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The effects of contract-type mismatch and matching frictions on unemployment duration: evidence for Portugal

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Abstract

This paper analyses the impact of matching frictions in the Portuguese labour market on individual unemployment hazard rates and unemployment durations. The coexistence of permanent contracts and temporary contracts in the Portuguese (dual) labour-market is akin to a matching friction, with a contract-type mismatch between jobseekers who prefer permanent contracts, whereas firms, in turn, prefer to offer temporary contracts. The paper uses a rich micro dataset which allows to compute a time and space varying contract-type mismatch index, over 86 local labour markets (job-centers of the Portuguese Public Employment System) and five years. Employing discrete time hazard models and a stock-flow matching mechanism, we find that local labour markets with higher contract-type mismatch rates are characterized by lower hazard rates and longer unemployment duration. Improving the desirability of temporary contracts and information about local contract-type mismatch rates may reduce matching frictions and average unemployment duration.
The eects of contract-type mismatch and matching frictions
on unemployment duration: evidence for Portugal
Antonio Menezes
a
and Dario Sciulli
b
a
University of the Azores, Faculty of Economics and Management and CEEAplA;
b
University of Chieti-Pescara,
Faculty of Economics
ABSTRACT
This paper analyses the impact of matching frictions in the Portuguese
labour market on individual unemployment hazard rates and unem-
ployment durations. The coexistence of permanent contracts and
temporary contracts in the Portuguese (dual) labour-market is akin to
a matching friction, with a contract-type mismatch between jobseekers
who prefer permanent contracts, whereas rms, in turn, prefer to oer
temporary contracts. The paper uses a rich micro dataset which allows
to compute a time and space varying contract-type mismatch index,
over 86 local labour markets (job-centers of the Portuguese Public
Employment System) and ve years. Employing discrete time hazard
models and a stock-ow matching mechanism, we nd that local
labour markets with higher contract-type mismatch rates are charac-
terized by lower hazard rates and longer unemployment duration.
Improving the desirability of temporary contracts and information
about local contract-type mismatch rates may reduce matching fric-
tions and average unemployment duration.
ARTICLE HISTORY
Received 15 April 2021
Revised 27 March 2022
Accepted 28 May 2022
KEYWORDS
Unemployment duration;
matching frictions; dual
labour markets; survival
analysis; stock-flow
matching functions
1. Introduction
Certain European labor markets have experienced, over the last decades, a dual structure,
where temporary contracts make up a significant share of overall employment relationships
and coexist with permanent contracts, under stringent differences (see Bentolila, Dolado, &
Jimeno, 2020 for a recent survey). This is especially true in Spain, Portugal, the Netherlands,
France, Italy, Germany, and Greece (Bentolila et al., 2020). The prevalence and persistence of
dual-labour markets across Europe have motivated studies on the performance of said labor
markets and their political economy aspects; early analyses include Saint-Paul (1996, 2000),
Dolado, Garcia-Serrano, and Jimeno (2002) and Boeri (2011), whereas Bentolila, Cahuc,
Dolado, and LeBarbanchon (2012), Cahuc, Charlot, and Malherbet (2016), Dolado (2017),
Bentolila et al., 2020) provide more recent reviews of theoretical and empirical insights on
European dual-labour markets and offer evidence that dual-labor markets may lead to higher
unemployment duration rates. Most interestingly, the co-existence of permanent contracts
and temporary contracts gives rise to a possible contract-type mismatch, as jobseekers
predominantly prefer permanent contracts, whereas firms offer mainly temporary contracts.
CONTACT Antonio Menezes antonio.jv.menezes@uac.pt University of the Azores, Faculty of Economics and
Management, Rua da Mãe de Deus 9500-321 Ponta Delgada Portugal
JOURNAL OF APPLIED ECONOMICS
2022, VOL. 25, NO. 1, 936–961
https://doi.org/10.1080/15140326.2022.2084687
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
This contract-type mismatch may be perceived as a matching friction (in the spirit of
Mortensen & Pissarides, 1994 and Pissarides 2000) and, consequently, may lead, per se, to
longer unemployment duration. Hence, a labour market reform, designed with a primary
intention to increase labour market flexibility at the margin via higher incidence of fixed-term
contracts (FTC), may have the unintended effect of leading, via this contract-type mismatch
effect, to longer unemployment duration.
Our paper sheds light to this important yet overlooked phenomenon by looking at the
Portuguese (dual) labor market, which has the second highest share of FTC among all EU
countries (see Bentolila et al., 2020 for a recent pan-EU perspective; Blanchard, 2007;
Blanchard & Portugal, 2017; Carneiro, Portugal, & Varejão, 2014; Centeno & Novo, 2012,
2014; Fonseca, Lima, & Pereira, 2018 for studies on the Portuguese dual-labour market,
albeit with different data sources and purposes than this study). We investigate the role
that matching frictions due to contract-type mismatch have in explaining unemployment
hazard rates in a dual-labor market where good jobs and bad jobs coexist using
a previously unexplored and, most importantly, rich micro dataset, which allows us to
contribute in a novel way to the empirical literature on matching frictions and unem-
ployment duration.
Since the 1990s labor market analysis has largely used matching functions in search and
match frameworks (see Mortensen, 1987; Mortensen & Pissarides, 1994 for seminal works;
Petrongolo & Pissarides, 2001 for an early survey; Petrongolo, 2001; Gregg & Petrongolo,
2005, Petrongolo; Coles & Coles, 2008; Petrongolo & Pissarides, 2008; Ebrahimy & Shimer,
2010; Christiano, Eichenbaum, & Trabandt, 2021 for more recent applications of matching
frameworks). Matching functions allow researchers to investigate the role of frictions in the
labour market, including how said frictions lead to unemployment duration and impact the
labor market effectiveness in matching jobseekers with available vacancies.
As highlighted by Petrongolo and Pissarides (2001), matching frictions derive from various
sources. For example, they depend on imperfect information about potential trading partners,
absence of perfect insurance markets, congestion from large numbers, among other factors.
Recently, most contributions devoted to estimating matching functions focused on the role of
heterogeneity of jobseekers in explaining frictions in the matching process. An important
argument put forward by such studies is that failure to consider the heterogeneity of
jobseekers may lead to a misspecification of the estimating matching function and, concomi-
tantly, to biased estimates of the estimating parameters and to misleading inferences on search
elasticities. Several authors (e.g., Burgess & Profit, 2001; van Ours & Ridder, 1995) found
evidence of job competition between different skill groups and between employed and
unemployed jobseekers. Fahr and Sunde (2001) found heterogeneity in matching technolo-
gies across members of different ages and education groups, indicating the importance of
disaggregating the matching function to explain the inner workings of the labour market and
to avoid the loss of important information. Hynninen and Lahtonen (2007) found that wider
heterogeneity of jobseekers in terms of their educational levels increases the importance of
frictions in the matching process. More recently, Lange and Papageorgiou (2020) explored
how jobseekers’ search behaviour heterogeneity impacts matching function specification and
search elasticities biases.
However, matching frictions may also arise from other sources, including labour market
reforms. The so-called “reforms at the margin” – which introduced temporary contracts and
were meant to reduce labour market rigidity – may constitute a potential source of matching
JOURNAL OF APPLIED ECONOMICS 937
frictions. The role of temporary contracts in the labour market is manifold. Certain authors
(e.g., Ichino, Mealli, & Nannicini, 2005) emphasize their role in making it easier for workers to
enter in the labour market and, in some cases, for workers to access permanent jobs. However,
several studies highlighted possible negative effects from temporary employment with respect
to traditional permanent relationships, contributing to rationalize the existence of segmented
labour markets divided into primary and secondary sectors and, specifically, a segmentation in
good and bad jobs.
2
Permanent jobs (good jobs) feature better working conditions, employ-
ment stability and good prospects of career advancements. Temporary jobs (bad jobs), in turn,
are associated with lower wages, lower job security and impediments to career advancements
(Bentolila et al., 2020). In a dual-labour market, where good and bad jobs coexist, it is likely
that one will find jobseekers having strong preferences for permanent contracts, while firms
may offer temporary contracts, since firms may use this contractual form to adjust their
workforce to business cycle conditions in a more cost-effective way or simply to reduce
expected labour costs. Therefore, a labour market characterized by a homogenous supply side,
with most jobseekers searching for a permanent job, and a heterogeneous demand side, where
temporary and permanent job offers coexist, may involve a high degree of (contract-type)
mismatch, and, hence, high average unemployment duration. In fact, it is likely that indivi-
duals looking for a permanent job will tend to first refuse offers if these offers are for temporary
jobs and only after a certain time they will start accepting those temporary job offers if these
individuals do not find a suitable permanent job meanwhile.
Our paper tests the hypothesis that higher contract-type mismatch is associated with
higher unemployment duration by leveraging on the space-and-time variation of con-
tract-type mismatch across Portugal over time. To that end, we estimate a matching
function using Portuguese rich micro data on individual transitions from unemployment
to employment or employment to employment. Our empirical strategy consists in
estimating individual reemployment probabilities with hazard models, as it allows for
more flexible specifications of the matching function when compared to estimates of an
aggregate matching function, since hazard models allow for a wide range of distributional
forms of unemployment durations. In addition, estimating individual reemployment
probabilities allows us to control for observed and unobserved heterogeneity at the
individual level, which are only implicitly considered in an aggregate matching function.
Despite the advantages of using hazard models to estimate matching functions, only
a few studies in the literature have done so. For example, Lindeboom, van Ours, and
Renes (1994) investigated the link between matching functions and hazard models to
study the relative effectiveness of alternative search channels. Petrongolo (2001) used
hazard function specifications to test the empirical relevance of the constant returns to
scale hypothesis in the matching technology.
We follow the literature and allow two possible approaches to estimate the matching
functions: the random matching and the stock-flow matching models.
3
Broersma and van
Ours (1999) argue that the estimates of the degree of returns to scale in the matching
technology depend on the data for jobseekers and posted vacancies used and emphasize the
importance of looking at comparable measures for flows and explanatory stocks. Gregg and
2
For example, see Dolado et al. (2002).See also Dolado, Jansen, and Jimeno (2007) and Bentolila et al. (2020) for
a theoretical framework on dual employment protection legislation.
2
For example, see Dolado et al. (2002).See also Dolado, Jansen, and Jimeno (2007) and Bentolila et al. (2020) for
a theoretical framework on dual employment protection legislation.
938 A. MENEZES AND D. SCIULLI
Petrongolo (2005) argue, in turn, that part of the instability of estimated matching functions
derives from problems of misspecification, due to the assumption of random search, rather
than a stock-flow matching technology. Ebrahimy and Shimer (2010) calibrate a stock-flow
matching model to replicate the cyclical volatility behaviour of key labour market metrics.
In our case, we use data from job-centers and consequently the stock-flow approach is
a better representation of the matching mechanism, since the existence of a matchmaker
(i.e., the job-center) makes it less likely that the same job offer is re-offered to the same
jobseeker, as allowed by the random matching approach.
4
We use a dataset from the IEFP (Instituto do Emprego e Formação Profissional), the
public entity responsible for all Portuguese public job placement centers, for the period
from 1998 to 2002. As documented in Bentolila et al. (2020) authoritative review of
European dual-labour markets, the share of FTC in Portugal has hovered around 20%
since 1998 until 2017 (Bentolila et al., 2020, (Figure 1) and during this period Portugal
has always experienced the second largest share of FTC in Europe (after Spain). In
addition, and as documented by Blanchard and Portugal (2017), the coexistence of
a large share of FTC and permanent contracts in Portugal has been accompanied by
striking and perennial differences in the respective levels of employment protection
legislation; in this sense, the period under analysis is fundamentally equivalent to the
current-day period (in the sense that the labour market objectively presents a dual-tier
structure, where “good” and “bad” jobs co-exist). Consequently, and in other words, the
period for which these (previously) unexplored data was obtained is akin to the nowadays
0 2 4 6 8 10
Density
-1 -.5 0 .5 1
HI
Figure 1. Distribution of the Heterogeneity Index. Source: own elaboration on IEFP data.
3
. . .
JOURNAL OF APPLIED ECONOMICS 939
period in a structural sense, as Portugal remains a country with a dual-labour market in
the recognized sense in the literature, which supports the timeliness and current rele-
vance of the dataset used.
This dataset provides information about personal and job-related characteristics of all
individuals who registered in the Portuguese job-centers and allows to construct spells of
individual unemployment duration and, quite interestingly, to identify the destination
contract (if permanent or temporary). In addition, the dataset allows us to construct
stocks and flows of unemployed jobseekers and vacancies offered for each month at the
job-center level. The dataset also contains information about vacancies, enabling us to
determine the number of vacant jobs available for each month at the job-center level. The
IEFP data provide information about the contract type sought by jobseekers and the
contract type offered by firms. Therefore, it allows to control the direct effects of the
desired contract on the hazard rates toward multiple destination states and to construct
a time and space varying index of the degree of the heterogeneity found between
contracts searched by jobseekers and contracts offered by firms which we use to under-
stand the relationship between such contract-type mismatch and unemployment dura-
tion. We estimate a competing risk discrete time hazard model with a (multinomial) logit
model for its flexibility and breadth of robustness checks allowed. The heterogeneity
between contract-type desired by jobseekers and contract-type offered by firms is
approximated by a contract-type mismatch index, which we include in the hazard
model in a flexible way as a categorical value. The mismatch index is calculated monthly
at the job-center level, and it thus reflects local labor market aggregate information, and
we leverage the space and time variation of the contract-type mismatch to assess how it is
associated with unemployment duration.
The remainder of the paper is organized as follows. Section 2 describes the data.
Section 3 describes the contract-type mismatch index. Section 4 presents the econometric
model. Section 5 presents the econometric results, including robustness checks. Finally,
Section 6 concludes.
2. Data
We use an IEFP dataset that provides information on individuals registered at jobcentres
in (Mainland) Portugal from 1997 to 2002. The IEFP is the agency responsible for
running the public employment services, and it is a division of the Ministry of Labour
and Solidarity. The IEFP is responsible for job brokering, vocational guidance, admin-
istering employment subsidies, vocational training, apprenticeship training and being
registered at a job-center is necessary to collect unemployment benefits, which explains
the widespread usage of their job-centers across the Portuguese territory and across the
span of the different socio-economic and demographic characteristics of jobseekers (see
Addison and Portugal 2002 for further details; see Teixeira & Nunes, 2009; Santos, 2010
for studies on IEFP data from an ALMP efficacy perspective). Coelho (2003) investigates
unemployment duration and vacancy duration using hazard models with the IEFP
dataset but does not consider contract-type mismatch (and its space-time variation).
Hence, and to the best of our knowledge, the IEFP dataset was never used to investigate
contract-type mismatch, or estimate matching models via hazard models, or, for that
matter, to study the implications of the Portuguese dual-labour market structure. The
940 A. MENEZES AND D. SCIULLI
IEFP dataset includes information about job vacancies offered by firms. The original
sample containing information on individuals is composed by more than 3 million of
observations. To avoid computational problems, we drew a randomized sub-sample
equal to 10% of the original sample. The IEFP dataset provides (daily) information
about the date of registration at the job-center and the date of placement, making it
possible to identify (multiple) spells of unemployment durations. Our duration analysis
focuses on unemployment spells starting since Jan. 1998 until Dec. 2002 with complete
information on all covariates considered. Spells without the date of placement are
considered censored. However, individuals may drop out of the job-centers if they fail
to present themselves at the job-centers’ control interviews. We eliminate from our
sample the spells that terminate in failure to report to the above-mentioned control
interviews to avoid misleading identification of censored unemployment durations
(which are less than 1% of total spells, in any case, an immaterial quantum). To make
our results easily comparable to studies in the literature, we analyse unemployment
duration on a monthly rather than a daily basis.
We only consider individuals aged 16–60 years old, for whom all information with
respect to all the covariates considered is available. This selection leaves us an unbalanced
panel composed by 164,627 spells and 133,234 individuals. We remark that more than
81% of individuals only experience one spell of unemployment in our samples. This is
mainly due to the (1) quite long duration of unemployment spells, which characterizes
the Portuguese labour market (see Blanchard & Portugal, 2001, 2017 for in depth
documentations of this perennial and salient feature of the Portuguese labour market)
and to the (2) period analysed in this paper (60 months); both factors also concur to
explain the high percentage of censored spells in our sample (about 62%).
We consider a plethora of personal and job-related characteristics to control for
observed heterogeneity at the individual level. Males and females are analysed separately.
Table 1 contains descriptive statistics (for sample used in estimations, and Table A5
reports full sample descriptive statistics).
We control for the following individual characteristics: age, introduced in a non-
linear way, marital status, disability status, number of dependent persons in the
household, and educational level. We also control for job-related characteristics. We
introduce a variable indicating if the individual is looking for his or her first job,
meaning that he or she has no previous work experience, and a dummy variable
indicating if the individual is employed at the onset of the job-search (on-the-job
search).
5
We consider a set of dummy variables indicating the motivation of the
registration at the job-center. These dummy variables flag if the individual: was
formerly a student; finished his or her educational career; finished a training period;
was dismissed; resigned; and if the individual registered because of the termination
of a temporary contract; the base category dummy is constituted by individuals with
no previous job experience. We control for the occupation of the individual,
through a set of dummy variables to distinguish between managers, supervision
activities and specialists, technicians, administrative workers, service workers,
4
Under the stock-flow matching technology, at the time an individual becomes unemployed he samples the existing
stock of vacancies for a suitable job. If he fails to find a suitable match among the existing stock of vacancies, then he
must wait to eventually be matched with the flow of new vacancies and he does not re-apply to the previously
searched stock of old vacancies.
JOURNAL OF APPLIED ECONOMICS 941
Table 1. Descriptive statistics.
Males Females
All Permanent contract Temporary contract All Permanent contract Temporary contract
Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev
Age 32.79 12.20 28.30 10.06 30.58 10.84 31.66 11.06 28.36 9.49 32.75 11.23
Age squared 1224.02 906.81 901.88 683.23 1052.84 771.41 1124.78 799.82 894.21 626.65 1198.91 812.83
Married 0.40 0.49 0.31 0.46 0.31 0.46 0.49 0.50 0.45 0.50 0.53 0.50
Disabled 0.01 0.10 0.01 0.10 0.01 0.10 0.00 0.06 0.00 0.06 0.00 0.07
No dependent persons 0.66 0.47 0.72 0.45 0.68 0.47 0.55 0.50 0.56 0.50 0.44 0.50
1 dependent person 0.15 0.36 0.13 0.33 0.14 0.35 0.23 0.42 0.23 0.42 0.28 0.45
2 dependent persons 0.12 0.32 0.10 0.30 0.11 0.32 0.16 0.36 0.15 0.36 0.20 0.40
3 or more dependent persons 0.07 0.25 0.05 0.23 0.07 0.25 0.06 0.23 0.06 0.23 0.08 0.27
Max 6 years of education 0.45 0.50 0.44 0.50 0.45 0.50 0.45 0.50 0.44 0.50 0.49 0.50
9 years of education 0.22 0.41 0.23 0.42 0.24 0.43 0.19 0.39 0.20 0.40 0.19 0.39
11–12 years of education 0.26 0.44 0.29 0.46 0.27 0.44 0.27 0.44 0.31 0.46 0.23 0.42
More than 12 years of education 0.07 0.25 0.03 0.17 0.03 0.18 0.10 0.29 0.05 0.22 0.08 0.28
Employed 0.03 0.18 0.05 0.22 0.04 0.19 0.04 0.19 0.05 0.22 0.03 0.17
First job 0.17 0.37 0.23 0.42 0.13 0.34 0.19 0.39 0.23 0.42 0.12 0.32
Student 0.07 0.25 0.09 0.28 0.06 0.24 0.07 0.25 0.08 0.28 0.06 0.23
Ex-student 0.07 0.26 0.10 0.31 0.05 0.21 0.08 0.28 0.10 0.30 0.05 0.21
End of training period 0.02 0.13 0.02 0.15 0.01 0.12 0.02 0.15 0.02 0.15 0.02 0.13
Dismissed 0.18 0.38 0.18 0.38 0.12 0.33 0.16 0.36 0.17 0.38 0.12 0.32
Resigned 0.13 0.34 0.12 0.32 0.10 0.30 0.10 0.30 0.10 0.30 0.07 0.25
End of temporary contract 0.34 0.47 0.28 0.45 0.44 0.50 0.35 0.48 0.29 0.46 0.52 0.50
Other motivation 0.18 0.38 0.20 0.40 0.20 0.40 0.20 0.40 0.20 0.40 0.16 0.37
Manager-Specialist 0.07 0.26 0.02 0.15 0.01 0.11 0.08 0.28 0.02 0.15 0.01 0.12
Technical 0.11 0.32 0.08 0.28 0.07 0.25 0.04 0.20 0.03 0.17 0.02 0.12
Administrative 0.13 0.34 0.12 0.33 0.14 0.35 0.20 0.40 0.21 0.41 0.16 0.37
Services 0.10 0.30 0.10 0.31 0.15 0.36 0.28 0.45 0.32 0.47 0.28 0.45
Agricultural 0.04 0.19 0.01 0.12 0.07 0.26 0.06 0.23 0.02 0.15 0.15 0.36
Blue-collar 0.37 0.48 0.42 0.49 0.34 0.47 0.11 0.32 0.17 0.37 0.08 0.28
Other 0.19 0.39 0.21 0.41 0.21 0.41 0.22 0.41 0.22 0.42 0.17 0.38
Young benefit 0.04 0.21 0.04 0.20 0.05 0.21 0.05 0.22 0.05 0.21 0.07 0.25
Unemployment benefit 0.07 0.26 0.04 0.19 0.04 0.19 0.09 0.28 0.05 0.22 0.05 0.23
Training 0.26 0.79 0.15 0.61 0.12 0.56 0.31 0.83 0.24 0.74 0.17 0.61
Local wage 53,930.8 30,445.6 60,209.3 24,247.5 66,324.8 21,271.6 55,389.3 29,411.7 61,509.6 22,989.3 64,678.5 20,188.7
Norte 0.34 0.47 0.37 0.48 0.09 0.29 0.32 0.47 0.36 0.48 0.08 0.27
(Continued)
942 A. MENEZES AND D. SCIULLI
Table 1. (Continued).
Males Females
All Permanent contract Temporary contract All Permanent contract Temporary contract
Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev
Centro 0.16 0.37 0.30 0.46 0.19 0.39 0.17 0.37 0.29 0.46 0.15 0.36
Lisboa 0.38 0.49 0.28 0.45 0.43 0.50 0.36 0.48 0.29 0.45 0.36 0.48
Alentejo 0.06 0.24 0.03 0.16 0.06 0.23 0.09 0.28 0.04 0.20 0.18 0.39
Algarve 0.05 0.23 0.02 0.12 0.23 0.42 0.06 0.24 0.02 0.13 0.23 0.42
Log-flow unemployment 5.80 0.60 5.62 0.62 5.74 0.60 5.75 0.61 5.63 0.59 5.62 0.63
Log-flow vacancies 4.48 1.02 4.54 0.90 4.70 0.96 4.45 1.05 4.54 0.91 4.50 1.03
Mismatch index (average) 0.30 0.34 0.15 0.25 0.63 0.30 0.31 0.35 0.17 0.26 0.62 0.32
(0.5, 1] 30.97 12.33 69.14 32.05 13.36 67.52
(0.3, 0.5] 9.60 7.93 13.50 9.82 7.84 12.02
(0.1, 0.3] 12.56 14.68 8.64 12.73 16.25 9.92
(−0.1, 0.1] 46.25 64.30 8.44 44.75 61.90 10.26
(−0.3, −0.1] 0.52 0.62 0.25 0.53 0.48 0.19
[−0.46, −0.3) 0.10 0.15 0.04 0.12 0.17 0.09
Unemployment duration (average) 18.66 14.37 11.16 10.58 10.39 10.17 18.45 14.15 11.73 10.64 10.88 10.31
# Spells 60,656 5,361 2,430 94,249 8,835 5,323
JOURNAL OF APPLIED ECONOMICS 943
agricultural and fishing workers, blue collars, and individuals without specific
occupations (interpreted here as no qualifications). Three variables are introduced
to control if the individual received unemployment or youth benefits or underwent
a training period during the registration at the job-center. Year dummies referring
to the beginning of the unemployment spell are also considered. Regional dummies
are introduced to control for possible specific regional labour markets effects. As
anticipated, according to the job-search theory framework, the probability of accept-
ing a job offer is related to the expected wage distribution, and, hence, we introduce
the mean wage offered by firms, evaluated monthly at the job-center level. Labour
market tightness variables are also introduced and are evaluated monthly at the job-
center level. To implement the stock-flow matching mechanism, we use stock and
flow values of unemployed workers and vacancies in the following way. The IEFP
data provide daily information of gross inflows of unemployed workers and vacan-
cies that allow us to construct the monthly magnitude of gross inflows of labour
market tightness variables and to reconstruct their stock values. To construct stock
values, we use information from the 1997 IEFP dataset, hence at the starting of the
period analysed we have at our disposal the accumulated flow values until
December 1997. The stock-flow approach is implemented using time-varying labour
market tightness variables, under the hypothesis that individuals look at the pool of
vacancies only in the first round (one month) of their search process, and, after-
wards, look at the gross inflow in the following rounds (months) of the search
process. Tightness of the labour market expressed in terms of stock values (V/U) is
about 0.075, while it is about 0.47 if expressed in gross flow terms (v/u). These
differences suggest that mean unemployment duration exceeds mean vacancy dura-
tion, a result in line with the literature (see Christiano et al., 2021).
According to the IEFP information 98% of jobseekers are looking for
a permanent contract at the onset of their job search,
6
while two-thirds of vacant
jobs offer a permanent relationship. A first consequence of this dyscrasia is that
some jobseekers may accept a contract-type different from the desired one. Table 2
reports the destination contract of the jobseekers who find a job via the job-center
services per declared desired contract-type; for those individuals, in particular, for
the ones looking for a permanent contract and do find a job via the job-centre, 69%
of males effectively find a permanent job, while this percentage decreases to 63%
among females. Among individuals looking for a temporary contract and do find
a job via the jobcentre, 46% of males effectively find a temporary job, while this
percentage increases to 57% among females.
3. A heterogeneity index for contract-type mismatch
The availability of data disaggregated both at the jobseeker level and at the job vacancy
level is an indispensable condition to the construction of a contract-type mismatch index.
The IEFP dataset gathers information from 85 job-centers for each month under
5
It should be noted that only 3% of registered individuals (the jobseekers) are employed and given the very small order of
importance of this quantum, the empirical work henceforth presented does not decompose the sample per employ-
ment status (yet controls for employment status). In addition, when presenting the results, we refer to the unemploy-
ment duration interchangeably with spell spent waiting for employment or re-employment.
944 A. MENEZES AND D. SCIULLI
Table 2. Desired and destination contracts.
Males Females
Looking for a PC Looking for a TC Looking for a PC Looking for a TC
59,351 1305 92,178 2071
97.85% 2.15% 97.80% 2.20%
Censored Uncensored Censored Uncensored Censored Uncensored Censored Uncensored
36,697 22,384 878 427 55,617 36,561 1296 775
62.29% 37.71% 67.28% 32.72% 60.34% 39.66% 62.58% 37.42%
Own means Employment-center Own means Employment-center Own means Employment-center Own means Employment-center
14,738 7646 282 145 22,656 13,905 522 253
65.84% 34.16% 66.04% 33.96% 61.97% 38.03% 67.35% 32.65%
PC TC PC TC PC TC PC TC
5283 2363 78 67 8728 5177 107 146
69.09% 30.91% 53.79% 46.21% 62.77% 37.23% 42.29% 57.71%
Source: Own elaboration on IEFP data
JOURNAL OF APPLIED ECONOMICS 945
investigation including the number of job vacancies available; therefore, we can analyse
the labour market demand side at an appropriately disaggregated level. To evaluate the
effects of contract-type heterogeneity – between permanent contracts searched by job-
seekers and permanent contracts offered by firms on unemployment duration, we
introduce an index (M, Mismatch Index) in the spirit of the Jackman and Roper (1987)
mismatch indicator.
7
The mismatch index, measured monthly (m) at the job-centre level
(j), is defined as the difference between the ratio of jobseekers looking for a permanent
contract and the pool of jobseekers, and the ratio of permanent contracts offered by firms
and the pool of vacancies:
Mjm ¼UPC
jm
Ujm VPC
jm
Vjm
with Mjm ¼ 1;þ1½ (1)
M is defined in the support region [−1, +1] with the following particular cases:
Mjm ¼1 if UPC
jm ¼0&VPC
jm ¼Vjm
0 if UPC
jm ¼0&VPC
jm ¼0jUPC
jm .Ujm ¼VPC
jm .Vjm
þ1 if VPC
jm ¼0&UPC
jm ¼Ujm
8
>
<
>
:(2)
M takes the value of zero (lowest mismatch) in case there are no jobseekers nor vacant
jobs with a preference for a permanent contract, or in case the percentage of jobseekers
looking for a PC is equal to the percentage of vacant jobs offering a PC. Full positive
contract-type mismatch (i.e., M takes value one) indicates that all jobseekers look for
a permanent contract and no permanent contracts are available. On the contrary, full
negative contract-type mismatch, (i.e., M takes value minus one) indicates that all
jobseekers workers look for a temporary contract and no temporary contracts are
available. Hence, higher absolute values of M indicate higher mismatch.
The average in-sample value of M is 0.31, which represents the average value of the
difference between jobseekers looking for a permanent relationship (97.8% of jobseekers)
and the percentage of permanent jobs offered by firms (66.7% vacant jobs). Table 3 and
Figure 1 illustrate the distribution of M values across job-centers.
As expected, positive contract-type mismatch is prevalent in the data, with negative
contract-type mismatch seldomly occurring. In addition, the distribution of M across
job-centers justifies a flexible empirical approach in the sense that the estimating strategy
allows for asymmetric effects between positive (M greater than zero) and negative
(M smaller than zero) contract-type mismatch on (re)employment probabilities.
4. The econometric model
We employ hazard models analysis taking into consideration that the start of the job-
search process coincides with registration at job-centers. As the dataset has interval-
censored data, discrete-time hazard models are estimated. According to the hazard
model analysis, the probability that a transition to employment will take place in
a given interval [a
j-1
,a
j
) is conditional on the time already spent in searching and is
6
The question about contract-preference is asked only at the onset of the registration at the job-center and the recorded
answer does not change over the unemployment/job-search as it is never asked again.
946 A. MENEZES AND D. SCIULLI
Table 3. Mismatch index by employment-center.
Region Job-centre Mean
Std.
Dev. Min Max Region Job-centre Mean
Std.
Dev. Min Max
Norte Viana do Castelo 0.421 0.254 −0.008 0.791 Lisboa Caldas da
Rainha
0.156 0.141 −0.059 0.661
Braga 0.064 0.151 −0.046 0.780 Abrentes 0.089 0.143 −0.024 0.977
Fafe 0.005 0.040 −0.034 0.209 Santarem 0.422 0.247 −0.041 0.910
Guimaraes 0.016 0.040 −0.026 0.189 Tomar 0.506 0.168 0.179 0.990
Vila Nova de
Famaliçao
0.145 0.172 −0.007 0.827 Torres Novas 0.138 0.150 −0.015 0.972
Amarante 0.099 0.128 −0.052 0.458 Amadora 0.641 0.449 −0.010 1.000
Matosinhos 0.000 0.026 −0.069 0.087 Cascais 0.556 0.393 −0.033 0.968
Penafiel −0.003 0.023 −0.054 0.152 Conde
Redondo
0.343 0.198 −0.044 0.731
Porto 0.421 0.224 −0.032 0.804 Picoas 0.431 0.319 −0.099 0.952
Povao do
Varzim/Vila do
Conde
0.013 0.063 −0.032 0.442 Loures 0.488 0.262 −0.017 0.992
Santo Tirso −0.011 0.024 −0.077 0.068 Moscavide 0.091 0.141 −0.052 0.538
Vila Nova de
Gaia
0.016 0.048 −0.026 0.291 Torres Vedras 0.034 0.073 −0.030 0.398
Vila Real 0.377 0.253 −0.069 0.923 Vila Franca de
Xira
−0.011 0.067 −0.230 0.257
Chaves 0.012 0.084 −0.034 0.556 Almada 0.611 0.162 −0.015 0.982
Bragança 0.006 0.029 −0.021 0.164 Barreiro 0.147 0.265 −0.119 0.885
Macedo de
Cavaleiros
0.124 0.183 −0.048 0.720 Montijo 0.045 0.128 −0.157 0.403
Mirandela 0.065 0.124 −0.051 0.616 Setubal 0.832 0.120 0.258 0.986
Torre de
Moncorvo
0.029 0.069 −0.043 0.345 Salvaterra de
Magos
0.678 0.249 −0.028 0.971
Felguiras −0.005 0.010 −0.035 0.019 Alcobaça 0.114 0.100 −0.020 0.424
Porto Ocidental −0.005 0.012 −0.045 0.025 Sintra 0.546 0.229 −0.455 0.784
Basto 0.458 0.410 −0.027 1.000 Alcantara 0.018 0.052 −0.053 0.192
Lamego 0.297 0.185 0.000 0.819 Benfica 0.781 0.175 −0.014 0.994
S. Joao de
Madeira
0.004 0.023 −0.028 0.158 Seixal 0.576 0.158 −0.102 0.889
Arcas de
Valvedez
0.337 0.206 0.000 0.857 Alentejo Alacer do Sal 0.785 0.247 −0.050 1.000
Barcelos 0.011 0.047 −0.030 0.304 Sines 0.260 0.285 −0.036 0.794
Maia 0.073 0.181 −0.018 0.979 Elvas 0.582 0.244 0.049 0.978
Valongo 0.043 0.119 −0.022 0.662 Portalegra 0.505 0.292 −0.021 0.961
Gondomar 0.077 0.152 −0.056 0.789 Estremoz 0.644 0.256 0.052 0.989
Valença 0.019 0.063 −0.015 0.432 Evora 0.338 0.209 −0.005 0.812
Centro Aveiro 0.229 0.195 −0.008 0.992 Beja 0.787 0.245 0.000 1.000
Agueda 0.055 0.075 −0.037 0.326 Ourique 0.053 0.156 −0.036 0.817
Coimbra 0.400 0.233 −0.016 0.970 Ponte de Sor 0.601 0.330 −0.054 1.000
Figueirada Foz 0.595 0.159 −0.020 0.984 Montemor
o Novo
0.458 0.290 −0.018 1.000
Lousa −0.031 0.246 −0.449 0.668 Moura 0.811 0.275 −0.011 1.000
Leiria 0.134 0.112 −0.041 0.370 Algarve Faro 0.874 0.142 0.218 1.000
Marinha Grande 0.198 0.141 −0.029 0.517 Portimao 0.883 0.152 −0.032 0.989
S. Pedro do Sul 0.003 0.018 −0.022 0.080 Vila Real de
Santo
Antonio
0.864 0.117 0.476 0.992
Viseu 0.003 0.059 −0.090 0.275 Loule 0.870 0.147 0.000 0.960
Guarda −0.005 0.029 −0.065 0.099 Lagos 0.617 0.223 −0.011 0.993
Castelo Branco 0.450 0.237 −0.005 0.882
Covilha 0.015 0.050 −0.028 0.207
Arganil 0.167 0.093 −0.014 0.493
Figueiro dos
Vinhos
0.185 0.144 −0.048 0.629
Tondela 0.674 0.173 −0.006 1.000
Seia 0.022 0.061 −0.036 0.280
Serta 0.074 0.095 −0.018 0.422
Pinhel 0.271 0.235 0.000 0.803
Source: Own elaboration on IEFP data
JOURNAL OF APPLIED ECONOMICS 947
estimated as a reduced form equation that considers the product of two probabilities: the
probability of the individual receiving a job offer and the probability of the individual
accepting it. The probability of the individual accepting a job offer corresponds to the
probability that the wage offer received exceeds his or her reservation wage. Thus, the
probability of the individual leaving unemployment (or his or her current employment
into the new job in case the individual is employed) can vary over the unemployment
spell (or on-the-job search spell if employed) according to changes in the probability of
receiving an offer and the reservation wage: adopting time-varying covariates controls for
this variation. The probability of exiting from the current state into a new job in period
j reads:
hj;Pr T2 ½aj1;ajÞjTaj1
(3)
Assuming unit length intervals, the realization j of the discrete random variable T is
the recorded spell duration. Discrete-time hazard models require that data are
organized into a “sequential binary form”. The data form an unbalanced panel of
individuals with the individual i contributing j = 1, 2, . . . t observations, where
j indicates the number of periods at risk of the event.
8
Because some individuals
transition into employment and possibly back into unemployment, multiple spells
q = 1, 2, . . . Q are observable.
We estimate the hazard functions by assuming a flexible discrete-time logistic
hazard model as our benchmark model. We also employ a discrete-time multi-
nomial logit hazard model to relax the assumption of independent competing risks
(considering correlated random effects). The contract-type mismatch index is
included in a categorical form to allow for flexible and asymmetric effects on
unemployment duration from positive versus negative contract-type mismatch. For
robustness, we compare results for the hazard model for two-competing outcomes
and for three-competing outcomes. To be specific, we note that the available data
allow to identify the destination contract (PC or TC) only if the individual accepts
a job offered at job-center level, while it remains unidentified if the individual
leaves unemployment by own means (OM). It follows that three destination states
(d) are possible and thus competing risks models are estimated and we consider
both cases of independent competing risks and non-independent competing risks,
for robustness and completeness sake of the analysis. We control for unobserved
heterogeneity to prevent estimation bias arising from omitted variables or from
measurement errors in the observables.
The augmented hazard function, for each risk, is given by:
h tjx;uð Þ ¼ exp γ tð Þ þ þuð Þ
1þexp γ tð Þ þ þuð Þ (4)
7
(Jackman and Roper (1987)) indicator reads: M¼1
2Piuivi
j j M20;1½ , where ui¼UiPiUi and vi¼ViPiVi
where U
i
and V
i
are the number of jobseekers and vacancies in category i (where i may indicate the sector, skill level,
region and so on).
948 A. MENEZES AND D. SCIULLI
where x is a set of time-fixed and time-varying covariates, including the mismatch index,
M, introduced according to a categorical specification,
9
β is a vector of unknown
parameters to be estimated, while u is a term capturing the role of unobserved character-
istics, such as motivation, ability and job-search effort.
To estimate this model, the survival and density functions that compose the likelihood
function cannot be conditioned on the unobserved effects. Therefore, the likelihood
contributions are obtained by integrating the random terms out. The discrete-time
likelihood function that incorporates the unobserved heterogeneity term is obtained by
summing up the discrete-time likelihood functions of each individual i and spell
q given by:
logL β;γ;σð Þ ¼ ð
þ1
1 X
Q
q¼1X
n
i¼1X
t
j¼1
yqijloghqij þ1yqij
log 1hqij
" #guui
ð Þdui(5)
where y
qij
is an indicator that assumes a value of one when the transition takes place in
month j (i.e., the spell is uncensored) and a value of zero otherwise, and σ is the vector of
unknown parameters in g
u
(u).
Our benchmark estimations assume a stock-flow matching mechanism (see
Petrongolo & Pissarides, 2001 for an early survey). Stock-flow matching is more compa-
tible with negative duration dependence than random matching, even if negative dura-
tion dependence may also be explained in terms of ranking or loss of skills during
unemployment. Positive duration dependence could be explained, for example, because
of unemployment benefits exhaustion.
5. Estimation results
5.1. Hazard rates and contract mismatch in local labour markets
Table 4 reports the estimated coefficients from discrete time hazard models with the
mismatch index introduced in a non-linear way.
These results are obtained under a competing-risk specification, separate for males
and females, in which we assume a piece-wise constant baseline hazard, normally
distributed unobserved heterogeneity and control for a plethora of individual and job-
related characteristics (see results in Table A1 in Appendix). The results support the
presence of duration dependence, in a statistically meaningful way, for both samples,
males and females, and regardless of if job seekers state they prefer permanent or
temporary contracts; it should be noted that these results are in line with is observed in
the literature. When we look at the effects of the individual and job-related characteristics
(Table A1 in Appendix), several noteworthy results surface, albeit all in line with the
literature, once more. Having a disability lowers the hazard rate, an effect which is
particularly relevant for males searching for permanent contracts. Interestingly, being
employed at the onset of registration at the job-center increases the hazard rate, ceteris
paribus, a result especially acute in a statistical sense for females looking for a permanent
8
To be more specific, a binary dependent variable was created. If the individual i’s survival time is censored, then the
dependent binary variable is always zero; if the individual i’s survival time is not censored, then the dependent binary
variable has a value of zero in the first j-1 observation and has a value of one in the last observation.
JOURNAL OF APPLIED ECONOMICS 949
contract. The status of the jobseekers (beyond employment) regarding why they are
registering at the job-center do have statistical significance as regressors for both samples.
It is also interesting to note that having received unemployment benefits immediately
prior to registering at the job-center decreases the hazard rate in a statistical sense for
both males and senses and for both types of sought after contracts; this result is
particularly interesting given the fact that unemployment duration is already controlled
for, in addition to the vast plethora of individual and job-related characteristics, suggest-
ing that there is something particular regarding either these individuals or how they are
perceived by the prospective employers (tentatively, a form of stigma). The same can be
said regarding having received training; in this sense, the data do not support that the
training received increases per se the subsequently experienced hazard rate. It is also
interesting to note that the local-labour market variables, monthly gross inflows of
unemployment and monthly gross inflows of vacancies, have the usual effects, with
unemployment inflows decreasing the hazard rates and vacancies inflows increasing
the hazard rates, both with statistically meaningful effects, for both males and females,
regardless of if the jobseekers prefer permanent or temporary contracts.
The predicted hazard rates are reported as a function of unemployment duration
in Figure 2.
10
For both males and females, the data show negative duration depen-
dence with hazard rates dropping with unemployment duration, a result in line with
Table 4. Hazard model (benchmark specification): Duration dependence, mismatch index and local
labour market parameters.
Males Females
Permanent contract Temporary contract Permanent contract Temporary contract
Coeff. s.e. Coeff. s.e. Coeff. s.e. Coeff. s.e.
Unemployment duration
1–3 months 1.396 0.055 *** 1.513 0.087 *** 0.882 0.043 *** 0.868 0.058 ***
4–6 months 1.010 0.057 *** 1.260 0.088 *** 0.800 0.042 *** 0.922 0.056 ***
7–9 months 0.490 0.064 *** 0.791 0.097 *** 0.296 0.047 *** 0.630 0.060 ***
10–12 months 0.272 0.069 *** 0.394 0.109 *** 0.007 0.053 0.041 0.072
13–18 months base-category
19–24 months −0.381 0.075 *** −0.172 0.117 −0.308 0.053 *** −0.372 0.075 ***
25–36 months −0.875 0.079 *** −0.714 0.128 *** −0.678 0.054 *** −0.781 0.079 ***
over 36 months −1.272 0.104 *** −1.090 0.181 *** −1.004 0.073 *** −1.184 0.113 ***
Mismatch index
(0.5, 1] base-category
(0.3, 0.5] 0.694 0.072 *** −0.470 0.072 *** 0.653 0.055 *** −0.478 0.051 ***
(0.1, 0.3] 1.031 0.063 *** −1.163 0.083 *** 1.006 0.048 *** −0.979 0.056 ***
(−0.1, 0.1] 1.319 0.055 *** −2.390 0.092 *** 1.242 0.043 *** −2.217 0.065 ***
(−0.3, −0.1] 1.185 0.188 *** −1.690 0.456 *** 1.176 0.139 *** −1.229 0.268 ***
(−0.3, −0.46] −0.203 0.724 −1.604 1.023 1.199 0.269 *** −1.621 0.724 **
Local labour markets
Log flow unemployment −0.380 0.023 *** −0.383 0.044 *** −0.306 0.018 *** −0.525 0.030 ***
Log flow vacancies 0.208 0.020 *** 0.228 0.032 *** 0.184 0.015 *** 0.296 0.023 ***
σ2
Tc !σ2
Tcσ2
Tcσ2
PC 0.937 0.085 1.222 0.075
1.483 0.145 1.643 0.108
Log-likelihood −28,003.3 −13,404.9 −47,043.5 −27,195.6
Observations 1,095,030 1,728,678
Source: Own elaboration on IEFP data
9
In order to account for non-linear/asymmetric effects of mismatch index on hazard rates, the continuous-type indicator
is used in a categorical form in the empirical specification.
950 A. MENEZES AND D. SCIULLI
the literature. Quite interestingly, the drop in the hazard rates is particularly
pronounced for jobseekers who seek permanent contracts, despite their gender.
Figure 3, in turn, reports the predicted hazard rates for different levels of the
mismatch index.
11
It is remarkable to note that for jobseekers who seek permanent
contracts the hazard rates are highest when heterogeneity or contract-type mismatch
is lowest or close to zero; this result is valid for both males and females. It is also
noteworthy to notice the drop in the hazard rates as the contract-type mismatch
index increases in absolute value. Integrating over all these results, the data support
an association between higher contract-type mismatch and lower hazard rates.
5.2. Robustness checks
In this section, we provide certain robustness checks. First, we employ a discrete-time
multinomial logit hazard model to relax the assumption of independent competing risks
by allowing for correlated random effects (Table A2). The analysis shows that random
effects are positively correlated in a statistically significant way. Once accounting for this
circumstance, the magnitude of the coefficients associated with the baseline hazard
declines up to 15%, indicating that the negative duration dependence weakened. The
Figure 2. Predicted hazard rate by unemployment spell duration. Source: own elaboration on IEFP
data.
9
In order to account for non-linear/asymmetric effects of mismatch index on hazard rates, the continuous-type indicator
is used in a categorical form in the empirical specification.
10
Control variables are evaluated at their average values.
JOURNAL OF APPLIED ECONOMICS 951
coefficients associated with the contract-type mismatch index and with the local labor
market variables also declined, even though in a quite negligible way. In sum, these
results suggest that while accounting for correlated competing risks may be conceptually
important, the essence of our findings remains unchanged.
Second, we exploit the presence of multiple spells (corresponding to about 20% of the
observations) to improve the identification of unobserved heterogeneity. We relax the
assumption that spells of the same individuals are uncorrelated by running a three-level
discrete-time logistic hazard model. This allows for random effects at the individual level
and random effects at the spell nested in individual level. Results reported in Table A3
show that it doesn’t emerge any clear correlation at spells level. However, once this aspect
is accounted for, the variability of unobserved heterogeneity is slightly altered. In addi-
tion, we note a slight reduction in the negative duration dependence for both males and
females and for both outcomes (permanent and temporary contracts). Quite interest-
ingly, changes in the role of the contract-type mismatch index and of local labor market
indicators for estimated hazard rates are negligible.
Finally, we run a supplementary analysis to test whether and how our benchmark
results change once jobseekers who find a job by own means are included in the sample.
Results are reported in Tables A4a and A4b (males and female, respectively) and indicate
(compared to the benchmark case) a slight decline in the duration dependence para-
meters for both permanent and temporary outcomes and for both males and females.
Incidentally, duration dependence is even smaller for individuals who find a job by own
means. Quite interestingly, when looking at the characteristics of the local labour market
Figure 3. Predicted hazard rate by Mismatch index. Source: own elaboration on IEFP data.
952 A. MENEZES AND D. SCIULLI
we find that they play a relatively negligible role for this pool of individuals. The
coefficients associated to the contract-type mismatch index are often not statistically
significant and small in magnitude. Prima facie, this result suggests that the degree of
contract-type mismatch between jobseekers and firms preferences is less relevant for
individuals who exit unemployment (or current employment) by own means. Similar
evidence emerged when looking at coefficients associated with log-flow unemployment
and log-flow vacancies.
6. Conclusions
This paper tests the hypothesis that higher labour market mismatch, defined as hetero-
geneity between contract-type sought by jobseekers and contract-type offered by firms, is
associated with longer unemployment duration. In this sense, labour market mismatch,
as found in a dual-labour market where permanent contracts (good jobs) and temporary
contracts (bad jobs) co-exist, acts as a matching friction, and may lead, per se, to longer
unemployment duration. In these circumstances, better information on job contract-type
availability may lead to more effective job-search strategies at the individual level, who
may revisit their expectations in a timely and informed way, avoiding, thus, excessive
exposure to long unemployment duration due to this form of matching friction.
Our mismatch index measures the degree of contract-type mismatch between declared
contract-type preferences of jobseekers and jobs offered by firms at the job-center level
and assesses the impact of contract-type mismatch on unemployment duration by
leveraging on the variation found across space and time (over 5 years) in Portugal on
contract-type mismatch at the job-center level (86 of them) while using in a novel way
a rich set of individual data for jobseekers and vacancies.
Results from a flexible discrete-time competing risk hazard (multinomial) logit model
under a stock-flow matching mechanism suggest a significant association of contract-
type mismatch at job-centers level on individual hazard rates. Among individuals finding
a permanent contract the hazard rate is highest in the absence of contract-type mismatch
and lower for extreme values of the mismatch index, with the hazard rate reaching its
lowest level when full positive contract-type mismatch occurs. In addition, local labour
markets characterized by positive values of the contract-type mismatch index are asso-
ciated with a higher incidence of exiting unemployment by accepting a temporary
contract, as individuals may hedge their position against a low likelihood of finding
a permanent contract. Finally, extreme values of the mismatch index, especially negative
values, are associated with a higher probability of finding a job by own means, suggesting
that in the presence of high contract-type mismatch individuals look for a job outside the
job-centers.
Our work indicates that the Portuguese labour market is characterized by substantial
contract-type mismatch: jobseekers prefer permanent contracts while firms offer both
permanent and temporary contracts but mostly temporary contracts. It follows that
contract-type mismatch is akin to a matching friction and is associated with longer
average unemployment duration. The underlying motives behind contract-type mis-
match may possibly lie in the undesirability of some temporary contracts because of
their characteristics, including possible negative effects on career advancements for some
workers on temporary-contracts. Improving temporary workers’ conditions and their
JOURNAL OF APPLIED ECONOMICS 953
labour market perspectives could improve the desirability of temporary contracts, con-
tributing to reduce matching frictions and average unemployment duration owing to
contract-type mismatch. Workers who are duly informed about actual contract-type
mismatch observed at the job-center level may formulate search-strategies which are
rational, including search-strategies which may involve revising preferences with respect
temporary-contracts. It may also be the case that some workers overestimate their own
individual probability of finding a permanent-contract, despite the level of contract-type
mismatch observed in their local labour market. This overestimation may be associated
with a well-documented cognitive bias (Kahneman, 2011), with individuals systemati-
cally overestimating their own ability and relative position with respect the overall
distribution. In this sense, better information on contract-type mismatch, coupled with
policies which render temporary-contracts more attractive, are likely to increase unem-
ployment exit rates and reduce average unemployment duration.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was supported by the
Notes on contributors
Antonio Menezes has a PhD in Economics from Boston College and is Associate Professor in
Economics at the University of the Azores, Faculty of Economics and Management.
Dario Sciulli has a PhD in Economics from University Tor Vergata and is Assocaite Professor in
Economics at University Chieti-Pescara, Faculty of Economics
ORCID
Antonio Menezes http://orcid.org/0000-0002-2001-1589
Dario Sciulli http://orcid.org/0000-0003-1844-1851
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Table A1. Hazard model (benchmark specification): other covariates.
Males Females
Permanent
contract
Temporary
contract
Permanent
contract
Temporary
contract
Coeff. s.e. Coeff. s.e. Coeff. s.e. Coeff. s.e.
Age 0.017 0.011 0.025 0.017 −0.012 0.010 −0.015 0.013
Age squared −0.001 0.000 *** −0.001 0.000 *** 0.000 0.000 *** 0.000 0.000
Married 0.055 0.054 −0.063 0.077 −0.059 0.034 * −0.050 0.045
Disabled −0.358 0.163 ** −0.388 0.252 −0.182 0.202 0.043 0.263
1 dependent person 0.017 0.060 0.080 0.086 0.072 0.038 * 0.227 0.050 ***
2 dependent persons 0.069 0.067 0.173 0.097 * 0.080 0.046 * 0.220 0.060 ***
3 or more dependent persons −0.091 0.083 0.130 0.115 0.029 0.065 0.231 0.079 ***
9 years of education 0.063 0.042 0.000 0.062 0.025 0.036 −0.063 0.049
11–12 years of education 0.029 0.043 −0.107 0.066 0.062 0.034 * −0.005 0.050
More than 12 years of education −0.363 0.096 *** −0.362 0.139 *** −0.061 0.062 −0.031 0.074
Employed 0.154 0.084 * 0.020 0.136 0.214 0.067 *** 0.042 0.106
First job −0.086 0.094 −0.320 0.160 ** −0.062 0.065 −0.444 0.112 ***
Student 0.118 0.099 −0.079 0.176 0.110 0.074 0.412 0.126 ***
Ex-student 0.258 0.098 *** 0.063 0.179 0.314 0.071 *** 0.472 0.127 ***
End of training period 0.345 0.127 *** 0.236 0.219 0.346 0.090 *** 0.170 0.133
Dismissed 0.254 0.056 *** 0.087 0.090 0.326 0.046 *** 0.280 0.067 ***
Resigned 0.139 0.063 ** 0.007 0.096 0.203 0.053 *** 0.112 0.079
End of temporary contract 0.183 0.052 *** 0.302 0.071 *** 0.312 0.042 *** 0.480 0.054 ***
Manager-Specialist −1.410 0.113 *** −1.785 0.196 *** −1.537 0.089 *** −1.984 0.139 ***
Technical −0.536 0.065 *** −0.741 0.107 *** −0.584 0.082 *** −1.207 0.134 ***
Administrative −0.378 0.058 *** −0.206 0.087 ** −0.142 0.043 *** −0.398 0.060 ***
Services −0.256 0.060 *** −0.134 0.086 −0.051 0.037 −0.307 0.049 ***
Agricultural −0.582 0.135 *** 0.715 0.118 *** 0.269 0.091 *** 1.155 0.076 ***
Blue-collar −0.006 0.043 −0.190 0.068 *** 0.437 0.044 *** −0.144 0.071 **
Young benefit 0.131 0.085 0.150 0.122 0.161 0.064 ** 0.288 0.076 ***
Unemployment benefit −0.161 0.090 * −0.405 0.142 *** −0.138 0.063 ** −0.216 0.087 **
Training −0.311 0.026 *** −0.378 0.046 *** −0.306 0.018 *** −0.469 0.030 ***
Local wage 0.000 0.000 0.000 0.000 0.000 0.000 *** 0.000 0.000
Norte 0.024 0.043 −0.674 0.088 *** −0.093 0.035 *** −0.802 0.068 ***
Centro 0.626 0.046 *** 0.215 0.074 *** 0.524 0.037 *** −0.045 0.057
Alentejo −0.457 0.103 *** −0.592 0.117 *** −0.276 0.070 *** −0.139 0.070 **
Algarve −0.414 0.129 *** 0.612 0.086 *** −0.507 0.101 *** 0.628 0.064 ***
Constant −5.870 0.254 ** −5.728 0.420 *** −5.646 0.212 *** −4.371 0.301 ***
Source: Own elaboration on IEFP data
956 A. MENEZES AND D. SCIULLI
Appendix
Table A2. Hazard model with correlated unobserved heterogeneity: duration dependence, mismatch
index and local labour market parameters.
Males Females
Permanent contract Temporary contract Permanent contract Temporary contract
Coeff. s.e. Coeff. s.e. Coeff. s.e. Coeff. s.e.
Unemployment duration
1–3 months 1.152 0.056 *** 1.188 0.089 *** 0.668 0.043 *** 0.533 0.059 ***
4–6 months 0.846 0.057 *** 1.047 0.089 *** 0.653 0.042 *** 0.692 0.056 ***
7–9 months 0.398 0.064 *** 0.676 0.097 *** 0.215 0.047 *** 0.501 0.060 ***
10–12 months 0.225 0.069 *** 0.335 0.109 *** −0.031 0.053 −0.022 0.072
13–18 months base-category
19–24 months −0.347 0.075 *** −0.124 0.117 −0.270 0.053 *** −0.305 0.075 ***
25–36 months −0.815 0.079 *** −0.628 0.128 *** −0.611 0.055 *** −0.667 0.079 ***
over 36 months −1.212 0.104 *** −0.990 0.181 *** −0.941 0.073 *** −1.058 0.113 ***
Mismatch index
(0.5, 1] base-category
(0.3, 0.5] 0.681 0.071 *** −0.487 0.071 *** 0.653 0.055 *** −0.466 0.051 ***
(0.1, 0.3] 1.011 0.062 *** −1.149 0.083 *** 1.000 0.048 *** −0.945 0.057 ***
(−0.1, 0.1] 1.282 0.055 *** −2.386 0.091 *** 1.227 0.043 *** −2.190 0.065 ***
(−0.3, −0.1] 1.124 0.187 *** −1.724 0.455 *** 1.180 0.138 *** −1.137 0.268 ***
(−0.3, −0.46] −0.182 0.722 −1.562 1.019 1.237 0.268 *** −1.438 0.723 **
Local labour markets
Log flow unemployment −0.378 0.023 *** −0.379 0.044 *** −0.296 0.018 *** −0.465 0.030 ***
Log flow vacancies 0.200 0.020 *** 0.219 0.032 *** 0.171 0.014 *** 0.268 0.022 ***
σ2
PC 1.037 0.093 1.346 0.082
1.870 0.166 2.265 0.128
cov σ2
PC;σ2
TC
1.372 0.127 1.441 0.095
Log-likelihood −122,309.9 −200,836.1
Observations 1,095,030 1,728,678
Source: Own elaboration on IEFP data
JOURNAL OF APPLIED ECONOMICS 957
Table A3. Hazard model with nested unobserved heterogeneity: duration dependence, mismatch
index and local labour market parameters.
Males Females
Permanent contract Temporary contract Permanent contract Temporary contract
Spell duration Coeff. s.e. Coeff. s.e. Coeff. s.e. Coeff. s.e.
1–3 months 1.221 0.055 *** 1.319 0.086 *** 0.762 0.042 *** 0.666 0.057 ***
4–6 months 0.882 0.057 *** 1.116 0.088 *** 0.706 0.042 *** 0.769 0.056 ***
7–9 months 0.418 0.064 *** 0.716 0.096 *** 0.243 0.047 *** 0.549 0.059 ***
10–12 months 0.235 0.069 *** 0.357 0.109 *** −0.017 0.052 0.007 0.072
13–18 months base-category
19–24 months −0.358 0.075 *** −0.146 0.117 −0.286 0.053 *** −0.333 0.075 ***
25–36 months −0.838 0.079 *** −0.670 0.128 *** −0.643 0.054 *** −0.723 0.079 ***
over 36 months −1.249 0.104 *** −1.056 0.181 *** −0.992 0.073 *** −1.148 0.113 ***
Mismatch index
(0.5, 1] base-category
(0.3, 0.5] 0.678 0.071 *** −0.486 0.071 *** 0.649 0.055 *** −0.467 0.051 ***
(0.1, 0.3] 1.014 0.062 *** −1.167 0.083 *** 1.006 0.048 *** −0.948 0.056 ***
(−0.1, 0.1] 1.295 0.055 *** −2.394 0.091 *** 1.233 0.043 *** −2.190 0.065 ***
(−0.3, −0.1] 1.133 0.186 *** −1.685 0.455 *** 1.174 0.138 *** −1.163 0.268 ***
(−0.3, −0.46] −0.185 0.721 −1.590 1.020 1.223 0.267 *** −1.555 0.724 **
Local labour markets
Log flow unemployment −0.378 0.023 *** −0.383 0.044 *** −0.298 0.018 *** −0.477 0.030 ***
Log flow vacancies 0.199 0.020 *** 0.221 0.032 *** 0.170 0.014 *** 0.271 0.022 ***
σ2
Pc [individuals] 0.849 0.082 1.093 0.069
σ2
Pc [spells] 0.000 0.000 0.000 0.000
σ2
Tc [individuals] 1.493 0.139 1.886 0.113
σ2
Tc [spells] 0.000 0.000 0.000 0.000
Log-likelihood −29,008.16 −13,975.15 −48,657.073 −28,585.07
Observations 1,231,895 1,959,714
Source: Own elaboration on IEFP data
958 A. MENEZES AND D. SCIULLI
Table A4a. Hazard model (including OM outcome): duration dependence, mismatch index and local
labour market parameters.
Males
Permanent contract Temporary contract Own means
Coeff. s.e. Coeff. s.e. Coeff. s.e.
Unemployment duration
1–3 months 1.221 0.055 *** 1.319 0.086 *** 0.620 0.035 ***
4–6 months 0.882 0.057 *** 1.116 0.088 *** 0.875 0.033 ***
7–9 months 0.418 0.064 *** 0.716 0.096 *** 0.503 0.035 ***
10–12 months 0.235 0.069 *** 0.357 0.109 *** 0.229 0.039 ***
13–18 months base-category
19–24 months −0.358 0.075 *** −0.146 0.117 −0.424 0.045 ***
25–36 months −0.838 0.079 *** −0.670 0.128 *** −0.704 0.046 ***
over 36 months −1.249 0.104 *** −1.056 0.181 *** −1.175 0.065 ***
Mismatch index
(0.5, 1] base-category
(0.3, 0.5] 0.678 0.071 *** −0.486 0.071 *** −0.017 0.034
(0.1, 0.3] 1.014 0.062 *** −1.167 0.083 *** 0.025 0.031
(−0.1, 0.1] 1.295 0.055 *** −2.394 0.091 *** 0.062 0.026 **
(−0.3, −0.1] 1.133 0.186 *** −1.685 0.455 *** 0.057 0.119
(−0.3, −0.46] −0.185 0.721 −1.590 1.020 0.445 0.292
Local labour markets
Log flow unemployment −0.378 0.023 *** −0.383 0.044 *** −0.081 0.017 ***
Log flow vacancies 0.199 0.020 *** 0.221 0.032 *** 0.121 0.011 ***
σ2
PC 0.849 0.082
σ2
TC 1.493 0.139
σ2
OM 0.820 0.043
Log-likelihood −29,008.2 −13,975.1 −72,269.4
Observations 1,231,895
Source: Own elaboration on IEFP data
JOURNAL OF APPLIED ECONOMICS 959
Table A4b. Hazard model (including OM outcome): duration dependence, heterogeneity index and
local labour market parameters.
Females
Permanent contract Temporary contract Own means
Coeff. s.e. Coeff. s.e. Coeff. s.e.
Unemployment duration
1–3 months 0.762 0.042 *** 0.666 0.057 *** 0.369 0.028 ***
4–6 months 0.706 0.042 *** 0.769 0.056 *** 0.653 0.026 ***
7–9 months 0.243 0.047 *** 0.549 0.059 *** 0.406 0.028 ***
10–12 months −0.017 0.052 0.007 0.072 0.132 0.031 ***
13–18 months base-category
19–24 months −0.286 0.053 *** −0.333 0.075 *** −0.230 0.034 ***
25–36 months −0.643 0.054 *** −0.723 0.079 *** −0.463 0.035 ***
over 36 months −0.992 0.073 *** −1.148 0.113 *** −0.858 0.050 ***
Mismatch index
(0.5, 1] base-category
(0.3, 0.5] 0.649 0.055 *** −0.467 0.051 *** −0.103 0.027 ***
(0.1, 0.3] 1.006 0.048 *** −0.948 0.056 *** −0.068 0.025 ***
(−0.1, 0.1] 1.233 0.043 *** −2.190 0.065 *** −0.021 0.022
(−0.3, −0.1] 1.174 0.138 *** −1.163 0.268 *** 0.134 0.088
(−0.3, −0.46] 1.223 0.267 *** −1.555 0.724 ** 0.392 0.211 *
Local labour markets
Log flow unemployment −0.298 0.018 *** −0.477 0.030 *** −0.070 0.014 ***
Log flow vacancies 0.170 0.014 *** 0.271 0.022 *** 0.121 0.008 ***
σ2
PC 1.093 0.069
σ2
TC 1.886 0.113
σ2
OM 1.044 0.037
Log-likelihood −48,657.073 −28,585.074 −112,053.290
Observations 1,959,714
Source: Own elaboration on IEFP data
960 A. MENEZES AND D. SCIULLI
Table A5. Full sample descriptive statistics.
Males Females
10% sample Full sample 10% sample Full sample
Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev
Age 32.790 12.200 32.807 12.259 31.662 11.06 31.650 11.026
Age squared 1224.02 906.81 1226.56 911.62 1124.78 799.82 1123.30 796.53
Married 0.404 0.491 0.405 0.491 0.486 0.50 0.485 0.500
Disabled 0.010 0.101 0.010 0.100 0.004 0.06 0.004 0.063
No dependent persons 0.663 0.473 0.663 0.473 0.552 0.50 0.554 0.497
1 dependent person 0.152 0.359 0.151 0.358 0.234 0.42 0.232 0.422
2 dependent persons 0.118 0.323 0.118 0.322 0.156 0.36 0.157 0.364
3 or more dependent
persons
0.068 0.251 0.068 0.252 0.058 0.23 0.057 0.233
Max 6 years of education 0.453 0.498 0.448 0.497 0.450 0.50 0.451 0.498
9 years of education 0.221 0.415 0.222 0.415 0.188 0.39 0.190 0.392
11–12 years of education 0.260 0.439 0.263 0.440 0.267 0.44 0.264 0.441
More than 12 years of
education
0.066 0.249 0.067 0.251 0.096 0.29 0.095 0.293
Employed 0.033 0.178 0.032 0.177 0.037 0.19 0.037 0.189
First job 0.165 0.371 0.163 0.370 0.188 0.39 0.188 0.391
Student 0.065 0.247 0.064 0.245 0.067 0.25 0.068 0.252
Ex-student 0.073 0.260 0.075 0.263 0.083 0.28 0.083 0.275
End of training period 0.016 0.125 0.015 0.122 0.024 0.15 0.024 0.152
Dismissed 0.180 0.385 0.183 0.387 0.157 0.36 0.157 0.364
Resigned 0.129 0.335 0.129 0.335 0.098 0.30 0.099 0.298
End of temporary
contract
0.343 0.475 0.343 0.475 0.354 0.48 0.354 0.478
Other motivation 0.190 0.380 0.188 0.375 0.215 0.40 0.214 0.390
Manager-Specialist 0.074 0.262 0.074 0.261 0.083 0.28 0.083 0.276
Technical 0.112 0.315 0.112 0.316 0.044 0.20 0.046 0.209
Administrative 0.132 0.338 0.129 0.335 0.201 0.40 0.198 0.399
Services 0.100 0.301 0.101 0.302 0.277 0.45 0.277 0.448
Agricultural 0.037 0.188 0.037 0.188 0.055 0.23 0.055 0.227
Blue-collar 0.372 0.483 0.372 0.483 0.114 0.32 0.115 0.318
Other 0.193 0.394 0.191 0.393 0.217 0.41 0.216 0.411
Young benefit 0.044 0.206 0.044 0.205 0.051 0.22 0.051 0.220
Unemployment benefit 0.071 0.257 0.070 0.255 0.088 0.28 0.087 0.281
Training 0.262 0.794 0.267 0.803 0.309 0.83 0.310 0.830
Local wage 53,930.84 30,445.60 54,007.95 30,408.16 55,389.32 29,411.66 55,470.29 29,385.30
Norte 0.338 0.473 0.340 0.474 0.318 0.47 0.318 0.466
Centro 0.162 0.369 0.161 0.368 0.167 0.37 0.169 0.374
Lisboa 0.382 0.486 0.382 0.486 0.364 0.48 0.362 0.481
Alentejo 0.063 0.243 0.063 0.243 0.089 0.28 0.089 0.284
Algarve 0.054 0.227 0.054 0.226 0.062 0.24 0.062 0.241
Log-flow unemployment 5.799 0.597 5.799 0.593 5.754 0.61 5.756 0.610
Log-flow vacancies 4.481 1.018 4.481 1.016 4.452 1.05 4.451 1.051
Heterogeneity index
(average)
0.303 0.345 0.304 0.345 0.314 0.35 0.314 0.349
# Spells 60,656 603,536 94,249 943,098
Source: Own elaboration on IEFP data
JOURNAL OF APPLIED ECONOMICS 961
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