Determinants of the decision to enrol in tertiary education
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Equity and Access to Tertiary Education: Demand for Student Loans in Italy
4. Determinants of the decision to enrol in tertiary education
This chapter is devoted to the understanding of the decision to engage in post-compulsory education with the
aim of informing public decision makers and lending institutions about socially relevant concerns related to
the presence of liquidity constraints that may affect the entry choice of less affluent but capable students.
One of the most important objectives of this study is to investigate whether student loans are an appropriate
policy tool to foster human capital investments across high-school students of different socio-economic
backgrounds. The knowledge of the availability of student loans may significantly affect the decision to
invest in tertiary education. The study also provides information about the relative importance of objective
“circumstances” – that is, characteristics for which students are not responsible – on the one hand and
personal characteristics and attitudes on the other. Circumstances that may condition the choice to stay in
education are, for example, family characteristics, availability of information, direct cost constraints,
opportunity costs, access to credit, or disposable family income. Personal characteristics and attitudes may
also act as barriers to post-compulsory education but are of the individual’s own responsibility. As Cigno and
Luporini (2009) note, young people who are sufficiently rich to pay for higher education resort to family
resources and do not participate in a loan scheme. Bright students would accept a loan only if they were
credit constrained.
While circumstances are of interest to guide policy action, individual responsibility is of interest for the
private rather than the public sphere. For example, when the decision is not free because constrained by
circumstances, such as insufficient income, then policies should act on leveling off the effect of
circumstances on the chance of enrolling in higher education. On the other hand, if an individual does not
face a liquidity constraint, but is not willing to invest extra effort in studying, then this situation should not
be of interest to policy makers. Individuals should be held responsible for their achievements and levels of
effort. The characteristics for which individuals can be held responsible are those under their full control. We
maintain that preferences and attitudes, but not the choices that are associated with them, belong to the
responsibility sphere independently from the fact that preferences are formed conditional upon the
environment of which a person is part without being able to modify it.
It is generally accepted that individuals can be held responsible for their choices and achievements only if
they enjoy similar opportunities. If this were not the case, we would be in the situation of comparing two
different choices subject to more or less stringent constraints and facing different opportunity sets. The
practical possibility to condition for different circumstances offering same opportunities of comparable
quality is rare. If family circumstances are unequal, students may be held responsible for not showing an
interest in taking a loan. By contrast, in the absence of loans, liquidity-constrained individuals may not be
held responsible for the choice to go to work rather than investing in tertiary education. The situation
changes for students enjoying similar opportunities because, for example, they belong to the same income
class, live in the same city and area, and attended a high school of the same type.
In line with Roemer (1985, 1992, and 1996) and Fleurbaey (2008), we adhere to the definition that a policy
effective in reducing the influence of circumstances outside individual command increases equality of
opportunity. An educational policy that reduces the impact of such circumstances as parental income,
education, employment and socio-economic status on the freedom of an individual choice is expected to
improve the distribution of opportunities across the student population. It is then crucial for policy-makers to
adopt appropriate policy actions aiming at equalizing opportunities across the student population and
removing barriers to education in order to make students fully responsible for their achievements as an
outcome of their sole efforts.
As noticed by Carneiro and Heckman (2002), the correlation between family income and college enrollment
is the result of short-run and long-run liquidity constraints. Families with high income in the adolescent years
have more opportunities to invest in the quality of education of their children and to produce the cognitive
and non-cognitive skills desirable to benefit from higher education. In this study, income from the child’s
formative years is not known, so we restrict the analysis to short-run liquidity constraints.
The decision to enroll: benefits and costs.
According to the mainstream human-capital investment model, high school students want to invest in tertiary
education if perceived future benefits exceed expected costs, all discounted to the time the decision is made.
22Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Benefits. Benefits can be identified primarily with the returns to the investment in education. Expected
benefits to education depend on the subjective evaluation of both employment possibilities and their quality
in terms of proximity to personal aspirations as well as on the level of expected lifetime earnings (financial
return). Focusing on the latter, benefits can be decomposed in a “net wage premium,” given by the wage
increase associated with an additional year of schooling, the “net employment premium” generated by a
marginal increase in employment probability, and in a “pension premium” corresponding to the present value
of higher retirement benefits derived from net wage and employment premia (De la Fuente and Jimeno 2005,
Boarini and Strauss 2007). For a given rate of time preference and presuming perfect foresight about entry,
transitory ad permanent incomes, the wage premium is the most important financial-returns component while
pension and labor market premia are less important (Boarini and Strauss 2007).
Detailed information about returns to investment in education and wage premia is crucial to effectively
inform policy makers. Reasonably, it is unlikely that students have a clear knowledge about wage
distribution, employment opportunities and stability of available jobs at the end of their education career.
The youngsters’ information set is probably formed by entry salaries from direct sources or indirectly from
their non-graduated age-mates who have already worked for a few years. Information about life-cycle
salaries is less precise. According to Betts (1996), undergraduates’ knowledge about salaries occurs mainly
in the fourth year of study.
The type of information that high school students may use to rationally form their wage and employment
expectations is shown in Table 4.1. The base wage is about 800 Euros. It increases of about 20% both for the
first and second level of university degree reaching approximately 1,000 and 1,200 Euros respectively.
Students may also know that chances to be unemployed in the first year after the attainment of the high-
school diploma, especially for the students with a general education at Liceo, can be as high as 56% as
compared to a chance of about 15% for a job seeker with a university degree.
We do expect, though, that high school leavers may have clear opinions about the relative importance of the
employment premium and the wage premium, as we ask in the questionnaire, but hardly their levels. What
we think that high school leavers know, with a reliable level of precision, is an estimate of their reservation
wage corresponding to the lowest level of wage they should receive in order to prefer working rather than
studying. Reservation wages are in general higher than market wages, especially for those students willing to
pursue tertiary education, and expected wages as well, which are also higher than actual wages (Brunello,
Lucifora and Winter-Ebmer 2001). The difference between reservation wages and prevailing market wages
gives a measure of the motivation either to search for a suitable job offer or to prefer higher education over
working. Brown and Taylor (2008) report that reservation wages are highly correlated with expected wages.
Other authors (Gorter and Gorter 1983) show that the reservation wage equals expected wage in about 44%
of the observed cases. In light of this evidence, the questionnaires ask only about reservation wages.
The level of personal self-esteem in assessing the potential to find a job and to attain a successful career also
affects the perception of additional earnings. This judgment may depend on past experience in terms of the
personal capability to link efforts and outcomes, learning ability in difficult matters such as mathematics,
relational ability, will power in studying and leisure activities, gender, habits, and other characteristics or
attitudes.
The subjective evaluation of the value attached to the investment in education, and indirectly of the potential
to realize future pay-offs, is revealed by the level of wage at which an individual prefers to work today rather
than after a period invested to acquire additional skills. The difference between the reservation wage and the
wage prevailing in the labor market for individuals with a high-school level of skills and knowledge provides
an indirect measure of the expected benefit from the investment in tertiary education. Similarly, the
difference between the reservation wage and the wage prevailing in the labor market for individuals with a
skill and knowledge profile of a graduate student offers a measure of the error individuals are making in
estimating future earnings and capabilities to pay back borrowed financial incentives.
The decision to enroll depends on the present value of net benefits, that is, the difference between benefits
and costs, both evaluated at the individuals’ specific rate of time preference. Benefits usually exceed costs
and, hence, obtaining a university degree is a worthwhile investment for individuals with average ability who
do not discount future earnings heavily and who are not overly risk-averse.
23Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Costs. Direct costs of tertiary education such as tuition fees, purchase of books or computers and living costs
related to lodging and transportation are not known with sufficient precision by high school students. Note
that it is important not to include the payment of the principal of a loan to avoid double cost counting.
Opportunity costs of education associated with foregone earnings are probably best estimated by the student.
There are also non-monetary costs of studying (“learning pain”), which greatly differ from one individual to
the other but are difficult to quantify. In general, last-year high-school students have only a vague idea of the
costs of post-secondary education thus justifying the indirect questionnaire method adopted in this study to
assess enrolment decisions via subjective measures.
In practice, researchers investigating the decision to invest in higher education do not observe either benefits
or costs. What is observed is the intention to invest in education. If the willingness to pursue post-secondary
education is revealed, then the utility generated by the choice is said to be associated with a positive net
benefit.
Risk and time preferences. The riskiness of university attendance may be an important factor in the
schooling choice. Some talented upper secondary students who can afford tertiary education may still
hesitate to attend university because of their aversion to risky prospects (Weiss 1972, Chen 2001). From the
point of view of an individual, investing in human capital is perceived as more risky than physical capital
(Levhari and Weiss 1974). Because of this evidence, we opted to run an experiment within the student
questionnaire aiming at eliciting students’ risk and time preferences.
Expected returns of education are estimated conditional on the realization of uncertain prospects such as
finding the preferred job combined with the desired level of salary given uncertain labor market conditions
and incomplete information about the personal capability of acquiring a satisfying level of ability during the
study course. Risk preferences are an important factor affecting the decision to invest in human capital
(Weiss 1971, Weiss 1972, Levhari and Weiss 1974, Chen 2002). A risk averse student who is not a top
performer may perceive as more risky, and a potential waste of time, to continue studying rather than looking
for a low-risk low-skilled job after the diploma. In general, if schooling is perceived as a risky prospect, then
the optimal number of years of schooling decreases when risk aversion increases. However, if investment in
education is perceived as a form of insurance, for example against high youth unemployment, then years
invested in education increase with the degree of risk aversion.
The present value of the estimated payoffs clearly depends on the personal discounting of time and the
propensity to delay consumption. Rates of time preference are not constant over time and gains are generally
discounted more than losses (Frederick, Loewenstein and O’Donoghue 2002). As it is reasonable to expect,
the ability to be forward-looking also depends on circumstances, efforts and characteristics. Myopic students,
for example, prefer present to future consumption. They discount future gains from education heavily,
making the choice to study less attractive compared with sure sources of income obtainable from entering the
job market. In general, impatient students request higher returns to pursue tertiary education.
Enrollment to tertiary education. Secondary school leavers in Italy, differently from other countries where
there is a long-standing tradition of post-secondary vocational education, simply face the choice of enrolling
into university or joining the labor market. About 54% of high school finishers declare at the time of the
interview the intention to go to the university, while 20% intend to enter the job market. The remaining 26%
is uncertain. About 32% of the students willing to enroll into university and 52% of the students preferring to
go to work are from the lowest two income quintiles. The distribution of uncertain students is more uniform
across income quintiles.
The proportion of low-income students with middle to high level of school performance who intend to enter
the job market is about 20% representing about the 4% of the total sample. This evidence shows that
liquidity constraints may not act as relevant barriers to participation. The proportion of skilled students
belonging to the lower portion of the income distribution who are still uncertain at the time of the interview
is higher than that of the less talented. This undecided proportion does not seem to invalidate the assertion
that access to tertiary education is equitable thanks to low fees and indirect and low psychic costs owing to
the high number of university campuses in the Veneto region.
These participation rates are in line with the rate reported by Cappellari and Lucifora (2009) who show that
the national enrollment rate after the Bologna process in 2001 is 62%. OECD (2009) reports that in Italy in
24Equity and Access to Tertiary Education: Demand for Student Loans in Italy
2006, 85% of upper secondary students finish the program, the same as the EU19 average, and 53% enter
tertiary education compared to a 55% for EU19.
It is important to be aware of the fact that in Italy, the comparative quality and reputation of a university has
a relatively low attraction power on upper secondary school leavers, because the study degree has the same
“legal value” everywhere. For example, the public sector as an employer is not allowed to prefer graduates
from a particular university over other graduates. The legal value provides equal access to public sector
employment and regulated professions such as lawyer, notary, engineer, physician, or business consultant,
independently of the university site where it is attained. Partly due to that, universities in Italy do not
compete among each other for the best researchers, teachers, students and public resources and the university
degree has a credential rather than a market-based value. Nonetheless, choices concerning university sites
may be affected by other factors such as distance to university or ease of transport connections. However, we
would like to remark that our interest is simply modeling the enrollment choice in tertiary education, be it in
the Veneto region or outside, not the choice about the University site.
4.1.The model
The intention to enroll in university, to go work or to be uncertain is modeled as an unordered response using
a multinomial logit model (MNL). The last-year high school student is asked to express the intention to
choose one alternative among the following three possible outcomes:
#Be uncertain (status 0)
"Enroll in university (status 1)
!Go to work (status 2)
We assume that the high school student reveals either the preferred intention, i.e. the choice that provides the
highest utility, or does not make the intention manifest. At the time of the interview, uncertain students are
assumed to have incomplete information about the benefits and costs associated with the choice, but know
their preferences with certainty. The intention to invest in tertiary education is revealed, if the net benefit is
evaluated to be greater than zero.
The utility function presents a stochastic component describing the heterogeneity unobserved by the
researcher as is traditionally assumed within a random utility framework. We represent the utility of student i
choosing alternative j$J as:
U j % X& j ' ( j ,
where X is a set of three subset of covariates X={FC, PREF, EFF}, & is the vector of parameters specific to
each alternative j associated with each element of the set of conditioning variables X and ( is the error term
assumed to be iid with a type 1 extreme value distribution capturing unobserved heterogeneity:
F )( * % exp ,- + exp )+( *./ .
The response probability of revealing intention j is:
) * , .
J
) *
P )y % j X * % P Ui j 0 Uil % exp X & j / 21 ' 1 exp )X &l *3
- l %1 /
4j 5 l ,
) *
where y is a random variable taking on the values {0,1,..,J} and exp X & j % 1 for j % 0 ensuring that
response probabilities sum up to unity. For P(y=0|X), the & vector associated with the base outcome category,
which is the uncertain state in our case, is set to zero for identification purposes. The estimated & coefficients
25Equity and Access to Tertiary Education: Demand for Student Loans in Italy
measure the change relative to status 0. The partial effects of the conditioning variables on the predicted
probabilities are clearly nonlinear, while the log-odds ratio is linear in X.
Out of 2,703 observations, 54% intends to enroll, 20% would prefer to go work, and 26% is still uncertain at
the interview date. Table 4.2 reveals that the decision to invest in human capital is positively related with
income. About 73% of the high school students belonging to the richest quintile intend to enroll in
university. The percentage of students of the lower two quintiles of the distribution deciding to invest in
higher education is about 40%, while about one fourth of the less affluent students intend to go to work.
Richer students are less uncertain. The table clearly shows that income is one of the relevant factors that may
affect the enrollment choice.
We do not directly observe benefits and costs, because of the intention nature of the data, but the factors
affecting the subjective evaluation of the present value of expected benefits and costs:
6 Family Circumstances (FC)
6 Individual characteristics, preferences and attitudes (PREF)
6 Outcomes and efforts (EFF).
We then proceed to investigate how intentional participation rates differ across individuals with dissimilar
characteristics, attitudes and outcomes, and living in diverse circumstances.
The conditioning variables. The enrollment decision is voluntary. The choice thus depends on a benefit-
cost calculus. Talent and inclination and other personality traits such as preferences towards risk and time
affect the subjective evaluation of both tangible and intangible benefits and costs. Families, social
institutions, media and other circumstances shape preferences, personality and cognition in a significant way
(Heckman 2009). In the present analysis, we do not explore this link and treat the sphere of circumstances as
separate from the sphere of cognitive and noncognitive capabilities such as motivation, perseverance, time
and risk preference, self-esteem, preference for leisure, loyalty, relational skills, degree of religiosity,
altruism (Cunha and Heckman 2009). Under a responsibility perspective, individuals are accountable for the
acquisition of both cognitive and noncognitive capabilities. We describe the group of factors affecting the
intention to enroll in tertiary education conditioning by post-secondary choice. The descriptive statistics are
presented in the set of Tables 4.3 to 4.5.
Family circumstances. There are marked infra-regional differences in the intention to enroll in university.
For example, Rovigo presents the lowest participation rate (29%) across the Veneto provinces. It is also the
province with the lowest income per capita, with the highest proportion of rural population and relatively
higher share of single earner families. The intention to enroll is especially high (67%) in the area of Padova
and Belluno where the university boasts a longstanding tradition.
The probability to undertake university studies is higher if a student lives in a residential rather than a
working class area, 57% as opposed to 45%. This location effect is detectable also when comparing urban
versus nonurban intention to enroll. Students who live in large cities have a higher propensity to continue
studying (64% versus 49%) than students from nun-urban areas, partly for cultural reasons and partly to
convenience due to proximity to the university site.
Family size does not vary across choice class. The average family has four members. As the stringency of
the liquidity constraint is closely related to family size, we construct a per capita income measure giving
each household member an equal weight considering that families with children of university age are not
likely to have very young children as well.
We also account for the family type on the basis of the number of earners in the household. A working
mother often has to substitute external help for her direct childcare services and the household organization
differs on many other accounts from that of a family with a single breadwinner. The double-earner household
model is the prevailing one in the Veneto region. The intention to enroll is higher among this family type
than in single-breadwinner families (58% and 43%).
Accounting for decision protocol heterogeneity is important for policy analysis (Giustinelli 2009). Child-
parent interactions and consultations are an inexpensive way of eliciting information about a decision that
may directly involve parents who attained a degree. This aspect can significantly affect how parents transmit
26Equity and Access to Tertiary Education: Demand for Student Loans in Italy
their background onto their children. The effectiveness of this process is in turn affected by the inherent
democracy of the information sharing mechanism. A joint style of family decision making is adopted in
74.4% of the cases. About 55% of joint choices are in favor of enrollment to tertiary education.
Father and mother educational backgrounds are distributed similarly across post-secondary choices of their
children. In 81% of the cases a high level of education of either the mother or the father is associated with an
intention to pursue tertiary education. This transmission factor is very high. It decreases with the level of
education. This pattern similarity comes from the assortative mating effect by educational strata of the
Veneto parents (Table D.3, Appendix D).
The effect of the occupational status of the father is also in line with expectations. The child of a father who
is a highly skilled white collar has a high probability to enroll (84%) as opposed to the child of a blue-collar
father (37%). The willingness to engage in tertiary education is lower when the mother is a housewife
probably because of a lower level of education.
Household income expressed in per-capita terms is an important factor. While the average per adult income
of the family of students intending to go to work and uncertain is about 600 Euros. The figure is 27% higher
for the families of the students who intend to participate in higher education. Households of large size are not
as frequent as in the past and liquidity constraints may not be as stringent.
Individual characteristics, preferences and attitudes. As illustrated in Table 4.4, gender differences in
willingness to invest in tertiary education are in favor of females as it is a common trend in Italy and in the
rest of Europe (59% vs 48%).
The academically-oriented track is a strong conditioning factor showing that the human capital investment
choice is made at an earlier stage of life especially in the case of students choosing a Liceo school. About
87% of those high-school leavers that belong to a Liceo intend to engage in tertiary education. The
proportion decreases at the 31% and 22% level for technical and vocational schools respectively. Parental
education is an important factor in affecting the choice of the high-school track. Among the students
attending a Liceo, about 65% have both parents without a graduate degree. In the case of students of
technical and vocational schools, the proportion of parents not holding a university degree is about 90%.
The questionnaire asks about students’ perception of the relative importance of university costs in
constraining the decision to the point of renouncing to personal aspirations. About 57% of those who do not
perceive costs as relevant for their enrollment choice decide to pursue tertiary education.
Employment, quality of employment and wage premium are very relevant factors. Of those students agreeing
with the statement that graduation is necessary to find a good job, 62% intend to go to university. A similar
proportion (60%) is found for those who think that graduation is necessary to find the preferred job.
Regarding the wage premium, an overwhelming majority of high-school leavers (80%) agrees that a degree
is needed to find a well-paid job, and of these, 57% intend to enroll in university. By contrast, only one in
five prospective university students does not think a degree is needed for a well-paid job. The proportion of
students who are neutral or disagree about the relevance of graduation to find a well-paid job, among those
students who are willing to enroll in university, is relatively higher as compared to the analogous proportion
of students not giving importance to the employment premium. About 89% of high school leavers of the
Veneto region, aspire to a skilled job.
As it is reasonable to expect, a large proportion (58%) of this group intends to continue studying. According
to Page et al. (2006), aspiration levels, which are in turn affected by family and social circumstances, also
affect participation choices and outcomes. At the same time, however, students are relatively pessimistic
regarding future job opportunities. As many as two thirds of the students do not trust that job opportunities
will be more frequent after graduation. About 53% are among those who intend to engage in tertiary
education. Aspirations and job opportunities are sought with greater or lower intensity depending on
individual’s risk seeking behavior and personal rate of time discounting. In order to learn to what extent
preferences for the present and for risky events affect investment decisions, we measured through lottery
experiments the individual level of aversion to risk time and the time discount rate as explained in Chapter 3
and Appendix B. As many as 68% of high school leavers are risk neutral. The distribution of discount rates is
less concentrated around the mean (53.4%). The proportion of last year high school students discounting
time heavily (34.6%) is higher with respect to the proportion of students highly averse to risk (20.2%).
27Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Interestingly, the difference in the distribution of each type of individual with a specific level of risk aversion
or time discount rate across students’ groups with different preferences towards university studies is similar.
Students who intend to go to the university are more prone to take a loan, especially when compared to those
who already know that they do not want to study. Note that the difference across groups is not as significant
as one may expect, because the question inquires about preferences for a general loan, not for a loan targeted
to students. Those who intend to go to work declare an average of 3.7, while prospective university students
declare 4.5.
Reservation wages, as discussed earlier, reflect the personal stock of abilities and knowledge. High-school
leavers may be overestimating the expected increase in lifetime earnings resulting from the investment or
they may have wrong expectations about events far in the future. Reservation wages, however, seem to
convey relevant information because the reservation wage of students intending to go work declare a
reservation wage which is 62% of the wage of about 1,800 Euros reported by prospective university students.
If compared with the wage structure presented in Table 4.1, we realize that the reservation wage for future
workers is relatively closer to the market wage than the reservation wage revealed by university candidates.
This is seems to be a robust evidence of the quality of this critical subjective information both about the
personal level of self-esteem, prevailing job market conditions and perceptions about returns to education.
Efforts and outcomes. The talent that a student is capable to express in terms of quality of outcomes
depends on her/his efforts, her/his innate endowment of ability and the socio-economic circumstances of
her/his learning environment. Abilities are both inherited and created (Cunha, Heckman, Lochner, Masterov
2005, Cunha and Heckman 2009). It is an important factor determining the enrollment decision.
School failure, as signaled by the repetition of a year, is highly correlated to family background and strongly
affects later choices (Mocetti 2007). In the sample, 18.3% of the students repeated at least one year
(Table 4.5). Of these, 29% intends to continue studying, compared with 59% of the non repeaters.
As expected, high-school performance is correlated with study intentions. The proportion of top students in
the sample, in terms of grade average, is 18%. A large share of these (77%) intends to enroll in university.
Within this group, we also observe the smallest percentage of uncertain students (14.7%) and the smallest
proportion of students (8%) wanting to work right after high school. About 61% of students with good
grades intend to play their chances at the university. Those who intend to pursue tertiary education also show
a higher average mark in mathematics (6.8) than others. Prospective university student also study longer
hours after school (2.7 hours) every day than others (about 2 hours). The investment in study activities does
not appear to preclude either the practice of sports or the interest in accessing the internet.
To shed further light on time preferences, the inclination to go to university is also intersected with
religiosity and smoking behavior. As shown at the bottom of Table 4.5, the share of prospective university
student is higher among highly religious persons and among non-smokers, respectively, compared with the
average population.
4.2.Results
Previous studies about Italian tertiary education show that important determinants of enrollment choices are
parental income, education, employment and socio-economic status. Intergenerational correlation in
educational attainment is higher in Italy than in other countries (Bratti et al. 2008, Checchi et al. 1999,
Checchi and Flabbi 2006). Checchi (2000, 2003) shows that transition to university strongly depends on past
schooling, but educational attainment of the parents is also important. Family income is not reported to be a
significant factor. On the basis of this evidence, the author lends more importance to cultural rather than to
liquidity constraints in explaining the transition to university. Cappellari and Lucifora (2009) find that the
probability of going to college is 15% higher than before the adoption of the Bologna process. This increase
is concentrated among able students from less favorable parental background and is interpreted by the
authors as an evidence of the existence of liquidity constraints. It should be noted that liquidity constraints, if
present, are less stringent than a cost prospect of 3 versus 5 years of university education, as it was before the
Bologna process.
28Equity and Access to Tertiary Education: Demand for Student Loans in Italy
The multinomial logit marginal effects reported in Table 4.6 for the sample of 2,703 high school leavers of
the Veneto region show distinctive traits.
Family circumstances are in general not significant. Differently from Attanasio and Kaufmann (2009), the
intra-family decision process about human capital investments is not an important factor conditioning the
school choice. Father education is positively associated with the intention to enroll. The statistically
significant factor is the possession of at least a diploma corresponding to a middle education level. Mother
education is not significant as it relates to the university choice. The distribution of fathers and mothers’
educational attainments is similar. The relatively higher significance of fathers’ education may be partly
explained with the association with full-time employment and a stable income source acting as a guarantee
fund for a medium term investment such as tertiary education. The observation that father’s occupational
status is statistically significant only for white collar fathers endowed with high skills lends support to the
above explanation. The higher the father’s level of skills acquired through education, the higher the
probability to enroll in higher education and the lower the probability of being uncertain. On the other hand,
as we have seen in Chapter 3, mothers’ education is significant in explaining students' school performance
recognizing their important role in skill formation also in the late childhood stage. Cultural family
background, thus, may affect enrolment in university by both influencing the choice of generalist schools,
whose mission is to form for tertiary education (Checchi 2000), and by shaping cognitive skills needed to
perform well in higher education. Interestingly, income is not a significant factor determining the enrollment
choice.
Gender is a significant characteristic. Females have stronger preferences towards higher education than
males. Relatively fewer women intend to go work after the diploma. As it is reasonable to expect, early
school tracking decisions play an important role. The cultural formation of Licei does not endow high school
leavers with the freedom of choosing to work after the diploma because the probability to find a job that
would match both the type of skills they can offer and their aspirations is low. The choice of a technical or
vocational school reveals a significantly greater market orientation. Moreover, students perceive university
costs as a binding constraint affecting their personal aspirations. Perceived cost constraints significantly
lower the probability to intend to enroll in university and increases the likelihood of being uncertain. It is
highly statistically significant and positively related in both students’ groups. As shown in Chapter 3,
recognition of binding costs is not strongly related with income levels. Therefore, this evidence is signaling
that students in both groups perceive that costs are limiting their aspirations, for example independence or
university site, rather than access to university. Students’ perceptions about the job market conditions are
highly statistically significant. Among those students who intend to go to work after the diploma the
disagreement about the relevance of graduation to find a job or the preferred job is significantly higher. The
opposite is true for high school leavers who are determined to pursue tertiary education. Expectations about
the importance of graduation in order to attain a wage premium are not statistically significant in either
group. College attendance decisions do not directly depend on the expected wage return, but place higher
weight to employment availability and quality. Students willing to enroll in tertiary education reveal a strong
and statistically significant disagreement towards unskilled jobs. Market oriented students reveal a higher
willingness to accept less skilled jobs and a significant confidence in the possibility to take advantage of job
opportunities after the diploma. Prospective university students also share similar confidence, but referred to
the post-graduation period. Risk attitudes and preferences towards time are not relevant among work-
oriented students. Moderate risk aversion is statistically significant, on the other hand, for university-oriented
students. Students not interested to continue studying are significantly less attracted by student loans. Interest
in student loans is weakly significant for the group of prospective university students. The revealed
reservation wage is a statistically significant factor determining the decision to join the labor market. The
lower the reservation wage, the more likely the student will join the labor market after high school, possibly
indicating a lower level of self-esteem as a student.
In fact, the profile of students willing to work after the diploma is characterized by highly statistically
significantly lower grades and a higher significant lower preference for sports. On the other side, level of
efforts as signaled by school performance, skills in mathematics and time devoted to studying are significant
factors shaping the enrollment decision of those who intend to engage in higher education.
29Equity and Access to Tertiary Education: Demand for Student Loans in Italy
4.3.Conclusions
This chapter has analyzed the relationship between intensity of preference for higher education and
determining factors classified as circumstances, individual characteristics and attitudes towards risk and
time, and measures of individual efforts among the high-school leavers of the Veneto region. The main
insights that the present investigation has brought to the fore are:
6 family circumstances are in general less relevant as compared with individual characteristics,
personality traits and level of efforts;
6 fathers employed in high skilled activities significantly influence attendance decisions;
6 income is not a binding constraint affecting the enrollment choice. Under this respect, tertiary
education could be an effective instrument of social mobility in the Veneto region;
6 past schooling is a relevant factor;
6 students do not expect much in terms of higher wages, but mainly perceive higher education as a
means to get better and more stable jobs;
6 prospective university students have a high distaste for low skilled jobs;
6 attitudes toward risk are statistically important mainly for university oriented students. Families seem
to fully insure their children from the riskiness of the investment in tertiary education;
6 reservation wages are informative for the enrolment decision, suggesting that they proxy returns to
education pretty well;
6 high-school achievement , is a necessary prerequisite to justify further investments in high education.
In general, liquidity constraints seem to be less important than individual preferences and inclinations.
Veneto high school leavers enjoy equal access independently of the parental economic background.
30Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Table 4. 1: Returns to education by degree level
University University
Diploma (First level) (Second Level)
Monthly net wage (Euros) 774 1033 1178
Unemployment rate after 1 year (%) 56 16.5 13.9
Source: AlmaLaurea "Condizione Occupazionale dei Laureati" XI Indagine 2008 and Ghiselli (2006): "Sbocchi occupazionali e formativi dei diplomati 2005"
Associazione Almadiploma.
Table 4.2: Choice for Tertiary Education by Income and Level of Average Proficiency
- High School Students -
Income Quintiles and level of school proficiency
I-II III IV-V
Choice for tertiary education mid mid mid
a
Obs. % low low low Total
high high high
b
Intention to enroll to university 1454 53.7 8.8 23.6 3.7 15.8 12.1 35.9 100
28.8c 53.7 27.6 66.9 44.2 76.4 53.7
Uncertain 707 26.1 20.3 26.4 10.7 10.7 16.6 14.9 100
32.4 29.3 38.9 22.0 29.6 15.5 26.1
Intention to enter the job market 542 20.0 31.7 19.9 11.9 7.0 19.1 10.1 100
38.7 16.9 33.3 11.0 26.1 8.0 20.0
Total 2703 100 16.4 23.6 7.2 12.7 14.7 25.2 100
100 100 100 100 100 100 100
Notes: Level of school proficiency, a- low =0-6, mid-high=7-10; b- Percentages are reported by rows; c- Percentages are reported by columns. Slight
deviations from 100% are due to rounding. For example: 8.8% of students willing to enrol in university are from lower-income families and low secondary-
school performers while 23.6% are from lower-income families and mid-high performers; 28.8% of all low performers from lower-income families intend to go
to university, while 32.4% of them are uncertain and 38.7% intend to enter the job market.
31Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Table 4.3: Family Circumstances by Post-Secondary Choice
- High School Students -
Obs. % No Univ. Uncertain Yes Univ.
Geographical location by municipalities
Belluno - Padova 421 15.6 12.5 20.7 66.8
Rovigo 152 5.6 40.7 30.3 28.9
Treviso - Venezia 631 23.3 20.2 27.6 52.1
Verona 835 30.9 20.1 23.5 56.4
Vicenza 664 24.6 19.7 30.7 49.5
Living aerea
Residential 2021 74.8 18.2 24.9 56.9
Working class 682 25.2 25.5 29.9 44.6
Urban vs non urban
Non urban 1903 70.4 22.1 28.4 49.4
Urban 800 29.6 15.0 20.7 64.2
Family size (avg)
Number of members 2703 100.0 4.1 4.1 4.0
Household type
Double-earner 1869 69.1 17.7 23.9 58.4
Single-earner 834 30.9 25.4 31.2 43.4
Family decision making
Single-parent decision making 691 25.6 22.0 28.4 49.6
Joint decision making 2012 74.4 19.4 25.4 55.2
Father education
Low 1052 38.9 31.1 32.2 36.7
Middle 1230 45.5 15.4 25.5 59.1
High 421 15.6 6.2 12.8 81.0
Mother education
Low 1111 41.1 29.9 31.4 38.7
Middle 1191 44.1 15.9 25.2 58.9
High 401 14.8 5.2 14.5 80.3
Father occupational status
Entrepreneur 640 23.7 19.4 28.9 51.7
Professional 329 12.2 11.2 18.2 70.5
Blue collar 669 24.8 29.9 33.0 37.0
White collar high skilled 205 7.6 6.8 8.8 84.4
White collar low skilled 602 22.3 17.3 23.4 59.3
Unemployed 258 9.5 24.4 31.8 43.8
Housewife
No 1852 68.5 17.9 24.2 58.0
Yes 851 31.5 24.9 30.4 44.7
Per-capita household income (avg)
Per-capita monthly income - Euros 2703 100.0 594.6 608.4 759.8
32Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Table 4.4: Individual Characteristics, Preferences and Attitudes by Post-Secondary Choice
- High School Students -
Obs. % No Univ. Uncertain Yes Univ.
Sex
Male 1363 50.4 24.9 26.8 48.3
Female 1340 49.6 15.1 25.5 59.3
High school type
Licei 1185 43.8 2.2 11.1 86.7
Technical and teaching institutes 1009 37.3 29.3 39.3 31.3
Vocational institutes 509 18.8 43.2 35.0 21.8
Perceived costs incidence
Yes 842 31.2 14.5 39.7 45.8
No 1861 68.8 22.6 20.0 57.4
Graduation is necessary to find a job
Agree 2006 74.2 13.2 25.1 61.7
Neutral or disagree 697 25.8 39.7 29.3 31.0
Graduation is necessary to find the preferred job
Agree 2193 81.1 13.3 26.4 60.3
Neutral or disagree 510 18.9 49.0 25.3 25.7
Graduation is necessary to find a well-paid job
Agree 2157 79.8 16.7 26.4 56.9
Neutral or disagree 546 20.2 33.3 25.1 41.6
Job aspiration (unskilled or skilled job)
Skilled job 2395 88.6 16.8 25.2 58.0
Unskilled job 308 11.4 45.4 33.8 20.8
Trust in future job opportunities
No 1808 66.9 16.8 29.9 53.3
Yes 895 33.1 26.6 18.5 54.9
Risk aversion
Low risk aversion 331 12.2 19.6 29.9 50.4
Risk neutrality 1827 67.6 19.8 25.5 54.7
High risk aversion 545 20.2 21.3 26.1 52.7
Time discount rate
Low 325 12.0 23.1 28.3 48.6
Medium 1444 53.4 18.7 26.9 54.4
High 934 34.6 21.1 24.2 54.71
Level of interest for a general loan
Likert scale (0=low,10=high) 2703 100.0 3.7 4.4 4.5
Reservation wage (avg)
Required monthly wage - Euros 2703 100.0 1119.0 1741.4 1804.9
33Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Table 4.5: Outcomes and Efforts by Post-Secondary Choice
- High School Students -
Obs. % No Univ. Uncertain Yes Univ.
Proportion of students repeating at least 1 year
No 2209 81.7 16.1 24.7 59.3
Yes 494 18.3 37.9 32.8 29.3
School grade (avg)
Low 1037 38.4 32.9 32.6 34.5
Middle 1175 43.5 13.8 25.3 60.9
High 491 18.2 7.9 14.7 77.4
Mathematics grade (avg)
Mathematics mark 2703 100.0 6.2 6.4 6.8
Daily time devoted to study (avg)
Minutes 2703 100.0 110.1 127.4 166.4
Leisure use: Yes if Sport 1st preference
No 1931 71.4 21.1 26.1 52.8
Yes 772 28.6 17.5 26.2 56.3
Leisure use: Yes if Internet 1st preference
No 2484 91.9 19.7 26.2 54.1
Yes 219 8.1 24.2 25.1 50.7
Religiousness
Not religious 606 22.4 19.6 24.8 55.6
Low 895 33.1 23.2 24.7 52.1
Medium 1049 38.8 17.8 29.3 52.9
High 153 5.7 18.3 18.9 62.7
Smoking behavior
No 1907 70.6 18.3 25.4 56.3
Yes 796 29.4 24.1 28.0 47.9
34Equity and Access to Tertiary Education: Demand for Student Loans in Italy
Table 4.6: Enrolment Decision by Post Secondary Choice
- Multinomial Logistic Regression, 2703 0bservations, High School -
Variables Coef. t Coef. t Coef. t
No Univ Uncertain Yes Univ
Geographical location by municipalities (d): Belluno-Padova -0.000564 (-0.12) -0.0590 (-1.55) 0.0595 (1.53)
Rovigo 0.00780 (1.03) 0.0123 (0.20) -0.0201 (-0.32)
Treviso-Venezia 0.00680 (1.47) 0.0203 (0.57) -0.0271 (-0.73)
Verona 0.00684 (1.61) -0.0110 (-0.33) 0.00412 (0.12)
Living area (d): Working class 0.00190 (0.62) 0.0411 (1.40) -0.0430 (-1.43)
Family Circumstances
Urban vs non urban (d) -0.000982 (-0.33) -0.0535 * (-1.97) 0.0544 * (1.96)
Family size (avg): Number of components 0.00200 (1.41) 0.00947 (0.68) -0.0115 (-0.81)
Household type (d): Traditional (single earner) -0.00271 (-0.44) 0.00151 (0.02) 0.00120 (0.02)
Family decision making (d): Joint decision making -0.00240 (-0.76) 0.0198 (0.71) -0.0174 (-0.61)
Father education (d): Middle -0.0128 ** (-3.26) -0.0675 * (-2.43) 0.0803 ** (2.82)
High -0.0129 * (-2.50) -0.0375 (-0.64) 0.0504 (0.85)
Mother education (d): Middle -0.00228 (-0.77) 0.0178 (0.62) -0.0155 (-0.53)
High -0.0109 * (-2.54) -0.0649 (-1.45) 0.0758 (1.67)
Father occupational status (d): Entrepreneur -0.000648 (-0.14) -0.0105 (-0.23) 0.0112 (0.24)
Professional -0.00124 (-0.21) -0.102 * (-2.12) 0.103 * (2.09)
Blue collar -0.000436 (-0.10) -0.0124 (-0.27) 0.0128 (0.27)
White collar high skilled 0.0244 (1.01) -0.160 ** (-2.66) 0.136 * (2.04)
White collar low skilled 0.00349 (0.63) -0.0479 (-1.04) 0.0444 (0.93)
1 if Housewife (d) 0.00822 (1.05) 0.0466 (0.73) -0.0548 (-0.84)
Per-capita monthly income – Euros (ln) -0.00433 (-1.39) -0.0304 (-1.07) 0.0347 (1.19)
Sex (d): Female -0.00821 * (-2.36) -0.00901 (-0.31) 0.0172 (0.58)
High school type (d): Technical and teaching institutes 0.0566 *** (4.44) 0.391 *** (13.78) -0.447 *** (-16.48)
*** ***
Vocational institutes 0.119 *** (4.17) 0.381 (9.56) -0.499 (-15.44)
Individual Characteristics,
Preferences and Attitudes
*** ***
Perceived costs incidence (d) -0.0104 *** (-3.33) 0.164 (5.87) -0.154 (-5.39)
*** ***
Graduation is necessary to find a job 0.0172 *** (3.71) 0.123 (3.67) -0.141 (-4.08)
*** ***
Graduation is necessary to find the preferred job 0.0236 *** (4.04) 0.143 (3.69) -0.167 (-4.19)
Graduation is necessary to find a well-paid job 0.00226 (0.68) -0.0101 (-0.28) 0.00786 (0.21)
Job aspiration (unskilled or not) (d) 0.0261 ** (2.93) 0.208 *** (4.39) -0.234 *** (-4.79)
Trust in future job opportunities (d) 0.0110 ** (2.76) -0.101 *** (-3.86) 0.0900 *** (3.32)
Risk attitude (d): Middle -0.000815 (-0.20) -0.0917 * (-2.31) 0.0925 * (2.27)
High 0.00312 (0.59) -0.0523 (-1.25) 0.0492 (1.13)
Time discount rate (d): Middle -0.00475 (-1.11) -0.0232 (-0.58) 0.0279 (0.68)
High -0.00171 (-0.41) -0.0493 (-1.20) 0.0510 (1.21)
Level of interest for student loan -0.00169 ** (-2.83) -0.00872 (-1.84) 0.0104 * (2.15)
Reservation wage – Euros (ln) -0.139 *** (-6.34) 0.0865 ** (2.75) 0.0525 (1.57)
Proportion of students repeating at least 1 year (d) 0.00920 (1.94) 0.0631 (1.73) -0.0723 (-1.92)
School grade (avg) -0.0137 *** (-4.07) -0.109 *** (-5.59) 0.123 *** (6.15)
Outcome and
Mathematics grade (avg) -0.00192 (-1.56) -0.0258 * (-2.26) 0.0277 * (2.38)
Efforts
Daily time devoted to study: Minutes -0.000832 (-0.78) -0.0283 ** (-2.80) 0.0292 ** (2.83)
Leisure use (d): Yes if Sport 1st preference -0.00635 * (-2.17) 0.00910 (0.31) -0.00276 (-0.09)
Yes if Internet 1st preference -0.00371 (-0.95) -0.0473 (-1.08) 0.0510 (1.14)
Religiousness -0.00276 (-1.67) 0.00241 (0.16) 0.000358 (0.02)
Smoking behavior (d) 0.00403 (1.23) 0.0398 (1.38) -0.0439 (-1.48)
Notes: Marginal effects; t statistics in parentheses
(d) – dummy variable is equal to 1 if the statement is true
* ** ***
p < 0.05, p < 0.01, p < 0.001
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