Departmental efficiency differences within a Greek university: An application of a DEA and Tobit analysis

Abstract

The allocation funds mechanisms for universities and their departments seem to be a complex procedure for state authorities. Consequently, the question of how efficient universities and their departments are has been imposed by governments and policymakers. In this paper, we assess research performance of academic departments within a single Greek university. A Data Envelopment Analysis (DEA) application with six model variants was used to estimate technical efficiencies relative to best practice performance. Furthermore, our input–output criteria are determined and used to measure the departments' academic performance. To deliver reasonable results a Tobit model is considered in a second stage to examine the degree to which “environmental effects” impact on departmental efficiencies. We find that “environmental effects” such as departmental infrastructure, age and schools' personnel have an important role.

Efficiency in education: a review of literature and a way forward

“…In addition, the production function can be influenced by various factors which are beyond the control of the evaluated observation. Observed in: Taylor and Johnes (1989), Beasley (1990Beasley ( ) (1995, Kao and Yang (1992), Johnes and Johnes (1993, Sinuany et al (1994), , , Athanassopoulos and Shale (1997), , Haksever and Muriagishi (1998), McMillan and Datta (1998), Sarrico and Dyson (2000), Thursby (2000), Ying and Sung (2000), Avkiran (2001), Korhonen et al (2001), Abbott and Doucouliagos (2002) Koksal and Nalcaci (2006), McMillan and Chan (2006), Agasisti and Salerno (2007), Anderson et al (2007), Fandel (2007, Tauer et al (2007), , Johnes and Yu (2008), Kao and Hung (2008), Kuo and Ho (2008), Ray and Jeon (2008), Worthington and Lee (2008) (2010), Dehnokhalaji et al (2010), Kantabutra and Tang (2010), Katharaki and Katharakis (2010), Kempkes and Pohl (2010), Rayeni and Saljooghi (2010), Agasisti et al (2011Agasisti et al ( ) (2012, Johnes and Schwarzenberger (2011), Kounetas et al (2011), Kuah and Wong (2011), , Thanassoulis et al (2011), WolszczakDerlacz and Parteka (2011), Eff et al (2012), …”

“…Research income/ Tuition fees/ outside funding Beasley (1990), Breu and Raab (1994), Beasley (1995), Athanassopoulos and Shale (1997), Heshmati and Kumbhakar (1997), Haksever and Muragishi (1998), Ying and Sung (2000), Dolton et al (2003), Koksal and Nalcaci (2006), Fandel (2007), Kempkes and Pohl (2010), Wolszczak-Derlacz and Parteka (2011). Size (number of students, student per class, proportion of boys and girls) Sfeir (1986) (1988), , Jimenez and Paqueo (1996), , Athanassopoulos and Shale (1997), Heshmati and Kumbhakar (1997), Thursby (2000), Mizala et al (2002), Hanushek and Luque (2003), Flegg et al (2004), Agasisti and Dal Bianco (2006) (2009), (2008), Koksal and Nalcaci (2006), Johnes and Yu (2008), Kao and Hung (2008), Ray and Jeon (2008), Worthington and Lee (2008), Agasisti and Johnes (2009), Agasisti and Pérez-Esparrells (2010), Bradley et al (2010), Essid et al (2010) (2013) (2014), Kounetas et al (2011), Kuah andWong (2011), Perelman andSantín (2011a), Wolszczak-Derlacz and Parteka (2011), , Kirjavainen (2012), …”

Levels of Technical, Allocative, and Groundwater Use Efficiency and the Factors Affecting the Allocative Efficiency of Wheat Farmers in Pakistan

“…Such variables are termed as two-sided censored variables. The most frequently used method to predict the values of such variables is the two-limit tobit regression model [17,28,[44][45][46]. In our case, the dependent variable represents a proportion, which requires that there should not be an excessive amount of censoring (values of zero and one).…”

Efficiency differences of government investment projects: an application of a DEA and Tobit analysis

“…After the efficiency scores are obtained, a Tobit regression method is applied to further analyse the factors that influence the DEA efficiency results. Although DEA-Tobit method has been applied in the management science and operations research fields, such as the influence of factors like experience and task on the efficiency of software engineers (Otero et al, 2012), efficiency differences between departments of a university in Greece (Kostas et al, 2011) and efficiency performances of Turkish electricity distribution companies (Çelen, 2013), it has not yet been used to evaluate the factors that influence the efficiency of GIP from an individual project perspective. Besides, the functional form of the productive relationship is not clear and the underlying distribution of the inefficiency project is hard to assume in GIP.…”