The effect of parents' background on youth unemployment duration
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The effect of parents’ background
on youth unemployment duration
*
Fernanda Mazzotta
October 2008
Abstract
Very Preliminary draft
Please don’t quote
Comment are welcome
The paper analyses the relation between the economic and cultural background
of parents and the unemployment of their adult children. A search theory model
is used to identify the effects exerted by the household’s economic
circumstances and the parents’ cultural/education level on the children’s
monetary and non-monetary constraints and chances of receiving job offers.
The empirical specification of the model features a simultaneous estimation
suggested by Lancaster (1985) of the duration of unemployment and of the
accepted wage in a new job. The data are drawn from the European
Community Household Panel considering the eight waves currently available
for Italy (1994-2001), and the sample consists of children aged under 36,
cohabiting with their parents during the search of a job. The main results are
that graduates from deprived social backgrounds find it more difficult to find
jobs than do graduates from affluent families and this is true in particular in
the South of Italy. While in the North, graduate, young male with ability are
favoured. Also, for other educated young unemployed children particular
important are work experiences and ability specially in the North These
findings once again raise questions about differential quality of education in
Italy in the North and South, and about the job-finding difficulties faced by
young people deriving form deprived family in particular where the labour
market is stagnant, regardless of individual abilities .
C3, J64, J62.
Keywords: Simultaneously equation models, unemployment, duration job
search, intergenerational mobility
*
University of Salerno
Department of Economics and Statistics
CELPE
mazzotta@unisa.it
11. Introduction
Individual school attainment and success in the labour market are important determinants
of income distribution and are often thought to be among the key factors explaining the
wealth of nations as well as cross-nation differences in economic growth. At the micro level,
it is customary to assume a strong correlation between schooling attainments and household
background variables (income and parents’ education). The effect of these variables on
individual schooling attainments can take various forms and the net impact is far from
obvious. While enrolled at school, young individuals receive parental support. On the one
hand, wealthier households may transfer more resources to their children and reduce the
opportunity cost of school attendance; on the other, the opportunity cost of spending time with
children is higher for these households. At the same time, innate ability, which is also
correlated with household background variables, should have an independent impact on the
decision to attend school and on labour market wages. If skill endowments are strongly
correlated with household background variables (especially the father's and mother's
education), those young individuals raised in households endowed with a high level of human
capital will have a high level of school ability but will also have a high level of market ability
(absolute advantage in the labour market). Moreover, the family background may influence
the reservation wage (or the accepted wage, since the accepted wage in a new job is an
increasing function of the reservation wage) of the adult children and thereby also influence
their decision whether or not to accept a given wage offer. Finally, the advantage in the labour
market may depend on the opportunity and the ability to activate the most effective job search
channel. In summary, household background variables (income and education) may influence
individual success in the labour market, exerting both a direct impact on the characteristics of
the labour supply (education, ability and reservation wage) and an indirect impact on the
labour demand (more job opportunities thanks to an efficient job search).
These simultaneous effects of the parents’ background variables on the opportunity cost,
the reservation wage and on both school and market abilities are the central concerns of this
study. The main objective is to estimate a structural model of unemployment duration and
answer the question of whether the household background influences the duration of
unemployment, after controlling for the children’s educational level, ability and the level of
the accepted wage in a new job. The household background variables are identified with the
economic condition of the household (monetary poverty condition) and the cultural level of
the parents. These two variables are used to distinguish between a liquidity constraints effect
(i.e. income constraints on the choice of a better education or of the optimal job) and non-
monetary effects linked to the family’s cultural origins. Such distinction is important for
designing better targeted policies to improve labour market performances.
The data used are drawn from the Italian sample of the European Community Household
Panel (ECHP), waves 1 to 8 (1994-2001), and includes all the unemployed children aged
under 36 (born after 1958 and before 1985) cohabiting with their families during the
unemployed period, reporting a completed duration of unemployment and an accepted wage
for a new job finding during the seven years of the survey.. All other young adults in the
labour market and under 36 are used to correct the model for sample selection. Young people
not in the labour force (students, non–active individuals, etc.) or never unemployed and those
aged over 35 are dropped from the sample.
2The next two sections review the literature on reservation wage and unemployment
duration. There follows a description of the data and of the sample selection model featuring
two simultaneous equations for estimation of the accepted wage in a new job and of
unemployment duration. The estimation results are then reported, and the paper concludes
with a summary of policy implications.
2. Literature
Research on the relation between parents’ economic and cultural background and
children’s success in the labour market has mainly addressed intergenerational mobility, with
economists focusing principally on the relation between the father’s and the son'/daughter’s
income. Since the milestone study published by Becker and Thomes in 1979, economists have
sought to measure the link between an individual’s socioeconomic position and that of his/her
father. Interest in the transmission of economic status from one generation to another is
generally prompted by the wish to determine the extent of the equality of opportunity in a
country.
Since Becker and Thomes’ study, a large part of the literature has sought appropriate
methods with which to measure mobility or to study the intergenerational correlations of
educational attainment, such as transition matrices (or some synthetic measures) and probit
(or ordered probit) estimations of the determinants of children’s success. As regards income,
the most frequently used measure of mobility is the regression coefficient relating a child’s
log earnings to his/her father's. A high value indicates a very marked persistence of economic
status, because an individual's position in the earnings distribution is largely a reflection of
his/her father’s position in his own distribution. A low value indicates a very mobile society
in which an individual's socio-economic position does not depend on that of the father. Data
availability is a crucial factor: in fact, information about the incomes of the two generations is
needed, and long panel data or cross-sections with retrospective information about parents'
income are typically used.
Although a large body of economic literature has studied the correlation between father’s
and son/daughter’s socioeconomic status, only fewer and more recent works have analyzed
the causes of this strong link. With specific reference to Italy, the possible causes of low
intergenerational mobility are seen to reside in the liquidity (monetary) constraints which
prevent individuals from taking advantage of incentives to acquire an education level more
compatible with their preferences. Consequently, the children of less well-off households
invest less in education because they are unable to attain the optimal level of education, where
optimal refers to the costs/benefits structure of investment in education. An additional alleged
reason is dependence on family origins, and it is strengthened by the so-called ‘peer effect’,
i.e. the social context within and outside the school. The offspring of poor and less-educated
parents must make greater efforts to acquire education because they live in a social context
which does not help them to learn. Policy measures designed to increase intergenerational
mobility should be double pronged, seeking to reduce liquidity constraints on the one hand,
while improving the workings of the education system, for example by fostering integration
3among students from different social backgrounds and reducing the initial disparities. A final
causal factor is ability, which according to an extreme hypothesis, is pre-determined by
genetics.
Checchi and Zollino (2001) and Checchi and Bertola (2001) have studied the effects of
parental background on scholastic performance, while Brunello, Checchi and Comi (2003)
have examined the effects on labour market performance. The findings are that, although
attendance at an expensive school is a necessary but not sufficient condition for better
performance, more important are the peer effects exerted by the neighbourhood and
differences in student cultural backgrounds originating in the family. Moreover, Checchi,
Ichino and Rustichini (1999) have compared Italy and the USA in regard of their different
school systems and the degree of intergenerational mobility. Their finding is that Italy is less
mobile than the USA, possibly because the way the educational system and the labour market
are structured does not offer real opportunities for the children of lower income families to
emerge and to retain the returns on their educational investment. In a world where family
background is important for labour market success – it is argued - an excessively centralized
education system with uniform quality, particularly at university level, does not necessarily
help poor children and may deprive them of a fundamental tool to prove their talent and to
compete with rich children.
Two approaches can be adopted in analysis of employment probabilities or hazards rate
from unemployment: a reduced form approach and a structural one. The former (see e.g.
Nickell, 1979; Lancaster and Nickell, 1980; Atkinsons et al. 1984; Narendranathan et al.,
1985) involves direct estimation of the hazard function, with the rate of job arrivals and the
reservation wage of the unemployed individual as the variables of interest. However, since the
specification of the hazard function makes no explicit reference to the behaviours of the
reservation wage, the results from the reduced form estimation do not allow to directly testing
various hypotheses on the reservation wage function postulated by the job search model.
Although a job search model is developed, it serves only as a basis for interpretations and
indirect inferences to be made from the results of the estimation. The structural approach to
estimation, on the other hand, uses information concerning the structure of the job search
model and imposes appropriate restrictions on the data. The ability to identify and estimate
the underlying structural relationship of a job search model is of importance to the
policymaker involved in the design and evaluation of policies affecting the job search process.
The availability of reservation wage data should makes it possible to estimate the structural
parameters of the job search models. Pioneering work in this area has been done by Lancaster
and Chesher (1983, 1984) and Lancaster (1985). Recognition of the potential two-way causal
link between unemployment duration and the reservation wage accounts for the techniques
developed in L-C (1984) and Lancaster (1985), where all the structural parameters are
estimated using the two-stage least squares method (2SLS). For the Italian case, direct
estimates of the reservation wage are provided by Mazzotta (1998), Bettio and Mazzotta
(2002) and Boeri and Pagani (1998), which focuses on the demographic and territorial
differences in asking wages. Of particular interest is the evidence of the great importance,
both theoretical and empirical, of desired working hours. (Bettio and Mazzotta, 2002). An
important aspect is the availability of a correct reservation wage. A new study (Bettio and
Mazzotta 2008) show that the reservation wage revailed in the ECHP is not the really
4reservation wage and more realistic is the first wage accepted for a new job2. Besides in a
simultaneously analysis it must take care to the time duration available, in fact the reservation
wage depends form the elapsed duration of search (until that moment) and for the accepted
wage the casual relation is with the completed duration. In the echp, we have the availabily of
the completed spell, I can reconstruct the elapsed or not completed duration of the job search,
but non è precisa ed a costo della perdita di molte osservazioni, dato che gli individui
possono aver interrotto la ricerca ed essere usciti temporaneamente durante il periodo
rilevazione. Inoltre, per le durate di ricerca che iniziano nel primo anno di rilevazione, se
l’individuo non trova un lavoro durante le otto rilevazioni, e non dichiara la durata della
disoccupazione, non si può ricostruire il momento in cui ha inziato a cercare un lavoro.
Per tutte queste ragioni si è scelto di analizzare la durata completa della disoccupazione
controllando per il salario di ingresso accettato.
To the best of my knowledge, however, there are no analyses that carry out structural
estimation of a job search model using both the accepted wage in a new job (or last
reservation wage) and the hazard function as is done here; nor are there analyses which
introduce family background variables to capture, on the supply side, the influence of the
family on the accepted wage and, on the demand side, family influence on the chances of
receiving job offers. By carrying out structural estimation of a job search model this study
seeks to determine whether, controlling for educational level, reservation wage and the ability
of children, family backgrounds still influence – and in which direction – the duration of
unemployment. For the reasons spelt out below, theory provides no clear a priori expectations
on the strength and the sign of this influence.
As noted, parents influence the occupational statuses of their children through the
educational attainment of the latter. Hence, the children of less well-off households chose the
best school or prolong the studies, then the expected marginal returns from the job search
increase. The cultural level of the parents also influences the reservation wage of the
offspring: the higher the educations of the father and mother, the higher the level attained by
the son. For example, Checchi and Zollino (2001) estimated that the son of a graduate has a
43% better chance of obtaining a college degree than the son of a non-graduate.
However, the economic and cultural condition of the parents may influence the labour-
market success of their children independently of education, by affecting the children’s
expectations, and therefore search for a suitable job. In fact, higher parental income directly
increases the amount of resources available for the children’s education. Higher parental
income increases the benefits received during the search, which boosts the children’s
reservation wage and the accepted wage in turn. However, if the reservation wage is
interpreted as a threshold wage, it may happen that a lower family income induces the
individual to ask for and accepted more in order to obtain a minimum standard of living for
the whole family.
2
Infatti, risulta che in Italia in media il salario di ingresso o re-ingresso nell’occupazione accettato dai
disoccupati meridionali non solo è inferiore in livello assoluto a quello nel resto del paese (€ 3.2 contro € 4 medi
al Centro Nord tra il 1994 e il 2001, a prezzi 1995), ma equivale ad una rinuncia del 31% rispetto al salario di
riserva orario dichiarato. La cifra corrispondente per il Centro-Nord è il 10%.
5Besides the accepted wage, family origins may also impinge on information asymmetries
on the labour demand and supply sides. That is to say, they may influence in various ways
both the information available to the children and the signal the latter send firms. The net
effect on intergenerational mobility, however, is unclear because poor and low-educated
families may favour information concerning with job offers lower pay, so that the duration of
unemployment is shorter, but at the same time intergenerational mobility is not improved.
3. Estimation Methodology: completed
durations and accepted wages.
Following Lancaster (1985) model there are two casual relationships between the reservation
wage (wr) and the duration of search (t), in one of which the reservation wage is a
deterministic function of the date and in the other the elapsed duration is a (stochastic function
of the reservation wage. The duration observed is both the date and a realisation of the
random elapsed duration in a way rather analogous to a market model in which the quantity
we observe is both the quantity supplied and the quantity demanded. Further, since the
accepted wage in a new job, w, is an increasing function of the reservation wage, a very
similar heuristic argument indicates that there will be two casual relation between w and t,
where t is the completed rather than the elapsed duration. 3: In fact, when a person accept a
occupation at date t, the wage accepted is a realisation of the random variable whose
distribution is that of the wage offer truncated on the left at wr(t).
Lancaster (1985) shows that the structural form of a model with completed duration and
accepted wage is:
log w = constant − η log t + Xβ + u1 [1]
log t = constant + α log w − Xθ + u2 [2]
Lancaster call this model the structural form because its coefficients are the structural
coefficients of the search model specification, η , θ , α e β . This form exhibits clearly the
two casual relations that we argued earlier should obtain between t and w . The first
equation say that people who have been searching a long time should have cut their asking
3
Lancaster Chesher (1984) assume that the wage offer distribution is the Pareto which is a constant elasticity
hypothesis for the hazards/reservation wage relation and can be thought as a log –linear approximation to the
true relation. The log normal, thought it may be of course be more accurate, does not have this interpretation as a
linear approximation
6wage a lot (if η >0) and therefore get a low wage in the new job. The second equation
corresponds to the idea that people who go back to work at a high wage have had a high
asking price and therefore have taken a long time to get an acceptable offer.
Noting that if at least one element of θ is zero while the corresponding element of β is not,
α and the remaining elements of θ , can be identified form the first moments of the data
regardless of the distribution of the error terms u1 and u2 as long as these have means that do
not depend on X . Contrarywise Lancaster argued that no variable in Xβ can fail to be
present in Xθ so that η e β are unidentifiable from the first moments of the data done.
Tuttavia, Lancaster note that, if one or more zero restrictions can be placed on θ , the equation
[2] can be consistently estimated by 2SLS and standard errors computed form the usual
formula since the covariance matrix of log w and log t is independent of X for small η .
Lancaster (1985) uses the number of dependent children in the job searcher’s household to
achieve identification. Dolton and O’Neill (1995) use the amount of benefits received by the
job seeker, the presence of children in the household and the presence of a working partner as
exclusion restrictions. Here I assume that the number of dependent (aged less than 15 year
old) and the private and social monetary transfers influences the costs of search and hence the
reservation wage, but not the job arrival rate or the wage offer distribution.
Another econometric issue is that the sample of the respondents reporting accepted wages
is restricted to those who are finding a job during the period 1995 - 2001. Consequently only
this group is generally used to estimate unemployment duration. However, this selective
sample may be unrepresentative of the population of workers, in fact excluded those
constantly unemployed, and estimations using the sample may yield biased regression
coefficients. Moreover, for the purposes of this study, the sample selected consists only of
persons identified as children at the beginning of the survey (1994), and this latter restriction
is necessary in order to estimate average education for the parents as well as the poverty
condition of the household in which the individual lives.
The first potential selection bias problem is the same as encountered in estimation of a
standard wage equation that uses only people in employment. Recall that Heckman's solution
to this problem is first to estimate a probit model that relates the probability of an individual
being in the labour force to a set of determinants, and then to use the probit estimates to
compute the inverse Mills ratio. This variable is then included as a covariate in the wage
equation. In the present case, however, there are two not independent decisions determining
the inclusion of the individual in the sample: the decision to accepted a job and the decision to
live in the original household Hence Heckman’s methodology must be adapted, and this
involves the step procedure described below.
1. In order to address selectivity the following model is run in step one
71 = Π 1 Z 1i + U 1 if w > w r (find a job)
Y1i =
0 otherwise (constantly unemployed )
[3]
1 = Π 2 Z 2 i + U 2 if u(in family) > u(out of family) (children)
Y2 i =
0 otherwise (not children)
where
Z 1 should contain all the exogenous variables in X 1 and X 2 . In practice, the two equations
are run using all the observations in the sample, whereby information collected exclusively
for the unemployed is lost (e.g. reservation wage and job offer variables).
Z 2 contains all the exogenous variables that influence the decision to cohabit with the
original household.
2. In step two the bivariate probit estimates from step one are used to calculate the two
selectivity bias terms. The corresponding expressions are (Meng and Schmidt, 1985; Baffoe -
Bonnie, 2004):
λ1i =
[
φ ( Z i Π 1 )Φ (Z i Π 2 − ρZ i Π 1 ) / 1 − ρ 2 ] [4]
F ( Z i Π1 , Z i Π 2 ; ρ )
λ 2i =
[
φ ( Z i Π 2 )Φ (Z i Π 1 − ρZ i Π 2 ) / 1 − ρ 2 ] [5]
F ( Z i Π1 , Z i Π 2 ; ρ )
3. In step three the selection bias terms are included on the right hand side of both the
reservation wage and the duration equation [1 and 2];
4. In step four the selectivity bias adjusted equations 1 and 2 are estimated using 2SLS (Hui,
1991; Haurin and Sridhar, 2003).
Per quanto concerne le variabili considerate, the crucial covariates for the purposes of this
study are the economic conditions and cultural level of the household in which the individual
lives. For the children of more affluent and better educated parents, a greater investment in
education may affect both labour supply and demand. If the concept of reservation wage is
used in the job search approach, a higher level of education results on the supply side in a
higher reservation wage: that is, a higher threshold at which the individual is willing to accept
a job. According to this approach, a higher family income means lower search costs for the
adult children, and as a consequence their reservation wages increase. However, if there is a
‘threshold effect’ (which the original model does not envisage), a lower family income may
induce the individual to decide on a higher reservation wage in order to reach a minimum
standard of living. Overall, therefore, on the supply side, a priori we would expect a high
8reservation wage or first accepted wage to be associated with a higher family income and then
the expected result is a negative association between better economic conditions in the family
and the probability that the children will find a job in the short term.4 On the demand side, a
higher education level favorably signals the abilities of the young person, thereby increasing
the probability that he or she will find a job.5
One aspect less considered in the literature on job search is the influence of the family’s
economic and cultural circumstances on the information asymmetries that characterise labor
supply and demand. As they search for work, the adult children of affluent families probably
have access to privileged information channels from which others are excluded, and which
signal the abilities of the former to employers. It is not easy to formulate hypotheses a priori
on the effects of economic and cultural circumstances defined as such, but it is likely that they
favor information about matches between the children of poor or lower-educated families
with less well-paid or less skill-demanding jobs: vice versa for the children of rich families. If
this is the case, we may find a reduction in unemployment among young people from poor
and lower-educated families without this improving intergenerational mobility.
Vector X other then the families’ backgrounds, includes the individual characteristics
such as ability in a foreign language, regularity in the completion of schooling6, sex, age, and
zone of residence. These are indicators of individual productivity, conditions in the local
labour market, and the intensity of the job search, and all may affect the rate of job arrivals.
Previous experience and job offers received proxy the signal sent by job seekers to potential
employers and consequently influence job offers received.
Infine, si ricorda che per l’identificazione dell’equazione della durata completa, I use the
number of dependent (aged less than 15 year old) and the private and social monetary
transfers. Assumendo che queste variabili influence the costs of search and hence the
reservation wage, but not the job arrival rate or the wage offer distribution.
4
However, a threshold effect would invert this relationship.
5
. The signal may differ according the family background. For instance, a degree held by the child of a poor
and/or lower- educated family would indicate greater ability than a degree held by the son of a more affluent and
better-educated family.
6
See the previous note.
94. Data and measurement issues
The data are drawn from the European Community Household Panel, waves 1-8 (survey
years 1994 - 2001) [Details of the survey are given in the Appendix]. The sample consists of
unemployed persons cohabiting and not cohabiting with their original families, aged under 36
years (born after 1958 and before 1985), and participating in all the waves. Those who did not
report a reservation wage once during the eight years of the survey and consequently did not
experience even one episode of active job search are considered ‘employed7’. ‘Unemployed’
refers to those who were jobless and searching for a job at least once during the seven years.
‘Cohabitants’ are those who live with their parent during the unemployment duration (1994),
all other subjects (heads of household, spouses, etc.) are treated as non-cohabitants with the
original family. The final sample consist of those unemployment who find a job and report e
completed duration of unemployment.
Resuming, the observations for the regression equations are unemployment young children
aged under 36 cohabiting with the family during unemployment who complete a duration of
unemployment during the 7 wave (from 1995 to 2001) finding a work during the period. The
remaining observations – unemployed people aged under 36 not cohabiting with the original
household (not adult children in 1994), and all children (cohabiting) constantly unemployed, –
are used in the selection equations. Young people not in the labour force (students, non–active
persons, etc.) or constantly in work and those aged over 36 are omitted altogether from
estimation.
Tab. 1
Table 1 lists all the covariates while average values for these variables are reported in the
appendix. Note that for time varying variables influencing unemployment duration, the
values chosen are those registered when employment begun.
The accepted wage is the net income form the work 8 In the present analysis the monthly
accepted wage is estimated conditional on the hours worked
The duration of unemployment is measured by the response to the question ''For how long
have you been seeking such work'' (number of months). Some missing data were recovered
using the following procedure. Information was obtained on the duration of unemployment by
calculating the difference between the year and month in which the current job began and the
year in which the previous job terminated or full-time education was completed. This
procedure is obviously susceptible to imprecision, but it served to fill up some missing data.
Solo la durata completa è disponibile dai dati, non c’è informazione sulle durate di ricerca non
complete. Tuttavia, quest’ultima potrebbe essere ricostruita, ma a costo di una notevole
7
Of course, all observations included reported non missing values for the covariates
8
Riscalando I dati considerando che nella ECHP i redditi rilevati in una survey si riferiscono ai redditi
guadagnati nell’anno precedente e tenendo conto della variazione del potere di acquisto negli anni (base 1995).
10riduzione delle unità di osservazione dovuta alla difficoltà a ricostruite la data di inizio della
ricerca.
Table 2
The household poverty is calculated by considering all household incomes net of the
children’s income from work, from transfers, and from financial capital. Considered to be
poor is a household whose disposable income net of the adult child’s income is equal to or
below the standard poverty line. Incomes are scaled using the modified OECD equivalence
scale, which yields an equivalent number of components by calculating the adult head of
household as 1, members aged 14 or over as 0.5, and members aged under 14 as 0.3. In merito
al momento temporale in cui valutare la condizione di povertà della famiglia si è deciso di
creare una variabile che consiste del numero di anni in cui la famiglia cade povertà nel
periodo precedente l’occupazione del figlio,in altri termini, durante il periodo di ricerca del
lavoro del giovane figlio. La linea di povertà è standard9
Table 3
9
Il 50% della valore mediano calcolato sul reddito totale disponibile equivalente.
115. Results
For reasons that will be clear from the commentary below, three set of estimates are
presented, in order of successive refinement. In the first one it is considered the monthly
reservation wage in Italy distinguendo per titolo di studio. whereas in the second and third set
of estimates the sample is broken down by education level and geographical region and is
proposed as the best set.
One first result from the first set of estimates on unemployment duration is that duration not
to depend significantly on the accepted wage for any educational level. Then we cannot say
that people who go back to work at a high wage must have had a high asking wage and
therefore have taken a long time to get an acceptable offer.
Graduates from deprived social backgrounds find it more difficult to find jobs than do
graduates from affluent families, since the estimates suggest that unemployment duration is
around 52% longer for the former. This differential in the length of unemployment decreases
but remains high at 19% among job seekers with upper secondary-school education. For the
latter, however, age matters more than family background, in that as age increases so does the
difficulty of finding a job. A turning point in the unemployment duration function occurs at
about 36 -37 years. INvece, la provenienza da famiglie con genitori poco istruiti favorisce
l’entrata nella occupazione per i diplomati.
Le altre variabili che significativamente influiscono sulla durata completa della
disoccupazione, sono la regolarità nel completare gli studi (ability) e la conoscenza di almeno
una lingua straniera e le esperienze di lavoro che per i graduates riducono la durata della
disoccupazione Per i bassi livelli di istruzione per ridurre la durata della disoccupazione ciò
che conto è l’esperienza lavorativo che rappresenta un segnale di abilità.
One might think that, besides the level, also the quality of education is inferior for poor
children, or that they have less ability. Although it was not possible to directly control for
quality in the estimation described here, ‘abiling’ and ‘regular’, which respectively stand for
an ability to speak English fluently, and conclusion of studies within the statutory time limit
proxy ability. However, only the first of them is found to significantly influence
unemployment duration among graduates, while for high-school diploma holders the ‘abiling’
coefficient is negative and significant –as expected - but that for ‘regular’ is positive. This
latter finding is puzzling and invites speculation that indecision about whether or not to
continue in full-time education reduces the intensity of the job search.
Previous studies (Mazzotta, 2007) have shown that the interaction between family
background, occupational status (unemployment rate) and economic condition (individual
poverty) is not the same in the three macro areas of Italy. In the South, where labour market
conditions are less favourable independently of children’s and parent’s education and/or
household economic condition, the adult offspring of poorly-educated parents faces higher
disadvantage with respect to residents in the North coming from equally poor families.
12In view of the well established importance of regional factors in the process of job search the
third and final set of estimates is broken down by education and broad region (tab. ?).10 The
results add fresh details to the salience of regional differences, but modify the preceding
results on whether and for whom family conditions matter. With regard to unemployment
duration, the disadvantage of living in the South is of 78% longer for graduates and 20% for
compulsory school holders if the parents are poor. In the south and for graduates is the only
variables which matter. Then w can say that graduates of poor family non riescono a superare
le rigidità dei canali informativi nel mercato del lavoro in cui si offrono, come accade anche
per chi ha solo la scuola dell’obbligo, anche se con minore svantaggio. Al sud, solo per i
diplomati non sussiste una maggiore difficoltà per chi proviene da famiglie mediamente
povere. Quindi la probabilità di trovare una lavoro ha un andamento a U rovesciata rispetto al
livello di istruzione al Sud.
Al nord invece, i laureati maschi, abili con esperienze di lavoro sono favoriti, rispetto alle
donne ed i meno abili. Invece, esperienza e giovane età sono fattori di successo per livelli di
istruzione medio bassi al Centro-Nord.
Tab 4
Tab 5
Tab 6
6. Summary and conclusions
Da completare
10
The difference between the Centre-North and the South was tested for graduates and individuals with below
upper-secondary educations. In the case of upper-secondary diploma holders (more numerous), the difference
was tested for all three areas.
13Appendix A
Survey
Selection of the sample
The data used are those on Italian households gathered by the European Community
Household Panel survey. Coordinated by Eurostat and carried out by national units, this
survey annually interviews around 6000 (7115 in 1994 and 5606 in 2001) households and
16000 individuals (17729 in 1994 and 13392 in 2001). The first survey was conducted in
1994 and the last in 2001. This study uses information collected in all waves and
consequently only individuals who experise an unemployment condition during the period.
The sample consisted of unemployed young children cohabiting with their original families in
the 1994, aged under 36 years that start a new job during the period 1994 - 2001, (n. 650
individual with 1495 observations). People not in the labour force (students, non-active
persons, housewives, etc.) or constantly at work were dropped. As ‘cohabitants’ I considered
those declared children during the unemployment duration all other subjects (heads of
household, spouses, etc.) are non-cohabitants with the original family. All the others –
unemployed people aged under 36 not cohabiting with the original household (not children in
1994), and all children (cohabiting) who are constantly in unemployed – were used to correct
the model for sample selection.
Tab B
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16TABLE
Tab 1 List of Covariates
RESERVATION WAGE UNEMPLOYMENT
EQUATION DURATION
Lnmonthly wage Lndur (months)
Ability Fluent English in Abiling 1/0 Abiling 1/0
social contexts
Completion of studies Regular 1/0 Regular 1/0
on schedule
Family Poverty condition of Number of time in poverty Number of time in poverty
Background the household
Average years of Highscparen1/0§ Highscparen1/0§
education for father
and mother
Lowscparen 1/0 Lowscparen 1/0
Medscparen 1/0 Medscparen 1/0
Local Labor Area of residence North1/0§ North1/0§
market
South&I1/0 South&I 1/0
Centre 1/0 Centre 1/0§
Local GDP growth rate
Gender Female1/0 Female1/0 Female 1/0
Age Children Age Etafig Etafig
Sample selection term Lambda1 Lambda1
(employed)
Sample selection term Lambda2 Lambda2
(cohabitants)
Job arrival rate Number of job offers Joboffer
and Wage offer (start 1994)
distribution
Job arrival rate Work experience Esper 1/0
and Wage offer (previous last job)
distribution
Number of members Child013
of household aged
under 14 (Log)
hours of work Lnor
offered (Log) per
month
Social benefits Lnsocialben
17Tab 2
Reservation wage, hours of work offered and unemployment duration by gender, education level and geographical area (sample averages)
Upper
University secondary Less than
Men Female degree school upper North South
Reservation wage (euros per 806,12 741,89 966,20 727,83 781,19 785,72 778,42
month)
New Wage (euros per month) 585,81 508,26 620,37 551,50 529,94 620,44 520,94
Completed unemployment duration 22,49 19,29 12,99 21,62 24,23 12,51 24,46
(months)
Elapsed Duration of unemployment 15,72 13,69 8,91 15,72 16,04 19 8,94
(months)
37,40 35,91 33,11 36,90 38,30 35,35 37,87
Hours of work offered (per week)
Hours of work (per week) 39,85 36,01 36,09 38,06 40,26 37,29 39,15
Tab 3 Poverty Lines
YEAR OF COMPETENCE 1994,00 1995,00 1996,00 1997,00 1998,00 1999,00 2000,00
Poverty Line (50% of median) (Euro)
Yearly Net equivalent total income 3822,74 4083,02 4152,31 4510,73 4738,49 5035,46 5267,86
18Table 4 Accepted wages and completed unemployment durations, Italy
Log duration Log duration Log duration
Laureati Diplomati Obbligo
Coefficient (S.E.) Coefficient (S.E.) Coefficient (S.E.)
Log monthly accepted wage -0,030 (0.455) +0.230 (0.298) -2.306 (2.985)
Age +0,074 (0.086) +0.266*** (0.048) +0.004 (0.344)
Age^2 -0,001 (0.002) -0.003*** (0.001) -0.0006 (0.004)
Gender
Female +0,265 (0.341) +0.267 (0.175) -0.411 (0.494)
Abilità
Regolar -0,125 (0.313) +0.359*** (0.148) +0.357 (0.506)
English -0,674*** (0.272) -0.195 (0.191) +0.196 (1.064)
Family background
Povertà della famiglia prima di trovare +0,519*** (0.219) +0.190*** (0.059) -0.025 (0.321)
lavoro
Livello basso Istruzione media del/I -0,491 (0.492) -0.951*** (0.327) +1.406 (2.766)
genitore/i
Livello medio Istruzione media del/I -0,324 (0.474) -0.676* (0.361)
genitore/i
Region
South & Island -0,937* (0.482) +0.796*** (0.185) +0.789 (1.063)
Tasso di crescita nell’area -0,061 (0.069) -0.0005 (0.041) -0.130 (0.146)
Media delle offerte nell’area +0,038 (0.091) +0.007 (0.037) +0.235 (0.461)
Esperienze di lavoro -1,315*** (0.285) -1.178*** (0.164) -1.679*** (0.471)
Sample selection correction
Lambda1 (employed) 0,157 (0.202) -0.147* (0.069) -0.462 (0.474)
Lambda2 (convivente) 0,484 (0.870) -0.445 (0.297) +1.135 (2.054)
Constant 1,028 (2.469) -2.000 (0.346) +15.147 (18.915)
N 191 824 480
R2 0,73 0.69 0.22
The equation estimated is the second of the number [2],with three zero restriction on θ, the omission of number of
youngest children con età inferiore ai 15 anni, reddito da benefici sociali e le ore lavorate.
19Table 5 Accepted wages and completed unemployment durations, Sud
Log duration Log duration Log duration
Laureati Diplomati Obbligo
Coefficient (S.E.) Coefficient (S.E.) Coefficient (S.E.)
Log monthly accepted wage +0.635 (0.645) +1.738 (1.364) -0.830 (0.664)
Age -0.096 (0.145) +0.246*** (0.086) +0.179* (0.098)
Age^2 +0.0006 (0.003) -0.003 (0.002) -0.003 (0.001)
Gender
Female 0.224 (0.753) 0.538 (0.548) -0.387 (0.342)
Abilità
Regolar -0.049 (0.510) 0.454 (0.355) -0.071 (0.228)
English -0.691 (0.610) -0.141 (0.488) +0.878 (0.629)
Family background
Povertà della famiglia prima di trovare lavoro 0.784* (0.448) 0.144 (0.131) 0.199** (0.097)
Livello basso Istruzione media del/I -1.024 (0.677) -0.989 (0.849) 0.096 (0.542)
genitore/i
Livello medio Istruzione media del/I -0.572 (0.815) -0.932 (0.873)
genitore/i
Region
Tasso di crescita nell’area -0.010 (0.120) -0.004 (0.082) -0.059 (0.071)
Media delle offerte nell’area +0.186 (0.218) +0.042 (0.072) -0.149 (0.138)
Esperienze di lavoro -0.991 (0.632) -2.199*** (0.562) -1.577*** (0.271)
Sample selection correction
Lambda1 (employed) +0.445* (0.268) +0.086 (0.298) -0.290 (0.183)
Lambda2 (convivente) +1.035 (1.420) -1.195 (1.451) 0.174 (0.613)
Constant 0.233 (4.747) -10.300 (9.671) 7.024 (4.897)
N 87 446 297
R2 0.76 0.29 0.7532
The equation estimated is the second of the number [2],with three zero restriction on θ, the omission of number of
youngest children con età inferiore ai 15 anni, reddito da benefici sociali e le ore lavorate.
20Table 6 Accepted wages and completed unemployment durations, Nord Centro
Log duration Log duration Log duration
Laureati Diplomati Obbligo
Coefficient (S.E.) Coefficient (S.E.) Coefficient (S.E.)
Log monthly accepted wage -0.013 (0.654) +0.023 0.187 1.983 (1.551)
Age +0.229* (0.123) +0.239*** 0.076 0.459 (0.324)
Age^2 -0.003 (0.002) -0.003* 0.001 -0.004 (0.005)
Gender
Female 0.704** (0.303) 0.428* 0.244 -0.575 (0.788)
Abilità
Regolar -0.629** (0.284) 0.560*** 0.217 -0.182 (0.672)
English -0.653* (0.330) -0.257 0.217 0.059 (1.327)
Family background
Povertà della famiglia prima di trovare lavoro +0.533 (0.382) 0.099 0.139 0.083 (0.236)
Livello basso Istruzione media del/I -0.657 (0.418) -0.489 0.422 -3.087 (2.618)
genitore/i
Livello medio Istruzione media del/I -0.450 (0.471) -0.237 0.458
genitore/i
Region
Tasso di crescita nell’area -0.138 (0.106) -0.002 0.059 +0.408 (0.322)
Media delle offerte nell’area -0.079 (0.122) 0.085 0.074 -0.275 (0.298)
Esperienze di lavoro -0.172*** (0.305) -0.748*** 0.240 -1.461*** (0.566)
Sample selection correction
Lambda1 (employed) +0.131 (0.241) -0.171 0.097 -0.153 (0.323)
Lambda2 (convivente) -0.448 (1.044) -0.623 0.484 -1.522 (1.546)
Constant -0.221 (3.512) -1.799 1.667 -12.347 (10.962)
N 104 378 183
R2 0.68 0.55 -0.60
The equation estimated is the second of the number [2],with three zero restriction on θ, the omission of number of
youngest children con età inferiore ai 15 anni, reddito da benefici sociali e le ore lavorate.
21Appendix
Tab A Average Values of the variables
Da completare
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