Exits from temporary jobs in Europe: A competing risks analysis

Transcription

1 Exits from temporary jobs in Europe: A competing risks analysis Abstract: Anna Cristina D Addio and Michael Rosholm $ January 2004 We study here the determinants of the transition out of temporary jobs using the waves of the European Community Household Panel with methods for discrete duration data. Specifically, we use a multinomial logit model (MLM) distinguishing between exits into permanent employment and non-employment. Two different specifications are presented, the first does not account for unobserved heterogeneity while the second does. In it, unobserved heterogeneity is assumed to follow a discrete distribution. This avoids the IIA assumption that is implied in the MLM. The competing risks model is estimated jointly for all the Member States as well as separately for Spain, UK and Denmark. There are some interesting differences, at the EU-level, between men and women. Some factors affect the hazard rates of both groups in the same way. The experience of non-employment before entering the temporary job, the labour market conditions, elementary occupations tasks, the private/public type of the job seem to increase job instability. Significant unobserved heterogeneity parameters are found. The duration dependence parameters suggest that in general for EU as a whole, very short contracts provide higher chances of labour market exclusion especially for men. JEL codes: J24, C41, C33, C35, J6 Keywords: mixed logit, discrete duration data, temporary jobs, panel data, multinomial-logit, unobserved heterogeneity HIVA (Higher Institute for Labour Studies), Katholiek Universiteit Leuven and CAM (Center for Applied Microeconometrics), University of Copenhagen. Corresponding author: anna.daddio@hiva.kuleuven.ac.be Department of Economics, University of Aarhus and IZA. $ The first author acknowledges the Department of Economics of the University of Aarhus and the Aarhus Business School for providing research facilities while visiting them. This study has been financed by the European Commission, DG Employment and Social Affairs in the framework of the contract for the statistical support to Employment in Europe (2003), Determinants of career stability, job quality and labour market transitions. We gratefully acknowledge EUROSTAT for the access to the European Community Household Panel and the New Cronos database. We would like also to thank Frank Siebern-Thomas, Alfonso Arpaia, Georg Fischer and Stefano Galgliarducci as well as the participants in the various meetings at the European Commission for their helpful comments. Views expressed represent exclusively the positions of the authors and do not necessarily correspond to those of the European Commission. The usual disclaimer applies. 1

2 1 Introduction In this study, we are concerned with the fates of individuals who hold temporary jobs in the European Union. Temporary jobs have been used actively in many EU countries to try to cope with high unemployment rates, the intention being that a temporary job could potentially provide a path of entry or re-entry into the core group of the labour market. Non-standard employment patterns (represented often by part time, agency work, temporary contracts or some other working arrangements) may offer a solution to a number of workers, but are often associated with poorer labour conditions (wage, working time, job security, working time, etc.) and thus likely also with lower job quality 1. The risk of experiencing precarious career paths can therefore be higher for people in such flexible working arrangements. Ultimately, it may be the case that individuals in these jobs are at risk of exclusion from the labour market. It is also important to consider the motives of employers offering such arrangements. From the employers perspective, temporary working arrangements may provide a way to cope with demand fluctuations in an environment where the ordinary labour market is characterized by permanent job types and thus fairly high firing costs (see Bentolila and Bertola, 1990; Bentolila and Saint-Gilles, 1994; Booth, 1997). While temporary employment may lower adjustment costs, it can worsen life s conditions for those individuals passing through it. Flexibility can be a choice on the firms side, but often it is accompanied by precariousness and instability on the employees side. The crucial question, thus, is whether, on average, temporary jobs help workers back into the labour market, or if they rather have the opposite effect. Although a growing number of analyses are performed, it is still unclear if these flexible jobs are associated with good performances in the subsequent career both in terms of wages and of working opportunities. Specifically, it is not clear whether they are stepping stones or dead-end jobs. To our knowledge, despite the growing number of analyses on temporary jobs executed for some countries (see e.g. for Spain, Dolado et al. 2002; Petrongolo and Güell, 2001; and for the UK, Booth et al. 2001; 2002a,b 2 ), no empirical analysis on this issue has been carried out, until now, on the European Community Household Panel (ECHP) as a whole. In order to obtain a 1 For a definition of job quality according to the EU, see Employment in Europe (2002). See also Salverda et al. (2001) 2 See also the volume 112 of the Economic Journal (2002) for a collection of articles on Temporary works in Spain, UK, France and Sweden. 2

3 general view of the European labour market we present here a study of the determinants of the transition out of temporary jobs using the ECHP data. The empirical analysis performed in this study is based therefore on the European Community Household Panel (ECHP), waves Since it is likely that different career prospects arise according to skills and behaviour of individuals, we estimate the probability of leaving temporary jobs on an annual basis and with two different destination states, ordinary jobs or unemployment/non-participation. The analyses are performed separately for men and women. The typical framework used in the empirical analysis of labour market transitions is the jobsearch approach (see for example, Burdett, 1978; Burdett and Mortensen, 1980; Lancaster, 1990; Devine and Kiefer, 1991). The duration of a labour market spell is modelled by specifying the conditional probability of leaving that spell; the hazard rate. The hazard rate out of a job can be seen as the sum of two conditional probabilities: the probability of a worker receiving an acceptable alternative job offer and the probability of a worker being (permanently) laid off. In general, workers will leave jobs if the expected utility flows available outside the job (plus any costs incurred by leaving) exceed those in the job. Firms will terminate a job if the profits from doing so, less firing costs, exceed the profits of maintaining the job. We might expect the hazard rate from jobs to fluctuate non-monotonically with duration. Job separation rates may increase initially, as workers and firms learn about the quality of the match, and only the satisfactory job matches will survive. Thereafter, job separation rates may decline (Jovanovic, 1979). Similarly, the acquisition of job or firm-specific skills implies a hazard rate that declines with job tenure (Becker, 1962; Mortensen, 1978). Still, the actual shape of the hazard rate out of temporary jobs is an empirical matter, as the theory above was never really intended for analysing temporary jobs and other specific contract types. It has been established by a large number of studies that flexible parametric or even semi-parametric duration models describe labour market data better than less flexible parametric models (e.g. Mayer, 1990; Lancaster, 1979; 1990). To allow for non-monotonic variation in the hazards with job tenure, and to capture a wider range of possible effects of spell duration on the hazard rate, we use a flexible hazard function. We only observe whether individuals leave their job within one-year intervals. The length of the job, T, is therefore assumed to be a discrete random variable. 3

4 The duration we are interested in, are temporary job durations and the destinations we focus on are permanent job and non-employment. Such an approach is referred to in the statistics literature as a competing risks model. Given the nature of the underlying data we use methods for discrete duration data (see Heckman, 1981a). Specifically, we use a multinomial logit model (MLM). Two different specifications are presented, the first does not account for potential unobserved heterogeneity while the second does. More particularly, unobserved heterogeneity is assumed to follow a discrete distribution with two points of support (see Heckman and Singer, 1984; Lindsay, 1985). A remark is in order, while the MLM in its classical form carried along the IIA assumption, this is not the case when allowing for unobserved heterogeneity associated to specific destinations as we do in the present case. Summarizing, we estimate flexible baseline hazards with and without unobserved heterogeneity. That is, we estimate the time dependence of each transition simultaneously, allowing each of them to have different time patterns and to be differently affected by the covariates. Our sample consists of individuals starting a temporary job during their participation in the waves of the ECHP. Competing risks model out of temporary jobs are estimated first jointly for all the European countries left after dealing with missing values and missing variables. In addition, we estimated the same models separately for three countries representative of different Welfare regimes: they are Spain, UK and Denmark. The results show some interesting differences in the determinants of the transition into permanent employment and non-employment between men and women at the EU-level. However, some factors affect the hazard rates of both groups in the same way. More particularly, the fact of having experienced non-employment before entering the temporary work arrangement, the labour market conditions (modelled through the unemployment rate), being occupied in some kind of elementary tasks, the private/public type of employment seem to be particularly important. All of them have the effect of reducing the probability of job stability and symmetrically that of increasing job instability. The results also show that in many cases (for EU as a whole and for the UK more particularly), once temporary jobs are held by older people those are at higher risk of exclusion, the same is true for less experienced workers, for women with young children and for those earning very low wages when in temporary job spells. In addition unobservables seem to play an important role in determining workers outcomes especially for women. Significant unobserved heterogeneity parameters are also found when estimated the hazard rates for Spain and the UK. Concerning duration 4

5 dependence, the parameters suggest that in general, at the EU-level, very short contract are associated with higher risks of labour market exclusion especially for men. When looking at the results for Spain and UK we notice that in those countries, very short contract seem on the contrary to represent a path to stability. The phenomenon is more pronounced in the UK, where also the probability of labour market exclusion is significantly reduced after a very short contract. The paper is organized as follow. Econometric models applied are illustrated in the next section. We describe the data and variables used in section 3. The results are discussed in section 4. Section 5 draws some conclusions. 2 Econometric models A problem, when estimating single risk duration models, is a potential aggregation bias; Individuals typically quit their job for different reasons (competing risks). Restricting the estimated coefficients for the baseline hazard and the covariates to be the same for all destination states might therefore be an unduly restrictive assumption. Therefore, the econometric model for the sequence of discrete choice models is a multinomial logit model; in each period, the person can either stay in the temporary job (the reference case), become unemployed/non-participant, or find a permanent job. This is one way of estimating a competing risks duration model in discrete time. It is well established that failing to control for unobserved heterogeneity in hazard models tends to create spurious negative duration dependence in the estimated baseline hazard, as well as bias the parameter estimates for the covariates (Lancaster, 1979; 1990). What causes this problem is the fact that the proportion of stayers in the risk set tends to increase over time. More particularly, unobserved heterogeneity could be an important consideration when estimating hazard rates from employment (Farber, 1994). Unobservable characteristics, such as motivation, effort, the propensity to take leisure on the job (to shirk ), or strong social or family pressure to remain in work may influence job tenure. Ignoring this unobserved heterogeneity can bias the effect of the covariates. Typically unobserved heterogeneity enters duration models as a random variable with a given distribution 3. 3 This variable is usually assumed to be independent across the cause-specific hazard rates (Katz and Meyer, 1990), common to all cause-specific hazards (Flinn and Heckman, 1982) or proportional to each other (Pickles and Davis, 1985). 5

6 To account for it, we will model unobserved heterogeneity with non-parametric procedures. The potential interest in modelling unobservables is to control for pure against spurious duration dependence (Heckman, 1981a,b). The model that we estimated can be formulated using transition probabilities. For the sake of notation, we focus on transitions from a state 0. Let P ( ) 0 d be the probability of leaving state 0 (in our case, a temporary job) and entering state m at temporary job duration, d. Let X id denote the observed vector of explanatory variables for individual i at time d. Define a fourby-one vector, D d, consisting of four dummy variables, where only one of them is 1, corresponding to the duration being equal to either 1, 2, 3 or 4 years (the longest possible duration in the sample in 1999 all observations are censored). To keep notation tractable, let d be the number of time periods spent in state 0 and j the time periods in calendar time spent in state 0. The probability of going from a temporary job (state 0) to state m (which is either a permanent job or unemployment/non-participation) can then be specified as a function of a linear index, P D + x where γ m and ' m ' m 0 m( dγ idβ ) estimated. The transition probability is specified as a multinomial logit: P 0m id 2 ' m ' m exp( Ddγ + xidβ ) ' k ' k ( Ddγ xidβ ) ( d x ) = 1+ exp + k = 1 m m β are vectors of coefficients to be The contribution to the likelihood function for a single individual is thus equal to L ( βγ, ) = d j= 1 ( γ + β ) + ( γ + β ) 2 ' k ' k 1+ exp( Ddγ + xidβ ) ' 1 ' 1 1 ' 2 ' 2 2 exp Dj xij cj Dj xij c j k = 1 where c, c are indicators for making the transition to each of the two possible destination 1 2 m m states at time j; permanent job (m=1) or non-employment (m=2). To account for potential unobserved characteristics we introduce unobserved heterogeneity into the model. We assume that the unobserved individual effect α im is destination state specific, time-constant, and independent of the observed characteristics. The probability of 6

7 being in state m in period d, conditional on observed and unobserved characteristics, is given by the following expression P m 0m id 2 ' m ' m m ( Ddγ + xidβ + αi ) ' k ' k k ( Ddγ xidβ αi ) exp ( d X, α ) = 1+ exp + + k = 1 To derive the likelihood contribution, we need to consider first the likelihood contribution conditional on the unobserved heterogeneity α. Using the same notation than previously we write the likelihood contribution for individual i as L 0 i ( βγ, α) i = d j= 1 ( γ + β + α ) + ( γ + β + α ) 2 ' k ' k k 1+ exp( Ddγ + xidβ + αi ) ' 1 ' ' 2 ' exp Dj xij i cj Dj xij i c j k = 1 We assume that the random effects are discretely distributed with unknown support points. Those points can be therefore interpreted as latent individual s types. This multinomial logit model is semi-parametric in the sense that the discrete density of the random intercept serves as an approximation to any probability density (Heckman and Singer, 1984; Lindsay, 1995). We assume that the unobserved individual effects are independent of the explanatory variables. Then the likelihood contribution for an individual can be obtained by integrating the conditional likelihood distribution i i s= 1 ( ) = ( = ) L β, γ, µ, π L β, γ α s π( s) where µ and π are the location points and the probability associated to them respectively, with s being the number of points. The outcome of the estimation would in this case be useful to assess labour market segmentation. In the analyses below, the expressions above are trivially modified by the introduction of sample weights. A note on the interpretation of the parameters is in order; The marginal effect of a certain variable, say, x k, on the probability of entering state 1, say, is equal to P x = P P 2 1 j 1/ k 1 βk jβk j= 1 that is not necessarily of the same sign as the parameter involved. However, for most purposes, we are interested in the probability of leaving for a certain destination state relative 7

8 to staying in a temporary job, the odds ratio, which is, say, P 1 / P 0. The marginal effect of a variable x k on the log odds ratio is P P 1 log = 0 β 1 k Hence, when we interpret the results below, bear in mind that the parameter informs us about the probability of leaving a temporary job for a certain destination state relative to the probability of staying. 3 The Data The data used in the study are extracted from the European Community Household Panel. More particularly from its waves The first year, however, is used only for calculating the origin state, which is used as an explanatory variable, and the last year is only used to calculate destination states. Hence, the first observations in the employed sample are from 1995 and the last ones are from The data set contains more than half a million observations. After splitting the sample into men and women, and conditioning on belonging in the age group 16-64, the sample contains 238,421 men and 284,730 women. We define temporary employment as jobs in fixed contract durations, Casual occupations and Other working arrangement. Ordinary jobs are defined as those held by individuals responding permanent job when asked about the nature of their job. Finally, there are two more states an individual can occupy, namely unemployment and non-participation. We select all those individuals who respond that they are in one of the categories corresponding to our definition of temporary employment. This results in a sample of 20,117 observations for men and 17,727 observations for women. For each observation, we know the origin state (what state did the individual occupy the previous year) and the destination state. This enables us to treat these data as (discrete time) duration data. Hence, we can analyse the time spent in temporary jobs and we can distinguish between three subsequent destination states, employment, unemployment, and non-participation. In the empirical analysis, we have merged two of the destinations, namely unemployment and non-participation, since they correspond to mobility in the wrong direction, namely, the direction leading potentially towards exclusion. When analysing duration models, one has to deal with the problems of censoring and unobserved heterogeneity. Right censoring is dealt with routinely, but left-censoring 8

9 temporary job spells that were in progress when we the individual was interviewed for the first time is a more complex issue (see D Addio and Rosholm, 2002; D Addio and Rosholm, 2004). For that reason, we have decided to remove all left-censored spells of temporary employment. This leaves us with 14,226 observations of men and 13,190 observations of women. Finally, we are interested in the extent to which some observed characteristics of individuals may explain the time they spend in temporary jobs and where they go subsequently. Therefore, in the empirical analysis, we use a set of explanatory variables. However, for some individuals, some of the necessary information is missing, and so we deleted those observations. In addition, because some variables are systematically missing in the dataset for some countries (e.g. job status, sector of occupation, health satisfaction), we had to leave out of the analysis all observations from Germany, Luxembourg and Sweden. This leaves us with our final samples for EU as a whole, which consist of 9,489 observations for men and 8,544 observations for women. There are 6058 men in the sample, with a total of 6587 temporary job spells of these spells end with a transition into a permanent job, while 1021 end with a transition into non-employment. There are 5834 women in the sample, with a total of 6238 temporary job spells of these spells end with a transition into a permanent job, while 1274 end with a transition into non-employment. 4 The dependent variable in the study is thus the number of years the individual spends in a temporary job and the subsequent destination state. In addition to the gamma-parameters (defined over each one-year interval) that account for duration dependence, the explanatory variables used in the analysis are the following: Individual and job related characteristics: A) An indicator for whether the individual was in unemployment/non-participation the previous year. The reference category is being in a permanent job. B) A set of age group indicators (age is the reference group) C) Two educational indicators, one for having higher educational attainments, and one for having secondary education, the reference being primary education. 4 Owing to the very few number of observations we have pooled men and women together in the estimations carried out for Spain, United Kingdom and Denmark. We have then used a dummy variable accounting for gender, it is equal to 1 for women. For Spain we have 5,390 observations about 3,234 men and 2,156 women respectively; 14.84% of them move into PE and 14.17% into NE. For UK, we are left with 607 observations, of which 293 about men and 314 about women; 37.89% of them make a transition into PE and 14.17% into NE. For DK, we have 547 observations, 303 of which about men and 243 about women; 39.98% make a transition into PE and 14.44% into NE. 9

10 D) Two indicators for marital status, one for being married, one for being divorced, separated or widowed, and the reference group has never been married. E) Eight different occupational codes 5 ; a. Occ1 Legislators, senior officials and managers b. Occ2 Professionals c. Occ3 Technicians and associate professionals d. Occ4 Clerical positions (REFERENCE CATEGORY) e. Occ5 Service workers and shop and market sales workers f. Occ6 Skilled agricultural and fishery workers g. Occ7 Craft and related trades workers h. Occ8 Plant and machine operators and assemblers i. Occ9 Elementary occupations F) Sector of occupation 6 a. Agriculture b. Industry c. Services G) Four firm size indicators a. less than 20 b c (REFERENCE CATEGORY} d e. 500 or more H) The monthly gross wage I) Job status 7 : 5 For Spain, UK and Denmark we have created a new categorical variable. It is coded as follows: newocc1 for individuals in occupational code 1, 2 and 3; newocc2 for clerical positions (occ4), the reference group; newocc3 for service workers and shop and market sales workers (occ5); newocc4 for individuals in occupational status 6, 7 and 8; newocc5 for workers in elementary occupations (occ9). 6 For Spain, Denmark and UK, we have introduced only one dummy variable capturing the fact of working in the service sector. 10

11 a. Supervisory b. Intermediate c. Non-supervisory (REFERENCE CATEGORY) J) Years of working experience K) Received training during the past 12 months L) Employed in the public sector M) Has child less than 12 N) Works part-time O) Bad health status (defined using the self-satisfaction indicator equal to 1) P) The aggregate annual national real growth rate of GDP Q) The average annual national unemployment rate R) Country dummies (Spain being the reference) Statistics of the structural indicators (P and Q above) are reported in Appendix Table 1 and in Charts 1 and 2. [Table 1 to be inserted here] [Charts 1 and 2 to be inserted here] Descriptive statistics by destination state and across genders, for EU as a whole, are reported in Table 2. [Table 2 to be inserted here] Descriptive statistics by destination state and across genders, for Spain, UK and Denmark, are reported in Table 3. [Table 3 to be inserted here] Tables 4 and 5 report some descriptive statistics for the duration of temporary contracts distinguishing according to the two competing exits, permanent employment and nonemployment, for EU as a whole and for Spain, UK and Denmark respectively. 7 For Spain, Denmark and UK, we have introduced only one dummy variable accounting for the non-supervisory status of the job. 11

12 [Tables 4 and 5 to be inserted here] Some of the features revealed by those statistics should be highlighted. First, we observe that the youngest and the oldest are more likely to move into non-employment than permanent employment after a temporary job. Second, those with higher education level seem to be better protected against the risk of facing exclusion from the labour market after having passed through a flexible work arrangement. In addition, concerning access to training, while the share of men is quite stable in the two destinations, there is a clear majority of trained women moving into permanent employment. Third, while single men are more likely to face non-employment after holding such a job contract, for women the opposite occurs. Concerning the occupational status we see that those being involved with elementary occupations and those in non-supervisory jobs are more at risk of becoming non-employed. The same occurs for jobs held in small firms and in agriculture (for men). The share of people in permanent employment and in non-employment after a temporary job in the public sector differs across genders. While for men this is likely to lead to non-employment, the converse happens for women. The proportion of people being non-employed before entering in a flexible working arrangement is much higher or those moving into non-employment than for those moving into permanent employment. Finally concerning wages and working hours, it is again clear that those flowing into nonemployment face lower incomes and work less hours. In addition, those working part-time have a higher risk of knowing subsequent job instability. 4 Results The results of the model estimated with and without unobserved heterogeneity on the whole set of Member States, when the destination is permanent employment and non-employment respectively, are reported in Tables 6 and 7 for men, and in Tables 8 and 9 for women. [Tables 6 and 7 to be inserted here] [Tables 8 and 9 to be inserted here] Unobserved heterogeneity parameters are reported in Table 10. [Table 10 to be inserted here] Log-likelihood values of the previous models and the number of observations used are presented in Table

13 [Table 11 to be inserted here] The results gathered through the estimation of the multinomial logit model with unobserved heterogeneity for Spain, the United Kingdom and Denmark, when the destination state are permanent employment and non-employment are illustrated in Table 12 and Table 13 respectively. [Tables 12 and 13 to be inserted here] The Unobserved heterogeneity parameters resulting from the above estimation are reported in Table 14. [Table 14 to be inserted here] Log-likelihood values are in Table 15 [Table 15 to be inserted here] 4.1 Results for the whole set of Member States Concerning unobserved heterogeneity, we remark that the locations of the mass points are significant for both men and women. Moreover, the introduction of unobserved heterogeneity clearly improves the likelihood values and a simple likelihood ratio test rejects the model without unobserved heterogeneity (see Table 10). In addition, the model with unobserved heterogeneity, allowing for correlation between the two destinations, no longer imposes the IIA assumption, which is implicit in the multinomial logit model. In the following discussion, we will therefore focus on the results where unobserved heterogeneity has been accounted for. We have represented graphically in Charts 3 and 4, the hazard functions for men and women, into permanent employment (PE) and non-employment (NE), respectively. [Charts 3 and 4 to be inserted here] We notice that both of them are non-monotonic. We also remark that this pattern is not easily deducted by looking at the gamma-parameters. The reason is that duration dependence is influenced by both sets of parameters, through the denominator of the multinomial logit specification. When considering the conditional probability of exiting temporary jobs into permanent employment, we note that for men it increases after two years to decrease again after the third year. For women, while the trend is similar for the first three years, it has an opposite sign after that moment, implying that the longer they have been in a temporary job, the higher the probability for them of flowing into permanent jobs. Conversely, for men 13

14 longer duration in temporary jobs decreases significantly their chances of subsequent job stability. The hazard rates into non-employment clearly confirm this situation, by suggesting that temporary jobs have different effects for men and women respectively. For the latter those jobs are likely to easy entry (or re-entry) into the labour market especially when long, for the former they are on the contrary a sort of step into instability especially when associated with longer contracts. It should however being noticed that for men and women, temporary jobs of less than two years increase (decrease) the probability of flowing into non-employment (employment). This suggests that very short contracts are not likely to contribute to job stability for people that hold them. Turning now to the coefficient estimates, we realize that there are many significant differences across genders. However, some characteristics seem to affect transition rates into PE and NE in the same way. We focus on them first, to discuss distinctive features afterwards. Related to transitions into PE, we notice that the origin state, i.e. the state occupied prior to entering the temporary job (the reference being employment), the labour market conditions (modelled here through the unemployment rate), being occupied in some kind of elementary tasks, and finally the private/public type of employment, affect the hazard rate of men and women in the same way. Namely, stepping into a temporary job after the experience of unemployment reduces the probability of getting a stable job afterwards. Symmetrically, it increases the probability of flowing into non-employment again. This result suggests the concept of exclusion in work as recently proposed in the literature 8, i.e. the existence of a social risk in jobs that - instead of protecting those that hold them - is likely to keep those individuals in lasting and uninterrupted instability paths in and out of the labour market. The result found suggests that when temporary jobs are held by individuals with low qualifications and in elementary occupations, they are not an efficient way to promote (re)- entry into the labour market, but a powerful tool to put them out of the labour market. Having temporary jobs in the public sector is associated with an increased risk of becoming non-employed. It is likely that in the public sector those jobs are used to accommodate temporary needs in terms of tasks and duration. The negative sign associated with this variable for both men and women in the transition into PE, points at the risk of such a practice. After 8 See Desmarez et al. (2001). 14

15 a temporary contract in the public sector (compared to private) individuals are more likely to flow again into NE or temporary contracts. In addition, holding these positions in small firms can also worsen the chances of getting stable jobs in the future. The unemployment rate has a positive impact on all transition probabilities. It is not so surprising when it comes to the transition into non-employment, but it is surprising for the transition into permanent jobs. It could imply that, when unemployment increases, temporary jobs are the first one to be destroyed by firms, thus generating an increasing outflow from temporary jobs in all directions. As we stated above, some effects differ across gender and we wish to explore them here. It is interesting to note that while many variables turn out to be significant in the characterization of men s transitions into PE and NE, this is not the case to the same extent for women. The results related to unobserved heterogeneity potentially suggest that a large fraction of the heterogeneity is accounted for by the included variables for men, while the converse seems to happen for women. Results for men. Holding a temporary job in the agricultural sector (compared to manufacturing) reduces the probability of finding a permanent job and increases the job of becoming non-employed. This result is certainly linked to season jobs and therefore it is quite understandable. Concerning wages, we see that the higher the wage, the lower is the probability of leaving the temporary job for any state. This is a straightforward reservation wage effect; when a job carries a high wage, the reservation wage is high and, correspondingly, the transition rate out of the state is low. The higher the occupational level, the lower is the transition rate into permanent jobs and the larger is the transition rate into non-employment. This could suggest that for higher occupational levels, holding a temporary job is stigmatising, while it is close to the norm for lower level occupations. Concerning education, it appears that those with primary education have the largest risk of becoming non-employed and the lowest chances of finding a permanent job, suggesting that this group is at risk of exclusion. An interesting result in men s transition into non-employment is the one associated with age. Older workers are also those at higher risk of leaving the labour market after a temporary 15

16 contract and are also the ones with a low probability of finding a permanent job. It thus seems that the older workers rather than the young are those who lose out in temporary jobs. The health status seems also to be important in men s transition rates. Having a bad perception of one s own health decreases significantly the probability of finding a stable job, and increases the one of being non-employed. This result is not surprising. Good economic conditions (as measured by the GDP growth rate) are likely to reduce the probability of experiencing non-employment after a temporary job, while the effect on the probability of finding a permanent job is insignificant. Results for women. We observe again the important effect attached to age. Being older (compared to prime age individuals) increases significantly the risk of entering nonemployment while at the same time old age is associated with a lower probability of finding permanent employment. In fact, holding a temporary job when old seems to lead almost certainly to exclusion from work. Working in agriculture and services (compared to the manufacturing) has the same negative effect. The part-time variable is not significant. Its sign suggest that part-time does not speed up the transition into PE, but at the same time it does not increase women s probability of becoming unemployed. Part-time could be potentially harmful when it is involuntarily, however in some situations it allows the flexibility necessary to strike a balance between working and family life. Women with young children have a higher probability of flowing into NE after a temporary job. In modern societies that rest on increasing participation rates of the female workforce to contribute to alleviate the problem of population ageing, this seems a striking result and leads us naturally to wonder whether women with children voluntarily leave the labour market after a temporary job or if there exist some barriers that prevent them from entering it in a stable way. This is of course an unanswered question, but the puzzle surfaces in this study. The GDP growth rate turns out to affect both transition probabilities positively, suggesting that in a cyclical upturn, there is some process of polarization going on among women. 4.2 Results for Spain, UK and Denmark Spain 9 Transitions into Permanent Employment (See Table 12). We remark that those individuals having experienced non-employment prior to entering a temporary job are again penalized. Their probability of subsequently entering permanent employment is significantly reduced. The 9 See also Dolado et al (2002) and Petrongolo and Guell (2001). 16

17 same is true for those working in elementary occupations, in non-supervisory job status, having held position in big firms, within the public sector and especially when working parttime. Less experienced and younger workers have also lower transition rates into stable jobs after a temporary job spell. Conversely, having held a flexible work arrangement in the service sector as well as having been rewarded with higher wages affect positively the probability of entering permanent employment. The sign associated with unemployment rate is again surprising. As we said before, this could be linked to the fact that when unemployment increases, temporary jobs are the first to be destroyed, with the consequence that an increasing outflow is generated from temporary jobs in all directions. Transitions into Non-employment (See Table 13): It is clear from the results associated with the exit into Non-employment that those holding high skilled positions have lower risks of entering this state after the experience of a temporary job. Similarly, higher education provides Spanish workers with a good protection against non-employment. Again low paid temporary jobs are associated with higher risks of labour market exclusion. The same effect is found for individuals that are divorced, married and widowed. As for the EU as a whole, having experienced non-employment before a temporary job does contribute negatively to subsequent job stability. If we look at the sign of non-supervisory job status, younger ages, the number of hours worked in part-time jobs, and in big firms, we realize that they are the same than those found in the estimation of the hazard rates towards permanent employment. This could mean that workers holding jobs with those characteristics are those that are most likely to be found in the flexible work arrangements since their probabilities of flowing into PE and NE are simultaneously negatively affected. Bad conditions on the labour market, represented by an increasing unemployment rates, contributes to increasing the transition into nonemployment and this suggests again the unstable character of these flexible working arrangements. The estimates of the unobserved heterogeneity parameters reveal that unobservables play an important role in determining workers outcomes on the Spanish labour market. The duration dependence parameters suggest as to them a probability of entering permanent employment that is slightly increasing in the first year, the probability of entering Non-employment being much higher. Chart 5 reveals however a decreasing probability of labour market exclusion after two years, for both men and women and this suggests that temporary jobs may be for some Spanish workers, a way of entering employment stability. 17

18 [Chart 5 to be inserted here] 10 United Kingdom 11 Transitions into Permanent Employment (See Table 12). Broadly speaking most of the results discussed for Spain hold also for UK. There are however some exceptions that should be underlined. First, older workers when compared to prime-age workers have higher risks of exclusion from the labour market as we have already noticed for the EU considered as a whole. Second, the same risk is faced by individuals working in small firms probably offering further lower employment protection. Third, having young children and reporting lower health satisfaction are associated with lower transition rates into permanent employment. Fourth, the sign of the variables accounting for the non-supervisory status of the job when carried out in services/trade workers occupational category suggests a positive impact of both these characteristics on the probability of subsequent labour market inclusion. Finally, better economic conditions positively affect outflows from temporary jobs in permanent jobs. Transitions into non-employment (See Table 13): The experience of non-employment before a temporary job spell has a negative impact on subsequent career prospects. The same effect is found for workers that are rewarded with low wages when holding flexible working arrangements. However the risk of labour market exclusion seems to be reduced for those workers having secondary educational attainments and having had access to training. The direction of the effects found for the variables accounting for the sector of occupation, for younger and less experienced individuals suggests that workers with those characteristics, or holding jobs within the service sector are most probably found in temporary jobs. In addition, it is likely that individuals that are less satisfied with their health are employed in flexible work arrangements. Concerning the macro economic indicators, while the (positive) sign of the unemployment rate coefficient doesn t surprise us, the (positive) sign of the growth rate does. It implies that an improvement in economic conditions in the UK is likely to lead to an increase in the inflow into non-employment. This could be explained whether temporary jobs are used both to face recession periods and to adjust rapidly to labour market conditions with the consequence of increasing inflows into permanent employment and non-employment simultaneously. Concerning unobserved heterogeneity, we notice that the two location points are highly significant and quite distant. Some unobservables are therefore likely to play a role in 10 In the charts 5, 6 and 7 we have drawn the hazard function for men and women respectively using the coefficient values associated with the gender variable. 11 See also Dex and McCulloch (1995); Arulampalam and Booth (1998); Booth et al. (2001; 2002a,b). 18

19 determining workers outcomes in the UK. Duration dependence out of Temporary job into PE is found to be increasing in the first year and to be decreasing afterwards as illustrated in Chart 6. The opposite trend is observed in the transition into non-employment. This suggests that very short contracts are for the workers in our sample a stepping-stone into the labour market. [Chart 6 to be inserted here] Denmark Very few insights can be gathered from the estimation of the transitions into permanent employment for Danish workers. Only individuals having a temporary job in the service sector are likely to know higher inclusion probabilities. All the other variables are not significant. 12 In the transition into non-employment, we remark however the significant negative impact of having entered a temporary job after a non-employment spell as well as of having being rewarded with lower wages. Both of these characteristics are related to higher risks of exclusion from the labour market for Danish workers. The same effect is found for those workers having secondary educational attainments, working in small firms especially when younger. Concerning duration dependence, owing to the very few number of transitions, we have constrained the hazard function to follow an exponential distribution (See Chart 7) [Chart 7 to be inserted here] The results show further that none of the unobserved heterogeneity parameters turn out to be significant. *** Summarizing the previous results, we notice some differences among Spain, Denmark and the United Kingdom, in particular when looking at the duration-dependence path into permanentemployment. However, it is important to underline that for all the three countries considered we found higher risks of exclusion for less experienced workers; for individuals having been rewarded with lower wages when employed in flexible work arrangements and for those who enter a temporary job after the experience of non-employment. In addition in the UK, older workers appear to be penalized by temporary jobs. The low pay dimension of job as well as 12 Temporary contracts are rarely used in Denmark. In a companion study by D Addio et al. (2003) it has been found that in Denmark (and especially for men) overall job satisfaction is negatively related to the fact of holding a temporary job. 19

20 the importance of previous history of the workers involved in this kind of work arrangement are important issues having in our view very heavy consequences on the subsequent career prospects (and choices) of the individuals concerned. Employment incentives can play a role whether these individuals are not penalized because of the fact they have been temporarily present on the labour market. The arbitrage between working and non-working and its effect on the replacement rate can of course be affected by working conditions associated with temporary jobs and by elements that are not strictly related with the job itself. 5 Conclusions In this study we have analysed the transitions from temporary jobs into permanent employment and non-employment respectively. In doing this we have used competing risks model in a discrete-time setting. This implies that we have specified the hazard function to be multinomial logit. We have used two different specifications, the former does not allow for unobservables while the second does. This last feature is particularly useful since it offers the opportunity of controlling for spurious or true state dependence in the hazard rate. In addition, it does not imply the IIA assumption that is implicit in the Multinomial logit model. We have used a very flexible hazard function, by allowing the hazard to vary freely on each interval of one year. Unobserved heterogeneity has been assumed to follow a discrete distribution with two points of support. The models have been estimated on the whole set of EU member states left after dealing with missing values and missing variables, introducing a set of country dummies. More particularly Sweden, Germany and Luxembourg have been excluded. We have also estimated the multinomial logit model with unobserved heterogeneity for Spain, the UK and Denmark separately. Some of the results allow us to point at some very important features of the European labour market. They show that labour market choices are affected by various considerations and are likely to differ across genders. Indeed, it appears that the passage through a flexible work arrangement at the EU level is a different experience for men and women. The coefficients attached to the duration dependence parameters show that women with longer temporary contracts are more likely to get a permanent job, while for men after the duration of the three years there is a clear negative path: transition rates into non-employment increases after that moment. While for men, long temporary jobs are in general associated with higher job insecurity, the converse is true for women. However, at the light of the result discussed above, 20

21 we can also say that this occurs probably for a limited category of women. Returning women, with children, are likely to be excluded again from the labour market. In general, we found that temporary jobs are associated in many situations with low incomes, especially when the individuals step in those jobs after a spell of non-employment. Indeed, previous labour market status is important in determining the probability of job instability (and therefore of exclusion) implying that unemployment has a severe penalty on subsequent job tenure. For those individuals entering a temporary job after the experience of unemployment, the employment is synonymous of dead-end jobs and not of stepping stones. The same is true for older workers, irrespective of their gender, and to some extent for the less educated (men). Being rewarded with lower wages when in flexible work arrangements has of course a bear on the whole life of individuals. Owing to the higher risk of being exposed to unemployment, this most likely implies the lack of social means during the whole life: lower benefits when unemployed, lower pensions when retired, lower health protection and finally almost certainly higher poverty and exclusion risk. The concept of exclusion in work seems therefore particularly relevant in this context and especially for the most vulnerable individuals. Giving a job to individuals is not enough to improve their living conditions. It seems to us that mainly jobs that involve adequate training allowing to upgrade the qualifications and skills of the person hired, can make a difference. In addition, the fact that lower wages are attached (on average) to temporary jobs could also affect future work decisions. If the arbitrage is between working and not working by comparing the levels of incomes in and out of work, individuals could decide that they are better off when unemployed (especially in countries with high levels of unemployment benefits) and therefore be trapped in that state, or, even worse, in poverty. Employment security and protection from dismissal may have a direct impact on job tenure. The view that workers should be protected from the loss of firm-specific capital has led many (European) countries to introduce severance pay for dismissed workers. It has been claimed that this compensation has a depressing effect on employment and participation rates (Lazear, 1990). Bertola (1990) has contested this view and found no correlation between the strictness of an employment regime and average employment levels. We have not controlled for tightness of the employment regimes in the different countries owing to the high number of missing values in the relevant composite indicator. It seems, however, that owing to the temporary character associated with many jobs, firms use in reality flexible work arrangements 21