1

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Please do not quote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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