The Evolution of Early Job Insecurity in Europe

Abstract

The present study proposes a meaningful multidimensional index of early job insecurity for European countries based on raw micro-data drawn from the European Union’s Labor Force Survey (EU-LFS), and captures its evolution over time, before and during the years of the post–2008 economic crisis. More specifically, a number of different indicators capturing various domains of early job insecurity are estimated, utilizing the data behind the EU-LFS survey for all European Union (EU) member states. These indicators are then composed into a single indicator of early job insecurity, which is used to apprehend and compare the degree of early job insecurity in EU member states, during these years. The proposed indicator captures the whole range of early job insecurity aspects, such as labor market conditions, job quality, school-to-work transitions, and job security, in an overall measurement providing a way of estimating and comparing early job insecurity among different countries. The results uncover the considerable differences between EU countries when early job insecurity is considered. Moreover, countries are ranked according to the degrees of early job insecurity for the years 2008-2014.

Introduction

It is widely recognized that unemployment and integration of young people in the labor market persist in being a major challenge for advanced economies, especially for the European Union. The growing job insecurity and systematic labor market and social exclusion of young people at the very beginning of their professional careers call for research to examine the characteristics of the problem and for policy to reach possible solutions. The extended debate though over the “threat of a lost generation” and its potential consequences, that include among others high risks of poverty, precarity, social exclusion, disaffection, insecurity, scarring, higher propensity toward drug use, offense, crime, and health problems, have not yet succeeded in the complete theorization and conceptualization of early job insecurity, let alone its assessment and quantification (Karamessini, Papazachariou, Parsanoglou, & Stamatopoulou, 2015).

Despite the growing interest in the study of early job insecurity, its measurement is not a straightforward process, because “ideal” indicators or definitions of early job insecurity do not actually exist (Dingeldey, Hvinden, Hyggen, O’Reilly, & Schøyen, 2015; Karamessini et al., 2015). This is even more true when one would like to compare the degrees of early job insecurity among countries. The present article proposes a composite index of early job insecurity that quantifies the degree of early job insecurity in a country and can be used for comparability purposes between European countries.

Our main objective here is to construct a single measurable early job insecurity indicator that would incorporate comprehensively different indicators from separate domains of the phenomenon (labor market conditions, job quality, school-to-work transitions, and job security), that is, an index that takes into account objective, measurable indicators connected to the different domains of early job insecurity for which reliable and comparable data exist. This composite index can allow for comparison of the labor market situation of young individuals in different European countries, although simultaneously it can provide information concerning the domain that contributes more (or less) to the estimated degree of early job insecurity in the country.

More specifically, the present article provides a composite index of early job insecurity based on 13 different indicators that can be measured using raw data drawn from the European Union’s Labor Force Survey (EU-LFS), to estimate and compare early job insecurity among European countries. Another important reason for wanting to construct a composite index is that when attempting to compare the degrees of early job insecurity among different countries or study the evolution of early job insecurity over time, the analysis becomes complicated and the mapping of the diversity between different indicators in different countries for different years simultaneously becomes unrealistic. This study attempts to remedy these problems by producing a single measurement of early job insecurity. More specifically, the article seeks to address the following points:

1. Provide a single measurable index of early job insecurity for European countries for the years 2008-2014.

2. Disentangle the degrees of early job insecurity for each country, by providing respective degrees (scores) for each specific domain (labor market conditions domain, job quality domain, and transition domain).

3. Compare countries in respect to early job insecurity.

4. Study the evolution of early job insecurity through the years of the crisis.

The article is organized in the following way. It first gives a brief overview of the key literature and current discussion on early job insecurity (see section “Discussion on Early Job Insecurity”). The section “Early Job Insecurity Indicators With Evidence From the EU-LFS” explores the estimation of the early job insecurity indicators for European countries based on the EU-LFS micro-data; the section “Constructing an Overall Index of Early Job Insecurity” examines the construction of the composite index and presents the results for these countries. Countries are ranked from countries of high early job insecurity to countries with low early job insecurity for the years of the economic crisis 2008-2014; the section “Discussion” provides the reader with a discussion based on the results of the section “Constructing an Overall Index of Early Job Insecurity.” Finally, the section “Conclusions and Further Steps” includes the conclusions of the study and suggestions for future research.

Discussion on Early Job Insecurity

Job insecurity relates to employees’ overall concern about the continued existence of the job and its valued characteristics in the future (Vander Elst, De Witte, & De Cuyper, 2014). It has become central to discussions about the changing quality of jobs from the 1980s and gained initial theoretical salience through analyses of labor market segmentation (Gallie, Felstead, Green, & Inanc, 2017). Job insecurity is also significant as a concept and heuristic tool in class theories (Goldthorpe, 2000); moreover, it became of central importance later on, in the 1990s, in the discussions on labor market flexibility (Gallie et al., 2017). Job insecurity along with the discussion on precarious labor and precarity are not used as simple descriptive notions neither as an anti-euphemism of flexibility. It is part of the discourses developed since the early 1980s to encompass diverse forms of insecure or precarious work: informal, nonstandard, atypical, non-declared, flexible, alternative, irregular, freelance concealing dependent work, and so on, that have appeared since then (Karamessini et al., 2015). In general, job insecurity is approached whether/or both as a subjective experience or/and as an objective phenomenon (De Witte & Näswall, 2003). More particularly, in psychological but also in sociological literature, job insecurity is very often examined as a perceived situation, where employees might experience different feelings and levels of uncertainty within the same objective situation (Klandermans & van Vuuren, 1999; Sverke, Hellgren, & Näswall, 2002). Subjective perceptions of job insecurity can bear two components: a cognitive and an affective one (Anderson & Pontusson, 2007; Näswall & De Witte, 2003). The cognitive component refers to the individual’s estimate of the probability that he or she will lose his or her job in the near future, whereas the affective component is relevant to the fear, worry, or anxiety of losing one’s job (Chung & van Oorschot, 2010). Another distinction in the literature differentiates “quantitative” and “qualitative” job insecurity (Hellgren, Sverke, & Isaksson, 1999; Sverke, Hellgren, & Näswall, 2006), as well as “job tenure insecurity” and “job status insecurity” (Gallie et al., 2017). Tenure job insecurity refers to the anxiety about the loss of employment, while status job insecurity relates to anxieties about the threat of loss of valued features of the job.

Besides the “powerlessness to maintain desired continuity in a threatened job situation” (Greenhalgh & Rosenblatt, 1984) or the “discrepancy between the level of security a person experiences and the level he or she might prefer” (Jacobson & Hartley, 1991), researchers have carried out empirical research to define job insecurity through its characteristics. Common denominator, however, of the different ways employees perceive their situation as job insecurity is the condition that it cannot but be an involuntary phenomenon; employees who deliberately choose for certain reasons an uncertain job, for example, a temporary part-time job for a specific period of time, cannot be considered as cases of job insecurity (De Witte, 2005; Sverke et al., 2002). In general, involuntary unsettledness is regarded as the core meaning of job insecurity (Dingeldey et al., 2015). It is worthy to note that even if for methodological purposes researchers insist on the subjective characteristics, most of the studies that accept this twofold nature of job insecurity combine both dimensions, while some connect empirically perceived insecurity with objective conditions, such as the employment status (Bernhardt & Krause, 2013; Klandermans, Hesselink, & van Vuuren, 2010).

Objective situations cannot be disregarded especially when it comes to comparing degrees of job insecurity in different countries in cross-national contexts. In this regard, researchers try to search out the most advantageous indicators linked with separate parts of the economic process (Karamessini et al., 2015). A crucial factor that is taken into account is labor market and (some) of its characteristics. Thus, job insecurity is most often linked to the threat of unemployment, the long-term unemployment rate, the prevalence of internal or external labor markets within an economy or a specific sector, and other factors that hamper individuals in finding or keeping a job. If we want to be somewhat pragmatic when measuring job insecurity in different countries, the analysis depends to a considerable extent on what variables the relevant available cross-national comparative data sets allow us to apply.

It is widely accepted that young individuals are among the most vulnerable groups with regard to their position in the labor market. Indeed, young people face higher risk of unemployment, unstable employment, flexible jobs with part-time and temporary contracts, and much turnover between employment, unemployment, and inactivity. Job insecurity and systematic labor market and social exclusion of young people at the very beginning of their professional careers are defined as early job insecurity. When it comes to measuring early job insecurity and labor market exclusion, one can see that this is far from being a straightforward procedure. The existing studies are using a wide variety of different methodologies usually employing a variety of indicators and models—descriptive statistics and indicators drawn from data sets to present a general image and explain differences among countries, school-to-work transitions (Eurofound, 2014), event history analysis, and survival functions (Betti, Lemmi, & Verma, 1994; Scherer, 2005). This dynamic perspective is examined by multiple indicators and models such as time after graduation until the first job, rate of transitions from employment to unemployment, or inactivity (Alvarez, Ciocchini, & Konwar, 2008; Bosch & Maloney, 2007; Brzinsky-Fay, 2007, 2014; Christodoulakis & Mamatzakis, 2009; Eurofound, 2014; Flek & Mysikova, 2015; Karamessini, Symeonaki, Parsanoglou, & Stamatopoulou, 2019; Karamessini, Symeonaki, Stamatopoulou, & Papazachariou, 2016; Karamessini, Symeonaki, Stamatopoulou, & Parsanoglou, 2019; McVicar & Anyadike-Danes, 2002; Quintini, Martin, & Martin, 2007; Scherer, 2001, 2005; Schoon, 2001; Sigle-Rushton & Perrons, 2006; Symeonaki, Karamessini, & Stamatopoulou, 2019a, 2019b; Symeonaki & Stamatopoulou, 2015; Symeonaki, Stamatopoulou, & Karamessini, 2018; Ward-Warmedinge & Macchiarelli, 2013).

In the present analysis, early job insecurity is perceived as an objective phenomenon and the overarching goal is to compose different objective indicators of early job insecurity into a single measure in a standardized way to provide a useful, comparable statistical measure of overall early job insecurity. The multidimensional early job insecurity index identifies multiple domains of early job insecurity at the country level in the labor market, the quality of jobs, the school-to-work transitions, job finding rates, and separation rates. It uses micro-data from the EU-LFS survey, and all the indicators needed to construct the measure come from the same survey.

Finally, we must stress that we consider tenure early job insecurity (mainly linked to the labor market outcomes domain), status early job insecurity (in terms of contractual status linked to the quality of job domain), and early job insecurity identified in school-to-work transitions. In other words, we examine early job insecurity in a wider sense, that is, not as an individual situation and/or condition but as a phenomenon that occurs to differentiated degrees at the level of countries.

Early Job Insecurity Indicators With Evidence From the EU-LFS

For the purpose of the present analysis, we focus on individuals in the age cohort 15 to 29. While in most contexts, EUROSTAT’s definition included, a young individual is considered a person aged 15 to 24, for this study; the upper limit is prolonged to 29 years. The reasons for this choice would be to maximize the number of cases included in the analysis (in some countries there would be limitations caused by a smaller sample) and, more importantly, to capture more information on the postgraduation employment experience of young people who have completed tertiary education. School-to-work transition has been steadily slowed down in many countries and is frequently completed after the mid-20s. Many young individuals carry on with their education beyond the age of 24 years (Karamessini, Symeonaki, & Stamatopoulou, 2016). As mentioned above, the proposed indices provide a set of measures of early job insecurity for countries across Europe, based on three different domains that refer to distinctive traits of early job insecurity:

1. The labor market conditions,

2. The quality of job,

3. The transition probabilities related to job security and transitions from school to the labor market.

More specifically, each of these domains includes a set of indicators and each indicator is estimated using raw data drawn from the EU-LFS survey. Therefore, the overall index combines information from these three domains to produce an overall standardized measure of early job insecurity. The overall index of early job insecurity, and each of the domains, can be used to rank every country according to the degree of early job insecurity experienced by young individuals living there. Figure 1 explains the seven stages of constructing the overall index of early job insecurity.

Figure 1.

Overview of the methodology used to construct the overall index of early job insecurity.

Conditions that need to be satisfied for an indicator to be included in a domain are the following:

• its measurability, that is, only indicators that can be measured can be considered,

• the ability of the indicator to reflect the labor market situation of young individuals, and

• the possibility to rank the values of the indicator, that is, it should be clear whether high values of the indicator point to high or low degrees of early job insecurity (e.g., if the indicator is Youth Participation Rate, it is clear that high values of the indicator reflect low degrees of early job insecurity).

The Labor Market Conditions Domain

This domain includes indicators commonly used in the literature to reflect the labor market situation of young individuals. We note here that job tenure insecurity has been shown to vary with the economic cycle, reaching its highest levels in times of high unemployment (Chung & van Oorschot, 2010). Actually, high national unemployment rates accentuate both tenure and status job insecurity (Gallie et al., 2017). By all accounts, long-term unemployment will have an even more negative effect on early job insecurity. The indicators incorporated in this domain are as follows: the Youth Employment Rate $(YER)$ , the Youth Unemployment Rate $(YURate)$ , the Youth Unemployment Ratio $(YURatio)$ , the incidence of Long-Term Unemployment $(LTU)$ , and the $NEET$ (not in Employment, Education, or Training) rate. The indicators are estimated using raw data drawn from the EU-LFS survey according to Equations 1 to 5:

$$YER=\frac{\mathrm{Number}\mathrm{of}\mathrm{employed}\mathrm{individuals},15-29}{\mathrm{Total}\mathrm{population},15-29},$$

(1)

$$YURate=\frac{\begin{array}{l}\mathrm{Number}\mathrm{of}\mathrm{unemployed}\\ \mathrm{individuals},15-29\end{array}}{\begin{array}{l}\mathrm{Number}\mathrm{of}\mathrm{individuals}\mathrm{in}\\ \mathrm{the}\mathrm{labour}\mathrm{force},15-29\end{array}},$$

(2)

$$YURatio=\frac{\begin{array}{l}\mathrm{Number}\mathrm{of}\mathrm{unemployed}\\ \mathrm{individuals},15-29\end{array}}{\mathrm{Total}\mathrm{population},15-29},$$

(3)

$$LTU=\frac{\begin{array}{l}\mathrm{Individuals},\mathrm{aged}15-29,\\ \mathrm{unemployed}\ge 12\mathrm{months}\end{array}}{\begin{array}{l}\mathrm{All}\mathrm{unemployed}\mathrm{individuals}\\ \mathrm{aged}15-29\end{array}},$$

(4)

$$\begin{array}{l}NEET=\mathrm{Young}\mathrm{individuals},\mathrm{aged}15-29,\mathrm{who}\\ \mathrm{are}\mathrm{not}\mathrm{in}\mathrm{employment},\mathrm{education},\mathrm{or}\\ \mathrm{training}\mathrm{as}\mathrm{a}\mathrm{percentage}\mathrm{of}\mathrm{the}\mathrm{total}\\ \mathrm{population},15-29.\end{array}$$

(5)

Table 1 includes the estimated values of these indicators for the years 2008-2014 in each member state based on the EU-LFS data.

Table 1.

Indicators of Labor Market Conditions Domain, 2008-2014.

Country/year	YER	YU rate	YU ratio	LTU	NEET
AT
2008	63.4	7.0	4.8	1.0	8.9
2009	62.4	9.1	6.2	1.3	9.6
2010	62.3	8.1	5.5	1.3	9.1
2011	63.4	7.5	5.2	1.1	8.5
2012	63.5	8.1	5.6	1.3	8.2
2013	62.9	8.6	5.9	1.3	8.6
2014	61.9	8.9	6.0	1.5	9.3
BE
2008	45.7	12.7	6.7	4.2	12.0
2009	43.9	15.1	7.8	4.7	12.8
2010	43.2	16.1	8.3	5.9	13.0
2011	43.5	13.6	6.8	4.8	13.8
2012	42.7	14.6	7.3	4.7	14.4
2013	41.4	16.5	8.2	5.5	14.9
2014	41.5	16.4	8.1	6.6	14.1
BU
2008	40.8	9.4	4.2	4.2	18.5
2009	38.7	11.9	5.2	4.1	20.8
2010	39.4	16.6	7.8	6.9	23.5
2011	37.2	19.1	8.8	9.6	24.7
2012	37.5	20.8	9.9	10.9	24.7
2013	36.7	21.8	10.2	10.8	25.7
2014	38.0	17.7	8.2	9.3	24.0
HR
2008	45.4	16.0	8.7	7.8	13.0
2009	43.7	17.8	9.5	8.1	14.9
2010	40.3	23.5	12.4	11.2	17.6
2011	36.0	28.3	14.2	14.7	19.1
2012	33.4	31.2	15.2	18.1	19.7
2013	31.6	34.1	16.3	18.4	22.3
2014	34.8	32.3	16.6	16.6	21.8
CY
2008	56.1	6.5	3.9	0.8	10.9
2009	52.8	9.8	5.7	0.8	11.5
2010	52.3	11.9	7.1	2.1	12.9
2011	50.4	14.8	8.8	2.9	14.8
2012	47.2	20.4	12.1	5.5	17.3
2013	43.2	27.5	16.4	9.9	20.4
2014	44.9	26.2	15.9	9.7	19.5
CZ
2008	45.8	6.4	3.1	2.2	10.7
2009	43.7	11.6	5.8	2.5	12.7
2010	42.6	13.0	6.3	4.4	12.9
2011	42.7	11.8	5.7	3.7	12.1
2012	42.9	13.1	6.5	4.4	12.9
2013	44.3	12.3	6.2	4.0	12.8
2014	45.7	10.7	5.5	3.4	12.1
DK
2008	72.0	6.3	4.9	0.3	5.0
2009	67.5	10.5	7.9	0.4	6.5
2010	63.1	12.8	9.2	1.3	7.3
2011	62.4	13.1	9.4	1.5	7.6
2012	60.2	13.0	9.0	1.6	8.2
2013	59.7	11.9	8.1	1.5	7.5
2014	59.8	11.4	7.7	1.4	7.3
EE
2008	50.1	8.6	4.7	1.9	11.4
2009	42.6	21.0	11.3	5.1	18.3
2010	40.9	24.6	13.4	9.7	18.1
2011	46.4	17.7	10.0	8.0	14.7
2012	47.4	15.4	8.6	5.8	15.1
2013	48.6	13.8	7.8	5.0	14.3
2014	50.7	11.5	6.6	4.1	13.8
FI
2008	56.6	12.0	7.7	0.6	8.9
2009	52.1	16.0	9.9	1.0	11.3
2010	51.6	15.6	9.5	1.6	10.5
2011	52.8	14.8	9.2	1.1	10.0
2012	53.3	14.3	8.9	1.1	10.4
2013	53.0	15.1	9.4	1.1	10.9
2014	52.3	15.8	9.8	1.2	11.8
FR
2008	47.5	13.3	7.3	3.4	12.6
2009	46.4	16.8	9.4	4.4	14.7
2010	46.1	16.8	9.3	5.2	14.8
2011	45.4	16.7	9.1	5.1	14.7
2012	44.3	17.7	9.5	5.1	15.1
2013	44.1	18.5	10.0	5.6	13.8
2014	43.4	18.9	10.1	6.1	14.2
GR
2008	42.9	16.2	8.3	6.5	14.8
2009	42.3	18.8	9.8	6.4	15.9
2010	39.0	24.4	12.6	9.7	18.6
2011	33.2	34.8	17.7	16.4	23.0
2012	28.6	43.7	22.2	24.7	26.8
2013	25.6	48.7	24.2	29.1	28.5
2014	27.1	45.0	22.1	29.4	26.7
HU
2008	38.9	12.7	5.7	4.8	15.9
2009	35.9	17.3	7.5	5.9	17.9
2010	35.0	18.8	8.1	8.2	17.7
2011	34.8	17.6	7.4	7.0	17.6
2012	35.1	19.4	8.5	7.1	18.7
2013	36.9	17.7	8.0	6.7	18.4
2014	40.8	13.9	6.6	5.0	16.4
IT
2008	39.1	15.3	7.1	6.2	19.3
2009	36.0	18.3	8.1	7.5	20.5
2010	34.1	20.3	8.7	9.0	22.0
2011	33.4	20.5	8.6	9.8	22.5
2012	32.3	25.4	11.0	12.6	23.8
2013	29.1	29.8	12.3	15.9	26.0
2014	28.3	31.6	13.1	18.5	26.2
LV
2008	50.2	11.2	6.3	1.8	13.6
2009	40.6	26.5	14.6	5.5	20.8
2010	40.1	28.3	15.8	10.1	20.7
2011	41.9	23.0	12.5	9.3	19.1
2012	45.4	20.8	11.9	7.5	17.2
2013	47.9	16.4	9.4	5.7	15.6
2014	50.3	14.4	8.4	4.0	15.2
LT
2008	41.1	9.5	4.3	11.9
2009	37.3	21.3	10.1	3.7	15.0
2010	33.6	27.2	12.6	9.1	17.0
2011	34.6	23.2	10.5	8.9	14.7
2012	38.0	19.6	9.3	6.2	13.9
2013	41.1	17.1	8.5	4.3	13.7
2014	44.2	14.7	7.6	4.1	12.9
LU
2008	42.7	13.4	6.6	3.2	9.2
2009	46.3	10.8	5.6	1.7	7.5
2010	43.5	8.2	3.9	1.8	6.1
2011	42.3	9.8	4.6	2.1	6.6
2012	42.8	10.9	5.2	1.9	7.6
2013	41.9	11.0	5.2	1.8	7.2
2014	43.4	12.6	6.2	1.6	6.5
NL
2008	75.6	4.1	3.2	0.5	4.6
2009	74.3	5.4	4.2	0.6	5.3
2010	70.2	7.1	5.3	0.9	5.7
2011	68.8	7.9	5.9	1.2	5.9
2012	68.5	9.3	7.1	1.4	6.5
2013	67.3	10.9	8.2	2.1	7.5
2014	66.3	10.5	7.8	2.3	7.6
PO
2008	45.0	12.0	6.1	3.0	12.7
2009	44.7	14.2	7.4	3.3	14.0
2010	43.9	16.8	8.9	4.0	14.8
2011	43.4	17.5	9.2	5.1	15.2
2012	43.2	18.4	9.7	6.2	15.7
2013	42.8	18.9	10.0	6.8	16.2
2014	44.4	16.5	8.8	5.8	15.5
PT
2008	50.9	13.3	7.8	4.0	11.9
2009	48.3	15.6	8.9	4.9	12.5
2010	45.1	18.0	9.9	6.3	13.6
2011	43.9	22.2	12.5	6.9	13.9
2012	40.1	27.8	15.4	9.7	15.6
2013	37.8	28.9	15.3	12.2	16.4
2014	39.0	25.4	13.3	10.6	14.6
RO
2008	40.4	11.8	5.4	5.1	13.2
2009	39.5	13.8	6.3	4.2	15.7
2010	40.6	14.9	7.1	4.9	18.9
2011	39.9	15.7	7.4	6.1	19.5
2012	40.2	15.4	7.3	6.5	19.3
2013	40.2	15.8	7.6	6.7	19.6
2014	41.0	15.6	7.6	6.0	19.9
SK
2008	43.2	14.1	7.1	7.9	15.3
2009	39.9	19.2	9.5	8.7	17.3
2010	37.9	23.4	11.6	13.4	19.0
2011	37.4	23.3	11.3	13.3	18.7
2012	38.0	24.1	12.0	14.3	18.8
2013	38.0	24.3	12.2	15.6	19.0
2014	39.4	21.3	10.7	12.8	18.2
SI
2008	54.8	8.3	5.0	2.1	7.5
2009	51.1	11.6	6.7	2.9	9.3
2010	49.8	13.7	7.9	4.9	9.4
2011	47.8	14.8	8.3	5.4	9.4
2012	44.8	17.2	9.3	6.6	11.8
2013	43.5	19.0	10.2	8.2	12.9
2014	42.9	18.9	10.0	8.0	12.9
ES
2008	52.3	18.2	11.6	2.0	15.3
2009	44.4	28.5	17.8	5.3	19.9
2010	41.4	31.7	19.2	9.5	20.0
2011	38.7	34.6	20.5	11.9	20.6
2012	34.5	40.3	23.3	15.1	22.2
2013	32.6	42.4	24.0	17.8	22.5
2014	33.1	39.7	21.7	16.7	20.7
SE
2008	54.3	14.4	9.1	0.6	8.0
2009	50.5	18.3	11.3	1.1	9.9
2010	50.6	18.4	11.4	1.6	8.3
2011	52.8	16.7	10.6	1.5	7.9
2012	52.3	17.4	11.0	1.5	8.4
2013	53.7	17.2	11.2	1.4	7.9
2014	55.0	16.7	11.0	1.3	7.8
CH
2008	70.9	5.7	4.3	1.0	6.9
2009	69.3	7.8	5.9	1.5	8.8
2010	69.8	7.3	5.5	1.4	7.9
2011	70.6	6.8	5.2	1.4	7.7
2012	69.8	7.2	5.5	1.5	7.6
2013	69.7	7.5	5.7	1.4	7.7
2014	70.0	7.6	5.7	1.6	7.5
2015	70.3	7.1	5.4	1.5	7.2

Source. EU-LFS, 2008-2014.

Note. YER = youth employment rate; YU = youth unemployment; LTU = long-term unemployment; NEET = not in Employment, Education, or Training; EU-LFS = European Union’s Labor Force Survey.

The Quality of Jobs Domain

Employees may be differentially vulnerable to job insecurity because of their contract status. A consistent finding is that employees on temporary contracts are more worried about job tenure security than those on permanent contracts (Gallie et al., 2017). However, the empirical evidence is ambivalent as to whether part-time employees are in general more exposed to job tenure insecurity than standard/full-time contract workers (Gallie, White, Cheng, & Tomlinson, 1998; Green, Felstead, & Burchell, 2000). Nevertheless, involuntary part-time employment is considered to be a clear indication of early job insecurity. In this domain, we take into account information concerning the job’s quality for young individuals aged between 15 and 29. The incidence of temporary employment $(TE)$ , the incidence of part-time employment $(PTE)$ , and the incidence of involuntary part-time workers $(IPTE)$ are measured. More precisely,

$$\begin{array}{l}TE=\mathrm{The}\mathrm{percentage}\mathrm{of}\mathrm{the}\mathrm{total}\mathrm{number}\mathrm{of}\mathrm{employees},\\ \mathrm{aged}15-29,\mathrm{who}\mathrm{have}\mathrm{a}\mathrm{fixed}-\mathrm{term}\mathrm{contract}.\end{array}$$

(6)

It must be noted that according to the methodology of the EU-LFS survey, the concept of fixed-term contract is only applicable to employees and not to self-employed. The $PTE$ for young individuals is estimated as follows:

$$\begin{array}{l}PTE=\mathrm{The}\mathrm{incidence}\mathrm{of}\mathrm{part}-\mathrm{time}\mathrm{employment}\\ \mathrm{estimated}\mathrm{as}\mathrm{a}\mathrm{percentage}\mathrm{of}\mathrm{all}\mathrm{employed}\\ \mathrm{individuals}\mathrm{aged}\mathrm{between}15\mathrm{and}29.\end{array}$$

(7)

In the EU-LFS survey, part-time work is recorded as self-reported by individuals, that is, the distinction between full-time and part-time work is made on the basis of the spontaneous answer given by the respondent. As part-time employment is not always a matter of personal choice—some people may be working part-time because they cannot find a full-time job—it is also important to measure the incidence of involuntary part-time employment $(IPTE)$ (Equation 8):

$$\begin{array}{l}IPTE=\mathrm{The}\mathrm{incidence}\mathrm{of}\mathrm{involuntary}\mathrm{part}-\mathrm{time}\\ \mathrm{employment}\mathrm{estimated}\mathrm{as}\mathrm{a}\mathrm{percentage}\mathrm{of}\\ \mathrm{the}\mathrm{total}\mathrm{part}-\mathrm{time}\mathrm{employment}\mathrm{for}\mathrm{young}\\ \mathrm{individuals}\mathrm{aged}\mathrm{between}15\mathrm{and}29.\end{array}$$

(8)

Table 2 presents the results of these indicators for European countries for the years 2008-2014.

Table 2.

Indicators of Quality of Jobs Domain, 2008-2014.

Country/year	TE	PTE	IPTE
AT
2008	24.1	18.0	13.9
2009	24.6	18.4	14.0
2010	25.4	19.7	15.9
2011	25.8	18.6	13.5
2012	24.7	19.5	11.1
2013	24.4	20.5	14.6
2014	24.7	22.2	14.6
BE
2008	18.9	16.1	34.4
2009	19.8	17.4	26.7
2010	19.7	18.2	25.5
2011	21.3	18.8	23.3
2012	20.3	19.4	21.4
2013	20.2	19.1	19.7
2014	21.1	19.1	23.3
BU
2008	7.3	2.3
2009	6.8	2.7	39.5
2010	6.9	3.0	34.6
2011	5.6	3.0	51.3
2012	7.3	2.9	54.3
2013	9.7	3.6	55.6
2014	9.1	3.8	50.2
HR
2008	25.9	4.7	30.3
2009	26.2	4.8	39.9
2010	28.7	6.0	43.4
2011	32.6	6.0	37.6
2012	34.2	3.7	30.0
2013	35.6	3.7	33.5
2014	40.7	7.1	42.2
CY
2008	14.7	8.2	47.0
2009	13.7	10.6	48.4
2010	14.3	11.1	51.0
2011	12.3	12.0	63.5
2012	12.7	14.6	61.2
2013	18.2	19.2	67.0
2014	21.6	20.9	74.2
CZ
2008	10.5	3.9	8.6
2009	12.4	5.6	9.7
2010	14.9	5.7	12.2
2011	15.0	5.3	21.4
2012	17.2	6.3	21.2
2013	19.1	8.1	18.4
2014	21.2	7.3	16.8
DK
2008	20.1	43.0	8.8
2009	19.4	47.1	10.9
2010	18.9	48.5	11.7
2011	20.1	49.3	11.2
2012	19.1	51.1	12.0
2013	19.7	51.7	12.0
2014	19.8	52.4	11.1
EE
2008	4.6	8.2
2009	6.0	12.5	17.5
2010	7.4	13.6
2011	8.6	12.5	9.0
2012	7.4	13.0	7.7
2013	7.7	12.6	8.3
2014	6.9	13.0
FI
2008	33.3	25.0	21.4
2009	32.2	26.6	23.3
2010	34.4	26.6	25.5
2011	34.6	26.3	27.2
2012	34.3	26.7	23.3
2013	34.3	27.0	23.1
2014	35.1	28.7	27.1
FR
2008	35.3	16.8	47.4
2009	33.9	16.8	47.1
2010	36.5	16.8	49.6
2011	37.3	17.0	48.1
2012	37.7	17.3	51.7
2013	37.9	18.1	54.6
2014	37.4	18.5	56.3
GR
2008	21.3	8.7	54.2
2009	21.9	9.7	55.3
2010	1.7	9.7	61.7
2011	19.5	10.9	66.6
2012	18.8	13.4	69.7
2013	19.8	13.9	72.5
2014	23.8	15.7	75.1
HU
2008	13.0	3.9	35.3
2009	14.8	5.1	43.3
2010	17.6	5.9	50.3
2011	15.5	6.9	54.9
2012	15.8	7.1	52.8
2013	17.8	6.5	53.5
2014	18.0	5.5	52.4
IT
2008	32.6	17.0	55.2
2009	32.5	17.0	62.2
2010	34.1	18.5	65.9
2011	36.6	19.1	71.4
2012	39.4	22.3	73.4
2013	39.8	23.3	79.7
2014	42.6	24.6	81.6
LV
2008	5.5	7.4	14.0
2009	6.4	10.3	35.5
2010	8.9	10.8	35.8
2011	8.0	9.9	37.9
2012	6.6	10.3	28.2
2013	6.3	8.6	25.8
2014	5.2	7.2	22.2
LT
2008	4.3	6.6
2009	3.5	7.3
2010	4.3	7.6	36.3
2011	5.6	9.1	34.5
2012	5.3	10.7	23.6
2013	5.0	9.8	24.6
2014	5.0	9.5	24.9
LU
2008	25.0	7.5	31.2
2009	23.4	13.0	18.1
2010	22.5	10.6	19.7
2011	22.5	12.1	24.0
2012	23.5	14.3	23.5
2013	22.4	16.9	22.3
2014	25.3	18.4	26.4
NL
2008	36.1	56.6	5.8
2009	37.3	58.3	8.6
2010	38.9	59.5	7.9
2011	38.7	60.7	9.4
2012	41.3	62.0	11.7
2013	43.7	63.1	12.7
2014	45.9	64.2	14.1
PO
2008	46.5	9.1	19.5
2009	46.0	8.9	23.8
2010	48.0	9.5	24.1
2011	48.8	9.2	31.5
2012	49.6	9.7	32.1
2013	50.6	9.6	35.4
2014	53.5	9.8	38.6
PT
2008	43.9	8.4	51.3
2009	43.4	8.7	51.5
2010	44.9	8.8	57.7
2011	45.8	12.2	54.1
2012	44.7	14.8	56.4
2013	48.0	15.8	56.4
2014	48.7	14.7	54.9
RO
2008	2.4	10.2	69.4
2009	2.1	9.7	64.9
2010	2.5	12.0	69.3
2011	3.1	11.8	63.1
2012	3.2	11.2	68.6
2013	3.5	11.0	72.0
2014	3.8	10.5	72.7
SK
2008	8.0	2.2	22.8
2009	7.3	2.9	27.5
2010	10.0	4.4	35.7
2011	11.8	4.7	36.7
2012	11.9	4.5	43.9
2013	13.0	5.4	40.4
2014	17.6	6.3	39.9
SI
2008	48.0	18.7	4.3
2009	47.9	21.7	6.7
2010	48.9	23.9	6.1
2011	49.2	22.3	5.8
2012	48.7	20.8	7.9
2013	48.7	21.7	9.8
2014	49.8	23.1	8.9
ES
2008	46.4	15.6	37.3
2009	42.5	17.1	45.4
2010	44.7	19.6	55.1
2011	47.1	21.3	58.0
2012	46.6	23.3	65.6
2013	49.3	26.5	68.2
2014	52.2	27.5	68.5
SE
2008	46.4	15.6	37.3
2009	42.5	17.1	45.4
2010	44.7	19.6	55.1
2011	47.1	21.3	58.0
2012	46.6	23.3	65.6
2013	49.3	26.5	68.2
2014	52.2	27.5	68.5
CH
2008	37.8	25.3	9.6
2009	39.9	26.4	8.9
2010	39.8	24.7	10.5
2011	39.6	23.6	12.0
2012	39.5	25.0	11.5
2013	39.5	24.6	10.0

Source. EU-LFS, 2008-2014.

Note. TE = temporary employment; PTE = part-time employment; IPTE = involuntary part-time employment; EU-LFS = European Union’s Labor Force Survey.

The Transition Domain

Another determinant of early job insecurity is related to the transitions of young individuals, either from school (education or training) to the labor market or from employment to unemployment or to inactivity, and vice versa. The transition from youth to adulthood is a period for young individuals related with the anticipation of socioeconomic emancipation. It is widely accepted, though, that some youth face difficulties in getting a firm foothold into the labor market. Apparently, for some young individuals, finding any employment, not to mention a sustaining and fulfilling employment, can be discouraging and disappointing, and the failure in proving oneself in the labor market arena as a creative and productive member can generate a sense of dissatisfaction and defeat, during a period that was, by all means, imagined otherwise. Moreover, young people who confront unemployment or a slow/late transition may experience long-term unfavorable effects in terms of future labor market success, income or family formation, and also well-being (Hellevik & Settersten, 2013; Schulenberg & Schoon, 2012). This may devaluate public and private investment in their education/training, which may cause a loss for the entire society. This grows to be notably acute in the frame of demographic phenomena, such as population aging in Europe, which puts additional pressure on Europe’s decreasing number of young individuals to integrate without delay into the labor market. Therefore, the study of labor market transitions and entry from school to employment is crucial. In this respect, we estimate the probability of an individual that has concluded education or training to enter each one of the three states: employment $(StE)$ , unemployment $(StU)$ , and inactivity $(StI)$ .

More specifically, in the EU-LFS survey, respondents are asked about their “current labor market state at the time of the survey” and their “situation with regard to activity 1 year before the survey.” These two variables are used in the present study to estimate the transition probabilities from school to the three labor market states. The variable “main labor status” (MAINSTAT) has been introduced in the EU-LFS survey to give the respondent’s own view of his or her main labor status. The purpose of this variable is different from the international definitions of employment status. It renders possible, for example, to identify students with jobs involving only a few hours’ work, who would be more likely to classify themselves as economically inactive students rather than as being employed. The information collected through these two questions is used to assess different kinds of mobility and transitions: between employment, unemployment, and inactivity and from education and training to employment, unemployment, and inactivity. The eight categories among which the respondents can choose for are the following:

1. Carries out a job or profession, including unpaid work for a family business or holding, including an apprenticeship or paid traineeship, and so on,

2. Unemployed,

3. Pupil, student, further training, unpaid work experience,

4. In retirement or early retirement or has given up business,

5. Permanently disabled,

6. In compulsory military service,

7. Fulfilling domestic tasks,

8. Other inactive person.

Categories are recoded, 1: Employment, 2: Unemployment, 3: Education/Training, 4: Retirement, 5 (5-8): Inactivity, and indicators reflecting the school to employment, unemployment, and inactivity are estimated according to the following conditional probabilities:

$$StE=prob\left\{\begin{array}{l}\mathrm{a}\mathrm{young}\mathrm{individual}\mathrm{is}\mathrm{employed}\\ \mathrm{at}\mathrm{time}\mathrm{t}|\mathrm{he}\mathrm{or}\mathrm{she}\mathrm{was}\mathrm{a}\mathrm{pupil},\\ \mathrm{a}\mathrm{student},\mathrm{in}\mathrm{further}\mathrm{training}\mathrm{or}\\ \mathrm{unpaid}\mathrm{work}\mathrm{experience}\mathrm{at}\mathrm{time}\mathrm{t}-1\end{array}\right\},$$

(9)

$$StU=prob\left\{\begin{array}{l}\mathrm{a}\mathrm{young}\mathrm{individual}\mathrm{is}\mathrm{unemployed}\\ \mathrm{at}\mathrm{time}\mathrm{t}|\mathrm{he}\mathrm{or}\mathrm{she}\mathrm{was}\mathrm{a}\mathrm{pupil},\\ \mathrm{a}\mathrm{student},\mathrm{in}\mathrm{further}\mathrm{training}\mathrm{or}\\ \mathrm{unpaid}\mathrm{work}\mathrm{experience}\mathrm{at}\mathrm{time}\mathrm{t}-1\end{array}\right\},$$

(10)

and

$$StI=prob\left\{\begin{array}{l}\mathrm{a}\mathrm{young}\mathrm{individual}\mathrm{is}\mathrm{inactive}\mathrm{at}\\ \mathrm{time}\mathrm{t}|\mathrm{he}\mathrm{or}\mathrm{she}\mathrm{was}\mathrm{a}\mathrm{pupil},\mathrm{a}\mathrm{student},\\ \mathrm{in}\mathrm{further}\mathrm{training}\mathrm{or}\mathrm{unpaid}\mathrm{work}\\ \mathrm{experience}\mathrm{at}\mathrm{time}\mathrm{t}-1\end{array}\right\}.$$

(11)

Moreover, two key indicators for capturing employment insecurity that are linked to certain transitions are the job finding rate $(JFR)$ and the job separation rate $(JSR)$ . In the present study, as is the case with most empirical studies (Hobijn & Sahin, 2007), we estimate the percentage of unemployed individuals at time $t-1$ , who are employed at time $t$ as the $JFR$ , and the percentage of employed individuals in time $t-1$ , who are not employed at time $t$ as the $JSR$ . These are estimated from the transition probabilities of going from unemployment to employment, and vice versa. Therefore,

$$JFR=prob\left\{\begin{array}{l}\mathrm{a}\mathrm{young}\mathrm{individual}\mathrm{is}\\ \mathrm{employed}\mathrm{at}\mathrm{time}\mathrm{t}|\mathrm{he}\mathrm{or}\mathrm{she}\\ \mathrm{was}\mathrm{unemployed}\mathrm{at}\mathrm{time}\mathrm{t}-1\end{array}\right\}\times 100,$$

(12)

$$JSR=prob\left\{\begin{array}{l}\mathrm{a}\mathrm{young}\mathrm{individual}\mathrm{is}\\ \mathrm{unemployed}\mathrm{at}\mathrm{time}\mathrm{t}|\\ \mathrm{he}\mathrm{or}\mathrm{she}\mathrm{was}\mathrm{employed}\\ \mathrm{at}\mathrm{time}\mathrm{t}-1\end{array}\right\}\times 100.$$

(13)

Table 3 provides the respective probabilities and percentages for all member states with available data for the years 2008-2014.

Table 3.

Indicators of Labor Market Conditions Domain, 2008-2014.

Country/year	StE	StU	StI	JFR	JSR
AT
2008	0.717	0.131	0.152	50.60	13.10
2009	0.699	0.176	0.125	42.65	12.40
2010	0.713	0.140	0.147	49.35	11.70
2011	0.704	0.130	0.145	47.75	9.95
2012	0.725	0.130	0.145	47.60	11.60
2013	0.707	0.152	0.141	43.70	8.0
2014	0.684	0.157	0.159	44.45	12.50
BE
2008	0.671	0.186	0.059	34.45	8.85
2009	0.573	0.276	0.054	29.40	9.80
2010	0.566	0.354	0.039	35.55	10.00
2011	0.615	0.302	0.047	33.95	8.20
2012	0.595	0.409	0.020	35.85	10.65
2013	0.587	0.343	0.011	34.10	8.50
2014	0.566	0.257	0.177	32.05	9.35
BU
2008	0.491	0.276	0.233	28.45	6.60
2009	0.459	0.333	0.208	21.60	9.70
2010	0.336	0.461	0.203	18.55	8.20
2011	0.296	0.450	0.254	17.05	6.75
2012	0.344	0.430	0.226	17.05	7.90
2013	0.352	0.443	0.205	20.80	6.40
2014	0.369	0.358	0.273	18.20	7.75
HR
2008	0.474	0.525	0.001	41.40	5.75
2009	0.444	0.554	0.002	31.65	8.45
2010	0.327	0.663	0.01	22.40	11.25
2011	0.330	0.652	0.018	25.65	11.25
2012	0.338	0.645	0.017	24.35	11.40
2013	0.304	0.695	0.001	22.10	13.50
2014	0.297	0.695	0.008	25.35	12.85
CY
2008	0.458	0.098	0.444	61.35	8.80
2009	0.383	0.140	0.477	57.20	10.30
2010	0.382	0.170	0.448	52.80	10.45
2011	0.335	0.218	0.447	45.50	9.45
2012	0.258	0.311	0.431	40.90	13.00
2013	—	—	—	—	—
2014	0.657	0.324	0.019	41.80	12.30
CZ
2008	0.755	0.217	0.105	47.00	8.15
2009	0.670	0.290	0.110	42.75	12.45
2010	0.607	0.300	0.092	38.30	11.05
2011	0.651	0.365	0.105	46.45	7.20
2012	0.571	0.363	0.097	46.50	12.55
2013	0.646	0.366	0.101	53.10	6.60
2014	0.657	0.324	0.019	59.65	4.65
DK
2013	0.655	0.231	0.114	39.20	4.00
2014	0.663	0.228	0.109	40.8	3.00
EE
2008	0.685	0.141	0.174	48.75	12.65
2009	0.481	0.333	0.186	27.75	24.90
2010	0.435	0.324	0.241	30.05	20.95
2011	0.496	0.220	0.284	39.25	9.90
2012	0.477	0.245	0.278	42.50	15.95
2013	0.560	0.184	0.256	40.00	14.50
2014	0.600	0.185	0.215	46.70	16.65
FI
2008	0.677	0.106	0.217	39.20	18.55
2009	0.597	0.200	0.203	28.40	22.05
2010	0.598	0.190	0.212	35.45	18.00
2011	0.616	0.154	0.230	39.35	20.65
2012	0.623	0.170	0.207	39.75	20.75
2013	0.642	0.168	0.190	32.20	15.60
2014	0.582	0.239	0.179	32.00	19.50
FR
2008	0.704	0.211	0.085	34.15	10.00
2009	0.652	0.268	0.080	35.60	13.90
2010	0.650	0.267	0.083	38.55	13.35
2011	0.673	0.256	0.071	39.10	12.95
2012	0.662	0.266	0.072	39.65	14.10
2013	0.646	0.282	0.072	36.60	9.10
2014	0.583	0.310	0.107	33.60	15.50
GR
2008	0.339	0.359	0.302	31.40	7.65
2009	0.304	0.419	0.277	26.95	10.05
2010	0.266	0.429	0.305	21.55	11.55
2011	0.194	0.524	0.282	13.20	13.35
2012	0.160	0.553	0.287	10.30	14.65
2013	0.193	0.562	0.245	12.00	12.70
2014	0.194	0.513	0.293	14.75	13.50
HU
2008	0.544	0.277	0.179	35.45	9.75
2009	0.486	0.345	0.169	33.85	14.45
2010	0.495	0.359	0.146	35.50	12.45
2011	0.466	0.381	0.153	34.45	11.6
2012	0.409	0.409	0.182	33.35	12.50
2013	0.474	0.384	0.142	36.00	7.90
2014	0.500	0.343	0.157	44.10	9.05
IT
2008	0.481	0.463	0.056	26.55	8.20
2009	0.400	0.537	0.063	22.05	10.30
2010	0.360	0.570	0.070	23.05	9.85
2011	0.360	0.563	0.077	23.00	9.90
2012	0.336	0.582	0.082	16.25	11.35
2013	0.296	0.611	0.093	18.00	10.50
2014	0.274	0.637	0.089	19.60	11.85
LV
2008	0.735	0.173	0.092	44.50	12.85
2009	0.550	0.333	0.117	27.25	21.20
2010	0.519	0.351	0.130	35.85	19.15
2011	0.542	0.314	0.144	28.75	15.20
2012	0.609	0.295	0.096	41.30	14.95
2013	0.634	0.231	0.135	48.90	14.60
2014	0.608	0.248	0.144	51.90	14.90
LT
2008	0.626	0.261	0.113	44.50	12.70
2009	0.477	0.394	0.129	33.05	20.60
2010	0.280	0.274	0.446	26.55	16.15
2011	0.573	0.322	0.105	40.25	10.15
2012	0.613	0.269	0.118	41.85	9.00
2013	0.653	0.231	0.116	41.00	7.40
2014	0.643	0.217	0.140	47.35	7.80
LU
2008	0.647	0.095	0.258	46.30	3.30
2009	0.675	0.063	0.262	46.50	3.40
2010	0.651	0.044	0.305	50.60	1.90
2011	0.715	0.060	0.225	53.80	2.70
2012	0.674	0.083	0.243	51.20	3.00
2013	—	—	—	—	—
2014	0.692	0.102	0.206	58.70	3.50
NL
2013	0.624	0.117	0.259	46.70	5.40
2014	0.638	0.263	0.099	38.10	5.90
PO
2008	0.678	0.217	0.105	39.15	9.20
2009	0.600	0.290	0.110	32.65	11.05
2010	0.609	0.300	0.092	35.55	12.15
2011	0.530	0.365	0.105	34.30	10.70
2012	0.540	0.363	0.097	31.40	10.50
2013	0.533	0.366	0.101	32.00	6.50
2014	0.535	0.340	0.125	32.65	9.15
PT
2011	0.450	0.492	0.058	33.15	14.50
2012	0.356	0.575	0.069	27.55	17.90
2013	0.410	0.518	0.072	29.20	14.60
2014	0.443	0.500	0.057	34.85	15.60
RO
2008	0.596	0.355	0.049	26.40	2.30
2009	0.516	0.433	0.051	16.70	4.05
2010	0.418	0.473	0.110	14.20	5.80
2011	0.450	0.450	0.100	16.25	8.40
2012	0.377	0.509	0.114	14.35	6.80
2013	0.409	0.508	0.083	14.60	6.70
2014	0.358	0.528	0.114	13.80	6.05
SK
2008	0.612	0.283	0.105	34.10	9.20
2009	0.612	0.381	0.007	29.15	8.30
2010	0.473	0.446	0.081	27.00	12.25
2011	0.470	0.470	0.060	28.30	9.80
2012	0.471	0.462	0.067	28.25	9.40
2013	0.486	0.468	0.046	25.80	8.40
2014	0.491	0.432	0.077	32.80	9.25
SI
2008	0.625	0.360	0.015	36.55	23.25
2009	0.549	0.421	0.030	26.50	26.50
2010	0.494	0.473	0.033	28.55	26.25
2011	0.481	0.462	0.057	29.20	26.10
2012	0.481	0.422	0.097	26.85	25.10
2013	0.436	0.500	0.064	26.10	27.8
2014	0.439	0.525	0.036	27.85	29.00
ES
2008	0.569	0.350	0.081	46.65	13.50
2009	0.445	0.454	0.101	33.30	18.95
2010	0.398	0.507	0.095	31.35	15.90
2011	0.375	0.529	0.096	28.40	15.95
2012	0.334	0.577	0.089	24.55	18.60
2013	0.233	0.524	0.243	23.40	19.30
2014	0.224	0.377	0.399	27.05	14.10
SE
2008	0.670	0.232	0.098	48.35	17.45
2009	0.574	0.317	0.109	34.55	22.80
2010	0.578	0.316	0.097	39.85	19.75
2011	0.634	0.278	0.088	42.55	18.70
2012	0.593	0.321	0.086	39.95	20.20
2013	0.600	0.320	0.080	39.50	19.4
2014	0.619	0.306	0.075	42.80	19.10
CH
2010	0.760	0.088	0.152	49.10	10.55
2011	0.801	0.058	0.141	50.90	12.35
2012	0.783	0.083	0.134	54.90	15.25
2013	0.796	0.087	0.117	53.50	13.2
2014	0.784	0.079	0.137	53.55	14.60

Source. EU-LFS, 2008-2014.

Note. StE = school-to-employment transition probability; StU = school-to-unemployment transition probability; StI = school-to-inactivity transition probability; JFR = job finding rate; JSR = job separation rate; EU-LFS = European Union’s Labor Force Survey.

Constructing an Overall Index of Early Job Insecurity

In the present section, we define the overall index of early job insecurity and estimate its values for all European countries for which we have the necessary data (variables). The procedure is further described in Figure 2.

Figure 2.

Methodological steps in constructing the multidimensional index of early job insecurity.

“Reversed scored” variables that need to be transposed for all variables to point to the same direction (higher values correspond to higher degrees of early job insecurity) are youth employment rate $(YER)$ , school-to-employment $(StE)$ transition probability, and job finding rates $(JFR)$ . Correlation analysis was performed, as required from theory—the construction of composite indices assumes that the indicators used to construct the indices must be co-related (correlation ≥ 0.3)—and indicated the exclusion of the following indicators: $TE$ , $PTE$ , $JSR$ , and $StI$ due to their low correlation with the other indicators. It must be stressed here that the composite index must include highly correlated indicators, for them to be included in the composite index. After the reverse-scored indicators are recoded and because our analysis is cross-national that means that an indicator having, for example, high values in a country with low degrees of early job insecurity and low values in a country with high degrees of early job insecurity will not be used and will be excluded because it does not add to the production of a comparable composite index. The results for temporary employment in the Netherlands, Switzerland, Bulgaria, and Romania are a typical example. In the Netherlands and Switzerland, the incidence of temporary employment is very high (45.9 in the Netherlands and 39.5 in Switzerland in the year 2014), while in Bulgaria and Romania, the incidence of temporary employment is low (9.1 in Bulgaria and 3.8 in Romania for that year). That simply means that high temporary employment percentages do not necessarily reflect high degrees of early job insecurity in the country. Correlation analysis suggests indicators that reflect early job insecurity in all countries.

Each domain is constructed separately, from the component indicators described in the sections “The Labor Market Conditions Domain,” “The Quality of Jobs Domain,” and “The Transition Domain,” and each country is assigned a domain score. The composite index is defined by Equation 14:

$$EJI=\frac{\sum _{i=1}^d{w}_{d_i}\cdot \frac{\sum _{j=1}^{d_i}{w}_{ij}\cdot zIn{d}_{ij}}{\sum _{j=1}^{d_i}{w}_{ij}}}{\sum _{i=1}^d{w}_{d_i}},$$

(14)

where $d$ is the number of domains (here, $d=3$ ); $d_i$ is the number of indicators in the i-th domain; $w_{ij}$ is the weight of the j-th indicator in the i-th domain; $w_{d_i}$ is the weight of the i-th domain; $In{d}_{ij}$ is the j-th indicator in the i-th domain, corresponding to the indicators defined in Equations 1 to 13; and $zIn{d}_{ij}$ is the z score of the j-th indicator in the i-th domain, $i=1,2,3$ .

Using Equation 14, we estimate the values of early job insecurity for the European countries. The ranking of the countries, the domain scores, and the early job insecurity scores are presented in Figures 3 to 9. This method yields a robust composite index which also follows the law of insufficient reason; that in absence of any indubitable basis of determining the weights assigned to different index variables, they all carry equal weights. In the last few years, after it was used for construction of the “human development index,” this method has won many adherents (Mishra, 2008).

Figure 3.

Domain scores and early job insecurity, 2008.

Figure 4.

Domain scores and early job insecurity, 2009.

Figure 5.

Domain scores and early job insecurity, 2010.

Figure 6.

Domain scores and early job insecurity, 2011.

Figure 7.

Domain scores and early job insecurity, 2012.

Figure 8.

Domain scores and early job insecurity, 2013.

Figure 9.

Domain scores and early job insecurity, 2014.

Discussion

Looking at the evolution of the early job insecurity scores among countries during the period we are examining, one can make several comments regarding both the situation in specific countries and the distance between countries. As for the former, one can notice a clear distinction between countries with low and high early job insecurity: the first category includes countries appearing in the right-hand side of the figures, known for their inclusive labor markets, such as Austria, Switzerland, and Luxembourg; in the second category, one can find mostly southern European countries, notably Greece, Italy, and Spain, but also Bulgaria and Croatia. Looking at Figures 3 to 9, one can easily draw the conclusion that the economic crisis has brought divergence in the degrees of early job insecurity among countries. The gap between the countries has grown leading to a far more greater distance between countries with low early job insecurity and countries on the other side of the graphs. It is important to mention that the gap has increased dramatically, reaching a peak in 2012 where the distance in early job insecurity between the “best” and the “worst” ranked countries was equal to 3.35.

Another significant aspect is the asymmetrical impact of the crisis. For certain countries, which are characterized by low early job insecurity, the situation has even improved during the crisis or has slightly deteriorated since 2013, for example, in Austria and Switzerland. On the contrary, for the countries figuring in the lowest ranks, early job insecurity has been steadily increasing, with the case of Spain showing a sharp increase from the beginning of the crisis.

One of the main advantages of the proposed multidimensional index is that it provides knowledge concerning the domains that contribute the most (or least) in the degrees of early job insecurity in each country for every year. It also informs about the changes in the importance of the domains that may have taken place during the studied years. To emphasize the potentialities of the proposed multidimensional composite, we could use as an example the cases of Greece, Switzerland, Hungary, and Romania. In 2008, Greece was found in the second position with particularly worrying scores in the “Quality of jobs” domain. The “Transition” domain was, on the contrary, the one that contributed the least. Greece climbed in the first position in 2014; however, the dimension that contributed the most in the high degrees of early job insecurity was the “Labor market conditions” domain. In fact, the score in this domain is the worst among all studied countries. The “Quality of jobs” domain was the one that presented the lowest contribution to early job insecurity in Greece in 2014. A completely different case is that of Switzerland. The first data that we have for Switzerland are for the year 2010, and the country exhibits the lowest degrees of early job insecurity among all studied countries during the years 2010-2014. It is also clearly visible that during those years, there were minor changes in the degree of early job insecurity and the way that the domains contributed to it. The domains are more or less of the same importance for all the years, with the “Quality of Jobs” domain being the domain where Switzerland exhibits its “worst” scores. In Hungary, one can notice that in the year 2008, the “Quality of jobs” domain contributed to the degree of early job insecurity present in the country to a very small extent. That changed during the next years with the “Quality of jobs” domain being the one that contributed the most to the degree of early job insecurity from 2010 to 2014 in Hungary. Romania is another example of a country where the dimension of “Quality of jobs” plays the most decisive role to the high degrees of early job insecurity exhibited in the country for all studied years.

Comparisons can be also made between countries that exhibit similar degrees of early job insecurity, Denmark and the Czech Republic, for example, in the year 2014. Both countries achieve similar scores of early job insecurity, but the way each domain contributes to this final score is completely different. Denmark’s score on the “Quality of job” domain is a positive one, meaning that involuntary part-time employment would not be a reason behind the early job insecurity score for Denmark, that year. In the Czech Republic, this is not the case, although the scores of early job insecurity are similar. Early job insecurity in the Czech Republic is positively affected to a greater extent by the “Transition” domain that year.

One can also detect countries that exhibit positive scores on one domain and negative scores on others, for example, Slovakia in 2008, Cyprus in 2009, Slovenia in 2010, and France in 2012, among others, which reveals the strengths and weaknesses of the country when early job insecurity is concerned.

It is clear therefore that the proposed composite index of early job insecurity gives us more information than just standard indicators such as youth unemployment rates or part-time employment rates. More specifically, it provides us with a powerful tool of

• comparing early job insecurity as a whole among countries for each year,

• comparing different dimensions of early job insecurity among countries for each year,

• examining the evolution of early job insecurity for each country throughout the studied years,

• examining the evolution of the significance of each dimension of early job insecurity in each country throughout the studied years, and

• examining the strengths and weaknesses of each country when early job insecurity is concerned for each year.

The composite index also helps us draw conclusions relevant to the relation between the labor market stagnation and early job insecurity. Through this index, we can conclude that for all studied countries and years, early job insecurity is not just a product of high unemployment rates. Labor market conditions play a decisive role, but involuntary part-time employment, a slow transition from school to employment, and low probabilities of going from unemployment to employment also play an important role, again to a greater or lesser degree, depending on the country.

Conclusions and Further Steps

In the present article we provided, based on a number of indicators that have been measured using raw data from the EU-LFS, a composite index of early job insecurity, to estimate and compare early job insecurity among European countries, during the years 2008-2014. It is obvious that early job insecurity differs among European countries. Countries with low early job insecurity can be identified (e.g., Switzerland, Denmark, Austria), whereas countries of high early job insecurity are also recognizable: Croatia, Italy, Spain, and Greece are the countries facing worrying degrees of early job insecurity. Apart from significant divergences among European countries, what is also interesting is the uneven impact of the financial crisis on each country. Even though all countries were momentarily affected in the beginning of the crisis, that is, in 2008, early job insecurity seems to be a significant repercussion in Greece, Italy, and Spain, countries where the labor market conditions, but also the access to the labor market and the quality of jobs have significantly deteriorated during the last years. The present article provides evidence based on empirical data that early job insecurity can be measured and it must be tackled because it exhibits worrying trends for many European countries. Further research will be pursued with the EU-LFS data for 2015 and 2016, while also considering different weights for each domain. This could be achieved either by

• implementing and testing different weighting scenarios, or by

• developing a fuzzy inference system for the estimation of the weights, based on theory and heuristic and empirical rules that describe the input–output relationship.