How & how much? : the growth-friendliness of public spending through the lens.
Date: 2020-10-30
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: EU Member State, econometrics, economic growth, fiscal policy, public expenditure, public finance

6

ISSN 2443-8022 (online)

How & How Much? 
The Growth-Friendliness 
of Public Spending 
Through the Lens

Alessandra Cepparulo and Gilles Mourre

DISCUSSION PAPER 132 | OCTOBER 2020

EUROPEAN ECONOMY

EUROPEAN 
ECONOMY

Economic and 
Financial Affairs

European  Economy  Discussion  Papers  are  written  by  the  staff  of  the  European  Commission’s 
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This  manuscript  was  completed  before  31  January  2020,  before  the  economic  impact  of  the  COVID-19 
pandemic reached Europe. 

Authorised for publication by Lucio Pench, Director for Fiscal Policy and Policy Mix. 

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European Commission 
Directorate-General for Economic and Financial Affairs 

How and How Much?                                                
The Growth-Friendliness                                               
of Public Spending through the Lens  

Alessandra Cepparulo and Gilles Mourre 

Abstract  

The paper analyses the growth-friendliness of public spending in EU countries over the decade 2007-2016. 
It  looks  into  the  composition,  performance  and  efficiency  of  public  expenditure  across  countries  and 
specific  functions  of  government.  This  approach  allows  for  some  granularity  in  the  analysis.  Using  a 
literature  survey,  semi-disaggregated  composite  indicators  of  performance  and  an  efficiency  frontier 
approach,  the  analysis  provides  a  rich  set  of  results  on  the  quality  of  public  spending,  giving  first 
indications on where room for improvements appears to be large. The overall results turn out fairly mixed, 
providing  a  nuanced  picture  of  the  growth-friendliness  of  public  expenditure  in  the  EU  both  in  terms  of 
level and change. 

JEL Classification: C14, E62, H11, H50. 

Keywords:  Quality  of  public  finance,  public  spending, government  efficiency,  government  performance, 
EU countries, DEA, Malmquist index, economic growth. 

Acknowledgements: We would like to thank for their comments Philipp Mohl, Giovanni Carnazza and 
participants  at  the  INFER  REM  Workshop  on  “New  Challenges  for  Fiscal  Policy”  at  Lisbon  School  of 
Economics  and  Management,  University  of  Lisbon  22  November  2019  where  a  preliminary  draft  was 
presented.  We are also grateful to Lucio Pench, Elva Bova, Julia Lendvai, Anna Thum-Thysen and Peter 
Voigt for useful suggestions. 

Contact: Alessandra Cepparulo, European Commission, Directorate-General for Economic and Financial 
Affairs,  alessandra.cepparulo@ec.europa.eu;  Gilles  Mourre,  European  Commission,  Directorate-General 
for Economic and Financial Affairs,  gilles.mourre@ec.europa.eu.  

EUROPEAN ECONOMY                                                                                      Discussion Paper 132 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CONTENTS 

1.    Introduction    ........................................................................................................................................ 5 

2.     Assessing the composition of public spending ...........................................................................   7 

2.1. The impact of public spending on GDP: a review of the literature                                                   7 

2.2. Is the composition of public expenditure growth-friendly?                   

 10 

3.     Looking at the performance of EU Expenditure.......................................................................... 12 

3.1.  Data and methodology                                                  

 13 

3.2.  Evolution in expenditure performance in the EU and the euro area as a whole                        15 

        3.3.  Results at member state level                                                                                                             16 

4. 

Efficiency of public expenditure .................................................................................................. 18 

4.1.   Methodology and data 

19 

4.1.1. Using two complementary empirical methods                                                                                     19 
4.1.2. Data source and treatment: using multiple output variable                                                               19 

4.2.     Results 

22 

5. 

Conclusions ...................................................................................................................................... 25 

REFERENCES  

ANNEX  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1.  INTRODUCTION  

Due to the limited fiscal space in some countries with high public indebtedness, the broad objective of 
improving the performance of public spending features prominently in the political agenda of national 
governments.  This  is  seen  as  a  way  to  improving  the  prospect  of  economic  growth  in  the  long  run, 
while preserving the equally important aim of containing spending developments to restore public debt 
sustainability and thereby ensure macroeconomic stability.  

Moreover, the public expenditure (excluding interest service) amounts to about half of the GDP in the 
EU and the euro area on average, reaching almost 55% in France and Finland (Graph 1). Its total size 
is  relatively  high  in  the  EU1  and  the  euro  area  compared  with  other  developed  countries  and  is 
expected not to abate considering the fiscal consequences of ageing population. 

Graph 1.  Primary expenditure dynamics (in % of GDP) 

49

47

45

43

41

39

37

35

33

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

OECD

EU28

EA

Note: OECD weighted average consider EU members of OECD as well as the following countries: 
United States, Japan, Australia, Canada, Korea, Iceland, Norway, Mexico and Switzerland. 

Source: Authors’ elaborations on Commission Spring Forecast 2018 and WDI data. 

Therefore, the adequacy of how public money is spent with respect to its objectives is a key issue for 
national governments and - not least - for citizens who will ultimately finance public expenditures by 
paying taxes. 

Public  expenditures  support  different  goals.  The  government  is  primarily  concerned  in  providing 
public good and services (to address market failure or fulfil specific political objectives) and ensuring 
a level of redistribution perceived as socially equitable. Enhancing economic growth is certainly also 
one  of  the  goals,  since  it  is  one  crucial  aspect  of  general  welfare  and  of  the  process  of  economic 
development (European Commission, 2012). More generally, whatever goal pursued, it is essential to 
ensure that spending is made in a way that is ultimately most conducive to growth or, at a minimum, 
least harmful to economic growth.  

1  The  United  Kingdom  (UK)  is  covered  by  the  analysis  and  the  EU  aggregate  refers  to  the  EU28  because  the  United 
Kingdom was still a Member State during the reference period of this paper (2007-2016). 

5 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
 
This  paper  focuses  on  the  growth-friendliness  of  public  spending.  It  does  not  cover  other  relevant 
goals  of  public  spending  such  as:  macroeconomic  stabilisation;  social  equity  and  redistribution 
including  the  fight  against  poverty;  the  satisfaction  of  users  and  more  broadly  economic  well-being 
and  quality  of  life  through  the  supply  of  public  goods  (e.g.  protection  of  environment  and  climate, 
public security and safety and international peace). These objectives may also indirectly impact long-
run growth and general welfare. Conversely, it is incorrect to automatically consider any spending on 
'growth-friendly'  expenditure  functions  as  effectively  productive.  For  instance,  whereas  better  and 
more effective infrastructure may enhance growth, this does not necessarily mean that any spending 
labelled as ‘infrastructure’ will lead mechanically to higher long-run growth.  

Academic  literature  generally  suggests  that  public  spending  composition  matter  for  GDP  growth. 
However, no consensus is found, when it comes to identifying the types of public expenditure most 
conducive  to  GDP.  There  is  no  authoritative  definition  of  what  constitutes  growth-enhancing 
spending.  Regarding  the  economic  classification  of  spending,  only  public  investment  could  be 
considered  growth-friendly,  at  least  in  the  advanced  economy.  The  functional  classification  is  more 
promising and more precise since it considers the very purpose of public expenditure. According to the 
literature some categories of expenditure seem to be associated with a higher GDP, such as education, 
health and infrastructure.  

In  this  respect,  some  caveats  should  be  recalled:  these  functional  categories  remain  aggregated  and 
may conceal large difference across sub-categories. The growth-enhancing effect of public spending 
will  also  crucially  depend  on  the  ability  of  the  government  to  achieve  outcomes  without  creating 
distortions, making the most of limited public resources, and on the quality of governance (Reinhart 
and  Rogoff,  2009;  Afonso  and  Jalles,  2011,  Nirola  and  Sahu,  2019).  In  particular,  Butkiewicz  and 
Yanikkaya  (2011),  considering  three  measures  of  government  effectiveness,  found  how  government 
effectiveness is an important determinant of the growth impact of government spending for developing 
nations while the results appeared mixed for the advanced economies. Pritchett (2000), instead, raises 
the  attention  to  the  fact  that  the  assumption  behind  empirical  research    –  government  is  a  cost 
minimising investor –  is uncertain, as the large country differences in public-sector efficacy is amply 
confirmed  (Dabla-Norris  et  al.,  2017).  Moreover,  further  factors  could  influence  the  expenditure  -
growth linkage, such as the financing resources and country fiscal position (Barrios and Schaechter, 
2008).  

This linkage will also vary according to, inter alia, the return on the specific project being funded, how 
efficiently public funds are used and the extent of the imbalance in the relative share between public 
and  private  capital,  giving  rise  to  diminishing  marginal  returns.  The  microeconomic  literature  on 
“white elephants” (e.g. Keck 1988, Ganuza and Llobet, 2018) explains specific cases of public money 
wastage. 

The paper provides a first indication on the composition, performance (effectiveness) and efficiency of 
public  expenditure  at  the  level  of  countries  and  specific  functions  of  government.  Surveying  these 
three  different  dimensions  of  the  quality  of  public expenditures  across  seven  categories  of spending 
and  each  EU  country  allows  for  some  granularity  in  the  analysis.  Based  successively  on  a literature 
survey, semi-disaggregated composite indicators of performance and an efficiency frontier approach, 
the  analysis  provides  a  rich  set  of  results  on  the  quality  of  public  spending,  giving  indications  on 
where room for improvements appears to be large.  

These analyses represent useful pieces of evidence-based analysis which may provoke or feed into a 
wider policy discussion, although no firm policy conclusion can be mechanically derived. In all rigour, 
this would require in - depth analyses – outside the scope of this paper – focusing on more specific 
expenditure  categories  and/or  countries  taking  additional  aspects  and  qualitative  evidence  into 
account. As a distinctive feature of the paper and in an attempt to capture the rich dimensions of public 
expenditure, we use multiple output indicators when looking at both the effectiveness (performance) 
and efficiency of each functional category of public spending.   

6 

 
The  paper  is  structured  as  follows.  Section  1  presents  a  review  of  the  literature  on  the  relationship 
between each expenditure item and long-run increase of GDP and uses the results thereof to evaluate 
the  quality  of  the  composition  of  public  expenditure  for  EU  countries  over  the  horizon  2007-2016. 
Section  2  measures  the  performance/effectiveness  of  government  expenditure  by  countries  and  over 
time, updating the analysis by Barrios and Schaechter (2008) based on composite indicators. Section 3 
digs deeper and assesses the efficient use of public spending by defining the ‘efficiency frontier’ for 
each functional category of public expenditures using a DEA approach. We also evaluate the change 
in  spending  efficiency  between  2007  and  2016  for  each  country  on  the  basis  of  the  Malmquist 
productivity index. The last section concludes. 

2.  ASSESSING THE COMPOSITION OF PUBLIC SPENDING  

2.1.     THE IMPACT OF PUBLIC SPENDING ON GDP: A REVIEW OF THE LITERATURE  

Table  1  summarises  the  results  of  the  rich  economic  literature  that  examines  the  complex  relation 
between various categories of public spending and economic performance in the long run, the latter 
being measured by the GDP. Both composition and size of expenditures play a role. We have surveyed 
around twenty five papers, focusing either on seminal research or most recent empirical contributions 
for the advanced economies.  

In  terms  of  spending  size,  the  neoclassical  theory  predicts  a  negative  relationship  between  public 
spending and GDP in the medium to long run because of the crowding out of private investment and 
the distortion due to high taxation (Bergh and Henrekson, 2011). This should be distinguished from 
the Keynes-inspired literature on fiscal multipliers, which looks into the short-term positive impact of 
public expenditure on GDP, via its effect on aggregate demand. A reconciliation of the two strands of 
theory comes from the Armey curve (1995) that, shaping an inverted U relationship between the two 
variables, implies the existence of a threshold above which the growth turns negative. 

In  terms  of  spending  composition,  the  neoclassical  growth  literature (Solow,  1956  and  Swan,  1956) 
highlighted the role of physical capital accumulation as one of the economic growth drivers2 (both in 
the  short  and  long  run).  By  contrast,  the  endogenous  growth  theory  (Barro  1991,  Acemoglu  2009, 
Aghion  and  Howitt,  2009)  points  to  other  variables  including  ‘intangible  investment’  in  the  broad 
sense such as public R&D and human capital investment3.  

Empirically  no  consensus  is  found  when  it  comes  to  identifying  the  spending  components  affecting 
economic growth both from economic and functional classification perspective4. The results from the 
literature change across regions, time span or adopted technique. As observed by Barro (1990) both 
sides  of  the  budget  –  expenditures  and  revenue  –  matter  for  growth.  Still,  not  all  the  studies  pay  a 
specific  attention  to  the  different  sources  of  expenditure  financing  but  almost  all  include  in  their 
empirical analysis financing variables to avoid biasing the econometric results.  

Focusing  on  the  expenditure  side,  both  the  economic  and  functional  classification  are  considered.5 
Looking  at  the  economic  composition,  Morozumi  and  Veiga  (2016)  suggest  that  public  capital 

2 The other two were the changes in labour-force availability and exogenous technological progress. 

3 Population dynamics and institutional characteristics were also considered. 

4 See for a survey Gemmell et al. (2013). 

5  The  economic  classification  considers  the  final  use  of  the  expenditure,  such  as  investment,  current  consumption  (e.g. 
intermediate  consumption  and  compensation  of  public  employees),  transfers  (e.g.  social  benefits  or  subsidies)  or  interest 
expenditure.  By  contrast,  the  functional  composition  considers  the  broad  objective  of  a  given  type  of  expenditure  (i.e. 
education, health, general public services, etc.). This functional classification is derived from the OECD Classification of the 
Functions of Government (COFOG), which identifies the purpose for which the funds are used. 

7 

 
                                                           
spending  enhances  GDP,  independently  from  the  financing  sources  (reallocation  from  current 
spending,  increase  in  revenue,  and  budget  deficit  deterioration),  especially  when  governments  are 
rendered accountable. The same positive effect associated with public capital spending is confirmed by 
Bose et al. (2007) while the role of revenues is insignificant.  

A  negative  impact  on  growth  is,  instead,  associated  with  current  spending  (Morozumi  and  Veiga, 
2016)  or  government  consumption  (Romero-Ávila  and  Strauch,  2008,  Alfonso  and  Furceri  2010, 
Gemmell  et  al.,  2011)6.  The  relation  is  still  negative  when  considering,  like  in  Barro  (1991), 
government  consumption  ratio to  GDP netted  out of the  ratio  of  government  spending  on  education 
and defence, retained more representative of public investment than public consumption. 

Table 1. The economic role of expenditure items to growth: a literature overview 

A detrimental role on GDP is also associated with subsidies. Afonso and Furceri (2010) find that their 
impact  is  even  more  negative  than  that  of  government  consumption.  Less  clear  is  the  role  of  social 
transfers:  according  to  Romero-Ávila  and  Strauch  (2008)  and  Afonso  and  Alegre  (2011),  they 
negatively  affect  the  growth  rate  of  GDP  per  capita  over  the  business  cycle,  whereas  Afonso  and 

6 Röger and In’t Veld (2009) and Coenen et al. (2010) running various simulations with DSGE model confirm these results, 
although it may be argued that the negative impact of current spending is derived by construction. 

8 

 
 
                                                           
Furceri  (2010)  and  Cashin  (1994)  show  the  opposite,  for  EU15  and  for  23  developed  countries 
respectively.  

Looking  at  the  functional  composition  of  expenditure,  the  results  are  unclear  for  social  protection 
expenditure, although tilted toward growth-friendliness. Feldstein (1974) and Docquier and Paddison 
(2003) find a growth-impairing impact for pension payments, which would discourage physical capital 
accumulation. On the other hand, Kelly (1997) finds a positive (and occasionally significant) growth-
enhancing  role  of  social  expenditure  over  a  sample  of  73  countries  over  the  1970-89  period,  and 
concludes  that  this  casts  doubt  on  the  often-assumed  absence  of  impact  on  GDP.  The  rise  in 
productivity induced by improved social cohesion and social welfare could explain this positive link. 
Barbiero and Cournede (2013) confirm this result, looking for long-term effects of several expenditure 
items  on  output  growth,  via  an  error-correction  model,  estimated  on  17  OECD  countries  over  the 
period 1970 to 2008.  

Expenditure on education has a positive impact (Glomm and Ravikumar 1997; Blankenau et al. 2007; 
Dissou et al. 2016; Bose et al. 2007). This result is even more pronounced if spending on education is 
financed  by  consumption  taxes  (Blankenau  and  Simpson,  2004;  Voyvoda  and  Yeldan,  2000). 
However, conducting a meta-regression analysis on 57 studies on the effect of education on economic 
growth,  Benos  and  Zotou  (2014)  identify  the  existence  of  a  substantial  publication  selection  bias 
toward a positive impact of education on growth7.   

Expenditure on health also appears to boost growth, either on its own (Wang, 2011; Elmi and Sadeghi, 
2012)  or  in  combination  with  other  expenditure  items  in  order  to  take  into  account  potential 
interdependencies (Barbiero and Cournede, 2013). 

Afonso  and  Alegre  (2011)  show  the  existence  of  a  positive  effect  of  infrastructures  (transportation, 
water,  electric  power,  etc).  In  general,  positive  effects  are  detected  for  investment  in  infrastructure 
such as transport (Canning and Fay, 1993), transport and communication (Easterly and Rebelo, 1993) 
and transport, communication and education (Milbourne et al., 2003). 

The economic effects of military expenditure are more uncertain, although the positive role claimed by 
Barro  (1991)  is  confirmed,  at  least  for  developed  countries,  by  the  meta-analysis  conducted  by 
Altpekin and Levine (2012) on 32 empirical studies.  

In  line  with  the  endogenous  growth  theory  (Romer,  1990),  investment  in  intangible  assets  has  been 
analysed  by  the  empirical  literature.  According  to  Szarowská  (2017),  public  R&D  expenditure  is  a 
significant  determinant  of  long-term  productivity  in  EU  countries.  By  contrast,  it  appears  non-
significant  in  CEE  countries  for  the  period  1997-2008  (Silaghi  et  al.,  2014).  More  generally,  in  a 
survey of empirical literature, Svensson (2008) finds that, although positive, the effect of public R&D 
on productivity is lower than that of private R&D spending.  

The  role  of  housing  expenditure  is  also  unclear.  While  Barbiero  and  Cournede  (2013)  observe  a 
dampening effect the opposite is found by Gemmel et al. (2016). 

Rather  than  focusing  on  a  specific  category  of  expenditure,  some  studies  offer  a  holistic  view, 
comparing the impact on GDP of all of the functional categories of spending. This is also the approach 
taken in this paper (see section 1.2 and section 2). Kneller et al. (1999), Bleaney et al. (2001), Moreno-
Dodson (2008 and Chu et al (2018) distinguish between productive (General public services, Defence, 
Educational, Health, Housing, Transport and communication) and non-productive expenditure (Social 

7 This means that papers showing significant positive effects of education are more likely to be published (Bom and Ligthart, 
2008). If this aspect is taken into account then the growth effect of education is found to be dependent on specific features of 
the studies: the inclusion of certain variables (education enrollment, education spending, political measures, initial output, and 
inflation) as well as estimation methods (cross-section instead of panel). 

9 

 
                                                           
security  and  welfare,  Recreation,  Expenditure  on  economic  services8).  They  identify  a  growth-
enhancing effect of productive government expenditure independently from the source of financing9.  

2.2.   IS THE COMPOSITION OF PUBLIC EXPENDITURE GROWTH-FRIENDLY? 

As mentioned above, the conflicting or equivocal results emerging from the literature make it illusory 
to find an undisputable definition of growth-enhancing spending. We will therefore focus on the items 
identified by a large batch of academic papers as most likely associated with economic growth.  

Considering the economic classification and focusing on EU as a whole, the growth-friendliest item, 
that  is  public  investment,  only  represents  6%  of  total  public  expenditure  in  2017  and  3%  of  GDP 
(Graph 2 and 3). Even adding the expenditure for compensation of employees in education and health 
(5% of GDP), the growth-friendly components will be (around 8%) still far from social benefits (about 
21%  of  GDP),  absorbing  the  main  share  of  the  expenditure  In  addition,  compared  with  2007,  these 
items also increased whereas public investment diminished by ½ pp. Social benefits increased by more 
than  2  %  of  GDP  also  because  of  the  crisis.  Intermediate  consumption  also  rose  though  to  a  lesser 
extent (0.2 % of GDP). Interest expenditure fell by 0.6 % of GDP. In order to assess the quality of the 
expenditure, we need to know their ultimate use, which requires a functional decomposition, as studied 
further below.  

Graph 2.  EU composition of public expenditure: economic classification  

2007

2017

20

15

10

5

0

Subsidies

Interest
expenditure

Investment

Other

Intermediate
comsumption

Compensation
of employees

Social benefits

Source: Commission Spring Forecast 2018.  

We then consider the functional classification, which is more relevant than the economic classification 
to  study  the  quality  of  public  spending  composition.  We  consider,  as  growth-boosting  expenditure 
items,  those  gathering  broad  consensus in  the literature  (see Table  1).  In  particular,  we  will  refer to 
public  spending  on  health,  education  (investment  in  human  capital),  transport  and  communication 
(investment  in  infrastructure)  as  well  as  R&D  (associated  with  innovation  and  technological 
development). We use the second level of Classification of the Functions of Government (COFOG) to 
construct the growth friendly categories, in particular public R&D, which is computed as the sum of all 
R&D spending across all COFOG categories (see Table A.4 in the annex).   

8  This  category  “Expenditure  on  economic  services”  encompasses  the  expenditure  on  fuel  and  energy,  expenditure  on 
agriculture, forestry, fishing and hunting and the expenditure on mining, mineral resources, manufacturing, and construction.   

9  A  similar  distinction,  although  based  on  a  smaller  selection  of  spending  categories  (public  infrastructure  investments, 
education  and  training,  R&D  and  health  care)  is  also  proposed  by  the  European  Commission  (2012),  when  assessing  and 
identifying possible actions to enhance the quality of public expenditures within the "Compact for Growth and Jobs", decided 
by the Heads of State or Government on 28-29 June 2012. 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
 
Graph 3.  Composition of public expenditure by country in 2017 (% of GDP) 

Source: Commission Spring Forecast 2018. 

According  to  COFOG  data,  in  2016  the  spending  on  growth-friendly  functions,  expressed  as  a 
percentage of total expenditure, registered a modest reduction, from 35.5% in 2006 down to 32.6% in 
2016 (Graph 4a). This conceals large differences across countries (Graph 4b). Some countries did not 
experience a substantial change of their spending composition in either direction. Around half of the 
EU countries, mainly central and northern countries (with RO being the exception) actually recorded a 
moderate rise in their share of growth-friendly expenditure (about 1 pp on average).  
Following  these  moderate  changes,  ‘growth-friendly’  expenditure  only  represented  between  25% 
(Cyprus)  and  40%  (Czechia)  of  total  expenditures  in  2016.  While  the  country  variation  is  far  from 
negligible, no clear pattern can be found (Graph 4c).  

Graph  4a.    EU  composition  of  public  expenditure  according  to  growth-friendliness  (Based  on 
COFOG -share of total expenditure) 

Note:  More  growth-friendly:  expenditure  on  education,  R&D,  health,  transport  and  communication;  less 
growth-friendly: general public services (excluding payments on debt transactions), public order and safety, 
defence,  economic  affairs,  environment  protection,  housing  and  community  amenities,  recreation-culture 
and religion; Interest expenditures: payments on debt transactions. 

Source: Eurostat.  

11 

 
 
 
 
 
 
 
 
 
Graph 4b. Developments in growth-friendliness of public spending (2007-16) by country (%GDP) 

2

0

2
-

4
-

IE PT HR LV HU IT PL EL ES CZ BGROCY UK MT LT SI AT FR SE EE FI NL SK DE BE LU DK

Source: Authors’ elaborations based on Eurostat data. 

Graph 4c.  Growth friendliness composition across EU countries (2016) 

Source: Authors’ elaborations based on Eurostat data. 

3.  LOOKING AT THE PERFORMANCE OF EU EXPENDITURE 

We  focus  now  on  the  performance  (put  another  way  effectiveness)  of  public  expenditure  using 
COFOG  categories.  With  this  term  we  mean  the  general  ability  of  public  expenditure  to  reach  its 
various  policy  targets  by  funding  a  large  quantity  or  a  high  quality  of  public  services.  The  latter  is 
represented by a set of output variables that are associated with a positive effect on growth according 
to  the  economic  literature.  Output  variables  could  capture  performance  in  purely  quantitative  terms 
(e.g. proportion of young population with tertiary education) or in more qualitative terms (PISA score 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
for primary education or survey-based perceived quality of education). All the variables are rescaled 
so that a high level of output is associated with a good performance.  

3.1.     DATA AND METHODOLOGY 

Following Barrios and Schaechter (2009), we compute a composite indicator, composed of seven sub-
indicators:  the  expenditure  composition,  education,  health,  R&D,  public  infrastructure,  public  order 
and safety and general public service. (Table A.1 in the annex). While the first sub-indicator largely 
depends on productive spending as presented in the previous paragraph, the other six composite sub-
indicators measure the performance of six individual categories of expenditure with output variables 
that are directly linked to the expenditure in the given category. As an example, in case of education 
we consider 5 outputs: PISA total score, educational attainment, youth educational attainment, early 
school leavers and quality of the educational system10.  

A  caveat  is  that  a  high  level  of  output  (capturing  the  quantity  or  quality  of  public  services)  can  be 
caused by other factors than public spending, such as structural reforms, the existence of an adequate 
regulation and an appropriate budgetary governance.  

Methodologically, the indicators of output should be distinguished from final outcomes (e.g. growth, 
productivity, quality of life) or environmental variables (e.g. the business cycle, behavioural changes 
in health consciousness, situation in the labour market affecting the return of educational investment), 
which  can  affect  both  output  and  outcome.  In  effect,  the  link  between  spending  categories  and 
economic growth/other final outcomes or the impact of environmental variables are more difficult to 
establish. The effect of these output indicators on the ultimate policy objectives (e.g. growth, quality 
of life)  is  not directly  observed  empirically  but  rather  based  on  theoretical  reasoning. The choice  of 
using direct output variables is also motivated by their availability and relatively easy measurement. 

Let  us  take  the  important  and  complex  example  of  education,  theoretical  contributions  emphasise 
different mechanisms through which education affects economic growth (Hanushek and Woessmann, 
2008;  European  Commision,  2010).  Education  improves  the  human  capital  of  the  labour  force, 
affecting  labour  productivity  and  pushing  toward  a  higher  equilibrium  output  level.  In  endogenous 
growth  theories,  education  increases  the  innovative  capacity  of  the  economy  and  thus  promotes 
growth.  In  particular,  education  facilitates  the  implementation  of  new  technologies.  Going  to  the 
specific outputs of education, according to the literature, if quantity measures are relevant (educational 
attainment,  youth  educational  attainment,  early  school  leavers),  the  quality  measures  are  even  more 
(Pisa score, quality of the educational system). Using test scores as a measure of labour-force quality 
in a sample of 31 countries, Hanushek and Kimko (2000) find that a one-standard-deviation increase 
in  the  quality  of  schooling  is  associated  with  an  increase  in  growth  seven  times  larger  than  the  one 
associated  with  a  one-standard-deviation  increase  in  the  quantity  of  schooling.  In  this  respect,  also 
Benavot (1992) shows how larger emphasis in science and mathematics in the school curriculum can 
positively influence the development of the economy. The Global Competitiveness Index of the World 
economic forum also combines qualitative aspects with quantitative measures (secondary and tertiary 
enrolment  rates).  Moreover,  human  capital  spillovers  may  increase  productivity  beyond  the  direct 
effect of education and can interact also with other outputs. Indeed, a better education leads to more 
informed  decisions,  affecting  both  personal  choices  (i.e.  health,  political  participation,  crime 
participation, etc.) and non-cognitive skills and attitudes (i.e. risk aversion, motivation, etc.) that can 
influence economic choices.  

10 In this section, while replicating the composite indicator, we will focus on its sub-components measuring the performance 
of  specific  spending  categories.  The  sub-indicator  relative  to  the  spending  composition  will  also  be  displayed  for  sake  of 
completeness:  its  developments  largely  confirm  the  analysis  of  growth-friendly  spending  composition  presented  in  the 
previous  section.  In  contrast  with  the  others,  this  composite  sub-indicator  measuring  the  growth-friendliness  of  the 
composition of public expenditure is constructed using policy input indicators, i.e. aggregates directly controlled by the policy 
maker. 

13 

 
                                                           
The  composite  sub-indicators  and  the  overall  composite  indicator  cover  the  28  EU  Member  States 
over  the  period  2007  to  2016.  This  period  was  largely  determined  by  the  availability  of  data. 
Moreover, as shown in Table A1 in annex each sub-composite indicator of performance is based on 
the same variables as used by Barrios and Schaechter (2009) except for the variables that have been 
discontinued  and  therefore  replaced  by  related  variables  from  different  data  sources.  These  were 
selected  on  the  basis  of four  statistical  properties:  statistical  reliability,  country  coverage,  timeliness 
and time  coverage. The  data  used  here are  normalised  as  done in the  Lisbon  methodology  Working 
Group (LIME) assessment framework (LAF)11. This approach allows for an easy interpretation of the 
results.  Specifically,  each  variable  is  standardised  with  respect  to  its  cross-country  average  and 
standard  deviation.12  To  facilitate  the  reading  and  interpretation  of  the  indicator,  the  LIME 
methodology  (LAF)  multiplies  the  standardised  value  by  10  as  a  magnifying-glass  coefficient  (see 
Wöhlbier et al. 2018, for an application to tax structure). This resulting score is trimmed for outliers 
beyond -30 and 30. Assuming a normal distribution of the indicators allows a simple interpretation of 
the score, as displayed in Table 2.  

Table 2. Distribution and classification of scores 

Continuous score 

Classification of score 

10 < x < = 30 

"very good" 

4 < x < = 10 

(over one standard deviation above the mean ) 
(≈ top 15% of the normal distribution i.e. 1st sextile) 
"good" 

(between 0.4 and 1 standard deviation above the mean) 
(≈ top 35% of the normal distribution minus top 15% i.e. 2nd sextile) 

- 4 < x < = 4 

"average" 

(between -0.4 and 0.4 standard deviation around the mean) 
(almost one third of the normal distribution around the mean i.e.  3rd & 
4th sextiles) 

- 10 < x < = - 4 

"poor" 

(between 0.4 and 1 standard deviation below the mean) 
(≈ bottom 35% of the normal distribution minus bottom 15% i.e. 5th 
sextile) 

- 30 < = x < = -10 

"very poor" 

(under one standard deviation above below the mean ) 
(≈ bottom 15% of the normal distribution i.e.  6th sextile) 

Source: European Commission (2008); Barrios and Schaechter (2009); Wöhlbier et al. (2018). 

The  methodology  behind  the  construction  of  the  overall  composite  indicator  and  its  sub-indicators 
follows  the  one  developed  by  Barrios  and  Schaechter  (2009)  with  one  big  difference  though.  All 
variables are standardised with the average and standard deviation frozen in the year 2007 in order to 
turn this indicator of the “relative” performance of countries for each given year into an indicator apt 
to  measure  the  evolution  of  their  performance  over  time  (with  the  performance  of  2007  taken  as  a 

11 See European Commission (2008). 

12 The standardised value is defined by the value of the indicator considered minus its mean and then divided by its standard 
deviation.  The  standardisation  parameter  is  the  unweighted  EU15  average  and  unweighted  EU15  standard  deviation.  The 
choice of EU15 is meant to remove the impact of the gradual catching-up process of post-2004 EU countries. The unweighing 
gives  equal  weights  to  policy  choices  irrespective  of  the  countries'  size.  Otherwise,    this  aggregate  indicator  risks  being 
largely tautological, since the LAF score is defined in relative terms compared with the EU average and standard deviation: so 
the EU has by construct an “average performance” . On way to go about the issue is to “freeze” the weights in 2007 level, as 
done here. As in Barrios and Schaechter (2009), the EU average corresponds to EU15 (before 2004 accessions), since more 
recently acceded Members States are still under catching-up process. 

14 

 
 
                                                           
 
benchmark)13. The resulting scores are then aggregated by expenditure category. We considered three 
standard  aggregation  methods  (unweighted  average,  random  weighting  and  factor  loadings  coming 
from a principal component analysis), which all yielded very similar results. Therefore, results will be 
discussed based on the principal component analysis method14, which has the advantage of relying on 
empirical observations rather than assumptions about the weights. The merit of this approach is also to 
ensure  comparability  with  past  results  from  Barrios  and  Schaechter  (2009).  The  overall  composite 
indicator  corresponds  to  the  average  of  the  value  of  composite  sub-indicators  across  all  spending 
categories.  

3.2. 

EVOLUTION IN EXPENDITURE PERFORMANCE IN THE EU AND THE EURO AREA AS 
A WHOLE  

The performance of public expenditures is found to have only marginally improved in the EU and the 
euro  area over  the  2007-2016  decade,  as  captured  by  the  overall  composite  indicator (see  Graph  5), 
with  some  reduction  during  the  Great  Recession,  as  could  be  expected.  The  indicator  for  the  EU 
remains somewhat below that of the euro area over the full horizon.  

Graph 5. Performance of Public Expenditures in the EU28 and EA19 - 2007 to 2016 

Source: Authors’ elaborations. 

As  regards  the  evolution  over  time,  the  improvement  in  the  indicator  before  2009  largely  reversed 
during the crisis with a modest recovery as of 2014. As a result, the overall spending performance in 
2016 turns out to be slightly above its 2007 level for both the EU and the euro area. At the same time, 
such changes in the composite indicator might be partly driven by the strong cyclicality of some of the 
variables, such as e.g. public investment. 

Looking at the evolution of the sub-indicators over time, the analysis suggests a broad improvement in 
performance in the growth-friendly categories of public expenditure (see Graph 6). Looking closer, the 
largest improvement regards health-related spending. More moderate improvement is also observable 

13  Otherwise,    this  aggregate  indicator  risks  being  largely  tautological,  since  the  LAF  score  is  defined  in  relative  terms 
compared with the EU average and standard deviation: so the EU has by construct an “average performance” . On way to go 
about the issue is to “freeze” the weights in 2007 level, as done here. As in Barrios and Schaechter (2009), the EU average 
corresponds  to  EU15  (before  2004  accessions),  since  more  recently  acceded  Members  States  are  still  under  catching-up 
process. 

14 In this case the factor loadings of the first factor are taken as weight. 

15 

 
 
 
 
 
 
 
 
 
 
 
                                                           
 
for spending on education, public R&D and to an even lesser extent on infrastructure. By contrast, the 
indicator of performance deteriorated in the area of public order and general public services. A similar 
pattern  is  found  for  the  euro  area.These  positive  developments  occurred  against  the  backdrop  of 
deterioration  in  the  growth-friendliness  of  the  spending  composition,  largely  driven  by  the  drop  in 
public investment(see also Graph 4a in the previous section). This seems to suggest that the efficiency 
of growth-friendly public spending more than offset the decline in their size. This will be analysed in 
the next chapter. 

Graph  6.  Growth-friendliness  and  performance  in  specific  spending  areas  in  the  EU: 
change over the decade 2007 – 2016 (bars) 

12

10

8

6

4

2

0

-2

-4

-6

-8

Composition

Education

Health

R&D

Infrastructure Public order

General
services

Source: Authors’ elaborations. 

Overall,  the  results  are  encouraging.  Even  if  the  overall  performance  of  public  expenditure  did  not 
improve  much over a decade, the performance of growth-friendly spending increased over the same 
period  (especially  for  education),  despite  the  reduction  in  their  total  size.  These  findings  remain 
indicative though. Some of the sub-indicators may react more slowly to a reduction in spending, given 
implementation lags and delayed impacts. Still output changes can also refer to factors other than the 
expenditure  but  still  under  government  control  (i.e.  structural  reforms,  regulations  etc.).  Similarly, 
some  sub-indicators  might  be  more  influenced  by  factors  beyond  the  control  of  the  government. 
Further analysis outside the remit of this study would be required to assess the precise nature of these 
changes and the role of governments in influencing them. 

3.3 .    RESULTS AT MEMBER STATE LEVEL 

Country-specific  results  show  some  dispersion  but  also  suggest  scope  for  improvement  in  the 
performance of public expenditures in the EU. In this respect, for the majority of the EU countries, no 
major changes have occurred between 2007 and 2016. Specifically, most Member States remain in the 
category they were in the pre-crisis years (Graph 7).  

On the positive side, Luxembourg and Ireland show an improvement passing from average [-4; +4] in 
2007 to good performance [+4; +10] in 2016. At the same time, four Member States (HR, HU, SK and 

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
DE) register a deterioration: the first three move from the 'poor' category to the 'very poor' category, 
while Germany shifted from the 'good' to the 'average' category. 

Graph 7.  Composite indicator score by country: 2016 vs. 2007  

         a) 2007 

b) 2016 

very good
good
average
poor
very poor

c) 2007 based only on growth-friendly spending        d) 2016 based only on growth-friendly spending 

very good
good
average
poor
very poor

Source: Authors’ elaborations. 

Compared  with  the  aggregate  efficiency,  the  situation  becomes  much  more  favourable  when 
considering  the  evolution  of  the  performance  of  growth-friendly  public  spending  by  country.  Four 
Member  States  continue  to  record  a  good  performance  (DE,  FR,  NL  and  SE).  The  performance  of 
seven Member States increased from average to good (BE, CY, ES, IE, IT, FI and MT), while AT, LU 
and  the  UK  continued  to  display  an  average  performance.  The  performance  of  five  Member  States 
improved from poor to average (CZ, GR, PL, PT and SI). The performance of three Member States 
improved from very poor to poor (BG, HR and LV). No Member State experienced a deterioration in 
their performance.  

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
As regards specific expenditure categories, no country is found to be among the best performers in all 
seven spending areas, with four countries (BE, DK, NL, SE) standing among the ten best performers in 
five out of seven areas (see Graph 8). Eight Member States are not among the ten best performers in 
any  spending  area,  which  suggests  clear  scope  for  improvement.  Again,  the  situation  appears  much 
more  favourable  for  growth-friendly  spending  areas  (red  dots).  Seven  countries  are  among  the  best 
performers in three growth-friendly spending areas out of four (AT, BE, DE, DK, FI, NL and SE). 

Graph 8. Number of specific public expenditure areas with top ranking, 2016 

Note:  The  bar  shows  the  number  of  sub-composite  indicators  for  which  Member  States  rank  among  the  ten  best 
performers in  2016.  The dot instead  shows  the  number  of productive  sub-composite indicators (Education, Health, 
R&D and Infrastructure) for which Member States rank among the ten best performers in 2016 

Source: Authors’ elaborations. 

4.   EFFICIENCY OF PUBLIC EXPENDITURE 

Public spending can affect an impact on the aggregate economy. The size of this effect depends also on 
the efficient way the resources are used. For example, Gonand (2007) refers to the impact on economic 
growth  of  increased  efficiency  of  public  spending  in  primary  and  lower-secondary  education.  More 
generally, Angelopoulos et al. (2008) find evidence on how government efficiency critically affects the 
relation between fiscal size and economic growth.  

Efficiency designates the optimal use of resources allocated to produce a certain output (Coelli et al., 
2005)15.  Simply  put,  this  is  the  ability  to  achieve  an  assigned  objective,  without  waste.  Public 
expenditure  is  considered  efficient  if,  given  the  available  technology,  the  inputs  allocated  to  the 
provision  of  public  goods  or  services  produce  the  highest  possible  output  performance.  This  is  the 
output-orientated definition. Alternatively, public expenditure is considered efficient if a given level of 
output  performance  is  produced  with  the  least  possible  resources.  This  is  the  input-orientated 
definition, which is used in this paper and applies best to public policy: given the scarcity of public 

15 See also for an overview of the efficiency definition and its measures Sickles and Zelenyuk (2019). The link among the 
inputs (specific categories of public expenditure) and the outputs is based on the literature or on theoretical reasonings. For 
example, in the case of the health category, Jaba et al (2014) find a significant relationship between health expenditures and 
life  expectancy.  Similarly,  Jackson  (2018),  by  surveying  the  literature  on  the  relation  between  spending  and  educational 
attainment, claims that spending matters in order to obtain better results. 

18 

 
 
                                                           
 
money and the distortionary nature of taxation, the key question for policymakers is how to reach a 
given policy objective, while saving public money. Public expenditure can turn out to be efficient in a 
country  with  a  relatively  low  level  of  output  since  it  devotes  very  limited  resources  to  it.  Inversely, 
public  spending  can  turn  out  inefficient  in  a  country  with  a  relatively  high  level  of  output  if  the 
provision of the output is very costly. 

4.1.     METHODOLOGY AND DATA   

4.1.1.   Using two complementary empirical methods 

For every function we compute i) the relative efficiency of public spending using EU countries data by 
using non-parametric approach (Data Envelopment Analysis-DEA16) on the latest available data at the 
time of the study (2016) and ii) the Malmquist productivity index to measure the increase or decline in 
efficiency compared to the pre-crisis period17 (2007).  

First, we use the DEA18 to compute an efficiency frontier for the 6 functional categories of spending 
defined by the first level of the COFOG functional classification and used in the previous section19. 
According  to  Mandl  et  al.  (2008),  looking  at  efficiency  by  spending  areas  is  more  effective  than 
considering  the  overall  public  spending  efficiency,  especially  when  dealing  with  cross-country  data. 
Based  on  linear  programming20,  DEA  captures  efficiency  in  relative  terms,  i.e.  compared  to  other 
observations. The comparison of input-output combinations across Member States allows us to draw a 
frontier of best-practice countries and to assess the position of Member States relative to this frontier, 
for every category of expenditure.  

In particular, we focus on the technical efficiency (Debreu, 1951 and Farrell, 1957, Sherman and Zhu, 
2006),  which implies that resources  are  not  wasted in  producing  a  unit  of  output. The  nature  of  the 
units under analysis (governments), the relatively broad definition of the government functions as well 
as the lack of explicit prices, for both input and output, are the reasons behind the choice of a non-
parametric  method.  Alternative  approaches  (e.g.  stochastic  frontier,  least  squares  econometric 
production  models,  total  factor  productivity  indices)  would  rely  on  parametric  assumptions  and/or 
would aim at capturing absolute efficiency by estimating an unobserved production function. A merit 
of  this  non-parametric  methodology  is  that  it  does  not  require  any  assumption  regarding  either  the 
functional  form  linking  inputs  to  outputs  or  data  distribution.  It  relies  only  on  general  axioms  of 
production theory such as: monotonicity, convexity, and homogeneity21. In addition, it allows the use 

16 See for an evolution of the Dea methods Charnes et al., (1978), Banker, et al. (1984), Boussofiane et al., (1991) and Yong –
Bae and Choonjoo, (2010). 

17 For expenditure education the comparison is made with 2005. 

18  See  also  on  this  topic:  Boussofiane  et  al.,  (1991)  and  Yong–Bae  and  Choonjoo,  (2010).  There  are  two  types  of  DEA 
models:  input-oriented  model,  looking  for  minimisation  in  inputs,  for  given  output  levels,  and  output-oriented  model, 
maximising outputs for given input values. 

19 Please see Cepparulo et al. (2016) for a first attempt in this direction.  

20  Suppose  that  there  are  N  decision-making  units  (here  countries),  each  producing  m  outputs  and  n  inputs.  The  decision-
making unit j uses the input vector xj to produce the output vector yj. The shadow price vectors (i.e. weights) for inputs and 
outputs are unknown and given by the vectors u and v. Then the efficiency (productivity) θ of the unit j will be the result of 
the following optimisation problem: 

𝑀𝑀𝑀𝑀𝑀𝑀𝑢𝑢 𝑣𝑣 𝜃𝜃 =

′

′

𝑢𝑢
𝑣𝑣

𝑦𝑦𝑗𝑗
𝑀𝑀𝑗𝑗

; 

𝑦𝑦𝑖𝑖
𝑥𝑥𝑖𝑖 ≤ 1 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑤𝑤 = 1,2, … . 𝑁𝑁

subject to  
𝑢𝑢

′

′

𝑣𝑣

21 As a working assumption, we consider EU countries as political homogenous units. 

𝑢𝑢 ≥ 0 𝑀𝑀𝑎𝑎𝑎𝑎 𝑣𝑣 ≥ 0

19 

 
                                                           
 
 
 
of  different  units  of  measurement  and  it  does  not  impose  any  limits  on  the  number  of  inputs  and 
outputs to be considered.  

Graphically, in the case of a single input-output, output (performance) is measured along the vertical 
axis in the graph, while input resources are on the horizontal axis, as illustrated in Graph 9. Efficient 
countries are located along the frontier, while inefficient countries are represented by dots underneath 
the frontier. Relative distance to the frontier defines the inefficiency or ‘efficiency gap’. This implies 
that a move toward the estimated frontier corresponds to a reduction of inputs with unchanged outputs. 

We compute robust efficiency scores22 to correct estimations by sample bias (Simar and Wilson, 2000; 
Simar  and  Wilson,  1998)  which  implies  that  the  sample  may  fail  to  incorporate  the  most  efficient 
governments,  causing  upwards  biased  estimates  of  input-oriented  efficiency.  Boostraps  at  different 
number of replications (500, 1000, 2000) and intervals of confidence (alpha = 0.1, 0.05 and 0.01) are 
performed in order to check for consistent results. 

Graph 9.  Data envelopment analysis: estimating the efficiency frontier 

Source: Authors’ elaborations. 

Second, we also use another technique to measure the development in efficiency over time, since the 
DEA remains a static approach. We compute the input oriented Malmquist productivity indices-MPI 
(Malmquist,  1953;  Caves,  Christensen  and  Diewert,  1982).  It  is  a  dynamic  non-parametric  method 
comparing  both  the  DEA-generated  efficiency  frontiers  and  the  distance  to  them  between  two  time 
periods. It measures the change in the overall efficiency of spending. A change in efficiency can be 
due  not  only  to  a  technical  efficiency  change  (TEC-reaching  the  efficiency  frontier)  but  also  to  a 
technological change (TC-a shift of the efficiency frontier), the index is defined as follows23: 

𝑀𝑀𝑀𝑀𝑀𝑀𝑡𝑡,𝑡𝑡+1 = 𝑇𝑇𝑇𝑇𝑇𝑇 ∗ 𝑇𝑇𝑇𝑇 = �

𝑡𝑡+1
𝐷𝐷𝑡𝑡+1
𝐷𝐷𝑡𝑡

𝑡𝑡 � ��

𝑡𝑡
𝐷𝐷𝑡𝑡+1
𝑡𝑡+1
𝐷𝐷𝑡𝑡+1

𝑡𝑡
𝐷𝐷𝑡𝑡
𝑡𝑡+1�
𝐷𝐷𝑡𝑡

22 We use the package rDEA — Robust Data Envelopment Analysis (DEA) for the software R. 

23 A simple form is: 

𝑀𝑀𝑀𝑀𝑀𝑀𝑡𝑡,𝑡𝑡+1 = �

𝑡𝑡
𝐷𝐷𝑡𝑡+1
𝑡𝑡
𝐷𝐷𝑡𝑡

𝑡𝑡+1
𝐷𝐷𝑡𝑡+1
𝑡𝑡+1
𝐷𝐷𝑡𝑡

20 

 
 
 
                                                           
 
 
 
where  D  represents  the  distance  of  each  observation  unit,  in  the  input  output  space,  the  subscript 
represents the set of inputs/outputs at a specific time (t or t+1) and the superscript represents the time 
when  the  frontier  is  set  (t  or  t+1).  t+1  denotes  here  a  subsequent  point  in  time,  not  necessarily  a 
consecutive year. The technical efficiency (TEC) change compares the distance to the frontier between 
t  and  t+1,  even  if  the  referential  -  the  technological  frontier  -  has  shifted  in  the  meantime.  The 
technological  change  (TC)  compares  the  distance  of the  observed  unit  in  t  to the  frontierbefore  and 
after the frontier shifts. It also compares the distance to the frontier in t+1 before and after the frontier 
shifts,  and  takes  the  average  of  these  two  measures  to  capture  the  impact  of  the  shift  in  the 
technological frontier.24 

4.1.2.    Data source and treatment: using multiple output variable 

Measuring  the  efficiency  of  public  spending  carries  a  number  of  difficulties  as  regards  the  timely 
availability of data as well as the measurement of both input and output variables. In addition, DEA 
approach is heavily dependent on the specification and definition of inputs and outputs (Sarkis, 2007). 

Following the literature (Afonso and Aubyn, 2008; Afonso et al., 2010; Dutu and Sicari, 2016; Afonso 
and Kazemi, 2017), we consider public expenditure, measured in euro, as input. Expenditure data are 
mainly taken from the COFOG database (at the first level of disaggregation) but we also use other data 
sources  as  far  as  available.  Given  that  the  performance  indicators  are,  in  many  cases,  of  structural 
nature, they are likely to have been influenced by past input over a longer time horizon. Therefore, for 
the DEA computations, when available (see Table A2), input variables were calculated as the average 
expenditure on the category over the past ten years. Besides, where the outputs can be influenced also 
by  private  input,  e.g.  in  the  case  of  health  and  R&D,  total  expenditure  (public  and  private)  is 
considered.  

Regarding  the  output  of  public  spending,  there  is  an  inherent  difficulty  in  measuring  it.  One  has  to 
choose indicators that are considered to be relatively strongly influenced by public spending and less 
by external, so-called environmental factors. In several cases, the indicators used reflect the outcome, 
rather than the output strictly speaking, which cannot be measured directly. Therefore, the choice of 
output is based on the choice of reasonable indicators of output, coming out of the literature and also 
frequently monitored by policy  makers when implementing concrete policies. Therefore, we use the 
database by Barrios and Schaechter (2009), which propose a set of carefully chosen indicators, in line 
with existing literature. In contrast with Afonso and Schuknecht (2019) concentrating on single output 
variables,  we  assess  expenditure  functions  based  on  multiple  individual  output  variables  (see  Table 
A1)25.  

To avoid scaling issues and ensure data comparability, the data are mean-normalised. This is required 
since multiple outputs are used. Then, as the analysis is based on the working principle that outputs are 
better  when  larger in  value,  we  take  the inverse  where  the  output  variables  are built in the  opposite 
way (such as the early school leavers’ outputs). Besides, as the DEA method assumes the absence of 
random noise, measurement errors, and outlier cases in the data, the detection of outliers is performed 
by adopting a non-parametric approach proposed by Daraio and Simar (2007). Finally, as the choice 
and  the  number  of  inputs  and  outputs  is  also  relevant  in  discriminating  between  efficient  and 
inefficient units, we check, for each expenditure category, the compliance of our sample size with the 

24 In more technical terms, TC is the geometrical unweighted average of two ratios. The first ratio compares the distance of an 
observed unit in the first period t vis-à-vis the ‘old’ frontier (in t) with the distance of the same unit in the same period t vis-à-
vis the ‘new’ frontier (in t+1). The second ratio compares the distance of the observed unit in the second period t+1 vis-à-vis 
the ‘old’ frontier (in t) with the distance of the same unit in the same period t+1 vis-à-vis the ‘new’ frontier (in t+1). 

25 In short, a category of spending, as inputs, could be related to a combination of outputs, with possible trade-off between 
individual outputs variables 

21 

 
                                                           
rules  of  thumb  emerged  in  the  literature26  (Boussofiane  et  al.,1991;  Golany  and  Roll,1989;  Bowlin, 
1998). 

4.2.  

RESULTS 

To  recall  and  to  ease  the  interpretation,  a  DEA-based  score  of  one  points  to  a  (relatively)  efficient 
country,  located  at  the  frontier.  A  score  between  zero  and  one  signals  an  inefficient  country.  The 
difference  between  1  and  efficiency  score  represents  the  efficiency  gap,  that  is,  the  room  for 
improvement: how much a country can reduce the input to get the same output, that is a given quantity 
or  quality  of  public  services.  Therefore,  we  are  not  here  measuring  the  quantitative  or  qualitative 
performance of spending. We only consider that the government has chosen to provide a given level of 
public services, which should be produced efficiently.  

A  Malmquist  productivity index  greater  than  one  will  indicate  progress in efficiency  (covering  both 
technical efficiency and technological progress) while a value lower than one will imply an efficiency 
decline. An index equals to one refers to the case when no change occurred. 

Looking at the efficiency of specific spending categories, infrastructure (Graph 10, d) appears the type 
of expenditure for which major efficiency gains can be envisaged (larger differences between 1 and 
the  DEA-efficiency  score).  Several  eastern  European  countries  could  significantly  increase  the 
efficiency of their infrastructure spending. For those countries, no improvement is observed since the 
pre-crisis  period  (2007),  with  the  exception  of  Czech  Republic.  Only  10  countries  have  Malmquist 
productivity  index  larger  than  one  (in  grey  in  Table  3).  By  contrast,  the  majority  is  affected  by  a 
decline in efficiency, mainly related to an adverse shift in the technological frontier (see Table A3 for 
the decomposition of the Malmquist productivity index of Table 3). This result can be related to the 
large investment cutback during the crisis. 

Other  spending  categories  where  inefficiencies  (average  distance  to  the  frontier)  are  still  relatively 
large are research and development, public order and safety and general public services (Graph 10). In 
the last one, progress has been already observed since 2007 for the majority of the EU countries (Table 
3), largely attributable to technical efficiency gain.  

Lower  albeit  still  large  are  inefficiencies  in  the  health  sector  (Graph  10a)  affecting  mostly  northern 
European countries, confirming previous findings (Dutu and Sicari, 2016). Efficiency increase regards 
only  10  countries  (driven  mostly  by  higher  technical  efficiency),  while  the  other  countries  show  a 
decline, connected to a loss in efficiency of scale. 

By  contrast,  education  for  secondary  and  tertiary  education  (when  considering  outputs  other  than 
Pisa), is the spending category where inefficiencies are the lowest on average. Primary education (Pisa 
scores) is the spending category where the highest number of countries recorded an increase in overall 
efficiency from 2007.  

Finally,  looking  at  the  recent  progress  made  in  terms  of  spending  efficiency  (Table  3),  it  should  be 
noted  that  the  majority  of  the  cases  occurred  in  growth-friendly  spending  areas,  especially  in 
education, health and infrastructure (around 40 out of 65). 

DEA-based  scores  (Graph  10)  also  allow  for  a  first  tentative  assessment  of  spending  efficiency  by 
country, which should be confirmed, nuanced or invalidated by further in-depth analysis, beyond the 
scope of this quantitative study. Regarding the five best DEA-based scores (in descending order), no 
country is efficient in all the public spending categories: only Cyprus, Germany, Finland, Sweden and 
Malta are comparatively efficient in three spending categories while over two thirds of the countries 
are at least relatively efficient in one sector.  

26 See for more details on this aspect Sarkis (2007) 

22 

 
                                                           
Cyprus, Spain, Malta, Bulgaria and Portugal seem relatively efficient regarding health care spending. 
Regarding  education  spending,  (relatively)  efficient  countries  are  -  in  descending  order  -  Germany, 
Sweden, Finland, Denmark and Estonia for primary education (captured by PISA score) and Sweden, 
Lithuania,  Luxemburg,  Czechia,  Finland  and  Ireland  for  secondary  and  tertiary  education  (taken 
together). These results except for CZ are also confirmed by previous findings (Canton et al, 2018). 
Cyprus, Germany, the United Kingdom, Malta and France appear relatively efficient for infrastructure. 
Estonia,  Luxembourg,  the  United  Kingdom,  Lithuania  and  the  Netherlands  seem  to  show  high 
(relative)  efficiency  regarding  general  public  services.  Austria,  Germany,  Finland,  Belgium  and 
Sweden  are  relatively  more  efficient  regarding  public  order  spending.  Regarding  R&D,  the  front-
runners are Cyprus, Malta, Bulgaria, Latvia and Slovakia.  

Graph 10.  Bias-corrected efficiency for each expenditure category27 

a)  Health 

 b) Education (PISA)  

1

9
.

8
.

7
.

6
.

lv dk lu de nl

ie be lt se at

fi sk fr ee hu hr cz pl ro it si el pt bg mt es cy

lu cy uk pt

it bg ie mt

lv sk fr pl es be si

num

lt nl at cz ee dk fi se de

95% confidence interval

corrected-bias efficient score

95% confidence interval

corrected-bias efficient score

c)  Education (other)  

d) Infrastructure 

8
.

6
.

4
.

2
.

0

9

.

8

.

7

.

6
.

5
.

1

9
.

8
.

7
.

es it pt ro uk mt at

fr sk bg nl dk hu pl

lv ee cy de si

ie fi cz lu lt se

sk cz hu hr pl bg ro lu ee lv at el be si se pt

fi

ie lt

it nl es dk fr mt uk de cy

95% confidence interval

corrected-bias efficiency score

95% confidence interval

corrected-bias efficient score

e) General public services   

f) Public order  

8

.

6
.

4
.

2
.

8
.

6
.

4
.

2
.

el hu hr it cy be mt pt ro at pl

fr

fi es se si dk de sk bg lv cz ie nl

lt uk lu ee

bg pl uk sk ro ee hr

lv nl

lt hu es fr mt

it

si dk se be fi de at

95% confidence interval

corrected-bias efficient score

95% confidence interval

corrected-bias efficient score

27 Robust efficiency scores shown here are based on 2000 boostrap replications and confidence interval of 0.05.  

23 

 
                                                           
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
g) R&D 

8
.

6
.

4
.

2
.

0

si se fi at dk es cz de ee fr hu it pt be ie lu uk hr nl

lt pl el sk lv bg mt cy

95% confidence interval

corrected-bias efficient score

Source: Authors’ elaborations. 

A majority of countries experienced a deterioration of their efficiency (Malmquist index) in a majority 
of  spending  areas.  Around  half  of  the  EU  countries  displayed  an  improvement  in  two  categories  or 
less, meaning that they experienced some deterioration in at least 5 categories out of 7. One third of 
Member  States  show  an  improvement  in  3  out  of  7  categories,  namely  Austria,  Bulgaria,  Germany, 
Estonia, Croatia, Italy, Latvia, Malta and Poland. This means that they experienced a deterioration in 
efficiency  in  4  categories out  of  7. The  few  exceptions  were  Spain,  which  shows  an  efficiency  gain 
(Table 3, cells in grey) in 5 out of 7 categories since the pre-crisis period, and Cyprus, Portugal and 
Luxembourg, which display an efficiency gain in 4 out of 7 categories.  

Comparisons  with  previous  studies  are  limited  due  to  differences  in  the  sample,  the  horizon,  the 
approach, the output and input data used. 

Table 3.  Efficiency development (Malmquist productivity index between 2007 and 2016) 28 

Health 
1,01 
0,97 
0,82 
1,03 
0,93 
0,913 
0,89 
1,09 
n.a. 
1,07 
0,93 
1,02 
0,95 
1,05 
n.a. 
1,03 
0,99 
1,05 
0,95 
0,96 
0,94 
n.a. 
1,01 
0,87 
0,90 
1,04 
0,93 
n.a. 

AT 
BE 
BG 
CY 
CZ 
DE 
DK 
EE 
EL 
ES 
FI 
FR 
HR 
HU 
IE 
IT 
LT 
LU 
LV 
MT 
NL 
PL 
PT 
RO 
SE 
SI 
SK 
UK 
Note: in grey the countries showing a gain in efficiency. 
Source: Authors’ elaborations. 

Infrastructure 
0,96 
0,64 
0,99 
1,12 
1,17 
0,58 
0,77 
1,02 
0,57 
1,15 
0,51 
0,95 
0,91 
0,74 
1,25 
0,94 
1,08 
1,22 
0,38 
1,15 
1,03 
0,63 
1,31 
0,13 
0,56 
0,86 
0,49 
0,74 

Public order 
0,91 
n.a. 
1,71 
n.a. 
n.a. 
0,97 
0,45 
2,90 
n.a. 
1,41 
0,61 
0,72 
1,33 
n.a. 
n.a. 
n.a. 
n.a. 
n.a. 
n.a. 
0,51 
0,74 
2,62 
n.a. 
1,84 
0,694126 
n.a. 
2,02 
0,82 

R&D 
0,27 
0,45 
0,94 
0,62 
0,78 
0,27 
0,30 
0,58 
0,90 
0,84 
0,41 
0,34 
1,18 
0,61 
0,53 
0,70 
0,87 
0,80 
0,89 
0,72 
0,33 
0,86 
0,65 
Outlier 
0,26 
0,70 
1.0 
0,57 

Public 
services 
1,22 
1,66 
1,52 
1,60 
0,66 
1,21 
1,36 
0,39 
2,26 
1,52 
1,25 
0,90 
1,64 
1,53 
0,74 
1,29 
1,074 
0,79 
1,12 
1,54 
0,57 
1,22 
1,10 
0,73 
0,53 
1,68 
0,96 
0,61 

Education 
(PISA) 
1,06 
0,96 
n.a. 
n.a. 
1,07 
1,07 
0,96 
n.a. 
n.a. 
1,09 
0,96 
1,02 
n.a. 
Outlier 
0,97 
1,10 
n.a. 
1,07 
1,06 
n.a. 
0,95 
1,08 
1,07 
Outlier 
1,09 
n.a. 
0,95 
0,95 

Education 
(other) 
0,95 
1,12 
1,07 
1,18 
0,93 
1,08 
0,85 
0,94 
n.a. 
0,88 
1,01 
0,98 
n.a. 
0,96 
1,12 
0,88 
0,94 
1,16 
0,97 
1,23 
1,11 
0,86 
1.0 
0,94 
0,96 
0,83 
0,84 
0,98 

28 For education the years of reference are 2005-2015 based on available data. 

24 

 
 
                                                           
 
 
 
 
 
5.  CONCLUSIONS 

The paper provides a first indication on the composition, performance (effectiveness) and efficiency of 
public  expenditure  at  the  level  of  countries  and  specific  functions  of  government.  Reviewing  these 
three  different  dimensions  of  the  quality  of  public expenditures  across  seven  categories  of spending 
and  each  EU  country  allows  for  some  granularity  in  the  analysis.  Based  successively  on  a literature 
survey, semi-disaggregated composite indicators of performance and an efficiency frontier approach, 
the  analysis  provides  a  rich  set  of  results  on  the  quality  of  public  spending,  giving  indications  on 
where room for improvements appears to be large.  

The  overall  results  turn  out  fairly  mixed,  providing  a  nuanced  picture  of  the  quality  of  public 
expenditure in the EU both in terms of level and change. In particular, no Member State is found to be 
among  the  most  performing  (effective) or  efficient  in  all seven  spending  areas. No  country  is  either 
among  the  least  performing  or  efficient  in  all  spending  areas.  No  country  improves  or  worsens  its 
situation  in  all  areas.  This  said  and  based  on  the  indicators  used  in  the  paper,  some  countries  are 
relatively  less  efficient  than  others  in  a  majority  of  spending  areas,  signalling  some  room  for 
improvement.  

Regarding the situation in 2016, the total share of growth-friendly spending (education, R&D, health, 
transport and communication infrastructures) only represents a third of total expenditure. In addition, 
many  countries  still  have  a  large  scope  for  increasing  the  efficiency  of  their  public  expenditure. 
Specifically,  inefficiencies  (average  distance  to  the  efficiency  frontier)  are  still  relatively  large  in 
infrastructure  –  possibly  due  to  widening  public  investment  gap  during  the  crisis  –  and,  to  a  lesser 
extent, in research and development, public order and safety and general public services. Education for 
secondary and tertiary education - an important growth-friendly area - is the spending category where 
inefficiencies are the lowest on average.  

Looking at the change over the decade 2007-2016, the average performance of growth-friendly public 
spending increased in the EU (especially for education), showing a stronger capacity of meeting their 
objectives, although their total size receded somehow in the EU over the same period. This is in line 
with  the  finding  that  the  great  majority  of  the  cases  of  increasing  spending  efficiency  occurred  in 
growth-friendly  spending  areas,  especially in education,  health and  infrastructure.  Primary  education 
(Pisa  scores)  is  the  spending  area  where  the  highest  number  of  countries  recorded  an  efficiency 
increase.  However,  looking  at  all  spending  categories  and  not  only  at  the  growth-friendly  ones,  the 
overall performance  of  public  expenditure  in the  EU  suffered  from  the  Great  Recession and  did  not 
improve  much  over  a  decade.  A  majority  of  EU  countries  also  experienced  a  deterioration  of 
expenditure efficiency in a majority of spending categories. 

Finally, methodological caveats and limitations should be borne in mind. It should be stressed that the 
assessment  of  spending  quality  follows  a  statistical  approach  exploiting  an  existing  database  of 
quantitative indicators. Thus, one should refrain from drawing strong conclusions on the basis of this 
assessment, which only represents a – valuable – first step to trigger an informed economic discussion. 
Concrete  policy  recommendations  should  be  based  on  in-depth  analysis  focusing  on  specific 
expenditure areas. Additional aspects, which are specific to expenditure areas and/or Member States 
and cannot be reflected in this general assessment, need to be taken into consideration. The clear merit 
of this study remains its ability to analyse all spending areas in a comprehensive way and from various 
angles (composition, performance and efficiency).  

25 

 
 
 
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31 

 
 
 
 
 
 
 
 
 
 
 
 
Description 

 Data source 

Output for 
efficiency 
analysis 

ANNEX 

Table A1. List of variables  

dimensions 
(used for the 
performance 
analysis) 

COMPOSITION 
EXPENDITURE 

Public investment (%GDP) 

Public investment (% primary expenditure) 

Public Investment / Public Consumption ratio 

Productive expenditure % GDP (Sum of Public spending on 
transport and communication, R&D, education and health) 
Productive expenditure % primary expenditure (Sum of Public 
spending on transportation and communication, R&D, 
education and health) 

AMECO 

AMECO 

AMECO 

EUROSTAT 

EUROSTAT 

OECD 

EUROSTAT 

EUROSTAT 

EUROSTAT 

WEF 

EUROSTAT  

EUROSTAT 

EUROSTAT  

EUROSTAT 

EUROSTAT 

WIPO 

EUROSTAT 

EUROSTAT 

WEF 

WEF 

WEF 

EUROSTAT  

WEF  

EUROSTAT & CIA 

WORLD BANK 

WORLD BANK. 

WEF 

WEF 

UNODC 

EUROSTAT  

WEF 

WEF 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

PISA total score 

Educational attainment 

  EDUCATION 

Youth educational attainment 

   HEALTH 

Early school leavers 

Quality of the educational system 

Life expectancy at birth 

Life expectancy at 65 

Health adjusted life expectancy - 
Females 
Health adjusted life expectancy - 
Males 
Infant mortality 

Patents granted to residents 

Patent applications 

Number of patent applications 

R&D 

Technological Readiness 

R&D innovation index 

Quality of scientific research institutions.  

Tertiary graduates per inhabitants 

Quality of math and science education  

Length of motorways 

Length of railways 

Fixed line and mobile phone subscribers 

internet users per inhabitants 

Quality of electricity supply 

Overall infrastructure index 

Persons convicted 

Crime reported by police  

Business cost of crime 

Judicial independence 

PUBLIC 
INFRASTRUCT
URE 

PUBLIC 
ORDER & 
SAFETY 

32 

 
 
 
 
 
 
 
 
 
 
     
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Organised crime 

Reliability of police services 

Security property rights 

Persons killed or injured in road traffic accidents 

Irregular payments and bribes  

Corruption perception index 

Favouritism in decisions of government officials  

GENERAL 
PUBLIC 
SERVICES 

Public trust of politicians 

Diversion of public funds 

Burden of government regulations  

Wastefulness of public spending index 

Government effectiveness 

E-government index 

WEF 

WEF 

WEF 

OECD 

WEF 

Transparency 
International 

WEF 

WEF 

WEF 

WEF 

WEF 

World Bank 

UNPACS 

x 

x 

x 

x 

x 

Note: the variables in grey are those only used for efficiency purposes but not part of the list used for the 
computation of the composite indicator. 

Table A2. Efficiency analysis: shorter list of input variables 

INPUT 

ORIGINAL DATA SOURCE 

Per capita expenditure on Education 
expenditure by level of education  

World Bank 

Total R&D expenditures (public+ private) 

World Bank 

Public order 

General public services 

Infrastructure 

Eurostat 

Eurostat 

Eurostat 

Per capita expenditure on health care(public+ 
private) 

World Bank 

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A3. Malmquist index decomposition 

Health 

Public order 

Infrastructure 

R&D 

Public services  Education (PISA)  Education (other) 

TEC 

TC 

TEC 

TC 

TEC 

TC 

TEC 

TC 

TEC 

TC 

TEC 

TC 

TEC 

TC 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

. 

. 

. 

. 

1.0 

1.0 

1.0 

1.0 

0.9 

0.9 

0.9 

1.1 

1.0 

0.9 

1.0 

0.9 

1.1 

. 

. 

1.0 

1.1 

1.0 

1.0 

1.0 

0.9 

. 

. 

1.0 

1.0 

0.9 

1.0 

0.9 

1.0 

0.9 

1.0 

. 

. 

. 

1.0 

. 

0.8 

1.0 

1.0 

1.0 

1.0 

1.0 

1.6 

. 

. 

1.0 

0.5 

2.7 

1.0 

1.0 

1.0 

1.0 

0.9 

1.0 

0.9 

1.0 

1.0 

1.0 

1.0 

1.4 

0.6 

0.7 

1.2 

0.4 

0.9 

2.4 

. 

. 

. 

. 

. 

. 

1.0 

. 

0.9 

1.0 

1.6 

0.8 

1.0 

. 

1.0 

1.8 

0.7 

. 

. 

. 

. 

. 

. 

. 

. 

1.1 

0.9 

0.9 

1.1 

1.0 

1.1 

1.0 

1.1 

1.2 

0.9 

1.1 

1.1 

0.9 

1.1 

1.1 

UK 

. 

. 

1.6 

1.1 

1.6 

1.8 

1.9 

0.9 

1.3 

1.7 

0.9 

1.9 

0.8 

1.6 

1.5 

1.2 

2.1 

1.6 

1.8 

2.0 

0.6 

1.9 

1.7 

1.0 

2.2 

0.2 

0.9 

1.4 

0.8 

1.2 

0.6 

0.6 

0.6 

0.9 

0.6 

1.3 

0.6 

1.0 

0.6 

1.1 

0.6 

0.6 

0.6 

0.6 

0.6 

0.8 

0.6 

1.2 

0.6 

1.3 

0.6 

0.8 

0.6 

0.7 

0.6 

1.6 

0.6 

1.0 

0.6 

1.0 

0.6 

1.2 

0.6 

1.2 

0.6 

1.6 

0.6 

1.2 

0.6 

1.2 

0.6 

0.7 

0.6 

1.2 

0.6 

1.0 

0.6 

0.6 

0.5 

0.6 

1.1 

0.6 

1.3 

0.6 

1.1 

0.5 

0.5 

0.7 

0.6 

0.7 

0.5 

0.5 

0.7 

0.7 

0.7 

0.5 

0.5 

0.7 

0.6 

0.6 

0.6 

0.7 

0.5 

0.7 

0.6 

0.5 

0.7 

0.7 

0.5 

0.7 

0.7 

0.5 

1.2 

1.7 

1.5 

1.7 

0.7 

1.3 

1.4 

0.4 

2.3 

1.5 

1.3 

0.9 

1.7 

1.6 

0.7 

1.2 

1.1 

0.8 

1.2 

1.6 

0.6 

1.2 

1.1 

0.7 

0.5 

1.6 

1.0 

0.6 

1.0 

1.0 

1.0 

0.9 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

. 

. 

. 

. 

1.1 

1.0 

1.1 

1.1 

1.0 

1.1 

1.0 

1.0 

. 

. 

. 

. 

1.0 

. 

. 

. 

. 

1.0 

1.1 

1.1 

1.1 

1.0 

1.1 

1.1 

1.0 

1.0 

1.1 

0.9 

. 

0.9 

1.0 

1.0 

. 

0.9 

1.0 

1.0 

1.1 

1.0 

. 

. 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

1.0 

. 

. 

1.0 

1.2 

1.1 

1.3 

1.0 

1.1 

0.9 

1.0 

0.9 

1.0 

1.1 

1.0 

1.2 

1.0 

1.0 

1.3 

1.0 

1.3 

1.2 

1.0 

1.0 

0.9 

1.0 

1.0 

0.9 

1.0 

0.9 

1.0 

1.0 

0.9 

0.9 

1.0 

0.9 

1.0 

0.9 

1.0 

0.9 

1.0 

0.9 

0.9 

0.9 

0.9 

1.0 

0.9 

1.0 

0.9 

1.0 

1.0 

0.9 

0.8 

0.9 

1.0 

1.1 

1.0 

1.0 

. 

. 

0.9 

0.9 

1.0 

1.0 

1.0 

1.0 

Note: TEC corresponds to technical efficiency change and TC to technological change. 

34 

 
 
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
Table A4. Correspondence table: COFOG, growth-friendly 
expenditure and other spending areas covered by the analysis 

 COFOG 

Growth-friendly expenditure  

Spending areas à la Barrios and Schaechter 
covered by the analysis on  
Performance (PCA)  
& Efficiency (DEA& Malmquist) 
General public service 
R&D 

R&D 
Public order and safety 
R&D 

R&D 

R&D 

R&D 

01 General public Service 
0105 R&D 

02 Defense 
0204 R&D 
03 Public order and safety 
0305 R&D 

04 Economic affairs 
0405 Transport 
0406 Communic. 
0408 R&D 
05 Environmental protection 
0505 R&D 

06  Housing  and  community 
amenities 
0605 R&D 

07 Health 
0705 R&D 
08  Recreation,  culture  and 
religion 
0805 R&D 
09 Education 
0907R&D 
10 Social protection 
1008 R&D 

Transport & Communication 
R&D 

Infrastructure 

R&D 

R&D 
Health 
R&D 

R&D 
Education 
R&D 

R& D 

R&D 

R&D 

R&D 
Health 
R&D 

R&D 
Education 
R&D 

R&D 

35 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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