QUADERNI DISCUSSIONE ESTRATTO SCRITTI DI STATISTICA ECONOMICA MOVING FROM UNIVERSITY TO WORK: THE CASE OF A FACULTY OF ECONOMICS IN THE SOUTH OF ITALY

Transcription

1 DIPARTIMENTO DI STATISTICA E MATEMATICA PER LA RICERCA ECONOMICA FACOLTÀ DI ECONOMIA UNIVERSITÀ DEGLI STUDI DI NAPOLI PARTHENOPE NAPOLI SCRITTI DI STATISTICA ECONOMICA QUADERNI DI DISCUSSIONE a cura di Claudio Quintano MOVING FROM UNIVERSITY TO WORK: THE CASE OF A FACULTY OF ECONOMICS IN THE SOUTH OF ITALY CLAUDIO QUINTANO ROSALIA CASTELLANO ANTONELLA D AGOSTINO ESTRATTO DIPARTIMENTO DI STATISTICA E MATEMATICA PER LA RICERCA ECONOMICA UNIVERSITA DEGLI STUDI DI NAPOLI PARTHENOPE 2006

2 265 MOVING FROM UNIVERSITY TO WORK: THE CASE OF A FACULTY OF ECONOMICS IN THE SOUTH OF ITALY (*) Claudio Quintano Rosalia Castellano Antonella D Agostino (**) 1. INTRODUCTION The transition from university graduation to the first job is considered one of the principal interchanges in the life cycle of individuals because it represents a vulnerable period for economic, social and psychological reasons (Oecd, 1992, Paul et al., 1993; Teichler, 2000). It can also be more marked than the transition from high school to university because it brings challenges that individuals may not have faced before or even anticipated. In Italy, over the past 10 years, the problem of the transition from university to the first job has been analyzed by many researchers and many sample surveys have also been conducted by single universities, giving an overview of youth labor market (Giambalvo, 1996; Ortu, 2000; ISTUD, 2001, Boero et al., 2001 and Checchi, 2002). There is great interest in this topic because the occupational perspective of graduates plays a determining role in measuring the university s capacity to (*) This paper was supported by the 2005 Endowment Funds of the Department of Statistics and Mathematics for Economic Research of University of Naples Parthenope in the framework of the research The Transition from University to Work. This work is coordinated by C. Quintano and it is the result of the common work of the Authors. R. Castellano is the Author of Sections 1, 2., 4.1. and A. D Agostino is the Author of Sections 3., 4.2., 5. The same fund has financed the off-print (**) Claudio Quintano is Full Professor of Economic Statistics, Rosalia Castellano is Full Professor of Economic Data Collection Procedures and Quality Control, Antonella D Agostino is Researcher in Economic Statistics, Department of Statistics and Mathematics for Economic Research of University of Naples Parthenope. Quintano C. (a cura di) (2006), Scritti di Statistica economica 12, Quaderni di discussione, Dipartimento di Statistica e Matematica per la Ricerca Economica, Università degli Studi di Napoli

3 266 C. Quintano, R. Castellano and A. D Agostino create an interchange with the labor market. This problem is especially serious in Italy where no strong link exists between the academic and the working worlds. Generally, this objective reality is more strongly felt in the geographical areas not characterized by bright professional perspectives because of an imbalance between high quality human capital and working opportunities at their level. In fact, the south of Italy has the highest youth unemployment rates, at both low, and high, educational levels. The question is: can individuals produce positive developmental outcomes in the transition from university to work, most importantly in the face of difficult contextual conditions? Using statistical terms, researchers in this field refer to the external efficacy of the university system (Gori, 1992; Bini and Pratesi, 2001; Chiandotto, 2002) because it is well known that the efficacy of a service grows as long as the obtained output is equal to the expected one. The attribute external derives from the capability of the university system to create an exchange with the economic and social context in the real word outside. In this context, the evaluation of the observable (for example the duration of an individual s university career, the marks obtained at graduation, the social and cultural background of the graduates, the gender, etc.) and/or unobservable factors that influence that efficacy itself, is of critical importance to the implementation of specific active policies. A great deal of statistical literature exists on what to measure and why, in order to obtain information useful for policy makers (Gori et al., 1998; CHEPS,1999) and a lot of empirical analysis has been carried out (Biggeri and Bini., 1999; Biggeri et al., 2001; Quintano et al., 2004). The purpose of this paper is to identify the factors that influence the university to work transition of graduates in Economics at Parthenope University of Naples, in order to conjecture possible scenarios and to predict future behaviors. Our study becomes is particularly interesting because this faculty is very young (about 10 years) and it is situated in a part of Italy that offers poor employment opportunities. In order to achieve our aim we look for the social and personal background features which lead to successful job hunting. Particular relevance is also given to the contractual condition of the first job in order to understand its

4 Moving from University to Work 267 effective stability. The empirical experience is preceded by an overview of possible statistical models that can be used in such a framework. The choice of the statistical methods is crucial because it depends on the data s structure and on the specific interest of the research. If available data allow several analyses, it is important, wherever possible, to study this transition from different angles, something that is very unusual in empirical studies. In Section 2, we briefly describe the background of analysis and in Section 3 we introduce a critical review of the theoretical framework of the transition from university to work. Then, we outline the sample survey used for the empirical analysis. Next, we turn to the evidence derived from the empirical results obtained and finally contractual conditions of the first job is discussed. 2. BACKGROUND Even though the transition in Italy from university to work is less complicated than the transition from high school to university, it presents problems that could be eliminated or at least reduced if they could be correctly analyzed. Among the negative aspects of a university career are the long duration of studies and the low quality of work available (Chiandotto, 2003). The crucial question is how to quantify the advantages of a university education The results of many Italian surveys conducted at national or local levels agree that university graduates have a better chance of finding a job than non graduates. The other fundamental question is: what are the individual and background factors that facilitate the transition from university to work? Empirical evidence shows that the choice of a scientific or economic field of study facilitates the transition (Istat, 2003, Chiandotto et al., 2004). Even if Italy has seen a progressive feminization of the labor market, women are still at a disadvantage with respect to men. The mark at graduation influence the working conditions of graduates. Young people with high marks are more selective in the labor market and they usually postpone the transition from

5 268 C. Quintano, R. Castellano and A. D Agostino school to the work (Almalaurea, 2003 and 2004). The first years after the transition are characterized by an elevated job mobility: in fact a high quota of young people change job frequently in the first three years after graduation. The time interval between graduation and the first job is typically very long (about 12 months) and even longer for women (up to 15 months). There is a marked difference in unemployment rates between northern and southern Italy; whose origin goes back many centuries in our history. Graduates in the south have more difficulty in finding a job and longer waiting times (Barbieri and Scherer, 2001; Istat, 2003). Our study attempts to verify the empirical evidence found in other Italian universities, by analyzing a reality that represent one of the widest catchment area for economic studies of Italy and moreover is situated in a southern Italian framework that presents a lot of problems regarding transition from school to work. We also aim to predict some possible scenarios, given fixed characteristics of graduates, by estimating statistical models that analyze both the outcome of transition at a fixed time and the duration of the transition. 3. METHODOLOGICAL FRAMEWORK Data structure is crucial for discussing the methodological approaches that can be used to study transition from university to work. All the statistical models presented are related to the availability of individual observations and they help to understand how particular factors can influence individual outcomes. First of all it is important to specify what is meant by university to work transition. A given set of n individuals at a fixed time t achieves its goal: the university degree. The transition takes place after this time t. From the viewpoint of the labor market, there will be a positive outcome for the i-th (i=1 n) individual, if at time t the transition to having a job has ended. In order to simplify, let us suppose that the time t occurs at same time as the interview that we fixed at t, so for the moment t =t (usually in such studies, t is one,

6 Moving from University to Work 269 two or even three years after graduation). From a methodological point of view the researcher observes a cross-section of a stochastic process that is expanding in time, because he does not know what will happen to the i-th individual after t nor does he know what has happened to the him between t and t. Focusing the attention on time t, the expected outcome of the i-th graduate can be considered a realization of a discrete cross-sectional stochastic process. Let y be the discrete stochastic variable, the h-th state (h=1 K) represents the outcome at time t of a cohort of students who graduated at time t. The specific characterization of states of the variable depends on available information and/or on the particular goal of the analysis; the simplest specification would be job / no job, in which case the transition from university to work is seen as the alternation of only two states 1. It is also true that at time t, the individual i may be employed but the job may not be his first or most recent one and the researcher doing this cross-sectional survey cannot distinguish between the two cases if no retrospective questions are made. In addition let x be a vector of covariates that represent individual and background factors. The aim of the researcher is to estimate the conditional expected probability of being in a particular state as a function of x. The statistical approach that can be used is a discrete choice model. The specification of the model depends on the K alternatives and on the hypothesis of the underlying distribution; for K=2 we generally have binary logit or probit models, and for k>2 multinomial logit or probit models (Draper, Smith, 1981, Amemiya, 1985). In order to generalize, k>2 alternatives are considered. For example, with the logit specification the conditional probability for the i-th graduate to be in the k-th alternative is modeled as: 1 There can be more than two states because graduates who have not found a job can choose to continue studying or do a training course etc.

7 270 C. Quintano, R. Castellano and A. D Agostino Pr ( y = k x ) i i = h exp K exp = 1 ( x β ) i k ( x β ) i h (1) where β 1 =1 for identification. This model is very easy from both methodological and computational points of view and it allows us to identify the factors that have a significant and positive influence on the probability of finding a job at a fixed time. Different profiles of estimated probabilities can be calculated by fixing observed individual and social characteristics and substituting them one at a time. Sometimes the data s structure is more complex because of an implicit hierarchy of the data itself that must be taken into account in order to understand whether clustering can have an effect on the outcome. This is the case, for example, of different degree courses or different faculties. The idea is that people sharing the same context can have similar behaviors and this creates a correlation among observations that cannot be neglected. If individual observations in the same cluster are correlated, the statistical model must consider it. In other words, the simple discrete choice model is replaced with a multilevel model with a discrete response variable (Goldstein, 1995). In this case a stochastic component is added to the model in order to create correlation among observations belonging to the same group. The simplest way is to hypothesize a random variable U j shared by all individuals i=1 n j at macro-level j, so that units differ randomly from one another. Let y ij be the value of the discrete variables at micro-level i and macro-level j. Equation (1) modifies as:

8 Moving from University to Work 271 Pr y ij = k x ij, U j = K exp h = 1 x exp ij β x k ij + σ k U β j h + σ h U j (2) where β 1 =1 for identification. In this case, the researcher is interested in estimating the conditional expected probability of being in state k as a function of a vector of covariates x, and in estimating the variability associated with each level of nesting. The degree of resemblance between micro-units belonging to the same macro-unit can be expressed by the intraclass correlation coefficient (Snijders and Bosker, 1999). Several complex specifications of such model exist; for a detailed discussion see: (Longford, 1993; Goldestein, 1995). Let us go back to time t of the survey. Suppose that now the interest is focused not on the states of variable y, but on the stochastic variable d that is defined as the period of time from t (time of graduation) to t (time of obtaing a job). In this case the realization of the stochastic process is represented by the waiting time (duration) in an unemployed state until the graduate i finds a job. The aim is to estimate the conditional expected duration up to a state transition. All the theory comes from the well known survival analysis (Cox and Oakes, 1984); different specifications exist for durations: totally parametric, semiparametric or non-parametric and there are two different approaches on the basis of the assumption made on the time, i.e. discrete or continuous. Since in empirical analysis a parametric duration model in continuous time is used, we refer to it only for simplicity. The idea is to specify a probability distribution for the duration d as a function of a vector of covariates x. The fundamental concept is the hazard rate, i.e. the instantaneous probability that episodes in a specific interval terminate provided that the event has not occurred before the beginning of this interval. For example, according to the log-logistic hazard rate (Blossfeld et al., 1989) a regression model is obtained by parametrization of the parameter λ with regard to the covariates in the form λ(x)=exp(x β).thus,

9 272 C. Quintano, R. Castellano and A. D Agostino λ ( d x ) = exp 1 + x exp ' β α x ( d ) α 1 ( d ) ' α (3) β There are a multiplicity of parametric specifications and the choice among them depends on the empirical hazard rate. In the university to work transition, as we pointed out before, there can be more than two destination states after the university degree; for this reason the researcher can decide to apply multistate duration models, where there is a specific hazard rate for each competitive end state. Thereby, the respective parameters of the covariates as well as the other parameters of hazard rate may depend on the destination state. Postulating a log-logistic model, the transition specific hazard rates have the same form as equation (3) but they will be identified by the m-th destination (m=1 M). Moreover, the discrete stochastic process can be analyzed after time t at a fixed temporal date such as six months, one year or more later, so as to have longitudinal observations. The panel survey would be useful to study the dynamics in the labor market of a set of graduates at time t and therefore it gives us information additional to that of the simple transition from university to work between two points in time. It is obvious that a longitudinal survey involves larger costs and longer times to take data. On the contrary, to have information before the time t, if there is not a panel survey, retrospective questions can be made in order to reconstruct the job history before the event at time t. If data are structured in this way the statistical models have to be dynamic. A complete panorama of these methods would be too long to include in this context, so only a brief discussion is dedicated to them. From a longitudinal point of view the interest is concentrated in the estimation that the dynamics of the conditional expected probabilities will be in particular states of the labor market in different times as function of individual and context factors or in the time spent in

10 Moving from University to Work 273 particular state before a transition to another state. In the first case, longitudinal models can be used as marginal, transition, random effect or fixed effect models, and in the second case as multistate-multispell duration models (Diggle, Liang and Zenger 1994; Lancaster, 1990). Many other considerations can be made; in particular, the transition from university to work can be analyzed more accurately if the kind of work obtained is considered. In the contextual reality of Italy the first job is frequently not at the same level as the educational title obtained and for this reason it is important to distinguish between work with and without contract or in more general terms between stable and non stable work. In order to synthesize the discussion, Tab presents the condition for realization of the various statistical approaches, their practical heuristics aspects, their advantages and limits.

11 274 C. Quintano, R. Castellano and A. D Agostino Tab Statistical methods for studying transition from university to work Statistical Approach Condition for realization Practical heuristic Advantages Discrete choice model Multilevel Model Duration models (single and multistate) Dynamic Model Multispellmultistate duration model Cross-sectional individual observations Cross-sectional clustering of individual observations Cross-sectional individual observations with information on date of beginning and end of the spell Longitudinal individual observations Longitudinal individual observations with information on waiting intervals aspects Estimation of the conditional expected probability of being in a particular state as function of individual and contextual factors Estimation of the conditional expected probability of being in a particular state as function of individual and contextual factors and estimation of the variability associated with each level of nesting Estimation of the conditional expected duration up to a state transition. Estimation of the dynamics of the conditional expected probabilities of being in particular states at different times as function of observable and unobservable characteristics that can be fixed or casual effects Estimation of expected duration in a given state as function of observable and unobservable characteristics Specification is easy and estimation is possible with many commonly used statistical packages Allows consideration of resemblance among microunits belonging to the same macro unit Allows estimation of interval before the event or events of interest Estimation of dependence and correlation between Y and x in only one equation Allows estimation of time interval in a given state before a transition Limits Gives a picture only at a fixed time t Gives a picture only at a fixed time t Multistate models can present the problem of unobserved heterogeneity Estimation can be complicated. Problems of left censor and estimation can be complicated

12 Moving from University to Work DATA AND RESULTS 4.1. A BINARY CHOICE MODEL FOR THE FIRST JOB The empirical analysis is based on the data from a cross-sectional survey conducted in February 2003 on four cohorts of graduates (1999, 2000,2001,2002) of the Faculty of Economics at the Pathenope University of Naples. The survey includes retrospective information on the starting date of the first job. The student population of this faculty has grown rapidly over the past four years; for this reason it is very interesting to observe the occupational and professional patterns of its graduates. The survey response rate is approximately 80% (N=651) 2. At the interview date the occupational condition is as follows: 55% employed, 10% unemployed, 22% in office training, 7% already employed before graduation and 6% outside the labor force. Bearing in mind that our main goal was to study the transition to the first job, we excluded the latter three groups in order to emphasize the distinction between the truly unemployed and employed graduates after graduation. The empirical analysis examines the factors significantly influencing the transition from university to first job of our graduates, first by considering only the outcome at fixed time 2003 and second by studying the time to obtaining the first job. The first step in our analysis is to estimate a binary choice model for the first job. The dependent variable is dichotomous: 1 indicates that the graduate is employed at time of interview (February 2003) and 0 otherwise. To overcome the problem of sparse data, we compact the cohort effect. Only the significant variables are included: two dummies for cohort effect (1 if cohort 2001 and 1 if cohort 2002), a dummy for the mark obtained at graduation (1 if over 100), a dummy for gender (1 if male), a dummy for age at graduation (1 if under 31), a dummy for the father s employment situation (1 if retired), an We used the Standard definition in telephone survey for the non response rates (The American Association for Public Opinion Research Standard Definitions): 2% refusal and break-off, 14% no-contacts and 4% other cases.

13 276 C. Quintano, R. Castellano and A. D Agostino interaction effect between gender and mark at graduation. Results are shown in Tab Tab Logistic regression estimates Covariates Estimates p-value Intercept Cohort Cohort <.0001 Mark at graduation Gender Mark at graduation *gender Father retired Age at graduation Likelihood ratio chi-square <.0001 Pseudo-Rsquare Excluded category: cohort , mark at graduation <100, female, father non retired, age at graduation >31. Very few variables have a significant effect except the obvious strongly negative cohort effect especially for the last cohort (2002) with respect to the first two: the probability of being unemployed increases as the time since graduation decreases. Positive effects on the probability of being employed are the mark and age at graduation and gender. The threshold of 100/110 for the significant effect of the mark at graduation is not very high, but our faculty is characterized by relatively low graduation marks: on average 97 for men and 101 for women, with very low standard deviations. The probability of being unemployed increases as the time spent in university increases. The threshold age of 31 years for the dummy variable seems very high, but descriptive statistics show an average graduation age of 27 years. Without considering the interaction between gender and graduation mark, men have a higher chance of being employed than women and this result is in line with most similar studies, but the negative effect of the interaction decreases this difference. In order to better understand the estimates, predicted probabilities of being employed have been computed for different individuals. First of all, characteristics of a benchmark individual are: graduation mark>100/110, male, age under 31, father working, graduation in Characteristics are substituted one at time in order to evaluate their impact on the probability of finding the first job. Tab

14 Moving from University to Work 277 presents these predicted probabilities. Since the cohort effect is an obvious result, we will discuss it below. The probability of the first job for the benchmark individual is very high (0.93), but decreases by 10% if he graduates after age 31. This tell us that spending a long time at university is negative for obtaining a job. The probability of being employed decreases by only 1% if the individual is female. It is interesting to note that a low graduation mark increases the probability of being employed by about 1% for men but decreases it by 8% for women. This result suggests that gender discrimination has a marked effect among people with lower academic results. Essentially, these findings are in line with the other studies conducted in Italy: age, graduation mark and gender play a critical role in the probability of finding a job after graduation. No other individual or contextual variables seem to come into play, with the exception of having a retired father, which may reflect elderly parents with consequent shorter time to create a link in the labor market for their children 3. Tab Predicted probabilities of being employed Characteristics Estimates BASE 0.93 Graduation mark< Female 0.92 Father retired 0.88 Age at graduation > Cohort 2002 Cohort Female and graduation mark< BASE: graduation mark>100/110, male, under 31 years old, father working, graduation in A reflection on the cohort effect is de rigueur. It is quite obvious that the earlier cohorts of graduates have a very high probability of being employed in It would be a serious problem if this trend were contradicted: it would mean that the time from graduation had no influence on the probability of having a job today. In any case, in this study it is important to check for a cohort effect 3 The last statement should be considered in a framework in which the possibility of finding a job is based on personal knowledge.

15 278 C. Quintano, R. Castellano and A. D Agostino because our aim is to see whether other factors have a significant effect on the transition from school to work ANALYSIS OF TIME TO FIRST JOB In the previous paragraph we analyze only the probability of being employed at a fixed time. Since we have different cohorts we also declare that the cohort effect is obvious. Since the probability of being employed is lower for the more recent cohorts than for the earlier ones because of the shorter time lapse between graduation and interview. At this point it becomes crucial to see whether patterns in duration to first job differ across cohorts, and whether within a given cohort the relative chances of finding a job differ across graduates. For this reason the outcome variable we study is the number of months before a graduate obtains his first job. For our purposes a single spell duration model is specified 4. The sample contains 417 observations. The distribution of acrosscohort durations is summarized using the Kaplan-Meier survivor function and the four curves show differences 5. The relative position of the curves reflects both the different characteristics of graduates across cohorts and any differences in experiences within demographic groups. The median duration is not equal across cohorts: the longest time to transition for half the population is that of cohort 2000, and those of cohorts 1999 and 2001 are almost identical. Tab displays four duration values (d=6, d=9, d=16, d=22) of survival function across cohorts. 4 Although the possible exit destination can be not only an occupation state, we decided to concentrate the analysis on a competitive risks net. All destinations different from occupation were excluded from the analysis. 5 The log-rank test (Breslow, 1970) was applied. The Mantel-Haensezel statistic is The hypothesis of equality across cohorts is refused at 5% level.

16 Moving from University to Work 279 Tab Survival function values at fixed duration times Duration d COHORT 1999 COHORT 2000 COHORT 2001 COHORT months months months months Median The estimated across-cohort hazard rates show a bell-shaped form: the probability of finding a job first increases and then decreases. For this reason, the effect of other variables on duration to the first job is computed with estimates of a log-logistic parametric model that has a flexible shape for transition rate at first rising monotonically up to a maximum and then falling monotonically. We allow for across-cohort differences by including in x dummy variables for the 1999, 2000 and 2001 cohorts. Tab summarizes the results from estimates of equation (3). Only significant covariates were included in x. We will deal only briefly with estimate results, as they are not easy to interpret and we prefer to compute the estimate hazards for different individuals and to compare them. Tab shows that besides the cohort effect only four variables have a significant effect on the duration: graduation mark, age at graduation, gender and father s occupational position. The remaining variables are non significant and thus excluded from analysis. The significant effect of the interaction between gender and graduation mark is interesting. The estimated coefficients for cohort are negative, suggesting longer waiting periods for the earlier cohorts. The highest difference in estimated coefficient is between the 2000 and the 2002 cohort. Age at graduation has a positive effect, meaning that younger graduates find a job more quickly. Gender and graduation mark have to be evaluated taking into account that their interaction has a negative effect. Scale parameter is also significantly different from zero. Thus we conclude that increasing youth unemployment experience leads to an increasing and then decreasing rate of finding the first job. Tab reports the estimated hazards at the fixed times of 6, 9, 16 and 22 months after graduation and the

17 280 C. Quintano, R. Castellano and A. D Agostino expected median duration for a set of individuals, substituting the characteristics one at a time starting from the benchmark individual (male, >100 graduation mark, under 31 years old, father working and graduation in 2001). Tab Log-logistic hazard duration model estimates Covariates Estimates p-value Intercept <.0001 Cohort Cohort Cohort Graduation mark Gender Graduation mark*gender Retired father Age at graduation Scale parameter α < Mean Log-likelihood Excluded category: cohort 2002, graduation mark<100, female, father not retired, age at graduation over 31. Tab Estimated hazard at fixed duration and expected medians Estimated hazard at Duration: Expected median (std) Characteristics 6 months 9 months 16 months 22 months BASE (0.99) Graduation mark< (0.63) Female (0.81) Father retired (1.44) Age at graduation> (3.09) Cohort (1.57) Cohort (1.49) Cohort (1.09) Female and graduation (1.09) mark<100 BASE: graduation mark>100/110, male, under 31 years old, father working, graduation in The hazard curve increases and then decreases, with slight individual variations. The interaction between graduation mark and gender implies that although males with low marks find work sooner than those with high marks (maybe because cleverer grads tend to search for their ideal job), female grads with high averages find work sooner than their male counterparts. Generally,

18 Moving from University to Work 281 age plays a fundamental role: students who have taken several years longer to reach graduation are also slower in finding a job. The cohort effect is strong: the first two cohorts show lower hazard than the last two. The highest expected duration is for graduates who have spent the longest time at university. Their expected median duration is about 14 months, instead of the benchmark individual s 8 months. It seems paradoxical, and interesting from a social point of view, that the shortest median duration is for graduates with low averages at graduation. One would expect that a brilliant university graduate would have the best chance of finding a job immediately. In Italy, and especially in the south, the first or even definitive job is often below the optimal level of the graduate and this explains the shorter median duration for grads with a low average: the higher the academic record, the longer the search for a suitable type of job. On the contrary, female grads with high average on graduation wait for a job about 7 months, less than their male counterparts (8 months) and less again than females with low graduation mark (9 months). The cohort effect seems to favour the later cohorts with respect to earlier two. This is probably due to recent increasing flexibility in the labor market, which has created more, but less permanent, jobs for young people. This additional model focusing on the duration of unemployment before the first job offers a more complete and detailed picture of the transition from university to work of graduates in Economics. Empirical results tell us that individual characteristics with a significant effect from a cross-sectional point of view are the same as those that influence the duration of the transition. However, the latter model allows us to predict median duration of the state of unemployment, and this is a useful parameter for policy makers. A comparison of our results with other empirical evidence reveals our interesting interaction between gender and graduation mark. Even if it is well know that gender discrimination exist in the Italian labor market, differences between males and females are affected by the graduation mark. If one look at female grads of high academic quality discrimination would appear to be waning.

19 282 C. Quintano, R. Castellano and A. D Agostino 5. DISCUSSION How long does it take university graduates to make the transition from school to work, and which are the factors that have a positive influence on this transition? The answer depends in part on when we consider the transition complete. In our empirical analysis we assume that the transition is made when the graduate finds the first job, as in most sample surveys that study this problem. However from a social and interpretive point of view the quality of this job can be relevant and, we feel, a non negligible aspect of the transition. Regardless of the statistical method used, having a full-time job with an indeterminate contract implies stable employment in time for the graduate; on the contrary, a job with a limited-time contract or even worse without contract cannot imply the same quality of employment. For this reason, we continued the empirical analysis by showing the distribution of graduates according to the contractual condition of the first job across cohorts (see tab. 7). The percentage of graduates with indeterminate contracts is higher in the first cohort, but the trend is not a regular decrease across cohorts. The incidence of this type of contract is high in any case and this is a positive result, although there is a non negligible quota of grads working with no contract in all cohorts. This means that an underground economy exists and its weight can be important. On the other hand, it is not easy to pinpoint the threshold between stable and non stable work. If we assume that a stable job implies a permanent duration in the time, we can consider together only workers on indeterminate contracts, family assistants and self-employed workers. We used this classification in another statistical analysis, by doing a logit regression of stable work with respect to a set of covariates. We do not present the results because no covariates were significantly different from zero, and thus factors that help to define the differences between being employed or not employed, and also the duration of unemployment, are unable to explain differences in stability of the contractual condition of graduates; it is likely that others reasons that do not appear in the

20 Moving from University to Work 283 present survey have to be taking into account. In any case, the overall view of percentages in Tab is quite comforting, considering that the geographic area of the analysis is characterized by a high degree of precariousness in employment for young people at all educational levels. Tab % Graduates according to contractual condition of the first job Percentage of employed with Cohort Cohort Cohort Cohort Total Worker on indeterminate contract Worker on limit time contract Junior clerk contract Seasonal or occasional contract Permanent collaboration contract Family assistant Interim work contract Mandate agency contract Self-employed worker No contract In conclusion, the transition from university to work of our graduates seems to end in stable work in almost all cases, which logically is a positive indicator of a traditional concept of the Italian labor market. The drawback is that the underground economy is not negligible at all.

21 284 C. Quintano, R. Castellano and A. D Agostino REFERENCES ALMALAUREA, (2003), Profilo dei laureati 2002, Bologna. ALMALAUREA (2004).Relazione sulla condizione occupazionale dei laureati in Italia, Bologna. AMEMIYA, T., (1985). Advances Econometrics. Harvard University Press, Cambridge, MA. BARBIERI P.AND SCHERER S. (2001) Logici e razionali? Comportamenti strategici dell offerta di lavoro nella transizione scuola-lavoro: un confronto fra Nord e Sud Italia, presentato al convegno Aiel Qualità del processo formativo ed esiti sul mercato del lavoro, Milano, novembre BIGGERI, L., BINI, M. (1999). A Multilevel logistic model for the analysis of Italian universities effectiveness. Proceedings of Annual Meeting of American Statistical Association. Baltimore (August 1999). BLOSSFELD H.P., HAMERLE A., MAYER K.U. (1989), Event History Analysis, Hillsdale, NJ: Lawrence Erlbaum Associates. BUGGERI, L., GRILLI, L., BINI, M., (2001). The transition from university to work: a multilevel approach to the analysis of the time to obtain the first job. Journal of Royal Statistical Society Serie A, 162 (2), BINI, M., PRATESI, M., (2001). Un modello multi-livello per stimare l efficacia esterna della formazione universitaria con disegno di campionamento. Atti del Convegno Processi e Metodi Statistici di Valutazione, Roma, 4-6 giugno 2001, Società Italiana di Statistica. BOERO G., MC KNIGHT A., NAYLOR R., SMITH J., (2001). Graduates and graduate labour market in Uk and Italy, presentato al Convegno Aiel Qualità del processo formativo ed esiti sul mercato del lavoro, Milano, novembre CHECCHI (2002). Formazione e percorsi lavorativi dei laureati dell Università degli Studi di Milano, Working Paper n.14, Facoltà di Scienze Politiche, Università degli Studi di Milano. CHEPS (1999). University Funding Mechanisms and Related Issus. Roma, Murst, Osservatorio per la valutazione del sistema universitario. CHIANDOTTO, B. (2002). Profilo e condizione occupazionale dei laureati dell Ateneo fiorentino ad uno, due e tre anni dal conseguimento del titolo. Università degli Studi di Firenze. CHIANDOTTO, B., BACCI, S., BERTACCINI, B. (2004). I laureati e diplomati dell Ateneo Fiorentino dell anno 2000: profilo e sbocchi professionali. Università degli Studi di Firenze.

22 Moving from University to Work 285 CHIANDOTTO, B. (2003). Valutazione e monitoraggio dei processi formativi nell Università di Firenze. COX, D.R., OAKES, D. (1984). Analysis of Survival Data, London: Chapman and Hall. DIGGLE, P.J., LIANG, K.Y., ZENGER, S.L. (1994). Analysis of Longitudinal Data. Clarendon Press, Oxford. DRAPER, N.R., SMITH, H. (1981). Applied Regression Analysis, 2d ed. New York: Wiley. GIAMBALVO, O., (1996). Tempi di attesa per l inserimento nel mercato del lavoro di una leva di laureati, Rivista Italiana di Economia, Demografia e Statistica, vol. L (1), Roma. GOLDESTEIN, H. (1995). Multilevel Statistical Models. Edward Arnold, Londra. Gori, E., (1992). La valutazione dell efficienza e efficacia dell istruzione. Atti della XXXVI Riunione Scientifica della Società Italiana di Statistica, CISU, Pescara, GORI, E., FABBRI, D., UKOVICH, W. (1998). Alcuni suggerimenti per la costruzione di un sistema di monitoraggio e valutazione dell università, Osservatorio per la valutazione del sistema universitario, MURST, Roma. ISTUD, (2001). Osservatorio sul lavoro giovanile ad alta qualificazione. Sintesi primo rapporto, Associazione ISTUD per la cultura di gestione. ISTAT, (2003). Caratteristiche dell indagine sull inserimento professionale dei laureati: Indagine 2001, Roma, ISTAT, (2003). I laureati e il mercato del lavoro: Inserimento professionale dei laureati, Differenze territoriali, Indagine 2001, Roma, 2003, LANCASTER, T., (1990). The econometric analysis of transition data, Cambridge University press, Cambridge. LONGFORD, N.T., (1993). Random Coefficient Models. Clarendon Press. Oxford. OECD, (1992), From Higher Education to Employment, 4 voll., Paris. ORTU, A., PUGGIONI, G., TEDESCO, N., (2000). Inserimento nel mercato del lavoro dei laureati in Scienze Politiche dell Università di Cagliari, in Valutazione della didattica con sistemi computer-assisted, Cleup. PAUL J.J., TEICHLER U., VAN DER VELDEN R., (1993) (a cura di), Higher Education and Graduates Employment, in European Journal of Education, 35, 2, giugno (special issue) THE AMERICAN ASSOCIATION FOR PUBLIC OPINION RESEARCH, (2004). Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. 3rd edition. Lenexa, Kansas: AAPOR.

23 286 C. Quintano, R. Castellano and A. D Agostino TEICHLER U., (2000), New Perspectives of the Relationships between Higher Education and Employment, in Tertiary Education and Management, 6, 2, pp TUMA, N:B., HANNAN M.T., (1984). Social Dynamics: Models and Method, New York: Academic Press. QUINTANO C., CASTELLANO R., D AGOSTINO A., (2004). The Determinants of the Transition from the University to Work: the Case of the Graduates in Economics at the University of Naples Parthenope, Atti della XLII Riunione Scientifica della Società Italiana di Statistica. SNIJDERS, A.B., BOSKER, R.J., (1999), Multilevel analysis. An introduction to basic and advanced multilevel modelling. Sage Publications.

24 Posizione degli Autori Riassunto - Summary - Résumé 541

25

26 557 Claudio QUINTANO Professore Ordinario di Statistica economica Rosalia CASTELLANO Professore Ordinario di Rilevazione e controllo di dati economici Antonella D AGOSTINO Ricercatore di Statistica economica Dipartimento di Statistica e Matematica per la Ricerca Economica, Università degli Studi di Napoli Parthenope. Riassunto La transizione dall Università al lavoro: il caso di una Facoltà di Economia nel Sud Italia I dati rilevati tramite un indagine condotta nel Febbraio 2003 su 4 coorti di laureati alla facoltà di economia dell università di Napoli Parthenope situata nel sud Italia, sono utilizzati per analizzare quali siano i fattori individuali e di contesto che influenzano la transizione dall università alla prima occupazione in modo tale da creare un valido supporto ai politici. La ricerca mette in evidenza come il genere e il voto di laurea giochino un ruolo fondamentale nella transizione. L analisi empirica si contestualizza nel più generale contesto di metodi statistici che generalmente sono usati per studiare la transizione scuola-lavoro e inoltre sono condotte due analisi differenti che mettono in evidenza come lo stesso problema possa essere studiato da differenti angolature.

27 558 Summary Moving from University to Work: the Case of a Faculty of Economics in the South of Italy Data from a cross-sectional sample survey conducted in Februry 2003 on four cohorts of graduating classes in the economics faculty of the Parthenope University of Naples situated in the south of Italy, are used to analyze which individual and background factors influence the transition from university to the first job in order to create a valid support for policy makers. We find that interaction between gender and degree mark plays an important role on the transition. The empirical analysis is contextualized in the general framework of statistical methods generally used for studying the school-to-work transition and two different analyses are conducted with the aim of emphasizing how different statistical methods can provide insight into problem from several point of view. Resumé La transition de l université au monde du travail: le cas d une faculté d économie dans le Sud d Italie Les donnés enregistrées avec une enquête menée dans le février 2003 sur 4 cohortes de titulaires d un diplôme universitaire à la faculté d économie de l université de Napoli Parthenope placée dans le sud d Italie, sont utilisées pour analyser les facteurs individuels et de contexte qui influencent la transition de l université à la première occupation de façon de créer un valable support aux politiques. La recherche met en évidence comme le genre et la note du diplôme université havent à rôle fondamental dans la transition. L analyse empirique est contextualisée dans le tableau plus général de méthode statistiques qui sont utilisée pour étudier la transition école-travaille et de plus deux différent

28 559 analyses sont menées qui met en évidence comme un problème peux être étudié des différent points de vue.

29 La presente pubblicazione, per la quale sono stati adempiuti gli obblighi previsti dalle norme per la consegna obbligatoria di esemplari degli stampati e delle pubblicazioni di cui alla legge del 2 febbraio 1939 n. 374 e successive modificazioni, è soggetta alle norme vigenti in materia di tutela del diritto di Autore come previsto nella legge 22 aprile 1941 n. 633 e successive modifiche. Pertanto, è vietata la riproduzione non autorizzata, anche parziale, con qualsiasi mezzo effettuata compresa la fotocopia e la masterizzazione in conformità anche di quanto previsto dalle modifiche e integrazioni introdotte dalla legge 18 agosto 2000 n. 248 e dal Decreto Legislativo n. 28 del La pubblicazione, finita di stampare nel mese di febbraio 2006 è stata depositata in copia cartacea e su supporto informatico (CD-ROM) presso i seguenti uffici: n. 1 copia presso il Dipartimento di Statistica e Matematica per la Ricerca Economica Università degli Studi di Napoli Parthenope, Via Medina, Napoli, protocollo Registro dello Stampatore n. 10 dell anno 2006; n. 4 copie presso l Ufficio Stampa della Prefettura di Napoli consegnata all Ufficio in qualità di stampatore; n. 1 copia presso l Ufficio Stampa della Procura Generale della Repubblica di Napoli consegnata all Ufficio in qualità di stampatore; n. 1 copia agli uffici della Questura di Napoli sezione Digos consegnata all Ufficio in qualità di Editori della Rivista Scritti di Statistica Economica.