Census on Youths NEET: Report on Findings FINAL REPORT_ETC.pdfTraining (NEETs). It includes an...

YOUTH

GUARANTEE

ESF-3.231

CENSUS ON YOUTHS NEET:

REPORT ON FINDINGS

Operational Programme II – Cohesion Policy 2007-2013

Empowering People for More Jobs and a Better Quality of Life

Part-financed by the European Union

European Social Fund (ESF)

Co-financing rate: 85% EU Funds; 15% National Funds

Investing in your future

Youths NEET Census Report 2015

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Table of Contents List of Tables .................................................................................................................................. 5

List of Figures ................................................................................................................................. 7

Executive Summary ........................................................................................................................ 8

Literature Review.......................................................................................................................... 10

1.1 Review of the Literature ...................................................................................................... 10

1.1.1 Introduction .................................................................................................................. 10

1.1.2 Current state-of-affairs ................................................................................................. 11

1.1.3 Calculating YU and the concept of NEETs .................................................................. 13

1.1.4 YU and the labour market ............................................................................................ 17

1.1.5 Causes of NEETs .......................................................................................................... 18

1.1.6 Effects of YU ................................................................................................................ 19

1.1.7 Transitions and Interventions ....................................................................................... 23

1.2 General Conclusions from the review ................................................................................. 28

Methodology ................................................................................................................................. 30

2.1 Introduction ......................................................................................................................... 30

2.2 Research Questions ............................................................................................................. 30

2.3 Sampling, data collection and ethical considerations.......................................................... 31

2.4 The Research Instrument ..................................................................................................... 35

2.4.1 Type of Questions ......................................................................................................... 35


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2.4.2 Translation of the questionnaire ................................................................................... 35

2.4.3 Type of Language used ................................................................................................. 36

2.4.4 Questionnaire sections .................................................................................................. 36

2.4.5 Pilot Study .................................................................................................................... 38

2.5 Data Analysis procedures .................................................................................................... 38

2.5.1 Cluster Analysis ............................................................................................................ 39

2.6. Conclusion .......................................................................................................................... 40

Results ........................................................................................................................................... 41

3.1 Introduction ......................................................................................................................... 41

3.2 Personal Information and Social Demographics (Section A) ............................................. 41

3.2.1 Demographic information (A1) .................................................................................... 41

3.2.2 Family members (A2) ................................................................................................... 42

3.3 Education and Training (Section B) .................................................................................... 45

3.3.1 Level of Education (B1) ............................................................................................... 45

3.3.2 Future Plans (B2) .......................................................................................................... 46

3.4 Work Experience and Interests (Section C) ........................................................................ 48

3.5 Feedback on the Youth Guarantee (Section D) ................................................................... 51

3.6 Additional Section - Open-ended questions (Section E) ..................................................... 55

3.7 Interviewer Observations (Section F) ................................................................................. 59

3.8 Cluster Analysis .................................................................................................................. 62


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3.8.1 Cluster Analysis of Targeted Population (including ineligible participants) ............... 62

3.8.2 Cluster Analysis of Population Excluding Ineligible Participants ............................... 69

3.9 Follow-up Interviews .......................................................................................................... 71

3.9.1 Rationale ....................................................................................................................... 71

3.9.2 Methodology ................................................................................................................. 72

3.9.3 Results .......................................................................................................................... 74

3.9.4 Further breakdown of above results ............................................................................. 80

3.9.5 Overall conclusion of the follow-up interviews ........................................................... 86

3.10 Conclusion ......................................................................................................................... 86

Conclusion .................................................................................................................................... 87

4.1 Introduction ......................................................................................................................... 87

4.2 Summary of major findings ................................................................................................ 87

4.2.1 Census ........................................................................................................................... 87

4.2.2 Follow-up Interviews .................................................................................................... 92

4.2.3 Putting it all together .................................................................................................... 93

4.3 Limitations .......................................................................................................................... 96

4.3.1 Limitations related to the Census ................................................................................. 96

4.3.2 Limitations related to the follow-up interviews ........................................................... 97

4.4 Recommendations .............................................................................................................. 97

4.4.1 Preventative Measures .................................................................................................. 98


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4.4.2 Activation Measures ................................................................................................... 100

4.4.3 Other Recommendations ............................................................................................ 102

4.5 Concluding note ................................................................................................................ 102

References ................................................................................................................................... 105

Appendices .................................................................................................................................. 111

Appendix A.1: Census Consent Form ..................................................................................... 112

Appendix A.2: Census Questionnaire ..................................................................................... 114

Appendix B: Follow-up Interview Questionnaire ................................................................... 124

Appendix C: Speculations ....................................................................................................... 127


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List of Tables

Table 1: Number of attempts made during the Youth NEET Census 2015.................................. 32

Table 2: Population and Sample distributions by gender, age group and district ......................... 34

Table 3: Reasons for stopping education or training .................................................................... 45

Table 4: Getting into education ..................................................................................................... 47

Table 5: Reasons for currently not being employed ..................................................................... 48

Table 6: Industries that interest respondents ................................................................................. 49

Table 7: Aspects that would help respondents to become employed ........................................... 51

Table 8: Reasons for not applying for the Youth Guarantee Programme ..................................... 51

Table 9: Reasons for possible participation in the Youth Guarantee Programme ........................ 52

Table 10: Perceptions of society and locus of control .................................................................. 53

Table 11: Barriers for getting into work, education or training .................................................... 54

Table 12: The one thing respondents enjoy doing most ............................................................... 55

Table 13: Hindering factors to education, training and employment ........................................... 56

Table 14: Factors that encourage educational, training, or job opportunities ............................... 57

Table 15: Implementing factors for further training/education and/or employment .................... 58

Table 16: Interviewer Observations .............................................................................................. 60

Table 17: Distribution by Age and Gender of Population for Maltese youth NEETs .................. 62

Table 18: Classification of Targeted Population .......................................................................... 62

Table 19: Distribution by Age and Gender of Population for District 1 youth NEETs ................ 63




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Table 25: Classification by District .............................................................................................. 69

Table 26: Gender * District Cross-tabulation ............................................................................... 70

Table 27: Age Code * District Cross-tabulation ........................................................................... 70

Table 28: Mean Ranks by District ................................................................................................ 71

Table 29: Analysis of Call Attempts ............................................................................................. 74

Table 30: Type of Employment .................................................................................................... 75

Table 31: Hours per week ............................................................................................................. 75

Table 32: Employment Start Dates ............................................................................................... 76

Table 33: Education Institute ........................................................................................................ 76

Table 34: Other Education Institutes ............................................................................................ 77

Table 35: Course Start Dates ........................................................................................................ 77

Table 36: Future Employment Plans............................................................................................. 78

Table 37: Future Education Plans ................................................................................................. 79

Table 38: Interviewee ................................................................................................................... 79

Table 39: Interest in Youth Guarantee – Core NEETs ................................................................. 82

Table 40: Breakdown of Floating NEETs .................................................................................... 83

Table 41: Breakdown of Transition NEETs ................................................................................. 84

Table 42: Category of Participants ............................................................................................... 86


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List of Figures

Figure 1: Sample distribution by district ...................................................................................... 41

Figure 2: Venn diagram – Where did you live when growing up? ............................................... 42

Figure 3: Clustered Barchart – Time spent with family and friends per week ............................. 44

Figure 4: Pie Chart – Current Life Satisfaction ............................................................................ 44

Figure 5: Fluidity of NEET Categories ......................................................................................... 85

Figure 6: Putting it all together ..................................................................................................... 96


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Executive Summary

Employment and inactivity are undoubtedly critical and determining features for a country’s

social and economic state of well-being. Youth Unemployment and Inactivity (hereafter YU) is

even more of a crucial topic as it affects individuals who are still entering or adapting to the work

ambience and therefore can be more vulnerable as negative effects can spill over and be felt for a

long time. Youths are the foundation of wealth creation and social stability of future generations.

This report provides data from a commissioned Census study conducted on the Island State of

Malta between July and September 2015 and a series of follow up interviews conducted during

the month of October 2015. This study investigated the state of youths who are generally

classified as ‘Not in Employment, Education or Training’ (NEET) and explores trends in terms

of attitudes and predispositions, as well as, cluster distribution in terms of key factors (e.g.

location, age and gender) for those who are formally Classified as NEETs.

Results clearly suggest that the NEETs population in Malta is highly heterogeneous and seems to

consist, in decreasing order, of Transition NEETs, Floating NEETs, and Core NEETs. These

sub-categories could also possibly be further sub-divided into two further sub-segments, with

one sub-segment demonstrating traits somewhere in between those of a Transition and Floating

NEET, and second sub-segment demonstrating traits somewhere in between those of a Floating

and Core NEET.

Further insight is required to determine the profile of and numbers making up the various NEET

typologies to be able to address more specific interventions and to have methods to identify

NEET predispositions among student populations, thereby adopting a proactive and preventative

rather than a reactive approach to reducing the number of youth NEETs in Malta.


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The report is divided into four parts. Section 1 provides an overview of the literature in this area

related to YU and, where possible, with direct reference to youth NEET. Section 2 provides

details on the methodology adopted for this Census study. Section 3 presents results while

Section 4 provides evidence-based conclusions and specific recommendations.


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Literature Review

1.1 Review of the Literature

This section offers a review of literature related to Youths Not in Employment, Education or

Training (NEETs). It includes an overview of; general statistics related to NEETs (both local and

EU), the concept of NEETs, causes and effects of NEETs, and previous interventions.

1.1.1 Introduction

Unemployment is an important indicator with both social and economic dimensions (Eurofound,

2013). Rising levels of unemployment may result in a loss of income for individuals, increased

pressure with respect to government spending on social benefits, and a reduction in tax revenue.

From an economic perspective, unemployment may be viewed as unused labour capacity

(Eurofound, 2012).

The European Foundation for the Improvement of Working and Living Conditions (Eurofound,

2012) states that the future of Europe depends upon the 94 million Europeans aged between 15

and 29. This generation will live in an era of full globalisation and has to cope with the

responsibility of an ageing population. This puts a lot of pressure on governments to ensure that

youths develop into highly skilled individuals, find employment, and contribute to developing a

country’s human capital (NIACE, 2013). If countries truly want to fully exploit the potential of

young people, they need to be productively employed and integrated into society.

Generally, youths tend to be more likely to experience periods of unemployment, as they tend to

be less sure of and settled in their occupational choices, and more mobile than adult workers.

Thus, short term YU may not necessarily have negative connotations (O’Higgins, 2010).


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However, when these spells of unemployment become persistent, it may lead to long-term

unemployment and permanent labour market disengagement.

Long-term unemployment is of particular concern, as it increases the risk of long-term exclusion

of young people from the labour market and society (Eurofound, 2014). Young people are

particularly vulnerable to the adverse consequences of long-term unemployment; it is recognised

that the loss of work experience early on in life, with its implied loss of human capital, is likely

to have scarring effects on future labour force participation and earnings (Eurostat, 2014). This

leads some scholars to claim that youth unemployment poses a ‘wage penalty’ on future

earnings, which is incurred even if individuals avoid being unemployed again. In the longer

term, it also implies that unemployed young people may not have sufficiently secured well-paid

jobs to accrue occupational pension rights or to make substantial contributions to other private

schemes; an increasing concern in light of demographic changes in the EU.

1.1.2 Current state-of-affairs

YU statistics are important to the extent that they reveal the state-of-affairs of the phenomenon

and thus can urge policy makers in activating policies and initiatives while addressing specific

urgencies. As at August 2015, the EU has the current statistics on unemployed youth:

While across all countries the unemployment rate of young people has always been

typically higher than that of adults, the recent economic and financial crisis hit young

people extremely hard. More than 4.5 million young people (aged 15-24) are

unemployed;


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Although it has decreased – from more than 23% in 2013 to less than 21% today – the

youth unemployment rate is still very high in the EU (with peaks of more than 40% in

several countries);

The EU youth unemployment rate is more than double the overall unemployment rate

(20% compared to 9%);

Overall employment rates of young people fell by more than 4 percentage points

between 2008 and 2014 – about 8 times as that for adults;

More than 7 million people in the 15-24 age group are classified as NEETs;

11% of those aged 18-24 are early school leavers;

High youth unemployment boils down to labour market mismatches, inadequate skills,

limited geographical mobility and/or inadequate wage conditions.

These figures and trends indicate that YU cannot be simply ‘added on’ to the general

unemployment scenario for adults but requires specific attention and specific policies that take

into consideration the social and psychological realities of the target audience.

And in the case of Malta? According to a Eurofound study conducted by Mascherini et al (2012),

Malta falls within what the authors refer to as ‘Cluster 2’. This cluster is characterised by having

a majority of NEETs who are inactive. It includes southern Mediterranean – countries like

Greece and Italy – and eastern/central European countries – like Bulgaria, Hungary, Romania,

Poland and Slovakia. With the exception of Poland and the Czech Republic, the countries in this

cluster are generally those with the highest NEET rates in Europe. The share of NEETs who are

female is much higher than the EU average. While the majority of NEETs are inactive, as in the

first cluster, these characteristics seem to be driven by very different dynamics. In particular, in

most of the countries, the majority of NEETs have no work experience or have less work


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experience than the EU average. In addition, in most of these countries, the share of discouraged

workers is higher than the EU average. While in most, a large proportion of NEETs have a lower

education level, the share of those with a tertiary education who are NEET is well above the EU

average. The characteristics of this cluster, namely the high rate of NEETs without work

experience, the high share of discouraged workers and the high share of NEETs among those

with a tertiary education, seem to indicate structural problems in the transitions from the

education system to the labour market. Here, ending up as NEET seems to be involuntary and the

result of various barriers that hinder a successful transition.

Further trends specific to the Island state of Malta are the following (Mascherini et al., 2012):

YU makes 14% of the total unemployed;

There are roughly as many young females as males who are unemployed;

The composition of young people in employment are generally classified as ‘employees’

(rather than self-employed, implying that young people are still dependent on ‘finding a

job’ rather than ‘creating a job’);

20% of all youths are in part time employment and 18% are in temporary employment;

40% of young people are in temporary employment because they could not find a

permanent job.

1.1.3 Calculating YU and the concept of NEETs

Earlier on, reference was made to NEETs to describe that category of youths who are ‘idle’.

Indeed, some of these published stats reflect the current state of this category too. However, one

must qualify the concept especially in view of the method adopted to calculate YU rates.


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The concept of NEETs has multiple facets and this needs to be clarified. One category refers to

youths who are out of employment and schooling because of social and economic reasons (such

as: family conflict, lack of attachment to school, unemployed parents). However, other categories

may be termed as NEETS for personal reasons (such as: anti-social behaviour) and some may

also choose to be NEET. Indeed, Mascherini et al (2012) state that: “NEETs are a very

heterogeneous population. The largest subgroup tends to be those who are conventionally

unemployed. Other vulnerable subgroups include the sick and disabled and young carers. Non-

vulnerable subgroups include those simply taking time out and those constructively engaged in

other activities such as art, music and self-directed learning (our emphasis).” (p. 2).

Rather than using the term NEET to describe all youths who are not in Employment, Education,

or Training, it should be used as an umbrella term that encompasses different types of NEETs.

Research (RaE, 2007) suggests that NEETs fall into three separate categories:

Core NEETs- Youths with social and behavioural issues, including those who come from

families where “worklessness” and unemployment is accepted as a norm and not much

importance is given to further and higher education. NEETs falling into this group may

tend to be “predisposed” (e.g. Youths from families with a: low Socio-Economic status,

history of drug abuse, violence/crime, and unemployment) and are more likely to become

NEETs if the right guidance and support is not given in their early youth years. Research

(e.g. Capsi et al., 1998) suggests that early personal and family characteristics may affect

labour-market outcomes, as not only do they restrict the accumulation of human capital

(e.g. education), but also because they directly affect one’s ability to enter and remain in

employment. The predictors for unemployment and early school leaving in this sub-group

of NEETs (e.g. Human Factors, Social Capital, Personal Capital) reach back to early


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childhood, thus it is plausible to say that they influence one’s ability to penetrate the

labour market years before these youths actually enter the workforce. Failure to account

for prior social, psychological, and economic risk factors in the development of Policies

targeted at this classification of NEETs may hinder the affectivity of such policies.

Floating NEETs- Youths who lack direction and motivation. They tend to have spells of

being a Youth NEET in between further education courses or employment with no

training. This group also contributes to NEETs churn, the movement of young individuals

between different phases of activity but return to NEETs status repeatedly after

unsuccessful attempts at education, training, or employment. These NEETs are keen to be

engaged in employment, education, or training and make an effort to seek employment or

training. However, they lack clear direction and thus require a certain amount of support

and guidance whilst they are seeking and engaged in employment or education

Transition NEETs- Those young individuals who have chosen to take time out before

progressing onto further or higher education opportunities. These individuals are likely to

return to education, training, or employment, but it is not always clear when this will

occur. This classification of NEETs usually possesses support and guidance from

relatives and friends and is simply on a study break or employment break. These

transition periods are often quite short and last 3-6 months.

This variation on who qualifies as NEETs should flag one to caution about the possibility of

generalizing characteristics of the NEETs group or even concluding about their potential

consequences on the labour market. This implies that one must be specific of which category of

youths qualified as NEETs one is referring to. It also means that different categories of NEETs

may require different interventions as different categories don’t share similar experiences of


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education and employment. In addition, caution must be taken when calculating the NEET

population vis-à-vis the general YU rate. While the YU rate refers just to the economically active

members of the population who were not able to find a job, the NEET rate can be understood as

the share of the total population of young people who are currently not engaged in employment,

education or training, some of whom out of their own choice and sometimes for specific reasons.

This is why the number of young people who are NEET in Europe (N=7,469,100 of 15–24-year-

olds in 2011) is higher than the number who are unemployed (N=5,264,800 in same age bracket

and same year), but the NEET rate (12.9%) is lower than the youth unemployment rate (21.3%).

In fact, the denominator of the two rates is different: In the case of the YU rate this is worked out

as:

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑜𝑢𝑛𝑔 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑜𝑢𝑛𝑔 𝑝𝑒𝑜𝑝𝑙𝑒 𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑎𝑙𝑙𝑦 𝑎𝑐𝑡𝑖𝑣𝑒

In the case of NEETs, this is worked out as:

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑜𝑢𝑛𝑔 𝑁𝐸𝐸𝑇

𝑇𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑦𝑜𝑢𝑛𝑔 𝑝𝑒𝑜𝑝𝑙𝑒

Thus, in the YU rate the denominator is constituted only by those who are economically active

(N=24,711,200, 15–24-year-olds in 2011), whereas the denominator of the NEET population is

constituted by the total population of young people (N=57,862,300 in 2011). As the

denominators of the two rates are different, the rates are not directly comparable. For younger

people (those aged 15–19 years), who are much more likely to be in full-time education, the

difference between the NEET rate and the unemployment rate is even greater. Therefore this

implies that caution needs to be exercised to retain an important distinction between unemployed

youths and NEETs who are one subset of youths who are ‘unemployed’, even though they share


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many similar experiences of being unemployed, in particular those who are so involuntarily. The

rest of this review refers to YU to include those who are NEET for reasons beyond their active

choice.

1.1.4 YU and the labour market

Writing on YU, Clark and Summers (1982) stated that “The central difference between the

traditional and new views of youth unemployment lies in their conception of turnover. The

former emphasizes the infrequency of job finding and the consequent lengthy duration of

unemployment, while the latter focuses on the brevity and frequency of unemployment spells”

(p. 200).

According to Breen (2005), there is a wide variation in YU among OECD countries. These

variations are a function of institutional and economic differences between countries. Coping

with a job loss in a weak labour market – when job offers are scarce and competition among job

seekers is fierce – is difficult for anyone. In spite of these variations, O’Higgins (2001) notes that

there are a number of features which are remarkably constant in quite different natural contexts.

First, YU is higher than adult unemployment; and second; it is closely linked to adult

unemployment.

O’Higgins (2001) argues that attention in YU needs to be paid to both the demand and the supply

side of the youth labour market. Breen (2005) specifies that economic variations are a function of

the overall state of the labour market while institutional factors include the educational system

and the degree to which employers are prevented from dismissing workers. Therefore, YU is

high in highly regulated labour markets and where the educational system does not send very

clear signs about workers’ abilities and skills, and thus tied partly to the vocational stream.

Moreover, MacDonald (2011) argues that YU is to be seen from a wider perspective such as


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labour market inequality, the labour market itself and social class reproduction. Research shows

that weaker labour market conditions and lower wages increase the likelihood that youths stay at

home with their parents, as well as remain at school (Mroz & Savage, 2006). In addition, the idea

that ‘up-skilling’ YU is a solution is not fully correct; unemployment is also the product of

market forces and therefore more on the demand side rather than the supply end; a similar view

is shared among other authors (e.g. Bell & Blanchflower, 2010). Indeed, a key development in

all this argument is ‘underemployment’ and it is recognised that underemployment is the 21st

century global normality for youths in the labour market. For a number of authors in the area

(e.g. Scarpetta, Sonnet & Manfredi, 2010) special efforts should be made to avoid youths

entering the labour market without a recognised and valued qualification, encouraging teenagers

to stay longer at school and above all, tackle school drop-outs.

1.1.5 Causes of NEETs

There are no hard and fast predictors why some youths end up either out of schooling or out of

employment, sometimes for a long period of time. Ironically, some evidence points out at the

fact that teachers, educators and also family members can well be precursors for the ‘creation’ of

NEETs. For example, a very recent comparative but exploratory study between Italy and France

showed similar patterns. Agrusti and Corradi (2015) investigated teachers’ perceptions and

conceptualizations of low educational achievers in upper secondary schools, analysing their

response to the issues connected to low attainment in terms of teaching strategies. From the study

emerged strong communicative barriers between teachers and students that could lead teachers to

stigmatize low achievers in their academic failure, in generating lack of self-esteem and

disengagement in young people. Alfieri, et al., (2015) investigated the link between family

variables (parents’ educational level, relationship quality, intrusiveness, support, and autonomy)


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and the NEET status of over 9000 young Italians. The results revealed that parents’ educational

level and support have a protective effect on the risk of becoming a NEET for both genders. In

particular, lack of autonomy had a specific negative impact for males while parental

intrusiveness had an impact mainly for females. These findings are coherent with the results of

other research (see, for example, Bynner & Parsons, 2002), which maintain that parents with low

educational levels have less expertise when giving their children advice about educational

choices thus guiding their children toward poor and inefficacious choices or toward making no

choice at all. In another study, Bäckman et al., (2014) explored the causes of NEETs among

three Swedish birth cohorts. They found that the individual-level risk factors for NEET-status

were the same in all three cohorts. These included indicators of resource deficiencies during

childhood and adolescence, social problems, ill-health and educational failures. Generally, the

effect of risk factors decreased slightly across cohorts. However, the importance of both upper

secondary school failure and criminal involvement increased over time. Moreover, belonging to

the NEET-group in early adulthood had an independent effect on the development of subsequent

labour-market risk for both men and women. Many of these patterns have been corroborated by

other studies elsewhere and using more longitudinal designs. For example, Bynner and Parsons

(2002) concluded that poor educational achievement is a major factor in entering NEET; in

addition, inner city living for boys and lack of parental interest in their education for girls were

also important predictors.

1.1.6 Effects of YU

Scarpetta, Sonnet and Manfredi (2010), Bell and Blanchflower (2010) and Smyth (2008)

recognise that for disadvantaged youths lacking basic education, failure to find a first job or

keeping one’s job for long can have negative long-term consequences on their career prospects


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that some reports refer to as ‘scarring’. According to these authors, youths who are left behind

generally lack a diploma, come from immigrant / minority background and / or live in

disadvantaged/rural/remote neighbourhoods. Such realities also leave a huge impact on the kind

of wages such youths will earn (O’Higgins, 2001).

In addition, long spells of YU often create permanent scars through the harmful effects on a

number of other outcomes, including happiness, job satisfaction and health, many years later

(e.g. Bynner & Parsons, 2002). It is crucial that such youths find a job as soon as possible or be

offered to participate in a programme to obtain a qualification (Scarpetta, Sonnet & Manfredi,

2010). YU also has economic consequences. Spells of involuntary unemployment can lead to

sub-optimal investments in human capital among young people in the short run (Mroz & Savage,

2006) and welfare and fertility rates in the long term (Jimeno & Rodriguez-Palenzuela, 2002).

We review the effects of YU from three realities: crime, health, and skill-shortages.

Youth and crime

The consequences of YU are a cause for concern as they are serious and persistent (O’Higgins,

2001). According to several authors there is a link between YU and crime and increases in YU

induce increases in crime (Backman et al., 2014; Fougere, Kramarz & Pouget, 2006). Gregg

(2001) postulates that avoiding low educational achievement and preventing the build-up of

substantial periods in unemployment as youths may reduce the extent to which a minority of

youths spend a large part of their working lives unemployed. A similar view is shared by others

like O’Higgins (2001). MacDonalds (2011) highlights the notion that understanding ‘youth

culture’ and ‘youth transition’ into employment cannot dismiss the broader phenomena like

criminal activity, leisure and drug using. Baron (2008) has suggested that people’s interpretation


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of their labour market situation plays a role in shaping their responses to it. Findings reveal that

the effect of YU on crime is mediated and moderated primarily by other variables like anger

which is the result of negative subjective interpretations of the economic situation and a

continued attitude which is aversive towards the labour market and more in tune with alternative

source of self-funding like crime. To attract the young away from crime, there are multiple

potential routes with education being an obvious one; more specifically, education has to pay

either directly through apprenticeships or indirectly by paying bonuses to low income families

whose children succeed at school (Fougere, Kramarz & Pouget, 2006).

Youth unemployment and health

Studies also indicate a close association between YU and happiness as an index of well-being

and mental stability (Bell & Blanchflower, 2010). Chaudhry, Marelli and Signorelli (2010) and

Hammer (2007) argue that YU is harmful as it results in discouraged workers and increases the

chances of social exclusion from society and the labour market while increasing the chances of

ill-health. Unfortunately, while most people know that unemployment is linked to anxiety and

depression, it is often assumed that this is a fairly transient condition (Weich & Lewis, 1998).

However, studies have provided consistent patterns of such associations over time and across

temporal cohorts. For example, Schaufeli (1997) tested two hypotheses: the causation hypothesis

that assumes that unemployment leads to poor mental health and the selection hypothesis that

assumes that poor mental health reduces the likelihood of finding a job. The causation hypothesis

was confirmed for school leavers; furthermore, employment and further education increased

levels of mental health among school leavers. Attention has also been directed on the

intergenerational transmission of unemployment. Schoon et al (2012) reviewed the literature and

concluded that although the academic prospects of the child are largely dependent upon a


22

family’s socio-economic circumstances, these factors cannot on their own explain why

adolescents become NEET. Anger (2012) has suggested that the intergenerational transmission

of personality could also impact on the child’s economic prospects. A post-hoc analysis of

Rentfrow et al.’s (2015) self-selected sample (N=386,375) showed that on average all the Big

Five personality traits of 18 to 24 year old NEETs (N=2426) in England were significantly

different from their norms for their age. Furthermore, there were significant differences in their

personality traits across 7 self-reported income brackets. Thus there is good reason to believe

that intergenerational transmission of personality and socio-economic prospects coalesce (c.f.

O’Higgins, 2001) in the context of parental unemployment to create the longer lasting mental

health problems that are seen in the NEET populations (e.g. Serbin & Karp, 2004).

Youth Unemployment and an Unskilled Workforce

The Europe 2020 strategy highlights that Malta needs to make the best possible use of its greatest

asset – human capital (Commission, 2015). This is an urgent matter as the country has one of the

lowest employment rates in the EU (63.2%), a very high number of early school leavers (22.6%),

and a low amount of students attaining tertiary education (22.4%).

The main findings of Cedefop’s skill demand and supply forecast for the European Union for

2010-20, indicate that over the coming years, workforce tends will shift towards more skill-

intensive jobs. This poses a major problem for countries with a high number of Youths NEET

and Early School Leavers.

Research (e.g. Tamesberger & Bacher, 2014) shows that the majority of Youths NEET’s highest

level of education is a School Leaving Certificate. Resulting in the skill set of NEETs being

relatively low. As stated earlier, Youths are the future. Thus, if a country wants to grow and

http://www.tandfonline.com.ejournals.um.edu.mt/author/Tamesberger%2C+D

http://www.tandfonline.com.ejournals.um.edu.mt/author/Bacher%2C+J


23

develop its economy, it is this generation that must be developed into highly skilled individuals,

find employment, and contribute to developing Malta’s human capital.

In today’s rapidly changing economy and external pressures mean that organisations need to

have a diverse and high calibre talent pool if they are to innovate and act with speed, in order to

keep ahead of the competition, grow, and sustain a strong Market presence (Ashton & Morton,

2005).

The unique talents among employees, including; flexibility, innovation, superior performance,

high productivity, and personal customer service are the ways that employees contribute to

maintaining an organisation’s success in their sector (Ashton & Morton, 2005). Taking into

account the important role of employees, organisations are increasingly looking at their human

resources (human capital) as a unique asset and means to maintaining productivity and overall

organisational success.

For this reason, it is essential that any policies and initiatives targeted at Youths NEET should

focus on; ensuring youths remain in education and equipping them with the employability skills

required to obtain and maintain employment.

1.1.7 Transitions and Interventions

Gregg (2001) argues that the evidence of scarring offers a strong justification for early

intervention to prevent long-term YU. Put differently, the evidence indicates that unemployment,

while young, creates permanent scars (Bell & Blanchflower, 2010). In providing workable

solutions to YU one must understand the plausible causes of YU to ensure that the right policy

and dose of intervention is provided. Back in 1982, Freeman and Wise provided two interesting

and plausible reasons; and not mutually inclusive. They suggested that the traditional view holds


24

that the problem of YU is one of job availability whereas the more contemporary views holds

that it is employment instability that is the crux of the joblessness problem. They explained that

the central difference between the two views lies in the conception of turnover. The old view

emphasises the infrequency of job finding and the consequent lengthy duration of unemployment

while the latter focuses on the brevity and frequency of unemployment spells. They had argued

that while youths respond strongly to labour market conditions, the inability to find work was a

greater culprit than the desire to hold jobs.

Additional explanations as for YU are more related to institutional characteristics. For example,

Jimeno and Rodriguez-Palenzuela (2002) suggest two institutional characteristics that seem to be

associated with YU: First, those that have a positive impact on the overall cost of the standard

labour contract (e.g. employment protection) are likely to make young workers less attractive for

firms, since given the average job expectations of young workers, their average productivity

tends to be lower; secondly, an institutional setting that does not make provisions for more

contractual flexibility for the particular characteristics of the young workers would have the

youth in disadvantage relative to more experienced prime age workers (Bell & Blanchflower,

2010), especially if the general labour market is predominantly rigid (see also Breen, 2005).

Another explanation with implications for specific interventions comes from Cinalli and Giugni

(2013). They argue in favour of two types of policies to address YU: unemployment regulations

(varying from exclusive to inclusive policies) and labour market regulations (varying from rigid

to flexible). Their analysis is that YU varies as a function of the interplay between the dose of

each policy. For instance, precarious work conditions are likely to be high where labour market

regulations tend to be flexible and unemployment regulation is exclusive. On the other hand,

where the labour market policies are rigid but unemployment regulations are more inclusive, full


25

protection is more likely and hence better YU protection; an analysis which is partly similar to

that by Breen (2005).

These explanations provide real contexts for the onset of several factors that give rise to the

susceptibility of YU such as low educational achievement, financial deprivation and behavioural

problems in childhood (Gregg, 2001). Indeed, a complex mix of individual risk factors and

institutional determinants are responsible for the high rates of YU (e.g. de Goede et al., 2000;

Isengard, 2003; Kelly, McGuinness & O’Connell, 2012). The more intense each of these factors,

the more difficult it is to support young people in their transition into decently paid employment.

Moreover, evidence shows that when the negative effects of prior employment on earnings are

large and persistent, the tendency to be out of employment tapers very slowly over time (Mroz &

Savage, 2006) suggesting that quality employment is a pre-requisite.

Breen’s (2005) study illustrates that institutional factors interact with market forces to shape the

levels of YU in OECD countries. The results reinforce the view that systems of vocational

training which teach specific skills and incorporate a strong work-based element provides a

prevention to YU by offsetting the negative effects of extensive worker protection against

dismissals. O’Higgins (2001) states that vocational education policy needs to be clearly linked to

the world of work; it is vital that an adequate skills recognition system be in place.

According to Scarpetta, Sonnet and Manfredi (2010), a short term policy priority is to provide

adequate support to youths most at risk of losing contact with the labour market. According to

the authors, governments should not underestimate the difficulties of implementing a labour

market policy based on acquiring skills first, work later, particularly for disadvantaged youths.

The authors emphasise the need to promote more extensive apprenticeship contracts for low


26

skilled youths where they can acquire at the same time skills and work experience. As noted by

many (e.g. Choudhry, Marelli & Signorelli, 2010), low quality employment has been found in

the mismatch between the knowledge acquired through formal education and the skills required

by the labour market. Indeed, these authors claim that the educational system requires a

progressive shift of the ‘sequential and rigid system’ towards a ‘dual and flexible system’.

Otherwise the NEET generation will continue to expand with dreadful economic and social

consequences. In addition, it is suggested that reducing the gap between regulations for

temporary and permanent contracts will likely contribute to promote the smoother transition of

new comers, including youth from entry jobs with short duration to more stable jobs that offer a

career prospect. On the other hand, Freeman and Wise (1982) had concluded that given a

shortage of jobs, training and job matching offer little prospect for making a significant

contribution to the solution of the YU problem; an issue also raised by more recent authors

(O’Higgins, 2001). On the other hand, they concur with more recent authors that direct job

creation through public employment or private sector subsidies appears to offer the most

promising structural approach to the youth unemployment problem. Thus, the literature suggests

a two prong approach: job creation AND provision of skills and experience useful to young

people for the long term; but above all, the key to a successful and active youth employment

policy and labour market programmes is a healthy overall economic situation (O’Higgins, 2001;

Bell & Blanchflower, 2010).

In addition, interventions should also be directed at employers. For example, Bell and

Blanchflower (2010) suggest helping employers offer more apprenticeships and creating

schemes that convince employers that employing young people is attractive, healthy and viable.


27

Success of interventions

Several programmes and initiatives exist and continue to be proposed to alleviate those youths

who are NEET from idleness and to get them back ‘on track’. For example, the New Deal for

Young People programme in the UK aims to increase the likelihood of entering a job for those

with 6+ months of unemployment. The New Deal operates via a 2 stage intervention: it provides

youths with support and monitoring of job searches; and provides placements.

However, interventions have not always been as successful as planned. Studies in this regard are

not conclusive. For example, Chen (2011) conducted a study of NEETs in Taiwan. The study

showed that most respondents did not become NEETs by choice; they did so for economic

reasons. Most participants thought that the programme was of little practical help to them and did

not increase their chances of employment but that it gave them social and emotional support and

helped them feel better about themselves. Many also agreed that the monetary allowance offered

by the programme was a good incentive for participation. Similarly, Alegre, et al., (2015),

evaluated a couple of transition programmes for students in Catalonia (Spain) and their effects on

participants’ labour and educational outcomes. Results showed that both programmes are

ineffective in increasing the labour prospects of participants although they were effective in re-

engaging youths in formal education. The positive effects were concentrated among the 16-18

year olds but there was almost no effect for all other age groups. MacDonald (2011) has spelt

out at least 3 problems related to NEETs. The first problem is that NEETs covers a very

heterogeneous population which is often overlooked; and hence so are variations in the NEETs

population very often overlooked! Secondly, NEET is construed to be a policy to address a

problem with young people rather than the absence of good jobs. Hence the emphasis tends to be

more at providing services to young unemployed rather than creating quality employment


28

opportunities for them; thirdly, NEET is unable to capture the dynamism and power of youth

transitions that have become less linear (more complex) and less stable (less predictable).

Indeed, MacDonald (2011) raises the alarm that NEET may run the risk of moving between

insecure low paid jobs, poor quality training schemes and unemployment. Moreover, NEET

policies are often, erroneously, focused too much on the working class and not the ‘missing

middle’ such as ‘graduate unemployment’. Other authors have spelt out indirect criticism

regarding NEETs for adopting poor policy measures which has impacted on YU. For instance,

Lahusen, Schulz and Graziano (2013) argue that the situation of YU is exacerbated for three

reasons: first is the lack of proactive and more rigid policy approach at EU level that would

imply for instance evaluating existing youth benchmarks and the Youth Guarantee every year to

ensure the delivery of results and progress. Hence, it seems there is a lack of commitment of the

EU institutions to translate general aims into specific policy instruments; secondly, the European

field of YU policies is dominated by a piecemeal approach; and thirdly, the current initiatives in

terms of YU policies commit themselves to the EU’s own concept of ‘flexicurity’ arguing that

the YU problem can be abated by increasing the flexibility of the labour market and by

enhancing job and social security conditionality; the same reasons used by employers to justify

higher YU because, as noted by Lahusen, Schulz and Graziano, (2013), young people do not

have access to social benefits, especially if they have never worked.

1.2 General Conclusions from the review

One can draw several conclusions from this overview of YU and NEETs and which are relevant

to this specific study. First, it is clear that the NEET population is a heterogeneous one. People

may be out of employment and education for personal reasons and out of choice. Hence a clear

working definition may be warranted when describing interventions and programmes for


29

support; second, it is clear that interventions cannot be out of context. Highly regulated labour

markets and/or inflexible employment legislation may actually decrease the chances of youths

finding quality jobs and increase the chances for ill-health and crime; third, the creation of

quality jobs for youths in NEET is more likely to have an impact. Rather than finding them a job,

activities should match the personality and profile of the job seekers while campaigns should

address the added-value of employing youths; finally, economic prosperity and quality

vocational education have an influence of keeping NEET rates low.


30

Methodology

2.1 Introduction

This section offers a description of the methodological approaches adopted for this project and

the operational definitions of the variables used. It includes the research questions addressed,

sampling and data collection procedures, the research instrument used, ethical considerations,

and the data analysis procedure.

2.2 Research Questions

The aim of this project was to provide a comprehensive picture of needs of Maltese youths aged

16 to 24 who are not engaged in any formal employment, education or training (therein referred

to as NEETs). More specifically, this project attempts to answer the following research

questions:

a) What are the personal and social demographic characteristics of Maltese youth NEETs?

b) NEETs and Education:

a. What is their level of involvement in education?

b. What plans do they have related to education and training?

c) What work experience do these youths have and what are their career plans (if any) for

the future?

d) What is their opinion on the Youth Guarantee Programme? i.e. What would motivate

them to participate in this programme and what barriers/obstacles are preventing them

from getting into work, education or training?


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Since the above research questions are quite broad, each question was divided into various sub-

questions to enable a better understanding of the needs of Maltese youth NEETs. More

information is provided in Section 3.3. Answers to these questions put Maltese policy makers in

a better position to ensure that the policies and initiatives that are in place, as well as those that

are to be proposed, truly reflect the needs of Maltese youth NEETs.

2.3 Sampling, data collection and ethical considerations

The population of NEETs aged 16 to 24 (both inclusive) according to the Employment and

Training Corporation (ETC) database dated April 2015, comprises of 6,749 youths.

Each person in this sampling frame received a letter from the Project Leader of the Youth

Guarantee Scheme inviting them to participate in a 20-minute face-to-face interview in the

comfort of their home during the months of June and September. Participants were informed

that; they were free to withdraw from the interview at any time, there were no risks involved or

any negative consequences on them or their families, and their responses would be combined

with those of the other responses to see the ‘big picture’. The final report would not contain any

names and hence confidentiality was guaranteed. Additionally, they were informed that the

findings of the study will be used to ensure that all future initiatives concerning the Youth

Guarantee will reflect the true needs of the target group. They were also provided with a contact

number in case they had any questions about this interview or their involvement.

The Research Company was provided with a database containing information regarding the

potential participants’names, gender, birthdates, and identity card numbers. The researchers

aimed to conduct a Census – i.e. to target every single person in this sampling frame.


32

In order to ensure a high standard of quality interviewers, all interviewers were trained prior to

commencing data collection. The interviewers were split into two cohorts - those in Cohort A

were trained between 22/06/2015 and 28/06/2015 and those in Cohort B were trained between

30/06/2015 and 06/07/2015.

The interviews then took place between 29/06/15 and 18/09/2015, were the interviewers made up

to five attempts, on five different days and weeks, at five different times to reach the participants

at their own residence. Prior to conducting the actual interview with a Participant, both the

interviewer and the interviewee were required to sign a consent form (Appendix A.1). In the case

where participants were under the age of 18, consent to participate in the interview was required

from parents/legal guardians/carers, as well as from the participants themselves.

Table 1 provides a breakdown of the the number of attempts made.

Table 1: Number of attempts made during the Youth NEET Census 2015

No. of attempts

0* 1st 2

nd 3

rd 4

th 5

th Total

751 5998 3637 2375 1631 1248 14889

*called MEDE or the Research Company further to receiving the census letter from the ministry and claimed to be

not NEET or refused to participate.

The above attempts resulted in 279 successful interviews and 4 incomplete (the interviewers felt

that these 4 participants were not in the right mental condition to complete the survey). This

meant that there were 6466 case closed scenarios resulting in unsuccessful interviews. The latter

was grouped into four categories, as recommended by Saunders, Thornhill and Lewis (2015).


33

Unsuccessful Interviews:

a) 699 refused to respond (refusal by participant, refusal to participate by others, respondent

under 18 and no consent from parents was given);

b) 4,035 were ineligible to respond (respondent claimed to be in education and training or

employment);

c) 539 were not located (vacant property, address does not exist, respondent changed

address);

d) 1,185 respondents were located but unable to make contact (i.e. no one at the door

following the fifth attempt, respondent permanently unavailable).

Eight cases could not be categorised due to data input errors. Once the ineligible Participants are

excluded, the total response rate of this study is 10.3% and when the unreachable (i.e. non-

location and non-contact) are also excluded, the active response rate is 28.2% (Neuman, 2005).

With a population of 2714 95% confidence in the estimate/s, and assuming that 50% of the

sample will have the specified attribute/s - a worst-case scenario for for multi-purpose surveys

as recommended by deVaus (2002) - a sample size of 279 yields a margin of error of 5.6% which

marginally exceeds the standard 5%.

Table 2 provides a breakdown of the modified population (N = 2714) and sampling distributions

by gender (male, female), age (16-17, 18-20, 21-24), and district (1 = Gozo & Comino, 2 =

Northern, 3 = North Harbour, 4 = South Eastern, 5 = South Harbour, 6 = Western).


34

Table 2: Population and Sample distributions by gender, age group and district

Category Population Sample

Gender

Male 1563 (57.6%) 154 (55.2%)

Female 1151 (42.4%) 125 (44.8%)

Age group

16-17 1014 (37.3%) 78 (28.0%)

18-20 936 (34.5%) 104 (37.3%)

21-24 764 (28.2%) 97 (34.8%)

District

1 191 (7.0%) 18 (6.5%)

2 439 (16.2%) 36 (12.9%)

3 823 (30.3%) 86 (30.8%)

4 400 (14.8%) 33 (11.8%)

5 492 (18.2%) 70 (25.1%)

6 369 (13.5%) 36 (12.9%)

Total 2714 (100.0%) 279 (100.0%)

Chi-squared tests of independence revealed that the sample and population distributions did not

differ significantly from each other with respect to gender (χ2 = 0.66, df = 1, p = 0.419) and not

quite significantly different with respect to district (χ2 = 11.02, df = 5, p = 0.051). However, the

age group proportions differed significantly from each other (χ2 = 11.42, df = 2, p = 0.003); this


35

may be attributed to the relatively high refusal rate by the 16-17 age group resulting in an under-

representation of the latter.

2.4 The Research Instrument

Further to conducting a thorough literature review, the Youth Guarantee Questionnaire was

purposely designed for the present study. This was an essential contributor to the value and

credibility of this research project. As Jesson, Matheson and Lacey (2011) state, a review of

previous literature ensures that there is a comprehensive understanding of the research topic, of

what has already been done in previous studies, how it has been researched, and what the key

issues truly are.

The final version of the Youth Guarantee Questionnaire is presented in Appendix A.2.

2.4.1 Type of Questions

The Youth Guarantee Questionnaire utilised both closed-ended (Sections A – D of the

Questionnaire) and open-ended questions (Section E of the Questionnaire). Although closed-

ended question were essential to generate a clear picture of the characteristics of, and challenges

faced by, Maltese Youth NEETs, it was essential to include a qualitative aspect in this

questionnaire. The open-ended questions moved beyond ‘how often’, and ‘how many’ and

towards ‘what’ and ‘why’. This allowed for a deeper and further understanding of key issues

concerning Youth NEETs.

2.4.2 Translation of the questionnaire

Two versions of the questionnaire were created in order to give participants the option to choose

between answering questions in Maltese or English. As inconsistencies in translated


36

questionnaires may disrupt the accuracy of results and final recommendations, a back-translation

was used to translate the original questionnaire from English to Maltese and back to English in

order to ensure that the original questionnaire was accurately translated into Maltese (i.e. running

a quality check on the original translation). This form of translation is viewed as one of the most

effective methods of translation, as it ensures that a high quality and reliable translation of the

original document is developed.

2.4.3 Type of Language used

It is important to point out that the questionnaire did not include any overly “technical” language.

All questions and responses were written with clear objectives, concise language, complete

sentences, and simple words. “Don’t know” and “not applicable” responses were also used in the

specific sections within the questionnaire. In some cases, “don't know” or “not applicable”

represented some respondents' most honest answers to some of the questions. Respondents who

felt they were being coerced into giving an answer they do not want to give often do not

complete the questionnaire thus it is important to offer other options.

2.4.4 Questionnaire sections

The questionnaire was divided into 6 main sections:

Personal Information and Social Demographics- The scope of these questions was to provide

general information on the Participant (e.g. “What is your date of birth?”), their family (e.g.

“How many family members are currently living in your household?”), and their personal

background (e.g. “How often do you spend time with your friends every week?”).


37

Education and Training- The scope of these questions was to provide information regarding the

level of education and training of the Participant (e.g. “What is your highest level of

education?”) and to assess the Participant’s current opinions/thoughts about education and

training (e.g. “Imagine you were given the opportunity to participate in some form of educational

or training courses, what sort of courses would you prefer?”).

Work Experience and Interests- The scope of these questions was to provide information

regarding the work-related experience of the Participant (e.g. “If you have any work experience,

how would you describe it?”) and any work interests (e.g. “Which industries interest you

most?”).

Feedback on the Youth Guarantee- The scope of these questions was to provide information

regarding why the Participant did not participate in the Youth Guarantee (e.g. “Do you know

about the Youth Guarantee Programme?”) and what would make them want to participate in it.

The interviewer first read an explanation about the Youth Guarantee to ensure that the

Participant had a clear understanding of the initiative.

Additional questions- The scope of this section was to allow the Participant to provide more

information about their current situation through a series of open-ended questions (e.g. ‘’What

would you like to see implemented by the Government to encourage you to take further

training/school and/or seek employment?”). It added a qualitative aspect to the research. It also

served as a validation of responses given in previous sections of the questionnaire. The addition

of qualitative data greatly added to this research by offering further insights into the specific and

individual challenges faced by Youth NEETs in Malta. The data derived from the following

questions allows the Government of Malta to craft the Youth Guarantee Initiative to reflect the


38

true needs of Maltese Youth NEETs, and thus ensure that the initiative is a success and

encourages Youths to engage and remain in Employment, Education, or Training.

Interviewer observations- The scope of these questions was to provide relevant information and

feedback from the interviewer’s point of view obtained during the interview. Interviewers were

asked to complete an interviewer checklist that focuses on collecting information regarding the:

appearance, verbal and physical behaviour, and attitude of the Participant, his/her surroundings

(residence), and other people present at the time of the interview. This checklist added another

layer to the data collected and allowed for a clearer and more detailed picture of Youth NEETs in

Malta to be developed.

2.4.5 Pilot Study

Prior to collecting data, a pilot study was conducted with a sample of 50 Youth NEETs in order

to;

Determine whether the questions, as presented, elicit the required meaning by

respondents.

Ensure that the terminology used in the questions asked is comprehendible and that the

appropriate response options are available.

Ensure that the questionnaire is not too long.

2.5 Data Analysis procedures

Data collection was conducted between 29th

June and 18th

September, 2015. Data was inputted

into an Excel spreadsheet, with the responses to the closed ended questions being transferred to

an SPSS V22 spreadsheet and subjected to statistical analyses. This consisted of descriptive


39

summaries using frequency tables for categorical data and measures of central tendency – mean

(M) or median (Md) - accompanied with measures of spread - standard deviation (SD) or

range(R) - for the ordinal/interval/ratio data. In providing pictorial representations, bar charts,

pie charts, venn diagrams and box plots were used. For the ‘other-please specify’ responses, the

information was summarised by counting various aspects of the content (i.e. content analysis).

Finally, for the open-ended questions, thematic analyses were performed to generate themes

across the data sets that are important to the description of the phenomena under investigation.

2.5.1 Cluster Analysis

The data was cluster analysed in two phases. The first phase of the analysis involved clustering

the target population into gender and age groups, with age nested in gender. This was performed

across all districts overall and separately. Additionally, the respondents were grouped into the

following categories: participated in survey, ineligible (not NEET), refusal, not located, and

located but no contact.

In the second phase of the analysis, a cluster analysis was conducted on a sample that excluded

the ineligible group (i.e. Participants claiming to be not NEETs). After grouping the respondents

into the four categories highlighted above, cross-tabulations of (i) gender by district and (ii)

gender by age group were obtained and the Chi-squared test of Independence was used to

determine whether significant association existed between (i) district and gender and (ii) district

and age group; the Cramer V being used as a measure of effect size. A Kruskal Wallis H test was

also used to determine whether chronological age by district on the basis of mean ranks and in

the presence of a significant statistic, Mann Whitney U (post-hoc) tests were used to determine

which groups differed significantly from each other in mean ranks; the Bonferroni correction

being used to control for the inflation of Type I error rate resulting from multiple comparisons.


40

2.6. Conclusion

This section has provided a description of the research method (research questions, data

collection, sampling technique, procedure to carry out the research, ethical considerations, and

data analysis procedures) adopted to answer the research questions in this project. The next

section reports the research findings based on the survey responses.


41

Results

3.1 Introduction

This section presents the main findings of the Youths NEET Census. As respondents were given

the freedom to; withdraw from the interview at any point and refuse to respond to questions they

did not feel comfortable answering, the total sample size on which percentages are based varies.

Hence, the sample size for each question is provided and the percentages provided presented

represent valid percentages. The compete survey questionnaire is provided in Appendix A.2.

3.2 Personal Information and Social Demographics (Section A)

3.2.1 Demographic information (A1)

55.2% of the 279 respondents who participated in the survey were male. Their mean age was

19.37 years (SD= 2.44) with ages ranging from 16 to 24. Figure 1 illustrates a break down of the

sample by district.

Figure 1: Sample distribution by district


42

3.2.2 Family members (A2)

The 279 respondents in the sample were asked to indicate where they lived when growing up

(A2.1). The Venn diagram in Figure 2 shows that 79.6% grew up with their biological parents,

14.3% in single parent households, 7.2% with grandparents and 2.9% lived in some other

household (adoptive families, orphanages or with other relatives). Circa 4.0% grew up in a

combination of households.

Figure 2: Venn diagram – Where did you live when growing up?

Out of the 253 respondents that indicated the number of family members currently living in their

household (A2.2), 32.0% reported that they live with one parent, 67.2% with two parents while

the remaining 0.8% did not live with their parents at the time of the study. They live on average

with circa two siblings (N = 221, M = 1.85, SD = 1.26), with this number ranging from 0 to 8.

Out of 190, 46.8% were the youngest sibling, 17.9% were the middle sibling, 33.7% were the

eldest sibling, while 1.6% were twins/triplets. In the ‘other – please specify’ category, 42

respondents stated that other persons live with them and these include among others


43

grandparents (16), husband and kid/s (6), partner only (4), partner and kid/s (4), uncles/aunties

(3), one child (2), and husband (2).

Moreover, out of 270 respondents, 11.8% reported that they have children (A2.3); 75.0% have

one child, 20.8% have two children and 9.4% have three children. From the latter, 75% stated

that their kids live with them.

The mean age of the respondents’ fathers (A2.4) was 51.04 years (SD = 7.11), with ages ranging

from 34 to 71. Based on 251 respondents, it was revealed that their father’s current employment

(A2.5) was mainly full-time (57.8%), followed by unemployed (13.5%), self-employed (8.8%),

part-time (2%) or casual employment (2.0%). In the ‘other’ category, 40 (15.9%) reported that

the father was either a pensioner (13), deceased (8), borded out (2), a prisoner (1), or that they do

not know what his occupation is (14).

The mean age of the respondents’ mothers (A2.6) was 47.89 years (SD = 6.82), with ages

ranging from 33 to 68. Based on 276 respondents, it emerged that the mother’s current

employment (A2.7) was mainly unemployed/inactive (65.2%), full-time (16.7%), part-time

(9.8%%), or self-employed (8.8%). The remaining 14 (5.1%) opted for the ‘other’ category,

stating that their mother was either a homemaker (8), hosts foreign students (2), a pensioner (3),

or that they do not know what her occupation is (1).

When asked how often they spend time with family members per week (A2.8) and friends

(A2.9), the vast majority of the 279 respondents (i.e. 84.2%) reported that they spend over 2

hours per day with their family but in the case of friends, the results are more varied as illustrated

in the cluster bar chart below in Figure 3.


44

Figure 3: Clustered Barchart – Time spent with family and friends per week

Finally, the general satisfaction with life at the time of study (A2.10) indicated that out of 279

respondents, 52% were ‘so and so’, 44.1% highly satisfied and the remaining 3.9% ‘not at all

satisfied’. A pictorial representation is exhibited in Figure 4.

Figure 4: Pie Chart – Current Life Satisfaction

050

100150200250

2+ hrs perday

1 hr perday

1-4 hrs perweek

hardly seethem

Family 235 15 22 7

Friends 97 25 68 89

Fre

qu

en

cy

Hours spent per week with family and friends


45

3.3 Education and Training (Section B)

3.3.1 Level of Education (B1)

The mean school leaving age for the 277 respondents (B1.1) was 16.67 years (SD = 1.82), with

ages ranging from 11 to 23. With respect to qualifications (B1.2), most of the respondents are in

possession of a School Leaving certificate 45.5%, 32.1% have at least an O level, 10.1% have a

diploma, and 3.2% have at least an A level. From the 10.8% that selected the ‘other –please

specify’ category, 8 were in possession of an undergraduate degree, 1 obtained a Masters degree,

5 stated MCAST or ITS without highighting the qualification, while the remaining 4 were not in

possession of any qualification.

When asked to highlight if they have learning difficulties such as dyslexia, ADHD or dyspraxia

(B1.3), out of 276 responses, 16.4% stated that they do. From these 45, 21 feel that they were not

given adequate support, with two opting not to reply to this question.

275 respondents provided reasons why they stopped their education or training (B1.4). The

single largest group reported that they were not interested in education (44.7%). A breakdown of

the responses is presented in Table 3.

Table 3: Reasons for stopping education or training

Reason

Frequency

(%)*

I wasn't interested in education 123 (44.7)

Personal reasons 73 (26.5)

Financial reasons 29 (10.5)


46

Medical reasons 15 (5.5)

Family pressures 15 (5.5)

Time 7 (2.5)

*frequency is more than 100% as more than one option could be selected.

In the ‘other – please specify’ category, the following reasons were the most prevalent: finished

their course (15), continued or plan to continue (13), preferred to work early (10), bullying at

school (2).

When asked if they have been involved in any further learning such as short term courses or

apprenticeships (B1.5), 276 replied. From these 42.8% said that they did. When asked to specify

such courses, the following were the most prevalent: MCAST courses (17), ETC courses (13),

Youth Guarantee (8), hairdressing (8), & ECDL (7).

3.3.2 Future Plans (B2)

When the respondents were asked if they have any plans to engage in any educational or training

courses (B2.6), 276 replied and from these 56.7% reported that they have plans. They specified

the following: hairdressing (15), security/police/soldier (14), computing/IT (13), childcare (9),

beautician (9). Additionally, 18 mentioned MCAST, 8 A-levels and/or the University of Malta,

and 3 a Masters without specifying the area of interest.

The respondents were asked to indicate the sort of courses they would prefer if they were to

participate in some form of educational or training courses (B2.7). Here, they could select more

than one type of course. 260 replied to this question and the following is a summary of the

reponses: part-time courses (84), classroom based courses (67), full-time courses (66), day


47

classes (62), individual attention (49), evening classes (49), and online courses (37). In the

‘other-please specify’ category, 23 respondents mainly highlighted that they were not interested

(8), that they don’t know (8)

In question B2.8, the respondents were provided with six statements and they had to indicate

whether or not they believed that each statement would help them get into education. Table 4

provides a descriptive summary of the responses:

Table 4: Getting into education

Courses N Yes No

Guaranteed employment upon completion of

the course

273 255 (93.4%) 18 (6.6%)

Clear information about education and

training opportunities that suit my career

ambitions

275 245 (89.1%) 30 (10.9%)

The opportunity to work whilst studying 274 241 (88.0%) 33 (12.0%)

Better English, Maths or Computer Skills 275 227 (82.5%) 48 (17.5%)

Financial incentives 276 225 (81.5%) 51 (18.5%)

Boosting my self-confidence 274 193 (70.4%) 81 (29.6%)

From the responses, one can conclude that the vast majority of the respondents believe that all

these initiatives will help them getting into education.

Furthermore, out of 277 repondents, 63.5% reported that they enjoyed their time at school

(B2.9a). Out of 274, 77.0% stated they would gladly further their education if they had the


48

opportunity to do so (B2.9b) and out of 276 94.9% stated that they believe that education and

training are important for the future (B2.9c).

Finally, respondents were asked to indicate whether the majority of their friends are in education

or training (B2.10). Out of 275 responses, 43.3% stated ‘Yes’, 36.5% stated ‘No’’ and the

remaining 20.2% reported ‘don’t know’.

3.4 Work Experience and Interests (Section C)

Out of 277 respondents, 76.2% (i.e 211) reported that they have work experience (C1). When

asked to specify their employment type, the following emerged: part time (53.1%), full-time

(45.5%), apprenticeship (9.5%), casual work (7.6%), and volunteer work (5.2%). The

percentages do not add up to 100% since the respondents could select more than one option.

These respondents were also asked to describe their work experience. Since they might have had

different experiences at different work places, they could select more than one option. 44.1%

described their experience as ‘Great’, 38.9% as ‘OK’, 13.2% didn’t like the work they were

doing , 18.0% were not treated fairly, and 2.8% hated it.

In question C2, we asked the respondents (273) to specify why they were currently unemployed

using six specific statements and an ‘other – please specify category’. A summary of the replies

is presented in Table 5 below.

Table 5: Reasons for currently not being employed

Reason Frequency (%*)

Have not found the job that I like 134 (49.1)

I don’t have the necessary qualifications 70 (25.6)

Personal reasons 62 (22.7)


49

Medical reasons 19 (7)

Not interested to work 10 (3.6)

Family pressures 9 (3.2)

*percentage of respondents choosing this specific option

In the ‘other –please specify’ category, the respondents predominantly highlighted the following

reasons: to raise young children (14), just finished studying, waiting for results or going to

continue studying (9), to take care of a family member (4). When asked if they would you like to

find some form of employment (C3), 257 from 276 respondents (i.e. 93.1%) replied in the

affirmative. When all respondents were asked to reveal their career plans for the future (C4), 275

replied as follows: full-time employment (189 -68.7%), part-time employment (45 -16.4%), no

plans 9.5% (26) and ‘other’ 5.54% (15),with the most prevalent being: self-employed (5),

continue or following their studies (C5) and 81.5% (from 271) believe that they have the support

needed to achieve the above plans (C6).

The respondents were asked to indicate which industries interest them most (C7). From the 277

that repied to this question, over a third (36.1%) , the retail industry. Here, the respondents could

choose more than one industry. More details are exhibited in Table 6.

Table 6: Industries that interest respondents

Industry Frequency (%*)

Retail 100 (36.1)

Public sector 72 (26.0)

Catering 63 (22.7)

Beauty 52 (18.8)


50

Hospitality 48 (17.3)

Healthcare 40 (14.4)

Manufacturing 39 (14.1)

Construction 18 (6.5)


In the ‘other – please specify’ category, the following industries were included: IT/ computing

and technology (15), security/police/soldier (13), childcare (6), accounts/finance (5).

The survey also sought to determine whether being out of work had any effects on the

respondents’ wellbeing (C8). Once again, the respondents could select more than one effect. Out

of 278 respondents, 60.4% reported no effect, while the others highlighted stress (25.1%),

staying indoors (14.7%), eating unhealthy (12.9%), depression (10.7%), smoking (11.5%), self-

harming (2.5%) and drinking too much alcohol (2.1%). In the ‘other – please specify’ category

(3.9%), the effects mentioned were feeling nervous and anxious (3) lack of money to go out (2)

and difficulty to buy day-to-day necessities (2).

Repondents were also asked to report where they see themselves in a year from now (C9). The

278 replies were categorised as follows: (i) working full-time or part-time (55.4%), (ii) in full-

time or part-time education (16.1%), (iii) owning a business (5.8%), and (iv) the same as today

(5.0%); 16.9% do not know what they will be doing while those who opted for the ‘other’

category (1.4%) stated that they want to go abroad (2) or that they intend to get married (1).

In question C10, the respondents were provided with three statements and they had to state

which of them would help them get into work. Here, they could select all those that apply. 255

respondents answered this question. Table 7 provides a descriptive summary.


51

Table 7: Aspects that would help respondents to become employed

Aspect

Frequency

(%*)

Advice about applying for jobs 170 (66.7%)

Boosting my self confidence 155 (60.8%)

Better English, Maths or Computer

skills 141 (55.3%)


Finally, the respondents were asked to identify whether the majority of their friends were

employed at the time of study (C11). 277 replied, of which 57.0% stated ‘yes’, 23.8% stated ‘no’

while the remaining 19.1% stated ‘don’t know’.

3.5 Feedback on the Youth Guarantee (Section D)

The respondents were asked if they knew about the Youth Guarantee Programme (D1). Out of

270 respondents, 30.4% replied that they knew about it. The latter (83) were then asked to

specify why they did not apply for the Youth Guarantee Programme. A summary of the findings

are presented in Table 8.

Table 8: Reasons for not applying for the Youth Guarantee Programme

Statement Frequency

I'm not sure 36 (43.4%)

It didn’t interest me 18 (21.7%)

I think it involves too much work 4 (4.8%)


52

In the ‘other – please specify’ category, the respondents stated that: (i) they did not know enough

about it (7), (ii) they had already taken part previously in the programme (5),

Participants were asked to provide reasons for possible participation in the Youth Guarantee

Programme (D2). 254 replied and a summary of the findings is presented in Table 9.

Table 9: Reasons for possible participation in the Youth Guarantee Programme

Statement

Frequency

(%)

Promise of employment or the opportunity for education, employment

or training 209 (82.3%)

Financial incentives 171 (67.3%)

Support and encouragement from my family 147 (57.9%)

Constant support and guidance from professionals 143 (56.3%)

Local school or employer opens days aimed at encouraging youths like

me to get back into work or education 134 (52.8%)

Basic classes in reading, writing, maths and computer skills 112 (44.1%)

Question D3 requested participants to state how they perceive themselves in society and their

locus of control in life. Table 10 provides the list of statements and a summary of the responses.


53

Table 10: Perceptions of society and locus of control

Statement Yes No

Valid

Yes

(%)

I have support from family and friends to plan the

future

262 13 95.3%

No matter what the government does, there will always

be a lot of people not in employment, education or

training

255 18 93.4%

Given the right support, I could contribute a lot more

to this country

250 26 90.6%

I am in control of how my life will turn out 182 91 66.7%

My full potential is being used 134 142 48.6%

I have little chance of ever getting a job 129 139 48.1%

I am not part of society 71 204 25.8%

I feel discriminated against 67 209 24.3%

The respondents were presented with eight statements reflecting possible barriers or obstacles

that could prevent them from getting into work, education or training (D4). A summary of the

responses is presented in Table 11.


54

Table 11: Barriers for getting into work, education or training

Statement Yes No NA

Valid

Yes

(%)

My lack of work experience 200 75

72.7%

I am worried I will not have enough time with

my child 20 12 242 62.5%*

My lack of required skills and qualifications 162 110

59.6%

My family or close friends are not able to

provide childcare 14 15 244 48.3%*

There are no suitable jobs in my locality 103 167

38.1%

My lack of self-confidence 95 181

34.4%

Financial factors – cost of transport, clothes,

etc. 92 182

33.6%

I do not want to lose the flexibility/freedom I

currently have 75 199

27.4%

I don’t know how to prepare for a job or

qualification 73 199

26.8%

Prejudice or discrimination 58 216

21.2%

Working will not improve my financial

situation 45 222

16.9%

I have a learning difficulty or disability 34 241

12.4%

I have a mental health condition 17 256

6.2%

I am concerned about losing the security of

social benefits 14 257

5.2%

My parents/partner/friends do not want me to

work 8 266

2.9%

*NA excluded


55

Finally, question D5 asked the respondents to state whether any of their friends were

participating in the Youth Guarantee programme. From 276 responses, 5.8% replied ‘yes’,

42.2% replied ‘no’’ while the remaining 52.0% replied ‘don’t know’.

3.6 Additional Section - Open-ended questions (Section E)

The respondents were asked to identify the one thing they enjoy doing most (E1). The 279

replies were grouped as follows (see Table 12):

Table 12: The one thing respondents enjoy doing most

Statement

Frequency

(%)

Doing outside activities and hobbies (e.g. swimming,

gardening, shopping)

127 (45.5%)

In-house activities (e.g. playing with computer, listening to

music, reading)

68 (24.4%)

Practicing an activity similar to the job I would like to do 29 (10.4%)

Going abroad and socializing / working with others 17 (6.1%)

Looking after / being with family members 17 (6.1%)

Relaxing at home/sleeping 9 (3.2%)

Don’t know/nothing 5 (1.8%)

Working with animals 3 (1.1%)

Watching some form of sport 2 (0.7%)

Others (being creative) 2 (0.7%)


56

Question E2 requested respondents to mention those factors that are hindering them from

seeking educational, training, or job opportunities. Respondents were instructed to forget about

all the previous questions they had answered. The 271 responses obtained were grouped as

follows (see Table 13):

Table 13: Hindering factors to education, training and employment

Statement

Frequency

(%)

Psychological, medical and logistical constraints

(immobilizers)

68 (25.1%)

No apparent/valid reason (either no constraints, laziness or

simply doesn’t care)

58 (21.4%)

Lack of suitable/proper training and job conditions

46 (17.0%)

Lack of right/sufficient qualifications 25 (9.2%)

Family Responsibilities 24 (8.9%)

Not interested in education and training but want a job 18 (6.6%)

Studying / working for better opportunities 13 (4.8%)

Financial difficulties 11 (4.1%)

Other 4 (1.5%)

Need of a break 2 (0.7%)

Dependent on family 2 (0.7%)


57

The respondents were requested to highlight factors that would encourage them to seek

educational, training, or job opportunities (E4). The 275 responses were grouped as follows (see

Table 14):

Table 14: Factors that encourage educational, training, or job opportunities

Statement

Frequency

(%)

Financial reasons 133 (48.4%)

The opportunity to learn further and have more

opportunities

42 (15.3%)

The idea of having my dream job come true 37 (13.5%)

Don’t know / not interested 33 (12.0%)

The opportunity to grow, be independent and have a better

future

26 (9.5%)

My family and friends 4 (1.5%)

The respondents were also asked to highlight what they would like to see implemented by the

Government to encourage them to take further training/ education and/or seek employment (E5).

The 270 valid responses were group as follows (see Table 15)


58

Table 15: Implementing factors for further training/education and/or employment

Statement

Frequency

(%)

Making more training programmes accessible/creating more

programmes

55 (20.4%)

Improving work experiences coupled with more active

learning schemes

40 (14.8%)

Improve career advisory services and information (e.g.

Open days)

35 (13.0%)

Increasing employment opportunities for young people

through new investment

27 (10%)

The opportunity to grow, be independent and have a better

future

26 (9.6%)

Better financing support schemes 16 (5.9%)

Other (e.g. better recruitment, positive discrimination) 6 (2.2%)

More childcare and family friendly work schemes 5 (1.9%)

Improved social benefits 1 (0.4%)

Apart from these, 47 (17.4%) respondents stated ‘don’t know’ while another 12 stated ‘nothing’.

Question E5 requested the participants to state whether they believed their family would support

and encourage them to take further training / education and/or seek employment. 277 replied

and from these 268 (96.7%) stated ‘yes’, 5 (1.8%)stated ‘no’ while the remaining 4 (1.4% )stated

‘not much’.


59

Finally, the respondents were requested to mention their dream job (E6). The 276 responses were

for the majority of them grouped as follows: performing arts including filming and creative

occupations (44), helping and caring professions (29), security services and civil protection

occupations (27), general employment (21), entrepreneur/small business owner (20),

engineering/ technology / IT / architectural design (19), manual jobs (18), sports (12),

hospitality, hotels and catering (11), company occupations - accountancy, marketing,

management (11), technical occupations - mechanics and IT technicians (9),clerical work (9),

maritime / aviation (6).

3.7 Interviewer Observations (Section F)

The interviewers were asked to rate a number of statements on three point Likert-type items (1 =

disagree, 2 = Neutral, 3 = Agree) related to appearance, verbal/physical behaviour and attitude of

the individual participants as well as the presence of others (human traffic) and the surrounding

conditions during the interview.

With respect to appearance, the vast majority of participants showed awareness of personal

grooming (76.0%) and they looked healthy – not obese or too thin, and alert (76.5%). In the case

of physical and verbal behaviour, the vast majority of the participants were polite (92.1) and

NOT rude (87.8%), arrogant (90.0%), aggressive (90.7%), intimidating (90.0%) or used foul

language (90.0%). With respect to the attitude of the participants, the respondents were generally

willing to participate in the questions (88.5%), took the interview seriously (73.8%) and seemed

truthful in the answers (79.2%).

In general, the participants’ houses were generally clean and habitable (70.3%) and they

appeared to feel comfortable and safe in their house (81.0%). Additionally, other relatives were


60

present during the interview (66.7%), the participants appeared to have a healthy relationship

with their family members (72.3%) and the other family members encouraged the participants to

participate in the interview (58.0%). More detailed statistical output is presented in Table 16.

Table 16: Interviewer Observations

F1. Appearance Disagree Neutral Agree

Md

(R) M (SD)

Participant showed awareness of

personal grooming 13 54 212 3 (1-3) 2.71 (0.55)

Participant looked healthy (not

obese, not too thin, alert, etc.) 18 47 212 3 (1-3) 2.70 (0.58)

F2. Verbal/Physical abuse

The participant was polite* 3 18 257 1 (1-3) 2.91 (0.32)

The participant was rude 245 7 2 3 (1-3) 1.04 (0.24)

The participant was arrogant 251 3 0 3 (2-3) 1.01 (0.11)

The participant was aggressive 253 1 0 3 (2-3) 1.00 (0.06)

The participant was intimidating 251 3 0 3 (2-3) 1.01 (0.11)

The participant used foul language 251 3 0 3 (2-3) 1.01 (0.11)

F3. Presence of other people

Other relatives were present

during the interview 59 12 186 1 (1-3) 2.49 (0.84)

The participant appeared to have a

healthy relationship with their 5 40 203 3 (1-3) 2.80 (0.45)


61

family members

The other individuals present

encouraged the participant to

participate in the interview 3 35 162 3 (2-3) 2.80 (0.44)

The other individuals present

discouraged the participant to

participate in the interview* 177 6 0 3 (2-3) 1.03 (0.18)

F4. Attitude

The participant was willing to

participate to the questions 6 26 247 3 (1-3) 2.86 (0.40)

The participant took the interview

seriously 8 65 206 3 (1-3) 2.71 (0.51)

The participant seemed truthful in

the answers 5 52 221 3 (1-3) 2.78 (0.46)

F5. Surroundings

Participant’s house was clean and

habitable 11 42 196 3 (1-3) 2.74 (0.53)

Participant appeared to feel

comfortable and safe in the house 4 26 226 3 (1-3) 2.87 (0.38)

*Reversed items


62

3.8 Cluster Analysis

3.8.1 Cluster Analysis of Targeted Population (including ineligible participants)

The population of Maltese youths aged 16 to 24 according to the NSO Demographic Review

2013 published in August 2015 was 46,942. According to the ETC (as at April 2015), the

number of youth NEETS in Malta was 6,749 (14.4%). Table 17 provides a breakdown by gender

and age for 6,749 youths NEETs.

Table 17: Distribution by Age and Gender of Population for Maltese youth NEETs

Male Female

3,856 (57.1%) 2,893 (42.9%)

16-17 18-20 21-24 16-17 18-20 21-24

1,350

(35.0%)

1,422

(36.9%)

1,084

(28.1%)

1,114

(38.5%)

908

(31.4%)

871

(30.1%)

Following the interview procedure, these 6,749 individuals were classified as follows (Table 18):

Table 18: Classification of Targeted Population

Participated in survey* 283 (4.2%)

Ineligible (not NEET) 4,035 (59.8%)

Refusal 699 (10.4%)

Not Located 539 (8.0%)

Located but unable to make contact (no 1,185 (17.6%)


63

contact after 5 attempts)

Data input error 8 (0.1%)

Total 6,749 (100.0%)

*4 incomplete surveys (interviewer concluded that participant was not in the right mental state to complete interview)

The following section will now provide a breakdown of the above information by district as

described by the NSO.

District 1 – Gozo and Comino

The population of youths aged 16 to 24 in District 1 according to NSO was 3,621. According to

the ETC, the number of youth NEETS within this district was 917 (25.3%). Table 19 provides a

breakdown by gender and age for these 917 youth NEETs.

Table 19: Distribution by Age and Gender of Population for District 1 youth NEETs

Male Female

479

(52.1%)

438

(47.7%)

16-17 18-20 21-24 16-17 18-20 21-24

235

(49.1%)

147

(30.7%)

97

(20.3%)

256

(58.4%)

112

(25.6%)

70

(16.0%)

Following the interview procedure, these 917 individuals were classified as follows:

Participated in survey 18 (2.0%)

Ineligible (not NEET) 726 (79.2%)


64

Refusal 20 (2.2%)


Located but unable to make contact 99 (10.8%)

Total 917 (100.0%)

District 2 - Northern


the ETC (as at April 2015), the number of youth NEETS within this district was 1,141 (15.2%).

Table 20 provides a breakdown by gender and age for these 1,141 youth NEETs.


Male Female

660

(57.8%)

481

(42.2%)

16-17 18-20 21-24 16-17 18-20 21-24

237

(35.9%)

235

(35.6%)

188

(28.5%)

156

(32.4%)

162

(33.7%)

163

(33.9%)

Following the interview procedure, these 1,141 individuals were classified as follows:

Participated in survey 37 (3.2%)*


Refusal 139 (12.2%)


65


Located but unable to make contact

Data input error

140 (12.4%)

1 (0.1)

Total 1,141 (100.0%)

* 1 incomplete survey (interviewer concluded that participant was not in the right mental state to complete interview)

District 3 – Northern Harbour

The population of youths aged 16 to 24 within this district according to NSO was 12,667.

According to the ETC, the number of youth NEETS within this district was 1,894 (15%). Table

21 provides a breakdown by gender and age for these 1,894 youth NEETs.


Male Female

1070 (56.5%) 824(43.5%)

16-17 18-20 21-24 16-17 18-20 21-24

307

(28.7%)

436

(40.7%)

327

(30.6%)

281

(34.1%)

265

(32.2%)

278

(33.7%)



Ineligible (not NEET) 1,071 (56.5%)

Refusal 210 (11.1%)


66



Data input error 1 (0.1%)

Total 1,894 (100.0%)

* 2 incomplete surveys (interviewer concluded that participant was not in the right mental state to complete interview)

District 4 – South Eastern


According to the ETC, the number of youth NEETS within this district was 803 (10.1%). Table

22 provides a breakdown by gender and age for these 803 youth NEETs.


Male Female

471(58.7%) 332(41.3%)

16-17 18-20 21-24 16-17 18-20 21-24

156

(33.1%)

192

(40.8%)

123

(26.1%)

124

(37.3%)

110

(33.1%)

98 (29.5%)




Refusal 104 (13.0%)




67

Data Input Error 3 (0.4%)

Total 803 (100.0%)

* 1 incomplete surveys (interviewer concluded that participant was not in the right mental state to complete interview)

District 5 – South Harbour


the ETC, the number of youth NEETS within this district was 1,101 (13.1%). Table 23 provides

a breakdown by gender and age for these 1,101 youth NEETs.


Male Female

658

(59.8%)

443

(40.2%)

16-17 18-20 21-24 16-17 18-20 21-24

238

(36.2%)

234

(35.6%)

186

(28.3%)

157

(35.4%)

154

(34.8%)

132

(29.8%)




Refusal 94 (8.5%)



Data Input Error 1 (0.1%)


68

Total 1,101 (100.0%)

District 6 - Western


According to the ETC, the number of youth NEETS within this district was 893 (13.1%). Table

24 provides a breakdown by gender and age for these 893 youth NEETs.


Male Female

518 (58.0%) 375 (42.0%)

16-17 18-20 21-24 16-17 18-20 21-24

177

(34.2%)

178

(34.4%)

163

(31.5%)

140

(37.3%)

104

(27.7%)

131

(34.9%)




Refusal 132 (14.8%)


Located but unable to make contact

Data Input Error

164 (18.4%)

2 (0.02%)

Total 893 (100.0%)


69

3.8.2 Cluster Analysis of Population Excluding Ineligible Participants

Once the ineligible participants were excluded, the following picture emerged (see Table 25):

Table 25: Classification by District

District

Classification 1 2 3 4 5 6 Total

Participated* 18 (9.4) 37 (8.4) 87(10.6) 35(8.8) 70(14.2) 36(9.8) 283(10.4)

Refusal 20(10.5) 139(31.7) 210(25.5) 104(26.0) 94(19.1) 132(36.0) 699(25.8)

Not Located 54(28.3) 122(27.8) 201(24.4) 50(12.5) 77(15.7) 35(9.5) 539(19.9)

Located,

no contact 99(51.8) 140(31.9) 324(39.4) 208(52.0) 250(50.8) 164(44.4) 1185(43.7)

Data input

error 0(0.0) 1(0.2) 1(0.1) 3(0.8) 1(0.2) 2(0.5) 8(0.3)

Total 191 439 823 400 492 369 2714

Note: column percentages are presented in brackets; * includes 4 incomplete surveys

It shows that the highest and lowest participation rates occurred in District 5 = South Harbour

(14.2%) and District 2 = Northern (8.2%) respectively; the highest refusal and lowest rates

occurred in District 6 = Western (36.0%) and District 1 = Gozo & Comino (10.5%) respectively;

the highest rates for ‘not located’ occurred in District 1 = Gozo & Comino (28.3%) followed

closely by District 2 = Northern, with the lowest rate recorded in District 6 = Western (9.5%);

the highest rates for ‘located but no contact’ occurred in District 4 = South Eastern (52.0%)

followed closely by District 1 = Gozo & Comino (51.8%) and District 5 = South Harbour

(50.8%), with the lowest rates emerging from District 2 = Northern (32.1%).


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There was no significant association between gender and district (χ2(5) = 1.75, p = 0.88), with

circa 58% males and 42% females across districts. A cross tabulation is presented in Table 26.

Table 26: Gender * District Cross-tabulation

Count

District Total

1 2 3 4 5 6

Gender

Male 108(56.5) 246 (56.0) 469(57.0) 232(58.0) 295(60.0) 213(57.7) 1563(57.6)

female 83(43.5) 193(44.0) 354(43.0) 168(42.0) 197(40.0) 156(42.3) 1151(42.4)

Total 191 439 823 400 492 369 2714

With respect to age, there was a significant difference between age category and district, with

respondents in District 1 more likely to be aged 21-24, those in Districts 3 and 5 more likely to

be aged 18-20, and those in Districts 2, 4 and 6 more likely to be aged 16-17 (see Table 27).

Table 27: Age Code * District Cross-tabulation

Count

District Total

1 2 3 4 5 6

Age Code

16-17 60(31.4) 170(38.7) 284(34.5) 162(40.5) 176(35.8) 162(43.9) 1014(37.4)

18-20 65(34.0) 133(30.3) 298(36.2) 138(34.5) 187(38.0) 114(30.9) 935(34.5)

21-24 66(34.6) 136(31.0) 241(29.3) 100(25.0) 129(26.2) 93(25.2) 765(28.2)

Total 191 439 823 400 492 369 2714


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When chronological age was used, the Kruskal wallis test revealed that age varied significantly

by district in the mean ranks (χ2(10) = 24.16, p<0.01). A descriptive summary of mean ranks is

presented in Table 28. A series (n = 15) of Mann Whitney U post-hoc tests (with significance set

at p ≤ 0.0033 after applying the Bonferroni correction) revealed that this overall age difference

was mainly due to the age difference between the District 1 = ‘Gozo & Comino’ and District 6 =

‘Western’ (p = 0.001); the oldest and the youngest across the 6 districts respectively.

Table 28: Mean Ranks by District

District N Mean Rank

1 191 1494.26

2 439 1368.52

3 823 1394.38

4 400 1298.89

5 492 1352.95

6 369 1257.28

Total 2714

3.9 Follow-up Interviews

3.9.1 Rationale

Whilst conducting the Census, 4,035 youths from the target group claimed that they did not meet

the YG eligibility criteria set out by ETC (i.e. under 25 years of age, residing in Malta or Gozo,

not receiving non-contributory social benefits, and not in employment and registered as


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unemployed, or not in education, or not in training, or inactive) and should in theory not be on

the database.

Based on the Census aims, those that claimed not to be NEETs could not be interviewed using

the Census questionnaire. Research clearly suggests that Youths NEET are a heterogeneous

population and cannot simply be defined by strict parameters, thus rather than disregarding this

seemingly Not NEET population, further investigation into these 4,035 youths was conducted in

order to identify/explore:

1. If those declaring to be Not NEETs were truly Not NEET or seem to be hesitant or even

lying;

2. Whether there are any issues in relation to the parameters used when including a Youth

on the NEETs database;

3. The Youth’s declaring not to be NEETs’ current situation in relation to Employment,

Education, and Training;

4. These same Youth’s future plans in relation to Employment, Education, and Training.

3.9.2 Methodology

Prior to conducting any follow-up interviews with a sample of these 4,035 youths, at the end of

September, MEDE revised their Database used to account for NEETs in relation to the Database

it had originally provided the Research Company at the start of the Census. This led to the

following results:

1. 1,049 youths who resulted being NEETs at the time of elaboration of the database have

found employment in the meantime. ETC confirmed that the registration date of those


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youths with the Corporation is between the date of the production of the database and the

date in which they were interviewed;

2. 61 youths are now registered unemployed with ETC;

3. 494 youths phoned MEDE or the service provider after being notified about the interview

declaring that they were actually in education or training;

4. 201 youths resulted in education following a revision of the information available in the

database.

On the basis of the outcome of the Census conducted and following the revision of the Database

of NEETs by the ETC, there still existed a discrepancy of 2,230 youths who claimed to not be

NEETs, while according to the ETC database they should be NEETs. This new target group was

to be interviewed by telephone using the questionnaire found in Appendix B, but for ethical

considerations, the ETC imposed a limitation on the sampling method to be used, this by only

allowing the attainment of telephone contact details for those targeted participants whose

telephone number was available via the public online telephone directory.

In this manner, a sample of 745 from the 2,230 youths was generated. All 745 participants were

contacted by telephone (each participant was called up to a maximum of 3 times, the interview

could be conducted with the participant or an immediate relative) and asked to participate in a 5

minute phone interview. These interviews aimed at obtaining more information on the Youth’s

actual status (in relation to employment and education), whilst evaluating whether they could be

hesitant in providing this further information or even possibly lying. Table 29, lists the number

of attempts made to reach participants.


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Table 29: Analysis of Call Attempts

Number of Attempts

1st Attempt 2

nd Attempt 3

rd Attempt Total No. of Attempts

745 365 290 1400

The above attempts resulted in 531 completed interviews (margin of error = 3.72 %). This meant

that there were 214 case closed scenarios resulting in unsuccessful interviews. The latter was

grouped into the following categories:

a) 20 refused to respond (refusal by participant, refusal to participate by others);

b) 34 were not located (wrong number, respondent changed address);

c) 157 respondents could not be reached following the the third call attempt;

d) 3 cases could not be categorised due to data input errors.

3.9.3 Results

Respondents’ Demographics

Out of 531 youth NEETs who participated in the follow-up interviews, 55.4% were male and

44.6% were female. At the time of data collection the respondents had a mean age of 18.76 years

(SD = 2.40).

Current Situation - Employment

When asked about current employment, 360 (67.8%) Participants reported that they were not

currently employed, whilst 165 (31.1%) Participants stated that they were currently employed. 6

(1.1%) Participants did not answer the question.


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Participants who were currently employed were asked to indicate:

a) The type of employment – 94.5% (n = 156) provided the following answers:

Table 30: Type of Employment

Category N Frequency (%)

Full- Time Job 101 64.7

Part-Time Job 49 31.4

Casual Worker 4 2.6

Summer Job 2 1.3

Total 156 100.0

b) The number of hours per week worked – 143 (86.7%) provided the following answers:

Table 31: Hours per week


Up to 20 hours 26 18.2

21-29 hours 16 11.2

30-39 hours 9 6.3

40+ hours 92 64.3

Total 143 100.0


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Out of the 165 respondents claiming to be in employment, 102 (61.8%) gave further details

relating to the start date of employment. Table 32 provides a detailed breakdown of the start

dates.

Table 32: Employment Start Dates

Year

Total

2010 2011 2012 2013 2014 2015

2 1 7 12 16 64 102

From all the participants in employment, 52 participants started working following April 2015.

Current Situation – Education and Training

When asked to indicate whether currently attending any training or educational programmes,

347 (65.3%) of Participants answered ‘yes’. The Participants were asked to name the education

institute they were attending, of which 343 (98.8%) highlighted the following:

Table 33: Education Institute


University of Malta 75 21.9

MCAST 50 14.6

ITS 1 0.3

Junior College 9 2.6

Other 208 60.6

Total 343 100.0


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Table 34 highlights the most prevalent institutions attended by Participants who selected the

“other” option.

Table 34: Other Education Institutes

Category N

Sir M.A. Refalo (6th

Form Gozo) 74

Foreign University 36

St Martin’s College 23

Giovanni Curmi (Higher Secondary Naxxar) 19

Out of the 347 respondents claiming to be in education, 289 (83.3%) gave further details relating

to the start date of their course. Table 35 provides a detailed breakdown of the start dates.

Table 35: Course Start Dates

Year

Total

2010 2011 2012 2013 2014 2015

7 4 4 19 110 145 289

From those participants starting education or training in 2015, 140 started studying following

April 2015.

286 (99%) highlighted the national qualifications framework level of the course: PhD (4),

Masters (25), Degree (101), Diploma (43), Certificate (108), and other (5) – the latter including a

foundation course and a pilot license course.


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Current Situation – Youth Guarantee Programme

Out of the 45 (8.5%) respondents who stated that they were neither in training/education or

employment, 22 (48.9%) stated that they would be willing to participate in the Youth Guarantee

Programme.

Future Plans – Employment

When asked about future plans for the next 6 months, 96.4% (512) Participants replied, with

results as illustrated in Table 36.

Table 36: Future Employment Plans


Full-time work 186 36.3

Part-time work 80 15.6

No plans to work 197 38.5

Change Jobs 10 2.0

Other 39 7.6

Total 512 100.0

Participants who selected “other” predominantly specified the following; Summer jobs (22),

don’t know (7), and apprenticeship (3).

Future Plans – Education and Training

When asked about future plans for the next 6 months, 95.9% (509) Participants replied, with

results as illustrated in Table 37.


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Table 37: Future Education Plans


Complete current course 296 58.2

Start new course 62 12.2

No plans 133 26.1

Other 18 3.5

Total 509 100.0

Participants who selected “other” predominantly specified the following; don’t know (9) and

sitting for SEC/MATSEC examinations (3).

Interviewer Feedback

Interviewers were asked to state who the interview was conducted with. Table 38 illustrates the

results.

Table 38: Interviewee


Participant 219 41.2

Mother 212 39.9

Father 59 11.1

Brother 12 2.3

Sister 11 2.1

Left Blank 8 1.5


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Uncle/Aunt 5 0.9

Grand Parent 4 0.9

Partner 1 0.2

Total 531 100.0

The interviewers were asked to rate a number of statements (1 = Agree, 2 = Disagree) related to

whether it was felt that the respondents were willing to respond to the questions and seemed

truthful in their responses.

Out of 520 responses (98%), the interviewers agreed that the vast majority of the participants

were willing to respond to the questions (96.7%) and seemed truthful in their responses (97.7%).

Interviewers disagreed with the statement that Participants were unwilling to give precise

responses (96.9%). 11 (2.1%) of the interviews had this question left blank.

3.9.4 Further breakdown of above results

A further breakdown of the above results was carried out in order to identify any obvious trends

and patterns from the responses obtained. The principle patterns that were looked out for in order

to confirm the major findings of the Census cluster analysis were as follows:

a) Number of respondents demonstrating youth NEET traits;

b) Potentially sub-dividing this group into NEET sub-categories demonstrating a

heterogeneous population;

c) Main reasons behind the number of respondents wrongly classified as youth NEETs.


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The following definitions and clearly defined criteria were used to extract the number of

respondents demonstrating youth NEET traits from their answers to the follow-up interviews:

- Core NEETs: Youths with social and behavioural issues, including those who come from

families where “worklessness” and unemployment is accepted as a norm and not much

importance is given to further and higher education.

- Floating NEETs: Youths who lack direction and motivation. They tend to have spells of

being a youth NEET in between further education courses or employment with no training.

This group also contributes to NEETs churn, the movement of young individuals between

different phases of activity but return to NEETs status repeatedly after unsuccessful attempts

at education, training, or employment.

- Transition NEETs: Those young individuals who have chosen to take time out before

progressing onto further or higher education or employment opportunities. These individuals

are likely to return to education, training, or employment, but it is not always clear when this

will occur.

The number of respondents that demonstrated youth NEET traits based on their responses and

above mentioned criteria were 266 (50.1%).

Core NEETs

Furthermore, based on the above definition for a Core NEET, the following criteria; not in

education, not in employment and no future educational or employment plans were used to

determine the potential number of Core NEETs. This resulted in 9 (3.4%) of all the participants

classified as NEETs being potentially classified as Core NEETs.


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Furthermore, it can be pointed out that when the said participants were asked whether they would

be interested in attending the Youth Guarantee Programme, the following results were provided:

Table 39: Interest in Youth Guarantee – Core NEETs

Core NEETs N Frequency %

Not Interested 5 55.6

Interested 2 22.2

Don’t Know 2 22.2

Total 9 100.0

Floating NEETs

While considering the definition of a Floating NEET, the following four criteria where used in

order to best identify the number of respondents falling under this category:

1. Not in employment but studying - Attending a course up to a maximum of O-level

standard (MQF – Level 3);

2. Employed but not in education or training – Employment start date post- April 2015 and

carrying out manual or low-skilled jobs;

3. Employed and in education – Carrying out manual labour and attending a course specific

to a low-skilled job;

4. Not working & not studying but has employment or educational future plans.

A total of 40 (15%) respondents were classified as Floating NEETs. The results for all the

participants falling under the 4 different criteria are provided below:


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Table 40: Breakdown of Floating NEETs

Criteria N Frequency %

Criteria 2 21 52.5

Criteria 3 15 37.5

Criteria 1 3 7.5

Criteria 4 1 2.5

Total 40 100.0

Transition NEETs

Based on above definition for a Transition NEET, it resulted that 122 (45.9%) of the participants

classified as NEETs are potentially Transition NEETs. Furthermore, this population of

Transition NEET was broken down according to the following criteria:

1. Not employed but in education or training – start date of current course or training

programme post-April 2015;

2. Employed and in education or training – start of both employment and educational

programme post-April 2015;

3. In employment but not in education or training – start date of employment post-April

2015;

4. Not in employment or education but has concrete future plans.


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Table 41: Breakdown of Transition NEETs

Criteria N Frequency %

Criteria 1 88 72.1

Criteria 3 15 12.3

Criteria 4 11 9.0

Criteria 2 8 6.6

Total 122 100.0

Additionally, the results show that of the 88 (72.1%) participants who fall under Criteria 1 - not

in employment but in education or training, 41 (46.6%) currently attend the University of Malta,

and a further 26 (29.5%) attend MCAST.

It is also important to note that the 11 (9.0%) participants who fall under Criteria 4 - not in

employment or in education but have concrete future plans, 6 (54.5%) participants plan on

working on a full time basis, 3 (27.3%) plan to work on a part time basis, 1 (9.1%) is currently

taking some time off after having completed a degree, and another 1 (9.1%) participant is re-

sitting exams.

Fluidity of NEETs population

Finally, a total of 95 (35.7%) of the participants classified as NEETs, did not fit a precise NEET

category as they could not be clearly associated to the above described criteria for each NEET

category. This indicated a potential fluidity within the three NEET segments. Of this group of 95,

31 (32.6%) demonstrated Floating NEET traits with a strong Core NEET trait element


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incorporated. Additionally, 64 (67.4%) participants demonstrated Transition NEET traits with a

strong risk of becoming Floating NEETs.

Figure 5: Fluidity of NEET Categories

Database management issues

The number of respondents that were clearly wrongly inputted into the youth NEETs database

and that have been in employment, education of training prior to April 2015, have been grouped

into 4 prevailing categories. 265 respondents were deemed to be incorrectly on the youth NEETs

database due to the fact that they were either working or studying abroad prior to April 2015,

were studying in a private further and higher education institute in Malta prior to April 2015,

were attending 6th

form in Gozo prior to April 2015, or were inputted due to apparent human

error.

Core NEETs

Floating NEETs

Transition NEETs


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3.9.5 Overall conclusion of the follow-up interviews

The above information was used to classify the 531 respondents under the following categories:

Table 42: Category of Participants

Category N Frequency %

Transition NEET

Transition/Floating

Floating NEET

Floating/Core

Core NEET

122

64

40

31

9

23.0

12.1

7.5

5.8

1.7

Human Error

Gozo Issue

Abroad

Private F&HEI

98

74

51

42

18.5

13.9

9.6

7.9

Total 531 100.0

3.10 Conclusion

In this section, a summary of data collected from the survey has been provided as recorded by

the interviewers. No additional interpretations have been added or implications proposed. In the

light of these findings, conclusions and recommendations will be presented in the next section.


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Conclusion

4.1 Introduction

This section provides an overall summary of the major findings of the project, its limitations, and

recommendations that could better guide policy makers in their quest to reduce the number of

youths that are not in education, training or employment and to bolster the growth potential of

the Maltese economy.

4.2 Summary of major findings

4.2.1 Census

Findings related to analysis of completed interviews

The following are the most salient patterns that emerged from the results of the completed

surveys:

Parental Influence: It is clear that the majority of NEETs are still living with their parents

(90%) and have ongoing interactions with family members. This implies that they are

heavily influenced by the family and may either get low exposure to the labour market or

poor career guidance (e.g. Mroz & Savage, 2006). In addition, the majority of

participants reported to have a traditional family set-up; namely the father as breadwinner

and the mother as homemaker. This has implications on how they would likely perceive

the labour market and their career prospects.


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Parenthood: 11.1% of the participants interviewed reported to be parents themselves. The

implications of this are that they may be unable to work to raise children and also because

they find it a major problem where to leave their children.

Education: The vast majority have at least a School Leaving Certificate, O-level or

diploma. A small number also have a tertiary level of education. In addition, 43% have

claimed to have undergone some form of apprenticeship or training experience after

leaving school. This suggests that these youths may be better defined as Floating NEETs

(RaE, 2007) and have potential for learning provided that the education model used is in

tune with their needs. In fact, more than half of the participants, in spite of having some

basic form of qualification, are demotivated and disinterested in the current form of

education.

Role of Education: 95% of the respondents explicitely claimed that education and

training is important for employment. This suggests that the respondents are well aware

of the connectivity between education and employment and this connection deserves to

be addressed and exploited, although 54% claim not to have what it takes to achieve their

plans. This link has been well documented by the literature (e.g. MacDonald, 2011;

Scarpetta, Sonnet & Manfredi, 2010).

Work-experience: Although 76% have some form of work experience, mostly doing odd

jobs, only 41% had a full-time job experience. This implies that the majority of

participants have never had the benefits of planning long term careers or settling down in

specific roles. Those doing part-time jobs were not doing so on a voluntary basis but

because they had no better options. In fact, 70% claimed to be more in support of having

a full-time job.


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Plans for the future: The majority of respondents reported to have plans for their future

careers or potential job opportunities. This still leaves a good 43.3% claiming that they

have no plans on how they should best approach job opportunities or career prospects.

Employment sectors: One can clearly notice that the majority of the respondents would

preferably go for the more traditional sectors. This implies that either they are still

aversive to new employment realities or that they have very little exposure to new

emerging sectors. In fact, the new sectors are more likely to offer the best salaries and

opportunities for growth. Moreover, only 6% of the participants claimed to consider

starting their own business which indicates they are more into job seeking rather than job

creation. This is in line with previous European survey findings (e.g. Mascherini et al.,

2012).

Effects of inactivity amongst NEETs: 39.5% of the participants claimed to have

contracted some form of ailment including those of a psychological nature as a

consequence of having no work suggesting that the NEETs status can have addition

debilitating effects which in turn makes it more difficult to actively seek and engage in

employment. Instances of unemployment may impact individuals adversely and have an

effect on their self confidence and sense of helplessness (c.f. Bell & Blanchflower, 2010;

Chaudhry, Marelli & Signorelli, 2010; Hammer, 2007).

Knowledge of the Youth Guarantee Programme: 70% of the respondents never heard

about this programme and 52% do not know if their friends are actually participating in

the programme, implying that the programme is not well-exposed. Moreover, had they to

participate in the programme, the most recurring reason to do so would be financial

(68%) followed by a quest for more support (57%). This indicates that candidates either


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have or see no significance in the scheme for engaging in employment or have little

understanding about the scheme. In line with the literature (e.g. Alegre et al., 2015; Chen,

2011), such schemes and programmes are not always in practice as effective as initially

planned and their purpose requires further thought.

Motivation for employment: It transpired from the results that the most recurring trend

amongst participants was to exhibit an external locus of control (i.e. one dependent on

fate such as luck, misfortune and support from others). This indicates that participants are

lacking in intrinsic motivation to seek for themselves better opportunities and may signify

a degree of helplessness with regards to their destiny. This does not only corroborate with

the previous finding that some are experiencing ill-health ailment due to economic

inactivity but also corroborates with the fact that youths in Malta prefer finding a job

rather than creating a job. Elsewhere, participants highlighted a number of factors which

are determinant for not finding employment. These included fear, lack of trust and

confidence, lack of work experience, lack of qualifications and a lacking support

structure related to childcare. Such findings concur with the literature related to the

impact of unemployment amongst youths (e.g. O’Higgins, 2001; Scarpetta, Sonnet &

Manfredi, 2010).

Enjoyment at work: It was clear from the responses that participants derive greatest

enjoyment from training in the work and NOT for the work (e.g. Breen, 2005). This was

in fact consolidated by the fact that respondents prefer job opportunities that allow them

to express themselves; jobs that provide them with a learning experience and jobs that

give them satisfaction and financial rewards.


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Observation by interviewers: The majority of the participants were observed to be well-

groomed, healthy and alert. They were also reported to have good behaviour/manners and

in general were willing to participate. In addition, they live in comfortable homes and

have a good relationship with family members.

Cluster analysis

The following are the most salient patterns that emerged from the results of the cluster analysis

of the Census Data:

The study showed that 59.8% of the targeted audience (4,035 NEETs) were youths that

declared to be in education, training or employment at the time of the Census.

There was a relatively lower rate of participation accompanied by higher rates for

‘ineligible’ for NEETs in the Northern part of Malta comprising District 1 = Gozo and

Comino and District 2 = Northern.

There was a relatively higher rate of ‘refusal’ and ‘not located’ for NEETs in the

Southern and Western part of Malta comprising Districts 4, 5 and 6.

Across the 6 districts, there were no significant discrepancies by gender, with circa 58%

males and 42% females across districts. In the case of age, there was a significantly

higher proportion of younger youth NEETs in District 6 = Western and older youth

NEETs in District 1 = Gozo & Comino.

The Census resulted in the Target population being segmented into 5 categories, namely;

Interviewed, Ineligible (not NEET), Refusal, Not Located, and Could not reach after 5

attempts. The ineligible portion of the target population was much larger than expected


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and hence further investigation through a follow-up interview was carried out with a

sample from this category to assess the characteristics of this sub-group.

4.2.2 Follow-up Interviews

The following are the most salient patterns that emerged from the results of the follow-up

interviews:

Although these participants or their relatives claimed not to be NEETs at the time of the

Census, there seems to be a good amount of these participants that are in reality still

NEETs in need of help, support, guidance, and socio-economic integration. This further

highlights the fact that conventional methods of reaching out to this segment of youths

will not reap the desired benefits.

Whilst in April 2015 (the time the database of Youth NEETs was created by ETC)

participants may have been inactive (i.e. not in employment, education or training), at the

time of data collection the vast majority of the follow-up interview participants were

either employed or engaged in an education programme. This highlights the fluidity of a

NEETs population. An individual may be a NEET for only a short amount of time. This

was also evident when, at the end of September 2015, the ETC validated the 4,035

participants claiming to be not NEETs during the Census and found that in fact 1,805 of

these participants had between April and September 2015 found employment or entered

an education programme. It thus seems that a significant proportion of the youth NEET

population could in fact be Transition NEETs.

The majority of participants who are employed are engaged in full-time employment.

Being engaged in full-time employment at a young age may be linked to an individual


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not developing the required knowledge or gaining the education needed to become a

successful knowledge worker. Malta’s greatest asset is Human Capital and over the

coming years the major workforce trends will shift towards skill-intensive jobs. These

jobs require individuals to possess higher levels of education than a school leaving

certificate, hence it is essential that each individual develops their knowledge to the

fullest extent possible.

The database parameters used by the ETC to put together a youths NEET population at

any given moment in time do not seem to cater for individuals who are studying or

working overseas, studying in state education institutes in Gozo, studying with a private

education institute in Malta, or attending some form of non-mainstream/formal education

programme.

The majority of participants claimed to have future plans, suggesting that there is an

element of self-belief and internal locus of control in this sample of youths. Yet, the

Participants may need guidance and support to execute their plans.

4.2.3 Putting it all together

Together, the results of both the Census and the Follow-up interviews highlight 4 important

points:

Issues with the NEETs Database: through this study it is plausible to assume that there

are a few issues with the Database used to identify and keep track of the NEETs

population. Such issues include:


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o Parameters for inclusion or exclusion from the database are rigid whilst the target

group is highly fluid and subject to variance and change (e.g. a youth may only be

a NEET for a few months).

o The parameters do not seem to cater for particular variables, such as; private

universities/schools, studying or working abroad, and studying in Gozo.

Indications of different categories of NEETs: The results of both data collection phases

clearly indicate that there seem to be distinct sub-categories within the Youths NEET

category. These sub-categories can be defined as:

o Core NEETs. Individuals who are not currently employed or in education, have

no future plans in relation to employment or education, have a negative

educational experience, and have minimum motivation.

o Floating NEETs. Individuals who would like to engage in some form of education

or employment, however are unsure of what plan to action. These participants

need guidance and support to develop a plan that suits their career ambitions,

goals, and capabilities. These action plans are essential for keeping an individual

on track and preventing them from becoming Core NEETs.

o Transition NEETs. These are individuals who are taking a short break from their

education or employment plans. This could be due to transitioning from education

into employment (i.e. searching for a job), to taking a gap year to travel or gain

experiences outside full-time employment or education, to waiting for a particular

educational course to become available, to waiting for results of education courses

being followed. These individuals all have a set career or educational plan.


95

The need for further research: This project indicated the presence of these youth NEET

population sub-categories. However, further research is required to confirm the precise

numbers of and relevance of the above mentioned descriptors of each sub-category, for

the Maltese youth NEET population. This could not be evaluated through this project

given the objectives and limitations of the study. However, the data obtained from this

project could be used to develop a research instrument that would be highly focused and

sensitive to the criteria that make up the NEET sub-categories.

The need for further and consistent investigation by the ETC: In both the Census and

follow-up interviews, a number of dubious cases arose (e.g. no contact made after 5

attempts for the door-to-door census and 3 attempts for the follow-up telephone calls,

could not locate or make contact with, refusal to participate in the interviews). Whether

these individuals are actual NEETs or not are not known and cannot be confirmed

through this project.

The above points are illustrated in Figure 6 below:


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Figure 6: Putting it all together

4.3 Limitations

4.3.1 Limitations related to the Census

Three main limitations related to the Census have been identified:

Firstly, this project sought to obtain a representative sample of Maltese youth NEETs aged

between 16 to 24. To do so, the project targeted every single person in the ETC sampling frame

and up to five attempts were made on five different days at five different times to reach the

respondents at their own homes. In spite of this, the total response rate was 11.5% and the

sample size obtained (279) just falls short of the minimum required sample size of 331, resulting

in a margin of error of 5.5% (Lenth, 2006-9). The resulting sample was representative by gender

and district, but skewed with respect to age (with a particular underepresentation in the 17-18 age


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group category). Additionally, we cannot exclude that those not participating are in fact the more

risky group.

Secondly, since the study utilised a face-to-face survey, there is always the possibility of social

desirability bias by some respondents as well as interviewer bias (Saunders et al., 2015),

although it has to be highlighted that the interviewers were trained prior to the interviews.

Thirdly, since the sampling frame was compiled during April 2015 and the study was conducted

during July/September 2015, there were cases that had to be excluded because the participants

were no longer NEETs. Additionally there could be others who at the time of writing have

become NEETs.

4.3.2 Limitations related to the follow-up interviews

Due to data protection reasons, it was impossible to generate a true random sample from the

2,230 youths identified as not NEETs. The sample utilised in the follow-up interviews was

generated by checking the available telephone numbers on the local public telephone online

directory, this generating a sample of 745 participants.

Generating a sample using the above methods led to biases that may possibly cloud the results of

the follow-up interviews and incomplete conclusions about the whole population from the data

collected.

4.4 Recommendations

In the light of the findings that emerged from the Census and follow-up interviews, the following

recommendations are being proposed. These recommendations have been divided into 2


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sections; Preventative measures and Activation measures, and are intended to minimise the

chances of a youth becoming NEET, enhance the possibilities of Maltese youth NEETS

returning to education/training or in getting better employment opportunities. It is essential that a

long-run strategy that focuses on preventing youths from becoming NEETs runs in parallel with

a remedial, short-run strategy, for those already NEETs or at high risk of becoming such.

4.4.1 Preventative Measures

These measures focus on ensuring that youths have the required measures in place to prevent

them from becoming NEETs and also on ensuring that Transition and Floating NEETs do not

become Core NEETs. Such measures include:

1. Educating parents about the importance of giving value to education and employment.

The respondents of this study stated that they spend the majority of their time with their

parents. Children are also highly influenced by their parents and tend to model their

parents’ behaviour. Thus what a parent says and does will have a great impact on a

child’s mind-set and approach to work and education. It is essential that support is given

to parents to ensure that they develop the right mind-set required to support their children

in becoming “active” in the labour market and education system.

2. Educational Experience and Purpose of Education. It is critical that work is done to

decrease the amount of youths who drop-out from school at an early age. Such youths

leave school with very low levels of qualifications and a deficient set of skills (general as

well as practical/technical) and thus face the highest unemployment risk. Low achievers

and potential low achievers must be identified and supported at early stages of

adolescence and youth.


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a. Engagement programmes in secondary schools i.e. putting measures in place that

prevent youths becoming NEETs once they reach 16. Ideally, these programmes

should target individuals who are predisposed to become “core NEETs”. Such

measures could take the form of new subjects that focus on the workplace. These

programmes should aim at providing young people with a set of skills that will

prepare them for employment, and to keep in school those who would otherwise

leave the education system at an early stage.

b. Comprehensive learning: The general schooling and education experience of most

of the participants that were interviewed during the Census or follow-up calls was

not positive to the extent that many have complained of the lack of interest in

schooling and their lack of motivation for education. In addition, many of the

participants exhibited a high degree of low self-confidence. Therefore it is highly

recommended to rethink the current educational model being provided to potential

NEETs (i.e. those predisposed to become NEETs) and to offer more vocational

oriented courses that offer a hands on work experience (youths must be able to

understand the purpose of education). It is also suggested that teachers in this

sphere provide special attention to the person’s sense of well being and health (i.e.

a more holistic educational model). It is recommended that teachers and educators

are provided more training with respect to how to deal with students that show

signs of pre-disposition towards becoming NEET.

3. Social engagement measures for youths related to sports, culture and adventure can also

be very useful for instilling and/or triggering personality traits which are beneficial life

skills, such as enhancing competitiveness, self-discipline, and motivation.


100

4. Youth Workers and Career Counsellors. These experts must be involved in the

preventative measures and not only in activation measures. It is these individuals that can

work with youths and ensure they develop their “Self” and are given guidance in relation

to the repercussions of certain behaviours/decisions taken. This is of particular

importance for youths who are predisposed to become Core NEETs due to a low socio-

economic status and family issues.

4.4.2 Activation Measures

These measures focus on ensuring that the required initiatives are implemented to reduce the

number of NEETs. Such measures include;

Redefining the term NEET: Before any new activation initiatives are put in place it is

critical that policy makers redefine the term NEETs in Malta and develop and implement

policies for each classification of NEET – core, floating and transition.

Student support services: One third of the participants stated that lack of work had a

negative impact on their health and well-being. It is therefore recommended that a

specific team is set up composed of social workers, educational psychologists, and youth

workers to support in the development programme of these potential NEETs. This could

take the form of an extensive profiling exercise among students to give this the necessary

attention before the problem escalates. One way to co-–ordinate such activities would be

to bring all the resources from existing Government agencies which are related to this

sector (e.g. FSS, Agenzija Zaghzagh, and ETC) and centralise them.

Awareness campaign: Over two-thirds of the respondents had not heard about the Youth

Guarantee Programme. To this end, it is being recommended that a fresh and more active


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campaign is initiated to bring potential NEETs and NEETs more cognizant about the

benefits of the programme together, with a view to increase participation in up-and-

coming similar schemes. Furthermore, sending letters or long documents to NEETs does

not work. The Government should work on coming up with innovative ways of

promoting schemes and initiatives e.g. videos (one could develop a film which will be

shown in all schools at age 12), use of social media, user friendly websites etc. Further to

this, its also highly recommended to involve previous NEETs who have successfully

made use of the Programme as official ambassadors of the initiative, thus serving as Peer

Educators.

Apprenticeship: Should the Youth Guarantee Programme be stuctured ‘learn first, work

later’, it is highly probable that the uptake, like in other countries, will be very low. It is

therefore imperative that the learning experience is infused into the work experience.

This could take the form of Public Private Partnerships whereby preference for work is

given to NEETs. This is more important given that foreign experience with such schemes

shows that doing the odd job for pure financial reasons at such a young age increases the

chances of longer spells of economic inactivity and increases experiences of “scarring”.

In addition, providing more quality employment opportunities will reduce the chances of

getting themselves socially excluded.

Childcare: A number of participants are parents, thus having responsibilities which may

restrict them from actively seeking employment opportunities. This seems to suggest that

while childcare services are offered for free, it is clear that these parents are unaware of it

and it is therefore recommended that more focused information campaigns targeting

NEETs are available.


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Career services: The survey showed that the majority of participants would opt for the

traditional employment sectors (e.g. retail, security, public service) or the ‘usual’ jobs

(e.g. clerks, salesgirls, soldiers and hairdressers). Career advisory services and job centres

present in each district should be provided to direct these individuals towards seeking

opportunities in less traditional sectors or better still, consider establishing co-operatives.

4.4.3 Other Recommendations

Database Management: One of the key issues identified through this research project was that the

parameters used to identify a youth as a NEET are flawed. These parameters need to be amended

in order to remove youths who are studying at private universities or institutions, abroad, and in

Gozo from the list. These youths are not NEETs. This indicated a possible fragmentation of

Government databases, and thus centralisation of a youth database with input from all relevant

stakeholders a potential way forward to mitigate this risk. Also, due to the fluidity of the target

group (youths may move in and out of being a transition NEET or between different NEET sub-

categories), the database must also be updated and recalibrated at regular 3 month intervals.

4.5 Concluding note

This project sought to develop a more knowledgeable picture of a specific group of individuals

in Malta known as youth NEETs.

Following the major findings of the Census and follow-up interviews, it can be stated that the

youth NEET population in Malta is highly heterogeneous and seems to consist, in decreasing

order, of Transition NEETs, Floating NEETs, and Core NEETs. These sub-categories could also

possibly be further sub-divided into two further sub-segments, with one sub-segment

demonstrating traits somewhere in between those of a Transition and Floating NEET, and second


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sub-segment demonstrating traits somewhere in between those of a Floating and Core NEET.

However, further insight is required to determine the profile of and numbers making up the

various NEET typologies to be able to address more specific interventions and to have methods

to identify NEET predispositions among student populations, thereby adopting a proactive and

preventative rather than a reactive approach to reducing the number of youth NEETs in Malta.

Measures need to be more holistic and engaging. Policy makers need to ensure that such

strategies take into consideration the youth’s self, their parents, the provision of education with a

clear purpose, the possibility of participating in activities that bring about more self-discipline

and motivation, together with socio-economic factors that can attract youths to further their work

skills development, and find suitable jobs. Furthermore, there needs to be a ‘task force’ for the

core/floating NEETs that may be at greater risk of socio-economic exclusion comprising of

youth & community officers and social workers. These professionals also need to be equipped

with the right resources and skills to ‘detect’ youths showing traits of core/floating NEETs at a

more tender age, not only at the age of 16/17.

Furthermore, given the significant amount of dubious cases that have originated from this

research, i.e. youth NEETs refusing to be interviewed and that could not be located after 5

attempts to reach them at their residence or 3 attempts to contact them by telephone at home, it

could also be stated that a proportion of those databased as youth NEETs could in fact also be

youths intentionally ‘skiving’ the formal labour and/or educational systems, rather than solely

‘skiving’ through learned helplessness.

There were also issues with the database of youth NEETs, listing youths as NEETs when they

should not make that list. The findings of this research therefore lead to the division of the youth


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NEETs target group as available to the ETC as at April 2015 into three: NEETs classification,

Dubious classification, and Database issue classification. The knowledge and understanding of

these divisions garnered through this research could now form the basis of an action plan

implemented by Government to evaluate precise youth NEET numbers by sub-category,

eliminating dubious cases through investigation and database issues through centralisation, and

thus ensuring its efforts, resources and funds are focused, effective and efficient.

As a consequence, it is highly probable that the number of youth NEETs thought to be NEETs by

the ETC at the start of this research, is significantly lower than 6,749. Despite limitations arising

from Data Protection issues, thereby limiting the Research Company from conducting a follow-

up interview with every reported NOT NEET case or else to adopt a random sample strategy, an

attempt has been made to provide number ranges for the above classifications (see Appendix C).

This project also sought to give an understanding as to why the NEETs identified on ETC’s April

database did not subscribe to the YG (only 554 of over 7,000 applied) and what will enable them

to reapply next time round. The results of the project, in line with the findings of RaE (2007),

clearly highlighted that the list of youth NEETs is not homogenous and that a one-size-fits-all

approach to the creation of youth activation measures will not work or have the desired results.

In fact, an interesting observation is that the Youth Guarantee or similar programmes were first

used in a number of countries in the EU around 2007/2008, and where thus created at a time

prior to the findings of RaE (2007) and the sub-categories of youth NEETs being published. We

could also state the Youth Guarantee programme in itself is successful, however, in its current

format, it is predominantly useful for Floating NEETs. Furthermore, better awareness campaigns

of such programmes targeting youth NEETs with Floating characteristics will make the

programme even more successful.


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Appendices

Appendix A.1: Census Consent Form

Appendix A.2: Census Questionnaire

Appendix B: Follow-Up Interview Questionnaire

Appendix C: Speculations


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Appendix A.1: Census Consent Form

Youth Guarantee – ESF 3.231

Dear Participant,

The Government has created a series of policies and initiatives aimed at encouraging youths to

engage in Education, Training, and Employment. The Government wants to ensure that these

policies and initiatives truly do reflect the needs of these Maltese Youths by conducting a

Census.

As you are currently not engaged in any formal employment, education, or training, you have

been selected to participate in this Census which will be conducted through a 20 minute face-to-

face interview in the comfort of your home.

This Census seeks to:

1. Explore Youths NEETs’ opinions regarding the Youth Guarantee initiative.

2. Identify the expectations Youths NEET have regarding their inclusion in the

education or the labour market.

3. Shed light on the Youths NEET population characteristics.

It is important to point out that:

1. You are free to withdraw from this interview at any point.

2. The risks to you are none. Your answers will not have any negative consequences

on you or your family.

3. The results of this study will be analysed with the findings forming part of a final

report that will be submitted to the Ministry of Education and Employment. The

findings of this study will be used to ensure that all future initiatives concerning

Youths NEET will reflect the true needs of the target group.

Interviewer Signature:

Interviewer Name:

Date:

By signing this consent form I am herewith confirming that I have read and understood the

information and am consenting to participate in the interview wilfully and to contribute to the

knowledge being gathered.

Signature:


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Name:

Date:

In the case the Participant is under 18: By signing this consent form you are indicating that

you have read and understood this document, and agree to allow your child to participate in this

interview.

Signature:

Name:

Relation:

Date:


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Appendix A.2: Census Questionnaire

Questionnaire

Date:

Time in: Time out:

Interviewer:

Cell Leader:

Locality (street, town):

Participant Code:

Section A: Personal Information & Social Demographics

A.1 Personal Info

1. First Name:

2. Surname:

3. Date of Birth:

4. Gender:

5. ID Card No. :

A.2 Family Members

1. Where did you live when growing up? (Choose all that apply)

a. With both biological parents,

b. Grandparents,

c. Single parent household,

d. Other, please specify

2. Number of Family Members currently living in your household (Choose all that apply

and write the amount for each section in the place provided)

a. Parents

b. Siblings(are you the eldest/youngest/middle-circle the appropriate)

c. Other, please specify

3. Do you have any children? Yes or No

If yes:


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i. How many?

ii. Do they live with you? Yes or No

4. Father’s Age:

5. Father’s Employment type (Choose one)

a. Full time employment,

b. Part time employment,

c. Casual worker,

d. Self-employed,

e. Unemployed,

f. Other, please specify

6. Mother’s Age

7. Mother’s Employment type(Choose one)

a. Full time employment,

b. Part time employment,

c. Casual worker,

d. Self-employed,

e. Unemployed,


8. How often do you spend time with your family every week? (Choose one)

a. 2 or more hours per day

b. An hour per day

c. 1-4 hours per week

d. I hardly ever see them

9. How often do you spend time with your friends every week? (Choose one)

a. 2 or more hours per day

b. An hour per day

c. 1-4 hours per week

d. I hardly ever see them

10. How would you rate your satisfaction with your life right now?

a. Not satisfied at all

b. So and so

c. High satisfaction


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Section B: Education and Training

B.1 Level of Education

1. How old were you when you left school? _____

2. What is your Highest Level of Education? (Choose one )

a. School leaving Certificate (secondary school),

b. O Levels,

c. A Levels,

d. Diploma,

e. Other, please specify

3. Do you have any learning difficulties? (e.g. Dyslexia, ADHD, Dyspraxia etc.) ? Yes or

No

a. If yes, do you feel you were given adequate support at school? Yes or No

4. Why did you stop your Education or Training? (Choose all that apply);

a. Financial Reasons

b. Personal Reasons

c. Medical Reasons

d. Family Pressures

e. Time

f. I wasn’t interested in Education

g. Other, please specify:

5. Have you been involved in any further learning (e.g. short training courses,

apprenticeships etc) since leaving school? Yes or No

a. If yes, please specify _________________

B.2 Future Plans

6. Do you currently have any plans to engage in any educational or training courses? Yes or

No

a. If Yes, please specify __________________

7. Imagine you were given the opportunity to participate in some form of educational or

training courses, what sort of courses would you prefer. (Choose all that apply)

a. Class-room based

b. Online

c. Part-time

d. Full-time

e. Evening classes


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f. Day classes

g. One-on-one

h. Other, please specify:

8. Do you believe the following will help you get into education?

a. Clear information about education and training opportunities that suit my career

ambitions Yes or No

b. Better English, Maths, or Computer skills Yes or No

c. Boosting my self-confidence Yes or No

d. Financial Incentives Yes or No

e. Guaranteed employment upon completion of course Yes or No

f. The opportunity to work whilst studying Yes or No

9. Do you agree or disagree with the following statements?

a. I enjoyed my time at school. Yes or No

b. Given the opportunity, I would gladly further my education. Yes or No

c. I believe education and training are important for my future. Yes or No

10. Are the majority of your friends currently in Education or Training?

Yes or No or Don’t Know

Section C: Work Experience and Interests

1. Do you have any work experience? Yes or No

If yes,

i. Please specify. (Choose all that apply)

a. Part-time work

b. Full-time work

c. Volunteer work

d. Apprenticeship

e. Casual work

ii. If you have ever had any work experience, how would you describe it?

(Choose all that apply)

a. Great, I was treated fairly and really enjoyed working

b. It was ok

c. I didn’t like the work I was doing

d. I wasn’t treated fairly

e. I hated it


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2. Why are you currently unemployed? (Choose all that apply)

a. I do not have the required qualifications

b. Personal Reasons

c. Medical Reasons

d. Family Pressures

e. I am not interested in working

f. I haven’t found a job that I like

g. Other please specify __________________

3. Would you like to find some form of employment? Yes or No

4. What are your career plans for the future? (Choose one)

a. To gain full-time employment,

b. To gain part-time employment,

c. I don’t have plans,

d. Other, please specify

5. Do you know how you are going to achieve the above plans?

Yes or No or Not Applicable

6. Do you believe you have the support needed to achieve the above plans?

Yes or No or Not Applicable

7. Which Industries interest you the most?(Choose all that apply)

a. Manufacturing

b. Retail (Shops)

c. Construction

d. Catering (Restaurants)

e. Hospitality (Hotels)

f. Beauty

g. Healthcare

h. Public Sector

i. Other, please specify

8. Has being out of work had any of the following effects on your wellbeing?(Choose all

that apply)

a. Stress

b. Depression

c. I rarely leave the house

d. Smoking too much

e. Eating unhealthy foods

f. Drinking too much alcohol


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g. Self-harming

h. Feeling physically ill

i. None of the above

j. Other, please specify

9. Where would you like to see yourself in a year from now?(Choose one)

a. Working full or part time

b. In full or part time education or training

c. The same as today

d. Owning my own business

e. I don’t know


10. Which of the following, if any, do you think would help you get into work? (Choose all

that apply). a. Advice about applying for jobs

b. Better English, Maths, or Computer skills

c. Boosting my self confidence

11. Are the majority of your friends currently Employed? Yes or No or Don’t know

Section D: Feedback on the Youth Guarantee

1. Do you know about the Youth Guarantee Programme? Yes or No

If the participant answers NO to the above question please read out the following: Malta’s

Youth Guarantee focuses on empowering participants though providing Youths with direct

access to career experts, youth workers, and training interventions. It spans over 18 weeks, the

first 6 weeks focus on training and personal support, whilst the remaining 12 weeks are

dedicated to job exposure or return to the education system. Participants are also offered a

financial allowance during the duration of the 18 weeks.

If YES, why didn’t you apply for the Youth Guarantee Programme?

a. It didn’t interest me

b. I think it involves too much work

c. I’m not sure

d. Other, Please specify

2. What would make you participate in the Youth Guarantee Programme? (Choose all that

apply) a. Financial Incentives

b. A promise of an interesting Job or Training opportunity


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c. Constant Support and Guidance from professionals

d. Support and encouragement from my family

e. Basic classes in reading, writing, maths and computer skills

f. Local School or employer open days aimed at encouraging Youths like me to get

back into work or education

3. Do you agree with the following statements?

a. No matter what the government does, there will always be a lot of people not in

employment, education or training Yes or No

b. Given the right support, I could contribute a lot more to this country Yes or No

c. I have support from family and friends to help plan for my future Yes or No

d. My full potential is being used. Yes or No

e. I am in control of how my life will turn out Yes or No

f. I am not part of society Yes or No

g. I have little chance of ever getting a job Yes or No

h. I feel discriminated against Yes or No

4. Do you agree with the following being barriers or obstacles that prevent you from getting

into work, education or training?

a. My lack of work experience Yes or No

b. My lack of the required skills and qualifications Yes or No

c. My lack of self-confidence Yes or No

d. Working will not improve my financial situation Yes or No

e. Financial factors- e.g. cost of transport, clothes Yes or No

f. I don’t know how to prepare for a job or qualification Yes or No

g. I have a learning difficulty or disability Yes or No

h. I have a mental health condition Yes or No

i. Prejudice or discrimination Yes or No

j. My parents/partner/friends do not want me to work Yes or No

k. There are no suitable jobs for me in my locality Yes or No

l. I am concerned about losing the security of social benefits Yes or No

m. I do not want to lose the flexibility/freedom that I currently have Yes or No

n. My family or close friends are not able to provide childcare Yes or No or N/A

o. I am worried I will not have enough time with my child Yes or No or N/A

5. Are any of your friends participating in the Youth Guarantee?

Yes or No or Don’t Know


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Section E: Additional Section .

1. What is the one thing you enjoy doing most?

2. What is hindering you from seeking educational, training, or job opportunities? (forget

about all the previous questions you have answered)

3. What would encourage you to seek educational, training, or job opportunities? (forget

about all the previous questions you have answered)

4. What would you like to see implemented by The Government to encourage you to take

further training/ schooling and/or seek employment?

5. Do you believe your family will support and encourage you to take further training

/schooling and/or seek employment?

6. What would be your dream job? _________________________

Section F: Interviewer observations

TO BE FILLED IN BY THE INTERVIEWER

Please circle the number that mostly applies to each statement;

Appearance

Agree Neutral Disagree Not

Applicable

a.The participant showed awareness of personal 1 2 3 4


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grooming.

b.The participant looked healthy (i.e. not obese,

too thin, alert etc)

1 2 3 4

Verbal and Physical Behaviour


Applicable

a.The participant was polite

1 2 3 4

b.The participant was rude

1 2 3 4

c.The participant was arrogant

1 2 3 4

d.The participant was aggressive

1 2 3 4

e.The participant was intimidating

1 2 3 4

f.The participant used foul language

1 2 3 4

Human Traffic/ Presence of Other People


Applicable

a.Other individuals (e.g. parents, siblings,

relatives etc) were present during the interview.

1 2

3 4

b.The participant appeared to have a healthy

relationship with their family members

1 2 3 4

c.The other individuals present encouraged the

participant to participate in the interview

1 2 3 4

d.The other individuals present discouraged the

participant to participate in the interview

1 2 3 4

Attitude of the Participant


Applicable

a.The participant was willing to respond to the

questions.

1 2 3 4

b.The participant took the interview seriously. 1 2 3 4


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c.The participant seemed truthful in their answers.

1 2 3 4

Surroundings


Applicable

a.The participant’s house was clean and habitable.

1 2 3 4

b.The participant appeared to feel comfortable

and safe in their house.

1 2 3 4

Any Further Comments: (Please state if anything in particular stood out about the Interview,

Participant, or Home of Participant)


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Appendix B: Follow-up Interview Questionnaire

Date:

Time:

Interviewer:

Participant Personal Information:

6. Name & Surname: _______________________

7. Age: _______

8. Gender: Male or Female

9. Participant No. : _______________

Current Situation:

1. Are you currently Employed? YES or NO

If yes, please give the following details:

i. Name of Company/Industry _____________________________

ii. Job Title ________________________________________________

iii. Type of Employment (Mark the appropriate)

__Summer

__PT Job

__FT Job

__Casual Worker

iv. Work Hours per week (Mark the appropriate)

__10-20


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__21-30

__31-40

__Over 40

2. Are you currently attending any training or educational programmes? YES or NO

If yes, please give the following details:

i. Name of Education Institute (Mark the appropriate)

__UoM

__MCAST

__ITS

__Junior College

__Other______________________

ii. Area of study _____________________

iii. Level of Course (Mark the appropriate)

__PhD

__Masters

__Degree

__Diploma

__Certificate

__Other ______________

iv. Duration of Course __________________________


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3. (If the Participant answered no to questions 1 & 2). Would you be willing to participate

in the Youth Guarantee? Yes or No. If No, Why?

Future Plans:

1. What are your employment plans for the next 6 months?

__Work Full-Time

__Work Part-Time

__I don’t plan to work

__Change jobs

__Other, Please Specify

2. What are your education/training plans for the next 6 months?

__Complete my current course

__Stop current course

__Start a new course

__I have no education/training plans

__Other ____________________________

Interviewer Feedback:

1. With who was the interview conducted. Please mark the appropriate:

__Mother

__Father

__Participant

Sibling (specify)


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Relative (specify, )

Other (please specify, )

2. Please tick the column that mostly applies to each statement;

3. Further Comments

Appendix C: Speculations

The following tables use the target group cluster data obtained through the census and follow-up

interview attempts to emerge with number ranges for the NEETs, Dubious cases and Database

issue classifications. These number ranges are to serve as a foundation for the direction and

implementation of action plans focused towards the reduction of numbers in all three

classifications. Further research and investigation is recommended to consolidate these number

ranges.

Agree Disagree

a. The participant was willing to respond to the questions.

b.The participant was unwilling to give precise responses.

c.The participant seemed truthful in their answers.


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The population of Maltese youths aged 16 to 24 according to the NSO Demographic Review

2013 published in August 2015) was 46,942. According to the ETC (as at April 2015), the

number of youth NEETS in Malta was 6,749. Following the Census interview procedures, these

6,749 individuals were classified as follows:

Census Classification

Participated in survey and confirmed NEETs 283 NEETs

Ineligible (claimed not NEET by participant) 4,035 *

Refusal to participate in survey 699 Dubious

Not Located due to wrong address, vacant

property etc.

539 Database

Located but unable to make contact after 5

attempts

1,185 Dubious

Data input error 8 Database

Total 6,749

*The Ineligible group became a new target group for Follow-up interviews and further

investigation. Following the follow-up interview procedures, these 4,035 participants were

classified as follows:

Follow-up Classification

Participated in survey and confirmed NEETs 266 NEETs

Participated in survey and confirmed database

issue

265 Database


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Ineligible (confirmed not NEET by ETC but

were NEET as at April 2015)

1,744 NEETs

Refusal to participate in interview 20 Dubious

Could not contact due to wrong telephone details 37 Database

Could not contact – no contact details (ethical

issues)

1,485 **

Unable to make contact after 3 call attempts 157 Dubious

Registered unemployed with the ETC 61 NEETs

Total 4,035

**The 1,485 participants were further sub-divided using the percentage of NEETs: Dubious:

Database obtained from the results of the follow-up interviews as a proxy:

Classification

Percentage of sample confirmed NEETs = 35.7% 531 NEETs

Percentage of sample confirmed database issue = 40.5% 602 Database

Percentage of sample confirmed dubious cases = 23.8% 352 Dubious

Total 1,485

Clustering the above Census and Follow-up interview data into the three identified classifications

leads to the following breakdown of the original target group of 6,749:

Confirmed NEETs 2,885

Dubious cases 2,414


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Database issues 1,450

Total 6,749

Furthermore, the follow-up interviews with the 266 confirmed NEETs also provided insight into

the sub-division of the NEETs population as follows:

Core NEETs 3.4%

Core/Floating NEETs 11.7%

Floating NEETs 15.0%

Floating/Transition NEETs 24.1%

Transition NEETs 45.8%

Total 100%

Census on Youths NEET: Report on Findings FINAL REPORT_ETC.pdfTraining (NEETs). It includes an...

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