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The job quality of young higher education graduates in Portugal:
Contractual arrangements and wage differentials
Fátima Suleman*
Instituto Universitário de Lisboa (ISCTE-IUL), DINÂMIA’CET-IUL, Lisboa, Portugal
Fatima.Suleman@iscte.pt
Maria da Conceição Figueiredo
Instituto Universitário de Lisboa (ISCTE-IUL), BRU-UNIDE, Lisboa, Portugal
Conceicao.Figueiredo@iscte.pt
*Corresponding author
Abstract
This article explores the wage differentials among young graduates engaged in different contractual
arrangements. We use linked employer-employee data – “Quadros de Pessoal”, for 2012, to examine
the quality of jobs of young graduates in Portugal. We estimate the impact of flexibility (stability) and
full (part) working time on wages. More specifically, this study examines the impact of four types of
contractual arrangements, notably Standard, Underemployed, Insecure, and Non-Standard. Empirical
analysis adopts the treatment effect model to deal with imprecise and inconsistent estimates arising
from the OLS earnings model. It is assumed that graduates themselves can select, or at least accept, the
contractual arrangement; therefore we use a treatment-outcome model for multinomial choice of
contractual arrangements. The results of the impact of four types of contract suggest that stability
benefits graduates whether they have full or part contract. The findings show that graduates’ labour
market is segmented in Portugal. Furthermore, labour market ranks graduates on the basis of field of
education. Graduates from health, mathematic and statistics, and transport services earn higher wages.
Keywords: young graduates; contractual arrangements; wage differentials; endogenous
selection; Portugal.
Very preliminary draft submitted to LEED 2017 Workshop
Faculty of Economics of University of Coimbra (FEUC)
Coimbra, Portugal July 14-15, 2017
Please do not quote
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Introduction
The starting point in the development of a theory of wages is based on perfect competition,
where buyers and sellers meet to transact a wage rate. However, labour market outcomes did
not give evidence on the predicted one wage. Wage theories have progressed towards new
directions holding that labour markets are imperfect and different from most other markets.
Part of the dispersion in earnings arises from heterogeneity of workers and jobs.
Since the pioneer work by Becker (1964), economists have conducted considerable
empirical research to explore how individual characteristics impact wages. Therefore, wage
differentials reflect differences in education, experience, skills and innate abilities and other
innate characteristics like gender and race. An associated explanation for the dispersion in
wages is that jobs are different. While some have pleasant working conditions, other jobs
involve unfavourable qualitative characteristics or risks. The association between jobs and
wages has been examined in the context of two apparently contrasting theoretical frameworks.
Compensating wage differentials (CWD, hereafter) theory explores the attempts to
compensate workers for non-wage characteristics of jobs. The theory is focused on the job
heterogeneity, rather than workers characteristics. The higher wage is to compensate workers
for undesirable working conditions and, at same time, to attract them for those jobs. It thus
serves as an incentive to workers to voluntarily perform dirty, dangerous, or unpleasant work.
Likewise, for employers compensating wage differential represents financial penalty because
of unfavourable working conditions offered to workers.
Despite the relevance of such approach, empirical evidence is limited especially
because the working conditions measures that should imply compensation remain undefined
(Duncan and Holmund, 1983). While jobs with risks of death or injury give clear support to
CDW arguments, other jobs are far less supportive (Brown, 1980). Recent literature tests CWD
hypothesis for flexible work arrangements: De la Rica and Felgueroso (1999) compare wage
differentials between permanent and temporary workers, while Graaf-Zijl (2012) compare the
differentials between on-call and fixed term jobs; Weeden (2005) explores the impact of
flexible schedules and flexible work locations; Hamersma et al. (2012) focus on temporary and
multiple job holders. The findings suggest that there is a compensation for flexible
arrangements, namely on-call workers have higher wages to compensate quantity flexibility
(Graaf-Zijl, 2012); and flexible work entails higher wages than fixed-schedule and fixed-location
counterparts (Weeden, 2005). Fernandez and Nordman (2009) also confirm the presence of
compensation for unfavourable working conditions but indicate that amplitude and
significance of the compensating differential is expected to differ along earnings distribution.
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In contrast with CWD, the labour market segmentation (LMS) literature considers that
poor (good) characteristics of jobs are associated with low (high) wages. The model of dual
labour market rests on the division between primary jobs, characterised by high-wage and
high quality jobs, and secondary jobs with low-wage and low quality jobs that affect workers´
ability to access essential goods and economic well-being, while raising the risk of erosion of
labour rights. Furthermore, various arguments have been provided over the last thirty years to
explain the changes in the characteristics of jobs and the widespread use of flexible
contractual arrangements that contribute to the new labour market segmentation (Hudson,
2007; Kalleberg, 2011).
This is particularly the case of the literature that examines the LMS arguments in the
context of higher education graduates. Bertrand-Cloodt et al. (2012) suggest that graduates in
flexible jobs face a set of negative consequences notably large wage penalties, a poorer job
match and less training participation than graduates entering into permanent jobs.
Another literature examines the drivers of low quality jobs of graduates. Khan (2010)
underlines the economic context of the time of graduation. Her findings indicate large,
negative and persistent consequences of completing graduation in worse economic conditions.
Differently from macroeconomic drivers, Bertand-Cloodt et al. (2012) pointed out that
Dutch graduates from some fields of education are more vulnerable to cyclical variations in
employment than others. More importantly, the authors underline that the reduction of
demand for some fields force graduates to accept flexible jobs. Lombardo and Passarelli (2011)
found that the field of education is the core determinant of job quality of graduates in
Southern Italy. As reported, graduates in Engineering and in Pharmacy enjoy high quality job
being assigned to stable, well matched and better paid jobs. Grave and Goerlitz (2012) found
empirical evidence on the higher wages of graduates from arts and humanities than from
other fields. Moreover, these differentials are largely correlated to job and firms
characteristics and tend to persist, at least, for the first years of the careers (five/six years).
The results suggest that labour market ranks education programs and this ranking explains the
quality of jobs of higher education graduates in countries under study. However, demand side
variables play a crucial role in this process.
The reported literature gives an illustrative picture of the debate surrounding the
young graduates’ labour market, particularly supported in country-based case studies.
However, there are questions that require further attention. Our argument is that a full
understanding of the quality of jobs of graduates requires an in-depth examination of supply
and demand side factors. Despite the attention received, the literature fails in giving a
comprehensive picture of the interactions between those factors. Is there a wage benefit for
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low quality jobs? Does labour market rank graduates on the basis of field of education? Are
graduates from Bologna more vulnerable to non-standard contracts? What particular firms’
characteristics influence the quality of jobs? In the light of literature, we submit to empirical
test the following hypotheses:
Hypothesis 1: Graduates in non-standard jobs suffer a wage penalisation.
Hypothesis 2: Field of education affects the wages of young graduates.
Data and methodology
The dataset
We examine the impact of contractual arrangements on wages using Portuguese LEED –
Quadros de Pessoal, for 2012 and our sample includes graduates from bachelor and master
degrees employees who are under the age of 35 years (n = 136,484). Young graduates may be
hired through different contractual arrangements; we therefore combine two relevant
features of this specific labour market namely, flexibility (stability) and part (full) working time.
Our strategy paralleled the work by Mocan and Tekin (2003) and Tansel and Kan (2012) which
assumed multiple dimensions of contracts. Table 1 summarises the variety of contractual
arrangements examined in this research.
Table 1: Contractual arrangements of young graduates
Working time Type of contract
Full-time Part-time
Stable Standard Underemployed
Flexible Insecure Non-standard
It is thus possible to determine whether the combination of flexible (stable) contracts
and full (part) working time correlate with lower (higher) wages. However, it is argued that
field of education, workers and firms’ characteristics shape inequality and wage differentials
among young graduates. The model includes a set of control variables notably field of
education, gender, migration status, tenure, occupation, internship status, firm size, industry
affiliation, and regional distribution. Table 2 provides summary statistics of variables used to
estimate the determinants of wages of young graduates in Portugal.
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TABLE 2: Descriptive statistics [mean, (SD)]
Mean Standard Deviation
Hourly wage (ln, [euro])
2.052 [8.49]
0.412 3.754
Gender (Male=1) 0.388 0.487 Age (years) 29.36 3.171 Native (Yes=1) 0.984 0.127 Graduate before Bologna (bachelors) 0.735 0.441
Graduate after Bologna (bachelors) 0.186 0.389
Master (Yes = 1) 0.079 0.269
Internship (Yes = 1) 0.020 0.140 Tenure (years) 3.071 2.841 Fields of Education (most relevant)
Teacher Training and Education Science 0.057 0.231 Art 0.019 0.135 Social and Behavioural Science 0.080 0.271 Business and Administration 0.160 0.367 Engineering and Engineering Trades 0.148 0.355 Health 0.159 0.366
Occupation (most relevant) Managers 0.031 0.173 Professionals 0.542 0.498 Technicians and Associate Professionals 0.159 0.365 Clerical Support Workers 0.169 0.374 Services and Sales Workers 0.077 0.266
Region North 0.267 0.442 Center 0.141 0.348 Lisbon 0.517 0.500 Alentejo 0.031 0.174 Algarve 0.024 0.156 Azores 0.001 0.025 Madeira 0.018 0.134
Firm Size 10 to 49 0.274 0.446 50 to 249 0.280 0.449 250 to 499 0.095 0.293 500 to 999 0.120 0.324 At least 1000 0.232 0.422
% stable contract within firm 0.730 0.267 Sector (most relevant)
Manufacturing 0.105 0.307 Wholesale & Retail Trade 0.125 0.331 Information & Communication 0.097 0.296 Financial & Insurance Activities 0.087 0.281 Professional, Scientific & Technical Activities 0.102 0.303 Administrative & Support Service Activities 0.060 0.238 Education 0.055 0.227 Human Health & Social Work Activities 0.226 0.418 Other Service Activities & international bodies 0.032 0.175
N 136,492
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The estimates in Table 2 show that three fields of education are relevant among
employed graduates, notably business, health and engineering. It should be noted that most of
graduates work in high-level jobs, as can be seen from the proportion of employees in
professional jobs.
Table 3 displays the distribution of young graduates among the four types of
contractual arrangements. We note wage differentials across arrangements, but more
importantly, the type of contract plays a greater role than working time in those differences.
The estimates show Bologna graduates prevail in Non-Standard (flexible and part-time)
suggesting that this type of arrangements might be an option for entry-level jobs during
transition from school-to-work.
Table 3: Descriptive statistics by type of contractual arrangement: mean
Standard Underemployed Insecure Non-standard
Hourly wage (ln, [euro]) 2.152 [9.28]
2.023 [8.71]
1.875 [7.02]
1.875 [7.43]
Gender (Male=1) 0.394 0.328 0.391 0.289 Age (years) 30.040 29.454 28.230 27.491 Native (Yes=1) 0.987 0.978 0.976 0.981 Graduate before Bologna (Yes=1) 0.811 0.763 0.606 0.541 Graduate after Bologna (Yes=1) 0.115 0.205 0.298 0.416 Master (Yes = 1) 0.074 0.033 0.096 0.043 Internship (Yes = 1) 0.009 0.025 0.037 0.065 Tenure (years) 4.116 3.744 1.240 0.703
Fields of Education (Yes=1)
Teacher Training and Education Science
0.127 0.137
Art 0.069 0.087 Social and Behavioural Science 0.082 0.081 0.077 0.064 Business and Administration 0.185 0.130 Engineering and Engineering Trades 0.145 0.170 Health 0.193 0.153 0.104 0.072
Occupation (Yes=1) Managers 0.040 0.016 Professionals 0.573 0.494 Technicians and Associate
Professionals 0.159 0.032 0.174 0.002
Clerical Support Workers 0.160 0.507 0.196 0.436 Services and Sales Workers 0.052 0.091 0.085 0.052
Region (Yes=1) North 0.261 0.261 0.275 0.461 Center 0.138 0.117 0.152 0.099 Lisbon 0.527 0.573 0.492 0.548 Alentejo 0.031 0.029 0.032 0.032 Algarve 0.021 0.012 0.033 0.010 Azores 0.001 0.000 0.000 0.001 Madeira 0.021 0.008 0.016 0.005
Firm Size 10 a 49 0.249 0.299 0.323 0.250
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50 a 249 0.251 0.299 0.333 0.295 250 a 499 0.095 0.058 0.098 0.074 500 a 999 0.139 0.089 0.090 0.062 At least 1000 0.265 0.255 0.156 0.320
% stable contract within firm 0.836 0.780 0.551 0.445 Sector (Yes=1)
Manufacturing 0.108 0.114 Wholesale and Retail Trade 0.247 0.112 Information and Communication 0.101 0.103 Financial and Insurance Activities 0.116 Professional, Scientific and Technical
Activities 0.106 0.074 0.105
Administrative and Support Service Activities
0.150
Education 0.160 0.221 Human Health and Social Work
Activities 0.253 0.225 0.186 0.120
Other Service Activities & international bodies
0.082 0.082
N 85,671 2,518 43,160 5,143
Econometric model
Firstly, we use OLS regression model to explore the drivers of wages of young graduates, in
which the dependent variable is the hourly wage in logarithm form. However, the major
problem in this estimation is the possibility of inconsistent estimators due to endogenous
selection bias associated with the choice of contractual arrangement. Empirical analysis adopts
the treatment effect model (Wooldridge, 2010) to deal with imprecise and inconsistent
estimates arising from the OLS earnings model.
We assume that young graduates themselves can select, or at least accept, the
contractual arrangement. For this reason, we follow Deb and Trivedi (2006a, 2006b) and use a
treatment-outcome model for multinomial choice of contractual arrangements. The treatment
effects approach is suitable for dealing with endogenous selection as in the case of contractual
arrangements in our wage determinants model (Imbens and Angrist, 1994; Maddala, 1983).
Neglecting selection leads to correlation of the errors terms and consequently to an omitted
variable bias.
However, multiple arrangements (as opposed to binary) call for the multinomial choice
model (Deb and Trivedi, 2006b), which is in fact an extension of the treatment model applied
to multinomial choice. The model assumes joint distribution of endogenous treatment and
wages using latent factor structure and applies a maximum simulated likelihood approach for
estimation. These econometric solutions are captured in mtreatreg Stata command (Triventi,
2014) and presuppose a model with two sets of equations: the selection and the outcome
equations. It should be stressed that the matrix of covariates z_i does not necessarily require
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additional variables relative to x_i to be identified. We decided to not include an exclusion
restriction or instrument in the treatment equation, as suggested by Deb and Trivedi (2006a).
Therefore, latent factors enter into the outcome and treatments equations in the same
way as observed covariates and incorporate unobserved characteristics related to the choice
or acceptance of a type of contract. On the other hand, since latent factors enter the likelihood
function but are unknown, the maximisation of the likelihood function is performed through
simulation by drawing several random numbers from a standard normal distribution. A formal
representation of the model is given for the choice of contractual arrangement, where each
individual i chooses a type of contractual arrangement j from a set of four choices
where is the control group (undeclared and flexible). Let denote the utility
associated with the hourly wage of individual i with contractual arrangement j
where denotes a set of exogenous covariates with parameters , are i.i.d. error terms,
and are latent factors which incorporate unobserved characteristics common to the
individual i ’s status choice and outcome (logarithm of hourly wage). The are assumed to be
independent of . As a normalisation , so the expected utility of j-th status is the
differential utility relative to that stable and full-time arrangement. Let be binary selection
variables representing the observed contractual arrangement choice and .
Also let . The mixed multinomial logit structure for the probability of
contractual arrangement choice can then be represented as
The expected outcome equation for individual i is formulated as
where is a set of exogenous variables and denote the treatment effects relative to the
stable and full-time arrangement. The expected value of the log hourly wage, , is a
function of the latent factors so that it is affected by unobserved characteristics which also
affect the selection a contractual arrangement. The interpretation of the factor-loading
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parameters is as follows: when is positive (negative), unobserved factors which increase the
probability of selecting j-th contractual arrangement also increase (reduce) the hourly wage.
In order to estimate parameters of the model, latent factors are assumed to be i.i.d.
Draws from the standard normal distribution and simulation-based method are used to
maximise the log likelihood. Provided the number of draws is sufficiently large (we select 200
draws), maximisation of the simulated log likelihood is equivalent to maximising the log
likelihood. Parameters of this model are identified when , but Deb and Trivedi (2006b)
recommend including some variables in which are not included in .
The impact of contractual arrangements on wages of young graduates
The OLS estimates displayed in Table 4 indicate that graduates in Standard - stable and full-
time, contracts have wage benefits comparatively to all other arrangements. Furthermore, the
estimates suggest that it is the contractual flexibility that generates large wage differentials. As
can be seen, graduates in Insecure contracts, which cross flexibility and full-time, suffer higher
wage penalty (-0.11) comparatively to stable and full-time arrangements.
Table 5 displays the estimates of the treatment-outcome model for multinomial choice
to control for endogenous selection bias. The results show that the estimates from OLS and
the treatment model vary considerably. The corrected estimates from treatment model
reported in columns 5 show some marked differences, especially in relation to the impact of
contractual arrangements. Furthermore, the lambda ( ), which measures the impact of
selection, is statistically significant for the three arrangements indicating that our prediction of
endogenous selection was correct.
The OLS estimates are therefore biased and the analysis should proceed on the basis of
the treatment approach estimates. Moreover, the test of degree of substitutability between
contractual arrangements demonstrated the non-violation of the IIA assumption. The findings
from the wage equation are consistent with wage differentials among the range of contractual
arrangements. More importantly, the penalisation appear to be higher the OLS estimates have
suggested. For example, graduates in Insecure arrangements earn 18% less than the
counterfactual group of graduates in Standard contracts. Furthermore, the estimates show
sharp differences among graduates in Underemployed and Non-Standard arrangements. In
sum, treatment model estimates corroborate wage differentials among contractual
arrangements suggesting that high wage correlate with Standard contracts.
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Table 4: Wage differentials across contractual arrangements: OLS estimates Model Estimates
Contractual arrangements (a)
Underemployed: Stable and Part-time (Yes = 1) -0.0184** (0.008)
Insecure: Flexible and Full-time (Yes = 1) -0.110*** (0.002)
Non-Standard: Flexible and Part-time (Yes = 1) -0.015*** (0.006)
Gender (Male=1) 0.087*** (0.002)
Native (Yes = 1) -0.060*** (0.008)
Level of Education (b)
Graduation (Bachelor degree) after Bologna (Yes = 1) -0.122*** (0.002)
Master (Yes = 1) -0.001 (0.003)
Internship (Yes = 1) -0.173*** (0.006)
Tenure (years)
0.022*** (0.000)
Fields of Education (c)
Teacher Training and Education Science (Yes = 1) -0.120***
(0.004) Art (Yes = 1) -0.075***
(0.009) Social and Behavioural Science (Yes = 1) -0.072***
(0.004) Business and Administration (Yes = 1) 0.0.15***
(0.004) Engineering and Engineering Trades (Yes = 1)
-0.028***
(0.005) Firm Size
(d)
50 a 249 (Yes = 1) 0.103***
(0.003) 250 a 499 (Yes = 1) 0.120***
(0.003) 500 a 999 (Yes = 1) 0.114***
(0.003) At least 1000 (Yes = 1) 0.129***
(0.003) Constant 2.100***
(0.009) N 136,484 R
2
0.383
Standard errors in brackets; Reference categories: (a)Stable and Full-time; (b) Graduate (Bachelor) before Bologna; (c)Health;(d) 10 a 49 workers. (*) p < 0.10; (**) p< 0.05; and (***) p< 0.01. Controls include all fields of education,
sectors and occupations.
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Table 5: Wage differentials and contractual arrangements: Endogenous MNL treatment model
Underemployed Insecure Non-
Standard ln (hourly
wage)
Gender (Male=1) 0.149*** (0.052)
-0.129*** (0.019)
-0.103** (0.044)
0.0860*** (0.002)
Native (Yes = 1) -0.300** (0.152)
-0.278*** (0.061)
0.277** (0.138)
-0.063*** (0.008)
Level of Education (b)
Graduate (Bachelor) Bologna (Yes = 1) 0.514*** (0.062)
0.415*** (0.021)
0.609*** (0.041)
-0.114*** (0.002)
Master (Yes = 1) -0.470*** (0.121)
0.105*** (0.030)
-0.311*** (0.088)
0.001 (0.003)
Internship (Yes = 1) 0.591*** (0.147)
0.720*** (0.056)
1.052*** (0.091)
-0.163*** (0.006)
Tenure (years)
-0040*** (0.009)
-0.681*** (0.006)
-1.009*** 0.021)
0.017*** (0.001)
Fields of Education (c)
Teacher Training and Education Science (Yes = 1)
0.650*** (0.109)
1.107*** (0.050)
1.677*** (0.104)
-0.109*** (0.004)
Art (Yes = 1) 1.610*** (0.128)
0.875*** (0.072)
2.710*** (0.123)
-0.061*** (0.009)
Social and Behavioural Science (Yes = 1) 0.260** (0.111)
0.717*** (0.044)
0.867*** (0.111)
-0.066*** (0.004)
Business and Administration (Yes = 1) -0.560*** (0.124)
0.253*** (0.042)
-0.066 (0.117)
-0.012*** (0.004)
Engineering & Engineering Trades (Yes = 1)
-0.191 (0.121)
0.764*** (0.043)
0.432 (0.121)
-0.021*** (0.005)
Firm Size (d)
50 a 249 (Yes = 1) -0.023 (0.058)
0.137*** (0.022)
0.211*** (0.053)
0.104*** (0.003)
250 a 499 (Yes = 1)
-0.571*** (0.099)
-0.144*** (0.030)
-0.006 (0.080)
0.119*** (0.004)
500 a 999 (Yes = 1) -0.308*** (0.093)
-0.599*** (0.031)
-0.300*** (0.080)
0.109*** (0.003)
At least 1000 (Yes = 1) -0.007 (0.072)
-0.522*** (0.027)
0.736*** (0.062)
0.126*** (0.003)
Constant -3.694*** (0.180)
0.430*** (0.070)
-3.471*** (0.168)
2.134*** (0.010)
Contractual Arrangements(c)
Stable and Part-time (Yes=1)
-0.102*** (0.011)
Flexible and Full-time (Yes=1)
-0.179*** (0.006)
Declared and flexible (Yes=1)
-0.089*** (0.010)
(Stable and Part-time)
0.089*** (0.008)
(Flexible and Full-time)
0.085*** (0.006)
(Flexible and Part-time)
0.090*** (0.010)
N 136,492
Standard errors in brackets; Reference categories: (a)Stable and Full-time; (b) Graduate (Bachelor) before Bologna; (c)Health;(d) 10 a 49 workers. (*) p < 0.10; (**) p< 0.05; and (***) p< 0.01. Controls include all fields of education,
sectors and occupations.
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The estimates in Table 5 are also consistent with wage differentials among graduates
of different fields of education. Graduates from health appear to earn more than all others
unless they are from mathematic and statistics or transport services. Furthermore, some fields
of education impose larger penalisation. This is particularly the case of teaching (-0.121);
media and journalism (-0.125) and social services (-0.113).
The findings show that labour market might distinguish generations of graduates.
Young people graduated after the implementation of Bologna reform suffer a non-negligible
wage penalisation (-0.122) comparatively to previous cohort. This probably explains lower
wages of master graduates (-0.0007), suggesting that the recent generation of masters may
correspond to previous cohort bachelors. Furthermore, internship is common among
graduates during transition from higher education and labour market. Those graduates earn
less almost 16% than the others with an employee status.
The other estimates displayed in Table 5 are consistent with gender wage differentials
since male earn more 9% than female graduates. On the hand, migrants enjoy benefits in the
labour market; Portuguese employees earn less 5.8% than migrant young graduates.
The impact of employers’ characteristics is assessed through the dimension of the firm.
The findings show that the largest firms (>1000 employees) pay higher wages. However,
graduates also benefit from working in large firm, the ones with 250-499 employees.
Table 6 summarises main findings illustrating the incidence of and wage differentials
among graduates in the four contractual arrangements examined in this study. The estimates
show low incidence of part-time jobs, especially in stable contracts (1.84%).
Table 6 The incidence of graduates in contractual arrangements and wage differentials
Working time Type of contract
Full-time Part-time
Stable Standard
62.8%
(reference category)
Underemployed
1.84%
(-9.7%)
Flexible Insecure
31.6%
(-16.4%)
Non-standard
3.8%
(-8.5%)
It should be highlighted that flexibility imposes greater wage penalty in the labour
market of young graduates in Portugal. Furthermore, it has to be noted that Standard
arrangements prevail even tough in the context of young graduates’ labour market and
worsening labour market conditions.
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Concluding remarks
This paper contributes to the research agenda on job quality of higher education graduates.
The goal is to examine the impact of contractual arrangements on wages of young graduates in
Portugal in 2012. The wage benefits of Standard contracts corroborate the prediction of labour
market segmentation arguments in that high wages are associated with stable contracts, while
low wages are linked to flexible contracts. So, graduates enjoy good or bad job characteristics
(Kalleberg, 2011). Furthermore, our findings indicate that it is the job flexibility that
contributes particularly for wage differentials. These findings are in the line with our
Hypothesis 1 in that graduates in Non-Standard arrangements suffer wage penalisation.
We also found the impact field of education on wages. Graduates from health,
mathematic and statistics, and transport services earn higher wages (Hypothesis 2). These
findings are some different from Grave and Goerlitz (2012) but corroborates the argument
that labour market ranks education programs and this ranking explains the quality of jobs of
higher education graduates in Portugal.
The wage penalisation of master graduates seems somehow striking. We suggest that
the recent generation of masters may correspond to previous cohort bachelors. This argument
deserves however further scrutiny, which should compare wage differentials among previous
bachelors and recent masters.
The preliminary evidence achieved shows that the labour market of young graduates is
segmented and job flexibility (stability) is an important driver of wages. Furthermore, some
relevant differences arise from individual characteristics (gender and migration), disciplinary
fields of education and employers characteristics (size, industry affiliation). Policy makers
should address job quality drivers in a comprehensive perspective linking individual and
employers characteristics. This is only possible if LEED are available for research, as is the case
of Quadros de Pessoal.
Acknowledgments
This research was possible thanks to the kindness of the Office for Strategy and Studies (GEE),
of the Ministry of Economy and Employment for access to the data, Quadros de Pessoal.
14
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