the determinants of overeducation: evidence from the...
TRANSCRIPT
The determinants of overeducation:
Evidence from the Italian labour market
Preliminary Draft
Clemente Pignatti Morano*
Research Department International Labour Organization
Geneva – 1211 +41 22 799 6251
The author is grateful for the comments received at the Winter School “New Skills and Occupations in Europe: Challenges and possibilities”, organized by the Centre for European Policy Studies (CEPS) in Brussels, 25-27 November 2013.
Abstract: The paper analyses the determinants of overeducation in the Italian
labour market for workers with an MSc degree, using data from the National
Labour Force Survey during the 2006-2011 period. The results confirm some of
the findings obtained by previous studies and they show that overeducation is
higher among youths, couples and foreign citizens. Interaction terms added to
the baseline regression reveal that higher overeducation among youths mostly
concern female workers and it is related to youth employment with temporary
contracts. Moreover, the study shows that overeducation is lower for workers
employed in big firms and for those with full-time contracts. The subject of study
also plays an important role, as workers who have obtained a more scientific
(e.g. engineering, scientific sciences) or a very specific (e.g. medicine,
architecture) degree are less likely to be overeducated. Previous conditions in
the labour market also affect current matching, with overeducation positively
correlated with previous unemployment status and negatively correlated with
previous student status. The paper finally examines whether labour market
conditions affect the likelihood of being overeducated. The results show mixed
evidence across age categories, but they reveal that unemployment increases
overeducation for workers below the age of 25.
1. INTRODUCTION 4
2. LITERATURE REVIEW 5
2.1 The beginning of the literature on overeducation 5
2.2 The determinants of overeducation at the individual level 5
2.3 Overeducation and macroeconomic conditions 7
3. MEASURING OVEREDUCATION 7
4. DATA AND METHODOLOGY 9
5. THE RESULTS 11
5.1 Main microeconomic results 11
5.2 Interaction of microeconomic variables 15
5.3 The role of unemployment in explaining overeducation 16
6. CONCLUSIONS 19
BIBLIOGRAPHY 23
4
1. Introduction
An extensive literature documents the microeconomic determinants of overeducation
in the labour market and it aims at assessing its consequences, especially in terms of
earnings’ losses (Leuven and Oosterbeek, 2011). Competing theories have been
proposed, for example considering overeducation only as a temporary status related
to occupational mobility (Johnson, 1978; Jovanovic, 1979; Sicherman, 1991; Groot,
1996); or rather as a long-term phenomenon connected to labour market rigidities or
individual unobserved heterogeneity (Clarck et al., 2012; Chevalier, 2003; Rubb,
2003). The first objective of this paper is to contribute to this literature by examining
the microeconomic determinants of overeducation in the Italian labour market, where
the phenomenon has been seldom analysed (Di Pietro and Urwin, 2003).
From a policy point of view, research on the microeconomic determinants of skills
mismatch and overeducation is motivated by the considerable increase in the
proportion of workers with a graduate degree in developed economies, which could
potentially lead to a reduction in the returns to education (Dolado et al., 2003; Leuven
and Oosterbeek, 2011).
The paper then aims at expanding the literature on overeducation by studying
whether labour market conditions affect the likelihood of being overeducated. Indeed,
while there is a growing literature providing evidence of the role played by labour
market conditions at the time of graduation in affecting future career paths,
considerably less attention has been paid to explaining the dynamics that at the
beginning of the career trigger these long term consequences (Liu et al, 2012). In
particular, the overeducation literature has not extensively targeted the question
whether negative labour market conditions increase the likelihood of being
overeducated, by for instance reducing the number of available jobs.
The answer to this question would widen the understanding of the long-term labour
market consequences of economic downturns and it would also help in the design of
active labour market policies (e.g. school to work transition) and unemployment
benefit schemes.
The rest of the paper is organized as follows. Section 2 reviews previous studies and
theories on overeducation; Section 3 examines the measurement issues related to the
concept of overeducation; Section 4 presents the data and methodology used in this
paper; Section 5 analyses the results; Section 6 concludes.
5
2. Literature review
2.1 The beginning of the literature on overeducation
Skills mismatch and overeducation have started being extensively analysed by the
academic literature in the 1970s, when concerns emerged in the US over the fact that
the supply of skilled workers seemed to outpace its demand (Berg, 1970). “The
Overeducated American” by Freeman (1976) represents one of the most influential
studies of that period. The author compared the average income of college graduates
and high school students at the time of entry in the labour market and reported a
decrease in the relative wage premium by 24 per cent between 1969 and 1974.
The topic immediately triggered an extensive debate and Smith and Welch (1978)
soon challenged the results obtained by Freeman. In particular, they added four years
to the analysis and accounted for the differences in age at the time of entry into the
labour market between high school and college graduates. They reported a
considerably smaller fall in the wage premium – equal to 8 per cent – and concluded
that the evidence was more in line with an overcrowded labour market.
The attention on skills mismatch was then revitalised by Duncan and Hoffman (1981),
who presented one of the first studies based on the comparison between the amount
of education supplied by a worker and the one required for his/her job. In particular,
the wage equation they introduced allowed to distinguish the returns to education
between years of required education, overeducation and undereducation (Leuven and
Oosterbeek, 2011). Their results showed that the returns of an additional year of
required education was twice as much as the return to an additional year of
overeducation.
Since then, an important part of the literature has followed the seminal study of
Duncan and Freeman by analysing the magnitude and persistence of earnings’ losses
associated to overeducation. Numerous other studies have instead used binary
outcome models to examine the determinants of overeducation at the individual level.
The following subsection reviews this second field of research.
2.2 The determinants of overeducation at the individual level
It is difficult to conduct a comprehensive review of the studies on the determinants of
overeducation at the individual level, since the specifications of the models greatly
differ. 1 Moreover, little motivation is generally presented for justifying the variables
that are included as controls and those who are not. Similarly, some of the included
1 See Leuven and Oosterbeek (2011) for a comprehensive review of the literature on skills mismatch.
6
variables such as job tenure might be endogenous, while different strategies have
been followed to measure other variables such as individual ability. However, it is
possible to draw conclusions on the relation between overeducation and some
individual variables such as age, gender, ethnicity and ability.
Age: There is a general agreement in the literature over the fact that the probability of
being overeducated decreases with age. Different labour market theories can explain
this empirical finding. First, labour market search theories predict that the quality of
job matches increases throughout the career (McGuinness and Wooden, 2009).
Moreover, young workers are more likely to be overeducated because employers may
compensate for their shorter working experience with additional education
(Sicherman and Galor, 1990). Finally, theories of career mobility predict that the skills
acquired as overeducated may increase the probability of being promoted, thus
presenting overeducation as an optimal strategy at the beginning of the career
(Leuven and Oosterbeek, 2011).
Gender: Different studies have documented that women are more likely to be
overeducated than men. The motivation generally relates to the fact that when man’s
wage represents the primary source of income, key households’ decisions – such as
house location and childcare – are taken in order to maximise his labour market
opportunities (Dolton and Silles, 2001). The choices available to women in the labour
market are therefore necessarily restricted – for instance only to part-time work –
leading to a higher probability of being overeducated (Frank, 1978; Ofek and Merrill,
1997).
Ethnicity: The analysis of the role of ethnicity in explaining overschooling has been
relatively constrained by the difficulties related to comparing educational systems
across countries. However, different studies have found a positive relation between
being a minority and employment as overeducated (Green et al., 2007; Battu at al.,
2004; Sharaf, 2013). The reasons advocated behind this phenomenon do differ. For
example, additional education might be required to compensate for other
shortcomings such as the lack of proficiency in the country’s language (Battu et al.,
2004). Alternatively, this might be simply the result of labour market discrimination,
of either taste based or statistical form (Altonji and Blank, 1999).
Ability: Being employed as overeducated can also be the effect of lower individual
ability, which is compensated by the employer with additional schooling. Even in this
case, the results of the literature have been limited by the difficulties in having access
to information on individuals’ skills. However, studies that include some measures of
individual ability generally find a negative correlation with overeducation (Allen and
Van der Velden, 2001; Chevalier and Lindley, 2009; Green and McIntosh, 2007).
7
2.3 Overeducation and macroeconomic conditions
Considerably less attention has been paid in the literature to analysing whether
overall macroeconomic conditions influence the probability of being overeducated.
The hypothesis is that negative labour market conditions restrict the number of
opportunities that an individual looking for a job faces, while raising the risk of
remaining unemployed. This increases the probability of accepting a job below the
level of education he/she has acquired (Dolton and Silles, 2001).
The theoretical literature has recently started modelling this mechanism. The first
contributions that have introduced matching models with heterogeneous agents are
of Pissarides (1999) and Acemoglu (1999). One of the first studies specifically
targeting job competition by educational level has been conducted on Spain and finds
evidence of an increase in overeducation during the 1980s as a result of biased
technological changes (Collard et al., 2002). Other contributions have expanded the
model by considering on-the-job search by mismatched high skilled workers (Gautier,
2002; Dolado et al., 2003) and endogenous skills requirements (Albrecht and Vroman,
2002).
The empirical literature has mostly studied whether labour market conditions at the
time of graduating play any effects on future careers’ paths. Kahn (2010) and
Oreopoulos et al. (2012) provide evidence that in America college students graduating
during a recession are affected by persistent earnings’ losses. Only recently, there
have been some attempts to explain these negative shocks by referring to
overeducation and skills mismatch. Liu et al. (2012) use a panel database on college
graduates from Norway to show how skills mismatch at the beginning of the career
plays a significant role in explaining future career losses. In particular, they find that
overeducation has a large countercyclical trend and that an increase in
unemployment rate by 1 per cent increases the probability of being overeducated by
3.4 per cent. Similarly, Hagedorn and Manovskii (2010) show how labour market
tightness affects the quality of job matches. Dolton and Silles (2001) also look at
unemployment rate as a potential determinant of over-education among college
graduates in the UK, however without finding a statistically significant result. 2
3. Measuring overeducation
An important feature that characterizes any study on overeducation relates to the
measure of overeducation that it is used. The issue is to compare individuals’
educational attainments with the educational requirements of their job. The more
challenging task is then related to defining the required level of education for a
2 Some policy papers have also recently documented the rise in skills’ mismatch and reported the lack of research on its macroeconomic determinants (CEDEFOP, 2010).
8
particular job or occupation and three methods have been mainly used in the
literature (Leuven and Oosterbeek, 2011).
Self-assessment: The first method uses answers to surveys that ask workers the
educational requirement associated to their job or occupation. This method has been
widely used and its main strength relates to the fact that it is potentially based on all
the information that is needed to define overeducation. However, the first problem is
that workers tend to overstate the educational requirements of their job (Hartog,
2000). Additionally, the questions reported in the surveys differ substantially. Some
interviews refer to recruiting standards (Duncan and Hoffman, 1981; Galasi, 2008);
while others to the skills needed to perform the job (Hartog and Oosterbeek, 1988;
Alba-Ramirez, 1993). Moreover, some studies include both formal and informal
education and training, while others refer specifically to the highest educational
degree obtained. Coherently, it has been reported that the same person responds
differently to these similar questions (Green et al, 1999).
Job analysis: A second approach is based on databases – such as the Occupational
Information Network in the US – that report information on occupational categories.
External analysts determine the required level of education related to each occupation
and this is translated into the corresponding years of education. The positive feature
of this measurement technique is that it is less subject to individual bias and that the
assessments are based on the levels of technology associated with each occupation
(Dolton and Silles; Leuven and Oosterbeek, 2011). However, updates in the
classification are infrequent and this might reduce the validity of the benchmarks
(Hartog, 2000). Moreover, these classifications are not conducted in many countries,
thus limiting comparability of studies. Additionally, the translation of educational
requirements into years of schooling is subject to debate (Halaby, 1994).
Realized matches: The last method deducts the required level of education from the
realized job matches in the labour market and it has first been proposed by Verdugo
and Verdugo (1989). In particular, the required level of education is derived as the
mean level of education of the workers in the same occupation. Most commonly, the
three-digit categorization of professions is used. Workers are then classified as over
or under qualified if their educational attainment is at least one standard deviation
above or below the mean educational level in their occupation (Verdugo and Verdugo,
1989).3 This method is criticized because it is the result of demand and supply forces,
rather than a genuine comparison of required and acquired levels of education
(Leuven and Oosterbeek, 2011). Moreover, the one standard deviation benchmark is
arbitrary and the results could be subject to outliers in small databases. Generally, it
3 Kiker et al. (1997) propose to use the mode rather than the mean for computing the required level of education.
9
has been documented that the realized matches approach underestimates
overschooling (Groot and van den Brink, 2000).
4. Data and Methodology
The present study uses data from the Continuous Labour Force Survey (Rilevazione
Continua delle Forze di Lavoro) conducted by the Italian National Institute of Statistics
(ISTAT). This survey is not directly available to the public, but it can be provided upon
request. The Continuous Labour Force Survey represents the main source of
documentation of the Italian labour market and it has been conducted since 1959. In
order to comply with European standards, the methodology has been reviewed in
2004. The main innovation has been the adoption of a new frequency for the
interviews, which were previously conducted during one single week in each quarter
and are now distributed throughout all weeks of the year. The reference sample
consists of all households resident in Italy.
The ISTAT Continuous Labour Force Survey does not ask individuals about the level of
education required for their job, thus precluding the use of the self-assessment
method for measuring overeducation. Similarly, there are not reliable databases in
Italy in which external analysts convert jobs and occupations into levels of required
education, thus preventing the use of the job analysis method. However, the ISTAT
Labour Force Survey collects information on the highest degree obtained by each
worker. This paper therefore uses the realized matches approach to measure
overeducation. The categorization of workers in occupational groups follows other
studies and it is based on the 3-digit code. For each occupational group, the mean level
of education is computed and a worker is considered overeducated if his/her highest
educational attainment is one standard deviation above the average level of education
of the relevant occupational group.
Notwithstanding the limitations of the realized matches approach, some
considerations should justify its use in this specific context. First, the very large
dimension of the sample – around 1.4 million employed individuals – considerably
limits the risk that outliers will influence the mean values obtained for the required
level of education. This is one of the main concerns when the analysis is conducted
with small panel databases, as the three-digit categorization of professions may lead
some sub-groups to have a very small number of units.4 Second, the short time period
under consideration – from 2006 to 2011 – considerably reduces the risk that the
measures of required level of education are inconsistent across years due to
substantial technological changes. Finally, the measure of overeducation obtained in
this paper can be compared with the results presented by other studies. In particular,
4 In order to avoid this problem, all occupational groups with less than 100 observations have been eliminated. However, this has generated the exclusion of only 3 occupational groups.
10
13.75 per cent of the working age population between 20 and 64 years old results
being overeducated. Leuven and Oosterbeek (2011) report that the studies of
overeducation that use the realized matches approach present on average a share of
overeducation equal to 13 per cent.
After having computed overeducation, we restrict the analysis to workers with an MSc
degree – around 120,000 employed individuals in the database. This is done in order
to avoid pooling together categories of workers for which the nature and
determinants of overeducation might differ. This problem has been recently raised in
the literature of overeducation by Arcidiacono et al. (2010). The MSc category is
chosen among the other graduate categories because of its significantly higher
dimension. In particular, 80.73 per cent of workers with a degree above the high
school level in the database have obtained an MSc degree. However, we confirm the
results obtained for MSc students by running a regression that also includes BA and
PhD graduates and that is presented in the Appendix (Table 1C).
We exclude workers employed in the armed forces due to the atypical nature of their
profession and we restrict the analysis to workers between 20 and 64 years old. This
represents a slight modification as compared to the traditional definition of working
age population (15-64 years old), needed to include only workers who have obtained
a postgraduate degree. Summary statistics for workers with an MSc degree are
provided in Table 1A in the Appendix.
The results should be interpreted relatively to the base worker, which is a man
between 50 and 54 years old. He is an Italian citizen, with an MSc degree in human
sciences, living in a couple – either married or not. He works in the service sector in a
firm with less than 10 employees, having the professional status of clerical worker. He
has a full-time and permanent job and he was already employed one year before the
interview. Regional dummies corresponding to the 19 regions reported in the survey
have been introduced, together with quarterly dummies for each quarter from 2006
to 2011. Although these dummies are not included in the results, it is worth noting
that the base worker lives in the region of Lombardy in the first quarter of 2008.
Employment and unemployment rates are regional quarterly data available from
ISTAT database. Finally and in order to account for the possible differences in the
effect of unemployment on overeducation across age groups, we interact
unemployment rate with the different age categories.
We use a probit model reporting marginal results with robust standard errors, as this
is the most common approach used in the literature (Leuven and Oosterbeek, 2011).
Sampling weights have been used in order to take into account of the design of the
survey. The resulting regression is described by the following equation:
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where Y is the dependent variable equal to 1 if the worker is overeducated and 0
otherwise; α is the intercept; Θ represents the time dummies; C the regional dummies;
is a set of individual characteristics; Z unemployment rate and its interaction with
the age groups; ε is the error term.
Interactions between microeconomic variables are added to the baseline regressions
at different stages to test additional hypotheses. The resulting regressions take the
following form:
where X and W are two sets of individual characteristics and their product represents
the interaction term.
5. The results The analysis of the results reveals that many individual determinants of
overeducation behave according to the predictions and the paper confirms most of the
findings obtained by previous microeconomic studies. Interaction terms are then
added at different stages and they reveal important combined effects on
overeducation of age and gender and age and the nature of the employment relation.
Finally, the results related to the effects of unemployment on overeducation differ
across age groups, but evidence suggests that unemployment increases the likelihood
of being overeducated for workers below the age of 25.
5.1 Main microeconomic results Different theories predict that the quality of job matches increases with age and the
empirical literature on skills mismatch has coherently documented how the
probability of being overeducated is higher among youths. Accordingly, the results of
this paper show how the likelihood of being overeducated decreases with age, with all
coefficients being statistically significant. As expected, the coefficients are stronger for
those age groups that are more distant from the category that has been dropped (50-
54 years old). However and quite surprisingly, the magnitude of the coefficient
remains fairly constant up to the age of 40 – suggesting that the generational penalty
is rather persistent (Table 1).
Foreign workers are also expected to be employed as overeducated more often than
national citizens. This might reflect either employers’ rational expectations about
their work quality – statistical discrimination – or rather employers’ distaste for
foreign workers – taste based discrimination. Additionally, lack of comparability of
university degrees across countries may disadvantage foreign citizens that have
12
completed their studies in their home country. According to these predictions, the
results show how foreign workers – defined as workers without the Italian citizenship
– are more likely to be overeducated than Italian workers. The effect is particularly
strong, with the probability of being overeducated increasing by 13 per cent among
foreign citizens in all the different specifications.
Family composition can also play a role in explaining overeducation by modifying the
opportunity choices from which an individual looking for a job can draw. For example,
workers in a couple are likely to be constrained in their job search by the decisions of
the other member of the couple. Consistently, the results show how being single
decreases the likelihood of being overeducated with respect to workers who are in a
couple – either married or not. 5 The coefficient related with other forms of family
composition – e.g. family groups – is instead not statistically significant.
An additional factor potentially explaining overeducation is the subject of the degree
obtained. This relation has been rarely analysed in the literature, while it can be of
particular interest as it is consistent with the contemporaneous presence of a
generally overeducated workforce and the shortage of skilled workers in some
specific sectors. The hypothesis in this case is that less specific degrees could lead to
an increase in the likelihood of being overeducated, as the worker is not perceived as
having acquired a specific set of knowledge. Alternatively, we would expect to have
higher overeducation among the students of those subjects for which there is an
oversupply of workers. The results confirm these hypotheses and reveal that –
compared to the omitted group of those with a degree in human sciences – workers
who have obtained a more scientific degree like sciences and engineering are less
likely to be overeducated. Similarly, degree qualifications such as architecture and
medicine, which have a direct link to specific professions, decrease the likelihood of
being overeducated.6
Examining the role played by previous conditions inside or outside the labour market
allows us to determine whether previous career paths influence the quality of future
matches – e.g. through signalling mechanisms. The results show that having been
unemployed in the previous year increases the likelihood of being overeducated on
the current job with respect to the omitted category of those who were employed in
the previous year. This is consistent with the idea that previous unemployment
condition may reveal the low quality of the applicant and it thus increases the
likelihood that the employer requires additional schooling for the same position. On
the other hand, having been a student in the previous year decreases the likelihood of
5 The interaction between the family composition and the female dummy reveals how single women are relatively more likely to be overeducated than single men (not reported). 6 In Model 5 in the Appendix (Table 1B) we interact the subject of study with the female dummy. The results show that women are relatively less overeducated in those areas (e.g. engineering, science) where they are underrepresented in our database. The opposite applies for degrees where there is a majority of women graduates (e.g. medicine).
13
being overeducated on your current job. This might be related to the fact that recent
students are more likely to be able to apply the skills that they have just acquired and
their knowledge should not be outdated. Not having been in the labour force in the
previous year has instead a generally positive but statistically not significant effect on
overeducation.
Given the relevance of micro enterprises in the Italian economy – equal to 94.5 per
cent of total enterprises – it is worth examining whether firm size plays any role in
determining the likelihood of being mismatched. The results show how indeed
overeducation decreases with firm size. This is consistent with the idea that bigger
firms have more accurate recruitment techniques that reduce the risk of hiring a
worker that does not match the educational requirements associated with the vacancy
(Dolton and Silles, 2001). Moreover, in big firms there is a wider range of positions
that enables the management to internally relocate workers in case of mismatch. The
only exception is represented by firms between 11 and 19 employees, where the
likelihood of being overeducated is slightly higher than in firms with less than 10
employees. However, the two categories of firms are likely to have similar
organizational strategies and this might explain the result.
Additionally, the results show how the probability of being overeducated is higher for
workers with part-time contracts with respect to their colleagues in full-time
employment. This is consistent with the idea that workers looking for a part-time job
are more constrained in their job search with respect to their colleagues that are
willing to work full time. An additional explanation relates to the fact that employers
may decide to prioritise – for instance in terms of training and career opportunities –
workers employed on a full time basis within the firm.7
Similarly and as expected (Dolton and Silles, 2001), the results related to the career
position reveal that workers at the top of the career ladder (directors and managers)
are less likely to be overeducated than those at the bottom (blue collar workers). The
only exception is represented by trainees – who are less likely to be overeducated
than the omitted category of clerical workers. This result can be explained by the
generally high-skilled nature of traineeships for workers with an MSc degree.
Finally, the results show that the likelihood of being overeducated significantly differs
across economic sectors. In particular, workers in the service sector are less likely to
be overeducated than those in the industry or in the agricultural sector. These results
can be interpreted by the different nature of employment in the three economic
sectors and the relatively less skilled nature of jobs in agriculture and industry.
7 In model 6 in the Appendix (Table 1B) we interact part-time employment with the female dummy. The results reveal how the positive relation between overeducation and part-time employment holds uniquely for women – which indeed represent 80 per cent of all part-time workers.
14
Age categories (50-54) 20-24 0.26633 *** (0.0069267)
(0.0310587) Agriculture 0.110089 ***
25-29 0.1902011 *** (0.0342618)
(0.0094174) Firm size (1-10) Firm 11-19 0.019767 **
30-34 0.1621838 *** (0.0086609)
(0.0078102) Firm 20-49 -0.07515 ***
35-39 0.157469 *** (0.0072935)
(0.0074218) Firm 50-250 -0.15446 ***
40-44 0.1123465 *** (0.0068573)
(0.0075801) Firm >250 -0.09104 ***
45-49 0.044284 *** (0.0079431)
(0.0077861) Subject of study (human science) Social sciences 0.296735 ***
55-59 -0.0368199 *** (0.0075454)
(0.0083244) Languages 0.084947 ***
60-64 -0.1041801 *** (0.0085047)
(0.0123402) Economics-Statistics 0.277781 ***
Personal characteristics No citizen 0.1370148 *** (0.006791)
(0.014712) Law 0.195905 ***
Single -0.0291957 *** (0.0079826)
(0.006799) Sciences -0.16239 ***
Other types of family -0.0226445 (0.0080331)
(0.0207206) Engineering -0.03347 ***
Female -0.0157498 *** (0.0092743)
(0.0048278) Architecture -0.02588 *
Career position (clerical) Director -0.3324635 *** (0.0137344)
(0.0059069) Medicine -0.10219 ***
Manager -0.2972218 *** (0.0100393)
(0.0044825) Other education 0.139797 ***
Blue collar worker 0.4815742 *** (0.0072574)
(0.0066239) Previous condition (employed) Unemployed previous year 0.053039 ***
Trainee -0.1328942 *** (0.0125812)
(0.0317739) Student previous year -0.06155 ***
Employment relation Part-time 0.0395409 *** (0.0183588)
(0.007273) Not labour force previous year 0.0556
Temporary -0.1092668 *** (0.0432931)
(0.0068519) Other previous year -0.02424
Sector (service) Industry 0.0906355 *** (0.0279634)
Table 1 Dependent variable is overeducation (equal to 1 if overeducated). Categories dropped are in parenthesis. Regional and quarterly dummies not reported
Probit regression reporting marginal results. Observations=119854; Pseudo R2=0. 0.2517; Log pseudo likelihood=-62155.376. Robust standard errors in parenthesis. Marginal effects
calculated at mean values for continuous variables and using discrete differences for dummy variables. Sampling weights have been used. ***/**/* Significance at 1/5/10 per cent.
15
5.2 Interaction of microeconomic variables
In order to test whether the age effects on overeducation described above are
similar for men and women, in Table 2 we present the results of the interaction
of the age categories with the female dummy – the entire regression results for
this specification are reported in Model 3 in Table 1B in the Appendix. The
results reveal how the higher probability of being overeducated among youths
mostly uniquely concerns female workers. Indeed, after having added the
interaction terms, the coefficients of the age categories up to 35-39 years old get
significantly reduced and they partially lose their statistical significance. By
contrast, the interaction terms between female and the age categories up to 35-
39 years old are all positive and statistically significant. Gender instead does not
seem to play a particular role in explaining overeducation above the age of 40,
probably reflecting how gender based discrimination decreases with age.
We then test whether the age effects on overeducation are related to the
different forms of employment that characterize youths and adults. In particular,
in Italy temporary employment is significantly concentrated among youths – 41
per cent of the workers below the age of 25 in our database holds a temporary
job. At the same time, temporary employment may affect overeducation, since
the short-term nature of the employment relation creates lower incentives for
both the employer and the employee to achieve an appropriate matching. To test
20-24 -0.044739 (0.0113552)
(0.0922506) Female 20-24 0.2847078 ***
25-29 0.0535541 ** (0.0573017)
(0.0228578) Female 25-29 0.1846796 ***
30-34 0.068661 *** (0.0167845)
(0.0191599) Female 30-34 0.1527845 ***
35-39 0.092077 *** (0.0148089)
(0.0182279) Female 35-39 0.1164717 ***
40-44 0.1048212 *** (0.0146354)
(0.0186714) Female 40-44 0.0291873 *
45-49 0.0201378 (0.0154397)
(0.0192299) Female 45-49 0.0303639 *
55-59 -0.0742792 *** (0.0155513)
(0.0204691) Female 55-59 -0.0037173
60-64 -0.1571762 *** (0.016606)
(0.027987) Female 60-64 0.0257851
Female -0.925454 *** (0.0255561)
Table 2: Age effects by gender
Note: Dependent variable is overeducation. The control variables included in the
regression are otherwise the same as in Table 4. Refer to Model 3 in Table 1B in the
Appendix for the entire regression results.
16
whether the predominance of temporary employment among young workers
explains their higher overeducation, we interact temporary employment with
the age categories.
The results confirm the prediction and show how for workers below the age of
25, their employment as temporary workers is the main driver of overeducation.
By contrast, temporary employment slightly reduces the probability of being
overeducated for workers in the middle of the age distribution. This can be
related to the different nature of temporary employment in the middle of the
career for a worker with an MSc degree – i.e. temporary external consultant.
Quite surprisingly, the interaction of temporary employment and the age
category 60-64 has a positive and statistically significant coefficient. This can be
related to the very atypical nature of temporary employment for the workers of
that age category – only 8 per cent of the workers between 60 and 64 years old.
5.3 The role of unemployment in explaining overeducation The paper finally looks at whether labour market conditions affect the likelihood
of being overeducated. The underlying hypothesis is that an increase in the
unemployment rate decreases the set of opportunities available to a worker,
increasing in this way the probability of accepting a job below the acquired level
of education. In order to account for the possible differences in the effect of
20-24 0.0675125 (0.0305698)
(0.092936) Temporary 20-24 0.1402548 *
25-29 0.1246447 *** (0.079654)
(0.0199708) Temporary 25-29 0.05316
30-34 0.1531035 *** (0.0338896)
(0.0163466) Temporary 30-34 -0.0309137
35-39 0.1517392 *** (0.0332822)
(0.0158178) Temporary 35-39 -0.0798889 **
40-44 0.1207718 *** (0.0334458)
(0.0163121) Temporary 40-44 -0.0787477 **
45-49 0.0355082 ** (0.0349841)
(0.0169869) Temporary 45-49 -0.0430384
55-59 -0.0740685 *** (0.0394297)
(0.018377) Temporary 55-59 0.0032069
60-64 -0.1559045 *** (0.0537485)
(0.026763) Temporary 60-64 0.2235773 ***
Temporary -0.084912 *** (0.0582132)
Table 3: Age effects by employment contract
Note: Dependent variable is overeducation. The control variables included in the regression are
otherwise the same as in Table 4. Refer to Model 4 in Table 1B in the Appendix for the entire
regression results.
17
unemployment on overeducation across age groups, unemployment rate is
interacted with the age categories. Indeed, the quality of job matches is likely to
be influenced by labour market conditions substantially more for workers that
are entering the labour market with respect to their colleagues with longer job
tenures. Employment rate is included as control variable in order to take into
account other macroeconomic fluctuations.
The results reveal how unemployment rate has a small, negative and generally
statistically not significant effect on overeducation. However, the coefficient of
the interaction terms between unemployment and the age group 20-24 is
positive and statistically significant in all the different specifications – from
Model 2 to Model 6 in Table 1B in the Appendix. Moreover, Wald tests reject the
null hypothesis that the sum of the coefficients of unemployment rate and its
interaction with the age group 20-24 is equal to zero. The coefficient of the
interaction between unemployment rate and the age group 25-29 is also positive
and statistically significant, but the coefficient is considerably smaller.
18
Age categories (50-54) 20-24 0.1390118 * Subject of study (human science) Social sciences 0.2968321 ***
(0.0728929) (0.0075512)
25-29 0.1527348 *** Languages 0.0852359 ***
(0.0188564) (0.0085021)
30-34 0.1516376 *** Economics-Statistics 0.2780281 ***
(0.0162025) (0.006792)
35-39 0.1481328 *** Law 0.1963445 ***
(0.0157488) (0.0079811)
40-44 0.1182333 *** Sciences -0.1623382 ***
(0.0162447) (0.008033)
45-49 0.0343125 ** Engineering -0.0332193 ***
(0.0169377) (0.009274)
55-59 -0.0740966 *** Architecture -0.0255594 *
(0.0182885) (0.013744)
60-64 -0.1392787 *** Medicine -0.1020426 ***
(0.0268741) (0.0100512)
Personal characteristics Female -0.0153279 *** Other education 0.1399372 ***
(0.0048227) (0.007257)
No citizen 0.1364267 *** Previous condition (employed) Unemployed previous year 0.0514686 ***
(0.0146993) (0.0126076)Single -0.0243246 *** Student previous year -0.0621656 ***
(0.0054378) (0.0182742)
Other types of family -0.0229392 Not labour force previous year 0.0572343
(0.0207357) (0.0433064)
Occupational status (clerical) Director -0.3327083 *** Other previous year -0.023703
(0.005912) (0.0279473)
Manager -0.2976268 *** Macroeconomic variables Employment rate 0.0023673
(0.0044844) (0.002708)
Blue collar worker 0.4814957 *** Unemployment rate -0.0017981
(0.0066321) (0.0032794)
Trainee -0.1337331 *** Unemployment 20-24 0.024098 **
(0.0316647) (0.009458)
Employment relation Part-time 0.0392702 *** Unemployment 25-29 0.0060054 ***
(0.0072902) (0.0022888)
Temporary -0.1084648 *** Unemployment 30-34 0.0014238
(0.0068627) (0.0019549)
Sector (service) Industry 0.0903209 *** Unemployment 35-39 0.0012109
(0.0069306) (0.0018497)
Agriculture 0.1107724 *** Unemployment 40-44 -0.0010618
(0.0341727) (0.0018718)
Firm size (1-10) Firm 11-19 0.0200799 ** Unemployment 45-49 0.0013471
(0.0086571) (0.0018728)
Firm 20-49 -0.0747855 *** Unemployment 55-59 0.0049242 **
(0.0072923) (0.0019891)
Firm 50-250 -0.1542278 *** Unemloyment 60-64 0.0047017
(0.0068583) (0.0030279)
Firm >250 -0.0909885 ***
(0.0079419)
Probit regression reporting marginal results. Observations=119854; Pseudo R2=0. 0.2519; Log pseudo likelihood=-62141.666. Robust standard errors in parenthesis. Marginal effects calculated
at mean values for continuous variables and using discrete differences for dummy variables. Sampling weights have been used. ***/**/* Significance at 1/5/10 per cent.
Table 4 Dependent variable is overeducation (equal to 1 if overeducated). Categories dropped are in parenthesis. Regional and quarterly dummies not reported
19
6. Conclusions
This paper has analysed the determinants of overeducation in the Italian labour
market for workers with an MSc degree using data from the National Labour
Force Survey during the 2006-2011 period.
Most results related to the microeconomic variables are in line with those
obtained by previous studies, although the conclusions are still relevant due to
the scarcity of studies on overeducation in Italy (Di Pietro and Urwin, 2003). In
particular, youths are more likely to be overeducated than adults and this effect
is particularly important for female and temporary young workers. The paper
also confirms that previous conditions in the labour market affect current
matching, with overeducation positively correlated with previous
unemployment status and negatively correlated with previous student status. An
important extension of this study is related to the analysis of the effect of the
subject of study on overeducation. The results reveal how workers who have
obtained a more scientific (e.g. engineering, scientific sciences) or a very specific
(e.g. medicine, architecture) degree are less likely to be overeducated. This result
provides for a more accurate answer to the generic question faced by individuals
on whether their investment in higher education pays off.
Finally, the paper has analysed the role of labour market conditions on
overeducation and it finds evidence of a positive effect of unemployment on
overeducation for young workers below the age of 25. This result seems to
provide evidence for an asymmetric effect of negative labour market conditions
on the quality of job matches. Possible policy considerations related to this result
may concern the design and implementation of active labour market policies
aimed at favouring the school-to-work transition of graduates entering in the
labour market (e.g. job matching). The government might be willing to focus
these policies during economic downturns or in regions characterized by high
unemployment rates. Longer unemployment benefit schemes that encourage
looking for an appropriate job match may also limit overeducation at the
beginning of the career.
Number Percentage Number Percentage
Age category 20-24 597 0.3% Professional status Director 15,262 12.3%
25-29 13,950 8.0% Manager 37,122 29.9%
30-34 26,248 15.1% Clerical worker 65,861 53.1%
35-39 30,255 17.4% Blue-collar worker 5,442 4.4%
40-44 27,251 15.7% Trainee 377 0.3%
45-49 24,528 14.1% Employment relation Full-time 153,597 88.3%
50-54 23,390 13.4% Part-time 20,341 11.7%
55-59 19,629 11.3% Permanent 197,608 92.3%
60-64 8,090 4.7% Temporary 16,435 7.7%
Personal characteristics Female 88,772 51.0% Subject of study Human sciences 19,476 11.9%
Male 85,166 49.0% Social sciences 10,594 6.5%
Foreign citizen 6,970 4.0% Law 21,191 13.0%
National citizen 166,968 96.0% Economics/Statistics 25,631 15.7%
Single 32,236 19.2% Scientific sciences 19,235 11.8%
Couple 133,619 79.7% Engineering 19,937 12.2%
Other type of family 1,885 1.1% Architecture 8,875 5.4%
Economic sector of activity Agriculture 1,449 0.8% Medicine 14,980 9.2%
Industry 19,007 10.9% Other degrees 23,279 14.3%
Service 153,482 88.2% Condition previous year Unemployed 6,021 3.5%
Firm size <10 28,287 20.8% Employed 163,369 94.3%
11-19 employees 15,132 11.1% Student 2,708 1.6%
20-49 employees 28,363 20.8% Other 1,086 0.6%
50-250 employees 43,385 31.9%
More than 250 employees 20,937 15.4%
Table 1A Descriptive statistics for the population between 20-64 with an MSc degree
20-24 0.2662959 *** 0.1390118 * -0.044739 0.0675125 0.1154219 0.1393574 *
25-29 0.1903321 *** 0.1527348 *** 0.0535541 ** 0.1246447 *** 0.145333 *** 0.1529568 ***
30-34 0.1619348 *** 0.1516376 *** 0.068661 *** 0.1531035 *** 0.1479009 *** 0.1518206 ***
35-39 0.1570213 *** 0.1481328 *** 0.092077 *** 0.1517392 *** 0.1476106 *** 0.1480166 ***
40-44 0.1120436 *** 0.1182333 *** 0.1048212 *** 0.1207718 *** 0.116242 *** 0.1178334 ***
45-49 0.0442103 *** 0.0343125 ** 0.0201378 0.0355082 ** 0.0304436 * 0.0342881 **
55-59 -0.0367968 *** -0.074097 *** -0.0742792 *** -0.0740685 *** -0.077175 *** -0.0735186 ***
60-64 -0.1044033 *** -0.139279 *** -0.1571762 *** -0.1559045 *** -0.1453259 *** -0.1390101 ***
20-24 female 0.2847078 ***
25-29 female 0.1846796 ***
30-34 female 0.1527845 ***
35-39 female 0.1164717 ***
40-44 female 0.0291873 *
45-49 female 0.0303639 *
55-59 female -0.0037173
60-64 female 0.0257851
Female -0.0152305 *** -0.015328 *** -0.0925454 *** -0.0151413 *** -0.0139219 -0.0182053 ***
No citizen 0.1362358 *** 0.1364267 *** 0.1302172 *** 0.1386206 *** 0.1341353 *** 0.1365823 ***
Single -0.0240502 *** -0.024325 *** -0.0182479 *** -0.0242351 *** -0.0266215 *** -0.0239429 ***
Other types of family -0.0226776 -0.022939 -0.0216014 -0.0227205 -0.0239355 -0.0232689
Director -0.332519 *** -0.332708 *** -0.3427779 *** -0.3342707 *** -0.3341278 *** -0.3330507 ***
Manager -0.2973528 *** -0.297627 *** -0.3005567 *** -0.2982952 *** -0.2987212 *** -0.2977066 ***
Blue collar worker 0.4815727 *** 0.4814957 *** 0.4840649 *** 0.4815768 *** 0.4822032 *** 0.48179 ***
Trainee -0.1333408 *** -0.133733 *** -0.1350213 *** -0.1573886 *** -0.133327 *** -0.1335614 ***
Director female
Manager female
Blue collar worker female
Trainee female
Firm 11-19 0.0198933 ** 0.0200799 ** 0.0238443 *** 0.0205507 ** 0.016252 * 0.0203505 **
Firm 20-49 -0.074936 *** -0.074786 *** -0.0693815 *** -0.074317 *** -0.0776483 *** -0.0745468 ***
Firm 50-250 -0.1543631 *** -0.154228 *** -0.1489313 *** -0.1537694 *** -0.1554977 *** -0.1539925 ***
Firm >250 -0.0910563 *** -0.090989 *** -0.0871389 *** -0.0914261 *** -0.0927908 *** -0.0908761 ***
Part-time 0.0397372 *** 0.0392702 *** 0.03694 *** 0.0409218 *** 0.0421094 *** -0.0043226
Part-time female 0.0519615 **
Temporary -0.1091496 *** -0.108465 *** -0.1149274 *** -0.084912 *** -0.1094127 *** -0.1075495 ***
Temporary 20-24 0.1402548 *
Temporary 25-29 0.05316
Temporary 30-34 -0.0309137
Temporary 35-39 -0.0798889 **
Temporary 40-44 -0.0787477 **
Temporary 45-49 -0.0430384
Temporary 55-59 0.0032069
Temporary 60-64 0.2235773 ***
Social sciences 0.2966998 *** 0.2968321 *** 0.2911389 *** 0.295454 *** 0.293918 *** 0.2965805 ***
Languges 0.0851126 *** 0.0852359 *** 0.0847683 *** 0.0856845 *** 0.0822339 *** 0.0848096 ***
Economics-Statistics 0.2778641 *** 0.2780281 *** 0.2760245 *** 0.2767232 *** 0.2912766 *** 0.2776277 ***
Law 0.1961233 *** 0.1963445 *** 0.190504 *** 0.1948342 *** 0.2151909 *** 0.19622 ***
Sciences -0.1622704 *** -0.162338 *** -0.1657393 *** -0.1640022 *** -0.0675345 *** -0.1627132 ***
Engineering -0.0331814 *** -0.033219 *** -0.0296776 *** -0.0343503 *** -0.0250111 * -0.0340419 ***
Architecture -0.0258143 * -0.025559 * -0.0327165 ** -0.0244297 * 0.0049152 -0.0254183 *
Medicine -0.1019428 *** -0.102043 *** -0.1107441 *** -0.1034551 *** -0.172262 *** -0.1022281 ***
Other education 0.1400429 *** 0.1399372 *** 0.135769 *** 0.138757 *** 0.0481838 *** 0.1397926 ***
Social science female 0.0083019
Languages female 0.0028933
Economics-Statistics female -0.0338179 **
Law female -0.0387611 **
Sciences female -0.1556326 ***
Engineering female -0.0520891 **
Architecture female -0.060506 **
Medicine female 0.1459181 ***
Other education female 0.1473159 ***
Unemployed previous year 0.0533527 *** 0.0514686 *** 0.0511004 *** 0.0495423 *** 0.0500733 *** 0.0516332 ***
Student previous year -0.0610797 *** -0.062166 *** -0.0509131 *** -0.0798217 *** -0.0618537 *** -0.0612817 ***
Not labour force previous year 0.0552413 0.0572343 0.0534599 0.0502196 0.0574696 0.0554576
Other previous year -0.0244333 -0.023703 -0.0290015 -0.0248903 -0.0238251 -0.0241656
Industry 0.0907206 *** 0.0903209 *** 0.0901992 *** 0.0896821 *** 0.088207 *** 0.0898788 ***
Agriculture 0.1102183 *** 0.1107724 *** 0.1109193 *** 0.1115381 *** 0.1307082 *** 0.1102366 ***
Employment rate 0.0023673 0.0020448 0.0023358 0.0024398 0.0024037
Unemployment rate -0.001798 -0.0017515 -0.0019701 -0.0013286 -0.001747
Unemployment 20-24 0.024098 ** 0.0238159 *** 0.0209416 ** 0.0263892 *** 0.0239634 **
Unemployment 25-29 0.0060054 *** 0.0055611 ** 0.0057815 ** 0.0067013 *** 0.0059473 ***
Unemployment 30-34 0.0014238 0.0013209 0.0015594 0.0016031 0.0013969
Unemployment 35-39 0.0012109 0.0005814 0.0018237 0.0011114 0.0012
Unemployment 40-44 -0.001062 -0.0010766 -0.0006432 -0.0010859 -0.001039
Unemployment 45-49 0.0013471 0.0012816 0.0014108 0.0016674 0.0013278
Unemployment 55-59 0.0049242 ** 0.0051177 ** 0.0049383 ** 0.0048999 ** 0.0048632 **
Unemloyment 60-64 0.0047017 0.0047606 0.0056523 * 0.0048806 0.0046759
Model 6
Table 1B Dependent variable is overeducation (equal to 1 if overeducated). Model 1 is equal to Table 1 in the paper; Model 2 to Table
4 . Other models present interaction of microeconomic variables discussed in the text. Regional and quarterly dummies not reported
Model 1 Model 2 Model 3 Model 4 Model 5
Pseudo R2: 0.2520
Observations:
119845
Observations:
119846
Observations:
119848
Observations:
119850
Observations:
119852
Observations: 119854
Pseudo R2: 0.254 Pseudo R2: 0.2545 Pseudo R2: 0.2527 Pseudo R2: 0.2561 Pseudo R2: 0.2496
Age categories (50-54) 20-24 0.11977 *** Subject of study (human science) Social sciences 0.2280723 ***
(0.0326864) (0.0066149)
25-29 0.1062568 *** Languages 0.0640241 ***
(0.0148828) (0.0077361)
30-34 0.1506994 *** Economics-Statistics 0.242992 ***
(0.0129789) (0.0057183)
35-39 0.1528434 *** Law 0.1582693 ***
(0.0125742) (0.0070611)
40-44 0.1244839 *** Sciences -0.1294504 ***
(0.0130519) (0.0076826)
45-49 0.0572893 *** Engineering -0.0486209 ***
(0.0138478) (0.0083712)
55-59 -0.0659085 *** Architecture -0.0172139
(0.0159445) (0.012427)
60-64 -0.1095487 *** Medicine 0.2532391 ***
(0.0246553) (0.0054754)
Personal characteristics Female -0.0057045 Other education 0.0606685 ***
(0.0039961) (0.0064561)
No citizen 0.1643596 *** Previous condition (employed) Unemployed previous year 0.0581391 ***
(0.0108794) (0.0102474)
Single -0.008902 * Student previous year -0.0073996
(0.0045388) (0.0141673)
Other types of family 0.0206747 Not labour force previous year 0.0155393
(0.0172173) (0.0357681)
Career position (clerical) Director -0.2792865 *** Other previous year 0.0009343
(0.0058441) (0.023581)
Manager -0.265383 *** Macroeconomic variables Employment rate 0.0019909
(0.0041779) (0.0022732)
Blue collar worker 0.3943744 *** Unemployment rate -0.0021485
(0.0056793) (0.0027571)
Trainee -0.0628508 ** Unemployment 20-24 0.0130575 ***
(0.0267467) (0.004936)
Employment relation Part-time 0.0341 *** Unemployment 25-29 0.0096339 ***
(0.0059685) (0.001849)
Temporary -0.1070939 *** Unemployment 30-34 0.0038041 **
(0.0059792) (0.0016507)
Sector (service) Industry 0.0605196 *** Unemployment 35-39 0.0028166 *
(0.0059462) (0.0015617)
Agriculture 0.0886725 *** Unemployment 40-44 0.0004401
(0.0274922) (0.0015771)
Firm size (1-10) Firm 11-19 0.0146986 ** Unemployment 45-49 0.0005979
(0.007323) (0.0015728)
Firm 20-49 -0.0553496 *** Unemployment 55-59 0.003626 **
(0.006352) (0.0016935)
Firm 50-250 -0.1074838 *** Unemloyment 60-64 0.0024187
(0.006025) (0.0026024)
Firm >250 -0.0288584 ***
(0.0067396)
Table 1C Dependent variable is overeducation (equal to 1 if overeducated). Categories dropped are in parenthesis. Regional and quarterly dummies not reported.
Compared to other regressions, all workers whose educational attainment is equal or above the BA level have been included
Probit regression reporting marginal results. Observations=154166; Pseudo R2=0. 0.2005; Log pseudo likelihood=-94736.69. Robust standard errors in parenthesis. Marginal effects
calculated at mean values for continuous variables and using discrete differences for dummy variables. Sampling weights have been used. ***/**/* Significance at 1/5/10 per cent.
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