Youth labour market integration in Spain: Search time, job duration ...

Span. Econ. Rev. 7, 191–208 (2005) DOI: 10.1007/s10108-005-0097-7 c Springer-Verlag 2005

Youth labour market integration in Spain: Search time, job duration and skill mismatch Maite Blázquez Cuesta The Amsterdam Institute for Advanced Labour Studies (AIAS), University of Amsterdam, Plantage Muidergracht 4, 1018 TV Amsterdam, The Netherlands (e-mail: maite [email protected])

Abstract. In recent years young workers in Spain have had great difficulties in finding and maintaining a job. Furthermore, the early histories of new entrants into the Spanish labour market has typically involved young workers accepting jobs for which the required level of education is lower that the attained level. Using the ad hoc module of the Labour Force Survey (2nd quarter, 2000), in this paper I analyse the transition from school to work for a sample of Spanish youths who left education for the first time after 1990. I allow the search period after completing education, the duration of the first significant job, and the probability of being over-educated in that job to be correlated in a system of simultaneous equations which is estimated by maximum likelihood. The results suggest that over-educated workers experience shorter durations in their first significant job. I do not find a direct effect of search time on employment duration. However, there are unobserved factors that increase the first unemployed job search period after completing education, and that also increase the subsequent employment duration. JEL Classification: C34, J64 Key words: Search time, job duration, skill-mismatch, over-education

1 Introduction Although the analysis of the determinants of unemployment duration is well documented, very little is known regarding job spells after unemployment, and more generally regarding the long-term effects of unemployment on work careers. This is a crucial issue for the Spanish labour market, which has faced one of the highest unemployment rates in the OECD since the mid-eighties, with youth unemployment The data set has been provided by professor Alfonso Alba-Ram´ırez. I am also grateful to Juan José Dolado, Marcel Jansen, Ricardo Mora, José Ignacio Gar´ıa, Juan Francisco Jimeno and two anonymous referees for useful comments and suggestions. The usual disclaimer applies.

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rates approximately 20% higher than the average. Most of the rise in the unemployment rate is due to a substantial lengthening of the average unemployment spell, rather than increased inflows into unemployment. However, young people do not only face problems in obtaining their first job, but they also have difficulties in maintaining it. Furthermore, young workers entering the Spanish labour market typically accept jobs for which the required level of education is lower than the level they have attained. In the 1960s Spain had one of the lowest stocks of human capital in the OECD. Since then, the share of educated workers has increased remarkably (see Dolado et al., 2000). Nonetheless, this educational drive has been accompanied by high and persistent unemployment rates. In 1995 the Spanish unemployment rate was around 23%, about twice the average of other European countries (OECD, 1999). Looking at the unemployment rates by age group, more than 50% of the 16 to 19-year-old population and more than 40% of the 20 to 24-year-old population were unemployed (Labour Force Survey, 1995). It is also well known that the average unemployment spells of young workers in Spain are long-lasting. The ad hoc module on the school to work transitions, included in the Labour Force Survey (2nd quarter, 2000) reveals that Spanish youths spend, on average, around 28 months looking for a job after completing their education. Nonetheless, average employment durations at this first stage of their working life are around 24 months. Finally, it appears that the impressive evolution of the supply of highly-educated labour has not been accompanied by an equal increase in the supply of skilled vacancies, Dolado et al. (2000), thus resulting in over-education. These are some of the stylised facts that have characterised the poor performance of the Spanish youth labour market in recent years, and that have made the transition from school to work one of the key topics of current social research and policy interests. In this paper, I analyse the early labour market experience of Spanish youths. The data are taken from the ad hoc module of the Labour Force Survey (2nd quarter, 2000), which is specifically designed to analyse the transition from school to work for people aged 16–35 who left continuous education for the first time after 1990. I focus on how the first unemployed job search period after completing education is related to the subsequent employment duration and the quality of the accepted matches. The importance of this study is that I model search time, job duration and skill mismatch simultaneously. This estimation procedure allows me to control for unobserved heterogeneity that might lead to biased estimates. A priori the effect of search time on job duration may be ambiguous. On the one hand, we may argue that the length of the unemployment period adversely affects the outcome of the search process. Longer unemployment durations could be interpreted as an adverse productivity signal, or as a signal of low human capital, consequently reducing the arrival rate of job offers. But on the other hand, longer search periods might improve the quality of the match, resulting in longer employment durations. In this paper I try to shed some light on the relationship between search time, employment duration and the quality of the accepted matches for the Spanish youth labour market.

Youth labour market integration in Spain: Search time, job duration and skill mismatch

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The main results of this paper can be summarised as follows. First, search time, job duration and the probability of being over-educated should be estimated jointly. Second, I find evidence of a direct negative effect of over-education on employment duration. In contrast, there is no evidence of a direct effect of search time on employment duration, although there are unobserved factors that simultaneously increase the duration of the first period of unemployment after leaving education and the duration of the subsequent spell of employment. Finally, I find unobserved factors that increase search time and also reduce the probability of being overeducated in the first significant job. The paper is set out as follows. Section 2 provides a short review of previous research. Section 3 presents the econometric model and I describe the data set in Sect. 4. The empirical results are contained in Sect. 5, while Sect. 6 concludes.

2 Previous research Several international studies have recently analysed the transition from school to work. This literature is reviewed in Ryan (2001) who points out the need to develop nationally appropriate institutions in order to improve school to work transitions. Another significant work in this field is the Thematic Review of the Transition from Initial Education to Working Life (OECD, 2000). The Review was designed to obtain a better understanding of both how young people’s transitions changed during the 1990s and of the types of national policies and programmes associated with successful transition outcomes in OECD countries. It highlights certain peculiarities of the Spanish youth labour market that are worthy of mention, especially relating to the difficulties that young people experience in the early stage of the transition process. In particular, Spain together with Italy and Greece, have the least effective transition outcomes among OECD countries. The over-education phenomenon has also been the subject of many studies. Eurostat (2003) reveals that this phenomenon is particularly relevant in the Southern Mediterranean countries. These countries have experienced an intense upgrading of tertiary education over the last fifteen years, and this shift in the distribution of educational attainments seems to have outpaced the growth of skilled jobs, giving rise to proportions of between 15% and 25% of skill mismatch at the highest educational levels. Studying school to work transitions has become of paramount importance in understanding the poor performance of the Spanish youth labour market over the last two decades. Nonetheless, previous studies were concerned mostly with descriptive statistical methods, e.g. Albert et al. (2000), Dolado et al. (2000). Some studies have analysed the employment probabilities of Spanish graduates, Sáez Fernández and Rey Boullón (2000), Mora et al. (2000). Duration models have been estimated to investigate the determinants of exit rates from unemployment and employment for young and adult workers, Garc´ıa-Pérez (1997), Garc´ıa-Fontes and Hopenhayn (1996). And finally, some models have analysed the type of match in the first job, Lassibille et al. (2001). However, there is no study which pays particular attention to the relationship between the search period after finishing education, the duration

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in the first significant job and the presence of some type of mismatch between education and work. School to work transitions have also been analysed in other countries. Using Canadian data, Belzil (1995) finds a negative relationship between job duration and the preceding unemployment spell. He sets up an unemployment/re-employment duration model as a simultaneous (recursive) system. In Belzil (2001) a multi-state duration model with state specific unobserved heterogeneity is estimated to investigate whether the correlation between completed unemployment duration and subsequent job duration is explained by a job matching effect or simply by unobserved heterogeneity. Belzil’s results indicate that both hypotheses contribute to explain the observed correlation between unemployment duration and accepted job duration. With Norwegian data, Bratberg and Nilsen (2000) analyse the connection between search time, hourly earnings and job duration simultaneously, finding a positive relationship between the length of the search period and employment duration.

3 The econometric model 3.1 Motivation The statistical analysis is based on the principle that the length of the search period after completing education, the subsequent employment period and the presence of some type of skill mismatch may be connected in a system of simultaneous equations. First, unobserved factors increasing the length of the unemployment period may improve the quality of the match. Second, there could be unobserved factors that simultaneously affect the quality of the match and, at the same time, influence job duration. Finally, my approach allows an analysis of the effect of the length of the first unemployment period after completing education on the duration of the first significant job. The econometric model is close in spirit to Bratberg and Nilsen (2000). Since the possibility that the type of job match may be correlated with search time and employment duration could lead to biased results, I pay particular attention to the endogeneity between the first unemployed job search period, the subsequent employment period and the presence of some type of skill mismatch.1

3.2 Model specification In the literature there exists various versions of the Tobit model that may be appropriate for the analysis of survey data. The pioneer work was developed by McFadden (1973) and Amemiya (1973). More elaborate simultaneous equation models were introduced by Nelson and Olson (1978), Heckman (1974), Amemiya (1974) and 1 The unavailability of information on earnings does not allow me to include wages in the analysis. In contrast, the data set provides information on the mismatch between education and work. If better matches are related to higher wages, I would expect no significant bias in the estimation results.


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Lee (1978). However, for survey structures with nested sequences of questions as described above, these models are inappropriate. On the one hand, a nested logit model is inadequate because it fails to use the additional information provided by nested responses. On the other hand, simple Tobit models applied to each question separately are equally inappropriate because the nested data sets are sequentially censored and the method does not account for this selection bias. The econometric problem is to estimate sequentially censored equations in a manner that makes full use of the information while at the same time producing consistent and efficient estimators of the equations’ parameters. Lee (1992) proposed the so-called nested Tobit model, which is an hybrid of the Tobit and the nested logit model. The nested Tobit model uses the nested logit model structure while utilising the additional quantitative information as the Tobit model does. The model used in our analysis is based on the nested Tobit model proposed by Lee (1992). Let T ∼ G(t; x) be the time spent in some state, where G is the c.d.f of a density function g, and x is a vector of covariates. The hazard rate is defined as: λ (t, x) = lim

dt→0

g (t; x) Pr (t ≤ T < t + dt| T ≥ t; x) = dt 1 − G (t; x)

(1)

In this model, the distribution must be specified in such a way that allows modelling of simultaneous equations. The model must also allow for possible right censoring of both search and job durations. Let xs , ze and xo denote the vectors of covariates that affect search duration (ts ), job duration (te ) and the probability of being overeducated (over), respectively. The corresponding coefficient vectors are denoted βs , δe and βo . The equations to be estimated simultaneously are the following: ln ts = min (xs βs + u1 , ln τs ) over = xo βo + η ln ts + u2 ln te = min (ze δe + γ ln ts + αover + u3 , ln τe )

(2) (3) (4)

where τs and τe are the censoring times of the search and job duration respectively. If we assume that the error terms are jointly normal, that is:    2    σ1 σ12 σ13  u1   0         2      0 u σ σ σ ∼ N (5) , = u=    21 2 23   2      u3 σ31 σ32 σ32 0 then, the likelihood of the system may easily be constructed by recognising the three possibilities of censoring. Firstly, ts may be censored, in which case, neither employment duration, te , nor the probability of being over-educated are observed. Secondly, ts is uncensored while te is censored. And finally, both ts and te are uncensored. The likelihood contribution of an individual who is censored at τs is simply: xsi βs − ln τsi (6) Pr (tsi ≥ τsi ) = Φ σ1

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where Φ(.) denotes the c.d.f of the standard normal distribution. In the second case, ts observed without censoring and te censored, the individual contribution to the likelihood is: ∞ (7) Pr (tei ≥ τei , overi , tsi ) = f (ln tei , overi , ln tsi ) d ln tei τe

This can be expressed in terms of conditional probability, in the following way: Pr (tei ≥ τei , overi , tsi ) = Pr (tei ≥ τei | overi , tsi ) × foveri ,ln tsi (overi , ln tsi ) = (8) −1 xei βe − ln τe + Σ21 Σ11 yi 1 −1 −1/ −1 × (2π) |Σ11 | 2 exp − yi Σ11 Φ yi σ3·12 2 δei + γ ln tsi + αoveri , and: where xei βe ≡ zei 2 σ1 σ12 σ13 ; Σ12 = ; Σ21 = Σ12 Σ11 = σ21 σ22 σ23 ln ts − xsi βs y= over − xo βo − η ln ts

(9)

−1 2 σ3·12 = σ32 − Σ21 Σ11 Σ12

Finally, the contribution to the likelihood of those individuals with both search and job durations uncensored, is given by: (10) f (ln tsi , overi , ln tei ) = 1 2/3 −1/2 (2π) |Σ| exp − (yi , ln tei − xei βe ) Σ −1 (yi , ln tei − xei βe ) 2 Collecting all these results, taking natural logarithms of (6), (8) and (10), and summing over individuals, we obtain the following log-likelihood function: N xsi βs − ln τsi L (β, Σ) = + (1 − d1i ) ln Φ σ1 i=1 −1 yi xei βe − ln τe + Σ21 Σ11 − di1 (1 − d2i ) ln Φ σ3·12 1 1 1 −1 ln (2π) − ln (|Σ11 |) − yi Σ11 yi − d1i d2i {3 ln (2π) + ln (|Σ|) +(11) 2 2 2 −1 (yi , ln tei − xei βe ) Σ (yi , ln tei − xei βe ) where d1i and d2i define two indicator functions that allow me to distinguish between censored and uncensored observations of search and job duration, respectively. From this log-likelihood function, each parameter of β and is identified separately. These parameters are then estimated iteratively using the NewtonRaphson method.


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4 Data Empirical results are based on data from the ad hoc module on school to work transitions, included in the Labour Force Survey (2nd quarter, 2000), and conducted by the Instituto Nacional de Estad´ıstica (INE). The module consists of 14 additional questions designed to provide information on two main issues: the time spent searching for a job, and the maximum educational level attained by Spanish youths. The module provides retrospective longitudinal information regarding the transition from school to work of young people aged 16–35 who left education for the first time after 1990.2 It includes relevant information regarding the first significant job, which is defined as a non-marginal employment of at least 20 hours per week, which has started after leaving continuous education. It also provides information regarding certain key variables of interest in transition studies, notably social background, the level and type of education when first leaving education, the date of first leaving education and training, the search duration until the first significant job is found and the duration and nature of this first significant job. Furthermore, the data set allows me to identify the type of match in the first significant job. Measurement of the type of job match is based on the objective method, which consists of using the distribution of qualifications to construct an over-education index, Cohn and Khan (1995), Groot (1993), Verdugo and Verdugo (1989). A worker is defined as over-educated if his/her years of education are above the mean educational level of the corresponding occupation plus one standard deviation. Adequately educated workers are those whose educational level is higher than the mean educational level of the corresponding occupation minus one standard deviation and lower than the mean educational level plus one standard deviation. And finally, a worker is under-educated if his/her educational attainments are below the mean education of the corresponding occupation minus one standard deviation.3 The survey was administrated to more than 180,000 individuals. However, only about 15,000 school-leavers were asked about their first significant job obtained after leaving the educational system. After deleting observations with missing variables, I was left with a sample of 10,863 individuals. For these individuals I constructed an unemployment variable measuring the number of months form completing school until employment, assuming that school-leavers are also job seekers. For those who had still not found their significant job, the variable measures the number of months searching for that job. The only employment durations which I 2 Individuals who left education temporarily due to motherhood, serious illness, military or social service, and those waiting for a diploma to access higher levels of education are not included in the analysis. 3 Mean educational levels by occupation are constructed using data from the Spanish Labour Force Survey rotating panel, for the period 1990Q2 to 2000Q2. The classification of occupations provided by this data set follows the National Classification of Occupations (CNO-94), which is the most recent Spanish adaption of the International Standard Classification of Occupations (ISCO-88). We use the twodigit level of CNO-94 to compute mean educational levels by occupation. The over-education index is then constructed taking into account the mean educational level of the corresponding occupation associated to the year when the individual found the job.

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can observe are for those school-leavers with complete job search durations. This leaves me with a sub-sample of 8,432 individuals with uncensored search duration. Out of 8,432 individuals with complete unemployment durations, 6,006 were still employed (incomplete job durations), while the remaining 2,426 had already left their first significant job.4 There are several factors that may affect both unemployment and employment durations. One of these is the level of education. If there is competition for good quality jobs, individuals with higher educational levels are expected to be more likely to get a job offer after completing their education. However, education might increase the length of the search period because higher education is normally associated with higher reservation wages. Studying the effects of the field of education on early labour market integration is also of paramount importance in our analysis. Apart form the increased enrolment in higher education, one of the most relevant features of the Spanish university system is the relative importance of social science degrees. As pointed out by Dolado et al. (2000), the percentage of people in social sciences in 2000 was around 50% of the total amount of those enrolled in college education. Furthermore, in 1996 Spain had the highest percentage of people with a social science degree among the OECD group. In contrast, the percentage of people with engineering and other science degrees was relatively small. Another factor that may influence search time and employment duration is the local unemployment rate. We would expect the structural circumstances of the local market to play an important role in determining the time spent searching for a job and employment duration. A higher unemployment rate is likely to increase the time individuals spend searching for a job, but it might also induce workers to stay longer in a job. Furthermore, we have taken into account the time fixed effects to capture the impact of the business cycle on the job opportunities of young workers. Finally, one factor that may significantly affect the duration in the first significant job is the type of job match. Following the Matching Theory, Jovanovic (1979b) a match between a worker and a job can be treated as a pure experience good. The only way to determine the quality of a particular match is to form the match and to “experience it’. Under this reasoning, the presence of some type of mismatch clearly influences the separation of a worker from his/her job, either due to dismissal or voluntary separation. Table 1 contains the variable definitions, where I apply exclusion restrictions for some of the exogenous variables of the system. A reduced form equation for search time should include the following individual attributes: gender, non-Spanish nationality, age when completing education and its square, the highest educational level completed, and the field of education for those individuals completing tertiary education.5 Apart from these personal characteristics, I control the transition from school to work for local labour market conditions by explicitly including in the 4 For both unemployment and employment durations, individuals with two or less than two months of duration are included within the same category. 5 I consider the age when obtaining the diploma as that when beginning the job search. Furthermore, the field of education for people with tertiary/university education was classified into twenty five detailed categories which could be aggregated into nine broad categories.


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Table 1. Variable definition Variable

Equation

Definition

Male Ext Age

1, 3 1, 2 1, 2, 3

Unem.Rate

1, 3

Primary Secondary Education

1, 2, 3 1, 2, 3 1, 2, 3

Arts Social Science

1, 2, 3 1, 2, 3

Science Engineering

1, 2, 3 1, 2, 3

Agriculture

1, 2, 3

Health Services Over-educated Adeq. educated Under-educated O0 O1

1, 2, 3 1, 2, 3 3 3 3 2, 3 2, 3

O2 O3

2, 3 2, 3

O4 O5

2, 3 2, 3

O6

2, 3

O7

2, 3

O8

2, 3

O9

2, 3

Y91-Y00

1, 3

Dummy variable indicating individual is male Dummy variable indicating individual is non-Spanish Age (1): Age (years) when leaving the school Age (2): Age (years) when beginning the first significant job Unem. Rate (1): Local unem. rate in year education was completed Unem. Rate (2): Local unem. rate in year when beginning first job Primary education Secondary education Dummy variable indicating field of tertiary education: Education Dummy variable indicating field of tertiary education: Arts Dummy variable indicating field of tertiary education: Social Science Dummy variable indicating field of tertiary education: Science Dummy variable indicating field of tertiary education: Engineering Dummy variable indicating field of tertiary education: Agriculture Dummy variable indicating field of tertiary education: Health Dummy variable indicating field of tertiary education: Services Dummy variable indicating individual is over-educated Dummy variable indicating individual is adeq. educated Dummy variable indicating individual is under-educated Dummy variable indicating occupation: Armed Forces Dummy variable indicating occupation: Legislators, senior officials and managers Dummy variable indicating occupation: Professionals Dummy variable indicating occupation: Technicians and associate professionals Dummy variable indicating occupation: Clerks Dummy variable indicating occupation: Service workers and shop and market sales workers Dummy variable indicating occupation: Skilled agricultural and fishery workers Dummy variable indicating occupation: Craft and related trade workers Dummy variable indicating occupation: Plant and machine operators and assemblers Dummy variable indicating occupation: Elementary occupations Year dummy variables

Equation (1): Search duration Equation (2): Probability of being over-educated Equation (3): Job duration

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Table 2. Descriptive statistics Full sample (N = 10, 863)

ts (uncensored) (N=8,432)

ts (censored) (N = 2, 431)

Variable

Mean

Std. dev

Mean

Std. dev

Mean

Std. dev

ts te Male Ext Age (1) Age (2) Primary Secondary Education Arts Social science Science Engineering Agriculture Health Services Unem. rate (1) Unem. rate (2) Over-educated Adeq. educated Under-educated

27.835

25.707

22.430

0.499 0.105 3.991

0.385 0.010 19.200

0.486 0.101 4.086

0.325 0.214 0.026 0.033 0.182 0.037 0.089 0.008 0.049 0.021 20.321

0.468 0.410 0.161 0.179 0.386 0.189 0.286 0.091 0.216 0.142 6.356

26.137 22.614 0.496 0.106 3.960 3.828 0.462 0.413 0.157 0.178 0.393 0.193 0.306 0.097 0.211 0.143 6.089 6.148 0.470 0.482 0.191

19.918

0.521 0.011 19.437

30.117 24.345 0.561 0.011 19.505 21.555 0.309 0.219 0.025 0.033 0.191 0.039 0.104 0.010 0.047 0.021 19.894 18.016 0.330 0.632 0.038

0.398 0.206 0.033 0.037 0.162 0.033 0.044 0.005 0.060 0.021 21.801

0.489 0.405 0.179 0.187 0.368 0.178 0.206 0.070 0.236 0.145 7.011

model the unemployment rate of the entire population in the home region at the moment when the individual started the job search. Finally, I have included a set of dummy variables referring to the year when the individual started the job search. The equation for the probability of being over-educated in the first significant job includes the following explanatory variables: unemployment duration, nonSpanish nationality, age when beginning the first job, level and field of education, and a set of ten occupational dummy variables.6 Finally, the explanatory variables included in the employment equation relate to both, personal and job characteristics: gender, age when beginning the job, highest educational level completed and field of education, search time before getting the job, the type of job match, and a set of ten occupational dummy variables. I also took into account structural circumstances by including as an explanatory variable the local unemployment rate for the year of finding the job and a set of dummy variables indicating the year when the individual started the job. Summary statistics for the full sample, the individuals finding a job (complete job search durations) and individuals not finding a significant job after leaving the educational system, are given in Table 2.7 I find that 22.38% of the full sample is censored, while the remaining 77.62% corresponds to complete job search durations. It is worth mentioning the long period of time that Spanish youths have 6 7

Occupational dummy variables are constructed based on the one-digit level of CNO-94. Occupational and year dummies omitted.


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Table 3. Mean Search Duration (months) by sex and educational level Educational level

Primary Secondary Tertiary Total

Female (N = 5, 194)

Male (N = 5, 669)

Total (N = 10, 863)

31.118 28.400 25.164 27.381

33.011 28.207 23.366 28.250

32.311 28.299 24.368 27.835

Table 4. Mean Job Duration (months) by sex and educational level Educational level

Primary Secondary Tertiary Total

Female (N = 3, 700)

Male (N = 4, 732)

Total (N = 8, 432)

21.757 24.214 26.102 24.726

20.218 24.436 27.433 24.046

20.703 24.339 26.739 24.344

Table 5. Mean Search Duration (months) by sex and field of education Field of tertiary ed.

Education Arts Social Science Science Engineering Agriculture Health Services Total

Female (N = 2, 748)

Male (N = 2, 185)

Total (N = 4, 933)

27.379 26.895 25.923 24.646 21.124 27.756 23.859 20.061 25.164

27.527 27.480 24.869 23.393 21.505 21.821 22.299 22.500 23.366

27.406 27.142 25.577 23.956 21.455 24.182 23.551 20.764 24.368

to spend searching for a job after completing education. As shown in Table 2, it takes an average of 28 months for young people in Spain to find a job after leaving education. Tables 3 and 4 show that higher educational levels are related to both shorter unemployment durations and longer first job durations. This pattern is similar for both males and females. Although there are no significant differences in terms of average search and job durations for males and females, I can appreciate some gender differences as regards unemployment durations according to the level of education. On average, females have a slightly shorter unemployment duration. However, looking at different educational levels, I find that females who have completed their university education need more time to find a job after finishing their education. Tables 5 and 6 represent the mean search and job duration according to the field of tertiary education. As can be observed, those individuals with education, arts and social science backgrounds have the longest unemployment durations. This

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Table 6. Mean Job Duration (months) by sex and field of education Field of tertiary ed.

Education Arts Social science Science Engineering Agriculture Health Services Total

Female (N = 2, 072)

Male (N = 1, 900)

Total (N = 3, 972)

24.056 28.075 25.492 29.316 22.743 29.448 28.210 25.091 26.102

25.842 31.031 28.930 25.850 26.137 31.442 28.144 23.410 27.433

24.374 29.467 26.714 27.278 25.717 30.728 28.196 24.559 26.739

Table 7. Skill mismatch distribution (%) by occupation

O0: Armed Forces O1: Legislators, senior officials and managers O2: Professionals O3: Technicians and associate professionals O4: Clerks O5: Service workers and shop and market sales workers O6: Skilled agricultural and fishery workers O7: Craft and related trade workers O8: Plant and machine operators and assemblers O9: Elementary occupations Total

Undereducated

Adeq. educated

Overeducated

13.73 0.99 9.06 2.44 1.55 2.93 1.15 2.48 2.85 4.49 3.80

82.35 48.02 69.47 57.59 60.77 63.50 38.06 64.14 60.22 67.78 63.20

3.92 50.99 21.47 39.97 37.68 33.57 60.79 33.38 36.93 27.73 33.00

pattern is mainly observed for males but not for females, for whom the longest unemployment periods correspond to agricultural backgrounds. The shortest unemployment durations for females (around 20 months) are observe in engineering and services backgrounds. In contrast, the shortest unemployment durations for males are in engineering and agricultural backgrounds. Turning to job durations, the results suggest that an agricultural background is associated with the longest first job duration (around 30 or 31 months), while the shortest employment durations (around 24 months) correspond to education and services backgrounds. These results support the hypothesis that the field of education may play an important role as regards labour market integration of school leavers. Table 7 shows the distribution of skill-mismatch according to occupation. As can be observed, the majority of workers, 63.20%, are correctly allocated, 33.00% are over-educated, and the remaining 3.80% are classified as under-educated. In general, it can be seen how the over-education problem tends to be more pronounced among occupations with higher levels of qualifications while the opposite is observed for the under-education index. Finally, in Table 8 we can appreciate some differences in the mean job durations according to gender and occupation.


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Table 8. Mean Job Duration (months) by sex and occupation

O0: Armed Forces O1: Legislators, senior officials and managers O2: Professionals O3: Technicians and associate professionals O4: Clerks O5: Service workers and shop and market sales workers O6: Skilled agricultural and fishery workers O7: Craft and related trade workers O8: Plant and machine operators and assemblers O9: Elementary occupations Total

Female

Male

Total

19.300 27.342 29.956 26.314 24.074 21.876 33.121 26.689 25.011 19.171 24.726

31.271 38.341 29.549 28.942 24.441 23.034 28.517 23.129 21.994 17.377 24.046

30.098 34.202 29.774 27.472 24.180 22.270 29.380 23.438 22.735 17.882 24.344

5 Empirical results Maximum likelihood estimates of the system (2)–(4) are reported in Table 9. Wald tests for the null hypotheses of covariance terms, σ12 , σ13 , and σ23 significantly equal to zero have been applied. The results obtained suggest that the three equations should be estimated simultaneously, and that estimating them independently would yield biased results. To interpret the estimated coefficients of this simultaneous equation model, I have scaled the marginal effects in the duration equations by the fraction of complete observations.8 I have argued that the time spent searching may affect the job duration in two opposite ways. However, it turns out that there is no direct effect at all – the estimated coefficient of ln ts is not significant in the employment equation. Thus, there is no evidence of “lagged duration dependence” affecting job durations. Nonetheless, the covariance term σ13 indicates the presence of unobserved factors that increase the length of the search period and at the same time increase employment duration. Furthermore, the covariance term σ12 is negative and significant, suggesting the presence of unobserved factors that increase unemployment duration and which also reduce the likelihood of being over-educated. Finally, I find a direct negative effect of over-education on job duration.9 This result is in line with Sicherman’s (1991) hypothesis that over-education may be part of a career mobility process: workers may enter the labour market in a job for which they are over-educated, but gradually move to jobs that better match their educational attainments. Separate estimations of equations (3) and (4) are reported in Tables 10 and 11 in the Appendix. Interestingly, it turns out that this mis-specified model does not indicate any significant effect of over-education on employment duration. Besides, the separate estimations indicate a direct positive effect of search time on employment duration and a direct negative effect of search time on the likelihood of being over-educated. I also experimented with estimating only the equations (2) and (4) 8 In censored regression models, the marginal effect of some variable x on the outcome for a random k individual i in the sample, who may or may not be censored, is βk Pr (i is uncensored). 9 The covariance term σ , however, is positive although not very significant, which suggests the 23 presence of unobserved factors working in the opposite direction.

204


Table 9. Simultaneous equation model Eq (1): Ln ts Coef. t Ln ts (Ln ts )2 (Ln ts )3 Male Ext Age Age2

−0.026 −0.016 −0.154 0.003

−1.31 −0.16 −5.49 4.77

Education Primary Secondary Education Arts Social science Science Engineering Agriculture Health Services

0.066 −0.027 0.031 −0.027 – −0.005 −0.131 −0.219 −0.063 −0.112

1.27 −0.68 0.42 −0.41 – −0.09 −2.98 −1.88 −1.11 −1.39

0.007

4.29

Unem. rate

0.101 0.087 −0.019

1.57 3.31 −5.57

0.095 0.030

1.99 14.51

−0.529 −0.248 0.205 −0.064 – 0.001 0.039 0.051 0.133 0.130

−25.37 −13.66 6.00 −2.09 – 0.04 1.93 0.95 5.02 3.49

Type of job match Over-educated Adeq. educated Under-educated Constant

5.109

N Uncensored observations (%) Log likelihood ∗ Year

16.30

10,863 77.62

Eq (3): Ln te Coef t

Eq (2): Over Coef t

−0.739

−0.012

−0.16

0.079

1.60

0.057

4.26

−0.698 −0.323 0.063 −0.288 – 0.176 −0.051 −0.430 0.191 0.263

−2.78 −2.29 0.37 −2.19 – 1.36 −0.55 −1.95 1.49 1.56

−0.004

−0.97

−1.288 – 0.153

−2.94 – 1.35

4.420

12.65

−9.86

8,432

8,432 28.77

−22,362

dummies included in equations 1 and 3, and occupational dummies in equations 2 and 3.

together (see Table 12 in the Appendix). The results obtained are similar to those in Table 9, but again the mis-specified model does not indicate any significant effect of over-education on employment duration.   1.017∗∗∗ −0.282∗∗∗ 0.240∗∗∗   ˆ = 0.466∗∗∗ 0.106∗∗  Σ   ∗∗∗ 1.512 Wald test for H0 : σ12 = 0; 540.50 (p = 0.000) Wald test for H0 : σ13 = 0; 5.28 (p = 0.021) Wald test for H0 : σ23 = 0; 2.04 (p = 0.153) Turning to the effects of education on search and job durations, some points are worthy of mention. First of all, school leavers with tertiary education and a


205

social science background are less likely to leave unemployment after completing their education than those with engineering and agricultural backgrounds. This result reveals the problems faced by individuals with a social science degree in obtaining a job. This problem is even more important if we take into account that this sector of the population represents a very significant proportion of those investing in university education.10 Second, I find that individuals with higher educational levels are more likely to be over-educated. This result might be explained by the oversupply of highly educated school-leavers that has lead to a process of “bumping down”, with highly-educated workers taking jobs that used to be occupied by lowlyeducated workers, Dolado et al. (2000). Besides, I also observe that the incidence of over-education varies depending on the field of tertiary education. Finally, I find that lower educational levels are related to shorter employment durations. This result could be explained by the higher probabilities of dismissal experienced by lowly-educated workers. According to Jovanovic’s model the productivity of these workers tends to be below the reservation productivity, leading to higher exit rates from employment. This finding is in line with previous works, Garc´ıa-Pérez (1997), Garc´ıa-Fontes and Hopenhayn (1996). Non-Spanish nationality increases the likelihood of being over-educated. This result is in line with various studies that analyse the difficulties faced by “disadvantaged groups” to compete in the labour market.11 I also find that the probability of leaving unemployment is positively related to the age at which the individual completed their education. One reason may be higher search intensity, but it could also be that some employers prefer slightly older job applicants.12 In contrast, I find a positive and significant effect of the age when the individual found the job on both the probability of being over-educated and the employment duration. As expected, the results show a negative relation between the likelihood of leaving unemployment and the local unemployment rate of the year of finishing education. This result is probably due to a lower rate of job offers in periods of recession. In contrast, there is no significant effect of the unemployment rate of the year of starting the job on employment duration.

6 Concluding remarks In this paper I analysed the transition process from school to work for Spanish youths for the period from 1990 to 2000, using a new data set collected within the Labour Force Survey (2nd quarter, 2000). Using a simultaneous equations procedure I studied the relationship between search time after completing education, the 10

Looking at the results reported in Table 2, it can the observed that individuals with a social science background represent more than 40% of the total amount of people investing in tertiary/university education. 11 Battu and Sloane (2002) analysed the case of ethnic minorities in Britain, finding that non-whites have a higher probability of being over-educated. 12 The estimated coefficient of the variable Age2 (1) indicates a non-linear relationship between the unemployment duration and the age when starting the search period.

206


duration of the first significant job and skill mismatch, taking into account unobserved factors. Failing to control for the endogeneity of the previous job search period and the quality of the accepted matches would lead to the erroneous conclusion that over-education has no direct effect on employment duration. When taking this endogeneity into account, I find that over-educated workers experience shorter employment durations than those correctly allocated. Furthermore, when taking this endogeneity into account, I find no direct effect of search time on job duration, although there are unobserved factors that increase search time and also increase employment duration. The results also suggest that the level and field of education play an important role in labour market integration. First, individuals with tertiary education and engineering or agricultural backgrounds have shorter unemployment durations than those with a social science background. Second, the level and field of education are also quite significant in determining the probability of being over-educated. Overeducation is less likely among workers with lower educational levels. However, the incidence of over-education also differs among the different fields of tertiary education. Finally, I find that individuals with lower educational levels experience shorter employment durations. Apart from education, another factor that seems to play a key role in the transition process from school to work is the local unemployment rate. The results reveal that longer search periods are positively related to higher values of the local unemployment rate at the time of completing education. Appendix Table 10. Logit model for the probability of being over-educated

Ln ts (Ln ts )2 (Ln ts )3 Ext Age Education Primary Secondary Education Arts Social science Science Engineering Agriculture Health Services Constant N Log-likelihood ∗

Coef.

t

−3.335 1.524 −0.221 0.678 0.101

−8.00 8.29 −9.13 2.62 8.64

−3.325 −1.348 1.182 −0.333

−25.54 −14.07 6.75 −2.15

−0.047 −0.260 0.237 0.599 0.354 −0.172

−0.34 −2.44 0.81 4.54 1.93 −0.47

Occupational dummies included.

8,432 −3,905


Table 11. Cox model for employment duration

Ln ts Male Age Education Primary Secondary Education Arts Social science Science Engineering Agriculture Health Services Unem. rate Type of job match Over-educated Adeq. educated Under-educated N Uncensored observations (%) Log likelihood ∗ Year

Coef.

t

−0.209 −0.095 −0.029

−10.51 −2.02 −3.23

0.119 −0.016 0.271 0.151 – −0.248 0.001 0.337 −0.076 −0.157 0.004

1.29 −0.21 1.93 1.22 – −1.83 0.02 1.61 −0.64 −1.01 1.15

0.038

0.69

−0.126

−1.09 8,432 28.77 −20,023

and occupational dummies included

Table 12. Simultaneous equation model for search and job duration Eq (1): Ln ts Coef. t Ln ts Male Ext Age Age2 Education Primary Secondary Education Arts Social science Science Engineering Agriculture Health Services Unem. rate Type of job match Over-educated Adeq. educated Under-educated Constant N Uncensored observations (%) Log likelihood

−0.034 0.010 −0.205 0.004

−1.42 0.10 −5.86 4.87

−0.071 −0.091 0.032 −0.008

−1.25 −2.23 0.43 −0.13

−0.001 −0.135 −0.199 −0.067 −0.132 0.013

−0.02 −3.04 −1.71 −1.17 −1.64 6.42

Eq (2): Ln te Coef. t −0.008 0.081

−0.12 1.67

0.027

2.94

−0.086 −0.013 −0.183 −0.194 – 0.178 −0.053 −0.435 0.052 0.130 −0.004

−0.89 −0.17 −1.27 −1.56 – 1.40 −0.59 −2.03 0.46 0.82 −0.97

−0.076 −1.35 – – 0.150 1.33 5.660 14.59 4.319 12.82 10,863 8,432 77.62 28.77 −18,656

∗ Year dummies included in Eqs. 1 and 2, and occupational dummies in Eq. 2.

207

208


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