1 On the Measurement of Early Job Insecurity in Europe Maria Symeonaki 1, Glykeria Stamatopoulou 2 and Maria Karamessini 3 1,2,3 Department of Social Policy, School of Political Sciences, Panteion University of Social and Political Sciences, Athens, Greece ( Abstract. In the present paper 1 the estimation of different indicators that can be used in order to capture the extent and forms of early job insecurity is studied. This specific matter has been receiving increasing research and policy attention throughout the two last decades. The present study proposes a new composite index for measuring the degree of early job insecurity on the basis of the estimation of the transition probabilities between labour market states and school-to-work transitions, with raw data drawn from the European Union s Labour Force Survey (EU- LFS) for the year This indicator captures the whole spectrum of early job insecurity in a single measurement. Thus, an attempt is made to provide a new index of early job insecurity, connecting it also to school-to-work transition probabilities, that captures the extent of early job insecurity. Keywords: Early job insecurity, labour market transition probabilities, EU-LFS. 1 Introduction The measurement of early job insecurity and labour market exclusion is not a straightforward procedure, since ideal indicators for early job insecurity don t actually exist. Different indicators though, such as the unemployment rate, the youth unemployment rate, the youth to adult unemployment ratio, or the NEET indicator can serve as useful tools, when comparing job insecurity in different countries. When one wants to compare early job insecurity (EJI) among different European countries or study the evolution of early job insecurity over time, it is difficult, if not impossible, to take into account numerous indicators simultaneously. Thus, there is a strong need to provide one single indicator of early job insecurity that takes into account all possible indices connected to EJI for which we have reliable data to depend on. In the present paper we provide a 1 This paper has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No (NEGOTIATE Negotiating early job-insecurity and labour market exclusion in Europe, Horizon 2020, Societal Challenge 6, H2020-YOUNG-SOCIETY-2014, Research and Innovation Action (RIA), Duration: 01 March February 2018).
2 composite index of EJI based on a number of indicators that we measure using raw data drawn from the EU-LFS, in order to estimate and compare early job insecurity among European countries. When it comes to measuring early job insecurity and patterns of school-to-work transition, several methodological approaches have been proposed. In Karamessini et al. and in Dingeldey et al. an attempt was made to provide a definition of early job insecurity and to connect early job insecurity with school-to-work transitions. Symeonaki et al.[20, 22] studied the transition flows between labour market states for young individuals based on the EU-LFS and the EU-SILC data. In Eurofound the labour market situation of young people in Europe is presented, focusing in particular on the school-to-work transition, in terms of the amount of time it takes to start the first job after education, while also monitoring the more general transition to adulthood, the age at which young people leave the parental home. In Brzinsky-Fay sequences of school-to-work transitions are studied in ten European countries using the exploratory methods of optimal matching and cluster analysis. The process of labour market entry is observed for five years after leaving school by examining monthly labour market statuses. Christodoulakis and Mamatzakis applied a Bayesian approach that employed a Monte Carlo integration procedure to expose the empirical posterior distribution of transition probabilities from full-time employment to part-time employment, temporary employment and unemployment and vice versa, in the EU 15. Additionally, Alvarez et al. study the labour dynamics of the population by fitting a stationary Markov chain to the Argentine official labour survey. On the other hand, Betti et al. describe some aspects of school-to-work transitions by analysing the employment situation of individuals as a function of the time elapsed since the completion of education and training, with a special focus on the patterns in Southern European countries. Ward-Warmedinger and Macchiarelli present information on labour market mobility in 23 European countries, using the Eurostat s Labour Force Survey data over the period , whereas in Flek and Mysíková, the labour market flows, i.e. flows between employment, unemployment and inactivity, are analysed using Markov transition systems in order to draw conclusions on unemployment dynamics in Central Europe. Markov system analysis is also used in Symeonaki and Stamatopoulou in order to analyse labour market dynamics in Greece and in Karamessini et al. Markov systems are used to estimate the school-to-labour market entry probabilities for a number of European countries with raw data drawn from the EU-LFS datasets for Bosch and Maloney discuss a set of statistics for examining and comparing labour market dynamics based on the estimation of continuous time Markov transition processes. They then use these to establish stylised facts about dynamic patterns of movement with the aid of panel data from Argentina, Brazil and Mexico. Moreover, the socio-economic background and the degree to which it affects the transition process has also been studied in the literature, as individuals from poorer households have lower job prospects,
3 while educational background may postpone their first entry in countries with strong family support system. Educational qualification and skills also have a strong effect on transitions from school-to-work, as low educated people hardly escape from spells of unemployment and inactivity, restricted mostly on temporary contracts (Quintini et. al ). Additionally, Scherer shows that compared to Germany and Great Britain, in Italy the parental educational attainments has a negative effect on young people s speed of entry, as the more educated parents support their offspring in longer searches for better jobs. Gender plays an important role in young people s integration, since young women seem to face more problems relating to their transition than their male counterparts, with higher probabilities of being inactive or in non-standard employment for longer periods of time, while caring responsibilities also delay their entrance on labour market (Sigle-Rushton and Perrons; Plantenga et. al). The methods most commonly used to examine school-to-work transitions as a sequence and not as a single event are the optimal matching method and the cluster analysis (McVicar and Anyadike-Danes; Scherer; Schoon). Brzinsky-Fay presents the main advantages and disadvantages of sequence analysis in comparison to event history analysis. Here, in order to capture the whole spectrum of early job and employment insecurity we use indicators, referring to different aspects of EJI: indicators that refer to labour market outcomes and to quality of job, indicators for employment insecurity and for transition from school-to-work. These indicators, estimated for the age group, should be considered as complementary rather than competing and are combined into a single composite indicator of EJI. The results reveal that countries differ when early job insecurity is considered and the values of the proposed index vary between for Switzerland (lowest early job insecurity) to 1.01 for Greece (highest early job insecurity). The paper is onganised in the following way. Section 2 provides the estimations of the early job insecurity indicators for the European countries based on the EU-LFS data of Section 3 presents the new composite index of EJS and provides the results for these countries, sorting them from countries of low EJI to countries with high EJI. Section 4 provides the reader with the conclusions of the study and aspects of future work. 2 Indicators of early job insecurity As earlier mentioned, to capture the entire range of early job and employment insecurity we use indicators, referring to distinctive traits of EJI: indicators that refer to labour market outcomes and to quality of job, indicators for employment insecurity and for transition from school-to-work. These indicators are estimated for the age group, from raw data drawn from the EU-LFS survey. Table 1 provides the indicators that are measured and their description, thus offering information of how these were actually measured.
4 Typical indicators used for the measurement of early job insecurity provided in the present analysis are the Youth Participation Rate (Ind1), the Youth Employment Rate (Ind2), the Youth Unemployment Rate (Ind3), the Youth Unemployment Ratio (Ind4), the incidence of long-term unemployment (Ind5) and the NEET (not in Employment, Education or Training) indicator (Ind6). Indicators, directly linked to the quality of jobs, are the incidence of temporary and part-time employment (Ind7 and Ind8), the incidence of underemployed part-time workers (Ind9) and working intensity measured as the distribution of employees according to usual weekly hours worked (hour bands) (Ind10). Another important aspect is connected to the transition of young individuals from school (education or training) to work. It is well accepted that young people s pathways from school to sustained work have become more and more rough and irregular and the probability of someone who has completed full-time education to move effectively into full-time occupation decreases, whereas the probability of engaging into part-time or temporary employment increases. Therefore, it is important to highlight useful indicators that fall into the category of measuring school-to-work transitions. In this respect, we estimate the probability of an individual that has concluded education or training to enter each one of the three labour market states: employment (Ind11), unemployment (Ind12) and inactivity (Ind13). This part of analysis will be handled with the aid of Markov system theory. Two other useful indicators for measuring employment insecurity are the job finding rate and the job separation rate. In the present paper, as is the case with empirical studies (Hobijn and Sahin), we will use the percent of unemployed individuals at time t-1, who are employed at time t as the job finding rate (Ind14) and the percent of employed individuals in time t-1, who are not employed at time t as the separation rate (Ind15). Moreover, two indicators regarding relative changes in unemployment rates are: the Youth to Adult Unemployment Ratio (Ind16) and the Relative Unemployment Rate of those individuals with low skills to those individuals with high skills (Ind17), as it provides evidence of how education and training influences unemployment. Table 2 provides the reader with the estimations of all indicators that relate to labour market outcomes (Ind1 Ind6), for all European countries, for In an analogous way, Table 3 and 4 present the values of the indicators regarding the job quality for the same year and countries (Ind7 Ind10). The probabilities that can be used as indicators for school-to-work transition are given in Table 5 (Ind11 Ind13), followed by Table 6, which reveals the indicators for employment (in)security (Ind14 Ind15). Finally, Table 7 provides indicators concerning the relative changes in unemployment rates (Ind16 Ind17).
5 Table 1 Early job insecurity indicators, Ages: 15 24, EU-LFS, 2014 INDICATORS DESCRIPTION Ind1 Youth Participation Rate Number of individuals in the labour force, aged Total number of individuals, aged Ind2 Ind3 Ind4 Ind5 Ind6 Ind7 Ind8 Ind9 Ind10 Ind11 Ind12 Ind13 Ind14 Ind15 Ind16 Ind17 Youth Employment Rate Youth Unemployment Rate Youth Unemployment Ratio Incidence of long-term unemployment NEET rate Incidence of temporary employment Incidence of part-time employment Underemployed part-time workers Working time Probability of entry to employment from education and training Probability of entry to unemployment from education and training Probability of entry to inactivity from education and training Job finding rate Job separation rate Youth to Total Unemployment Ratio Relative UR low skills/high skills Number of employed individuals, aged Total Population, aged Number of unemployed individuals, aged Number of individuals in the labour force, aged Number of unemployed individuals, aged Total population, aged Young unemployed (12 months or more) as % of all young unemployed The population not in employment, education or training as a percentage of total population As % of all employees As % of all employed As % of total part-time workers Distribution of employees according to usual weekly hours worked (hour bands) Markov system Markov systems Markov systems Percent of unemployed at time t-1, who are employed at time t Percent of employed in time t-1, who are not employed at time t Youth unemployment rate (age: 15-24) Total unemployment rate (age>15) UR of those ISCED < 3 (HATLEV = 1 UR of those ISCED 3 (HATLEV = 2 or 3)
6 Table 2 Basic labour market indicators, 2014 Country Ind1 Ind2 Ind3 Ind4 Ind5 Ind6 Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxemburg Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland UK Notes: Not reliable results for IS. Small samples for LU, MT, SI. Sources: EU-LFS, 2014
7 Table 3 Basic labour market indicators, 2014 Country Ind7 Ind8 Ind9 Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland UK Sources: EU-LFS, 2014
8 Table 4 Working time indicators, 2014 Country Working time Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland UK Notes: Not reliable results for IS, LU, MT. Small samples for CY, EE, LV. Sources: EU-LFS, 2014
9 Table 5 Indicators for transition from school to work, 2014 Country School-to-Work Transition Probability School-to- Unemployment Transition School-to- Inactivity Transition AT BE BG CH CZ DK EE EL ES FI FR HR HU IT LT LV PL PT RO SE For the countries for which MAINSTAT and WSTAT1Y (or both) are EMPTY the respective transition probabilities cannot be estimated Sources: Own Calculations, EU-LFS, 2014
10 Table 6 Indicators for employment (in)security Country Job Finding Rate Job Separation Rate 2 Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany - - Greece Hungary Italy Latvia Lithuania Malta Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland For the countries for which MAINSTAT and WSTAT1Y (or both) are EMPTY the respective rates cannot be estimated. Sources: EU-LFS, In this report, we omit inactivity-unemployment flows and focus only on employmentunemployment flows. See Shimer (2007) and Barnichon (2009) for evidence supporting this choice.
11 Figure 1 displays the values of Job Finding Rates and Job Separation Rates for the European countries. Fig. 1. Job finding rates and job separation rates across European countries, 15 29, EU-LFS, 2014
12 Table 7 Relative changes in unemployment rates Country Youth to Total UR Relative UR, low skills/high skills Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxemburg Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland UK Notes: Not reliable results for LU and CY Sources: EU-LFS, 2014
13 3 A composite index of early job insecurity In the present section we define the composite index of early job insecurity and estimate its values for all European countries for which we have the necessary data (variables). The composite index is defined as: EJI = d w di i=1 d i w ij zind ij j=1 d i wij j=1 d wdi i=1, (1) where: d : the number of dimensions (here d=5) d i : the number of indicators in the i-th dimension w ij : the weight of the j-th indicator in the i-th dimension w di : the weight of the i-th dimension zind ij : the z-score of the j-th indicator in the i-th dimension. Using Equation (1) we estimate the values of EJI for the European countries. The values are presented in Table 8.
14 Table 8 Early Job Insecurity Indicator, EU-LFS, 2014 Country Early Job Insecurity Index 1. Switzerland Denmark Austria Estonia Lithuania Finland Czech Republic Sweden Belgium France Hungary Poland Romania Portugal Croatia Italy Spain Greece Conclusions In the present paper we provided a composite index of EJI based on a number of indicators that we measured using raw data drawn from the EU-LFS, in order to estimate and compare early job insecurity among European countries. It is obvious that early job insecurity differs among European countries. Countries with low EJI can be identified (Switzerland, Denmark, Austria for example), whereas countries of high EJI are also recognisable. Croatia, Italy, Spain and Greece are the countries facing worrying EJI. Figure 2 provides the map of early job insecurity for 2014.
15 Fig.2. Mapping Early Job Insecurity Early job insecurity can have multiple consequences: Systematic labour market exclusion of young people at the very beginning of their professional careers, the growing discourses over the threat of a lost generation, accompanied by a multi-faceted social malaise that includes among others high risks of poverty, precarity, social exclusion, disaffection, insecurity, scarring, higher propensity towards offence and crime, as well as (mental and physical) health problems, to name but a few. Therefore, it is very important to activate effective policies that can prevent the unfavourable effects of early job insecurity and youth unemployment. In this paper we have provided evidence based on empirical data that early job insecurity exists, it can be measured and it must be tackled since it exhibits worrying trends for a lot of European countries. Further research will be perused with the EU-LFS data for 2015.
16 References 1. E. Alvarez, F. Ciocchini and K. Konwar. A Locally Stationary Markov Chain Model for Labour Dynamics. Journal of Data Science, 7, 27-42, G. Betti, A. Lemmi and V. Verma. A comparative analysis of school-to-work transitions in the European Union. Innovation: The European Journal of Social Science Research, 18,4, , M. Bosch and W. Maloney. Comparative Analysis of Labor Market Dynamics Using Markov Processes: An Application to Informality. Discussion Paper Series, IZA DP, 3038, C. Brzinsky-Fay. Lost in Transition? Labour Market Entry Sequences of School Leavers in Europe. European Sociological Review, 23, 4, , C. Brzinsky-Fay. The measurement of school-to-work transitions as processes. About Events and Sequences. European Societies, 16, 2, , G. Christodoulakis and C. Mamatzakis. Labour Market Dynamics in EU: a Bayesian Markov Chain Approach. Discussion paper series, Department of Economics, University of Macedonia, no , I. Dingeldey, B. Hvinden, C. Hyggen, J. O Reilly and M. A. Schøyen. Understanding the consequences of early job insecurity and labour market exclusion: The interaction of structural conditions, institutions, active agency and capability, Negotiate working paper, D2.1.pdf. 8. Eurofound. Mapping youth transitions in Europe, Publications Office of the European Union, Luxembourg, V. Flek and M. Mysíková. Unemployment Dynamics in Central Europe: A labour flow approach. Prague Economic Papers, 24, 1, 73-87, B. Hobijn and A. Sahin. Job-Finding and Separation Rates in the OECD. Federal Reserve Bank of New York, Staff Report, 298, M. Karamessini, A. Papazachariou, D. Parsanoglou and G. Stamatopoulou. Indicators and data sources to measure patterns of labour market entry across countries, Negotiate working papers, 2015, D3.1.pdf 12. M. Karamessini, M. Symeonaki, G. Stamatopoulou and A. Papazachariou. The careers of young people in Europe during the economic crisis: Identifying risk factors. Negotiate working papers, 2016, The-careers-of-young-people-in-Eurpa-during-the-economic-crisis.pdf 13. D. McVicar and M. Anyadike Danes. Predicting successful and unsuccessful transitions from school to work by using sequence methods. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165, 2, , J. Plantenga, C. Remery and M. S. Lodovici. Starting Fragile: Gender differences in the youth labour market, European Commission, Luxembourg, G. Quintini, J. Martin and S. Martin. The Changing Nature of the School-to-Work Transition Process in OECD Countries. IZA Discusion Paper, 2582, W. Sigle-Rushton and D. Perrons. Employment transitions over the life cycle: a statistical analysis. LSE Working Paper Series, 46, 2013.
17 17. S. Scherer. Patterns of Labour Market Entry Long Wait or Career Instability? An Empirical Comparison of Italy, Great Britain and West Germany. European Sociological Review, 21, 5, , S. Scherer. Early Career Patterns: A comparison of Great Britain and West Germany. European Sociological Review, 17, 2, , I. M. Schoon. Transitions from school to work in a changing social context. YOUNG- UPPSALA-, 9, 4-22, M. Symeonaki, M. Karamessini and G. Stamatopoulou. Measuring School-to-Work Transition Probabilities in Europe with Evidence from the EU-SILC. 5 th Demographics Workshop, Valletta, Malta, 1-4 June, Eds Christos Skiadas, Sergei Silvestrov and Teresa Oliveira (Springer). 21. M. Symeonaki. Rate of convergence in fuzzy non-homogeneous Markov systems. SMTDA, Valletta, Malta, 1-4 June M. Symeonaki, M. Karamessini and G. Stamatopoulou, Gender-based differences on the impact of the economic crisis on labour market flows in Southern Europe. SMTDA, Valletta, Malta, 1-4 June M. Symeonaki and G. Stamatopoulou. A Markov system analysis application on labour market dynamics: The case of Greece. IWPLMS. Athens, M. Ward-Warmedinge, E. Melanie and C. Macchiarelli. Transitions in labour market status in the European Union. LEQS Paper 69, 2013.
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