Introduction
The role of human capital has been widely discussed in the literature, and empir-ical evidence of a positive relationship between quantitative (years of studying) and qualitative (knowledge acquired) education measures and earnings has been widely demonstrated.1 Individuals with a tertiary level of education have a greater chance of finding a job,2 a lower unemployment rate,3 a higher possibility of hav-ing a full-time contract,4 and earn more5 than those who do not have a university degree (OECD 2011). However, in recent decades, the problem of interrupted careers has become a growing concern, given that a substantial number of stu-dents enter the higher education system and leave without at least a first degree;6 according to Lambert and Butler (2006),
high drop-out rates are a sign either that the university system is not meeting the needs of its students, or that young people are using universities as a con-venient place to pass a year or two before getting on with their lives. In a mass access system with no selection and high youth unemployment rates, it may be quite rational for a student to sit around for a year or two before dropping out. But this is hardly an efficient use of public resources.
The Italian context is a particularly interesting case in point as “the reduction of drop-out rates is also at the core of recent reforms of the national university system, as increased retention has become the goal of many quality assessments and reorganizational efforts in Italian higher education institutions” (Belloc et al.
2010); indeed, even though it is not the aim of this chapter to discuss the insti-tutional setting of the Italian higher education system, it has to be said that its structure was reformed mainly in the 1990s and at the beginning of the 2000s, leading, as Agasisti (2009) emphasized, to a more competitive environment for the assignment of public funds (for an analysis of the potential causality between the reforms implemented and the dropout phenomenon, see, among others, Cappellari and Lucifora 2009; Di Pietro and Cutillo 2008). Universities have started to be funded according to their quality, and both quantitative and qualitative indicators
58 Roberto Zotti
were developed to accurately evaluate their productivity; among these indicators,7 there is also the number of students who leave university. Specifically, attention has mainly been paid to the transition between the first and second year, consid-ered as one of the weaknesses of the Italian higher education system (CNVSU 2011). In recent years the percentage of students dropping out after the first year has fallen but is still very high. Specifically, from academic year 2002–3 to aca-demic year 2008–9, on average 20.35 percent of entrants in Italian tertiary educa-tion institueduca-tions did not enroll in the second year (CNVSU 2011). Dung the same period, 18.02 percent of entrants could be considered inactive, meaning that these students did not acquire any credit during the first year of university (see Figure 4.1). Thus, because of both financial issues and the implications for employment, understanding the decision to withdraw has become a very important element of discussion in the higher education environment (see Belloc et al. 2010).
Using a unique administrative dataset of 56,807 first-year students from a large Italian university based in the South of Italy (the University of Salerno), from aca-demic year 2002–3 to acaaca-demic year 2010–11, this chapter examines the deter-minants of university dropout, focusing on the transition between the first and second year. The main goal is to contribute to the existing literature on students’
withdrawal, focusing on individuals’ basic demographics, educational back-ground and pre-enrollment characteristics, and households’ financial conditions.
The analysis focuses specifically on data from one university only; although this might give rise to concern regarding the external validity of the results obtained, in this way additional sources of heterogeneity which can influence students’
performance (namely the factors that influence the size of the teaching budgets across different institutions) are completely eliminated. In order to perform the analysis, a broader and more accurate dropout definition than the formal one used by university administration offices has been employed. Indeed, in line with some previous research,8 a student drops out both when he/she officially withdraws
Students who dropout after the first year (%) Inactive students (%) 30
25 20
% 15
10 5 0
2002–03 2003–04 2004–05 2005–06 2006–07 2007–08 2008–09
Figure 4.1 Post-reform persistence indicators, from 2002–3 to 2008–9
Dropping out from university 59 from the university (the so-called rinunciatari) on presenting a formal request to the student office, and when he/she does not renew his/her registration. Moreover, since the first attempts to understand the dropout phenomenon in higher educa-tion (see Tinto, 1975), an important issue has been failing to separate permanent from temporary dropout9 as well as transfer behaviors. Failure to make this dis-tinction has often led institutional and state planners to substantially overesti-mate the extent of dropout from higher education. Thus, in order to avoid putting together forms of leaving behavior different in their characteristics, students who do not renew their registration but ask to move to another university (differently from the approach used in some previous research10) are not considered as drop-outs. Moreover, students who do not renew their registration but are found to be enrolled in another faculty of the University of Salerno are not considered drop-outs either. The empirical evidence shows that students’ characteristics, such as type of high school attended, high school diploma score, gender, age, and house-hold financial conditions, play an important role in students’ decision to drop out. Estimates are robust to different structures of the error terms. The rest of this chapter is organized as follows. I begin by examining existing studies on higher education dropout. I then describe the dataset and the empirical strategy, and summarize the results. Finally, conclusions are presented, including some implications for policy.
Related literature
Student attrition in higher education institutions is a multifaceted problem, and economic, sociological, and psychological factors have to be taken into account.
Students may leave the tertiary education system for various reasons, such as a lack of social (i.e. participation in the university’s activities) and academic (i.e.
low grades) integration, information about other opportunities or their own abili-ties emerging after enrollment, a mismatch with the quality standards required by the institution, financial problems, an evaluation of the opportunity cost of educa-tion or an inaccurate prediceduca-tion about the returns from educaeduca-tion in the job market.
Since the early studies in the 1970s (see the theoretical model proposed by Tinto 1975),11 the empirical evidence shows that students’ social and academic integra-tion (referred to, respectively, as instituintegra-tional commitment and goal commitment) strongly influence whether they stay on (so-called persistence) at university ( Pascarella and Chapman 1983; Pascarella and Terenzini 1980). Specifically, the role of universities’ social and academic organization has been also investigated (for evidence on high school dropout, see Lee and Burkam 2003). Also, as in a labor economics scenario,12 whether or not the student drops out is related to the quality of the matching process with the higher education institutions. Specifically, the relationship between student ability and the quality of the universities has been taken in consideration. Low-ability students have a higher probability of dropping out from high-quality institutions than they have from low-quality institutions. In other words, university quality does matter (see Light and Strayer 2000;13 see also Hanushek et al. 2006 for the primary school environment). The higher the quality
60 Roberto Zotti
of the university’s teaching, the lower the student’s propensity to drop out (Johnes and McNabb 2004).14
Examining another aspect of the matching problem such as integration within the university, evidence has been found that students who attend university in the same region as their parental home have a higher dropout probability than others, since they may not be as well integrated with their colleagues as other stu-dents (Johnes and McNabb 2004). Based on Bean’s theoretical model (Bean 1980, 1982a),15 the empirical evidence suggests that student attitudes, the level of inte-gration into the university and factors external to the university environment (such as family approval of the choice made, the encouragement of friends to continue studying, the financial situation, and the perceived opportunities to change univer-sity) strongly influence the student decision to drop out (Bean 1982a, 1982b; Bean and Vesper 1990). Credit constraints might also be strongly related to the decision to leave the university. Students might not be able to finance the ex ante optimal level of higher education (Carneiro and Heckman 2005) or might even underes-timate the future schooling returns in term of higher earnings (Kjelland 2008).
Other factors which are linked to higher education students’ persistence are family related. The family’s socioeconomic status (Belloc et al. 2010) and the parent’s education seem to be inversely related to dropout (Cingano and Cipollone 2007; D’Hombres 2007;16 Di Pietro and Cutillo 2008;17 Cappellari and Lucifora, 2009). Regarding the role of the cultural and economic capacity of the family in the educational investment decision, see also O‘Higgins et al. (2007), for theo-retical and empirical evidence on high school dropout. Students who persist with higher education seem to come from families in which more open, more support-ive and less conflictual relationships have been built (Trent and Ruyle 1965) and where parents have higher expectations for their children’s education (Hackman and Dysinger 1970). Some factors are also related to high school, and evidence of the importance of pre-college preparedness has been found (Noel et al. 1985;
Fielding et al. 1998; Smith and Naylor 2001); students with a higher probability of dropping out seem to come from vocational school (Cingano and Cipollone 2007; Boero et al. 2005). High school diploma score has been shown to be an important predictor of persistence: students with a higher diploma score are less likely to drop out18 (Di Pietro and Cutillo 2008; Aina 2010). Tertiary education persistence also depends on work commitments. Full-time students have a lower probability of dropping out than part-time students (Bean and Metzner 1985; De Rome and Lewin 1984).
Data and empirical specification
The empirical analysis was carried out on a repeated cross-section of 56,807 first-year students19 from a large Italian university based in the South of Italy from 2002–3 to 2010–11. The institution includes nine faculties and around 50 degree courses. To give some idea of its size and financial commitments, in the last decade about €90 million have been invested every year on human resources (both academic and non-academic) and over 40,000 students are currently registered.
Dropping out from university 61 The total university turnover has been fluctuating in the same period around €100 million. Students mostly come from the neighboring area and are of middle- class background. The university has its headquarters in a small town which lies a few kilometers east of the main city in the area – a city whose population is slightly above 100,000 inhabitants, and whose income per capita lies around the national average – to which it is well connected by a motorway. The dataset gath-ers information about the students’ basic demographics (gender, age, residence),20 educational background and pre-enrollment characteristics (type of high school attended, high school final exam scores), households’ financial conditions (family self-declared income),21 and general information about university careers (having enrolled immediately after obtaining high school diploma, being a part-time stu-dent). Descriptive statistics are presented in Table 4.1.
Table 4.1 Definition of variables and sample means (Standard deviations in parentheses)
Variable name Variable definition Sample mean
Outcome variables
Dropout 1 if drops out at the end of the first year;
0 otherwise 0.4175
(0.4931) Gender
Males 1 if male; 0 otherwise 0.4590
(0.4983) Individual characteristics
Age Age in years at the beginning of the enrollment
year 21.0733
(5.1749) Age2 Age in years at the beginning of the enrollment
year squared 470.8637
(323.8445) Residence Residence distance from the University campus 42.1504
(59.7587) Residence2 Residence distance from the University campus
squared 5347.69
(38689.17) Type of high school
Scientlyc 1 if attended scientific lyceum; 0 otherwise 0.3328 (0.4712) Classlyc 1 if attended classical lyceum; 0 otherwise 0.1078
(0.3101) Linglyc 1 if attended linguistic lyceum; 0 otherwise 0.0509
(0.2198) Techninst 1 if attended technical Institution; 0 otherwise 0.2956
(0.4563) Profinst 1 if attended professional Institution; 0 otherwise 0.0941
(0.2920) Otherinst 1 attended other institutions; 0 otherwise 0.1186
(0.3233) (Continued)
62 Roberto Zotti
The econometric model is given by
yij* = +α Xijβ ε+ ij (4.1)
where the observed values of y are outcomes for individual i enrolled in faculty j.
X is a vector of exogenous variables, such as students’ individual characteristics, educational background and pre-enrollment characteristics, financial conditions, and enrollment information. β represents a set of parameters to be estimated and ε is an error term. For the identification of the dropout probability a binomial probit model has been used where y=1 if the student drops out22 and y=0 otherwise.
Results
Estimates of equation (4.1) are presented in Tables 4.2 and 4.3. For all the out-comes five estimates for the standard errors are reported. Column 1 in Table 4.2 reports standard errors robust to heteroskedasticity, column 2 reports standard errors clustered at faculty and year level, whereas column 4 reports standard errors clustered at curriculum and year level. Cluster-adjusted standard errors cor-rect for the possible correlation in performance of students enrolled in the same faculty and curricula over time. Faculties are the main organizational units where teaching takes places. Through the faculties, universities organize their activity in the various subject areas. Faculties coordinate subject courses and arrange them within the different degree programs; they appoint academic staff and decide, always respectful of the principle of freedom of teaching, how to distribute roles
Variable name Variable definition Sample mean
Diploma score
Score High school final exam score 78.8857
(12.5943) Family income
Low income 1 if declared family income from €0 to €12,000.00;
0 otherwise 0.4787
(0.4995) Medium income 1 if declared family income from €12,000.01 to
€32,000.00; 0 otherwise 0.3500
(0.4769) High income 1 if declared family income higher than
€32,000.01; 0 otherwise 0.1685
(0.3743) Enrollment characteristics
Gap_time 1 if enrolled in the year of the diploma; 0 otherwise 0.8163 (0.3872)
Part_time 1 if part-time student; 0 otherwise 0.0332
(0.1792) Table 4.1 (Continued)
Table 4.2 Estimated coefficients from the probit model for withdrawing students All covariates
(1) Dropout between year1 and 2
(2) Dropout between year1 and 2
(3) Dropout between year1 and 2
(4) Dropout between year1 and 2
(5) Dropout between year1 and 2 Gender (reference: females) Males 0.122*** (0.013)
0.122*** (0.018)
0.122*** (0.017) 0.122*** (0.017) 0.122*** (0.017) Individual characteristics Age 0.163*** (0.007) 0.163*** (0.009) 0.163*** (0.009) 0.163*** (0.009) 0.163*** (0.010) Age2
–0.002*** (0.0001) –0.002*** (0.0001) –0.002*** (0.0001) –0.002*** (0.0001) –0.002*** (0.0001)
Residence
–0.0006*** (0.0001) –0.0006*** (0.0001) –0.0006*** (0.0002) –0.0006*** (0.0002) –0.0006*** (0.0002)
Residence2
7.65e-07*** (2.86e-07) 7.65e-07*** (2.93e-07) 7.65e-07** (3.08e-07) 7.65e-07** (3.03e-07) 7.65e-07** (3.43e-07) Type of high school (reference: scientific lyceum) Classlyc 0.024 (0.020) 0.024 (0.035) 0.024 (0.034)
0.024 (0.028)
0.024 (0.026) Linglyc
0.161*** (0.028)
0.161*** (0.036) 0.161*** (0.036) 0.161*** (0.033) 0.161*** (0.030) Techninst 0.358*** (0.014) 0.358*** (0.026) 0.358*** (0.026) 0.358*** (0.020) 0.358*** (0.019) Profinst 0.528*** (0.021) 0.528*** (0.034) 0.528*** (0.032) 0.528*** (0.027) 0.528*** (0.026) Otherinst 0.339*** (0.021) 0.339*** (0.035) 0.339*** (0.031) 0.339*** (0.029) 0.339*** (0.033) (Continued)
All covariates
(1) Dropout between year1 and 2
(2) Dropout between year1 and 2
(3) Dropout between year1 and 2
(4) Dropout between year1 and 2
(5) Dropout between year1 and 2 Diploma score High School Marks
–0.023*** (0.0004)
Part_time 0.247*** (0.034) 0.247*** (0.036) 0.247*** (0.036) 0.247*** (0.035) 0.247*** (0.036) Faculties – Reference Economics Pharmacy 0.141** (0.058) 0.248*** (0.050) 0.248*** (0.051) Art & Philosophy
–0.146*** (0.017)
All covariates
(1) Dropout between year1 and 2
(2) Dropout between year1 and 2
(3) Dropout between year1 and 2
(4) Dropout between year1 and 2
(5) Dropout between year1 and 2 Educational Sciences
–0.203*** (0.021) –0.203*** (0.073) –0.203*** (0.064) –0.203*** (0.063) –0.203*** (0.074)
Maths, Phys. & Nat Sc.
0.048** (0.018) 0.048 (0.063) 0.048 (0.073) 0.048 (0.047) 0.048 (0.051) Political Sciences
0.049** (0.023)
0.049 (0.055)
0.049 (0.058)
0.049 (0.046) 0.049 (0.051) Year fixed effectsYesYesYesYesYes No. of obs.5680756807568075680756807 Wald χ2(30)7180.252928.383970.533136.676301.01 Prob > χ20.00000.00000.00000.00000.0000 Log-likelihood–34479.843–34479.843–34479.843–34479.843–34479.843 Pseudo R20.10670.10670.10670.10670.1067 (1) Standard errors robust to heteroskedasticity; (2) standard errors clustered faculty and year; (3) standard errors clustered faculty and year bootstrap (1000 replica- tions); (4) standard errors clustered curricula and year; (5) standard errors clustered curricula and year bootstrap (1000 replications) * p < 0.10, ** p < 0.05, *** p < 0.01
Table 4.2 (Continued)
Table 4.3 Estimated coefficients from the probit model for withdrawing students by gender All covariates
(1) Dropout between year1 and 2
(2) Dropout between year1 and 2
(3) Dropout between year1 and 2
(4) Dropout between year1 and 2
(5) Dropout between year1 and 2 MalesFemalesMalesFemalesMalesFemalesMalesFemalesMalesFemales Individual characteristics Age 0.148*** 0.172*** 0.148*** 0.172*** 0.148*** 0.172*** 0.148*** 0.172*** 0.148*** 0.172*** (0.010)(0.010)(0.012)(0.013)(0.012)(0.012)(0.012)(0.013)(0.014)(0.013) 2Age
–0.001*** (0.0001)
Type of high school (reference: scientific lyceum) Classlyc 0.060* (0.032) –0.004 (0.026) 0.121*** (0.040) 0.366*** (0.069) 0.121*** (0.036) 0.366*** (0.073) 0.121*** (0.037) Techninst
0.386*** (0.018) 0.341*** (0.023) 0.386*** (0.032) 0.341*** (0.030) 0.386*** (0.026) 0.341*** (0.030) 0.386*** (0.024) 0.341*** (0.030) 0.386*** (0.025) 0.341*** (0.031) Profinst 0.610*** (0.033)
0.471*** (0.028) 0.610*** (0.041) 0.471*** (0.039) 0.610*** (0.037) 0.471*** (0.043) 0.610*** (0.038) 0.471*** (0.034) 0.610*** (0.042) 0.471*** (0.035) Otherinst 0.506*** (0.053) 0.307*** (0.024) 0.506*** (0.054) 0.307*** (0.037) 0.506*** (0.052) 0.307*** (0.035)
0.506*** (0.058) 0.307*** (0.032) 0.506*** (0.060) 0.307*** (0.033) Diploma score High School Marks
–0.026*** (0.0007)
All covariates
(1) Dropout between year1 and 2
(2) Dropout between year1 and 2
(3) Dropout between year1 and 2
(4) Dropout between year1 and 2
(5) Dropout between year1 and 2 MalesFemalesMalesFemalesMalesFemalesMalesFemalesMalesFemales Enrollment characteristics Gap_time
–0.132*** (0.025) –0.054** (0.022) –0.132*** (0.030) –0.054** (0.023) –0.132*** (0.032) –0.054** (0.024) –0.132*** (0.028) –0.054** (0.025) –0.132*** (0.027) –0.054* (0.028)
Part_time 0.292*** (0.045) 0.219*** (0.052) 0.292*** (0.049) 0.219*** (0.050) 0.292*** (0.053) 0.219*** (0.048)
0.292*** (0.047) 0.219*** (0.054) 0.292*** (0.041) 0.219*** (0.0565) Year and faculties fixed effects
YesYesYesYesYesYesYesYesYesYes No. of obs.26080307272608030727260803072726080307272608030727 Wald χ2(29)3608.243152.931883.431903.623242.342116.772058.201786.482787.722628.37 Prob > χ20.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000 Log- likelihood–15939.01–18408.38–15939.01–18408.38–15939.01–18408.38–15939.01–18408.38–15939.01–18408.38 Pseudo R20.11710.08750.11710.08750.11710.08750.11710.08750.11710.0875 (1) Standard errors robust to heteroskedasticity; (2) standard errors clustered faculty and year; (3) standard errors clustered faculty and year bootstrap (1000 replications); (4) standard errors clustered curricula and year; (5) standard errors clustered curricula and year bootstrap (1000 replications) * p < 0.10, ** p < 0.05, *** p < 0.01
Table 4.3 (Continued)
68 Roberto Zotti
and workload among university teachers and researchers. Curricula are the small-est organizational units within the faculty and might be effectively the main place where common shocks may occur. The asymptotic approximation relevant for clustered standard errors relies on a large number of clusters (see Donald and Lang 2007). However, as a matter of robustness, non-parametric standard errors clustered at faculty, curriculum and year level based on a block bootstrap with 1000 replications (see Cameron et al. 2008) have been reported. Non-parametric standard errors are reported in columns 3 and 5.
With regard to individual characteristics, male students are found to be more likely to drop out than female students. Age is also significant and positively cor-related to dropout; more specifically, dropout has an inverse U-shaped relation-ship with age.23 Students whose residence is far from the university are less likely to withdraw; dropout has in this case a U-shaped relationship with residence.24 In other words, the increase in age and residence does not lead to a linear change in the dropout probability. Turning to the pre-enrollment experiences, in line with the main literature, results show that educational background is an important determinant of the dropout decision. Relative to those who have completed sci-entific lyceum schooling, other things equal, having completed technical or pro-fessional secondary school increases the probability of dropout. Furthermore, high school diploma score is important. The higher the diploma score, the less likely students drop out from the university. Staying with pre-enrollment char-acteristics, those who enroll in the university immediately after obtaining a high school diploma have a lower probability of dropout. The enrollment specification plays a role, too, as being a part-time student increases the probability of dropout.
Regarding students’ financial conditions (specifically, family declared income), those students with a medium declared income25 are less likely to drop out than those students with a low declared income. Results by gender are reported in Table 4.3.
Conclusions and discussion
This chapter examines the determinants of university dropout between the first and the second year, using a sample of 56,807 first-year students from a large Italian university based in the South of Italy, from academic year 2002–3 to aca-demic year 2010–11. Understanding of the decision to withdraw has become a very important element of discussions in the higher education environment and might have important policy implications in the particular context of the post- reform tertiary education system in Italy. Indeed, according to Cappellari and Lucifora (2009), the “system was often criticized for its inefficiencies in terms of low enrollment, high drop-out, excessive actual length of studies”. Moreover, higher education institutions are evaluated and then financially supported also on the basis of parameters and indicators, such as the dropout rate, especially between the first and the second year.
The empirical evidence (estimates are robust to different structures of the error terms) suggests that educational background and pre-enrollment characteristics
Dropping out from university 69 have an important role in the decision to leave university. Having attended a voca-tional secondary education institution increases the student’s attrition, and the higher is the diploma score the lower is the probability of dropout. According to the results obtained, well-trained students seem to be better integrated into the university system and there is a strong relationship between secondary school choice and parental background (educational, cultural and financial) to be taken into account. Secondary school track chosen also represents a channel through which the family environment (consolidating the intergenerational correlation in the educational attainment) influences the level of education completed (Checchi et al. 2013; Carneiro and Heckman 2005). Still in line with the main literature, evidence has been found that female, younger and full-time students and those who enrolled immediately after having attended high school are less likely to drop out. Regarding family socio-economic status (income), instead, estimates show that those students with a medium declared income are less likely to drop out between the first and second year. This result should be interpreted with care, mostly because a good measure of family income was lacking. Moreover, the family income variable may suffer from a partial correlation either with students’
educational background or with their parents’ level of education. It is also interest-ing to note that students whose residence is far from the university are less likely
educational background or with their parents’ level of education. It is also interest-ing to note that students whose residence is far from the university are less likely