BLUE-ETS

Transcription

BLUE-ETS

Blue-Ets Project - SSH-CT-2010-244767
BLUE-Enterprise and Trade Statistics
BLUE-ETS
www.blue-ets.eu
SP1-Cooperation-Collaborative Project
Small or medium-scale focused research project
FP7-SSH-2009-A
Grant Agreement Number 244767
SSH-CT-2010-244767
Deliverable 9.2
Dissemination level: PU
Report on decomposable indicators on industrial competitiveness and business
collaboration and CWE
Authors:
Part 1: Rosa Bernardini Papalia, Pinuccia Calia, Carlo Filippucci (UNIBO)
Part 2: Vincenzo Patrizii, Giuliano Resce (UNIFI)
Part 3: Alessandra Righi (Istat)
March 2013
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Deliverable 9.2
Report on decomposable indicators on industrial competitiveness and business
collaboration and CWE
Preface
According to the description of work, Work package 9 “New types of indicators-Applying results” of BLUE-ETS
project is about linking data to indicators and tailoring the former to the latter, not just to generic averages or
dimensions and concepts but in order to develop practices and tools which permit flexibility and choice and, in
particular, greater tailoring of the statistics and their matching with analytical requirements. The activities
carried out have looked at the challenges and the state-of-the-art in new, as yet mostly uncharted or badly
charted fields in order to assess how official statistics can adjust to reflect and catch structural jumps and
change.
Within this framework, Deliverable 9.2, “Report on decomposable indicators on industrial competitiveness and
business collaboration and CWE”, intends to provide an important contribution to new knowledge and
understanding of some specific issues. In particular, the deliverable, in compliance with the main tasks
described in the work plan, has been structured in three parts. The first part, made by Rosa Bernardini Papalia,
Pinuccia Calia, Carlo Filippucci from University of Bologna, deals with the methodological and theoretical
aspects of the measurement of competitiveness issue related to the construction of a multidimensional index;
the second part made by Vincenzo Patrizii and Giuliano Resce form University of Florence, affords the theme of
new indicators of competitiveness explaining the research carried out for the construction of an index for
measuring the role of Public Institutions (and policies) on industrial competitiveness; the third part made by
Alessandra Righi from Istat, deals with the theme of business collaboration and Collaborative Working
Environments focusing on the current initiatives in official statistics to develop new statistical indicators on
business collaboration and Collaborative Working Environment (CWE).
Concerning the competitiveness issue, analysis in the first part deals with the construction of a
multidimensional competitiveness index applied to Italian enterprise micro-data and on the methodologies to
decompose the multidimensional index. The index has been developed within the Information Theoretic
framework following an approach similar to one of the multidimensional inequality index of well-being. The
proposed tool allows analysing multidimensional competitiveness at different level of aggregation (country,
region or sector) with a micro-level foundation. Analysis has shown that new indicators can be developed to
translate the distribution of firms characteristics into measures of competitiveness designed to capture not
only average performance but also the heterogeneity of firm performance. The decomposition of the index
according to some characteristic of the units can supply information on the determinants of competitiveness.
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To this regard, the analysis could be extended to relevant characterization like the degree of
internationalization or innovativeness. Further work has been planned to investigate methods to assess the
relative importance of several potential explanatory factors simultaneously, as opposed to the traditional
decomposition methods.
The analysis in the second part deals with the development and implementation of a decomposable indicator
for public institutions’ contribution to competitiveness. Measuring competitiveness of a country in terms of
final relative prices sheds little light on how those prices are affected by public expenditure. By taking
productivity as a common factor to any index of competitiveness the analysis proposes to assess the role of
public sector policies by measuring the efficiency in public services provision. The methodology, which
integrates data envelopment analysis with principal component analysis, has been applied in a case study for
Italy investigating local public services provision and allowing for territorial as well as service differentiation.
Results show a large territorial variability in Italian local public services productivity and a differentiation in
terms of both levels of government and type of services. The index can provide a measure of effectiveness for
major types of public policies such as Health care, Public Transport, Water and waste management, Education,
Police, etc. This type of analysis allows identifying areas, services and tiers of government’s lower efficiency
that constitute a potential obstacle to growth. By means of this analysis, the long lasting problem of enhancing
economic growth gains an additional instrument that allows to identify with some degree of precision areas,
services and levels of government that constitute an obstacle to growth and a cause of waste in terms of public
expenditure.
Finally, concerning the part on Collaborative Working environments, the work performed has focused on an
analysis of the current initiatives in official statistics to develop new statistical indicators on business
collaboration and Collaborative Working Environment (CWE) as well as to improve the data availability not only
on the use of collaboration tools but also on the skills needed for this practice. Analysis stresses the
requirement for official statistics to investigate the opportunities offered to the enterprises by CWE tools and
to monitor the use of these practices in the enterprises more frequently. Two recent initiatives introduced by
Istat have been described. Among them the introduction of an ad hoc module in the Community Survey on ICT
Usage and e-Commerce in Enterprises could be considered as “best practices” to be extended to all EU NSIs
providing the basis for a new accumulation of knowledge that can be managed to the benefit of
competitiveness and productivity of the whole European economic system.
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INDEX
PART 1: AN INFORMATION THEORY BASED MULTIDIMENSIONAL APPROACH TO
COMPETITIVENESS MEASUREMENT ................................................................................... 6
1. INTRODUCTION....................................................................................................................... 6
2. CONCEPTUAL FRAMEWORK ..................................................................................................... 7
2.1 Assessment approaches .................................................................................................................................................... 8
2.2 Capabilities approaches .................................................................................................................................................... 8
2.3 Axiomatic approaches ....................................................................................................................................................... 9
2.4 Non-axiomatic approaches ............................................................................................................................................. 10
3. AN EXTENDED TWO STAGE INFORMATION THEORY BASED APPROACH .............. 13
4. AN APPLICATION TO FIRM COMPETIVENESS .............................................................. 17
4.1 Data and variables .......................................................................................................................................................... 19
4.2 Decomposition by subgroups of the population ............................................................................................................ 23
5. CONCLUDING REMARKS ........................................................................................................ 25
REFERENCES ............................................................................................................................. 27
ANNEX ..................................................................................................................................... 30
APPENDIX ................................................................................................................................ 35
PART 2: PUBLIC SECTOR CONTRIBUTION TO COMPETITIVENESS .............................. 39
INTRODUCTION ........................................................................................................................ 40
1. PUBLIC SERVICES BY TIERS OF GOVERNMENT ......................................................... 41
2. THE MULTIDIMENSIONAL NATURE OF PUBLIC SERVICES ........................................ 44
3. DIMENSION IN DEA MODELS .................................................................................................. 56
4. RESULTS ............................................................................................................................... 61
4.1 Services’ productivity ...................................................................................................................................................... 61
4.2 The scale problem ........................................................................................................................................................... 64
4.3 Public sector contribution to productivity ...................................................................................................................... 70
CONCLUDING REMARKS ............................................................................................................ 74
REFERENCES ............................................................................................................................. 75
ANNEXES .................................................................................................................................. 78
PART 3: REPORT ON INDICATORS ON BUSINESS COLLABORATION AND
COLLABORATIVE WORKING ENVIRONMENTS (CWE) ..................................................... 87
INTRODUCTION ........................................................................................................................ 87
1. COLLABORATIVE WORKING ENVIRONMENT (CWE) .................................................. 88
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2. CWE AND OFFICIAL STATISTICS............................................................................................... 90
2.2 Focus on recent ISTAT initiatives ................................................................................................................................... 91
3. IMPROVING DATA AVAILABILITY: TWO PROPOSALS ................................................. 93
3.1 A new CWE module in the Community Survey on ICT Usage and e-Commerce in Enterprises...................................... 93
3.2 A new module in the Business Census on complex business units................................................................................. 96
CONCLUDING REMARKS ............................................................................................................ 99
REFERENCES ........................................................................................................................... 100
ANNEX ................................................................................................................................... 102
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Part 1: An information theory based multidimensional approach to competitiveness measurement
Rosa Bernardini Papalia, Pinuccia Calia, Carlo Filippucci
Department of Statistics, University of Bologna, Italy
Summary
In this paper we are interested in analysing competitiveness as a function of profitability, productivity and
growth. Our first objective is to introduce a multidimensional economic performance index which is measured
by profitability, productivity as well as output growth. The basic idea is to analyse the firm competitiveness by
means of multivariate inequality indexes following the approach of Maasoumi (1986). An empirical analysis of
the multidimensional economic performance index is also provided in terms of profitability, productivity as well
as output growth using micro-data for Italian firms for the year 2008.
Keywords: Multidimensional inequality, Inequality decomposition, Information Theory, Competitiveness.
JEL classification: C20, D63
1. Introduction
In this paper we study the potentialities of a multivariate index approach for measuring competitiveness.
Competitiveness is a concept based on different aspects of the complex economic activity. Recently the use of
an index approach, at the macro level, has been proposed in order to summarize a whole range of different
aspects for the assessment of country competitiveness, although it is not without criticism (see Krugman,
1994). Our proposal as well is aimed at developing a multidimensional economic performance index of
competitiveness but at the micro-level. The final goal is to implement an instrument to analyze sectoral or
regional competitiveness, or along other similar dimensions.
To perform this task we develop a competitiveness index within the Information Theoretic framework
following the approach proposed by Maasoumi (1986) for a multidimensional inequality index of well-being.
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Our strategy is to construct a multidimensional inequality index following a two stage procedure. In the first
one, the attributes for each unit are aggregated via an aggregation function yielding a real number for each
unit. Different attributes are distributed differently; so the aggregation function is chosen according to the
distribution that most closely represents the distributional information in each attribute, using a multivariate
generalization of the generalized entropy measure of divergence (the Kullback-Leibler distance). In the second
one, a one-dimensional measure of inequality of the family of Generalized Entropy measures is computed.
An empirical analysis of the multidimensional economic performance index is provided in terms of profitability,
productivity as well as output growth. Profitability is an indicator of effectiveness that reflects how well the
goal of generating income has been met; productivity is a measure of efficiency, a sine qua non condition for
long term competitiveness; output growth is the dynamic aspect of competitiveness.
More specifically, the multidimensional competitiveness index is applied to micro-data for Italian firms for the
year 2008. We perform a sensitivity analysis of the performance of the index as the consequence of the choice
of different parameters and hypothesis implied by its structure.
This paper presents two elements of originality. First we introduce the IT-based multidimensional inequality
index to measure competitiveness starting from performance indicators. Second we use index decomposability
to investigate differences in inequality for sub-populations/groups of interest defined by firm characteristics
(size and economic activity).
The paper is structured as follows. In section 2 we introduce a conceptual framework of multidimensional
approaches to inequality measurement. Section 3 introduces the extended Information Theoretic approach to
account for potential differences across dimensions. Section 4 describes and discusses results of the empirical
application of the multidimensional economic performance index, while section 5 outlines final remarks and
future developments.
2. Conceptual framework
The relevant literature on multidimensional inequality measures refers to many different approaches.
Assessment approaches check the possession of selected/measured attributes that take into account
dimensional deprivations and allow to evaluate how much individuals are deprived (or non-deprived) on each
attribute taken individually. Capabilities approaches introduce the concepts of capabilities and functioning for
assessing the well-being of individuals (Sen, 1985 and 1993). Axiomatic approaches advocate to maintain the
multidimensional conceptualization of inequality measure and to aggregate the selected dimensions into a
composite index in accordance with some desirable axioms (Tsui 1995 and 2002; Chakravarty and al. 1998;
Bourguignon and Chakravarty, 2002 and 2003). Non-axiomatic approaches derive multidimensional inequality
measures based either on a aggregation of a series of indicators that have been previously aggregated across
individuals or on individual data that allow the retained well-being indicators to be aggregated at the individual
level first, and then across individuals.
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2.1 Assessment approaches
A) The cut-off method
Townsend (1979) was the first to introduce the concept of de-privation. The cut-off method consists to
evaluate the deprivation on each attribute by binary ranking of type of deprivation (or non-deprivation).
Formally, one constructs an increasing function ψj: R+ →{0,1} on each attribute which is then used to assign an
individual to the set of deprived individuals, Dj.
B) The “Fuzzy Set” Approach
The theory of “Fuzzy Sets” was developed by Zadeh (1965) on the basis of the idea that certain classes of
objects may not be defined by very precise criteria of membership. Let there be a set X and let x be any
element of X. A fuzzy set or subset A of X is characterized by a membership function μA(x) that will link any
point of X with a real number in the interval [0,1]. μA(x) is called the degree of membership of the element x to
the set A. If A were a set in the sense in which this term is usually understood, the membership function which
would be associated to this set would take only the values 0 and 1. But if A is a fuzzy subset, we will say that
μA(x) = 0 if the element x does not belong to A and that μA(x) = 1 if x completely belongs to A. But if 0< μA(x) < 1,
x belongs only partially to A. These simple ideas were easily applied to the concept of poverty. In some cases an
individual is in such a state of deprivation that she certainly should be considered as poor while in others her
level of welfare is such that she certainly should not be classified as poor. There are however also instances
where it is not clear whether a given person is poor or not. This is especially true when one takes a
multidimensional approach to poverty measurement, because according to some criteria one would certainly
define her as poor whereas according to others one should not regard her as poor. Such a fuzzy approach to
the study of poverty has taken various forms in the literature. Cerioli and Zani (1990) were the first to apply the
concept of fuzzy sets to the measurement of poverty. Their approach is called the Totally Fuzzy Approach (TFA)
and the idea is to take into account a whole series of variables that are supposed to measure each a particular
aspect of poverty. They considered the case of dichotomous, polytomous and continuous variables. Other
“Fuzzy” approaches have been proposed such as that of Vero and Werquin (1997) but because of space
constraints this approach will not be presented. Some authors (Cheli and Lemmi, 1995) have proposed to
modify Cerioli and Zani’s (1990) Totally Fuzzy Approach (TFA) and suggested what they have called the Totally
Fuzzy and Relative Approach (TFR).
2.2 Capabilities approaches
Sen (1985) was the first to formalize the concept of capabilities. Let f*(xi) represents the vector of commodities
possessed by individual (or household) xi (with i∈I). Let ψ(.) represents the conversion function of a commodity
vector into a vector of objective characteristics. Let gxi[.] represents a personal utilization function of xi
reflecting one pattern of use that xi can actually make. Let Lxi represents the set of utilization function gxi
among which individual xi can make its choice and let Vxi[.] represents the valuation of the vector of
functionings hxi which evaluates the level of well-being of xi. Thus, the achieved function vector hxi represents
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achieved functioning of individual xi when he chooses utilization function gxi for a vector of commodities f*(xi)
and can be given by the following equation:
hxi = gxi ° ψ(f*(xi)) = gxi[ψ(f*(xi))]
(1)
The vector hxi represents what individual xi is able to do and to be. The well-being of individual xi can then be
given as follows:
V`xi = Vxi[hxi] = Vxi[gxi ° ψ( f*(xi))]
(2)
By using Vxi, it is possible to characterize the valuations of well-being that individual xi can potentially achieved
and one can derived a set of vectors of achievable functionings Rxi(f*(xi)) and given by the following equation:
Rxi(f*(xi))= {hxi ⁄ hxi = gxi ° ψ(f*(xi)), ∀ gxi[.]∈Lxi}
(3)
Therefore, the vector of achievable functionings becomes:
Qxi(f*(xi))= {hxi⁄ hxi = gxi ° ψ(f*(xi)), ∀ gxi[.]∈Lxi.∧ ∀ f*(xi) ∈ X`xi}
(4)
and Qxi(f*(xi)) formally represents the set of capabilities of individual xi, i.e. the freedom that have individual xi
to choose among all possible functionings alternatives according to its personal characteristics and its social
environment.
2.3 Axiomatic approaches
The axiomatic approaches assume the multidimensional setting by aggregating the dimensions/attributes into
a composite index in accordance with the properties (or axioms) that are desirable rules governing the
dimensions and their relations by defining the specific functional form of the multidimensional index. The first
author to adopt an axiomatic approach in the context of multidimensional poverty indices were Chakravarty
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and al. (1998), followed by Tsui (2002) after its previous works on the axiomatization of multidimensional
indices of inequality (Tsui 1995), then Bourguignon and Chakravarty (2002 and 2003).
2.4 Non-axiomatic approaches
2.4.1 The approaches based on aggregated indicators
The principle of aggregation approaches is to combine and synthesize simultaneously several numerical values
into one index, named composite index, so that this composite index takes into account all individual values.
More specifically, within this class, in the Complementary Approach of Brandolini and D'Alessio (1998)
functionings are examined item by item to describe the characteristic of each dimension and to study their
correlation structure. The advantage lies in its simplicity, and the prerequisites for the inequality measure are
less demanding. The lack of synthesis and difficulty of giving a unified well-defined image are the main
disadvantages of this approach. Indeed, the plurality of elementary indicators conflicting existing
simultaneously within a same modelling can lead to difficulty in analysis and synthesis. In addition, it is likely to
get only a partial order when comparing observations between them. This constitutes a major argument in
favour of the construction of aggregate indexes for synthesizing information.
The composite index approach consists to construct a global composite indicator for each unit xi. Let M be a
pattern matrix, the problem is to determine an aggregation function Θ defined from Rm to R on a set of
individuals X≡{ x1 , x2 ,.. xn }such as:
∆x(xi,ξ)= Θγ(ψ1(f1(xi)),…, ψm(fm(xi)); λ1,..,λm)
(5)
The function Θ (and its properties) can be defined according to the Bourguignon and Chakravarty (2003) and
Chiappero (1994).
The most used aggregation function is the weighted mean of order β .
(
)
∆ x ( xi , ξ ) = Θγ .., Ψ j ( f j ( xi )),...;.., λ j ,...
=
[∑
Deliverable 9.2
λ (Ψ j ( f j (xi )))
j =1 j
m
]
β 1β
(6)
,
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where β represents a parameter which determines the substitution level between attributes and ∆x(xi)
represents aggregated index for individual xi of normalized degrees ψj(fj(xi)) associated to different elementary
indicators of deprivation, and λj the weighting system for each fj (with j = 1,..,m) such that λj≥0 and Σj λj =1.
This kind of aggregation operators is faced to many problems (Atkinson 2003), such as commensurability of
attributes and compensation/substitutability.
2.4.2 The Approaches based on Individual Data
For this class of approaches the comparisons are not based on a aggregation of series of individuals but on
individual level first and then across individuals.
A) Inertia Approach
The inertia approach is a parametric approach to the composite inequality indicator who is mainly based on
multivariate analysis techniques. A brief review of literature on inertia approaches for multidimensional
measurement of poverty was firstly proposed by Asselin (2009).
Filmer and Pritchett (2001) built a linear index of wealth based on asset ownership indicator variables using
Principal Components Analysis to derive the standardized first principal component of the variance-covariance
matrix of the observed unit assets (also called weights) for the asset indicators (Klasen, 2000; Filmer and
Pritchett, 1997; Asselin 2009; Kabubo-Mariara and al., 2010). Sahn and Stifel (2003) used factor analysis to
evaluate the potential of an asset-based index as an indicator of household economic welfare. In the case of
capability approach, Silber (2007) argued that the factor analysis would provide a theoretical framework for
explaining the (observed) functionings by means of capabilities represented by the latent factors but such a
model will not explain the latent variables. Booysen et al. (2007) used Multiple Correspondence Analysis to
build asset-based composite poverty indicators. This approach is helpful for the visualization of data and
variables on the same graph and is preferable in presence of categorical data since it can easily combine
quantitative variables and categorical variables (Asselin, 2009).
Ferro Luzzi and al. (2006) used cluster analysis to aggregate individuals according to how similar they are with
regard to their various scores of multiple deprivations. Hirschberg and al. (1991) proposed statistical cluster
analysis methods to explore different ways and levels for clustering of alternative and use cluster analysis to
see how different welfare units (countries) cluster together on the basis of the attributes considered, and in a
manner (metric) evidently consistent with that used to cluster the attributes.
B) The Distance Function Approach
The concept of distance function has been widely used in the efficiency analysis which highlights the distinction
between input and output distance functions (see Coelli et al., 1998). Lovell and al. (1994) were the first to
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apply this concept in the context of the analysis of household behaviour. Deutsch and Silber (2005) was then
developed it in the context of multidimensional poverty measurement.
Formally, one defines L(y) as the input set of all input vectors x which can produce the output vector y, that is:
L(y) = {x s.t. x can produce y}. The input distance function Din(x) is then defined by the following equation: Din(x;
y) = max{τ s.t. (x⁄τ) ∈ L(y)}.
Coelli and al. (1998) proved that the input distance function complies to four properties:
(i) Din(x; y) is increasing in x and decreasing in y.
(ii) Din(x; y) is linearly homogeneous in x.
(iii) If x ∈ L(y) then Din(x; y) ≥1.
(iv) Din(x; y) = 1 if x belongs to the “frontier” of the input set L(y) (the isoquant of y).
(7)
The distance between distributions
Assume we make a given experiment E which has n potential results x1 ,…, xn with corresponding a priori
probabilities q1 ,…, qn. It may however happen that we receive some information that implies a modification of
these a priori probabilities. In other words assume we have now received a message that transformed the a
priori probabilities q1 ,…, qn into a posteriori probabilities p1 ,…, pn with Σi pi = 1. The supplement of
information D(p,q) that is obtained when shifting from the distribution of a priori probabilities {q1 ,…, qn}to
that of the a posteriori probabilities {p1 ,…, pn} will be expressed as D(p, q) = Σi pi log (pi / qi). D(p,q) represents
actually the expected information of a message transforming the a priori probabilities into the a posteriori.
Note that this supplement of information D(p,q) may also be considered as a measure of the divergence
between the distributions {p1 ,…, pn}and {q1 ,…, qn} or as the difference between the entropy corresponding
to the distribution {p1 ,…, pn }and that relative to the distribution {q1 ,…, qn }, assuming the weights to be
chosen are those corresponding to the latter distribution. This measure of divergence D(p,q) is generally
positive and will be equal to zero only in the very specific case where pi = qi for all i (i=1 to n), that is when the
new message does not modify any of the a priori probabilities. D(p,q) will be maximal when there is a result xi
such that qi > pi = 0 because in such a case the probability a priori that the event xi would occur was nil
whereas now, after reception of the correcting message, the probability that it will occur is not nil any more
and thus the degree of surprise may be considered as infinite.
A most used measure of divergence is the Kullback-Leibler-Jeffreys measure J(q,p) (see, Kullback 1959).
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3. An extended two stage information theory based approach
Within the non-axiomatic approaches and in the context of the multidimensional measurement of inequality,
Maasoumi (1986) uses information theory both in the aggregation across attributes to obtain an index of wellbeing for each individual and in the aggregation across individuals to obtain the global inequality measure
(Maasoumi, 1986). Maasoumi was one of the first to introduce a measure of multidimensional inequality based
on the theory of information (Maasoumi, 1986, 1999; Lugo, 2005), suggesting to construct a multivariate
inequality index in a two stage procedure.
In the first stage, the attributes for each unit are aggregated via an aggregation function yielding a real number
for each unit. In the second, a one-dimensional measure of inequality of the family of Generalised Entropy
measures is computed. This approach is based on the idea that different indicators of economic welfare are
distributed differently therefore Maasoumi suggests an aggregation function with a distribution that most
closely represents the distributional information in each attribute. In particular he proposes a multivariate
generalisation of the generalised entropy measure of divergence (the Kullback-Leibler distance) or closeness
between the distribution of the aggregation function and the m densities related to the m dimensions (a
weighted sum of the pairwise divergence terms). Differences in the elasticity of substitution between the
attributes under consideration are here introduced by a priori assumptions on the parameter of the
aggregation function.
More specifically, in the first step, a function Si would summarize the information on all m attributes (x1i, x2i,…,
xmi) for each individual i in an efficient manner. Every attribute j has a distribution xj= (xj1, xj2,…, xjn) containing
all the information about the variable that can be accessed and inferred objectively. The aim is to select a
functional form for the aggregator function Si that would have a distribution as close as possible to the
distributions of its constituent members, xjs. The ‘optimal’ function Si(*) can be achieved by solving an
information theoretic (IT) inverse problem, based on distributional distances, where the divergences represent
the difference between their entropies. Within this IT framework, the distance function is the weighted
average of the relative entropy divergences between (S1, S2,.., Sn) and each xj = (xj1, xj2,.., xjn).
In the second stage an index of the generalized entropy family is applied to these weighted means.
Bourguignon (1999) proposed a slightly different approach. While in the case of the Maasoumi index
normalisation is obtained by the mean aggregation function, Bourguignon applies the value of the aggregation
function for the mean individual, i.e. the unit that is endowed with mean attributes. The multidimensional
Bourguignon index thus provides a more direct link with standard utilitarian social evaluation functions and
hence with multidimensional stochastic dominance criteria as outlined in Lugo (2005).
Following Massoumi, we propose to use an IT based approach in the approximation across individuals and
dimensions.
The distance function D is defined as the weighted mean of the relative entropy divergences between each Sj =
(Sj1, Sj2,.., Sjn) and each xj = (xj1, xj2,.., xjn) and can be reformulated as follow:
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 n
 S j
∑ S i j  ij
Dβ (S β X ; w) = ∑ w j
β (β + 1)  i =1  xi
j =1


m
1
β


 − 1  ,
 

(8)
where wj is the weight associated to the generalized entropy distance of each attribute.
(
)
The minimization of Dβ S β X ; w with respect to Si j provides the optimal functions given by1:
[
S i j ∝ ( β + 1) X ij− β
Si ∝
and
[∑
]
−1 β
δ (S i j )
j =1 j
]
− β −1 β
m
β ≠ 0, β ≠ −1 δ j =
wj
∑w
(9)
,
j
j
where w j are the positive weights associated to each jth attribute, δ j are the normalized weight of indicator j,
β is a parameter which determines the level of substitution between attributes in the aggregate function. The
greater the β the smaller is the degree of substitutability between them. When β = -1, the attributes are
perfect substitutes, so low levels on one of the attributes can be perfectly compensated by high levels on
another, and for β → ∞ attributes are not substitutes so the worst performer attribute is the only considered
in the aggregation function. The higher the β , the smaller is the elasticity of substitution between the
attributes under consideration.
If the restriction
S i j = 1 is imposed in minimizing Dβ (S β X ; w) with respect to Si j , the following optimal
∑
i =1
aggregation function:
Si
j
[δ X ]
=
∑ [δ X ]
n
i =1
1
− β −1 β
ij
j
When,
∑
j
− β −1 β
ij
m
w
j =1 j
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=m
β ≠ 0, β ≠ −1 δ j =
wj
∑w
(10)
,
j
j
[
S i j ∝ ( β + 1) X ij−β
]
−1 β
, otherwise if
∑
m
w
j =1 j
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 ( β + 1) − β 
X ij 
 m

−1 β
= m Si ∝ 
j
March 2013
For β =0, the minimization of the distance function D formulated as follow
[ (
)]
 m

Dβ (S β X ; w) = ∑ j =1 w j ∑ Si j log Si j xij 
i =1


β =0
,
(11)
provides the optimal aggregation function:
m
( )
Si = ∏ Si j
j =1
δj
β = 0, δ j =
wj
∑w
(12)
j
j
( )
S i j = xij .
For β =-1, the minimization of the distance function D formulated as follow:
[ (
)]
 m

Dβ (S β X ; w) = ∑ j =1 w j ∑ xij log xij S i j 
i =1


β = −1
,
(13)
provides the optimal aggregation function:
S i = ∑ j =1 δ j S i j
m
β = −1 δ j =
wj
∑w
(14)
j
j
Si j = X i j
Thus it turns out that the composite indicator S is a weighted average of the different indicators. In the general
case (9) S is a harmonic mean; in the case where β → 0, it is a geometric mean, while in the case where β → 1 it is an arithmetic mean.
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Moreover it is easy to interpret this composite indicator as a utility function of the CES type with an elasticity of
substitution σ = 1/(1+ β ) when β ≠ 0, -1, as a Cobb-Douglas utility function when β → 0, and as a linear utility
function when β → -1.
Having derived an aggregation function Si (composite index) for each individual i, in the second stage, following
Maasoumi we introduce an index of the generalised entropy family as a measure of the divergence between
two distributions that are: the size distribution of S and the uniform distribution that has the highest entropy
and represents equality of well-being. More specifically, at the second stage, a one-dimensional measure of
inequality of the family of Generalised Entropy measures is calculated:
(1+γ )
 1

1 n   S i 
;
Iγ (S ) = 
−
1


∑


(
+
1
)
n
S
γ
γ

i =1  


where S =
γ ≠ −1,0
(15)
n
∑S
i =1
n is the mean of the aggregation function indicator for the n units. Given the general form
i
of the aggregation function S in (15), the multidimensional index has the following expression in terms of the
original attributes:
1
1  n 1+γ −(1+γ )  m
 ∑ w j xi j
Iγ =
∑ n K

γ (γ + 1) n  i =1
 j =1

( )
where K =
−β




− (1+γ ) β

− 1;

γ ≠ −1,0
(16)
n
∑S
i =1
i
.
The formula (16) is equivalent to the more usual formulation of the generalised entropy index where
1+ γ = α :
α
 1
 
1 n  S i 
Iα ( S ) = 
  − 1 ;
∑
α (α − 1) n i =1  S 
 
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or equivalently:

 m

 δ ( x j ) − β
∑ j i
 1
1 n  j =1
Iα = 
∑  S
α (α − 1) n i =1 










−
α
β


 
− 1 ; β ≠ 0,1 α ≠ 0,1


 
(18)
where α is the “inequality aversion” parameter as in the univariate formulation; the lower the
inequality sensitive to changes in the lower part of the distribution.
α , the more is
This measure is accused to fail satisfying the multidimensional version of the Pigou-Dalton principle of transfer
known as Uniform Majorization (see Lugo and Maasoumi, 2008) 2.
4. An application to firm competiveness
The aim of the paper is to propose a tool that could be used to measure and compare competitiveness at
different level of aggregation (country, region or sector) but with a micro-level foundation. The interest on
measuring competitiveness at a firm-level is due to the consideration that firms are key players in the creation
of economic growth. Growth and jobs are produced by enterprises operating on competitive markets: “it is the
firms, not nations, which compete in international markets” (Porter, 1998). The focus must therefore be on the
global performance of the enterprise sector, as also recently stressed in Altomonte, Barba Navaretti, Di Mauro
and Ottaviano (2011) which advocate the broadening of the scope of the firm-level analysis and demonstrate
that firm-level data provides critical information for the design of appropriate competitiveness measures that
complement traditional macro analysis. Their conclusion is that “new indicators should be developed to
translate the distribution of firms characteristics into indicators of competitiveness designed to capture not
only average performance but also the heterogeneity of firm performance”.
In our view, competitiveness is a multidimensional concept that can be looked at from different levels:
country/region, industry, and firm level. While, at the macro level, there are some studies that analyze and try
the assessment of country competitiveness using a multidimensional approach, there is a lack of studies on the
measurement of firm competitiveness as a multidimensional phenomenon.
A rather general definition that can be applied at every level is that provided by OECD (1996): competitiveness
is “the ability of companies, industries, regions, nations, and supranational regions to generate, while being
2
Lugo and Maasoumi (2008) have proposed alternative approaches to the derivation of multidimensional poverty indices
using information theory: the aggregate poverty line approach and the component poverty line approach.
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and remaining exposed to international competition, relatively high factor of income and factor employment
levels on a sustainable basis”. At firm level, competitiveness can be defined as the ability of firm to design,
produce and or market products superior to those offered by competitors, considering the price and non-price
qualities (D’Cruz and Rugman, 1992). A someway more comprehensive definition is the one formulated by the
Research Centre for Competitiveness (1996): firm-level competitiveness is defined as “the company’s ability to
permanently offer consumers products and services, which are in compliance with the standards of social
responsibility, and for which they are willing to pay more than for the competitors’ products, ensuring
profitable conditions for the company. Condition of this competitiveness is that the company should be able to
detect changes in the environment and within the company, by performing permanent better market
competition criteria compared to the competitors”.
In the above definition some basic competitiveness features can be identified, in particular, the ability for
assuring the “efficiency” in the utilization of resources and the capacity to attain economic objectives
(“effectiveness”). A firm is thus competitive if it can produce products or services of superior quality or at lower
costs than its domestic and international competitors. It is, therefore, synonymous with a firm’s long-run profit
performance and its ability to compensate its employees and provide superior returns to its owners (Buckley et
al., 1988). In the narrow sense, such measures of competitiveness at the firm level comprise indicators of
economic and market performance, such as the development of sales, profits, and costs, as well as stock
performance.
However, conditions for assuring this competitiveness are also strictly related to the enterprise culture, the
management ability and the human resources of the company to adapt to changing conditions, to the ability to
influence the enterprise environment, innovate, develop or explore new technologies and markets. These and
other factors are indicated as sources of competitiveness in the literature by the most popular perspectives on
this issue. A review of these various perspectives and a classification of the connotations of firm-level
competitiveness can be found in Ambastha and Momaya (2004). Despite the variety of connotations of
competitiveness, and following Fischer and Schornberg (2007) we focus on three dimensions (and related
performance measures) in our application that refers to the features specified in the definition above.
Efficiency, measured by productivity, can be defined as the degree to which output are generated in terms of
input of any system and reflect how well resources are used. Productivity is a commonly used measure of
competitiveness and a good indicator of long term competitiveness; Porter defines competitiveness as
productivity growth that is reflected in either lower costs or differentiated products that command premium
prices (Porter, 1990). Some argues that it can be considered a necessary but not a sufficient condition for longterm competitiveness (Reinert, 1995; Fischer and Schornberg, 2007). Effectiveness (measured by profitability)
refers to the capability of reaching stated goals and objectives; so the focus of effectiveness is the achievement
as such not the resources spent and anything that is effective has to be efficient, but anything that is efficient
also needs to be effective. Since the ultimate goal of a firm is to generate income, effectiveness can be referred
to the firm’s ability to compensate its employees and provide returns to its owners. Profitability is a key
variable for assessing competitiveness (EU Commission, 2005) and an indicator frequently used. In addition to
efficiency and effectiveness, a growth indicator represents the dynamic component of competitiveness. To
quote Fischer and Schornberg (2007) “overall, we simultaneously consider competitive performance
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(effectiveness as the status of competitiveness), competitive potential (efficiency reveals something about the
ability to be competitive in the future) and competitive process (growth is our dynamic aspect of
competitiveness).
Profits may be an indicator for effectiveness, in fact the amounts of earned profits reflect how well the purpose
of generating income has been met. As a profit measure, we use gross operating surplus (GOS) calculated as
value added at factor cost less expenses for employees. Thus, GOS is the part of the value added which allows
for capital and entrepreneur compensation, to pay taxes, and as consequence eventually to finance all or part
of investment. GOS has been preferred over value added because the latter includes labour costs that can be
higher for highly-skilled employees, although real profits may be low. For the analysis we use the share of GOS
in turnover (TURN), an indicator which may also be called gross operating profit margin: M1=GOS/TURN.
Productivity is calculated as value added per employee: M2=VA/EMPL. The ratio of value added per employee
(labour productivity) is clearly influenced by changes in capital, as well as technical, organizational and
efficiency change (within as well as between firms), by economies of scale, degrees of capacity utilization and
measurement errors. Nevertheless, excluding the costs of intermediate inputs, labour productivity relates to
the single most important factor of production. "Labour productivity reflects how efficiently labour is combined
with other factors of production, how many of these other inputs are available per worker and how rapidly
embodied and disembodied technical change proceed" (OECD, 2001). While the use of GOS is preferable as a
profit measure, VA yields better results in terms of labour productivity exactly because it contains labour costs
thus implicitly the skill of labour is measured. M1 and M2 may both potentially be biased if the capital intensity
of industries varies considerably across areas and/or industries. More precisely, more capital - intensive sectors
will tend to display higher levels of GOS (and VA) even if real profitability is low since these sectors require
higher investments. Furthermore, more capital - intensive industries in general employ fewer staff (Fischer and
Schornberg, 2007).
About growth, the annual change of turnover value is calculated: M3=ΔTURN. Turnover has been used instead
of total production because it is a more reliable variable and results in fewer missing data.
4.1 Data and variables
We apply the multidimensional index to firm level data collected yearly by Istat (Italian Statistical Institute)
through the “Structural business surveys” on Italian enterprises according to specific criteria established by a
new European Community Regulation (No 295/2008) on structural statistics.
The data were collected using two different statistical surveys on business sectors covering the enterprises
operating in industrial and service sectors3. The first one is a census survey on enterprises with at least 100
employees (Survey on Accounting System of Enterprises - SCI). The second one is a sample survey covering
3
Economic activities covered by the two surveys are the sections from B to N and from P to S of the classification of
economic activities Nace Rev. 2.
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enterprises with 1-99 employees (Small and Medium Enterprise Survey - PMI). For the latter a random sample
with one stage stratified design is drawn, with equal sample inclusion probabilities. The population of
enterprises is stratified by economic activity (four digit code of the statistical classification of economic
activities NACE rev. 2), size class (number of employees), and location areas (regions). In each survey the
enterprise is the sample unit. Missing information (total or partial nonresponse) is recovered from
administrative sources (financial statements and fiscal data) for the most important variables. Both surveys
collect data on the main economic, financial and structural variables of Italian businesses. In particular, the
data refers to economic variables from business accounting (turnover, production, intermediate costs, etc.),
investments and assets, employment and labour cost. Data are used to calculate the value added and others
economic variables for the analysis of the economic performance of industrial and services sectors and to
assess the national accounts aggregates.
We use firm data from the two surveys with reference to 2008.
Firm distribution by size and economic activity in the sample are reported in Table 1 and Table 2. It is to be
stressed that 55% of firms have a very small size (less than 10 employees) and the 88% have less than 100
employees.
Table 1 here4
The distribution by economic activity shows that the 58% of the firms in the sample operate in the service
sector (among them the 17% are in the trade sector) and the 35% of firms operate in the industry sector (the
majority are manufacturing firms that represent the 32% of the whole sample).
Table 2 here
To calculate the two indicators of productivity and profitability we have first to obtain the firm value added.
Value added (VA) is obtained by the value of total production minus the value of costs for materials and
services. The Eurostat SBS definition is:
VA = Net Turnover +/- Change in stocks of finished goods and work in progress + Capitalised production (cost of
own capital formation) + Other operating income - Goods and services purchases - Other operating charges (Taxes on production) +/- Changes in stocks of raw materials and consumables.
The Gross Operating Surplus (GOS) is defined as Value added minus expenses for employees.
4
For all the tables see Annex at end of Part 1.
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The last indicator considered is the variation of turnover between 2007 and 2008. The value of turnover for the
previous year is recorded for every business in the PMI survey, while for firms in the SCI survey we had to
match data referred to 2008 with those on 2007. We retain firms whose data on all the indicators are
complete.
Table 3 reports some statistics for the indicators. We observe some extreme values for each indicator,
especially for profitability. We decide to operate the trimming of the 0.02% of the observations on the tails of
the distribution of each indicator.
Table 3 here
Correlations between indicators (Table 4) are positive but quite low: this confirms that the three indicators
emphasize different aspects of firm’s performance.
Table 4 here
Before applying the multidimensional index, we also use some normalization to transform the original
indicators. Two types of normalization are often used: standardization (deviation from the mean divided
standard deviation) or the min-max transformation ((value-min)/(max-min)). The second normalization has the
advantage to produce values in the range 0-1 so avoiding negative values as result of transformation. This
solves the problem of negative values in the raw data: because negative values are problematic in application
of inequality index, we decided to apply min-max transformation. Another advantage of the min-max
transformation is to reduce the effect of extreme values and outliers when the original distribution is markedly
skewed, as in our case.
As a first step we calculate the generalized entropy index for each indicator separately, applied to transformed
data.
We choose for α (the “inequality aversion” parameter) the values -2, -1, 0.333, 0.5: the lower the α the more
sensitive is inequality to changes in the lower part of the indicator distribution.
Table 5 here
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As we can see, the value of the GE index decreases markedly as the parameter α increases, in particular in the
range of negative values of α. The higher the absolute value of α the greater is the sensitivity of the measure to
inequality (transfer) in the tails areas of the distribution; so the smaller the α (negative values), the more
sensitive is inequality to changes in the lower part of the distribution, i.e. between firms with the worst
performances for each indicator. Hence, inequality in the distribution of each indicator is mostly due to
inequality between firms with the worst performances, and we expect some reduction in inequality by
improvements in the performances of the worst firms. Comparing the values of the GE indexes across
indicators for given values of α we notice that profitability has the highest values of inequality respect to
growth and productivity when small values of the parameter α are considered, while has the smallest value of
inequality as α increases and become positive. This indicates that inequality in profitability is marked more
between firms with the worst performances than between firms with the best performances, compared to the
other competitiveness dimensions.
At the first stage of the calculation of the multidimensional index of generalised entropy the function Sij is
estimated for each firm and dimension as described in section 3. The aggregation function Si is calculated
imposing an equal weighting scheme (weights equal to 1/3 attached to each indicator) using different values of
the parameter β related to the degree of substitutability between attributes (identical for all pairs of
attributes): we choose β ≥ -1 and specifically β = -0.5, 4 and 20, thus implying positive values of the elasticity of
substitution. The parameter β corresponds to different degree of substitution: the greater the β the smaller the
substitution elasticity. Then we calculate the multidimensional GE index using different values of α. The
aggregate functions are calculated for each individual firm and the GE index is then applied to this univariate
indicator.
Table 6 reports the GE multidimensional index for three different values of the parameter β and four values of
the parameter α.
Table 6 here
Comparing the pattern of inequality across the different values of β and α, we notice that inequality is much
more influenced by variations across β than variation across α. For each given parameter α, the comparison of
the GE index for different βs give us information on how different hypothesis on the degree of substitutability
between attributes influences the inequality measure. As β increases, the elasticity of substitution decreases:
so for high values of elasticity of substitution between attributes the value of inequality is quite small. On the
contrary the inequality is higher when the elasticity of substitution becomes smaller and attributes are nearly
complements (or equivalently when β increases, in particular when it changes from -0.5 to 4). In this case, a
bad performance on profitability, for example, cannot be compensated by a good performance measured on
productivity, and the index measures by far the performance on the worst indicator. However, the influence of
the parameter α is greater when indicators are poor substitutes, because inequality varies much more as α
increases in the case of low degree of substitution. So the smaller the substitutability between productivity and
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profitability (or growth) the more the inequality is affected by changes in the performance of the firms with the
worst performance.
Results are robust at different levels of disaggregation. The same analysis has been performed for a single
Italian region (Umbria) and results are summarized in tables A.1 to A.7 in appendix. The case study is
particularly suitable to represent the usefulness of our approach to study the competitiveness at local level.
The Umbria region assumes the character of the region-not region, that is, a political-administrative unit
dominated by centripetal and centrifugal forces which thus tend to enhance linkages and integration with
neighboring regions. The different areas are characterized by specific features and distinct growth path5.
The role of the selected dimensions seems to be confirmed. More specifically, as we can see, whereas the
values of univariate and multidimensional indexes are different, and in particular are larger for Umbria, they
behave in a similar manner for varying values of β and α.
4.2 Decomposition by subgroups of the population
The multidimensional GE index is decomposable by subgroups of population into two components: “Within”
and “Between” group inequalities. The former measures the inequality within the groups defined by individual
characteristics, the latter measures the inequality between the same groups.
Supposing there are G groups, indexed by g = 1,…,G and containing exactly n g individuals, defined by some
variable, the general formulae for decomposition of the multivariate GE index of the type (16) is as follow:
G
I γ ( S ) = I γ ( S .) + ∑ Pg−γ S 1g+•γ I γ ( S g )
(22)
g =1
where Pg = n g / n and S g • =
gth subgroup, K =
n
∑S
i =1
i
ng
∑S
i =1
i
/ K denote the population and the “attribute” shares, respectively, of the
and S . = ( S1• , S 2• ,..., S G• ) , and S g denote the n g vector of the relative shares
S ig = S i / KS g • .
5
(i) the rural high Valnerina area (Norcia and Cascia) projected to enhance the economic potential of cultural and
environmental specificities; (ii) Città di Castello and Umbertide characterized by a territorial organization of district type,
(iii) the area of Tevere's valley, re-organized in the rural Todi, the area relative to Perugia, Deruta, and an area of small and
medium enterprises with a significant systemic organizational structure, Marsciano;(iv) the territories of the Lake
Trasimeno, Orvieto, those of the Valle Umbra (Assisi, Foligno), and so on (the Terni, in the Gubbio area Gualdese), each
with its own characteristics and distinct growth path characterized by distinctive specificities.
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I γ (S .) is the multidimensional ”Between” group inequality (obtained attaching the mean value of the gth
group to each member of the gth group) while the second term of (22) is a weighted sum of “Within” group
inequalities.
Using the more common specification of the generalized entropy index in (17) the decomposition in “Within”
and “Between” group inequality is:
 ng
Iα = ∑ 
g =1  n

G
 µg

 µ
α


 I αg  + Iαb ;


α ≠ 0,1
(23)
where µ g is the mean of the aggregation function in the ght group, µ is the mean of the aggregation function
in the whole population, I αg is the multidimensional GE index in the gth group (calculated as in formula (17)),
and I αb is the “Between” inequality:
I αb
n g  µ g
=

∑
α (α − 1) g =1 n  µ
1
G
α


 − 1


(24)
Table 7 here
Table 7 shows the decomposition of the Multivariate GE Index in the “Within” and “Between” inequalities
across groups of firms with different sizes (the classification by firm size is reported in Table 1). As we can see,
the “Within” inequality accounts for the main part of the population inequality (more than 98%) for all values
of β and α. We can only note that the relative contribution of the “Between” inequality increases as α gets
higher and positive (so when the measure of inequality is sensitive to changes in the upper part of the
distribution) while decreases as β increases (so, when the elasticity of substitution is low).
Hence, differences in competitiveness across groups of firms with different size are greater when we assign
more weight to the upper part of the distribution of the competitiveness index, that is to firms with the best
performance.
On the other hand, since the relative contribution of the “Between” inequality lowers as the substitutability
between the dimensions of competitiveness decreases, we can conclude that differences in competitiveness
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across groups of firms with different size are negligible if we consider various competitive aspects as not
substitutes; this means that none specific size group outperforms or underperforms the others with respect to
all the dimensions simultaneously. Generally speaking, however, firm size does not explain much of the
inequality in the competitiveness indicator. Similar considerations are due to the decomposition of the GE
index by economic sectors (Table 8). . At more disaggregated level (Umbria region), as for the previous analysis,
the decomposition lead to similar results (decomposition by sector of activity is not reported).
Table 8 here
5. Concluding remarks
The goal of the paper is to introduce an extended IT-based multidimensional inequality index in measuring
competitiveness and to study the behaviour of the index at different values of the parameters that underline
varying hypothesis on the aversion to inequality and the degree of substitutability between pairs of
dimensions.
The preliminary results on application to firm level data at 2008 for Italian firms in industrial and service sectors
show that measurement assumptions may influence the results. In particular we find that results are
influenced according to varying degrees of substitutability between the dimensions of competitiveness more
than varying inequality aversion.
As the elasticity of substitution decreases inequality gets higher thus differences in competitiveness across
entities (sectors or regions) can be most appreciated when we assume low elasticity of substitution between
different dimensions of competitiveness. However, the smaller the substitutability between productivity and
profitability (or growth) the more the inequality is affected by changes in the performance of the firms with the
worst performance. These findings are robust for different levels of analysis (national/regional level).
We also explore the decomposition of the inequality index into within and between group inequalities
according to different characteristics of the units observed in order to understand the determinants of
competitiveness for some sub-populations/groups of interest. There is no empirical evidence on any influence
of firm size on competitiveness. To this regard, however, the analysis should be extended to relevant
characterisations other than size, like the degree of internationalization or innovativeness.
Another extension of the empirical investigation should be to consider further indicators in measuring
multidimensional competitiveness, adopting thus recent recommendations of the European Union.
In future work we plan to introduce a method to decompose multidimensional indexes based on regression
analysis and the Shapley value approach. The advantage of a regression based approach is that the relative
importance of many variables and groups of them to explain inequality are taken into account simultaneously.
As opposed to the traditional decomposition methods, this approach allows to consider all potential
explanatory factors simultaneously and to derive indicators of their relative importance in a simple and
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effective way. This is of interest since, generally, results on the relative importance do not differ substantially
from the traditional subgroup decomposition approaches and therefore this regression based Shapley value
approach might be a useful alternative in doing comparisons across geographical areas and over time.
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EU
Commission
(2005).
EU
sectoral
competitiveness
indicators.
Retrieved
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ANNEX
Table 1: Distribution by size (number of employees)
Size (# employees)
Frequency
Percent
Cumulate
51655
13580
11481
5010
7767
2074
1434
93001
55.54
14.60
12.35
5.39
8.35
2.23
1.54
100.00
55.54
70.14
82.49
87.88
96.23
98.46
100.00
Less than 10
From 10 to 19
From 20 to 49
From 50 to 99
From 100 to 249
From 250 to 499
500 or more
Total
Table 2: Distribution by economic activity
Economic activity
Frequency
Percent
Cumulate
585
0.63
0.63
30153
32.42
33.05
636
0.68
33.74
1586
1.71
35.44
Total Industry
32960
35.44
35.44
Construction
5645
6.07
41.51
15836
17.03
58.54
Transportation and storage
4799
5.16
63.70
Accommodation and food service activities
2550
2.74
66.44
Information and communication
4428
4.76
71.20
Financial and insurance activities
1006
1.08
72.28
Real estate activities
3262
3.51
75.79
Professional, scientific and technical activities
7673
8.25
84.04
Administrative and support service activities
5947
6.39
90.44
Mining and quarrying
Manufacturing
Electricity, gas, steam and air conditioning supply
Water supply; sewerage, waste management and remediation activities
Wholesale and retail trade; repair of motor vehicles and motorcycles
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Education
1094
1.18
91.61
Human health and social work activities
2946
3.17
94.78
Arts, entertainment and recreation
2722
2.93
97.71
Other service activities
2133
2.29
100.00
Total Services
54396
58.49
100.00
Total
93001
100.00
Table 3: Statistics for Labour productivity, profitability, growth of turnover (thousand 1000 euros at 2008) – All
observations
Variable
N
mean
cv
p25
p50
p75
Productivity
90655
38.59
5.48
14.15
27.42
46.28
Growth
90655
1634.62
34.32
-58.31
27.65
609.00
Profitability
90655
-4.22
-153.59
0.03
0.10
0.30
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Table 4: Correlations between growth, productivity and profitability indicators
Productivity
Growth
Productivity
1.0000
Growth
0.1138
1.0000
Profitability
0.0736
0.0029
Profitability
1.0000
Table 5: GE Index of productivity, profitability, and turnover growth (transformed min-max)
Dimension
α =-2
α =-1
α =0.333
α =0.5
Growth
0.74767
0.00536
0.00053
0.00052
Productivity
0.13654
0.00268
0.00107
0.00108
Profitability
1.62561
0.00564
0.00014
0.00012
α =-2
α =-1
α =0.333
α =0.5
-0.5
0.00019
0.00017
0.00015
0.00015
4
0.64119
0.00715
0.00061
0.00057
20
0.77551
0.00799
0.00075
0.00071
Table 6: Multidimensional GE index
Dimension
β
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Table 7: Decomposition of the Multidimensional GE Index by firm size
α=-2
Absolute
contribution
α=-1
Relative
contribution
Absolute
contribution
α=0.333
Relative
contribution
Absolute
contribution
α=0.5
Relative
contribution
Absolute
contribution
Relative
contribution
β = - 0.5
Within
0.000182
0.986062
0.000162
0.984373
0.000151
0.983203
0.000150
0.983126
Between
0.000003
0.013938
0.000003
0.015627
0.000003
0.016797
0.000003
0.016874
Population
0.000185
1.000000
0.000165
1.000000
0.000154
1.000000
0.000153
1.000000
β = 4
Within
0.641176
0.999985
0.007138
0.998641
0.000600
0.984066
0.000559
0.982916
Between
0.000010
0.000015
0.000010
0.001359
0.000010
0.015934
0.000010
0.017084
Population
0.641186
1.000000
0.007148
1.000000
0.000610
1.000000
0.000569
1.000000
β = 20
Within
0.775492
0.999981
0.007971
0.998192
0.000736
0.980730
0.000691
0.979519
Between
0.000014
0.000019
0.000014
0.001808
0.000014
0.019270
0.000014
0.020481
Population
0.775507
1.000000
0.007986
1.000000
0.000750
1.000000
0.000706
1.000000
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Table 8: Decomposition of the Multidimensional GE Index by firm sector of activity
α=-2
Absolute
contribution
α=-1
Relative
contribution
Absolute
contribution
α=0.333
Relative
contribution
α=0.5
Absolute
contribution
Relative
contribution
Absolute
contribution
Relative
contribution
β = 20
Within
0.000183
0.989671
0.000163
0.988374
0.000152
0.987437
0.000151
0.987371
Between
0.000002
0.010329
0.000002
0.011626
0.000002
0.012563
0.000002
0.012629
Population
0.000185
1.000000
0.000165
1.000000
0.000154
1.000000
0.000153
1.000000
β=4
Within
0.641181
0.999993
0.007143
0.999340
0.000605
0.992235
0.000564
0.991671
Between
0.000005
0.000007
0.000005
0.000660
0.000005
0.007765
0.000005
0.008329
Population
0.641186
1.000000
0.007148
1.000000
0.000610
1.000000
0.000569
1.000000
β = 20
Within
0.775499
0.999991
0.007979
0.999077
0.000743
0.990139
0.000699
0.989517
Between
0.000007
0.000009
0.000007
0.000923
0.000007
0.009861
0.000007
0.010483
Population
0.775507
1.000000
0.007986
1.000000
0.000750
1.000000
0.000706
1.000000
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Appendix
Table A.1: Distribution by size (number of employees) - Umbria
Size (# employees)
Frequency
Percent
Cumulate
Less than 10
1825
60.79
60.79
From 10 to 19
491
16.36
77.15
From 20 to 49
376
12.52
89.67
From 50 to 99
162
5.40
95.07
From 100 to 249
93
3.10
98.17
From 250 to 499
34
1.13
99.30
500 or more
21
0.70
100.00
Total
3002
100.00
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Table A.2: Distribution by economic activity – Umbria
Economic activity
Frequency
Percent
Cumulate
14
0.47
0.47
950
31.65
32.11
Electricity, gas, steam and air conditioning supply
18
0.60
32.71
Water supply; sewerage, waste management and remediation activities
38
1.27
33.98
Total Industry
1020
33.98
33.98
Construction
171
5.70
39.67
Wholesale and retail trade; repair of motor vehicles and motorcycles
515
17.16
56.83
Transportation and storage
116
3.86
60.69
74
2.47
63.16
Information and communication
136
4.53
67.69
Financial and insurance activities
45
1.50
69.19
Real estate activities
213
7.10
76.28
Professional, scientific and technical activities
271
9.03
85.31
Administrative and support service activities
143
4.76
90.07
Education
40
1.33
91.41
Human health and social work activities
84
2.80
94.20
Arts, entertainment and recreation
91
3.03
97.24
Other service activities
83
2.76
100
Total Services
1982
66.02
100
Total
3002
100
Mining and quarrying
Manufacturing
Accommodation and food service activities
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Table A.3: Statistics for Labour productivity, profitability, growth of turnover (raw data), (thousand euros at
2008) – Umbria
Variable
N
mean
p50
Cv
p10
p25
p75
Productivity
3002
28.73
24.19
2.16
2.48
12.21
39.26
Growth
2984
698.82
18.08
22.84
-678.98
-48.38
387.82
Profitability
2962
-14.16
0.11
-54.44
-0.06
0.035
0.35
Table A.4: Correlations between growth, productivity and profitability indicators - Umbria
Growth
Productivity
Growth
1.0000
Productivity
0.0446
1.0000
Profitability
0.0009
0.0246
Profitability
1.0000
Table A.5: GE Index of productivity, profitability, and turnover growth (transformed min-max) - Umbria
α=-2
α=-1
α=0.333
α=0.5
Growth
19.26807
0.09826
0.00102
0.00081
Productivity
23.15575
0.10774
0.00136
0.00115
Profitability
56.21388
0.16839
0.00086
0.00063
Table A.6: Multidimensional GE index - Umbria
α=-2
α=-1
α=0.333
α=0.5
-0.5
0.000550
0.000400
0.000286
0.000276
4
43.51370
0.257018
0.002614
0.001988
20
57.56939
0.295790
0.002657
0.002016
β
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Table A.7: Decomposition of the Multidimensional GE Index by firm size - Umbria
α=-2
Absolute
contribution
α=-1
Relative
contribution
Absolute
contribution
α=0.333
Relative
contribution
α=0.5
Absolute
contribution
Relative
contribution
Absolute
contribution
Relative
contribution
β=-0.5
Within
0.000546
0.994024
0.000397
0.991795
0.000283
0.988535
0.000273
0.988119
Between
0.000003
0.005976
0.000003
0.008205
0.000003
0.011465
0.000003
0.011881
Population
0.000550
1.000000
0.000400
1.000000
0.000286
1.000000
0.000276
1.000000
β=4
Within
43.513691
1.000000
0.257008
0.999963
0.002604
0.996379
0.001978
0.995244
0.000011
0.000000
0.000010
0.000037
0.000009
0.003621
0.000009
0.004756
43.513702
1.000000
0.257018
1.000000
0.002614
1.000000
0.001988
1.000000
Between
Population
β=20
Within
57.569378
1.000000
0.295782
0.999974
0.002650
0.997104
0.002008
0.996187
0.000008
0.000000
0.000008
0.000026
0.000008
0.002896
0.000008
0.003813
57.569386
1.000000
0.295790
1.000000
0.002657
1.000000
0.002016
1.000000
Between
Population
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Part 2: Public Sector contribution to competitiveness
Vincenzo Patrizii (Unifi) , Giuliano Resce (Unifi)
University of Florence, Italy
Summary*
Measuring country competitiveness in terms of final relative prices sheds little light on how those prices are
affected by public expenditure. By taking productivity as a common factor to any index of competitiveness we
propose to assess the role of public sector policies by measuring the efficiency in public services provision. The
analysis is carried out on the Italian local public services therefore allowing for territorial as well as service
differentiation.
Data envelopment analysis integration with Principal component analysis supplies a consistent methodology to
face the problem of large dimension and large variability in data caused by the intrinsic multidimensional
aspects of public services provision and utilisation.
Results show a large territorial variability in Italian local public services productivity and a differentiation in
terms of both levels of government and type of services. This provides evidence to identify areas, services and
tiers of government lacking efficiency and constituting a potential obstacle to growth.
*
For helpful comments and for support in data collection the authors wish to thank: Filippo Elba (Università di Firenze),
Maria Grazia Calza and Edoardo Pizzoli (Istat), Bruno Spadoni (Confservizi), Emanuele Proia (Asstra), Sara De Marco and
Fabian Mazza (Bureau Van Dijk). Generous access to Aida Database from Bureau Van Dijk is kindly acknowledged.
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Introduction
Competitiveness is often seen as a key indicator of a Country’s economic potential. Conventional
measures tend to concentrate on either the private sector of the economy, or to treat a Country’s economic
system as a peculiar large private company. In both cases factors cost and goods final price play a major role in
any measure of competitiveness6. A significant exception is provided by the Global Competitiveness Index
based on a variety of indices, aiming at including a large set of characteristics not directly observable in market
operations, such as ,for examples, people’s own assessment of how easy it is to run businesses in a Country
and the functioning of institution, public and private (World Economic Forum 2012) 7.
In this paper we follow this more general way to approach competitiveness because it provides a
framework in which to pose the question of how public institutions affect the competitiveness of a Country.
However, we depart from the Global Competitiveness Index way to include Public institutions because of its
heavy reliance on “subjective” measures. Our contribution goes in the direction of devising a more objective
way to link the working of Public institutions to a Country index of competitiveness. To this end we follow the
idea put forward by Krugman (1994) that although competitiveness could have many and even opposing
meanings they all have to be grounded on the concept of productivity. Indeed World Economic Forum (2008),
defines the national competitiveness as a set of factors, policies and institutions that determine the level of the
productivity of a country.
With productivity in mind the question of measuring the contribution by public institutions is very close
to the old question of getting a measure for the “real” value of public expenditure. Indeed public expenditure
can better be seen as the financial side of how public institutions operate in the economy. However, like in the
private sector, public expenditure does not provide the same contribution throughout the Country. It is known
that Public policy effectiveness varies significantly when judged on a territorial basis. The North –South growth
problem is a well known and lasting example. Therefore we want the measurement of Public sector
contribution to be area-specific. That is to say to be decomposable by territorial area. In addition, taking into
account the quest for increased efficiency in public expenditure, the overall measure of Public sector
contribution has to be decomposable in terms of policy objectives (type of public services).
To make such a measurement possible, the first problem is that of isolating the final services provided
by the Government, their cost and level of provision. Once these data are made available we estimate the
relative efficiency of each service, hence aggregate the efficiency index on a territorial basis (District level). This
index provides the basic tool to arrive at an efficiency adjusted measure of public expenditure. Its geographical
distribution provides indicators of how public policies affect overall and local development. Comparing the
index across services and tiers of government helps identifying areas and causes of inefficiency.
6
For Oecd practice see Mattine,Giorno (1990),. More recently; Neary (2006), Diewert, Nakamura (2007).
Porter (1990), World Economic Forum (2007) include indices referring to: Institutions, Infrastructures, Health and
Primary education, Higher Education and Training.
7
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The paper develops as follows: sec. 1 describes the way public services are provided in Italy according
to layers of government; sec. 2 documents the organisational aspect of provision and illustrates the different
measure of outputs needed to account for the services’ multidimensional nature; sec. 3 presents the basics of
Data Envelopment Analysis (DEA) and the characteristics of a Slack Based Measure (SBM) of efficiency
integrated with Principal Component Analysis in order to make the multidimensional characteristic of the data
manageable; sec. 4 presents the results and par sec.5 summarises and concludes.
1. Public services by tiers of government
If one had to rely only to national accounts data, the public sector’s contribution to the Country’s
productivity could be obtained by isolating the public component in the per capita GDP index. For the 20 Italian
Regions this exercise is reported in Figure 1. It comes out the conventional picture characterized by:
a)
The relatively high level of average productivity (per capita GDP) in some of the Northern regions (Aosta
Valley e Trentino Alto Adige8) is partially due to higher public contribution to GDP by mean of relative high local
public spending;
A North-South dichotomy whereby the Southern Regions show, on average, lower productivity despite
b)
relatively high level of per capita public expenditure. That is private per capita component to GDP is much lower
in Southern Regions than in rest of the Country;
c)
The more productive Regions have an average productivity above EU average.
8
They are both Special Statute Regions. A status granted also to Friuli Venezia Giulia, Sicily and Sardinia by Constitutional
Act. They benefit from higher level of per capita public spending and wider degree of independence in terms of own
legislation.
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Figure 1. Per capita GDP in Italian Regions (euro 2009)
Source: Istat (2012a)
This picture contains, however, the implicit hypothesis that public expenditure: i) has a productivity
one to one in terms of services and; and therefore productivity is uniform throughout the Country. We begin
by challenging this hypothesis.
Form National Accounts, services provided by the public sector (General government) sum up to 325
bln euro in 2009, that is 21% of GDP (ISTAT 2012). This, however, underestimates the actual role of the public
sector in terms of command over resources. Since mid-Eighties a new aggregate for the public sector:
“Enlarged Public Sector” (EPS) has been introduced which is larger than the conventional General government
as it also includes Local public companies9 (hereafter L) which supply public services although they are not
necessarily fully owned by bodies belonging to the General government. With reference to the EPS the
contribution to the Country’s GDP in terms of final services is of 599 bln Euro (39% of GDP) in 2009 (Ministero
dello sviluppo economico, 2012)10.
9
From a legal point of view they are private companies. However either because the companies are 100% owned by
public institutions or because as a matter of fact public institution take the role of residual claimant, the companies are
better seen as belonging to the public sector.
10
In Table 1, first column, total expenditure comes to be less than 599bln because Defense and other minor services are
not included.
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Lack of data prevents the analysis to cover all this level of expenditure. Data collection on public
services is non-systematic and does not guarantee full coverage in terms of services and tiers of government.
By gathering information from different sources, data have been collected to cover, in terms of expenditure
and level of services provided, the services listed in Table 1. Some minor services are excluded due to lack of
data in terms of output measurements11. Nevertheless, the analysis manages to cover a range of expenditure
that goes from 100% of Health services, to 15% of Security& safety, with an average over all services of almost
60% (see Tab 1).
Table 1: Public Services by tiers of government*
Expenditure Share
Share
Coverage
Expenditure
(2)
Services
Analyzed
Expen.
(
mln €)
%
Analysis by
Districts
%
area%
115,702.31
100.0
53.3
(1)
EPS (mln €)
Health
115,702.31(a)
100.0
Administration &
governance
127,529.78
46,764.07
36.6
21.5
Education
53,488.89
41,355.82
77.3
19.0
96.2
Public transport
14,761.56
3,594.97
24.3
1.7
62.4
Waste management
16,205.63
3,058.39
18.8
1.4
62.3
Social services
19,196.44
2,952.58
15.4
1.4
94.9
Security & Safety
13,930.62
2,089.54
15.0
1.0
100.0
Road maintenance
6,071.71
1,713.62
28.2
0.8
77.8
Total
366,886.95
217.231,30
59.2
100.0
84.8
85.3
(a)
Legend: (a) The share of social services provided by Regions is included in Health expenditure.
Source: (1) Ministero dello sviluppo economico (2012); (2) Health: Ministero della salute (2011b), and as
for Trentino Alto Adige and Calabria Istat (2012b). Administration: as for Regions COPAFF (2010).
Education: Ministero dell’Istruzione, Università e Ricerca (2012). Public transport and Waste
management Aida-Bureau Van Djik (2012). Social services Istat (2011c). Administration and governance
11
Also some of the major nationally provided services are excluded, such as Administration, Defence and Security . We
believe that exclusion of these services does not alter the results because the best working hypothesis would be that of
uniform distribution.
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(as for Districts and municipalities share), Security & safety and Road maintenance Ministero
dell’Interno (2012).
2. The multidimensional nature of Public services
In most cases Public services do not have a market: they are not sold but rather provided either for free
or at heavily subsidised fares. For instances the functioning of the institutional body (Administration &
management), Road maintenance, Security & Safety, Social services of Tab.1 belong to such a category. Even in
these cases of marketed services, when a cost oriented price does exit, externalities in provision and in
consumption are relevant to the extent that the overall benefit to the community is only indirectly linked to
users’ price (Transport, Health, Education, Waste management). In all cases, marketed and non-marketed
services, the collective benefits to the community are made of direct (consumers surplus) and indirect (external
effects) components. In attempting to account for them all one has to resort to a variety of measurements.
This is the root for the inherent multidimensional characteristics in public services provision.
In addition some of the benefits do not materialise in observable variables, but rather have the nature
of latent variables. Health services are probably the most obvious case although by no means the only one
among public services. The ultimate benefit of health services and, for that matter, of the whole health system,
is a generic variable of “good health”12. Such a variable is not directly observable as it is the outcome of
complex, unknown combination of observable variables (services actually provided). To recover latent variables
a large set of observable variables has to be collected and statistical Principal component – Factor analysis be
used in order to isolate the contribution to the latent variable from each of the observed ones.
Last, not least though, comes the role of Local Communities’ decision system. Some Communities
might see the externality components of public services, more than the private benefits, as the leading
motivation in deciding quality and quantity (hence the characteristics) of public services. Other Communities
could, instead, consider private benefits to be the guiding criterion. If in measuring public services one chooses
to look only at individual users, hence proceeds to collect measurements on some rather than other
characteristics of services, then the externality concerned Communities would be at a disadvantage.
For all these reasons output from public services should be measured by collecting data on as many
different aspects as possible in order to catch the relevant dimensions likely to affect Community’s welfare.
What follows is a description of the organisational aspects of service provision together with a list of
variables used to measure services’ quantity and quality.
Administration & governance
This expenditure is considered for each of the three tiers of government: Region, Districts and
Municipalities13. It is therefore treated as three distinct type of services according to the layer of government
each refers to. Description of detailed services included in this sort of catch-all name is rather difficult as they
are heavily characterised by joint cost in production and externalities in utilisation. Services include
12
The quality-adjusted life year (QALY) is the conventional measurement.
Regional expenditure for Administration has been obtained from netting total expenditure of capital outlay at the same
proportion as for the total, COPAFF (2010). For Districts and Municipalities expenditure data come from Ministero
dell’Interno (2012). Municipalities include only the County towns (capoluogo).
13
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management of personnel, the working of legislative bodies; expenditures to support economic development
when not directly attributed to that item (Istat 2009). Strategic planning and implementation of programs and
other services. Registration service (birth, marriage, death ceremonies and obtaining certificate copies) for
Municipalities is the bulk part. Businesses licensing and tax collection is also a relevant set of services falling
under this heading.
As this type of expenditure concerns the cost of people and offices responsible for the execution of
policies established by the governing board it seemed plausible to include, as a measure of output, the overall
efficiency index obtained from the analysis of the remaining services (see Table 1). Table 2 details the variables
taken as output measurements for this aggregate of services.
Table 2: Administration & governance
(a)
Variables by tier
of government
Groups of variables
R
Demographic data
Coefficient of
(b)
variation %
D
M
1
1
8
129,2
1
1
-
53,3
Production activities(d)
-
-
5
208,0
(e)
-
2
2
72,0
1
1
1
146,0
Surface area
(c)
Bills and resolutions
(f)
Efficient expenditure
Legend: (a) It refers to 88 District and 84 Municipalities (County town);
(b) For Municipalities includes: residents, immigrates, birth, deaths, new
entries, cancellations, number of families, cohabitants. For Districts and
Regions only inhabitants are considered; (c) Relevant mainly for Regions
as a proxy for environmental protection; (d) As for Municipalities:
number of active businesses, new entries, cancellations, hawkers and
stationary businesses; (e ) As for Municipalities and Districts, number of
bills and resolution from the Council and by the Cabinet. As some
legislation bodies are differently organised in SSR Regions, the variable
has been omitted for Regions; (f) It is the overall efficiency index for each
tier of government.
Source: Ministero dell’interno (2012), Istat (2012c), Istat (2013), Data
provided by Chamber of Commerce.
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Health
Among all services, Health bears the highest expenditure (ISTAT 2012e)14. Its governance is basically in
the hands of Regions. They are in charge of defining the organisational structure of Health Care Districts,
providing for a suitable territorial dimension. Although with significant differences at some local areas, Health
Districts tend to coincide with the second tier of local government: Districts15. Provision is organised at a local
level by administrative units Local Health Authority (ASL) in charge of 50,000 to 200,000 inhabitants. In the last
decades, population aging, changes in epidemiology and the quest for increased effectiveness and cost concern
have oriented health policy towards the reorganisation of territorial care through the development of
organisational models based on division of labour between the General Practioners, Hospitals and Specialists.
In addition, the contribution of families and patients in terms of “out of pocket” payments is undergoing a
continuous increase. This has seen a changing role for hospitals (towards increasing specialisation in terms of
professional know-how and technological equipment for treatment of acute illnesses) and strengthening of the
Primary care system (the front-office type of role towards patients). Form an organizational point of view
services are divided into three groups (cfr. Table 3).
Table 3: Groups of Health services
Services
Expenditure share % (2009)
1) Health care in public and workplaces
4.90
2) District health care
48.84
3) Hospital services
46.97
Source: Ministero della salute (2011b)
a) Health care in public and workplaces
Local Health Authority (ASL) are primarily concerned with protecting and promoting public health and
are responsible for achieving health objectives and targets set at national and regional level of government.
Each ASL has a health promoting department covering the following activities: prevention; children vaccination
(compulsory and recommended); Oncological screening; preventing occupational diseases and accidents;
immunization against specific and professional hazards; non-communicable diseases; health promotion and
education; preventing environmental hazards; food control and preventing food-related disease (obesity and
malnutrition); prevention, diagnosis and treatment of animal diseases, including infectious and contagious
diseases and zones; educational public programs to prevent lifestyles diseases related (smoking, food, alcohol);
14
Exependiture data come from Health: Ministero della salute (2011b). The gap for Trentino Alto Adige and Calabria has
been filled from Istat (2012b) for which the distribution by groups follows the national distribution. It is worth noticing
that Ministero della Salute (2011b) directly provides total expenditure by Group of Health services of Tab.3.
15
Act 833/78, states that as a rule Local Health Units comprise from 50,000 to 200,000 inhabitants. Exceptions can only be
granted by Regions depending on social and geographical peculiarities.
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environmental risk management, legal purposes assessment; issuing of medical certificates; infection control in
healthcare settings and patient safety. The variables considered as outputs for the analysis are in Table 4.
Table 4: Health care in public and work places
(a)
Number of
Coefficient of
variables
Variation %
Variables by group
Prevention departments
1
70,84
Pap tests
1
82,29
Mammography
1
101,64
3
87,76
1
78,77
5
97,86
Children vaccination
(b)
Anti-flu vaccines
People with healthy lifestyles
(c)
Legend: (a) It refers to all the 20 Regions; (b) It includes three sets of vaccines: Diphtheria/pertussis/tetanus; Polio; Hepatitis B; (c)
Number of: non-smokers; normal weight people; people that eat vegetables at least once a day
Source: Ministero della salute (2011b), Istat (2012b).
b) District health care
The name of this group of services comes form the geographical type of organization that turns out to
correspond to the actual Districts. Besides the misleading name, the services under this heading are those of
Primary care of international use (cfr. World Health Organization. Health systems). It is provided by General
Practioners, paediatricians and self-employed physicians working under a contractual agreement with the
National Health System (SSN). GPs and paediatricians initially assess the patient and are expected to provide
most primary care. They act as “front office” for access to secondary care services. Specialist doctors or
diagnostic tests are provided by public hospitals or by private ones. Admission in Hospital requires a
prescription either from the General Practioner or from doctors at the Emergency. Following surgery and
primary rehabilitation at the hospital, the patient might need services that belong to the Primary Care, such as
home care (home nurse and/or home assistance). Old people assistance and care are also provided by Primary
care organisations. Mental health care is provided by National Health System (SSN) in a variety of communitytype services: community mental health centers, community psychiatric diagnostic centres, general hospital
inpatient wards, and residential facilities. The set of variables used to measure this group of services is in Table
5.
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Table 5: District (Primary) Health Care
(a)
Number of Coefficient of
Variables by group
variables
variation %
Doctors
1
77,35
Paediatricians
1
82,59
Prescriptions
1
75,85
N. of CUP(a)
1
68,07
1
83,31
N. of SERT days
1
85,11
Days of rehabilitation assistance
1
114,93
3
87,17
Days in mental health hospitals
1
96,39
Elderly persons assisted at home
1
105,2
Days of elderly in residential facilities
1
178,82
N. of SERT users
(b)
Specialists services
(c)
Legend: (a) It refers to all the 20 Regions; (b) CUP: Central Booking Point;
(c) SERT: drug-addicts assistance; (d) sorted into: specialists and medical
devices test, diagnostic tests and other services.
c)
Hospital services
Hospital services have undergone a deep reorganization in order to gain efficiency by increasing horizontal
integration between Operational Units and vertical integration within types of specialty. National standards
have been set for allocation of hospital beds as to 4 per 1,000 inhabitants, including 0.7 beds for rehabilitation
and long-term care (cfr. Ministero della salute, 2011a). The aim being that of promoting a shift from
hospitalisation to day care and from day care to outpatient care and to support home and residential care
(France et al 2005, Thomson et al 2011). Currently, hospital care is delivered mainly by almost 700 public
structures. The major groups of hospital services are: a) Healthcare services provided during inpatient hospital
stay; b) Healthcare services provided during outpatient hospital admissions (day hospital); c) Rehabilitative
healthcare services. Usually, these service are provided in conventional hospitals (public or private under
contractual agreement with the National Health System) and in long term hospitals, nursing homes and
residential and semi-residential homes. Older and disabled individuals receive care through residential or semiresidential facilities and community home. Patients are given free choice for hospital while choice of specialist
is not allowed. Therefore, ASL have to pay for the treatment received by their residents from providers in other
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regions (outward mobility) and, in turn, they receive payments for services provided to patients coming from
other regions (inward mobility) (cfr. Lo Scalzo et al 2009).
(a)
Table 6: Hospital health services
Number of
Coefficient of
variables
variation %
Variables by group
Patients discharged
1
84,59
Non-residents outpatients
1
98,1
3
81,51
3
88,44
1
86,98
4
88,35
3
102,85
Hospital beds
(b)
Days in hospital
(b)
Discharged patients with DRG
Births
(c)
(d)
Waiting lists
(e)
Legend: (a) It refers to all the 20 Regions; (b) Sorted into long-stay, day
hospital and others; (c) Patients consistently classified according to DRG
(diagnosis-related-group) coding. It is a measure of effectiveness of
patients in-taking policy; (d) Sorted into Caesarean, Natural and Premature;
(e) Bookings with less than 60 waiting days, divided into echography, CAT
(computerized axial tomography) and optometric services.
Education
Expenditure for Education is the second highest level of expenditure after Health (ISTAT 2012e)16, and is
financed mainly by the Central government with contribution for selected services and objectives from
European Union, Regions, Districts and Municipalities.
From organisational point of view Education is state-controlled and all schools, both public and private,
are subject to comply with the curricula and teaching methods set by the Ministry of Public Education. Some
degree of autonomous decision making and responsibility has been recently granted to each individual school.
Education system is divided into 5 levels: Nursery, Primary school, Lower secondary school, Upper secondary
school and University. In the present work University is not considered because its distribution not being
uniform across the Country would cause a bias in the efficiency measurements.
16
Expenditure data come from Ministero dell’Istruzione, Università e Ricerca (2012) which includes expenditure at each
school level. Therefore no matter which level of government provides finance, all the expenditure is included. Even
included are fees and contributions for guided educational trips.
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The length of compulsory education has in recent years been extended up to the age of eighteen. At
primary level, which starts at the age of six to end at eleven, students are provided free education and
textbooks. Primary level education can be preceded by three years of pre-school nursery. Secondary education
is divided into two levels: Lower secondary school and the Upper secondary school.
The data collected in order to provide measurements of the educational output came from Ministero
dell’istruzione, università e ricerca (2012) and includes Nursery, Primary, Lower secondary and Upper
secondary schools.
The unit of analysis for the present work is the District to which is imputed a total expenditure for
Education (however financed: EU, Government, Region or Municipalities) made of the expenditure from each
individual school within the District boundaries. In very much the same way on the output side each District has
outputs given by the sum of outputs from each Government school within the District (cfr. Table 7).
(a)
Table 7: Education
Variables by group
variables
variation (%)
School
1
80
Classrooms
1
79.66
Sections
1
107.37
Pupils
1
112
Personal computer (b)
2
93.70
2
112.16
Classrooms with lan and wifi
2
120.76
Teaching staff (d)
1
98.78
1
105.65
1
98.22
Overhead projectors
(c)
Support teachers staff
ATA staff
(d)
(d)
Legend: (a) It refers to 101 Districts; (b) Sorted into desktop e laptop; (c)
Sorted into in room equipment and mobile; (d) Net of staff days of
absence.
Source: Ministero dell’Istruzione, Università e Ricerca (2012)
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Social services
Services are provided by a network of public and private partners with the Municipality supervising
provision through integrated procedures and an accreditation process. Finance from EU, Central government,
Regions and Private donors is collected by the Municipality17. The national Act 328/2000 on social welfare
makes Municipalities responsible for managing social services and benefits, and Regions responsible for
planning . Expenditure at the municipal level was 7 billion euros in 2009, with an average value per resident of
115.9 euros. The distribution of expenditure by type of services reveals that support to families with young
children accounts for 39.8 per cent of total expenditure; next are the services to Elderly and Disabled, with a
share of 21 and 20 per cent, respectively. Policies aiming at curbing poverty and social exclusion represent 8.3
per cent, to multi-purpose activities goes 6.3 per cen. Minor shares go to finance support to Immigrants and
Nomads (2.7 per cent) and different types of Addictions (0.9 per cent).
The distribution of expenditure across the Country varies significantly: ranging from a minimum of
about 26 euros in Calabria to a maximum of 294 euros in Trento, in 2009. Southern Regions and Islands
(except Sardinia) all fare below average. In these Regions the share of expenditure aimed at reducing poverty
or social exclusion is higher than the rest of the Country. While in the Northern Regions, with the exception of
Lombardy and Emilia-Romagna, Elderly and Disabled population related policies are above national average
(Istat 2011c).
(a)
Table 8: Social services
Number of
Coefficient of
variables
variation (%)
Families with Children
1
196,05
Disabled people
1
264,45
Drug and other addictions
1
351,03
Elderly people
1
238,11
Immigrants and Nomads
1
224,29
Poor and hardship adults
1
244,11
Multi-purpose activities
1
162,57
(b)
Variables
Legend: (a) It refers to 110 Municipalities (County town); (b) all
variables refer to the actual number of users.
Source: Istat (2011c)
17
Expenditure data come from Istat (2011c) which includes expenditure from all government level (citizens’ co-payment
included). Therefore expenditure from all government tiers and Local companies is included.
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Road Maintenance
Road building and maintenance is a service shared among different layers of government. Besides the
motorways system, mainly privately managed on a long term concession contract, the rest of the Country’s
road system is managed by Districts and by Central government (through Anas), Regions and Municipalities.
The destination of roads to layer of governemt is presently done according to Act (Dlgs 112/1998) which
transferred most of Central government roads (strade statali) to Regions. It is up to the Region the decision on
Districts road classification. Following the classification each Layer of government has to finance construction
and maintenance out of its own budget. The Country road network is about 183,705 km (excluding Municipal
roads) of which 4% are motor ways, 11% Central government roads (strade statali) and 85 % are managed by
Regions and Districts ( Ministero delle infrastrutture e trasporti 2011, Banca d’Italia 2011).
The distribution of motorways is not uniform across the Country: the Northern Regions have a higher
share. Districts and Regional roads show a higher share for Northern and Southern Regions (about 40% each)
with the Central regions at just about 20%. As far as Municipal roads the distribution sees the Northern
Municipalities with about 38%, Central 28% and Southern 34%.
In the present work, however, lack of data forces us to limit the analysis to road maintenance at
Municipal and District level 18.
Table 9: Road maintenance
Variables by group
(a)
Variables by tiers
Coefficient of
of government
Variation (%)
D
M
Municipal roads
-
1
178.95
District roads
1
-
52.11
Street lights
-
1
126.75
Lighted roads
-
1
153.13
Vehicles
1
1
155.46
Traffic restricted roads
-
1
131.61
Bicycle paths
-
1
136.3
Pedestrian areas
-
1
293.28
Accidents
1
-
154.08
Legend: (a) It refers to 85 Districts and 86 Municipalities (County Town).
Source: Ministero dell’interno (2012), Istat (2011d), Istat (2012d), Ministero
18
Data expenditure both for Municipalities and Districts come from Ministero dell’Interno (2012).
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delle Infrastrutture e dei Trasporti (2010), ACI (2011).
Public transport
Local transport services (city and suburban buses, underground and local trains) in Italy are provided by
companies private or publically owned (Central government, Regions Districts and mostly by Municipalities). In
all cases the service is provided under a concession type of contract (contratto di servizio) which while granting
a monopoly power it also subsidies the company. Persistent financial distress and worsening of services quality
have brought the national Parliament to a radical reform (D.lgs. 422/97 and 400/99). The reform basic idea is
that of granting concession only through tendering. In addition an incentive type of regulation is introduced
whereby companies are required to sign a contract which sets a limit to the subsidy and defines quality
standards and fares together with penalties in case of underperformance. Together with the incentive type of
regulation the reform has brought a substantial devolution from the Central Government to Regions and
through them to Districts and Municipalities of strategic function such as coordination of transport systems
within the Region, services long term planning (Bentivogli et al., 2008). As division of labor exists among level
of government which gives to Districts the duty of organizing ad providing suburban transport services, while
Regions take care of local train transport and the city bus service belongs to the Municipalities.
As far as the supply of services is concerned there tends to be an imbalance in terms of transport
infrastructures. Taking the index of network density given by network length per 100 sq km, the major
Municipalities have a high density index with no significant differences across the Country. Instead medium
and small Municipalities with low density index are more frequent in the Southern regions and the Islands.
Similar imbalance exists for indices of service supply. The number of coaches to population ratio takes highest
value in 30 Municipalities of which 21 are from the North. Symmetrically, out of 22 Municipalities faring the
lowest index level, as many as 22 are from the South (Istat, 2012d)19.
Table 10: Public transport
(a)
Variables by group
variables
variation (%)
Buses network
1
120.21
Tramways
1
482.9
Trolleybuses
1
366.82
19
Data expenditure in the case of public transport is the cost of service from each local company, for County Town
(capoluogo) Aida-Bureau Van Dijk (2012).
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Metro network
1
488.37
Funicular network
1
346
Buses, trams and metro coaches
3
308.03
Seats/km for busses, trams and metro
3
442.37
Bus and tram stops
2
144.14
Metro stations
1
488.11
Passengers per year
1
114.38
Legend: (a) It refers to 72 companies from 2008 to 2009
Source: CNEL (2012), Bureau Van Dijk (2012), Istat (2011d) and data provided
by ASSTRA (Associazione Trasporti, Roma).
Waste management
The service belongs to the devolved matter and is organized following subsidiarity principle: Regions
have a coordinating and planning role, which also includes setting regulations norms and environmental
standards and management of landfill sites (Act 22/1997, Decreto Ronchi); Districts have the role of
coordinating waste management and collection within the sub Regional area (ATO, Ambito Territoriale
Ottimale); Municipalities take the largest share of the service as they are responsible for operation activities: i)
General waste collection, which includes transportation to a transfer station, or drop-off site for recyclables, or
a disposal facility; ii) Transport services by means of a fleet of vehicles specially equipped for waste collection,
cleaning and transportation of waste to landfill sites; iii) Cleaning services for all public spaces and streets
under the Municipal’s jurisdiction; iv); Disposal services, including maintenance and operation of special
processing and collection facilities, waste transfer stations and landfill sites20.
In 2009, reference year for our analysis waste sorting (separate waste collection) was operating in all
the 116 County town of our Municipality sample. In that same year average waste collection per head was
604,3 kg. Which shows a reduction when confronted with previous years, mainly in the share of unsorted
collection, while there is an increase in sorted collection by then reaching 30.4%. Northern Towns are those
with the higher per capita level of waste collection: (660 kg), next come the North Ester Town (640 kg),
followed by Town from the Islands (602 kg) then those from the South and North West (555 kg). As for the
share of sorted waste collection to the total, North Eastern Municipalities reach top level of 44.5%, followed by
those from North West (39.7), the Centre (26.9), South (20.4) and the Islands (13.5) (Istat 2011e).
20
As for the case of transport, expenditure data refers to the running cost of the service as from the Local companies
budget balance, from Aida-Bureau Van Dijk (2012).
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Table 11: Waste management
(a)
Number of
Coefficient of
variables
variation (%)
Variables by group
Paper
1
227.09
Glass
1
222.06
Plastic
1
212.43
Metal
1
225.73
Dangerous waste
1
175.68
Organic waste
1
144.94
Undifferentiated waste
1
221.82
Legend: (a) It refers to 72 companies from 2008 to 2009
Source: CNEL (2012), Bureau Van Dijk (2012), Istat (2011e)
Security & Safety
Within the Municipal organisation, Security & safety is a service made up of three distinct agent corps:
Municipal, Businesses and Administrative Police. Although the bulk activity concerns traffic and road regulation
including fines and documents checking, there is asset of additional activities encompassing a great deal of
Municipal collective life aspects. According to National Act 65/1986 Local policemen are public officers,
therefore in addition to their duty coming from Mayor’s decisions, they also have to operate in accordance
with the local public Prosecutor. Businesses Police has a role of supervisor on shops and businesses activities
with respect to opening hours, safety, health conditions and consumers’ protection. Administrative police’s
principal role is that of enforcing regulation mainly as far as shops start up permission is concerned and is in
charge of the administrative procedure of fining21.
Table 12: Security & safety
Variables by group
Policemen
21
variables
variation (%)
1
260.22
Expenditure data come from Ministero dell’interno (2012) and refer to Municipalities.
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Police vehicles
1
176.1
Kilometres covered 1
211.85
Fines
255.83
1
Legend: (a) It refers to 2901 Municipalities
Source: Ministero dell’interno (2012).
3. Dimension in DEA models
The previous paragraph has documented how public services are characterized by many dimensions in
terms of outputs. This follows from the fact that public services provision aims at meeting not just one Local
community’s need but rather many (not even necessarily consistent). The choice of which need should
characterize more than others local services provision belongs to the domain of Local government’s choice and
should not condition the measurement of the efficiency. DEA models possess this characteristics although it is
gained at the price of loosing the possibility of making conventional statistical inference in terms of confidence
interval of estimates22 (cfr Cooper et al 2007).
This point can better be described by noticing that there exist two basic approaches to the estimation
of frontier function: parametric and non parametric. The parametric approach is characterized by the required
assumption of a specific functional form for the underlying production function (Ces, Translog). In addition to
the functional form it is also required specific assumptions about the error term distribution (truncated or one
side distribution). Under these assumptions, single or (input) demand system regression yields marginal
products or marginal cost or partial elasticities from which efficiency index can be built23.
Data Envelopment Analysis is non parametric in that it does not require the underlying production
function to belong to any specific functional form24. In addition it does not require the imposition of specific
assumption on the probability distribution of the error term. Instead it assumes that any deviation from the
efficient frontier is due to inefficiency, hence no random component need to be accounted for. This very last
assumption, while exposing DEA to error from poor data quality, provides the way to arrive at a single efficiency
index even in presence of multiple inputs and outputs. And it does this without the need to pre-assign weights
to inputs and outputs. Even better it evaluates the DMU (Decision making unit) efficiency by allowing input and
output weights to take the most favourable value for the DMU under assessment (cfr Cooper et al 2007). The
efficiency index comes as a result of comparing each DMU relative to all the others under the simple constraint
that all DMU lie on or below the efficient frontier. The efficiency index is obtained by scaling the inefficient DMU
against a convex combination of efficient DMU nearest to it. Therefore efficiency has a relative meaning: it is
relative to the efficient DMU. This implies that technological efficiency, that corresponding to a DMU operating
on the (engineer or mathematical) production function might not be the term of reference unless the efficient
22
In addition DEA allows to arrive at a production frontier in cases of simultaneous presence of multi input and output.
This potential has, however, been used only in the case of Social services (3 inputs), Roads Maintenance (2 inputs of which
Accidents is a “bad output”, cfr. Cooper et al., 2007, p. 368) and Public transport (2 inputs).
23
See for instance: Lovell and Schmidt (1988); Bauer (1990); Kumbhakar and.Knox Lovell (2000).
24
DEA originates from the work of Farrel (1957) and further developed by Charnes, Cooper and Rhodes (1978).
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DMU does in fact operates on it. If efficient DMU contains any inefficiency, that would not be brought to the
surface by DEA 25.
Figure 2: Technical efficiency and Productivity in Dea models
Output: y
CRS Frontier
D2
D1
.
D3
.
.C
.D
VRS Frontier
B.
.A
Input: x
O
One important distinction has to be made in using DEA models to measure productivity. This is done in
Figure 2 where four DMU (A,B,C, D) are depicted. They produce one output (y) out of one input (x). The set
made of all points between the piecewise production frontier A,B,C and the x axis describes the feasible
production set. DMU operating on the frontier (A,B,C) are technical efficient. The higher output by DMU C, as
compared to B, has been reached by using more input. DMU D, instead, is inefficient because by using the same
input as C it produces less output, hence it lies below the efficient frontier.
For DMU D to become efficient it would require a movement towards point D3 or to point C. Either
points would guarantee technical efficiency, although at a different level of productivity. Indeed productivity,
as measured by the ratio of output to input, is higher in D3 than in C due to decreasing returns to scale.
In short, if productivity is the aim of the measurement then inefficiency has two different sources:
technical inefficiency if the DMU lies below the production possibility set; scale inefficiency if the DMU does not
exploit return to scale. In Figure 2 it is DMU B the only one which is both technical and scale efficient, hence the
highest productivity.
The overall (global) Technical efficiency of DMU C (Cooper et al 2007):
(1)
can be decomposed into Pure (local) technical efficiency:
(2)
and Scale efficiency
(3)
Therefore:
(4)
25
Or by any parametric method for that matter. That is because parametric models, in as much as they rely on regression
analysis, take an “average” of observed values.
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An additive variant of (4) is at times more suitable to present results. For this purpose we define Scale
inefficiency (SI) as:
(5)
Then from the obvious definition of Pure Technical Inefficiency as PTI =1-PTE we get:
(6)
In this way the whole inefficiency (
is decomposed into two additive components: Scale
and Pure Technical Inefficiency. If no inefficiency is present (
) then the Dmu is at maximum
; otherwise productivity is less than maximum (
.
productivity and
As shown in Figure 2, the Technical efficiency index is obtained by DEA models under constant return to
scale assumption (CRS Frontier). The Pure technical efficiency index requires the variable return to scale
assumption ((VRS Frontier).
It is worth stressing that all ndices coming from (1to 6) have a double “relative” content. They are
relative to the DMU considered, in that the indices are not invariant to the inclusion (exclusion) of DMU in the
analysis. And they are relative to the reference term given by the efficient DMU. That is any inefficiency
contained in the best performing unit (efficient DMU) has no place in the efficiency indices. Therefore all indices
are “gross” of any lack of efficiency characterising the best performing DMU.
In addition, it is worth calling the attention on the fact that productivity according to any DEA model is
at most equal to one, as it is also made clear in (6)26. Attention should then be payed in comparing
conventional aggregate productivity, such as that provided by the ratio of GDP to population (see Fig. 1) and
productivity measured according to (1). Not only the two ratios have different denominators, but that given by
(1) is also subject to a scaling factor that makes its maximum value 1 and its minimum value 0.
The way we estimate the VRS and CRS frontier in Fig.2 is by mean of a Data Envelopment Analysis
model known as Slack Based Measure of efficiency (Tone 2001). The main characteristics of the inefficiency
index provided by this model are (for all see Cooper et al 2007):
- it is consistent with the Pareto-Koopmans efficiency;
- can be interpreted as a radial measure in the sense that the model sums up all the partial measures
(slacks) in a single measure
- it is “unit invariant” due to the characteristic of converting absolute slacks into proportional ones;
- it is not, however “translation invariant”, as it carrier the basic characteristics of additive models;
- It is monotonic increasing with respect to input and output slacks;
- it depends (as any DEA efficiency index) only on the reference set. That is, the index depends only on
the efficient frontier and non on how data are distributed around it;
- it is non parametric in the usual sense of not postulating any functional form for the efficient frontier.
The SBM model is:
Where:
26
This follows from the usual constraint in DEA model which requires (virtual) output be at most equal to (virtual) inputs
(See Cooper et al 2007).
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Y=[
]; is the n by s matrix of (s) outputs measurements. Each n by 1 column vector
(i=1,..,s)
represents one dimension along which output is being measured;
The number of DMU (n) is given by the number of Municipalities or Districts or Regions depending on
which layer of government the analysis is run;
], is the n by m matrix of inputs categories. For most services, however X is made of just
X=[
one column vector as expenditure is the only input considered27.
To complete the list of symbols: the transpose is (‘); t is a scalar, S and Z are column vectors of output
and input slacks, respectively. Then
and
are row vectors of output and input weight, respectively.
) which measures the efficiency of each
The objective function in (5) provides an index (
Dmu. Depending on whether the optimization problem (5) is solved under the assumption of constant return
to scare (CRS) or variable return to scale (VRS) the resulting efficiency index is that described in (1) or in (2),
respectively.
Model (5) is not suitable in cases where the number of variables (input and outputs) is large relatively
to the number of DMU. When this happens DEA is caught into the curse of dimensionality: its discriminating
power tends to vanish. But this is indeed the case of public services provision due to the multidimensional
characteristics of output.
We therefore integrate model (5) with Principal Component Analysis in order to reduce dimensions
while keeping the most of variance accounted for by remaining variables.
Integration of DEA model with Principal component analysis is due to Ueda, Hoshiai (1997) and Adler,
Golany (2001, 2007). The extension to SBM models is a novelty, not yet developed as far as we know.
We label such an extension as SBM_PC model:
and the
matrices of
Where in addition to the usual symbols we have introduced the
eigenvectors obtained from single value decomposition of the correlation matrix of X and Y, respectively.
Reduction in dimensions comes through the elimination of eigenvector with lower eigenvalue, usually
less than 1, while keeping at least 70-80 % of total variance. In this way DEA has a high discriminating power
despite the presence of large number of variables.
DEA model (6) once applied to each service provides efficiency indices which can then be examined
either service by service or by tiers of government. In any case there arises a problem of aggregation: over
service and over tiers of government.
At first sight it might seem that aggregation of indices from different type of services would lead to
inconsistency with DEA methodology. The argument being that if an aggregate measure of efficiency was aimed
27
At cases the number of inputs is greater than one: Social services (3), Roads Maintenance (2) and Public Transport (2)
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at then one should run a (all inclusive) DEA which simultaneously includes all tiers of government and all
services.
We think, on the contrary, that such a procedure would cause a mistake as it overlooks the fact that in
our analysis decision units are the single institutions in charge for each service provision. These institutions are,
in some cases, the Municipalities, in others, the Districts, the Regions or even the local public companies.
Grouping all services and expenditure, at, say, the District level would need the hypothesis that the District is
the decision making units for all these services. But that would not correspond to reality. Instead we are in a
situation where at the local level (we choose the district in order to territorially differentiate the results as
much as allowed by data availability) services provided by different institutions (DMU) and we seek an overall
(District) index of efficiency. Moreover if an all-inclusive DEA is resorted to, one would miss the information
contained in budget shares as weights of the different services.
We propose to aggregate indices across:
A)
ser
vices, for given layer of government, by a weighting system given by services’ budget shares within the
given layer of government; and
lay
B)
ers of government, for given service, by a weighting system given by layers of government expenditure
shares, within the given service.
Either way the aggregate index is a convex combination elementary efficiency indices. For the case of
aggregation across services (within a layer of government, in our case the Districts), we divide services into
two groups: those (j=1,..,J) 28 provided by the top layer, the Region; and those (i=1,..I)29 provided by the
District or layers below: Municipalities and local companies. For each of the
each service budget shares as:
Where
(
Districts we compute
) is the expenditure level for each of the group I services in District
;
while
(
) is the expenditure level for each of the group J services in District
. The
peculiarity for group J services (those provided at Regional level) is that Regional expenditure is attributed to
Districts, within the given Region, according to their share of Regional Public Sector value added (
The overall index of efficiency for District
.
is then given by:
28
With J=4 as there are only two services provided at the Regional level: Health made up of three distinct services (see
par. 2) and Administration.
29
Where I=9. Namely, three at District level (Administration, Education, and Road maintenance); four at Municipality level
(Administration, Social services, Road maintenance, and Security & safety); two for Local public companies (Public
transport and Waste management).
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Where
, (i=1,..,I) and
services30. And
, (j=1,..,J) are the efficiency indices for the
.
A second type of aggregation takes place for a given service
within a Region
Each DMU (
,
over tiers of government
. The aim being that of providing a Regional index of productivity for each service.
providing a given service within a Region,(no matter if at Regional, District or
Municipality level) share of expenditure (within a given Region r
The overall Regional (
Where
and the
is DMU
) is given by:
.) index of efficiency for service
efficiency index for service
is given by:
, and
.
4. Results
Applying DEA model (8) to public services of Tab.1 provides a large set of information which can be
summarised according to the efficiency and inefficiency measures as in (6). For convenience of presentation we
first deal with the efficiency results for each type of service, then we examine the scale type of inefficiency,
and, lastly, we summarize the results in terms of a territorial index of efficiency, with the Districts as terms of
reference (level of aggregation) 31.
4.1 Services’ productivity
Figure 3 provides a condensed representation of efficiency indices for all the services examined (see
Tab.1). The index being represented is that of Technical efficiency (i.e.: productivity) as described by (1) and
aggregated by service (i.e.: across level of government) following (10).
30
31
In cases of services with not available data for a given layer of government, their budget shares are set to zero.
That is we use the Districts to aggregate services’ efficiency indices.
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One first interesting result is about the indices for Health and Education. They are, on average, higher
than other services’ and more evenly distributed across Regions. It is of some interest to note that although
both services belong to local government decision making (Regions and Districts, respectively), yet the Central
Government keeps on both a strong hold in terms of mandatory minimum service level for health, and in terms
of internal service organisation for Education.
In keeping the attention on Health (it represents 53.3% of total expenditure for the services covered by
the analysis, see Tab. 1) turns out that Basilicata, Abruzzo and Veneto show the highest values. Abruzzo and
Basilicata belonging to Central and Southern Regions constitute a sharp contrast to the commonly held idea
that efficiency characterise only Northern Regions.
Even for education the top Regions are from the South (Molise, Sardegna). At least for Health and
Education, then, the conventional dichotomy North/South does not seem to hold in term of public services
efficiency.
In addition, it turn out that Special Statute Regions show low efficiency indices for Administration. This
results might find a plausible explanation in data heterogeneity. Special Statute Regions because of their wider
set of devolved matters end up providing services that are not provided by other Regions, therefore, for these
services there tend not to be a suitable accounting procedure at national level, making Administration even
more a catch all type of expenditure.
The remaining services (Road maintenance, Social services, Transport and Waste management) share
the common feature of efficiency indices highly differentiated across Regions. Thus providing a first important
hint for a significant diversity of efficiency in terms of territorial distribution.
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Figure 3: Public services productivity by Regions
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4.2 The scale problem
Scale inefficiency, see (5), measures of how much DMU’s dimension is constraining productivity. In the
present work, DMU’s dimension can, however, mean different things because we deal with two types of
dimensions: i) the size of the DMU providing each service, and ii) the size of the government’s tier. For instance,
while scale efficiency for the local company providing public transport depends on the size of the service
provided, most commonly measured in terms of total expenditure; instead scale efficiency for a given, say,
municipality does not depend on the level of a single service or expenditure, but rather on the aggregate level
of services (expenditure). In short we have two distinct scale efficiencies: that of each service (to be dealt with
in sec. 4.2.1) and that of each level of government (to be discussed in sec. 4.2.2).
As far as the meaning of these two types of scale inefficiency is concerned, the distinction comes from
the different role played by two production factors: technological and organisational. Scale inefficiency is
technologically based when the technical (engineer) aspect of production starts limiting productivity. Instead it
is organisationally (managerially) based when the coordination process in production becomes the limiting
factor. Of course these two factors tend to have a simultaneously role and to distinguish one from the other is
an almost impossible task. In our case, however, what distinguishes a “small” from a “large” level of
government is not just the dimension (scale) of each individual service provided but rather the complexity in
internal organisation. For example in providing Health services a small Region is different from a large Region
not much in the technical aspects in service provision but rather in the number of local health units to operate
and coordinate. Likewise what distinguishes a small Municipality from a large one is the amount of
coordination effort required to organise together many large services as compared to many small ones32. ).
Following Banker et al (1984), return to scale are increasing if in DEA model (8) we have that
constant if
productivity scale.
, decreasing il
. A DMU functioning with
,
is said to be at its most
4.2.1 Scale efficiency in major services
The conventional measure of scale inefficiency is obtained by examining a given service across level of
governments. The technical efficiency index has already been presented in par 4.1. We now look at the major
services in terms of expenditure levels: Administration (for each of the three tiers of government), Health
(Regions) and Education (Districts) together represent almost 90% of the expenditure for services included in
our analysis (see Table 1
32
This is not to say that large tier of government are less organisationally efficient. Indeed even organisational activities
can exhibit economies or diseconomies of scale.
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Fig. 4 shows
for Administration at each level of government (Regions, Districts and
Municipalities). In the case of Regions it turns out that in most cases they operate under increasing return to
scale. That is to say that productivity increases with dimension, providing a confirmation of the commonly held
position of small Regions being at disadvantage.
As for Districts, productivity does not seem to be linked to dimension. Returns to scale vary greatly
within the same dimension. This is also due to the limited variation in Administration expenditure across
Districts as compared to the same variation across Regions.
Municipalities, instead, present a large variation in scale and a correspondent large variation in
.
) operate at much lower level of productivity
Large Municipalities (Rome, Milan, Turin, Naples with
than smaller ones. The optimal scale being roughly less than 1/10 the size of large Municipalities. On the other
hand there is quite a large group of small Municipalities that operate under increasing returns, confirm the idea
that small Municipalities greatly suffer for the presence of fixed cost type of services (e.g. legislative bodies,
organisational structures).
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Figure 4: Scale efficiency for Administration
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Returns to scale in health services are decreasing for most Regions (Fig 5)33. Together with large range
in size, this implies that optimal size corresponds to a rather small dimension (about 2 bln Euro). Around this
size are regions like Abruzzo, Basilicata and Calabria. As health expenditure is 53.3% of total services
expenditure (Tab. 1) it follows that productivity in health services come to explain a large part of overall
productivity by level of government.
The widespread condition of decreasing returns when coupled with the observation that health
services organisation is based on Local Health Authority (ASL)34 hints to the possibility that decreasing returns
are due to the internal organization that limits the efficiency of coordination and management in the case of
medium and large Regions.
A similar situation is that of Education (Fig. 5) whose decision making unit is the District level of
government35. Returns to scale are heavily decreasing. Optimal size is below the 500 mln budget. Therefore
even in this case is plausible that decreasing returns are due to a lack of coordination that gets more severe as
size (expenditure) increases
33
For health services returns to scale are a weighted average of
computed for each of the three service group of
Tab 4. This is why no Dmu has
.
34
The Local Health Authority (ASL) are in charge of service provision on a territorial (district) basis. However, Regions
rather than Districts are the levels of government in charge for allocating expenditure, planning and for service
management, including the supervision over ASL belonging to the Regional area.
35
Strictly speaking the District do not have total freedom in educational service provision. Some of the basics services’
characteristics are set at national level. However coordination and building maintenance are managed at District level.
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Figure 5: Scale efficiency for Health and Education
4.2.2 Scale efficiency by levels of government
In order to study the scale efficiency by level of government we run a separate DEA by grouping
services according to the level of government they belong to. Therefore we assign Health and Administration
(the Regional share) to the Regions group; then Administration (the District share), Road maintenance and
Education to the Districts group; and Administration (Municipalities share), Transport, Waste management,
Social services, Road maintenance and Security & safety to the Municipalities group36.
For each group there is a single input given by the sum of expenditure from each service belonging to
the group and for output the set of outputs for each service as described in sec.237. The overall results are
grouped in Fig.6. It turns out that, on average, the sources of inefficiencies differ depending on levels of
government. Municipalities have a higher level of productivity (technical efficiency TE), while Districts on the
whole suffer from a high level of Pure technical inefficiency and (consequently) low level of scale inefficiency.
Scale inefficiency, is, instead, the main problem for Regions group.
36
Each group is made of: 20 Regions; 81 Districts; 25 Municipalities. The relative small number of Municipalities is due to
the difficulties of gathering enough data such that all services are present in all the municipalities included.
37
In order to have all services with just one input, in the case of Road maintenance at District level the variable
“Accidents” has not been considered.
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Figure 6: Productivity and inefficiencies by tiers of government
SI = Scale Inefficiency; PTI = Pure Technical Inefficiency; TE = Technical Efficiency
Detailed results for scale efficiencies are brought together in Fig.7. Only two Regions (Lombardia and
Lazio) operate under significant decreasing returns to sale, all the others face sharp increasing returns, leading
to the conclusion (already contained in Fig.6) that the major cause of Regions’ lack of productivity is the
dimension of operations. Returns to scale increase up to a budget of about 10 bln Euro. Considering that the
two Regional services, one (Administration, Fig.4) operates under increasing returns and the other (Health, Fig.
5) under decreasing return to scale, we can deduce that the increasing characterisation of return to scale for
the Regions group comes from Administration. That should in part be expected as it is a service where fix costs
dominate.
As for the Districts, Fig.7 show that they are more evenly distributed in terms of budget size: only three
are above the 400 bln. level (Rome, Milan and Turin) and none in the area between 200 and 400. Most Districts
concentrate at low budget levels where returns to scale are quite diversified hinting to the fact that budget size
is not the key variable to identify returns to scale in the case of Districts.
Municipalities in Fig.7 show a large variability in budget size and a clear correlation with returns to
38
scale . Municipalities operating with a budget at or above 300 mln are under significant decreasing returns. It
should be noticed, however that large municipalities are a minority and the average is dominated by small and
medium sized municipalities whose returns to scale are closer to constant and therefore scale inefficiency is
limited as shown by Figure 5.
38
Due to the large variability in budget size in the case of Municipalities both y and x scale are in log.
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Figure 7: Return to scales by tiers of government
4.3 Public sector contribution to productivity
Taking the indices from Fig.3 and aggregating them according to (9) we get the aggregate index of
productivity at District level depicted in Fig.8 (LHS). It shows the ranking of Districts according to public services
level of productivity. The major points worth stressing are:
i)
the
Northern Regions, which are the most productive in terms of conventional per capita-GDP have also high
levels of public efficiency (with the important exclusion of Special Stature Regions);
ii)
the
North-South dichotomy, which is apparent in the ranking based on per capita GDP (see Fig.9A), does not
hold in terms of Public sector productivity. Instead, a novelty situation emerges characterised by an EastWest distinction for the Central and Southern Regions.
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Altogether these results suggest that public sector productivity’s contribution to overall productivity
varies significantly at local level. Moreover there seem not to exist any systematic relationship between
conventional productivity measure (per capita GDP) and public sector productivity. In fact, situations are
observed where high private per capita GDP goes together with high public productivity (North-East Regions),
and situations where low private per capita GDP goes together with high public productivity (Regions on the
Eastern coast).
Special Statute Regions (SSR), both those in the North and the Islands have very low level of
productivity. On this point a caveat has to be mentioned and concerns the role of Administration and
governance services. We have already noticed that this service refers to activities difficult to isolate in terms of
“output” because a significant share of it concerns the functioning of institutional assembly and the
coordination other activities and services. In addition, Administration and governance is the service on which
SSR fare rather badly (see Fig. 3), probably due to the fact that activities specific to SSR end up being classified in
this group of services. To isolate the role played on final results by this group of services we have excluded it
from the analysis to obtain Fig. 6 RHS. The new ranking is significantly different for some, but not all, the SSR
(namely for Trentino e Sardinia, while Aosta Valley, Friuli Venezia Giulia and Sicily remain below the median).
Interestingly enough there is a change also for some of the Ordinary Statute Regions (mainly Tuscany and
Umbria) in the direction of worsening their ranking, and in this way it becomes more evident the East-West
distinction in Central Regions in terms of public services productivity. In short, the main conclusions drawn
from Fig.6 LHS still hold once Administration is excluded.
Figure 8: Public Sector Productivity index (2009)
A further result can be drawn if we assume that Public sector productivity index from Fig.8, is taken to
hold also for all final public expenditure in National accounts (i.e the public sector component of GDP). It is
clearly an assumption that leads only to speculative results because Public sector productivity index in Fig 6
concerns only services detailed in Table 1, and by no means they exhaust the services provided by the General
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government. In particular, national public goods type of services are not considered although most of the
services provided locally and some of those centrally financed are included 39. The aim of such an exercise is
that of showing how conventional measure of productivity (GDP per head) would be modified once public
sector efficiency is taken into account.
Staring form per-capita GDP from national accounts as distributed at Districts level (Fig.9A), we isolate
the Public sector contribution (Fig.9B) given by the per-capita final public expenditure.
39
For more details see notes to Table 1.
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Figure 9: Public Sector contribution to productivity (2009)
This is the conventional picture, characterised by the implicit assumption of 1 to 1 ratio between inputs
and outputs in the Public sector40. We replace this assumption by the Public sector productivity index of Fig 6,
hence calculate the inefficient expenditure41 in Fig.9C. That is the amount Public expenditure could be reduced
40
i.e.: productivity equal 1 in terms of our eq. (1).
Inefficient expenditure is given by the nominal public expenditure of Fig.9B by the inefficiency index (6)
which is equal to (1- productivity index of Fig.8).
41
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,
while keeping service at the chosen level. This reduced public expenditure together with the private
component of GDP leads to a new level of GDP which embodies an efficient level of final public expenditure.
Comparing the new level of per capita GDP to the original one at Districts level leads to changes in
rankings which are depicted in Fig.9D. The overall picture is rather familiar by now: public expenditure is
efficiency (productivity) enhancing both for the “rich” North- Eastern Districts and for the “poor” CentreEastern and Southern Districts. For almost all the Districts belonging to the Special Statute Regions (which
include the Islands) public sector tends to reduce productivity42. The same happens for some of the WestCentral Districts, namely some of those in Liguria, Tuscany and Lazio. In short, there is a significant
geographical variability.
This exercise in no way can constitute a reliable assessment of the whole contribution of public
expenditure, because important public services provided at national level are not included in the analysis.
However, most services provided at local level are included and the results together with the methodology go
to show that not only the “correction” in conventional national account picture can be substantial, but also
that qualitative results in terms of services and territorial variability can be of help in assessing specific policy
programs and the conduct of local governments.
Concluding remarks
We have shown how to measure public sector contribution to the national level of productivity. We have faced
two major difficulties. One being that data are not being systematically collected for this purpose. Therefore,
full coverage in terms of services, areas and levels of government is a serious problem. And so is that of having
a set of output and input measurements to consistently represent public services which are by their nature
characterized by high dimension both in provision and utilization. And this leads to the other difficulty that of
facing from a methodological point of view a problem of large dimension.
Systematic data collection on public services is the only satisfactory solution for the first problem. At the
present it can only partially be solved, as we have done, by recourse to integration in data sets. As for the
second problem, Data Envelopment Analysis integration with Principal component analysis provides a
satisfactory way to deal with large dimensions while maintaining a sufficient discriminatory power.
Results show that public sector contribution to overall productivity is very differentiated by type of public
services, by layers of government and by territorial areas. The long lasting problem of enhancing economic
growth thus gains an additional instrument that allows to identify with some degree of precision areas, services
and levels of government that constitute an obstacle to growth and a cause of waste in terms of public
expenditure.
42
This last observation has to be taken carefully due to the role of Administration services, see previous page).
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Roma.
Istat, (2012a), Conti economici regionali Anni 1995-2011, 23 novembre, Roma.
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Deliverable 9.2
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March 2013
Annexes
A1. Public sevices productivity
A2. Productivity by District area
Annex 1. Public Services Poductivity
District
Deliverable 9.2
A
B
C
D
E
F
G
H
I
L
Agrigento
0.20
0.74
0.43
0.23
n.a.
0.18
0.57
0.29
0.48
0.08
Alessandria
0.71
0.70
0.53
0.10
0.60
0.06
0.47
0.52
0.65
0.09
Ancona
0.50
0.74
0.55
0.10
0.20
0.22
0.61
0.25
0.64
0.39
Aosta
0.19
0.66
n.a.
n.a.
0.51
0.01
0.63
0.13
0.33
1.00
Arezzo
0.95
0.73
0.53
0.08
0.22
0.27
0.57
0.29
0.66
0.07
Ascoli P.
0.50
0.74
0.50
0.08
0.76
0.16
0.66
0.39
0.62
0.21
Asti
0.71
0.70
0.57
0.23
n.a.
0.24
0.57
0.26
0.64
0.04
Avellino
0.34
0.73
0.47
0.17
0.38
0.43
0.49
0.57
0.59
0.07
Bari
0.50
0.71
0.59
0.12
0.18
0.26
0.60
0.19
0.63
0.39
Belluno
0.75
0.81
0.62
0.04
n.a.
0.01
0.72
0.06
0.71
0.06
Benevento
0.34
0.73
0.59
0.15
0.24
0.08
0.46
0.53
0.62
0.05
Bergamo
0.37
0.79
0.55
0.16
0.50
0.12
0.63
0.22
0.63
0.09
Biella
0.71
0.70
0.50
0.02
n.a.
0.10
0.80
0.31
0.61
0.03
Bologna
0.90
0.77
0.54
n.a.
0.31
0.12
0.50
0.20
0.70
0.31
Bolzano
0.16
0.69
n.a.
0.12
n.a.
n.a.
0.47
0.15
0.36
0.53
Brescia
0.37
0.79
0.58
0.15
n.a.
0.18
0.60
0.18
0.64
0.11
Brindisi
0.51
0.71
0.54
0.06
n.a.
0.38
0.45
0.49
0.62
0.10
Cagliari
0.44
0.75
0.62
0.22
0.17
0.21
0.49
0.26
0.60
0.40
Caltanissetta
0.20
0.74
0.54
0.20
n.a.
0.60
0.51
0.59
0.52
0.05
Campobasso
0.50
0.72
0.66
0.06
0.15
0.30
0.55
0.72
0.62
0.74
Carbonia I.
0.44
0.75
n.a.
n.a.
0.10
0.09
0.47
0.33
0.59
0.05
78/111
March 2013
Deliverable 9.2
Caserta
0.34
0.73
0.45
0.02
n.a.
0.17
0.56
0.40
0.59
0.15
Catania
0.20
0.74
0.55
0.13
n.a.
0.03
0.33
0.17
0.51
0.20
Catanzaro
0.58
0.70
0.56
0.13
0.37
0.34
0.62
0.32
0.61
0.22
Chieti
0.86
0.84
0.49
n.a.
0.36
0.18
0.49
0.32
0.74
0.26
Como
0.37
0.79
0.63
n.a.
0.82
0.05
0.59
0.16
0.65
0.05
Cosenza
0.59
0.70
0.61
0.17
n.a.
0.35
0.53
0.54
0.64
0.36
Cremona
0.37
0.79
0.59
0.10
n.a.
0.32
0.55
0.24
0.66
0.04
Crotone
0.61
0.70
0.55
n.a.
0.12
0.27
0.44
0.67
0.62
0.06
Cuneo
0.71
0.70
0.55
n.a.
0.72
0.08
0.67
0.25
0.66
0.12
Enna
0.20
0.74
0.53
n.a.
n.a.
0.05
0.42
0.63
0.52
0.04
Ferrara
0.99
0.77
0.49
n.a.
n.a.
0.12
0.67
0.32
0.71
0.08
Firenze
0.83
0.73
0.50
0.04
0.17
0.09
0.67
0.18
0.63
0.32
Foggia
0.50
0.71
0.57
0.09
n.a.
0.98
0.89
0.18
0.62
0.15
Forlì
0.98
0.77
0.54
n.a.
n.a.
0.11
0.72
0.28
0.71
0.08
Frosinone
0.25
0.75
0.53
0.36
0.15
0.12
0.58
0.43
0.58
0.06
Genova
0.55
0.75
0.48
0.05
0.15
0.10
0.57
0.17
0.59
0.57
Gorizia
0.19
0.74
0.57
0.05
n.a.
0.02
0.49
0.24
0.47
0.11
Grosseto
0.95
0.73
0.45
0.04
0.15
0.08
0.63
0.61
0.64
0.06
Imperia
0.68
0.75
0.49
0.05
0.36
0.38
0.48
0.42
0.64
0.11
Isernia
0.51
0.72
0.76
n.a.
0.06
0.06
1.00
0.65
0.64
0.26
La spezia
0.67
0.75
0.55
0.01
n.a.
0.01
1.00
0.48
0.67
0.17
L'Aquila
0.86
0.84
0.59
1.00
0.11
0.04
0.56
0.45
0.78
0.34
Latina
0.25
0.75
0.47
n.a.
0.25
0.19
0.66
0.20
0.55
0.06
Lecce
0.50
0.71
0.53
0.17
0.12
0.22
0.52
0.64
0.61
0.17
Lecco
0.37
0.79
0.72
0.28
0.50
0.03
0.72
0.26
0.68
0.03
Livorno
0.94
0.73
0.48
0.05
0.19
0.01
0.67
0.19
0.64
0.10
Lodi
0.37
0.79
0.56
0.02
0.44
0.08
0.85
0.29
0.61
0.02
Lucca
0.95
0.73
0.55
0.05
0.44
0.05
0.59
0.24
0.65
0.08
79/111
March 2013
Deliverable 9.2
Macerata
0.50
0.74
0.54
n.a.
0.24
0.39
0.59
0.45
0.63
0.18
Mantova
0.37
0.79
0.53
0.09
n.a.
0.02
0.85
0.34
0.63
0.04
Massa C.
0.95
0.73
0.45
n.a.
1.00
0.25
0.58
0.38
0.65
0.05
Matera
0.72
0.94
0.52
0.28
n.a.
0.12
0.54
0.11
0.72
0.31
Medio C.
0.43
0.75
n.a.
n.a.
0.44
0.12
0.54
0.78
0.56
0.03
Messina
0.20
0.74
0.54
0.08
0.07
0.04
0.45
0.32
0.49
0.14
Milano
0.34
0.79
0.42
n.a.
0.34
0.05
0.62
0.22
0.59
0.46
Modena
0.95
0.77
0.55
0.10
n.a.
0.02
0.55
0.27
0.67
0.14
Napoli
0.30
0.73
0.43
0.06
0.17
0.15
0.48
0.14
0.55
0.56
Novara
0.70
0.70
0.50
0.16
0.65
0.10
0.63
0.25
0.63
0.07
Nuoro
0.44
0.75
0.57
0.33
0.47
0.04
0.57
0.97
0.60
0.10
Ogliastra
0.44
0.75
n.a.
n.a.
0.38
0.09
0.51
n.a.
0.60
0.03
Olbian T.
0.44
0.75
n.a.
0.20
0.30
0.16
0.56
0.72
0.59
0.07
Oristano
0.43
0.75
0.60
n.a.
n.a.
0.19
0.57
0.28
0.59
0.11
Padova
0.74
0.81
0.56
0.07
n.a.
0.18
0.50
0.26
0.72
0.20
Palermo
0.19
0.74
0.55
0.03
0.06
0.19
0.50
0.20
0.47
0.29
Parma
0.97
0.77
0.57
0.11
n.a.
0.06
0.48
0.40
0.68
0.09
Pavia
0.37
0.79
0.47
n.a.
0.28
0.10
0.62
0.54
0.63
0.06
Perugia
0.69
0.69
0.49
0.06
0.13
0.50
0.78
0.52
0.60
0.77
Pesaro e U.
0.47
0.74
0.61
n.a.
n.a.
0.16
0.58
0.42
0.64
0.22
Pescara
0.86
0.84
0.68
0.08
0.38
0.22
0.41
0.44
0.77
0.22
Piacenza
1.00
0.77
0.54
0.07
n.a.
0.17
0.51
0.32
0.70
0.06
Pisa
0.92
0.73
0.42
0.05
0.10
0.00
0.91
0.30
0.64
0.13
Pistoia
0.95
0.73
0.55
0.09
0.09
0.00
0.56
0.27
0.62
0.06
Pordenone
0.19
0.74
0.50
0.05
0.73
0.02
0.55
0.31
0.46
0.20
Potenza
0.68
0.94
0.58
n.a.
0.09
0.03
0.49
0.36
0.77
0.69
Prato
0.94
0.73
0.38
n.a.
0.23
0.14
0.84
0.24
0.57
0.04
Ragusa
0.20
0.74
0.51
n.a.
n.a.
0.06
0.82
n.a.
0.51
0.05
80/111
March 2013
Deliverable 9.2
Ravenna
0.98
0.77
0.52
0.10
n.a.
0.46
0.52
0.14
0.68
0.08
Reggio C.
0.59
0.70
0.53
0.12
0.31
0.32
0.51
n.a.
0.62
0.28
Reggio E.
0.97
0.77
0.57
n.a.
n.a.
0.08
0.67
0.29
0.69
0.09
Rieti
0.25
0.75
0.52
n.a.
0.15
0.02
0.88
0.87
0.58
0.02
Rimini
0.74
0.77
0.57
0.12
n.a.
0.07
1.00
0.25
0.68
0.06
Roma
0.25
0.75
0.42
0.09
0.14
0.09
0.29
0.10
0.52
0.82
Rovigo
0.75
0.81
0.58
n.a.
n.a.
n.a.
0.56
0.34
0.74
0.05
Salerno
0.34
0.73
0.53
0.03
0.41
0.27
0.49
0.24
0.59
0.18
Sassari
0.44
0.75
0.63
0.14
0.35
0.06
0.42
0.36
0.60
0.19
Savona
0.65
0.75
0.54
0.22
0.25
0.07
0.53
0.25
0.65
0.15
Siena
0.95
0.73
0.50
0.11
0.08
0.04
0.66
0.36
0.64
0.08
Siracusa
0.20
0.74
0.54
n.a.
n.a.
0.12
0.48
0.27
0.51
0.08
Sondrio
0.37
0.79
0.64
n.a.
0.52
0.15
0.84
0.34
0.68
0.03
Taranto
0.51
0.71
0.54
0.08
0.10
n.a.
0.50
0.66
0.62
0.19
Teramo
0.87
0.84
0.55
n.a.
n.a.
n.a.
0.52
0.54
0.76
0.18
Terni
0.64
0.69
0.60
0.05
n.a.
0.57
0.68
0.54
0.58
0.23
Torino
0.70
0.70
0.46
0.04
0.25
0.07
0.41
0.16
0.58
0.56
Trapani
0.20
0.74
0.50
0.24
n.a.
0.58
0.54
0.37
0.51
0.08
Trento
0.16
0.69
n.a.
n.a.
n.a.
0.02
0.47
0.12
0.36
0.47
Treviso
0.75
0.81
0.44
0.12
n.a.
0.11
0.68
0.16
0.69
0.15
Trieste
0.19
0.74
1.00
0.08
0.24
n.a.
0.44
0.22
0.50
0.29
Udine
0.19
0.74
0.36
n.a.
n.a.
0.19
0.70
0.19
0.45
0.40
Varese
0.37
0.79
0.52
0.02
0.45
0.06
0.60
0.15
0.58
0.08
Venezia
0.73
0.81
0.50
n.a.
0.33
0.12
0.44
0.36
0.71
0.21
Verbania
0.71
0.70
0.52
0.16
n.a.
0.08
0.68
0.23
0.62
0.04
Vercelli
0.71
0.70
0.51
n.a.
0.44
n.a.
0.52
0.44
0.59
0.04
Verona
0.74
0.81
0.55
0.04
n.a.
0.02
0.79
0.12
0.72
0.18
Vibo V.
0.61
0.70
0.57
n.a.
n.a.
1.00
0.40
n.a.
0.64
0.08
81/111
March 2013
Vicenza
0.74
0.81
0.58
n.a.
n.a.
0.10
0.69
0.27
0.73
0.14
Viterbo
0.25
0.75
0.50
0.17
0.17
0.03
0.73
0.29
0.58
0.04
Mean
0.55
0.75
0.54
0.12
0.31
0.17
0.59
0.59
0.61
0.19
Median
0.51
0.74
0.54
0.09
0.25
0.11
0.57
0.57
0.62
0.11
Legend: A=Administration; B=Health; C=Education; D=Public transport; E=Waste management;
F=Social services; G= Security & safety; H= Road Maintenance; I=Public Sector Productivity; L=”q”
Annex 2. Productivity by District area
District
A
B
C
D
E
Agrigento
14485.78
9244.619
5241.165
2540.598
11785.22
0.48 106 107
-1
Alessandria
26746.14
21626.07
5120.07
3330.33
24956.4
0.65
47
49
-2
Ancona
28721.47
22002.26
6719.21
4286.795
26289.06
0.64
33
34
-1
Aosta
34670.09
23952.39
10717.7
3527.956
27480.35
0.33
3
25
-22
Arezzo
27114.29
22882.3
4231.987
2777.085
25659.39
0.66
45
40
5
Ascoli P.
24492.14
20089.3
4402.839
2741.831
22831.13
0.62
60
59
1
Asti
24603.56
20240.07
4363.491
2787.559
23027.63
0.64
58
55
3
Avellino
17969.55
12963.16
5006.384
2955.299
15918.46
0.59
82
82
0
Bari
18451.9
13443.24
5008.659
3154.598
16597.84
0.63
78
77
1
29624
23074.8
6549.205
4629.359
27704.16
0.71
24
24
0
Benevento
16987.06
11815.04
5172.014
3187.055
15002.1
0.62
92
85
7
Bergamo
32150.67
28336.75
3813.915
2405.893
30742.65
0.63
7
5
2
Biella
27269.77
23035.16
4234.619
2567.357
25602.51
0.61
44
41
3
Bologna
34295.98
27057.62
7238.357
5088.078
32145.7
0.70
4
3
1
Bolzano
36058.96
27766.79
8292.176
2994.181
30760.97
0.36
2
4
-2
Brescia
31415.45
27002.74
4412.707
2831.424
29834.16
0.64
11
10
1
Brindisi
16331.01
11188.45
5142.554
3179.146
14367.6
0.62
96
94
2
Cagliari
22361.32
14856.63
7504.689
4476.652
19333.28
0.60
68
73
-5
Belluno
Deliverable 9.2
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F
G
H
I
March 2013
Deliverable 9.2
Caltanissetta
17349.48
11783.88
5565.598
2875.5
14659.38
0.52
90
92
-2
Campobasso
20708.1
13976.5
6731.597
4166.457
18142.96
0.62
74
74
0
Carbonia I.
15249.56
10911.9
4337.668
2577.175
13489.07
0.59 104 102
2
Caserta
16034.84
10866.82
5168.022
3029.606
13896.42
0.59
99
96
3
Catania
16474.69
10848.09
5626.6
2871.913
13720
0.51
94
99
-5
Catanzaro
18047.45
11069.21
6978.246
4260.93
15330.14
0.61
80
84
-4
Chieti
21722.51
16880.58
4841.932
3567.602
20448.18
0.74
71
68
3
Como
28209.25
24234.19
3975.063
2571.628
26805.81
0.65
37
29
8
Cosenza
16827.43
10893.79
5933.634
3812.94
14706.73
0.64
93
91
2
Cremona
28796.26
23707.34
5088.927
3334.031
27041.37
0.66
31
27
4
Crotone
14779.63
10360.86
4418.77
2748.582
13109.44
0.62 105 103
2
Cuneo
29547.25
24490.12
5057.133
3341.808
27831.93
0.66
22
3
Enna
15619.3
9365.459
6253.842
3231.87
12597.33
0.52 101 106
-5
Ferrara
27893.8
22668.05
5225.744
3712.293
26380.35
0.71
39
33
6
Firenze
31145.25
24678.41
6466.84
4053.165
28731.58
0.63
13
14
-1
Foggia
15404.17
10735.59
4668.578
2910.31
13645.9
0.62 103 100
3
Forlì
31624.13
26892.05
4732.074
3363.429
30255.48
0.71
10
7
3
Frosinone
23368.74
19549.29
3819.457
2216.949
21766.24
0.58
63
62
1
Genova
27531.65
21672.73
5858.924
3455.048
25127.78
0.59
43
46
-3
Gorizia
25605.7
19816.07
5789.626
2710.012
22526.08
0.47
55
60
-5
Grosseto
26096.61
20299.84
5796.771
3711.234
24011.08
0.64
52
53
-1
Imperia
25883
21128.57
4754.425
3060.866
24189.44
0.64
53
52
1
Isernia
19518.51
13305.55
6212.968
3978.575
17284.12
0.64
75
75
0
La spezia
25172.77
18161.9
7010.874
4710.163
22872.06
0.67
56
58
-2
L'Aquila
21945.43
13840.03
8105.405
6328.077
20168.11
0.78
70
71
-1
Latina
24522.42
20950.6
3571.824
1976.97
22927.57
0.55
59
57
2
Lecce
16423.59
12113.53
4310.057
2614.45
14727.98
0.61
95
90
5
Lecco
29934.86
26143.69
3791.172
2570.245
28713.93
0.68
18
15
3
83/111
25
March 2013
Livorno
26331.88
20340.78
5991.095
3861.586
24202.37
0.64
51
51
0
Lodi
26777.71
22306.08
4471.629
2725.553
25031.63
0.61
46
48
-2
Lucca
28795.54
24869.58
3925.962
2561.757
27431.33
0.65
32
26
6
Macerata
24657.6
19943.44
4714.157
2986.979
22930.42
0.63
57
56
1
Mantova
32120.5
27465.91
4654.583
2912.719
30378.63
0.63
8
6
2
Massa C.
22799.83
17914.69
4885.145
3197.204
21111.89
0.65
66
65
1
Matera
17438.53
12184.84
5253.691
3787.623
15972.47
0.72
88
81
7
Medio C.
14162.87
10697.3
3465.571
2042.836
12740.14
0.59 107 105
2
Messina
17858.45
11398.45
6459.996
3164.124
14562.58
0.49
85
93
-8
Milano
37334.91
31763.2
5571.705
3292.848
35056.05
0.59
1
1
0
Modena
33893.82
29389.42
4504.4
3035.055
32424.47
0.67
5
2
3
Napoli
16279.51
10674.99
5604.524
3067.156
13742.14
0.55
97
98
-1
Novara
27668.15
22708.91
4959.24
3101.108
25810.02
0.63
42
38
4
Nuoro
19249.49
12537.66
6711.821
4005.116
16542.78
0.60
76
78
-2
Ogliastra
17209.19
11271
5938.196
3583.365
14854.36
0.60
91
88
3
Olbian T.
23574.97
18534.43
5040.542
2982.665
21517.09
0.59
62
63
-1
Oristano
17563.34
10996.98
6566.364
3884.545
14881.52
0.59
86
87
-1
Padova
29630.08
24223.47
5406.605
3898.188
28121.66
0.72
23
18
5
Palermo
17501.67
10483.61
7018.065
3271.507
13755.11
0.47
87
97
-10
Parma
31699.4
26860.18
4839.217
3280.116
30140.3
0.68
9
8
1
Pavia
26681.76
21414.17
5267.596
3336.863
24751.03
0.63
48
50
-2
Perugia
24445.8
18414.45
6031.351
3649.014
22063.47
0.61
61
61
0
25648
20821.46
4826.542
3060.469
23881.93
0.63
54
54
0
Pescara
21161.38
16040.36
5121.014
3918.117
19958.48
0.77
73
72
1
Piacenza
29964.44
24853.89
5110.55
3574.786
28428.67
0.70
17
16
1
Pisa
29010.4
22391.53
6618.868
4215.887
26607.42
0.64
29
31
-2
Pistoia
26617.53
22418.04
4199.485
2625.03
25043.07
0.63
49
47
2
Pordenone
28685.32
23688.05
4997.275
2314.458
26002.5
0.46
34
37
-3
Pesaro e U.
Deliverable 9.2
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Deliverable 9.2
Potenza
18314.02
12176.79
6137.227
4738.992
16915.78
0.77
79
76
3
Prato
28024.16
24658.74
3365.417
1907.688
26566.43
0.57
38
32
6
Ragusa
17951.9
12917.89
5034.012
2572.575
15490.47
0.51
83
83
0
Ravenna
29198.76
24742.98
4455.772
3049.026
27792.01
0.68
27
23
4
Reggio C.
16162.35
10305.96
5856.384
3614.675
13920.64
0.62
98
95
3
Reggio E.
31258.76
27360.04
3898.718
2694.185
30054.23
0.69
12
9
3
Rieti
22218.25
17544.34
4673.915
2697.74
20242.08
0.58
69
70
-1
Rimini
30465.26
25898.81
4566.456
3090.469
28989.28
0.68
15
13
2
Roma
32288.07
25814.01
6474.054
3308.845
29122.86
0.51
6
12
-6
Rovigo
26377.59
21753.03
4624.552
3407.654
25160.69
0.74
50
44
6
Salerno
18043.25
13031.78
5011.476
2967.292
15999.07
0.59
81
80
1
Sassari
18812.75
12889.29
5923.461
3541.025
16430.32
0.60
77
79
-2
Savona
27708.73
22993.79
4714.939
3047.489
26041.28
0.65
41
35
6
Siena
28935.24
22796.95
6138.287
3914.407
26711.36
0.64
30
30
0
Siracusa
17925.54
11663.97
6261.573
3173.845
14837.82
0.51
84
89
-5
Sondrio
29283.25
22298.75
6984.494
4716.459
27015.21
0.68
26
28
-2
Taranto
17377.53
10790.44
6587.092
4104.972
14895.41
0.62
89
86
3
Teramo
21383.39
17139.09
4244.306
3207.892
20346.98
0.76
72
69
3
Terni
23241.02
18194.18
5046.837
2969.083
21163.27
0.59
64
64
0
Torino
27787.24
21905.19
5882.041
3434.893
25340.09
0.58
40
42
-2
Trapani
15766.01
10177.26
5588.755
2826.822
13004.08
0.51 100 104
-4
Trento
30830
22931.53
7898.471
2863.707
25795.24
0.36
14
39
-25
Treviso
29119.6
24939.56
4180.033
2903.4
27842.96
0.69
28
21
7
Trieste
29848.45
20830.82
9017.625
4470.375
25301.2
0.50
20
43
-23
Udine
28223.64
22597.66
5625.974
2548.975
25146.64
0.45
36
45
-9
Varese
29852.1
25684.8
4167.31
2418.803
28103.6
0.58
19
19
0
Venezia
29660.41
23559.92
6100.483
4304.167
27864.09
0.71
22
20
2
Verbania
22955.59
17787.08
5168.514
3199.479
20986.56
0.62
65
67
-2
85/111
March 2013
Vercelli
28462.62
22460.75
6001.872
3564.65
26025.4
0.59
35
36
-1
Verona
29734.33
24865.31
4869.019
3481.377
28346.69
0.72
21
17
4
Vibo V.
15598.5
10022.77
5575.728
3569.988
13592.76
0.64 102 101
1
Vicenza
30305.65
26319.91
3985.739
2898.234
29218.15
0.73
16
11
5
Viterbo
22736.85
18581.86
4154.991
2423.59
21005.45
0.58
67
66
1
mean
24446.71 19029.57 5417.142 3291.518
22321.09
0.61
54
54
0
median
24446.71 19029.57 5417.142 3291.518
22321.09
0.61
54
54
0
Legend: A=Percapita GDP; B=Percapita Private GDP; C=Percapita Public GDP; D=Percapita Efficient
Public GDP; E=New Percapita GDP; F=Public Sector Productivity; G=Rank for GDP; H= Rank for New
GDP; I=Change in Rank
Deliverable 9.2
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Part 3: Report on indicators on business collaboration and Collaborative Working Environments
(CWE)
Alessandra Righi
Italian National Institute of Statistics (Istat)
Summary
This Report is aimed at presenting initiatives to develop new statistical indicators on business collaboration and
Collaborative Working Environment (CWE) in official statistics as well as to improve the data availability not
only on the use of collaboration tools but also on the skills needed for this practice.
After a brief overview of what is meant by CWE or Unified Communications and Collaboration tools and what
are the practices introduced through these systems, the report describes the delay of official statistics in
dealing with the CWE and presents two recent initiatives to improve the information produced. Among these,
the introduction of an ad hoc module in the Community Survey on ICT Usage and e-Commerce in Enterprises
can be considered as a best practice to be extended at a European level.
Introduction43
In a context of market globalization the ability to collaborate within an organization or among different
organizations or communities is characterized by a rapid transition from a traditional proximity or geographical
collocation paradigm to a virtual collocation paradigm where professionals work together whatever their
geographical location.
The Collaborative Working Environment (CWE) concept is related to different types of knowledge workers who
intensively use information and communications technology (ICT) environments and tools in their everyday
working practices. E-mail, instant messaging, application sharing, videoconferencing, collaborative workspace
43
I would like to thank Donatella Fazio (Istat) -BLUE-ETS Project Coordinator - and Maria Grazia Calza (Istat) - BLUE-ETS
Coordination Staff - for their help in the finalisation of the paper; Alessandra Nurra and Stefano Menghinello (Istat) for the
fruitful collaboration; M. Francesca D’Ambrogio (Istat) - BLUE-ETS Coordination Staff - for the English language assistence
provided.
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and document management, workflow-management but also wiki pages and blogging are tools supporting
collaborations.
Different studies show that the CWE increases productivity and creativity through new ways of working in the
production and knowledge-intensive businesses, as also recognized by different Reports published or financed
by the European Commission (Stewing, Heimann, 2006; Stanoevska-Slabeva et al., 2008).
This Report, as a deliverable of the BLUE-ETS Project in 7th EU Framework Programme, is aimed at presenting
initiatives to develop new statistical indicators on business collaboration and Collaborative Working
Environment (CWE) in official statistics as well as to improve the data availability not only on the use of
collaboration tools but also on the skills needed for this practice.
After a brief overview of what is meant by CWE or Unified Communications and Collaboration tools and what
are the practices introduced through these systems, the Report in par.2 describes the delay of official statistics
in dealing with the CWE and in par. 3 presents two recent Istat initiatives to improve the information produced.
These experiences, and particularly the introduction of an ad hoc module in the Community Survey on ICT
Usage and e-Commerce in Enterprises, could be considered as “best practices” to be extended to all EU NSIs.
1. Collaborative Working Environment (CWE)
The term “virtual enterprise” has been used in articulating the strategy for the 21st century global
manufacturing enterprises. Many definitions have been proposed for this term, one of the most significant
indicates that the concept refers to a new organisational form characterised by a temporary or permanent
collection of geographically dispersed individuals, groups or organisation departments not belonging to the
same organisation – or entire organisations, that are dependent on electronic communication for carrying out
their production process (Travica, 1997).
There are three key elements in this definition: a) the distance (see Pallot, 2010, 2011); b) the use of ICT
systems to overcome any barrier; c) new forms of organization at the enterprise or corporate level.
Collaborative working environments (CWE) have been developed to enable these new working practices. CWEs
are a combination of infrastructures - both physical, information technology-based networks and social or
organisational structures - supporting people in their individual and collaborative work (Stanoevska-Slabeva et
al., 2008). These infrastructures allow to share online real time information, to exchange ideas in order to
achieve a common understanding of the problems and to achieve an effective and efficient collaboration
between different types of expertise in an organisation. CWEs enable enterprises to have several competitive
advantages using their computers, networks and best practices of communication (web-based conferencing
and collaboration, desktop videoconferencing, instant messaging) making them easily available for
collaboration.
The acknowledgment of the relevant role of CWEs in Europe came in 2006 by the Report of the European
Commission New Collaborative Working Environments 2020 - Report on industry-led FP7 consultations and 3rd
Report of the Experts Group on Collaboration@Work stating that the CWE is necessary because it can increase
productivity and creativity through new forms of employment in manufacturing and in knowledge-intensive
businesses (Stewing, Heimann 2006). In the mid 2000, in fact, the DG Information Society of the European
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Commission pushed the study of this phenomenon by setting up expert groups or collaborating to the
organisation of international conferences (Expert Group, 2004, 2006; Larsson, 2006).
As regards tools, common applications are considered:
1. Collaborative software designed to improve the performance of teams by supporting the sharing and
flow of information. It allows real-time collaboration and conferencing.
2. Peer-to-peer collaboration software permits users to communicate in real time and share files without
going through a central server.
3. Workflow systems facilitate the automation and management of business processes.
4. Documentation management systems manage a document through all the stages of its processing.
5. Knowledge management systems are IT systems that support the capture, organisation, and
distribution of knowledge (know-how).
6. Social network systems are IT systems that link people to others they know and, from there, to people
their contacts know. It is a way to leverage personal and professional contacts.
This type of environment is increasingly becoming an integrated collaboration environment which allows
organisations to take advantage of technological advances in computer processing power and video technology
and can reduce costs for companies.
There is an even closer integration between communication and collaboration tools, so that in the last years
these environments are defined Unified Communications & Collaboration systems to express the high degree
of coordination achieved (Verizon business, 2009).
UC&C represents a growing field but there is not always a clear awareness of what it is really and what it is
involved, at least according to the 2010 Report of the Polytechnic of Milan on the situation of 127 Italian
companies (DIG-Politecnico di Milano, 2010).
Groupware provides an essential support for the maintenance of knowledge management in many
organisations. Accordingly, the use of groupware systems has become widespread. Interoperability issues have
taken a vital role in the business sector (Martinez-Carreras et al., 2007)
The need to classify the tools to map the exact boundary of these virtual environment is increasing. LasoBallesteros presents the main results of the consultation process carried out in order to identify the three
building blocks needed for collaborative work environments together with the 12 research challenges on this
area (Laso-Ballesteros, 200&). In 2009 Riemer, Steinfield and Vogel (2009) show an attempt of classification of
eCollaboration features (table 1.1).
Table 1.1 – Examples of eCollaboration features and system building bloks
Communication
Email
Text chat
Instant messaging
Audio chat and conferences
Video chat and conferences
Deliverable 9.2
Coordination
Group calendars
Project management
Workflow management
Social networking
Voting
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Collaboration
Wikis
Group editors
Electronic whiteboard
Application sharing
Shared Office solution
March 2013
Discussion board
Blogs
Document archives
Version management
Source: Riemer, Steinfield and Vogel, 2009
However, it is essential to ensure flexibility in such environments by making the best use of knowledge and
skills. Collaboration, socialising and learning are key words in this environment.
In terms of human resources and skills necessary for successful collaboration environments, today knowledge
work is key to business success and researches show that typically it is accomplished in four different modes
essential to the process of building knowledge that in turn drives creativity and innovation (Nonaka, Takeuchi,
1995).
a. Focusing: every worker needs some time to concentrate and attend to specific tasks;
b. Collaborating: working with one or more people to achieve a goal, in a way that all perspectives are
equally respected;
c. Learning: if thinking is made visible to others, learning is accelerated and becomes an integrated part
of organisational culture;
d. Socialising: when people socialise and work with others in both formal and informal ways, both
learning and trust are built. Combining trust with an organisation’s intellectual capital creates the
necessary ingredients for innovation.
The absence of trust is considered to be a barrier to effective communication, information flow and knowledge
sharing. Trust makes information flow smooth and creates a knowledge sharing environment in which the
whole supply chain progresses and develops (McDermott et al., 2004).
Unfortunately, CWE practices do not yet support collaboration in a completely adequate way (Eicker et al.,
2007). Therefore it is necessary to have statistical information that can help in specifying the actual use of
these practices, the reasons why companies undertake them and the problems both for enterprises and
workers44.
2. CWE and Official Statistics
So far, the official statistics has insufficiently contributed to the understanding and the monitoring over time of
the CWE. Nevertheless, it should be remembered that the New Global project in 2009 made a proposal of a
questionnaire on CWE tools and practices which has not been implemented by the NSIs even in part
(Stanoevska-Slabeva et al., 2009). Even Eurostat, that with the NSIs is in charge of the Cummunity survey ICT
Usage and e-Commerce in Enterprises, has so far focused on this theme.
44
Recently the WES Survey has measured employee attitudes about work and the workplace using a paper-based
questionnaire (PSS, 2011).
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2.2 Focus on recent ISTAT initiatives
There is few information already collected in Istat on CWE and only since 2011. More, the topic has not always
been adequately identified and transferred to the items of the questionnaires to capture effectively the
characteristics of the CWE.
A first experience of data collection is in the Community Survey on ICT Usage and e-Commerce in Enterprises
2010/201145 where there is a question on enterprises adopting initiatives to replace physical travel using
telephone, internet and video conferencing. Of course, considering the phone among these tools, the results of
the survey were very positive: more than 50% of Italian enterprises have adopted these initiatives (table 2.1),
the share exceed 78% for ICT sector, but is bigger for enterprises over 250 employees (78,7%).
Table 2.1 - Enterprises that have adopted initiatives to replace physical travel with use of telephone, internet and
video conferencing by some characteristics, Italy –2011/2012 (%)
50,5
TOTAL
ECONOMIC SECTOR
Manufacturing
50,6
Energy
59,7
Construction
49,9
Services
50,3
ICT SECTOR (Industry and Services)
78,6
NUMBERS OF EMPLOYEES
10 - 49
48,7
50 - 99
58,3
100 - 249
70,5
250 and +
78,7
GEOGRAPHIC AREA
45
An annual survey carried out in accordance with EC Regulations no. 808/2004 and no. 1006/2009, following the criteria
and methods shared by all the countries of the European Union. The survey is representative of the universe of active
enterprises with 10 or more persons employed (in manufacturing sector, energy, construction, services). The survey is a
sample in the case of firms with less than 250 persons employed, while a census for those of larger size.
http://epp.eurostat.ec.europa.eu/portal/page/portal/information_society/introduction
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North-West
52,5
North-East
50,3
Centre
47,4
South
50,5
Source: Community Survey on ICT Usage and e-Commerce in Enterprises
A second relevant undertaken initiative on CWEs is the introduction of a specific question in the Business
Census of enterprises over 10 employees46 2012 (question no. 2.11) on the field till the end of January 2013.
Unfortunately, the introduced question intends to investigate specifically internal collaboration tools and not
those for global collaboration47 more generally considered in CWEs. More, besides specific CWE collaboration
tools (wiki or blogs), other items consider also tools not usually included in the core of the CWEs, that is the
Enterprise Resource Planning - ERP48 and the Customer Relationship Management – CRM, devoted to the
company’s interactions with customers.
Thus, even in a so important census occasion, the information collected on interesting aspects of collaboration
in the enterprise does not cover the specific aspects of the unified communications & collaboration with its
characteristic of distance working.
46
The Business Census of enterprises 2011 has a complex design; first of all a complete enumeration of business and
related variables on occupation carried out by administrative sources; secondly a direct survey on about 260 thousands
enterprises with complete enumeration of business larger than 20 persons employed and a sample selected from the
population of business equal or over 3 persons employed regarding topics generally not covered by the current surveys;
finally another survey on more than 3000 complex business and groups of business on internal organisation and related
issues. In particular the second .survey uses two different questionnaires for small and medium/large enterprises focusing
on competitiveness with modules on strategies for governance, human resource management, position on the national
and international markets, innovation, financial aspects, internationalisation, localisation choices.
47
The exact wording is: “What technological tools are used by the company to communicate, exchange or share
information IN IT?” a) intranet; b) wikis, corporate blogs; c) software dedicated to sharing information between business
functions (ERP); d) software designed to manage and analysis of information on customers (CRM); e) none.
48
Systems to integrate internal and external management information across the organisation embracing
finance/accounting, manufacturing, and marketing, and sometimes sales and service.
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3. Improving data availability: two proposals
3.1 A new CWE module in the Community Survey on ICT Usage and e-Commerce in Enterprises
The characteristics of this survey (to be an annual survey, harmonised at European level, on the use of ICT in
medium and large firms) make it as the best instrument for data collection on the theme of CWE, so far used in
a very limited extent.
The survey 2012/2013 (that is to start with the fieldwork) will present two important news about the subjects
of CWE:
1) at Community level, Eurostat introduced a module on the use of social media in the enterprise49
considering also some communication tools of the CWE (prospect 3.1).
Prospect 3.1 – Social Media ad hoc Module in Community Survey on ICT Usage and e-Commerce in Enterprises 20122013
B12.
In January 2013, did your enterprise use any of the following social media?
Yes
No
a) Social networks (e.g. Facebook, LinkedIn, Xing, Viadeo, Yammer, etc)

b) Enterprise's blog or microblogs (e.g. Twitter, Present.ly, etc)

c) Multimedia content sharing websites (e.g. YouTube, Flickr, Picasa, SlideShare,
etc)

d) Wiki based knowledge sharing tools

(add national examples; replace existing examples if necessary)
e) The enterprise did not use any of the above mentioned social media or used
them only for posting paid adverts
B13.
In January 2013, did your enterprise use social media to:
a) Develop the enterprise's image or market products (e.g. advertising or launching
-> go to
C1
Yes
No

49
Enterprises using social media are considered those that have a user profile, an account or a user license depending on
the requirements and the type of the social media. Use of Social Media refers to the enterprise’s use of applications based
on Internet technology or communication platforms for connecting, creating and exchanging content online with
customers, suppliers, or partners, or within the enterprise.
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products, etc)
B14.
b) Obtain or respond to customer opinions, reviews, questions

c) Involve customers in development or innovation of goods or services

d) Collaborate with business partners (e.g. suppliers, etc.) or other organisations
(e.g. public authorities, non governmental organisations, etc.)

e) Recruit employees

f) Exchange views, opinions or knowledge within the enterprise

Yes
No
In January 2013, did your enterprise have a formal policy for using social media?
(e.g. objectives, rules, procedures, etc)
2) More, Istat has decided to dedicate to the CWE50 a specific ad hoc module in addition to the one on
social media. So, five quite simple questions are introduced, because the survey is aimed at enterprises
of all sizes which are also not globalised (prospect 3.2). Two specific questions are devoted to
investigate whether the enterprise has used unified communications and collaboration systems
(defined as generalised systems available on the market that allow enterprise to use multiple
communication and collaboration tools to the various users to ensure full interoperability of voice, data
and video) or it has made use of on-line communication and collaboration systems specific to the
sector. This will allow to understand whether the trend toward UC&C - described in the literature - is
really significant and if there are many systems specific to the sector in which different enterprise
operate.
A more complex question is dedicated to understand who are the collaboration partners and where
are localised (nationally or abroad). Finally, the obstacles to overcome or to deal with using these tools
(privacy, copyright, interoperability, adaptability and language barriers) are investigated.
Prospect 3.2 – CWE ad hoc Module in Community Survey on ICT Usage and e-Commerce in Enterprises 2012-2013
proposed by Istat
50
CWE is defined as “work together with other parties, without meeting in person. Communication practices (voice, text
or multimedia) are handled by devices (fixed, portable, mobile) connected to Internet”.
These tools allow to access corporate resources from any location and can be used to work in the company or in mobility
from anywhere.
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B15. - Please indicate if in 2012 your enterprise has used the following online communication tools for collaborative working
environments
Yes
No
E-mailing
Instant messaging systems
Management of grouped documents (e.g. online documents repository, collaborative
writing, etc.)
Management of shared agendas
Videoconferences and webinars
Workflow Management
B16. Please indicate if in 2012 your enterprise has used Unified communication and collaboration systems
Yes
No
(generalized systems available on the market that allow enterprise to use multiple communication and collaboration tools to
ensure to the various users full interoperability of voice, data and video)
B17. Please indicate if in 2012 your enterprise has used online communication and collaboration systems specific to the sector
Yes
No
B18. - Please indicate if your enterprise in 2012 has made use of some online collaboration systems mentioned above in order
to collaborate with (specifying also the geographical location of partners/subjects)
Yes
In Italy
No
Abroad
Internal communication
Logistics or distribution partners
Businesses consultants and marketing
Other suppliers
Customers
Universities, high level educational institutes, public and private research centres
Other public sector organisations
B19. Please indicate which problems your enterprise believes are the most important while collaborating online
Yes
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Privacy or data security
Problems on copyright
Interoperability of processes and ICT systems
Difficulty in building trust among collaboration partners
Problems due to language or other cultural barriers
Difficulty in adapting employees’ working time to the demands of global collaboration
This module will be proposed to the ad hoc Eurostat Task Force coordinating the Survey as a best practice to
include also in the harmonised questionnaire to ensure that the information is collected in all EU countries.
3.2 A new module in the Business Census on complex business units
A second strategic opportunity to gather information comes from the introduction of an ad hoc CWE module in
the Istat Business Census questionnaire on complex business units (that will be carried out in September 2013).
The survey is addressed to bigger businesses and groups of business resident in Italy. Sample size is in the
range between 3.000-3.500 units. In particular, a complex business unit51 can identify either a single enterprise
or an enterprise group. The respondent unit is the enterprise or in the case of enterprise group the head of the
group (UCI definition). Foreign MNEs operating in Italy are investigated using the truncated head of group
resident in Italy as respondent unit. Personal interviews are carried out by a specialised consultancy company
selected by ISTAT on the basis of a public tender.
In prospect 3.3 are summarised the topics of the Census on complex business units which, as you can see,
cover many subjects in a really detailed way.
Prospect 3.3 – Topics of the questionnaire by section
Section 1 – Organisation of complex business unit
Section 2 – Management and strategic choices
51
A complex business unit is selected in the sample if it holds at least 500 person employed or a turnover larger than 100
million euros with respect to all activities carried out in Italy or abroad.
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Competitive position and medium term growth prospects
Characteristics of management
Governance and decision-making processes
Human Resource Management
Suppliers Management
Customer Management
Internationalisation
Knowledge and innovation processes
Administrative and financial management and management control
Programs, strategies and corporate functions
Section 3 - Relations with complex business units and new survey methods
This particular survey may be the occasion to investigate on CWE precisely in a sector of businesses which
should use distance collaboration more broadly to other sectors and seems to take advantage to a greater
extent of the opportunities offered by ICT and UC&C systems.
A single question is introduced, but more complex than those of the previous survey (which is aimed at
companies of all sizes and sectors not necessarily globalised).
The use of traditional CWE tools (including social networks) by the complex business units is analysed
distinguishing the subjects of these working relations (branch office, consultants, suppliers, customers, and
university/research centers) in Italy or abroad. This distinction has been introduced to see if the distance
determines actual differences in the use of different tools (prospect 3.3).
Since the NSIs in latest years are pushing the analysis of enterprise groups and corporates, Istat ad hoc module
on CWE practice and tools within complex business units will offer to Eurostat and other NSIs a best practice
with the aim of including it in similar questionnaires in all EU countries.
Prospect 3.3 – CWE ad hoc Module in the Business Census on complex business units 2013 proposed by Istat
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QUESTION – The enterprise group uses the following online communication tools for collaborative working depending on the
subjects with which they have been used and their territorial location
IN ITALY
Branch offices
Business
consultants
Suppliers
Customers
Universities
and Research
Centres
Customers
Universities
and Research
Centres
E-mailing and instant
messaging systems
Social networks (i.e. Facebook,
LinkedIn, Xing, Viadeo,
Yammer, etc.)
Wiki or knowledge blogs
Management of grouped
documents (e.g. on-line
documents repository,
collaborative writing, etc.)
Management of shared
agendas
Videoconferences and
webinars
Workflow Management
Unified systems of
communication and
collaboration (Microsoft, IBM,
Google)
Industry-specific on-line
collaboration tools
ABROAD
Branch offices
Business
consultants
Suppliers
E-mailing and instant
messaging systems
Social networks (i.e. Facebook,
LinkedIn, Xing, Viadeo,
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Yammer, etc.)
Wiki or knowledge blogs
Management of grouped
documents (e.g. on-line
documents repository,
collaborative writing, etc.)
Management of shared
agendas
Videoconferences and
webinars
Management of work flow
Unified systems of
communication and
collaboration (Microsoft, IBM,
Google)
Industry-specific on-line
collaboration tools
Concluding remarks
It is finally time that the Official Statistics assume responsibility to investigate the opportunities offered to the
enterprises by CWE tools and that the monitoring of the use of these practices in the enterprises possibly
happen annually.
For this to happen, given the lack of attention on this subject by NSIs, a scientific pressure on Eurostat and
other international organisations should be exercised.
For this reason, Istat launches two proposals for introduction of ad hoc questions in two different types of
surveys. These proposals incorporate the main conclusions from the guidelines on the study of the
phenomenon CWE provided in the second half of the 2000s by various task forces and projects in Europe, thus
properly addressing the issue but also trying to make it up to date with respect to the latest trends emerging in
this topic.
Both of these experiences can be exported in EU countries and provide the basis for a new accumulation of
knowledge on distance collaboration, emerging practices and skills needed to manage these processes to the
benefit of competitiveness and productivity of the economic system.
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Annex
Ad hoc CWE Module in the Community Survey on ICT Usage and e-Commerce in Enterprises,
2012-2013 (in Italian)
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