Stepping stones or traps? Immigrants’ transition into permanent jobs in France

Stepping stones or traps? 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Immigrants’ transition into permanent jobs in France Stephane Hlaimi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6522433/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this paper, I analyse the transition duration patterns into permanent employment in France. We show that immigrants are more likely to be unemployed at the end of their education. For natives, the transition rate from unemployment into temporary work is smaller than that from unemployment into regular work. However, the duration becomes longer for immigrants especially those of Maghrebian origin. Moreover, the probability of matching into regular employment after 1 year for a European immigrant is 06% points lower than that of a native but this gap becomes wider for Maghrebian immigrants and equals 10% after one year and even 20% after 3 years. Therefore, Maghrebians are more trapped within precarious work arrangements and can face discrimination or glass ceiling effect. JEL Classification: J61, J64, J68 Other Economics immigrants temporary jobs permanent employment stepping-stones Figures Figure 1 Figure 2 1. INTRODUCTION In France, immigration is becoming a very hot topic especially regarding the evolution of French labour market and economy. Some political parties argue that they represent a social and financial weight in terms of public expenditure. Accordingly, limitation of immigrants’ entrants and successful integration of immigrants have become major issues for public debate in France. However, some arguments are put forward to underline the importance of immigration as a wealth and a chance for France. Within the labour market, some jobs are refused by the natives and immigrants are overrepresented. This argument is particularly true for the services sector (Perrin-Haynes, 2008). However, regarding the longitudinal evolution of French labour market, one has to say that unemployment is anaemically increasing and that temporary jobs are spreading. In addition, this situation directly affects immigrants. Accordingly extra-European immigrants are more likely to hold precarious jobs in the French labour market. Their labour participation is lower and unemployment higher than for the native population or even the European immigrants (Richard, 2013 ). Moreover, they are overrepresented among the self-employed and among workers holding precarious work arrangements. Even second-generation immigrants still meet the same difficulties than the first generation (Algan et al, 2010 ). This situation is particularly relevant if we look at the labour market participation and unemployment rates (Gobillon et al, 2014 ). Unsurprisingly, these immigrants face greater difficulties in the labour market. According to the INSEE (2009), 57% of immigrants aged 18 to 64, arrived in France after age 18, have a job, against 69% for non-immigrants. Immigrant women are particularly affected by unemployment where their employment rate is 37.8% against 46% for native women. Immigrants are also twice as likely to be unemployed (12.7% for immigrant men in 2008, against 6.2% for men non-immigrants). Less present in the labour market, they are also more likely to work in low-skilled jobs, since 38% are workers or unskilled employees against 19% for non-immigrant. Such difficulties in the access into the labour market are in part due to a lack of skills. The INSEE states that almost half of immigrants have either a diploma or a degree in primary education level (against 20% for natives). But 25% of immigrants hold tertiary level (similar proportion of natives) though the unemployment rate of the formers three to four times higher than that of the latter. Accordingly, immigrants are more likely to accept temporary jobs with often accurate job insecurity and greater wage uncertainty. This paper seeks to provide evidence on the connection between immigration and temporary employment. We study whether temporary employment is a stepping stone into permanent jobs for immigrants in France. Even if recent literature on the stepping stone assumption suggest that fixed term contracts are a bridge into permanent work (Guell and Petrongolo, 2007), we argue that this effect is unequally observed among different ethnic groups. Temporary jobs might be a successful pathway for immigrants into the labour market for several reasons: First, employers have often difficulties observing the productivity of workers educated in a different environment. Temporary employment enables them to reduce information asymmetries and to screen workers without committing themselves to a permanent employment contract. Second, immigrants might be able to build up not only general but also country specific human capital, such as language skills. Finally, through temporary jobs, immigrants can face glass ceiling or even discriminatory practices. This prevents them to progress and to hold more stable jobs. We then observe different pathways of labour market transition and important ethnic differences. The key contribution of this paper is to focus on immigrant integration within the French labour market. We look to analyse if temporary employment is a bridge to permanent jobs for immigrants as job stability is an important step for immigrant’s integration. Following the approach of Abbring and Van den Berg ( 2003 ) we use longitudinal survey data to estimate a duration model. The model specifies the transition rates from unemployment to temporary jobs, from temporary jobs to regular work and from unemployment directly to regular work. Each transition rate is allowed to depend on observed and unobserved explanatory variables as well as on the elapsed time spent in the current state. To deal with selection effects, we allow the unobserved determinants to be dependent across transition rates. A particular focus will be given to ethnic differences regarding the first entrance into the labour market as our sample is carried out on individuals who left the school system for the first time in 2007. We address the issue of school-to-work transition and the modes of entrance into the labour market. This is a key question for policy makers to establish instruments in favour of the use of temporary work as a bridge to permanent jobs and as a tool helping immigrant integration. The paper is organized as follows. Section 2 presents the econometric model as well as the measures of the stepping stone effects. Section 3 presents the dataset, defines temporary jobs, and discusses some variables used in the empirical analysis. Section 4 discusses the estimation results, which are illustrated with some graphical overviews. We draw conclusions on the stepping-stone effect of temporary employment, covariate effects, the role of unobserved heterogeneity and the quality of the jobs found. Section 5 concludes. 2. ECONOMETRIC FRAMEWORK 1- Timing of events framework We apply the ‘timing of events’ approach. We use the methodology developed by Van den Berg et al. (2000). In our case, the econometric framework defines the transition rates from unemployment to temporary employment, from unemployment to regular employment and from temporary employment to regular employment. In general, the transition rate θ ij is defined as the rate at which an individual move from the state i to another state j, given that he survived in the state i until the current moment. The indices i and j can take the values: 1 for unemployment, 2 for temporary employment and 3 for permanent employment. We specify a mixed proportional hazard model for each transition rate. We denote X the vector of observed characteristics and λij(.) the baseline hazard, for the transition rate from state i to state j. Moreover, β ij is a vector of unknown parameters. The multiplicative unobserved random terms ε ij are state- and exit-destination-specific. Then, While the corresponding survival function is: The hazard rates depend only on the elapsed duration in the current state and not on earlier outcomes. Note that the initial-conditions problems are not raised since our data contain a natural starting point of each individual labour market history: the moment of school-leaving. We define the unemployment spell as the duration between entry into unemployment and entry into either regular or temporary work. A temporary job spell is defined as the duration between the start of the first temporary job and entry into regular employment. Thus, a temporary job spell may consist of multiple periods of (short) unemployment and temporary job spells. The total spell between the start of unemployment and regular employment is the sum of the unemployment spells and, eventually the temporary job spells. For every respondent, we suppose that the random term ε ij follows the same pattern across different spells. This unobserved heterogeneity term captures selective inflow into temporary work and permanent work. For identification purposes, we assume that individuals have no additional information about the timing of starting their jobs. In this context, anticipation arises if individuals abandon their research to pursue permanent employment when they begin a temporary job with a known duration. Following the approach suggested by Van den Berg et al. (2002), our baseline hazards have piecewise constant specifications. Accordingly, is a realization of the random variable. Individuals are associated to a unique set of values of, ,. We allow for two possible realizations for each ε ij , so n = 1, 2. We take the locations of the mass points as well as the associated probabilities to be unknown parameters. In addition, we impose the condition that if = then =. This effectively assumes that individuals who more easily find regular work from unemployment also find regular work more easily from a temporary position. Therefore, our specification involves four groups of individuals (four different combinations of mass points) and six mass point parameters. Note that the arrangement of mass points (,, ) replaces the constants in the vector of coefficients to estimate and, thus, are identified. The relation between the elements of (,, ) does not need to be monotone. As mentioned before, the extent to which is related to and determines the extent to which selectivity affects the association between having had temporary work or not on the one hand and entering regular work on the other. 2- Measuring the effect of temporary contracts To better capture the effect of temporary employment on immigrant’s transition, we consider two measurements: the stepping stone effect and the probability of permanent employment. We define the stepping - stone effect as the increase in the hazard rate of finding a permanent job after a sequence of temporary employment. This effect can be represented by two components: duration of unemployment t 1 and duration of temporary work t 2 . Accordingly, the causal effect of temporary employment is equal to Then a comparison of hazard rates from unemployment to permanent job and from temporary to permanent wok depends on the duration patterns even if on average, we can expect that the transition from temporary to permanent is higher than that from unemployment to permanent. Nevertheless, another quantity of interest can give us a interesting idea about the effect of temporary contract on the transition into permanent jobs. Following Card and Sullivan (1988), we can quantify the probability of regular work within t months in the absence of temporary work and define the cumulative probability function Where S 13 is the survivor function of the duration from unemployment into permanent work, S 12 the survivor function of the duration from unemployment into temporary work, S 23 the survivor function of the duration from temporary into permanent work. The decomposition of the equation above represents the population fraction of unemployed individuals who find regular work through either the temporary work channel or the direct channel. The first part of the expression equals the probability of moving into regular work by way of a direct transition from unemployment, whereas the second part equals the probability of moving into regular work by way of temporary work. However, this decomposition does not capture a causal effect of temporary work. Note that both terms are positive even if there is no effect at the individual level. In our empirical analysis, we will estimate separately for each ethnic group to put forward possible ethnic differences in the stepping stone effect. 3. DATA This study uses the Génération 2007 survey, a panel of young individuals who left the school system in 2007. The sample was followed for four years, and series of interviews were conducted on a sample of 24879 individuals. Each of these three interviews was based on a questionnaire administered by telephone. The “Generation 2007” survey mainly focuses on young people’s occupational pathways with a view to drawing a longitudinal picture of their school-to-work transition and early career experience. In each interview, an “occupational calendar” was used to collect details of the respondents’ situation month-by-month: employment, unemployment, inactivity, etc. Further questions were then asked about the various periods of employment, about the employers and conditions of employment (work contract, salary, position, type of occupation, etc.) and the respondents’ job satisfaction. The respondents were also questioned about their periods of unemployment, as to whether they had taken undertaken job-search, had undergone vocational training, received unemployment benefits or gave up their job search. A second calendar focusing on housing and the family was used to record any changes in the personal lives of the young people interviewed. In the Generation 2007 survey, we have extensive information on the labour market histories of the respondents. Individuals are asked about their labour market status since they left the school system, about all transitions made since then, and about their current labour market status. We use all immigrants and descendants of immigrants (2. generation) who left the school system in 2007. We define first generation immigrants as individuals having born outside France of two foreign parents. Second generation immigrants are born in France of at least one foreign parent. We also define three ethnic groups: natives, Europeans and maghrebians. We will not consider the other groups because of the weakness of the corresponding subsamples. First of all, we choose the group “without temporary employment experience” as the reference category. The median unemployment duration for the reference group is between 04 and 09 months; the median unemployment duration until the first temporary job is 5 months. Table 1 summarizes the individual and employment characteristics of the sample. Table 1 Sample averages of dependent and explanatory variables Variable All observations Transition from unemployment to permanent employment Transition from temporary employment to permanent employment Observed durations (in months): Unemployment 13.7 16.48 11.85 Temporary job 22.4 19.58 14.96 Permanent job 3.9 - - Ethnic group: Natives 89.73 74.33 85.25 Europeans 3.66 2.95 3.74 Maghrebians 6.61 8.75 4.31 Age (in years) 22,32 23.87 24.93 Gender: female 50.77 47.91 44.36 Marital status: Single 91.66 87.54 86.33 Married 09.34 05.90 06.22 Children at home 11.58 04.31 07.11 Working partner 18.11 08.47 09.85 Education: Primary 7.91 28.55 08.31 Secondary 45.01 53.33 38.43 Higher education 48.08 29.12 63.26 Region: Ile de France 12,36 10.34 14.29 West 39.56 31.67 38.81 North 10.44 17.45 11.89 East 11.38 12.34 14.48 South 22.46 25.31 18.96 Sector of activity Agriculture 08,34 13.36 07.34 Manufacturing 38.68 31.94 35.87 Construction 13.74 15.46 16.93 Services 31.91 29.31 36.03 Number of observations 24879 7654 13478 Each entry (except for age) gives the percentage of workers with a given characteristic in the sample. Source: Génération 2007 Regarding the labour market position, individuals can be in the following states: other function with same employer, employee at other employer, self-employed, co-working partner of self-employed, no paid job but looking for one, no paid job and not looking for one, military service and full-time education. From these labour market histories, we obtain both the sequence of labour market situations occupied and their duration. Figure 1 shows the total number of observed labour market transitions in our sub sample. Note that some types of transitions are not considered in our analysis below (in particular, the transitions to and from ‘not in the labour force’, the transitions to unemployment and the transitions from regular employment to temporary employment). This is because of the weakness of the subsample size of such transitions. Since the survey identify the sequence of labour market states and their durations, we can determine the unemployment spell as the duration between the end of school (start of unemployment) and the time when the individual moves into either permanent or temporary work—and the temporary job spell —the duration from the start of a temporary contract until the moment at which the individual moves to a permanent job. A temporary job spell ends with a permanent contract at the same employer or at another employer. Tables 2 a and 2 b report monthly transition probabilities across three labour market states: unemployment, temporary employment, and permanent employment. This suggests high persistence in unemployment and permanent employment. As expected, temporary employment state displays significant turnover, as shown in the bottom row of Table 2 . Table 2 a: Monthly transitions across labour market states for natives Month T + 1 Month T Unemployment Temporary employment Permanent employment Unemployment 0.9103 0.0637 0.0260 Temporary employment 0.2248 0.7421 0.0331 Permanent employment 0.027 0.0120 0.9863 Transition rates are computed according to the distribution of natives across labour market states at month t + 1, conditional on their status at month t. Source: Génération 2007. Table 2 b: Monthly transitions across labour market states for immigrants Month T + 1 Month T Unemployment Temporary employment Permanent employment Unemployment 0.9563 0.0408 0.0139 Temporary employment 0.2437 0.7430 0.0133 Permanent employment 0.0463 0.0371 0.9266 Transition rates are computed according to the distribution of immigrants across labour market states at month t +1, conditional on their status at month t. Source: Génération 2007. In our duration model, we focus on transitions of individuals who left the educational system for the first time in 2007. This leaves us with 111955 temporary employment spells. The duration of each contract is constructed using self-reported information from the various monthly interviews. Given that no contract identifier is supplied, in order to follow each single TC across interviews we rely on information concerning (i) the type of contract held; and (ii) the uncompleted duration of the present contract. The type of contract held can be permanent or temporary. The uncompleted duration of the present contract is expected to rise across interviews with calendar time, and to drop to zero whenever there is a contract change. We therefore consider a spell of temporary employment as completed when either we observe a change in the type of contract or a drop in the uncompleted duration of the present contract. Both tables display extremely strong persistence in the non-employment and the permanent employment states for natives and for immigrants. As expected, immigrants are more likely to be unemployed at the end of their education. In addition, for both immigrants and natives finding a permanent job is more difficult for unemployed than for individuals with temporary contracts. In generation 2007 survey, each spell of temporary employment can end with a new temporary job, a permanent contract, unemployment, returning to school or vocational training. The proportion of fixed term contracts that ends with a permanent jobs started around 04% in 2007 and has increased monotonically until 2010 (14%), showing some ups and downs during the studied period. This average proportion hides significant ethnic differences, and it is useful to see how ethnic groups are different regarding labour market statuses. Accordingly, we calculated the rates of transition for each ethnic group on a quarterly basis. Once again, ethnic differences are perceptible especially between European and non-Europeans. European immigrants show almost the same patterns than natives with a conversion rate around 17%. This can be explained by the employment legislation in France where European immigrants do not need work permit to be employed. For Maghrebian immigrants, the conversion rate is 4 points lower than that of immigrants. Interestingly, at the end of schooling, labour market transition seems to be segmented according to ethnic origin. To go one step further, we report the distribution of the three spells (temporary employment, permanent employment and unemployment) for each ethnic group and according to the duration of transition since the school leave. One has to note that school leavers can choose other tracks than employment. In our survey, the possible destinations are temporary employment, permanent employment, unemployment, training, apprenticeship, self-employment, and no work. We keep only individuals who transit into effective employment after they leave the education system. The other explanatory variables included in our regressions are individual characteristics such as gender, age, education (5 dummies), and marital status. Year dummies (referring to the year in which the individual obtained a conversion or, in case of censoring, to the year in which he/she was last interviewed) are also included in order to capture any time pattern in conversion probabilities. Finally, recipient of unemployment assistance dummy and sectoral unemployment rates are introduced to capture the effects of overall labour market performance on the conversion of contracts. Average sample values of these variables are reported in Table 4 , for both the whole sample and each type of destination. In Génération 2007 survey, we do not systematically observe the wage of every monthly sequence. This implies that the set of explanatory variables that we can use is restricted mostly to background and individual characteristics. Moreover, it is not always clear whether a job that starts and ends between two consecutive interviews is temporary or not. In case of doubt, we infer the type of contract from other variables. We use the data provided by the associated employer survey (this can be a temporary agency, on call or even subsidized public job) and the stated reason why transitions into and out of the job are made (reconversion, more job security or apprenticeship). In certain cases, these variables are missing, and we right-censor the unemployment spell at the moment of the transition into such a job. The latter occurred in 18% of all spells. Table 3 Sample averages of dependent and explanatory variables Variable Natives All immigrants Europeans Maghrebians Temporary job in months 13.6 14.7 13.9 15.6 Permanent job in months 4.7 3.7 4.2 1.8 Unemployment in months 09.3 11.4 10.2 13.5 Age 22.23 22.90 22.63 23.18 Gender: female 50.70 51.20 54.09 51.68 Education: primary 14.28 16.50 16.22 13.99 Education: secondary 30.13 34.61 18.07 39.08 Education: tertiary (2 years) 23.23 16.33 18.64 15.83 Education: tertiary 2 ( 4 years) 12.80 12.70 13.00 13.86 Education: tertiary (5 years or more) 17.38 19.08 19.33 15.92 Marital status: single 44.84 55.11 51.02 59.19 Marital status: married/engaged 54.79 44.66 48.70 40.62 Number of children: no children 75.79 78.20 78.27 78.13 Number of children: 1–2 18.47 21.57 34.41 16.76 Number of children: 3 and more 00.62 01.23 0.70 1.52 Regions: Ile de France 10.90 21.83 21.95 21.68 Southeast 18.94 22.90 20.50 25.40 Southwest 9.61 22.30 15.58 27.02 Northeast 25.19 14.35 14.90 13.91 Northwest 18.13 8.32 8.24 8.39 Centre 9.59 6.91 8.30 5.53 Number of observations 21156 2,444 869 1,575 Source: Génération 2007 4. EMPIRICAL FINDINGS 1-The stepping stone effect of temporary employment We begin the empirical analysis with the estimation of transition rates as functions of the elapsed durations in the states under consideration. We have the initial level of a transition rate (i.e., upon entry into the state under consideration), the shape of this rate is described by the parameters of the duration dependence function (see estimates in Table 4 ). Apart from these duration dependence parameters, the specifications only include time-invariant covariates. Table 4a: ethnic estimates of the log duration dependence functions from unemployment into permanent jobs Time durations Natives Europeans Maghrebians 1 quarter -0.359 (0.191) -0.427 (0.280) -0.461(0.113) 2 quarters 0.287 (0.181) - 0.289 (0.093) -0.196 (0.044) 3 quarters 0.3957 (0.103) 0.321 (0.129) -0.2138(0.029) One year 0.193 (0.201) 0.354 (0.138) 0.474 (0.239) 5 quarters 0.274 (0.104) 0.495 (0.203) 0.538 (0.018) 6 quarters 0.166 (0.102) -0 .22 1 (0.190) 0.586 (0.112) 7 quarters 0.864 (0.179) 0.527 (0.214) 1.535 (0.218) 2 years 1.081 (0.185) -0.364 (0.218) 1.646 (0.216) 9 quarters 0.951 (0.275) 0.515 (0.185) 1.673 (0.293) 10 quarters 1.074 (0.195) 0.804 (0.237) 1.589 (0.139) 11 quarters 1.033 (0.204) 0.921 (0.419) 1.678 (0.137) 12 quarters 0.942 (0.179) 0.958 (0.132) 2.102 (0.617) 13 quarters 0.763 (0.483) 1.007 (0.513) 1.872 (0.132) 14 quarters 1.037 (0.107) 0.941 (0.672) 2.257 (0.351) 15 quarters 1.346 (0.273) 1.158 (0.438) 1.832 (0.162) Standard errors in parentheses Source: Génération 2007 Table 4 a: ethnic estimates of the log duration dependence functions from temporary employment into permanent jobs Time durations Natives Europeans Maghrebians 1 quarter (reference duration) 2 quarters −0.042 (0.098) 0.063 (0.127) 0.269 (0.314) 3 quarters 0.094 (0.093) -0.036 (0.083) 0.322 (0.207) One year 0.113 (0.125) 0.075 (0.091) 0.373 (0.382) 5 quarters 0.145 (0.201) 0.104 (0.112) 0.468 (0.168) 6 quarters 0.182 (0.142) 0.137 (0.119) 0.434 (0.226) 7 quarters 0.235 (0.114) 0.169 (0.273) 0.560 (0.109) 2 years 0.625 (0.245) 0.274 (0.205) 0.587 (0.231) 9 quarters 0.863 (0.233) 0.348 (0.158) 0.773 (0.379) 10 quarters 0.946 (0.249) 0.679 (0.081) 0.851 (0.234) 11 quarters 1.038 (0.139) 0.834 (0.209) 0.962 (0.407) 12 quarters 1.106 (0.213) 1.029 (0.437) 1.153 (.203) 13 quarters 1.127 (0.196) 1.173 (0.236) 1.238 (0.165) 14 quarters 1.183 (0.172) 1.236 (0.102) 1.452 (0.202) 15 quarters 1.196 (0.264) 1.307 (0.395) 1.573 (0.172) Standard errors in parentheses Source: Génération 2007 For natives, the transition rate from unemployment into temporary work is smaller than the transition rate from unemployment into regular work. This implies that unemployed people are more likely to start on temporary work arrangements after an unemployment spell. However, the duration becomes longer for immigrants especially those of Maghrebian origin. Two arguments can be put forward. From one hand, extra-European immigrants need work permit and the payment of immigration taxes, while European foreigners can have access to the French labour market without neither authorization nor taxes. From another hand, Maghrebians can suffer from unrevealed discrimination and are more likely to face unemployment. In addition, the durations into permanent jobs are more likely to be longer for Maghrebians even if the gap between Europeans and immigrants is narrowing for temporary employment. However, once an individual is in temporary employment, the rate of flowing into regular work is at some time after the start of the search larger than otherwise. One might expect workers who accept a temporary job to be initially strongly attached to that job—for example, for contractual reasons. This is quite intuitive since newly employed temporary workers have a slightly lower rate into regular work than unemployed workers. Until 1 year after the start of the temporary job, the transition rate to permanent employment is lower than that from unemployment to regular work. Regarding ethnic differences, the gap is reducing, and immigrants and natives show approximately the same rates of transition. After a period of 24 months years, however, the transition rate from temporary into regular employment increases substantially. Since we have no information on the different employers to measure the length of temporary work, we cannot establish any effect of binding limit (Guell and Petrongolo, 2007). After 30 months, we are left with only 14832 individuals in our sample, which makes the estimated hazards and the transition rates rather imprecise. And the quality of estimates becomes more concise. Accordingly, the finding that the transition rate from temporary work to regular work increases during the temporary job indicates that the accumulation of human capital and the effect of work experience may be a major reason for employers to prefer individuals who have occupied a temporary job. This is true whatever the ethnic group[2]. Note that the results are not due to selection effects, since we corrected for observed and unobserved heterogeneity. As indicated earlier, the selection effect for which we correct might well be a self-selection effect, as is the case if some individuals search for temporary jobs and others do not. 1- The determinants of individual transition rates We now seek to show if the results presented above are uniformly valid for all types of individuals. Table 5 presents the determinants of individual transition rates. Comparing the coefficients for “unemployment to regular” with the coefficients for “temporary to regular” suggests that there are significant differences in the stepping-stone effect across different types of individuals. Table 5 the determinants of individual transition rates Unemployment to temporary jobs Unemployment to permanent jobs Temporary to permanent jobs Age −0.411** (0.084) −0.582** (0.064) −0.196** (0.075) Gender: Female −1.846** (0.369) 1.264** (0.461) 0.813* (0.831) Ethnicity (ref: natives) Europeans −1.643** (0.386) −0.822* (0.572) 0.922** (0.604) Maghrebians −1 .783** (0.392) −0 .941** (0.472) −1 .581** (0.841) Education (ref: no education) Primary level −0 .357** (0.274) −0 .635** (0.391) -0.236* (0.247) Secondary education level -0.562* (0.152) 0.345** (0.190) 0.824** (0.376) Baccalaureate level 0.433* (0.517) 0.361** (0.401) 0.979** (0.145) High education level 1.422** (0.106) 0.615** (0.293) 1.182** (0.263) Region (ref: ile de France) West 1.139** (0.379) 0.683** (0.203) 0 .572* (0.264) North 0.683** (0.293) −0 .738** (0.227) 1 .382** (0.302) East 0.793** (0.034) 0.836** (0.201) 0 .538** (0.358) South 1.492** (0.214) 0.273 (0.206) 0 .493** (0.390) Children (ref: no children) 1–2 children −0 .312* (0.047) 0.562** (0.253) 0 .238 ** (0.173) 3 children or more −0 .248 (0.107) 0.638** (0.293) 0.743* (0.281) working partner 1.483** (0.039) 0.431** (0.203) 0 .693** (0.593) Year 2007 −0.037 (0.082) -0.473** (0.063) -0.014 (0.206) Year 2008 0.537** (0.139) 0.268* (0.046) -0.062 (0.217) Year 2009 −0 .478** (0.203) −0.766** (0.102) -0.982** (0.237) Year 2010 -1.320** (0.573) -1.441*** (0.294) -1.538** (0.180) Sector of activity (Ref:Agriculture) Manufacturing 0.683* (0.306) 0.913** (0.227) 0.478* (0.402) Construction -0.522** (0.287) 0.113** (0.439) 0.386** (0.135) Services 0.734* (0.372) 1.237* (0.291) 0.673** (0.402) Log Likelihood 354.021 254.739 347.374 R2 0.1390 0.1234 0.1141 Number of observations 3408 741 1275 Standard errors in parentheses, significance levels are * for 10%, ** for 05% and *** for 1% Source: Génération 2007 An expected result is that transition rates into permanent jobs are higher in employable labour markets. However, the transition rate into temporary work is more difficult to predict since this work arrangement is less sensitive to economic fluctuations. Age effect is negative, which implies that older unemployed or temporary workers have longer transition rates into permanent jobs. Ethnic minorities show the same pattern especially for Mahgrebians. Being in a couple is positively correlated with the direct transition from unemployment to permanent work. This effect is due to the “marriage premium” where workers may be selected into marriage on the basis of characteristics valued by employers as well as by spouses (Cornwell and Rupert, 1997; Ginter and Zavodny, 2001). Having one or more children induces a higher transition rate from temporary to permanent work. This suggests that parent workers are more likely to provide more effort in order to earn enough of family income and thus may be more involved in transiting into a more secure regular position. For education controls, results suggest that holding tertiary level of education has a strong positive effect on the direct transition into to regular work. This confirms the fact that education remains the most important argument to find permanent jobs (Garrouste and Loi, 2011 ). For ethnic minorities, our estimates show different differences between and within immigrant groups. First, differences between male and female immigrants are observed. The stepping-stone effect is much higher for male immigrants than for native males, where the coefficient for temporary to regular work is positive and the coefficient from unemployment to regular work is negative. This can be explained by the fact that immigration in France is often associated with temporary jobs. However, being second generation immigrants is associated to a lower transition probability and a longer duration to move from unemployment into permanent jobs. This is particularly the case of Maghrebian immigrants who can suffer not only from the weakness of their levels of education but also the discrimination ethnic minorities are likely to face within the labour market. This is particularly true for immigrants who hold high education level where being immigrant and holding tertiary level induces lower transition into permanent jobs from both unemployment and temporary jobs. To better understand ethnic differences, we estimate cumulative probabilities of finding regular work through temporary jobs for Maghrebians, Europeans and natives. 3- Ethnic patterns of stepping stones effect Given the ethnic differences in the stepping-stone effect, it is interesting to see whether this is effect follows a particular pattern with respect to ethnicity. In particular, the probability of finding a permanent job after a spell of temporary employment can provide interesting prediction regarding the dynamics of ethnic groups within the French labour market. Accordingly, we estimated the cumulative probabilities of holding temporary jobs after a temporary contract separately for natives, Europeans and Maghrebians. The figure above shows that immigrants experience a higher stepping-stone effect than natives. For example, the probability of having found regular work after 1 year for a European immigrant is 06% points lower than that of a native. This difference become is widening for Maghrebian immigrants and equals 10% after one year and even 20% after 3 years. Such differences between natives and immigrants suggest that the transition rate of ethnic minorities is often higher. Immigrant workers are often trapped with temporary jobs for several reasons. From one hand, Maghrebians need some authorizations such as work permit and immigration taxes, which make their employment more difficult for employers. Also, Europeans have no restriction regarding their labour market access. From another hand, a discriminatory behaviour could be observed when employers choose to recruit and to convert temporary jobs into permanent positions. To resume, immigrants are often trapped in temporary jobs with a kind of glass ceiling effect that prevent them to progress. However, temporary jobs are also a first step to reduce immigrant’s unemployment. Accordingly, despite the important ethnic differences temporary employment can contribute to reduce labour market segregation and even immigrant integration within the country of origin. Nevertheless, employment legislation considerably hinders equality regarding labour market access to permanent jobs. Therefore, changing the rules regarding work permits for immigrants is likely to restore a kind of equality and to assist extra-European immigrants in their work transition. 5. CONCLUDING COMMENTS The evolution of French labour market and the anaemic unemployment have led to doubts as to whether some particular populations groups are more affected, and some forms of employment are widening. This is particularly true for immigrants who are generally more vulnerable within the labour market and can be devoted to temporary jobs. Nevertheless, temporary work may have potential as a means of integrating immigrants who would otherwise meet difficulties to find secure employment. In addition, there is a risk that these are dead-end jobs. Answering this question for individuals who left the educational system and who enter labour market is a key issue, since this put forward the connection between school and employment. In particular, first- and second-generation immigrants are exposed to exclusion and can suffer from discrimination and glass ceiling effects. In this research, we use the timing-of-events model to estimate causal effects of temporary employment on the transition into permanent job of immigrants in France. We mobilize longitudinal data drawn from Generation 2007 survey to analyse the effect of temporary employment for the employment opportunities of first- and second-generation immigrants who left the school system for the first time in 2007. Following the timing-of-events methodology, we defined the stepping-stone effect as the increase in the hazard rate of finding regular employment as a result of the acceptance of a temporary job. We studied duration dependence patterns and showed that newly employed temporary workers have a slightly lower rate into regular work than unemployed workers. As expected, immigrants are more likely to be unemployed at the end of their education. In addition, for both immigrants and natives finding a permanent job is more difficult for unemployed than for individuals with temporary contracts. For natives, the transition rate from unemployment into temporary work is smaller than the transition rate from unemployment into regular work. Unemployed individuals are more likely to start their professional life on temporary work arrangements. However, the duration becomes longer for immigrants especially those of Maghrebian origin. In addition, estimates indicate that the probability of matching into regular employment after 1 year for a European immigrant is 06% points lower than that of a native, but this gap becomes wider for Maghrebian immigrants and equals 10% after one year and even 20% after 3 years. The differences suggest that the transition of immigrant workers is longer and harder. Therefore, Maghrebians are more trapped within precarious work arrangements and can face discrimination or glass ceiling effect. Nevertheless, temporary employment can be viewed more positively as an effort to integrate immigrants into the labour market. Nevertheless, employment legislation still hinders equality regarding access to permanent jobs. Therefore, changing the rules regarding work permits for immigrants is likely to restore a kind of equality and to assist extra-European immigrants in their work transition. To resume, our results argue suggest that, in spite of a probable discrimination some ethnic groups are likely to face, temporary employment may be an efficient instrument of integrative labour market strategy. Temporary employment has two advantages: it allows immigrants to earn a wage and it also stimulates employment and reduces unemployment duration for some groups. Policy makers should create the institutional framework that helps immigrants in their labour search. This can be for instance an active plan of job placement and the harmonization of labour market authorizations for immigrants. Declarations Conflict of Interest Statement The authors declare that there are no conflicts of interest related to this study. No financial, personal, or professional relationships have influenced the research process, findings, or interpretations presented in this manuscript. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. References Abbring, J.; Van den Berg, G. (2003), “The Nonparametric Identification of Treatment Effects in Duration Models,” Econometrica 71, 1491-1517. Algan, Y., Dustmann, C., Glitz, A., and Manning, A. (2010). The economic situation of first and second-generation immigrants in France, Germany and the United Kingdom, The Economic Journal vol120(542): pp 4–30. Autor, D. (2009), “The Economics of Labour Market Intermediation: An Analytic Framework,” In David Autor, ed., Studies in Labour Market Intermediation, Chicago: The University of Chicago Press, 1-23. Garrouste C. and Loi M. (2011) School-to-work transitions in Europe: Paths towards a permanent contract. Joint research centre, European Commission, 2011. Gobillon, L., Rupert, P and Wasmer, E. (2014). Ethnic unemployment rates and frictional markets, Journal of Urban Economics , Volume 79, January 2014, Pages 108-120, Richard, J-L. (2013) Unemployment of people of foreign origin in France: The role of discrimination, Canadian Studies in Population, “Immigration and the Life Course,” special issue, 40, no. 1–2 (2013): 75–88 De Graaf-Zijl, M.; Van den Berg, G.; Hemya, A. (2011), “Stepping Stones for the Unemployed: The Effect of Temporary Jobs on the Duration until Regular Work,” Journal of Population Economics 24(1), 107-139.. Jahn, E.; Rosholm, M. (2010), “Looking Beyond the Bridge: The Effect of Temporary Agency Employment on Labour Market Outcomes, IZA Discussion paper No 4973. Footnotes 2. Note that the estimates are not affected by selection effects, since we corrected for observed and unobserved heterogeneity. The selection effect for which we correct might well be a self-selection effect, as is the case if some individuals search for temporary jobs and others do not. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6522433","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447589356,"identity":"58e92f19-1ba7-4256-bb56-efadb294778b","order_by":0,"name":"Stephane Hlaimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACA3YGhgMMBjZAJmPjAeK0MIO1pIG0NBCvBQgOg0nitJgzMz88dKPgvN3a9sNAW2psoglqsWxmMzicY3A7eduZRKCWY2m5DQQddpgBosXsAFALY8NhYrSwfwBqOZdsdv4h0Vp4QLYcsDO7QbwtPAVALckJZjeAtiQQ5Zfj7Zs/5/yxszc7n/7wwYcaG8JaYCARrDKBWOUgYE+K4lEwCkbBKBhhAACV/0i5Qle86AAAAABJRU5ErkJggg==","orcid":"","institution":"university of exeter","correspondingAuthor":true,"prefix":"","firstName":"Stephane","middleName":"","lastName":"Hlaimi","suffix":""}],"badges":[],"createdAt":"2025-04-24 16:08:07","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6522433/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6522433/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81355310,"identity":"9f3bf3f5-cc6d-4044-a540-28eb37ce42ca","added_by":"auto","created_at":"2025-04-25 07:26:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":698412,"visible":true,"origin":"","legend":"\u003cp\u003eethnic differences in the share of temporary jobs converted to permanent ones\u003c/p\u003e\n\u003cp\u003ePercentages are calculated on a three-month basis and according to the differences between employment and unemployment statuses.\u003c/p\u003e\n\u003cp\u003eSource: Génération 2007\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6522433/v1/92645c5b2132705c578d6a52.png"},{"id":81355308,"identity":"02fe7ae1-8234-4f32-9278-80a56cc8e1f2","added_by":"auto","created_at":"2025-04-25 07:26:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":699558,"visible":true,"origin":"","legend":"\u003cp\u003ethe cumulative probability of finding a permanent job after a temporary spell\u003c/p\u003e\n\u003cp\u003eSource: Génération 2007\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6522433/v1/d72cbeeddbcdfa9a19bf5c2c.png"},{"id":81357230,"identity":"4ed015d4-017c-47eb-95c4-9e3ce9135a2a","added_by":"auto","created_at":"2025-04-25 07:50:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2428328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6522433/v1/a5f3c5c8-1932-42d1-be86-cad04f6266ad.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eStepping stones or traps? Immigrants’ transition into permanent jobs in France\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn France, immigration is becoming a very hot topic especially regarding the evolution of French labour market and economy. Some political parties argue that they represent a social and financial weight in terms of public expenditure. Accordingly, limitation of immigrants\u0026rsquo; entrants and successful integration of immigrants have become major issues for public debate in France.\u003c/p\u003e \u003cp\u003eHowever, some arguments are put forward to underline the importance of immigration as a wealth and a chance for France. Within the labour market, some jobs are refused by the natives and immigrants are overrepresented. This argument is particularly true for the services sector (Perrin-Haynes, 2008). However, regarding the longitudinal evolution of French labour market, one has to say that unemployment is anaemically increasing and that temporary jobs are spreading. In addition, this situation directly affects immigrants.\u003c/p\u003e \u003cp\u003eAccordingly extra-European immigrants are more likely to hold precarious jobs in the French labour market. Their labour participation is lower and unemployment higher than for the native population or even the European immigrants (Richard, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, they are overrepresented among the self-employed and among workers holding precarious work arrangements. Even second-generation immigrants still meet the same difficulties than the first generation (Algan et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This situation is particularly relevant if we look at the labour market participation and unemployment rates (Gobillon et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnsurprisingly, these immigrants face greater difficulties in the labour market. According to the INSEE (2009), 57% of immigrants aged 18 to 64, arrived in France after age 18, have a job, against 69% for non-immigrants. Immigrant women are particularly affected by unemployment where their employment rate is 37.8% against 46% for native women. Immigrants are also twice as likely to be unemployed (12.7% for immigrant men in 2008, against 6.2% for men non-immigrants). Less present in the labour market, they are also more likely to work in low-skilled jobs, since 38% are workers or unskilled employees against 19% for non-immigrant.\u003c/p\u003e \u003cp\u003eSuch difficulties in the access into the labour market are in part due to a lack of skills. The INSEE states that almost half of immigrants have either a diploma or a degree in primary education level (against 20% for natives). But 25% of immigrants hold tertiary level (similar proportion of natives) though the unemployment rate of the formers three to four times higher than that of the latter. Accordingly, immigrants are more likely to accept temporary jobs with often accurate job insecurity and greater wage uncertainty.\u003c/p\u003e \u003cp\u003eThis paper seeks to provide evidence on the connection between immigration and temporary employment. We study whether temporary employment is a stepping stone into permanent jobs for immigrants in France. Even if recent literature on the stepping stone assumption suggest that fixed term contracts are a bridge into permanent work (Guell and Petrongolo, 2007), we argue that this effect is unequally observed among different ethnic groups. Temporary jobs might be a successful pathway for immigrants into the labour market for several reasons: First, employers have often difficulties observing the productivity of workers educated in a different environment. Temporary employment enables them to reduce information asymmetries and to screen workers without committing themselves to a permanent employment contract. Second, immigrants might be able to build up not only general but also country specific human capital, such as language skills.\u003c/p\u003e \u003cp\u003eFinally, through temporary jobs, immigrants can face glass ceiling or even discriminatory practices. This prevents them to progress and to hold more stable jobs. We then observe different pathways of labour market transition and important ethnic differences.\u003c/p\u003e \u003cp\u003eThe key contribution of this paper is to focus on immigrant integration within the French labour market. We look to analyse if temporary employment is a bridge to permanent jobs for immigrants as job stability is an important step for immigrant\u0026rsquo;s integration. Following the approach of Abbring and Van den Berg (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) we use longitudinal survey data to estimate a duration model. The model specifies the transition rates from unemployment to temporary jobs, from temporary jobs to regular work and from unemployment directly to regular work. Each transition rate is allowed to depend on observed and unobserved explanatory variables as well as on the elapsed time spent in the current state. To deal with selection effects, we allow the unobserved determinants to be dependent across transition rates. A particular focus will be given to ethnic differences regarding the first entrance into the labour market as our sample is carried out on individuals who left the school system for the first time in 2007. We address the issue of school-to-work transition and the modes of entrance into the labour market. This is a key question for policy makers to establish instruments in favour of the use of temporary work as a bridge to permanent jobs and as a tool helping immigrant integration.\u003c/p\u003e \u003cp\u003eThe paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the econometric model as well as the measures of the stepping stone effects. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the dataset, defines temporary jobs, and discusses some variables used in the empirical analysis. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the estimation results, which are illustrated with some graphical overviews. We draw conclusions on the stepping-stone effect of temporary employment, covariate effects, the role of unobserved heterogeneity and the quality of the jobs found. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes.\u003c/p\u003e"},{"header":"2. ECONOMETRIC FRAMEWORK","content":"\n\u003ch3\u003e1- Timing of events framework\u003c/h3\u003e\n\u003cp\u003eWe apply the \u0026lsquo;timing of events\u0026rsquo; approach. We use the methodology developed by Van den Berg et al. (2000). In our case, the econometric framework defines the transition rates from unemployment to temporary employment, from unemployment to regular employment and from temporary employment to regular employment. In general, the transition rate θ\u003csub\u003eij\u003c/sub\u003e is defined as the rate at which an individual move from the state i to another state j, given that he survived in the state i until the current moment. The indices i and j can take the values: 1 for unemployment, 2 for temporary employment and 3 for permanent employment. We specify a mixed proportional hazard model for each transition rate. We denote X the vector of observed characteristics and λij(.) the baseline hazard, for the transition rate from state i to state j. Moreover, β\u003csub\u003eij\u003c/sub\u003e is a vector of unknown parameters. The multiplicative unobserved random terms ε\u003csub\u003eij\u003c/sub\u003e are state- and exit-destination-specific. Then,\u003c/p\u003e \u003cp\u003eWhile the corresponding survival function is:\u003c/p\u003e \u003cp\u003eThe hazard rates depend only on the elapsed duration in the current state and not on earlier outcomes. Note that the initial-conditions problems are not raised since our data contain a natural starting point of each individual labour market history: the moment of school-leaving.\u003c/p\u003e \u003cp\u003eWe define the unemployment spell as the duration between entry into unemployment and entry into either regular or temporary work. A temporary job spell is defined as the duration between the start of the first temporary job and entry into regular employment. Thus, a temporary job spell may consist of multiple periods of (short) unemployment and temporary job spells. The total spell between the start of unemployment and regular employment is the sum of the unemployment spells and, eventually the temporary job spells. For every respondent, we suppose that the random term ε\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e follows the same pattern across different spells. This unobserved heterogeneity term captures selective inflow into temporary work and permanent work.\u003c/p\u003e \u003cp\u003eFor identification purposes, we assume that individuals have no additional information about the timing of starting their jobs. In this context, anticipation arises if individuals abandon their research to pursue permanent employment when they begin a temporary job with a known duration. Following the approach suggested by Van den Berg et al. (2002), our baseline hazards have piecewise constant specifications. Accordingly, is a realization of the random variable. Individuals are associated to a unique set of values of, ,. We allow for two possible realizations for each ε\u003csub\u003eij\u003c/sub\u003e, so \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2. We take the locations of the mass points as well as the associated probabilities to be unknown parameters. In addition, we impose the condition that if\u0026thinsp;=\u0026thinsp;then =. This effectively assumes that individuals who more easily find regular work from unemployment also find regular work more easily from a temporary position.\u003c/p\u003e \u003cp\u003eTherefore, our specification involves four groups of individuals (four different combinations of mass points) and six mass point parameters. Note that the arrangement of mass points (,, \u003cem\u003e)\u003c/em\u003e replaces the constants in the vector of coefficients to estimate and, thus, are identified. The relation between the elements of (,, \u003cem\u003e)\u003c/em\u003e does not need to be monotone. As mentioned before, the extent to which is related to and determines the extent to which selectivity affects the association between having had temporary work or not on the one hand and entering regular work on the other.\u003c/p\u003e\n\u003ch3\u003e2- Measuring the effect of temporary contracts\u003c/h3\u003e\n\u003cp\u003eTo better capture the effect of temporary employment on immigrant\u0026rsquo;s transition, we consider two measurements: the stepping stone effect and the probability of permanent employment.\u003c/p\u003e \u003cp\u003eWe define the \u003cem\u003estepping\u003c/em\u003e-\u003cem\u003estone effect\u003c/em\u003e as the increase in the hazard rate of finding a permanent job after a sequence of temporary employment. This effect can be represented by two components: duration of unemployment t\u003csub\u003e1\u003c/sub\u003e and duration of temporary work t\u003csub\u003e2\u003c/sub\u003e. Accordingly, the causal effect of temporary employment is equal to\u003c/p\u003e \u003cp\u003eThen a comparison of hazard rates from unemployment to permanent job and from temporary to permanent wok depends on the duration patterns even if on average, we can expect that the transition from temporary to permanent is higher than that from unemployment to permanent.\u003c/p\u003e \u003cp\u003eNevertheless, another quantity of interest can give us a interesting idea about the effect of temporary contract on the transition into permanent jobs. Following Card and Sullivan (1988), we can quantify the probability of regular work within \u003cem\u003et\u003c/em\u003e months in the absence of temporary work and define the cumulative probability function\u003c/p\u003e \u003cp\u003eWhere S\u003csub\u003e13\u003c/sub\u003e is the survivor function of the duration from unemployment into permanent work, S\u003csub\u003e12\u003c/sub\u003e the survivor function of the duration from unemployment into temporary work, S\u003csub\u003e23\u003c/sub\u003e the survivor function of the duration from temporary into permanent work. The decomposition of the equation above represents the population fraction of unemployed individuals who find regular work through either the temporary work channel or the direct channel. The first part of the expression equals the probability of moving into regular work by way of a direct transition from unemployment, whereas the second part equals the probability of moving into regular work by way of temporary work. However, this decomposition does not capture a causal effect of temporary work. Note that both terms are positive even if there is no effect at the individual level. In our empirical analysis, we will estimate separately for each ethnic group to put forward possible ethnic differences in the stepping stone effect.\u003c/p\u003e"},{"header":"3. DATA","content":"\u003cp\u003eThis study uses the G\u0026eacute;n\u0026eacute;ration 2007 survey, a panel of young individuals who left the school system in 2007. The sample was followed for four years, and series of interviews were conducted on a sample of 24879 individuals. Each of these three interviews was based on a questionnaire administered by telephone.\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;Generation 2007\u0026rdquo; survey mainly focuses on young people\u0026rsquo;s occupational pathways with a view to drawing a longitudinal picture of their school-to-work transition and early career experience. In each interview, an \u0026ldquo;occupational calendar\u0026rdquo; was used to collect details of the respondents\u0026rsquo; situation month-by-month: employment, unemployment, inactivity, etc. Further questions were then asked about the various periods of employment, about the employers and conditions of employment (work contract, salary, position, type of occupation, etc.) and the respondents\u0026rsquo; job satisfaction. The respondents were also questioned about their periods of unemployment, as to whether they had taken undertaken job-search, had undergone vocational training, received unemployment benefits or gave up their job search. A second calendar focusing on housing and the family was used to record any changes in the personal lives of the young people interviewed.\u003c/p\u003e\n\u003cp\u003eIn the Generation 2007 survey, we have extensive information on the labour market histories of the respondents. Individuals are asked about their labour market status since they left the school system, about all transitions made since then, and about their current labour market status. We use all immigrants and descendants of immigrants (2. generation) who left the school system in 2007. We define first generation immigrants as individuals having born outside France of two foreign parents. Second generation immigrants are born in France of at least one foreign parent. We also define three ethnic groups: natives, Europeans and maghrebians. We will not consider the other groups because of the weakness of the corresponding subsamples.\u003c/p\u003e\n\u003cp\u003eFirst of all, we choose the group \u0026ldquo;without temporary employment experience\u0026rdquo; as the reference category. The median unemployment duration for the reference group is between 04 and 09 months; the median unemployment duration until the first temporary job is 5 months. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the individual and employment characteristics of the sample.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample averages of dependent and explanatory variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll observations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTransition from unemployment to permanent employment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTransition from temporary employment to permanent employment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved durations (in months):\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnic group:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaghrebians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (in years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender: female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildren at home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegion:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle de France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector of activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eEach entry (except for age) gives the percentage of workers with a given characteristic in the sample.\u003c/p\u003e\n\u003cp\u003eSource: G\u0026eacute;n\u0026eacute;ration 2007\u003c/p\u003e\n\u003cp\u003eRegarding the labour market position, individuals can be in the following states: other function with same employer, employee at other employer, self-employed, co-working partner of self-employed, no paid job but looking for one, no paid job and not looking for one, military service and full-time education. From these labour market histories, we obtain both the sequence of labour market situations occupied and their duration. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the total number of observed labour market transitions in our sub sample. Note that some types of transitions are not considered in our analysis below (in particular, the transitions to and from \u0026lsquo;not in the labour force\u0026rsquo;, the transitions to unemployment and the transitions from regular employment to temporary employment). This is because of the weakness of the subsample size of such transitions.\u003c/p\u003e\n\u003cp\u003eSince the survey identify the sequence of labour market states and their durations, we can determine the \u003cem\u003eunemployment spell\u003c/em\u003e as the duration between the end of school (start of unemployment) and the time when the individual moves into either permanent or temporary work\u0026mdash;and the \u003cem\u003etemporary job spell\u003c/em\u003e\u0026mdash;the duration from the start of a temporary contract until the moment at which the individual moves to a permanent job. A temporary job spell ends with a permanent contract at the same employer or at another employer.\u003c/p\u003e\n\u003cp\u003eTables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb report monthly transition probabilities across three labour market states: unemployment, temporary employment, and permanent employment. This suggests high persistence in unemployment and permanent employment. As expected, temporary employment state displays significant turnover, as shown in the bottom row of Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ea: Monthly transitions across labour market states for natives\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMonth T\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent employment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTransition rates are computed according to the distribution of natives across labour market states at month t\u0026thinsp;+\u0026thinsp;1, conditional on their status at month t.\u003c/p\u003e\n\u003cp\u003eSource: G\u0026eacute;n\u0026eacute;ration 2007.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eb: Monthly transitions across labour market states for immigrants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMonth T\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent employment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTransition rates are computed according to the distribution of immigrants across labour market states at month t +1, conditional on their status at month t.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Source: G\u0026eacute;n\u0026eacute;ration 2007.\u003c/p\u003e\n\u003cp\u003eIn our duration model, we focus on transitions of individuals who left the educational system for the first time in 2007. This leaves us with 111955 temporary employment spells. The duration of each contract is constructed using self-reported information from the various monthly interviews. Given that no contract identifier is supplied, in order to follow each single TC across interviews we rely on information concerning (i) the type of contract held; and (ii) the uncompleted duration of the present contract. The type of contract held can be permanent or temporary. The uncompleted duration of the present contract is expected to rise across interviews with calendar time, and to drop to zero whenever there is a contract change. We therefore consider a spell of temporary employment as completed when either we observe a change in the type of contract or a drop in the uncompleted duration of the present contract.\u003c/p\u003e\n\u003cp\u003eBoth tables display extremely strong persistence in the non-employment and the permanent employment states for natives and for immigrants. As expected, immigrants are more likely to be unemployed at the end of their education. In addition, for both immigrants and natives finding a permanent job is more difficult for unemployed than for individuals with temporary contracts.\u003c/p\u003e\n\u003cp\u003eIn generation 2007 survey, each spell of temporary employment can end with a new temporary job, a permanent contract, unemployment, returning to school or vocational training. The proportion of fixed term contracts that ends with a permanent jobs started around 04% in 2007 and has increased monotonically until 2010 (14%), showing some ups and downs during the studied period. This average proportion hides significant ethnic differences, and it is useful to see how ethnic groups are different regarding labour market statuses. Accordingly, we calculated the rates of transition for each ethnic group on a quarterly basis.\u003c/p\u003e\n\u003cp\u003eOnce again, ethnic differences are perceptible especially between European and non-Europeans. European immigrants show almost the same patterns than natives with a conversion rate around 17%. This can be explained by the employment legislation in France where European immigrants do not need work permit to be employed. For Maghrebian immigrants, the conversion rate is 4 points lower than that of immigrants. Interestingly, at the end of schooling, labour market transition seems to be segmented according to ethnic origin. To go one step further, we report the distribution of the three spells (temporary employment, permanent employment and unemployment) for each ethnic group and according to the duration of transition since the school leave. One has to note that school leavers can choose other tracks than employment. In our survey, the possible destinations are temporary employment, permanent employment, unemployment, training, apprenticeship, self-employment, and no work. We keep only individuals who transit into effective employment after they leave the education system.\u003c/p\u003e\n\u003cp\u003eThe other explanatory variables included in our regressions are individual characteristics such as gender, age, education (5 dummies), and marital status. Year dummies (referring to the year in which the individual obtained a conversion or, in case of censoring, to the year in which he/she was last interviewed) are also included in order to capture any time pattern in conversion probabilities. Finally, recipient of unemployment assistance dummy and sectoral unemployment rates are introduced to capture the effects of overall labour market performance on the conversion of contracts. Average sample values of these variables are reported in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, for both the whole sample and each type of destination.\u003c/p\u003e\n\u003cp\u003eIn G\u0026eacute;n\u0026eacute;ration 2007 survey, we do not systematically observe the wage of every monthly sequence. This implies that the set of explanatory variables that we can use is restricted mostly to background and individual characteristics. Moreover, it is not always clear whether a job that starts and ends between two consecutive interviews is temporary or not. In case of doubt, we infer the type of contract from other variables. We use the data provided by the associated employer survey (this can be a temporary agency, on call or even subsidized public job) and the stated reason why transitions into and out of the job are made (reconversion, more job security or apprenticeship). In certain cases, these variables are missing, and we right-censor the unemployment spell at the moment of the transition into such a job. The latter occurred in 18% of all spells.\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample averages of dependent and explanatory variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNatives\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll immigrants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEuropeans\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaghrebians\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary job in months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent job in months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment in months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender: female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation: primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation: secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation: tertiary (2 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation: tertiary 2 ( 4 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation: tertiary (5 years or more)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status: single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status: married/engaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children: no children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children: 1\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children: 3 and more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e00.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegions:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle de France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSource: G\u0026eacute;n\u0026eacute;ration 2007\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. EMPIRICAL FINDINGS","content":"\u003ch3\u003e1-The stepping stone effect of temporary employment\u003c/h3\u003e\n\u003cp\u003eWe begin the empirical analysis with the estimation of transition rates as functions of the elapsed durations in the states under consideration. We have the initial level of a transition rate (i.e., upon entry into the state under consideration), the shape of this rate is described by the parameters of the duration dependence function (see estimates in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Apart from these duration dependence parameters, the specifications only include time-invariant covariates.\u003c/p\u003e\u003cp\u003eTable 4a: ethnic estimates of the log duration dependence functions from unemployment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003einto permanent jobs\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003eTime durations\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003eNatives\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003eEuropeans\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003eMaghrebians\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e1 quarter\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e-0.359 (0.191)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e-0.427 (0.280)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e-0.461(0.113)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e2 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.287 (0.181)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e- 0.289 (0.093)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e-0.196 (0.044)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e3 quarters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.3957 (0.103)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.321 (0.129)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e-0.2138(0.029)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003eOne year \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.193 (0.201)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.354 (0.138)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e0.474 (0.239)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e5 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.274 (0.104) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.495 (0.203)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e0.538 (0.018)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e6 quarters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.166 (0.102) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e-0\u003cem\u003e.22\u003c/em\u003e1 (0.190)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e0.586 (0.112)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e7 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.864 (0.179)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.527 (0.214)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.535 (0.218)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e2 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.081 (0.185)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e-0.364 (0.218)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.646 (0.216)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e9 quarters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.951 (0.275) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.515 (0.185)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.673 (0.293)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e10 quarters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.074 (0.195) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.804 (0.237)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.589 (0.139)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e11 quarters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.033 (0.204)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.921 (0.419)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.678 (0.137)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e12 quarters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.942 (0.179)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.958 (0.132)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e2.102 (0.617)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e13 quarters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.763 (0.483)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.007 (0.513)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.872 (0.132)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e14 quarters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.037 (0.107)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e0.941 (0.672)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e2.257 (0.351)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 40.1372%;\"\u003e\n \u003cp\u003e15 quarters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.346 (0.273)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 19.3825%;\"\u003e\n \u003cp\u003e1.158 (0.438)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 21.0978%;\"\u003e\n \u003cp\u003e1.832 (0.162)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eStandard errors in parentheses\u003c/p\u003e\n\u003cp\u003eSource: Génération 2007\u003c/p\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ea: ethnic estimates of the log duration dependence functions from temporary employment into permanent jobs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eTime durations\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNatives\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eEuropeans\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMaghrebians\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1 quarter (reference duration)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.042 (0.098)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063 (0.127)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.269 (0.314)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094 (0.093)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.036 (0.083)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.322 (0.207)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOne year\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113 (0.125)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075 (0.091)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.373 (0.382)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145 (0.201)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104 (0.112)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.468 (0.168)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182 (0.142)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137 (0.119)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.434 (0.226)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235 (0.114)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169 (0.273)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.560 (0.109)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.625 (0.245)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274 (0.205)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.587 (0.231)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.863 (0.233)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.348 (0.158)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.773 (0.379)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e10 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946 (0.249)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.679 (0.081)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851 (0.234)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e11 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.038 (0.139)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.834 (0.209)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962 (0.407)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.106 (0.213)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.029 (0.437)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.153 (.203)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e13 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.127 (0.196)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.173 (0.236)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.238 (0.165)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e14 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.183 (0.172)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.236 (0.102)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.452 (0.202)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e15 quarters\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.196 (0.264)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.307 (0.395)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.573 (0.172)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Génération 2007\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eFor natives, the transition rate from unemployment into temporary work is smaller than the transition rate from unemployment into regular work. This implies that unemployed people are more likely to start on temporary work arrangements after an unemployment spell. However, the duration becomes longer for immigrants especially those of Maghrebian origin. Two arguments can be put forward. From one hand, extra-European immigrants need work permit and the payment of immigration taxes, while European foreigners can have access to the French labour market without neither authorization nor taxes. From another hand, Maghrebians can suffer from unrevealed discrimination and are more likely to face unemployment. In addition, the durations into permanent jobs are more likely to be longer for Maghrebians even if the gap between Europeans and immigrants is narrowing for temporary employment.\u003c/p\u003e\n\u003cp\u003eHowever, once an individual is in temporary employment, the rate of flowing into regular work is at some time after the start of the search larger than otherwise. One might expect workers who accept a temporary job to be initially strongly attached to that job—for example, for contractual reasons. This is quite intuitive since newly employed temporary workers have a slightly lower rate into regular work than unemployed workers. Until 1 year after the start of the temporary job, the transition rate to permanent employment is lower than that from unemployment to regular work. Regarding ethnic differences, the gap is reducing, and immigrants and natives show approximately the same rates of transition.\u003c/p\u003e\n\u003cp\u003eAfter a period of 24 months years, however, the transition rate from temporary into regular employment increases substantially. Since we have no information on the different employers to measure the length of temporary work, we cannot establish any effect of binding limit (Guell and Petrongolo, 2007). After 30 months, we are left with only 14832 individuals in our sample, which makes the estimated hazards and the transition rates rather imprecise. And the quality of estimates becomes more concise.\u003c/p\u003e\n\u003cp\u003eAccordingly, the finding that the transition rate from temporary work to regular work increases during the temporary job indicates that the accumulation of human capital and the effect of work experience may be a major reason for employers to prefer individuals who have occupied a temporary job. This is true whatever the ethnic group[2].\u003c/p\u003e\n\u003cp\u003eNote that the results are not due to selection effects, since we corrected for observed and unobserved heterogeneity. As indicated earlier, the selection effect for which we correct might well be a self-selection effect, as is the case if some individuals search for temporary jobs and others do not.\u003c/p\u003e\n\u003ch3\u003e1- The determinants of individual transition rates\u003c/h3\u003e\n\u003cp\u003eWe now seek to show if the results presented above are uniformly valid for all types of individuals. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the determinants of individual transition rates. Comparing the coefficients for “unemployment to regular” with the coefficients for “temporary to regular” suggests that there are significant differences in the stepping-stone effect across different types of individuals.\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ethe determinants of individual transition rates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eUnemployment to temporary jobs\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eUnemployment to permanent jobs\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eTemporary to permanent jobs\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.411**\u003c/p\u003e\n \u003cp\u003e(0.084)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.582**\u003c/p\u003e\n \u003cp\u003e(0.064)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.196**\u003c/p\u003e\n \u003cp\u003e(0.075)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGender: Female\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−1.846**\u003c/p\u003e\n \u003cp\u003e(0.369)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.264**\u003c/p\u003e\n \u003cp\u003e(0.461)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813*\u003c/p\u003e\n \u003cp\u003e(0.831)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity (ref: natives)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropeans\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−1.643**\u003c/p\u003e\n \u003cp\u003e(0.386)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.822*\u003c/p\u003e\n \u003cp\u003e(0.572)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922**\u003c/p\u003e\n \u003cp\u003e(0.604)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMaghrebians\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−1\u003cem\u003e.783**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.392)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.941**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.472)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−1\u003cem\u003e.581**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.841)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (ref: no education)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary level\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.357**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.274)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.635**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.391)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.236*\u003c/p\u003e\n \u003cp\u003e(0.247)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary education level\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.562*\u003c/p\u003e\n \u003cp\u003e(0.152)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.345**\u003c/p\u003e\n \u003cp\u003e(0.190)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824**\u003c/p\u003e\n \u003cp\u003e(0.376)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBaccalaureate level\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433*\u003c/p\u003e\n \u003cp\u003e(0.517)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361**\u003c/p\u003e\n \u003cp\u003e(0.401)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.979**\u003c/p\u003e\n \u003cp\u003e(0.145)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh education level\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.422**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.615**\u003c/p\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.182**\u003c/p\u003e\n \u003cp\u003e(0.263)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eRegion (ref: ile de France)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.139**\u003c/p\u003e\n \u003cp\u003e(0.379)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683**\u003c/p\u003e\n \u003cp\u003e(0.203)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003cem\u003e.572*\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.264)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683**\u003c/p\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.738**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.227)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003cem\u003e.382**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.302)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.793**\u003c/p\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836**\u003c/p\u003e\n \u003cp\u003e(0.201)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003cem\u003e.538**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.358)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.492**\u003c/p\u003e\n \u003cp\u003e(0.214)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003cp\u003e(0.206)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003cem\u003e.493**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.390)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eChildren (ref: no children)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1–2 children\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.312*\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562**\u003c/p\u003e\n \u003cp\u003e(0.253)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003cem\u003e.238\u003c/em\u003e**\u003c/p\u003e\n \u003cp\u003e(0.173)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3 children or more\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.248\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.107)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.638**\u003c/p\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743*\u003c/p\u003e\n \u003cp\u003e(0.281)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eworking partner\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.483**\u003c/p\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.431**\u003c/p\u003e\n \u003cp\u003e(0.203)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003cem\u003e.693**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.593)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear 2007\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.037\u003c/p\u003e\n \u003cp\u003e(0.082)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.473**\u003c/p\u003e\n \u003cp\u003e(0.063)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003cp\u003e(0.206)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear 2008\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.537**\u003c/p\u003e\n \u003cp\u003e(0.139)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268*\u003c/p\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003cp\u003e(0.217)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear 2009\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0\u003cem\u003e.478**\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(0.203)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.766**\u003c/p\u003e\n \u003cp\u003e(0.102)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.982**\u003c/p\u003e\n \u003cp\u003e(0.237)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear 2010\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.320**\u003c/p\u003e\n \u003cp\u003e(0.573)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.441***\u003c/p\u003e\n \u003cp\u003e(0.294)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.538**\u003c/p\u003e\n \u003cp\u003e(0.180)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSector of activity (Ref:Agriculture)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683*\u003c/p\u003e\n \u003cp\u003e(0.306)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.913**\u003c/p\u003e\n \u003cp\u003e(0.227)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478*\u003c/p\u003e\n \u003cp\u003e(0.402)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.522**\u003c/p\u003e\n \u003cp\u003e(0.287)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113**\u003c/p\u003e\n \u003cp\u003e(0.439)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386**\u003c/p\u003e\n \u003cp\u003e(0.135)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734*\u003c/p\u003e\n \u003cp\u003e(0.372)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.237*\u003c/p\u003e\n \u003cp\u003e(0.291)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.673**\u003c/p\u003e\n \u003cp\u003e(0.402)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLog Likelihood\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e354.021\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e254.739\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e347.374\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1390\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1234\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1141\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3408\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e741\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1275\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eStandard errors in parentheses, significance levels are * for 10%, ** for 05% and *** for 1%\u003c/p\u003e\n\u003cp\u003eSource: Génération 2007\u003c/p\u003e\n\u003cp\u003eAn expected result is that transition rates into permanent jobs are higher in employable labour markets. However, the transition rate into temporary work is more difficult to predict since this work arrangement is less sensitive to economic fluctuations. Age effect is negative, which implies that older unemployed or temporary workers have longer transition rates into permanent jobs. Ethnic minorities show the same pattern especially for Mahgrebians.\u003c/p\u003e\n\u003cp\u003eBeing in a couple is positively correlated with the direct transition from unemployment to permanent work. This effect is due to the “marriage premium” where workers may be selected into marriage on the basis of characteristics valued by employers as well as by spouses (Cornwell and Rupert, 1997; Ginter and Zavodny, 2001).\u003c/p\u003e\n\u003cp\u003eHaving one or more children induces a higher transition rate from temporary to permanent work. This suggests that parent workers are more likely to provide more effort in order to earn enough of family income and thus may be more involved in transiting into a more secure regular position.\u003c/p\u003e\n\u003cp\u003eFor education controls, results suggest that holding tertiary level of education has a strong positive effect on the direct transition into to regular work. This confirms the fact that education remains the most important argument to find permanent jobs (Garrouste and Loi, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor ethnic minorities, our estimates show different differences between and within immigrant groups. First, differences between male and female immigrants are observed. The stepping-stone effect is much higher for male immigrants than for native males, where the coefficient for temporary to regular work is positive and the coefficient from unemployment to regular work is negative. This can be explained by the fact that immigration in France is often associated with temporary jobs. However, being second generation immigrants is associated to a lower transition probability and a longer duration to move from unemployment into permanent jobs. This is particularly the case of Maghrebian immigrants who can suffer not only from the weakness of their levels of education but also the discrimination ethnic minorities are likely to face within the labour market. This is particularly true for immigrants who hold high education level where being immigrant and holding tertiary level induces lower transition into permanent jobs from both unemployment and temporary jobs.\u003c/p\u003e\n\u003cp\u003eTo better understand ethnic differences, we estimate cumulative probabilities of finding regular work through temporary jobs for Maghrebians, Europeans and natives.\u003c/p\u003e\n\u003ch3\u003e3- Ethnic patterns of stepping stones effect\u003c/h3\u003e\n\u003cp\u003eGiven the ethnic differences in the stepping-stone effect, it is interesting to see whether this is effect follows a particular pattern with respect to ethnicity. In particular, the probability of finding a permanent job after a spell of temporary employment can provide interesting prediction regarding the dynamics of ethnic groups within the French labour market. Accordingly, we estimated the cumulative probabilities of holding temporary jobs after a temporary contract separately for natives, Europeans and Maghrebians.\u003c/p\u003e\n\u003cp\u003eThe figure above shows that immigrants experience a higher stepping-stone effect than natives. For example, the probability of having found regular work after 1 year for a European immigrant is 06% points lower than that of a native. This difference become is widening for Maghrebian immigrants and equals 10% after one year and even 20% after 3 years.\u003c/p\u003e\n\u003cp\u003eSuch differences between natives and immigrants suggest that the transition rate of ethnic minorities is often higher. Immigrant workers are often trapped with temporary jobs for several reasons. From one hand, Maghrebians need some authorizations such as work permit and immigration taxes, which make their employment more difficult for employers. Also, Europeans have no restriction regarding their labour market access. From another hand, a discriminatory behaviour could be observed when employers choose to recruit and to convert temporary jobs into permanent positions.\u003c/p\u003e\n\u003cp\u003eTo resume, immigrants are often trapped in temporary jobs with a kind of glass ceiling effect that prevent them to progress. However, temporary jobs are also a first step to reduce immigrant’s unemployment. Accordingly, despite the important ethnic differences temporary employment can contribute to reduce labour market segregation and even immigrant integration within the country of origin. Nevertheless, employment legislation considerably hinders equality regarding labour market access to permanent jobs. Therefore, changing the rules regarding work permits for immigrants is likely to restore a kind of equality and to assist extra-European immigrants in their work transition.\u003c/p\u003e\n\n\n\n\n\n\n\n"},{"header":"5. CONCLUDING COMMENTS","content":"\u003cp\u003eThe evolution of French labour market and the anaemic unemployment have led to doubts as to whether some particular populations groups are more affected, and some forms of employment are widening. This is particularly true for immigrants who are generally more vulnerable within the labour market and can be devoted to temporary jobs. Nevertheless, temporary work may have potential as a means of integrating immigrants who would otherwise meet difficulties to find secure employment. In addition, there is a risk that these are dead-end jobs. Answering this question for individuals who left the educational system and who enter labour market is a key issue, since this put forward the connection between school and employment. In particular, first- and second-generation immigrants are exposed to exclusion and can suffer from discrimination and glass ceiling effects.\u003c/p\u003e\u003cp\u003eIn this research, we use the timing-of-events model to estimate causal effects of temporary employment on the transition into permanent job of immigrants in France. We mobilize longitudinal data drawn from Generation 2007 survey to analyse the effect of temporary employment for the employment opportunities of first- and second-generation immigrants who left the school system for the first time in 2007.\u003c/p\u003e\u003cp\u003eFollowing the timing-of-events methodology, we defined the \u003cem\u003estepping-stone effect\u003c/em\u003e as the increase in the hazard rate of finding regular employment as a result of the acceptance of a temporary job. We studied duration dependence patterns and showed that newly employed temporary workers have a slightly lower rate into regular work than unemployed workers. As expected, immigrants are more likely to be unemployed at the end of their education. In addition, for both immigrants and natives finding a permanent job is more difficult for unemployed than for individuals with temporary contracts.\u003c/p\u003e\u003cp\u003eFor natives, the transition rate from unemployment into temporary work is smaller than the transition rate from unemployment into regular work. Unemployed individuals are more likely to start their professional life on temporary work arrangements. However, the duration becomes longer for immigrants especially those of Maghrebian origin.\u003c/p\u003e\u003cp\u003eIn addition, estimates indicate that the probability of matching into regular employment after 1 year for a European immigrant is 06% points lower than that of a native, but this gap becomes wider for Maghrebian immigrants and equals 10% after one year and even 20% after 3 years.\u003c/p\u003e\u003cp\u003eThe differences suggest that the transition of immigrant workers is longer and harder. Therefore, Maghrebians are more trapped within precarious work arrangements and can face discrimination or glass ceiling effect. Nevertheless, temporary employment can be viewed more positively as an effort to integrate immigrants into the labour market. Nevertheless, employment legislation still hinders equality regarding access to permanent jobs. Therefore, changing the rules regarding work permits for immigrants is likely to restore a kind of equality and to assist extra-European immigrants in their work transition.\u003c/p\u003e\u003cp\u003eTo resume, our results argue suggest that, in spite of a probable discrimination some ethnic groups are likely to face, temporary employment may be an efficient instrument of integrative labour market strategy. Temporary employment has two advantages: it allows immigrants to earn a wage and it also stimulates employment and reduces unemployment duration for some groups. Policy makers should create the institutional framework that helps immigrants in their labour search. This can be for instance an active plan of job placement and the harmonization of labour market authorizations for immigrants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflict of Interest Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest related to this study. No financial, personal, or professional relationships have influenced the research process, findings, or interpretations presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData Availability Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbring, J.; Van den Berg, G. (2003), \u0026ldquo;The Nonparametric Identification of Treatment Effects in Duration Models,\u0026rdquo; \u003cem\u003eEconometrica \u003c/em\u003e71, 1491-1517.\u003c/li\u003e\n\u003cli\u003eAlgan, Y., Dustmann, C., Glitz, A., and Manning, A. (2010). The economic situation of first and second-generation immigrants in France, Germany and the United Kingdom, \u003cem\u003eThe Economic Journal \u003c/em\u003evol120(542): pp 4\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eAutor, D. (2009), \u0026ldquo;The Economics of Labour Market Intermediation: An Analytic Framework,\u0026rdquo; In David Autor, ed., Studies in Labour Market Intermediation, Chicago: The University of Chicago Press, 1-23.\u003c/li\u003e\n\u003cli\u003eGarrouste C. and Loi M. (2011) School-to-work transitions in Europe: Paths towards a permanent contract. Joint research centre, European Commission, 2011.\u003c/li\u003e\n\u003cli\u003eGobillon, L., Rupert, P and Wasmer, E. (2014). Ethnic unemployment rates and frictional markets, \u003cem\u003eJournal of Urban Economics\u003c/em\u003e, Volume 79, January 2014, Pages 108-120,\u003c/li\u003e\n\u003cli\u003eRichard, J-L. (2013) Unemployment of people of foreign origin in France: The role of discrimination, \u003cem\u003eCanadian Studies in Population, \u003c/em\u003e\u0026ldquo;Immigration and the Life Course,\u0026rdquo; special issue, 40, no. 1\u0026ndash;2 (2013): 75\u0026ndash;88\u003c/li\u003e\n\u003cli\u003eDe Graaf-Zijl, M.; Van den Berg, G.; Hemya, A. (2011), \u0026ldquo;Stepping Stones for the Unemployed: The Effect of Temporary Jobs on the Duration until Regular Work,\u0026rdquo; \u003cem\u003eJournal of Population Economics\u003c/em\u003e 24(1), 107-139..\u003c/li\u003e\n\u003cli\u003eJahn, E.; Rosholm, M. (2010), \u0026ldquo;Looking Beyond the Bridge: The Effect of Temporary Agency Employment on Labour Market Outcomes, IZA Discussion paper No 4973.\u003c/li\u003e\n\u003c/ol\u003e "},{"header":"Footnotes","content":"\u003cp\u003e 2. Note that the estimates are not affected by selection effects, since we corrected for observed and unobserved heterogeneity. The selection effect for which we correct might well be a self-selection effect, as is the case if some individuals search for temporary jobs and others do not.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Exeter","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"immigrants, temporary jobs, permanent employment, stepping-stones","lastPublishedDoi":"10.21203/rs.3.rs-6522433/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6522433/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, I analyse the transition duration patterns into permanent employment in France. We show that immigrants are more likely to be unemployed at the end of their education. For natives, the transition rate from unemployment into temporary work is smaller than that from unemployment into regular work. However, the duration becomes longer for immigrants especially those of Maghrebian origin.\u003c/p\u003e\n\u003cp\u003eMoreover, the probability of matching into regular employment after 1 year for a European immigrant is 06% points lower than that of a native but this gap becomes wider for Maghrebian immigrants and equals 10% after one year and even 20% after 3 years. Therefore, Maghrebians are more trapped within precarious work arrangements and can face discrimination or glass ceiling effect.\u003c/p\u003e\n\u003cp\u003eJEL Classification: J61, J64, J68\u003c/p\u003e","manuscriptTitle":"Stepping stones or traps? Immigrants’ transition into permanent jobs in France","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 07:26:40","doi":"10.21203/rs.3.rs-6522433/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4e16ae7-fc6e-4666-85f9-72bc1dc6c541","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47643080,"name":"Other Economics"}],"tags":[],"updatedAt":"2025-04-25T07:26:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-25 07:26:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6522433","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6522433","identity":"rs-6522433","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}