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Evaluating the impact of the Bologna Process on the efficiency convergence of Italian universities: a non-parametric frontier approach

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Abstract

The Bologna Process (BP) promoted a series of wide-ranging reforms of higher education (HE) systems in order to improve the quality of teaching activities across Europe. This paper evaluates the effect of these reforms on the teaching efficiency of Italian universities during the period 2000–2010. We employ bootstrapped data envelopment analysis algorithms to assess teaching efficiency. Then, we examine the convergence of the Italian HE system using several panel data estimators. We find clear evidence that Italian universities have become more efficient over time, consistent with the goals of the BP, but that substantial improvement mainly occurs during the initial period of implementation. Our estimates also show a process of convergence in the performance of the Italian HE system, but we find strong evidence of persistent gaps at both university and regional levels. These empirical findings are robust to an alternative estimator, the empirical strategy, and the employed sample.

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Notes

  1. There is a considerable literature showing a duality in the Italian socio-economic system between the developed North-Centre and the less-developed South, also in human capital endowments (Di Liberto 2008).

  2. At present, the Bologna Process involves 47 countries.

  3. The Italian HE system has experienced a deep and unsystematic process of reform over the last decades. The reform process began in the late 80s with the introduction of the self-regulation principle in 1989 and of financial autonomy of HEIs in 1993, in line with the general trend of the European university sector system in favor of decentralization. More recently, the so-called Gelmini Reform (Law No. 240/2010) further modified the internal organization of universities.

  4. A slow pace in the implementation of the reform was not a specific feature of Italy. See, for instance, Bolli and Farsi (2015) on the reform in Switzerland.

  5. The studies that focus on research activities use output indexes related to HEIs’ prestige (Johnes and Yu 2008), external resources attracted to research activities (Johnes 2008; Agasisti and Pérez-Esparrells 2010), the number of published works and citations (St. Aubyn et al. 2009), and the number of Ph.D. degrees.

  6. For a more extensive discussion, see Cooper et al. (2007) and Fried et al. (2008).

  7. From an output-oriented perspective, efficiency is defined as the ratio of a DMU’s observed output to the maximum output that could be achieved given its input level (Farrell 1957).

  8. We assume an output-oriented model to maximize the outputs that could be produced given the inputs. Moreover, we assume a Shephard (1970) output-oriented distance function, and consequently, efficiency scores assume values between zero and one, that is, the reciprocal of the Farrell (1957) distance function.

  9. Notwithstanding their large use, DEA estimators have received criticism, since they rely on extreme points and could be extremely sensitive to data selection, aggregation, model specification, and data errors (Simar and Wilson 2008). Alternative approaches do exist to provide robust measures of efficiency at extreme data points based on partial frontiers and the resulting partial efficiency scores. A detailed survey of these approaches can be found in Simar and Wilson (2008). See also Wilson (2012) for a discussion on these approaches and for a proposed extension of order-m estimator obtained by Cazals et al. (2002).

  10. This function has two principal advantages: first, it is closely related with technical efficiency; and second, it allows the possibility of specifying a multiple-input, multiple-output technology without price information so being suitable for our models, which includes only data on quantities.

  11. We use a system GMM estimator (SYS-GMM) that accounts for possible endogeneity by using a valid instrument and allow jointly estimating the original level and the first-difference regressions (Blundell and Bond 1998). Another possible estimator is the two-step GMM estimator proposed by Arellano and Bond (1991) that uses lagged values of the variables as instruments in the first-differenced regression. However, these were shown to be weak, particularly for the regression in differences (see Blundell and Bond 1998).

  12. Estimation results are available upon request.

  13. However, in the considered period, new HEIs (in particular, online universities) have been established. The results we present in the following sections are also robust with respect to the full unbalanced sample including all universities that completed at least a first round of degree programs.

  14. As previously stated, the choice of both inputs and outputs strictly depends on the availability of data, and we are perfectly aware that the set of variables we include does not allow us to fully capture quality directly. However, we have included ENR_9, GRAD_R, and GRAD_Q in order to reflect the qualitative aspect of teaching efficiency that has been considered explicitly as a key issue in HE reforms in Europe.

  15. We employed the following degree classification grade (in parenthesis the Italian grades): first (summa cum laude); upper second (106–110); lower second (101–105); third (91–100); and finally (66–90). Then following Johnes (2006) the weights used are respectively: first = 30; upper second = 25; lower second = 20; third = 15; and finally = 10.

  16. More statistical details on the employed variables can be found in Table 15 in the “Appendix”.

  17. Table 14 in the “Appendix” summarizes these estimated models and reports the descriptive statistics of the employed variables, whereas further statistical details on the variables can be found in Table 15 also in the “Appendix”.

  18. With respect to the first step, technical efficiency has been estimated using the software package FEAR 1.15 (Wilson 2008), while Eqs. (2), (3) and (5) have been estimated using Stata v.11.2 SE.

  19. The output-oriented approach is usually preferable in this setting because inputs such as enrolled students and personnel are assumed to be fixed exogenously, at least in the short run.

  20. See Jondrow et al. (1982) for further details.

  21. Moreover, time-invariant technology is assumed when estimating intertemporal frontiers.

  22. In particular, Simar and Wilson (2008) observed that due the slow convergence rate of DEA estimators, the consistency of DEA estimates strongly depend on the number of observations and variables included in the model (i.e. the dimensionality space). The dimensionality space implies that, for a given sample, a parsimonious model tends to produce better estimates for the efficient frontier than large model. Moreover, for a given model specification, small sample tends to report higher efficiency rate than large sample.

  23. However, one may argue that a better empirical strategy to test HEIs’ efficiency convergence is to employ a contemporaneous frontiers approach as suggested by Casu and Girardone (2010), since the intertemporal frontier approach does not allow for the identification of year-specific effects and, moreover, requires time invariant assumption on the HEIs production process. For these reasons and to provide robustness to our empirical findings, we also run convergence analysis by using contemporaneous frontier estimates. Results are largely comparable with those reported in the next sections and are not reported to economize on space, but are available from the authors upon request.

  24. Furthermore, we observe that our estimates are comparable to previous findings available in the literature (Agasisti and Dal Bianco 2009), considering that we use a common frontier.

  25. Nevertheless, we formally test whether the frontier globally exerts constant (CRS) or variable (VRS) returns to scale with both the procedures developed by Banker (1996) and by Simar and Wilson (2002) for mod 1. The results of Banker (1996)’s test show that we cannot reject the null hypothesis of CRS at the conventional level of significance. Moreover, the procedure proposed by Simar and Wilson (2002) consists in smoothing the probability distribution of the efficiency estimates under the CRS and VRS formulations of the DEA model. A bootstrap re-sampling method (B = 2000) is implemented to develop a robust p value, which enables us to test whether HEIs operate under CRS or VRS. We cannot reject the null hypothesis of global VRS at 5 % confidence level (p values = 0.0927). This implies that, in what follows, we do not account for the size-effects related to Italian HEIs.

  26. However, mod 3 shows that those effects were probably overcome by a reduction of average degree score.

  27. In Sect. 6, we also employ panel fixed-effect estimators to control for such an effect.

  28. In a different sense, we can look at the implementation of the reform as a natural experiment that enables us to compare the average efficiency of HEIs exposed to the reform (i.e., those having the number of graduates higher than the cut-off), to the control group of the remaining HEIs.

  29. We estimate the SFA models reported here using the Maximum Likelihood Method with the R package Benchmarking by Bogetoft and Otto (2011). For a discussion on the approach proposed here, see Bogetoft and Otto (2011; chapters 7 and 8).

  30. Additional statistics on SFA technical efficiency scores could be found in Table 13 and the correlation matrix for all estimates can be found in Table 16 also in the “Appendix”.

  31. We wish to thank an anonymous reviewer for suggesting this point.

  32. Table 14 in the “Appendix” summarizes the estimated models.

  33. We are aware that the proposed method has received criticism (Cordero et al. 2009). Despite criticisms of the one-stage model, it remains popular and is the standard approach for considering such factors, especially in cases where the impact is recognized but not fully understood (Syrjänen 2004; Harrison et al. 2012). Furthermore, the method is more robust in the case of CRS.

  34. A detailed analysis of the pattern of research and teaching in a comparable sample of HEIs can be found in Guccio et al. (2015).

  35. An extensive literature deals with endogeneity problems with respect to convergence equation and growth models. Consistently with the empirical literature (see for instance, Casu and Girardone 2010; Maghyereh and Awartani 2012; Ayadi and Mouley 2013), we use a system-GMM estimator (Arellano and Bover 1995; Blundell and Bond 1998) to deal with this issue. Our results are consistent with those obtained through the GMM estimator (Arellano and Bond 1991) and related tables are available upon request.

  36. Results hold for mod 2 and mod 3, and related tables are available upon request.

  37. More statistical details can be found in Table 17 in the “Appendix”.

  38. Model selection tests have been provided in Table 18.

  39. To provide robustness of our convergence results, we perform several checks (related tables are available upon request). We estimate Eqs. (2), (3) and (5): (1) by using SFA scores; (2) by using bias-corrected efficiency scores (Simar and Wilson 1998); (3) by using scores estimated through contemporaneous frontiers to relax the assumption of time-invariant technology of production; (4) by employing the semiparametric estimator proposed by Simar and Wilson (2007); (5) by estimating models on the subsample of universities that have been established before the year 1997 to assess whether the case selection in terms of HEIs plays a role. Our findings are robust with respect to all the above mentioned checks.

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Acknowledgments

I would like to thank Prof. Victor Podinovski, two anonymous reviewers, and the associate editor for their insightful and constructive comments. We also thank Tommaso Agasisti, Isidoro Mazza, and the participants of the XXV annual Conference of the Italian Society of Public Economics (SIEP)—Pavia 2013, and of the international Workshop “New Issues of International and Public Economics”—Catania 2014, for their helpful suggestions on earlier versions. Any remaining errors are solely the responsibility of the authors.

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  1. Department of Economics and Business, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Calogero Guccio, Marco Ferdinando Martorana & Luisa Monaco

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  1. Calogero Guccio
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  2. Marco Ferdinando Martorana
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  3. Luisa Monaco
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Correspondence to Calogero Guccio.

Appendix

Appendix

See Tables 13, 14, 15, 16, 17 and 18.

Table 13 Average SFA efficiency scores estimates by year and geographical area
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Table 14 Descriptive statistics of variables: models that employ research as exogenously produced by HEIs
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Table 15 Descriptive statistics of employed variables in different models: average values per year
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Table 16 Correlation matrix between efficiency estimates
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Table 17 Average efficiency scores with respect to geographical areas (intertemporal frontier)
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Table 18 Panel data model selection test results
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Guccio, C., Martorana, M.F. & Monaco, L. Evaluating the impact of the Bologna Process on the efficiency convergence of Italian universities: a non-parametric frontier approach. J Prod Anal 45, 275–298 (2016). https://doi.org/10.1007/s11123-015-0459-6

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