3.3. Using non-discretionary factors to explain inefficiencies
The analysis via composite performance indicators and DEA analysis have assumed tacitly that
expenditure efficiency is purely the result of discretionary (policy and spending) inputs. They do not take
into account the presence of “environmental” factors, also known as non-discretionary or “exogenous”
inputs. However, such factors may play a relevant role in determining heterogeneity across countries and
influence performance and efficiency. Exogenous or non-discretionary factors can have an economic and
non-economic origin.
As non-discretionary and discretionary factors jointly contribute to country performance and efficiency,
there are in the literature several proposals on how to deal with this issue, implying usually the use of
two-stage and even three-stage models.^13 Using the DEA output efficiency scores computed in the
previous subsection, we will evaluate the importance of non-discretionary factors below in the context of
our new member and emerging market sample. We will undertake Tobit regressions by regressing the
output efficiency scores, δι, on a set of possible non-discretionary inputs, Z, as as follows
δi= f(Zi)+εi. (7)
Previous research on the performance and efficiency of the public sector and its functions that applied
non-parametric methods mostly used either FDH or DEA and find significant inefficiencies in many
countries. Studies include notably Gupta and Verhoeven (2001) for education and health in Africa,
Clements (2002) for education in Europe, St. Aubyn (2003) for education spending in the OECD,
Afonso, Schuknecht, and Tanzi (2005) for public sector performance expenditure in the OECD, Afonso
and St. Aubyn (2005a, b) for efficiency in providing health and education in OECD countries. De Borger
at al. (1994), De Borger and Kerstens (1996), and Afonso and Fernandes (2006) find evidence of
spending inefficiencies for the local government sector. Some studies apply both FHD and DEA
methods. Afonso and St. Aubyn (2005b) undertook a two-step DEA/Tobit analysis, in the context of a
cross-country analysis of secondary education efficiency.
4. A quantitative assessment of public sector performance and expenditure
efficiency
4.1. Some stylised facts for the EU new member states and comparative countries
As a first step of our quantitative analysis, we will provide some stylised facts i) about expenditure levels
and composition, and ii) about the relation between total expenditure and the level of economic
development and economic growth. This will help gauge the situation of the new EU member countries
and comparable industrialised and emerging market countries from a broader, global perspective.
The country sample which will be used in the efficiency analysis includes the ten EU new member states,
(Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic, and
Slovenia); two candidate countries, (Bulgaria, and Romania); three “old” member countries that
underwent a catching up process after entering the EU, (Greece, Ireland and Portugal); and finally nine
countries that can also be considered as emerging markets, (Brazil, Chile, Korea, Mauritius, Mexico,
Singapore, South Africa, Thailand, and Turkey). The selection of countries was determined by the search
for a sufficient number of countries which can be compared with the new EU members and for which
reasonably good quality data is available so that an expenditure efficiency analysis becomes meaningful.
(^13) See Ruggiero (2004) and Simar and Wilson (2004) for an overview.