are in basic prices which include taxes on the production process (such as business rates and any vehicle excise duty paid by businesses) but exclude taxes (less subsidies) on products. By contrast, the GDP is measured in market prices. The difference between the two is called the basic price adjustment (BPA), and reflects the impact of taxes and subsidies on market prices (Chamberlin, 2010). In general, the relationship between GDP and GVA can be written as follows: GDP at market prices = GVA at basic prices + taxes on products - subsidies on products. Moreover, GVA at factor cost + (Production taxes less Production subsidies) = GVA at basic prices. Additionally, the real Regional GVA estimates are obtained by dividing the nominal Regional GVA by the Regional Retail Price Index (RPI) for each corresponding year, with the base year of 2005.
Figure 7 Yearly UK and Regional Log Per Capita Gross Value Added (in £) and Unemployment Rate (UR) 16-64, 1989-2012
Source: Office for National Statistics, 2014d (for per capita GVA) and 2014e (for UR).
Note: yellow bar indicates periods of a fall in GVA and increase in the unemployment rate, whereas red bar represents the increase in unemployment rate after the level of GVA has risen back.
Figure 7 also shows that the unemployment rates by region generally follow the unemployment rate trends at the national level. Similar to the figure of real GVA by region, the unemployment rate for the northern regions is also higher than the unemployment rate of the southern regions in all periods. Moreover, the unemployment rate for the northern regions almost always higher than the national unemployment rate. This may support the notion that regions in the southern part of the UK could have more job opportunities available as compared to those regions in the northern part. In addition, almost every period of recession is accompanied by an increase in the unemployment rate. One exception is during the early 2000s recession, where the increase in the unemployment rate only apparent in the case of southern regions, whereas the unemployment rate for the northern regions tended to decrease.
For the purpose of this study, we make use of the UK British Household Panel Survey (BHPS) dataset for waves 1-18, and joined them with its successor study of the Understanding Society (US) data for waves 1-5. Our sample consists of individuals aged 16-65 in each wave who have not yet retired. We estimate multinomial logit models to account for multiple labour market states based on the self-reported current labour market status (or economic activity) of respondents in each survey. In addition, not only do we estimate the three standard labour market states, i.e. employment, unemployment, and inactivity (out of the labour force), but we also include the economic activities of being in education or training as alternatives. Classifying the labour market statuses into these categories enables us to distinguish between individuals who are NEET (those who are not in education, employment or training) with those who are Non-NEET or EET (those in education, employment or training).
Lastly, there are at least two reasons why the analysis in this chapter might be important to support analyses in the next chapters. First, our analysis in this chapter provides a picture of the characteristics of individuals who are in a particular labour market state. Secondly, our results from this chapter can be used as a benchmark to analyse the dynamic nature of transitions between different labour market states (transition probabilities) which will be the main purpose of the next chapters.