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Local Governments' efficiency: is there anything new after Troika's intervention in Portugal?
Maria Basílio CIGES and Management Department, Polytechnic Institute of Beja (IPBeja), Escola Superior de Tecnologia e Gestão, R. Pedro Soares, Campus do Instituto Politécnico de Beja, 7800-295
Beja. Phone: +351 284 311 541 [email protected]
Clara Pires CIGES and Management Department, Polytechnic Institute of Beja (IPBeja), Escola Superior de Tecnologia e Gestão, R. Pedro Soares, Campus do Instituto Politécnico de Beja, 7800-295
Beja. Phone: +351 284 311 541 [email protected]
Carlos Borralho CIGES and Management Department, Polytechnic Institute of Beja (IPBeja), Escola Superior de Tecnologia e Gestão, R. Pedro Soares, Campus do Instituto Politécnico de Beja, 7800-295
Beja. Phone: +351 284 311 541 [email protected]
José Pires dos Reis CIGES and Management Department, Polytechnic Institute of Beja (IPBeja), Escola Superior de Tecnologia e Gestão, R. Pedro Soares, Campus do Instituto Politécnico de Beja, 7800-295
Beja. Phone: +351 284 311 541 [email protected]
Área Temática: I - Setor Público e Não Lucrativo
2
Local Governments’ efficiency: is there anything new after Troika’s
intervention in Portugal?
ABSTRACT
The austerity policies being implemented in many European countries and in Portugal,
particularly as a consequence of the bailout agreement signed between the Portuguese
Government and the Troika, bring measures to increase performance and reduce costs. The
analysis of Local Governments’ efficiency and the assessment of its determinants is highly
relevant for policy purposes. The aim of this research is to evaluate the efficiency of the 278
mainland municipalities in Portugal with a two-stage procedure, combining DEA methods in
a first phase with fractional response models in the second stage. The analysis is performed
for 2010 and 2015, before and after the Troika’s intervention in Portugal. Results show a
similar pattern for both years, in the two stages. Performing a Chow test, no structural change
occurred from 2010 to 2015, suggesting that the reforms implemented in municipalities did
not succeed.
Keywords: Municipalities, Data Envelopment Analysis, Efficiency, Fractional
Response models.
JEL classification: C14, C35, H72, D60
3
1. Introduction
The topic of efficiency and performance measurement has been long investigated in public
administration. Under the New Public Management paradigm which emerged in the 1980’s,
the challenge has been to create governments that “works better but costs less”1. But how to
reconcile the trade-off between more and better services to citizens with fewer resources?
How to effectively measure governments’ outcomes? These are still questions hard to answer.
In recent years this topic has taken a renewed importance. For countries like Portugal,
due to its economic and financial situation that emerged from the sovereign and international
crisis. In fact, from May 2011 to May 2014, the country was under an economic and financial
assistance program negotiated between the Portuguese authorities and the European
Commission (EC), the European Central Bank (ECB) and the International Monetary Fund
(IMF) - the Troika. The bailout agreement required fiscal consolidation and several structural
reforms. Among them, the reorganisation of local government administration in order to
reduce the number of parishes,2 to improve efficiency and quality, and to reduce costs.
The goal of this research is twofold. First, we assess the relative efficiency of each
Portuguese municipality for the years 2010 and 2015, before and after Troika’s intervention
in Portugal using Data Envelopment Analysis (DEA) methods - standard DEA and Inverted
DEA. Second, we adopt fractional response models, to reveal potential determinants of these
efficiency levels. It should be emphasized that as far as we know, no other study concerning
Portuguese municipalities has used these methodologies.
1 This phrase turned into a slogan after the publication of a report in 1993, by Al Gore (then vice-president under
the Bill Clinton presidency in the United States) untitled “Creating a Government that Works Better and Costs
Less”.
2 The merger of civil parishes was one of the solutions suggested by the Troika to increase efficiency of local
governments, in line with the argument of scale economies (Tavares et al., 2012). The number of parishes in
Portugal was reduced from 4260 (in 2010) to 3092 (in 2015).
4
The main contribution of this research is to increase the discussion about efficiency
and its drivers in Local Government entities and to provide empirical evidence about the
impacts of the recent financial crisis on Portuguese municipalities.
The plan of this paper is structured as follows. Section 2 provides a short description
of the characteristics of Portuguese municipalities. Section 3 reviews some of the recent
literature using DEA and second-stage procedures to evaluate local governments’ efficiency.
Section 4 explains the research methodology and the econometric approach, detailing the
data. Section 5 presents and discusses the results. Finally, Section 6 draws the main
conclusions and avenues for future research are highlighted.
2. Characteristics of Portuguese Municipalities
According to the Portuguese Constitution, there are three types of local governments: parishes
(freguesias), municipalities, and administrative regions. From these three types,
municipalities assume more importance, taking into account their political decision power and
financial expression (Jorge et al., 2008). Parishes are small jurisdictions with few own
competencies and administrative regions are not yet implemented in Portugal mainland. There
are currently 308 municipalities, 278 are located in Portugal mainland and the remaining 30
are overseas municipalities belonging to the autonomous regions of Azores and Madeira.
Local governments in Portugal have their own budgets and property, and are all
subject to the same legal and institutional framework. Recent years have witnessed a
progressive trend of decentralization of competencies from the Central Government to local
authorities. However, the weight of local governments in general government finances is
small compared to other European countries. As mentioned in Carvalho et al. (2016), local
expenditures of Portuguese municipalities in 2015, accounted for only 15% of total public
5
expenditure and local revenue for 17% of total public revenue, which is the lowest when
compared to the European average (29% and 32%, respectively).
Municipal public expenditures are divided into capital and current expenditures. The
former include investment, their main component, capital transfers to parishes, financial
assets and liabilities, and other capital expenditures. As for current expenditures, their sub-
components are expenditures on goods and services, financial expenditures, human resources,
current transfers to parishes, and other current expenditures. The main sources of municipal
revenue are transfers from the central government, local taxes (being the property tax the
largest own-revenue source of municipalities) and other revenues (fees and fines, property
income, and financial liabilities, among others).
As mentioned in Costa, Veiga and Portela (2015), Portugal is an interesting case study
because municipalities are all subject to the same rules and legislation and have the same
policy instruments and resources at their disposal.3
Following the Local Administration Reform implemented in 2012, a number of legal
reforms were introduced changing significantly the financial, control and reporting
framework of Portuguese municipalities. Some of these changes resulted directly from the
bailout agreement.
The most significant legal diplomas are the current Local Finance Law (Law 73/2013)
and Local Authorities Law (Law 75/2013). Portuguese municipalities’ main competences are
established in art. 23 of this last diploma and includes: urban and rural infrastructure, energy,
transport and communications, education, patrimony, culture and science, sports and leisure,
healthcare, social services, housing, civil protection and police, environment and basic
sanitation, consumer protection, social and economic development, territory organisation and
external cooperation.
3 They may be considered a set of homogenous entities, a prerequisite to use a DEA approach.
6
3. Literature Review
Performance and efficiency are difficult to measure, particularly for non-profit organizations.
In what concerns local governments outcomes, it is even a more difficult task, given the
nature of the services provided that are market-aside and difficult to valuate.
One way to access efficiency is to use Cost Accounting information. Several attempts
were made to develop a Cost Accounting subsystem to be applied to public entities; however
the great majority of the municipalities have not implemented it yet. More recently, with the
new accounting system (SNC-AP, the Accounting Standardization System for Public
Administrations), it is expected to ascertain the cost of services provided by municipalities to
the populations, and thus to allow an effective control of their efficiency and effectiveness
(Carvalho et al., 2016). The implementation of Management Accounting systems in Public
Administrations is of great importance in the current context in which the various entities are
faced with the need to properly manage the resources at their disposal and to manage public
institutions efficiently and economically. These systems will provide managers with a set of
tools essential to decision making, including planning and execution of control functions.
The literature about local governments’ efficiency is extensive. According to Narbón-
Perpiña and De Witte (2017a), adopting a systematic literature review and with a focus on
local government efficiency from a global point of view4, from 1990 to 2016, 84 empirical
studies were identified. The large majority of these studies have adopted only one approach,
mainly a nonparametric, being Data Envelopment Analysis (DEA) the most used, followed by
Free Disposal Hull (FDH). A wide variety of input and output variables were used depending
on the competences of local governments in each country and naturally, on the availability of
4 By contrast, some studies have concentrated the analysis on a particular local service. For instance water
provision (Picazo et al. 2009, Byrnes at al. 2010) or refuse collection (Bosch et al. 2000).
7
data. The collection of data and particularly, the measurement of local services are complex
and difficult tasks.
The operational environment strongly affects the performance of local governments.
Municipalities face different environmental conditions, namely different social, demographic,
economic, political, financial, geographical and institutional conditions (Narbón-Perpiña &
De Witte, 2017b). In order to take into account the effects of these external factors, a two-
stage procedure is common in nonparametric efficiency studies. In the first phase, DEA
efficiency scores are obtained. In the second phase, a regression model is estimated for DEA
scores in order to examine the effect on the efficiency of potential relevant factors that are
beyond control (the so-called environmental, contextual or non-discretionary variables).
From the 84 empirical studies mentioned above, 63 included environmental variables
in the analysis, and 40 of them adopted a two-stage analysis. In most cases, the second-stage
regression relies on Tobit models (17 papers) or bootstrapped truncated regressions (11
papers), or even Ordinary Least Squares OLS (12 papers), as mentioned in Narbón-Perpiña
and De Witte (2017b). To the best of our knowledge, fractional response models were not
tested in this context. This option will be discussed in the next section.
Specifically about Portugal, five studies have evaluated the efficiency of local
governments. Afonso and Fernandes (2006) studied 51 municipalities in the region of Lisbon
and Vale do Tejo, for the year 2001. This analysis was extended to the entire Portuguese
mainland in Afonso and Fernandes (2008). In this last paper, the authors used DEA with one
input variable (per capita municipal expenditures) and one output variable (the Local
Government Output Indicator - LGOI - a composite index to proxy for the global performance
of the municipality) for 2001. The results by regions showed that Alentejo (0.654) and
Algarve (0.608) have the highest values, on average. The Centro region was, on average, the
least efficient (0.237). On a municipal level the results were quite uneven. In a second stage,
8
through a Tobit analysis, the most relevant environmental factors, which contribute positively
to increase efficiency, were the level of education (secondary or tertiary); municipal per capita
purchasing power; and geographical distance to the capital of district.
Jorge et al. (2008) used data from 2004 municipal accounts and estimated DEA scores
to 274 municipalities in Portugal mainland (three were excluded due to unavailability of data).
With an input-oriented approach and grouping the municipalities by size, the results showed
that larger municipalities tend to be more efficient.
Cruz and Marques (2014) used the super-efficiency DEA model of Andersen and
Petersen (1993) in a first stage using data for the year of 2009 and for all the Portuguese
municipalities (308). To explore the determinants of economic efficiency, in a second-stage,
different models were compared, namely, Tobit, OLS and double-bootstrap. The results seem
to indicate that concentration of population; illiteracy rate; net debt and purchasing power of
the population have a negative effect on the municipalities’ performance.
With a different approach, Cordero et al. (2016) applied time-dependent conditional
frontier estimators to assess the performance of the 278 municipalities for the period of 2009-
2014. One of the strongest limitations of two-stage approaches is that the results may be
biased and inconsistent due to the existence of serial correlation among the estimated
efficiencies obtained with nonparametric methods (Simar & Wilson, 2007, 2011). To deal
with this shortcoming, the authors adopted conditional measures of efficiency, to directly
account for the effect of external variables on the estimation of the efficiency measures of
local government performance. The results suggested that the average efficiency level has
remained stable over the period of 2009-2014, with a slight improvement in the last year.
Larger municipalities performed better, but the gap between these and small municipalities
narrowed notably after the local reforms implemented in 2013. The Lisbon region exhibits the
most efficient municipalities. Concerning the environmental variables, population density and
9
socioeconomic factors do not have a significant impact on efficiency, while a coastal location,
the number of civil parishes and the level of net debt affect municipalities’ efficiency.
4. Methodology
Several methods have been developed to estimate efficiency, and can be classified into two
main groups: parametric (namely, Stochastic Frontier Approach – SFA, Distribution Free
Approach – DFA) and nonparametric (Data Envelopment Analysis – DEA, Free Disposal
Hull - FDH). The main difference between parametric and nonparametric methods relies on
the assumptions about the random errors and on the underlying distribution. While the
parametric approaches have the advantage of decomposing deviations between “noise” and
pure inefficiency, the nonparametric approaches classify the whole deviation as inefficiency,
but otherwise they have the advantage of not imposing a particular parametric functional
form, avoiding misspecification errors. Nonparametric methods have received the authors’
preference because they have less-restrictive assumptions, greater flexibility and can easily
handle multiple outputs and inputs (Ruggiero, 2007).
DEA is based on the idea of technical efficiency, measured by the ratio of output to
input. It allows the identification of the efficient and inefficient units in a comparison of each
unit with its peers (within the group). This programming technique was first developed by
Charnes, Cooper and Rhodes (1978) – CCR model - and since then it has been used to assess
efficiency in areas such as health, prisons, courts, schools and universities and, more recently,
transit and banking.5
DEA compares the relative performance of each decision-making unit (DMU) with the
“best” performance. It is a tool to access the relative performance of homogeneous units. The
advantages of DEA include its ability to accommodate a multiplicity of inputs and outputs;
5 For more details about the DEA methodology, see for instance, Ray (2004).
10
there is no need to specify a particular functional form for the production frontier, and no
prior establishment of rules for the weights is necessary. In addition it works particularly well
with small samples. By contrast, several limitations may be pointed out, namely: the
assumption that there is no random error (any deviation from the estimated frontier is
considered inefficiency); the results’ sensitiveness to the selection of inputs and outputs; and
the number of efficient DMUs on the frontier tends to increase with the number of input and
output variables. As a rule of thumb, the number of DMUs is usually required to triplicate the
number of variables.
The efficiency scores obtained with constant returns to scale (CRS) indicate the
overall technical efficiency (OTE).6 Banker, Charnes and Cooper (1984) develop the use of
variable returns to scale (VRS) – BCC model - which allow the decomposition of OTE into a
product of two components:
𝑂𝑇𝐸 = 𝑃𝑇𝐸 𝑥 𝑆𝐸
where, PTE is pure technical efficiency obtained under VRS and relates to the ability of
managers to use municipalities’ resources. These scores are higher than or equal to those
obtained under CRS. SE is scale efficiency and refers to exploiting scale economies and
measures whether a municipality produces at an optimal size of scale. SE is obtained by
dividing OTE by PTE.
The mathematical characteristics of the BCC model allow the DMUs that have the
lowest value in one of the inputs (or the highest value in one of the outputs) to be considered
efficient, even if the other variables do not have the best relations (Ali, 1993). These DMUs
are called false efficient, efficient from the start or by default.
6 The efficiency scores may be obtained with constant returns to scale (CRS) or variable returns to scale
(VRS). With CRS, the assumption is that DMUs are capable of linearly scaling the inputs and outputs, without
affecting efficiency. In other words, there is no significant relationship between the scale of operations and
efficiency. With VRS, an increase in inputs does not result in a proportional change in the outputs.
11
In order to better understand the results obtained by standard DEA models, some
complementary models emerged, such as the super-efficiency model (Andersen & Peterson,
1993) or the Inverted DEA model (Yamada, Matui & Sugiyama, 1994). Although these are
not very recent in the literature, there are few empirical studies that used these approaches.7
Most of the time, the limitations of the DEA standard model are ignored.
In this empirical application, we will use Inverted DEA (IDEA), based on the
inversion between inputs and outputs, to identify false efficient DMUs in the BCC model.
While standard DEA models are considered optimistic - DMUs are considered efficient
through the subset of variables that are more favourable to them - IDEA models represent a
pessimistic assessment of each DMU, since the inverted frontier is composed of the worst
DMUs with poor practices, and it represents an “inefficient frontier”. Thus, the higher the
level of inverted efficiency, the lower the efficiency of the DMU.
To provide a more realistic assessment of the “true” efficiency score, we follow Meza
et al. (2007) and compute a compound PTE* score, following:
𝐶𝑜𝑚𝑝𝑜𝑢𝑛𝑑 𝑃𝑇𝐸∗ = 𝑃𝑇𝐸𝐷𝐸𝐴 + (1 − 𝑃𝑇𝐸𝐼𝐷𝐸𝐴
∗ )
2
Because IDEA reflects a measure of inefficiency, its complement is used to get the
average of efficiency levels. Lastly, the normalized compound efficiency score is computed,
dividing each DMU’ value by the higher value.
In the second stage, we perform individual regressions for each year and using the
whole sample (through a pooled cross-section regression) against several potential exogenous
7 Notably exceptions using Inverted DEA models include Meza et al., (2007), Entani et al., (2002), Pimenta et al.
(2004) and Mello et al., (2005), although not in Local Governments’ context. Authors using DEA super-
efficiency models include Cruz and Marques (2014) and Liu et al. (2011).
12
variables. The efficiency scores are proportions, therefore classified as a fractional response
variable, ranging from 0–1. We use the generalized linear models (GLM) approach, first
proposed by Papke and Wooldridge (1996), with robust standard errors and quasi-maximum
likelihood estimation. Several functional forms for the conditional mean of y that enforce the
conceptual requirement that 𝐸(𝑦|𝑥) is in the unit interval, may be used. We have,
𝐸(𝑦|𝑥) = 𝐺(𝑧) (1)
where G(. ) is a known nonlinear function satisfying 0 < G(. ) < 1. The logistic and standard
normal specifications for G(. ) are symmetric about the point 0.5 and consequently approach 0
and 1 at the same rate. Given our data and testing for both models, logit and probit, a
fractional logit model was used.8
Here, it is important to stress that traditional linear models or Tobit approaches to
second-stage DEA analysis do not constitute a reasonable data-generating process for DEA
scores. Following Ramalho, Ramalho and Henriques (2010), the standard linear model using
OLS is not appropriate since the predicted values of 𝑦 may lie outside the unit interval and the
implied constant marginal effects of the covariates on 𝑦 are not compatible with both the
bounded nature of DEA scores and the existence of a mass point at unity in their distribution.
A different approach is to use a two-limit Tobit model, but this is also problematic. In DEA
applications, there is an accumulation of observations at unity, and this is a natural
consequence of the DEA methodology rather than the result of a censoring mechanism. In
addition, efficiency scores of zero are typically not observed.
Two particular problems arise from using DEA scores in the second-stage regression
analysis: first, the input/output variables used in the first stage may be correlated with the
explanatory variables used in the second stage; and second, DEA scores are dependent on
8 For a detailed analysis of fractional response models (FRMs), see Ramalho, Ramalho and Murteira (2011).
13
each other, which is against the within-sample independence requirement. Therefore, the
standard approaches to statistical inference are invalid and the estimated effects of the
environmental variables on DMUs efficiency may be inconsistent. Simar and Wilson (2007)
proposed two alternative bootstrap methods to construct confidence intervals, and although
these algorithms solved some of the theoretical limitations of traditional regression models,
they still impose a restrictive condition of separability between the input-output space and the
space of external variables that should be tested in advance. This separability restriction
implies to assume that the exogenous variables included in the second stage cannot affect the
support of the input and output variables included in the first stage, which is often an
unrealistic assumption (Cordero et al., 2016).
In this research, we follow a different approach, adopting FRMs following Ramalho et
al. (2010).9 Under the assumption that DEA scores are observed measures of the relative
performance of units in the sample, FRMs are the most natural way of modelling bounded,
proportional response variables such as DEA scores. Advantages of using FRMs include the
fact that they may be estimated by quasi-maximum likelihood, unlike Tobit models, FRMs do
not require assumptions about the conditional distribution of DEA scores or
heteroskedasticity patterns.
4.1. Data and variables
The empirical analysis is based on the 278 mainland municipalities. We adopt a similar
approach as Afonso and Santos (2008) and Cordero et al. (2016) and the 30 overseas
municipalities were excluded. Differences in the territorial organization, the fact that
inhabitants of the islands may have different needs from those living in continental Europe,
9 Other approaches were also tested. See for instance, the nonparametric kernel regression (Balaguer-Coll, Prior
& Tortosa-Ausina, 2007), or the conditional nonparametric approach (Asatryan & De Witte, 2015; Cordero
et al., 2016).
14
and the status of ultra-peripheral regions that allow them to receive additional European
Union’s funds, are among the reasons for that exclusion (Costa et al., 2015).
Following the criteria of Carvalho et al. (2016), we group municipalities by population
size: small ≤ 20 000 inhabitants; medium > 20 000 and ≤ 100 000 inhabitants; large >
100 000 inhabitants. Results by size and by regions (NUTS II) are presented in Table 1.
Table 1: Distribution of municipalities by size and NUTS II
NUTS II Small Medium Large Total
regions N % N % N %
Norte 46 53.5% 30 34.9% 10 11.6% 86
Centro 63 63.0% 35 35.0% 2 2.0% 100
Lisboa 1 5.6% 6 33.3% 11 61.1% 18
Alentejo 45 77.6% 13 22.4% 0
58
Algarve 7 43.8% 9 56.3% 0
16
Portugal mainland 162 58.3% 93 33.5% 23 8.3% 278
The majority of municipalities are small (58.3% of the total), particularly in the
Alentejo, Centro and Norte regions. By contrast, the Lisboa region has 11 municipalities with
more than 100 000 inhabitants.
The choice of the inputs and outputs is critical for the DEA model. Our model uses
one input variable - current expenditures - and four output variables:
Resident population (number);
Area of the municipality (measured in Km2);
Kindergarten and primary education students (number of enrolled students);
Senior citizens, older than 65 years (number).
According to Narbón-Perpiña and De Witte (2017a), current expenditures are the most
widely used input indicator to measure the costs incurred by municipalities to provide local
services. Capital expenditures are not included since they are highly volatile, being affected
15
by large infrastructure investments made by municipalities. As already mentioned, the
selection of outputs must proxy the specific municipalities responsibilities, defined by law.
We use resident population and the area of the municipality as measures of the total demand
of public services.10 Education and social services (the care for elderly) are measured by the
number of enrolled students in kindergarten and primary education and by the number of
senior citizens older than 65 years old, respectively.
For the years of 2010 and 2015, standard DEA models were estimated using CRS and
VRS and the IDEA model was estimated adopting VRS (the BCC model), with an input
minimization orientation. With an input-oriented DEA model, the goal is to study how much
input quantities can be proportionally reduced in order to keep the same quantities of outputs
produced. This is a rational option for local governments that are facing pressures to reduce
costs and simultaneously to maintain a certain level of services.
The input data used to perform the DEA analysis was obtained from the
municipalities’ annual reports from Portal Autárquico (available online at
http://www.portalautarquico.pt/). Concerning output variables, data was collected from the
Portuguese National Statistics Institute.
Differences in the operational environment of each municipality may affect
performance outcomes. Any study about local governments’ efficiency that do not control for
this heterogeneity, will have only a limited value (Cruz & Marques, 2014).
Therefore, in the second stage, exogenous factors were tested as independent variables
to explain the compound efficiency scores. These factors that are beyond municipalities’
control were grouped in several dimensions in order to characterize the operational
environment of each municipality. We consider the following variables:
10 These two indicators are among the most used, 46 papers used total population and 10 papers the municipal
area. For more details, see Narbón-Perpiña and De Witte (2017a).
16
Table 2: Exogenous variables definition
Dimension Variable Definition
Social and demographic
determinants
Population density (-) The number of inhabitants of each municipality divided by its
extension (measured in squared kilometres)
Education level (+) The number of students enrolled in primary, secondary and
tertiary education divided by the resident population
Economic determinants Unemployment (-) The percentage of unemployment related to the working
population of each municipality
Political determinants Democratic
participation (+)
It represents the political participation of the citizens in local
elections. It is measured by the number of votes divided by the
total number of citizens entitled to vote
Financial determinants Debt (-) Measured by the weight of financial liabilities on total
municipalities' assets
Geographical determinants Coastal (+) A dummy variable to account if the municipality is on the littoral,
has a coastal area (1) or not (0)
Territory organization Parishes (-) Number of civil parishes
Data on each municipality was obtained for the years of 2010 and 2015. Concerning
the variables, the dummy on the coastal location and the variable of Democratic participation
were computed by the authors, as well as the variable Debt, using information available on
http://www.portalautarquico.pt/. It should be noted that Democratic participation is related
the local elections of 2009 and 2013 and is used as a proxy for the years 2010 and 2015,
respectively. The remaining variables were obtained through online INE and PORDATA
databases.
We expect that if the population concentration is larger, it will increase the costs of
providing public services (due to higher complexity, congestion) and it will lower the average
efficiency. However, the effect of this variable on efficiency is not straightforward, see, for
instance, Afonso and Santos (2008) or Lo Storto (2016).
By contrast, more educated citizens will demand better public services, which will
have a positive effect on the efficiency level of each municipality. The same effect is expected
concerning the democratic participation variable. Geys, Heinemann and Kalb (2010) reported
that higher voter involvement is indeed associated with increased government efficiency.
17
Regarding unemployment and debt, it is expected that municipalities’ facing high
levels of unemployment and debt to be less efficient. Several authors reported that higher
income citizens paying greater taxes will require more and better local services and facilities
(Afonso & Fernandes, 2008; Asatryan & De Witte, 2015). Municipalities’ debt has been a
major concern in recent years and we expected higher levels of indebtedness to affect
negatively efficiency, as a higher share of resources will be employed in payments: interests
and amortizations (Cruz & Marques, 2014; Geys, 2006; Geys & Moesen, 2009). A positive
relation with efficiency is expected for coastal location. Municipalities near the coast are able
to achieve higher levels of development and as a consequence, to increase tax revenues
(Cordero et al., 2016). Lastly, the number of civil parishes is included to ascertain if their
reduction has the expected effect of increasing efficiency, supporting the argument of scale
economies. The expected effect, of these variables on the efficiency score, is presented in
brackets in the table above and Appendix A presents the descriptive statistics.
It should be noted that there is a wide variety of efficiency’ determinants that may be
considered and results are strongly dependent on the variables and models chosen by the
authors. Many determinants present ambiguous and mixed effects and results should be
interpreted with caution since they are country-specific (for a detail analysis of these results,
see Narbón-Perpiña & De Witte, 2017b).
5. Empirical Results
5.1. First-stage DEA results
All the results were obtained using DEAP software. It should be emphasized, that the results
by municipality are quite uneven (results on each municipality are presented in Appendix B).
Next table reports the results by NUTS II regions, for both years under analysis.
18
Table 3: Average DEA scores for municipalities grouped by regions for the years 2010 and 2015
NUTS II N
2010 2015
regions OTE PTE SE PTE* OTE PTE SE PTE*
Norte 86 0.622 0.733 0.838 0.678 0.609 0.721 0.839 0.666
Centro 100 0.617 0.758 0.815 0.707
0.623 0.768 0.810 0.712
Lisboa 18 0.650 0.736 0.896 0.645
0.701 0.768 0.924 0.685
Alentejo 58 0.621 0.761 0.818 0.666
0.642 0.796 0.813 0.699
Algarve 16 0.418 0.490 0.860 0.424
0.432 0.506 0.859 0.435
Portugal mainland 278 0.610 0.734 0.831 0.669 0.616 0.744 0.830 0.677
Considering the scale of the Portuguese mainland municipalities, on average, results
for both years show that around 17% of inefficiency is explained by scale inadequacy.
However, better results are presented by the Lisboa region, particularly in 2015 (SE = 0.924).
At this purpose, we should recall that 11 of the 18 municipalities of the Lisboa region are
large municipalities, what suggests the existence of economies of scale.
Next, we will focus our analysis on the PTE* score because, as explained before, it is
expected to present a more realistic efficiency measure. The analysis of the results by regions
shows a similar pattern for both years. PTE* is the lowest for the Algarve region and the
highest for the Centro region in both years under analysis. Additionally, all regions exhibit a
slight, but positive trend from 2010 to 2015, with the exception of the Norte, where the
average PTE* score declined 1.77%.
For the full sample of the 278 Portuguese municipalities the average PTE* score is
0.669 in 2010, and 0.677 in 2015, which means that the average municipality in the sample
could have theoretically achieved roughly the same outputs, with about 33.1% and 32.3% ,
fewer resources, respectively.
Grouping the results by municipality’s size, we have the following results (Table 4).
19
Table 4: Average DEA scores by municipalities’ size for the years 2010 and 2015
Comparing 2010 and 2015, a slightly improvement has occurred, in all the three
groups of municipalities, being more notorious in large municipalities. Focusing our attention
on the PTE*, large municipalities grew 4.43% on the average efficiency score. Comparing our
results with previous findings, it is important to note that Jorge et al. (2008) and Cordero et al.
(2016) also report that, on average, large municipalities were more efficient.
Using simple comparisons, the results for both years appear to be not significantly
different. To give support to that claim, we compute a two-group mean comparison test, a
paired t-test achieving -1.37 and as such, not rejecting the null hypothesis, i.e., there is no
statistically significant difference between the average PTE* score for the years of 2010 and
2015.
5.2. Second-stage regression results
In the second stage of the analysis, the efficiency’ determinants were investigated in
2010 and 2015, adopting the fractional logit model. The estimations were conducted using
STATA 14. To avoid collinearity problems, a statistical test was performed using the VIF
(variance inflation factor) measure available in STATA, with no problems identified (mean
VIF in 2010 of 1.25 and, in 2015 of 1.35).11 These results were confirmed by the correlation
matrix (not presented here, for convenience purposes).
11 VIF is an indicator of how much of the inflation of the standard error could be caused by collinearity. As a
rule of thumb, values above 10 should be a cause for concern and must be corrected.
Municipalities N
2010 2015
Size OTE PTE SE PTE* OTE PTE SE PTE*
Small 162 0.561 0.741 0.754 0.644 0.554 0.741 0.751 0.648
Medium 93 0.657 0.695 0.945 0.699
0.683 0.724 0.944 0.711
Large 23 0.761 0.843 0.907 0.722
0.788 0.853 0.927 0.754
Portugal mainland 278 0.610 0.734 0.831 0.669 0.616 0.744 0.830 0.677
20
Next, we applied a specification error test (linktest in STATA) which demonstrated
the meaningfulness of the covariates chosen and a correct assumption for the specified link
function. Table 5 presents the regression results from the fractional logit model for the years
of 2010 and 2015, with robust standard errors and quasi-maximum likelihood estimation. The
third column presents a pooled cross-section regression including all observations and a
dummy for the year (1 if the year is 2015 and 0 for the base year of 2010) and column 4, an
expanded model with interactions between this dummy variable and all the explanatory
variables, to perform a Chow test, in order to check for a structural change across time,
comparing both years.
Table 5. Regression results – Fractional logit regressions
Dependent variable: PTE*
2010 (1) 2015 (2) Pooled Regression
(3)
Expanded Regression
(4) βa AMEb
βa AMEb Unemployment -0.005 -0.0076
-0.033*** -0.0583
-0.019 -0.005
Parishes (nº) 0.016*** 0.0474
0.012*** 0.0262
0.015*** 0.016***
Democratic particip. -2.201*** -0.3029
-1.539*** -0.1966
-1.874*** -2.201***
Debt -1.028*** -0.0483
-0.842*** -0.0274
-0.942*** -1.028***
Pop. density -0.000*** -0.0088
-0.000** -0.0054
-0.000*** -0.000***
Education level 1.681*** 0.0649
1.701*** 0.0550
1.689*** 1.680**
Coastal Dummy -0.449*** -0.0243
-0.361*** -0.0188
-0.403*** -0.449***
Year Dummy
0.008 -0.2
Unemployment_year
-0.029
Parishes_year
-0.004
Democratic part_year
0.662
Debt_year
0.186
Pop. Density_year
0
Education level_year
0.022
Coastal Dummy_year
0.089
Constant 1.977***
1.777***
1.860*** 1.977***
Number of observations 278
278
556 556
Pseudo R2 0.0209
0.0115
0.0159 0.0162
Correlation (y yhat)^2 0.2433
0.1419
0.1916 0.1957
Linktest (p-value hatsq) 0.317 0.692 0.28 0.295 Legend: * statistically significant at 10% level, ** at 5% level, *** at 1% level.
y – observed values; yhat –fitted values (prediction); hatsq - prediction squared. a - Regression coefficients; b – AME – Average Marginal Effects Robust t statistics in parentheses
21
In the individual specifications (1) and (2), almost all the variables appear statistically
relevant in explaining the efficiency of the municipalities, with the exception of the
unemployment rate in 2010. Additionally, the results are very similar comparing both years.
The opposite effect on efficiency levels was obtained for the variables democratic
participation, coastal dummy and the number of parishes. However unexpected, these
findings were in line with previous studies. Cruz and Marques (2014) also find that the
number of civil parishes seem to affect local governments’ efficiency positively. Reinforcing
this idea, Cordero et al. (2016) reported that the reorganization of civil parishes implemented
in 2013 has enhanced more substantially the efficiency of more divided municipalities, i.e.,
those with a higher number of civil parishes.
Concerning the proximity to the sea (coastal dummy), Carosi, D’Inverno and Ravagli
(2014) argued that municipalities with a littoral zone, can be subject to seasonality, which
could have a negative correlation with efficiency.
Contrary to what expected, the coefficient on democratic participation exhibits a
negative sign. This means that the higher the democratic participation of the citizens of each
municipality, the lower its efficiency. Apparently, this result suggests that the higher political
involvement is a citizens’ response to force improvements in inefficient municipalities. The
same negative effect was reported in Cruz and Marques (2014).
Discussing the expected results, we have a strong and positive effect of the variable
Education level on efficiency. A more educated population contributes to the improvement of
performance of each municipality, supporting the findings of Afonso and Fernandes (2008)
that already demonstrated the importance of the education level on efficiency.
A higher concentration of the population affects negatively the municipality’s
efficiency, in line with the results of Geys (2006). Although, it is not a strong economic result
22
if we analyse the average marginal effect. Debt and unemployment rate also report a negative
sign.
Performing a Chow test, comparing models 3 and 4, in order to explore the existence
of a structural change comparing the years of 2010 and 2015, it is possible to see that none of
the interactions terms were statistically significant and the Likelihood Ratio test performed
returned a p-value of 0.999, meaning that we cannot reject the null hypothesis, i.e., all the
coefficients on the dummy and related interactions terms are zero.12 That suggests that no
meaningful changes occurred from one year to the other. On this regard, Cordero et al. (2016)
already noted that over the period 2009-2014, no relevant changes were detected, leading to
the conclusion that the reforms to improve municipalities’ efficiency were not successful.
6. Conclusions
The current austerity forces public institutions to show value for money. Resources
should be spend as effective and efficient as possible. The main purpose of this research is to
evaluate efficiency of municipalities in Portugal mainland and to explore the importance of
potential factors in improving their performance. The analysis is conducted in two different
years 2010 and 2015, to detect if significant changes occurred after the reforms implemented
in local governments administration.
Answering our first question - Is there anything new after Troika’s intervention in
Portugal, concerning Local Governments’ Efficiency? – We find evidence that no significant
difference exists before and after the Troika intervention in Portugal.
12 The same conclusion and identical p-value is achieved if we compare the full model (3), with the two sub-sets
of the data (model 1 and 2).
23
This is verified in the DEA analysis, with similar results in both years. The average
PTE* score changed slightly from 0.669 to 0.677 and PTE* is the lowest for the Algarve
region and the highest for the Centro region. In addition, large municipalities tend to perform
better. In the second stage, through fractional logit models, the results obtained are very close
comparing the years of 2010 with 2015. Education level and the number of parishes affect
positively efficiency. The opposite effect is verified with debt, unemployment, proximity to
the sea and democratic participation of the citizens. The Chow test performed confirmed that
no structural change occurred from 2010 to 2015, providing further evidence to the claim that
no meaningful changes were detected concerning local governments’ efficiency after the
Troika’s intervention in Portugal.
Some policy recommendations may be drawn from this research. From the DEA
analysis, there is evidence that some efficiency improvements may be possible in several
municipalities. In the second stage, debt and the number of parishes effectively influence
municipalities’ efficiency levels. But only debt has the expected effect. The reduction of the
number of parishes (a measure adopted to fulfil the requirements of the bailout agreement) do
not appear a successful measure.
From a methodological point of view, this paper uses more realistic DEA scores,
adopting a compound measure of efficiency (the average between standard DEA and Inverted
DEA scores). In the second stage, fractional response models are used. To the best of our
knowledge, this is the first empirical study adopting these methodologies in order to measure
and explain the efficiency of local governments.
As already mentioned, results are strongly dependent from the variables and methods
used in the analysis and conclusions (with the correspondent policy implications) should be
country specific. However, it should be noted that our results are, to a certain extent, in line
with previous studies about the efficiency of local governments in Portugal. Topics for further
24
research include to test additional exogenous variables for the second stage and to account for
possible effects of reversed causality (for instance, with debt and efficiency levels) and
endogeneity. Unobserved factors that affect a municipality’s score of efficiency in one year
will also affect its score in a different year requiring more advanced methods (panel data
methods) to handle this feature.
25
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Appendix A: Descriptive statistics of the variables used in the regression
Appendix B: Detailed DEA results for all the Municipalities
Municipality 2010 2015
OTE PTE SE Compound
PTE* OTE PTE SE
Compound PTE*
Abrantes 0.785 0.791 0.993 0.826
0.837 0.856 0.978 0.856
Águeda 0.66 0.661 0.999 0.739
0.776 0.799 0.971 0.837
Aguiar da Beira 0.484 0.803 0.603 0.691
0.47 0.78 0.603 0.704
Alandroal 0.44 0.505 0.872 0.289
0.634 0.753 0.843 0.679
Albergaria-a-Velha 0.662 0.743 0.891 0.780
0.69 0.777 0.888 0.802
Albufeira 0.224 0.247 0.905 0.139
0.256 0.269 0.953 0.154
Alcácer do Sal 0.897 1 0.897 0.775
0.829 1 0.829 0.706
Alcanena 0.527 0.664 0.794 0.674
0.559 0.698 0.8 0.683
Alcobaça 0.785 0.807 0.973 0.851
0.839 0.853 0.983 0.887
Alcochete 0.43 0.549 0.783 0.564
0.535 0.614 0.871 0.587
Alcoutim 0.774 0.908 0.853 0.512
0.683 0.851 0.802 0.488
Alenquer 0.641 0.654 0.979 0.721
0.664 0.701 0.947 0.765
Alfândega da Fé 0.302 0.409 0.737 0.230
0.448 0.617 0.726 0.559
Alijó 0.612 0.768 0.796 0.784
0.531 0.661 0.804 0.713
Aljezur 0.425 0.576 0.738 0.512
0.405 0.557 0.728 0.486
Aljustrel 0.575 0.687 0.838 0.679
0.587 0.711 0.825 0.730
Almada 0.843 1 0.843 0.842
0.869 0.927 0.938 0.817
Almeida 0.574 0.664 0.865 0.549
0.699 0.804 0.869 0.731
Almeirim 0.604 0.664 0.91 0.723
0.72 0.797 0.904 0.823
Almodôvar 0.757 0.811 0.934 0.690
0.722 0.781 0.924 0.662
Alpiarça 0.479 0.756 0.634 0.660
0.548 0.88 0.622 0.763
Alter do Chão 0.651 0.848 0.767 0.673
0.572 0.808 0.709 0.637
Alvaiázere 0.7 0.991 0.706 0.824
0.717 1 0.717 0.868
Alvito 0.622 0.961 0.647 0.670
0.577 0.978 0.59 0.645
Amadora 0.695 0.832 0.836 0.602
0.862 0.982 0.878 0.701
Variable Obs Mean Std. Dev. Min Max Mean Std. Dev. Min Max
PTE* 278 0.6690 0.1589 0.139 1 0.6775 0.1511 0.154 1
coastal Dummy 278 0.2302 0.4217 0 1 0.2302 0.4217 0 1
Unemployment 278 7.6284 2.2507 2.2 15.9 8.0385 2.3910 4 16.1
Parishes (nº) 278 14.5684 12.7764 1 89 10.3669 8.7444 1 61
Democratic particip. 278 0.6362 0.0758 0.4406 0.81118 0.5907 0.0913 0.3777 0.8139
Debt 278 0.2126 0.1273 0.0052 0.7025 0.1471 0.1164 0.0099 0.7314
Pop. density 278 311.6906 866.8126 5.2 7366.4 303.6540 830.7053 4.4 7413.7
Education level 278 0.1822 0.0614 0.0620 0.5250 0.1522 0.0556 0.0572 0.5290
2010 2015
30
Amarante 0.727 0.744 0.977 0.806
0.736 0.764 0.964 0.816
Amares 0.643 0.78 0.824 0.776
0.566 0.704 0.804 0.678
Anadia 1 1 1 0.982
0.869 0.923 0.942 0.903
Ansião 0.817 1 0.817 0.942
0.687 0.872 0.788 0.846
Arcos de Valdevez 0.745 0.766 0.973 0.761
0.664 0.711 0.934 0.656
Arganil 0.552 0.693 0.796 0.728
0.59 0.739 0.799 0.794
Armamar 0.354 0.619 0.573 0.535
0.344 0.617 0.558 0.507
Arouca 0.624 0.717 0.87 0.770
0.68 0.771 0.882 0.826
Arraiolos 0.865 0.946 0.914 0.823
0.862 0.954 0.904 0.884
Arronches 0.735 1 0.735 0.731
0.686 1 0.686 0.704
Arruda dos Vinhos 0.49 0.651 0.752 0.654
0.593 0.728 0.815 0.692
Aveiro 0.61 0.618 0.987 0.666
0.742 0.75 0.989 0.779
Avis 0.698 0.799 0.874 0.563
0.699 0.831 0.841 0.669
Azambuja 0.513 0.588 0.872 0.663
0.633 0.712 0.889 0.774
Baião 0.592 0.69 0.857 0.734
0.558 0.662 0.843 0.683
Barcelos 0.841 0.844 0.997 0.881
0.924 0.943 0.98 0.954
Barrancos 0.385 0.839 0.459 0.473
0.436 1 0.436 0.573
Barreiro 0.766 0.837 0.915 0.697
0.871 0.876 0.994 0.747
Batalha 0.668 0.84 0.796 0.832
0.576 0.706 0.817 0.707
Beja 0.858 0.881 0.973 0.897
0.851 0.865 0.984 0.855
Belmonte 0.604 0.981 0.615 0.807
0.448 0.778 0.576 0.696
Benavente 0.67 0.737 0.909 0.777
0.832 0.882 0.943 0.895
Bombarral 0.55 0.708 0.776 0.683
0.492 0.627 0.785 0.586
Borba 0.447 0.686 0.651 0.608
0.439 0.685 0.641 0.641
Boticas 0.538 0.73 0.737 0.636
0.537 0.728 0.738 0.678
Braga 1 1 1 0.882
0.822 0.882 0.932 0.778
Bragança 0.573 0.579 0.989 0.573
0.673 0.681 0.988 0.680
Cabeceiras de Basto 0.663 0.828 0.8 0.838
0.539 0.644 0.836 0.693
Cadaval 0.611 0.731 0.836 0.744
0.583 0.726 0.803 0.725
Caldas da Rainha 0.845 0.869 0.972 0.886
0.947 0.975 0.972 0.961
Caminha 0.416 0.49 0.849 0.481
0.442 0.534 0.828 0.503
Campo Maior 0.462 0.677 0.681 0.650
0.588 0.771 0.762 0.734
Cantanhede 0.794 0.803 0.989 0.839
0.949 0.978 0.971 0.977
Carrazeda de Ansiães 0.511 0.733 0.697 0.639
0.603 0.836 0.721 0.790
Carregal do Sal 0.602 0.859 0.701 0.796
0.669 0.952 0.702 0.861
Cartaxo 0.509 0.548 0.929 0.599
0.473 0.528 0.895 0.536
Cascais 0.485 0.541 0.896 0.338
0.497 0.526 0.945 0.369
Castanheira de Pêra 0.448 1 0.448 0.563
0.385 1 0.385 0.573
Castelo Branco 1 1 1 1
1 1 1 0.982
Castelo de Paiva 0.737 0.941 0.783 0.882
0.653 0.794 0.823 0.759
Castelo de Vide 0.52 0.781 0.666 0.602
0.526 0.848 0.62 0.659
Castro Daire 0.594 0.718 0.828 0.776
0.562 0.667 0.842 0.726
Castro Marim 0.318 0.45 0.706 0.367
0.35 0.482 0.726 0.386
Castro Verde 0.59 0.668 0.883 0.574
0.625 0.722 0.866 0.647
Celorico da Beira 0.301 0.45 0.669 0.407
0.437 0.627 0.697 0.657
Celorico de Basto 0.521 0.597 0.872 0.635
0.549 0.655 0.839 0.681
31
Chamusca 0.946 1 0.946 0.926
0.964 1 0.964 0.967
Chaves 0.788 0.799 0.986 0.854
0.598 0.609 0.982 0.581
Cinfães 0.73 0.848 0.861 0.868
0.546 0.636 0.858 0.689
Coimbra 0.601 0.717 0.839 0.664
0.769 0.856 0.898 0.814
Condeixa-a-Nova 0.659 0.798 0.826 0.757
0.576 0.708 0.814 0.683
Constância 0.354 0.801 0.442 0.509
0.4 0.859 0.466 0.559
Coruche 0.814 0.83 0.981 0.805
0.935 0.943 0.991 0.888
Covilhã 0.828 0.851 0.973 0.873
1 1 1 0.990
Crato 0.632 0.797 0.793 0.572
0.599 0.796 0.752 0.597
Cuba 0.414 0.778 0.533 0.641
0.49 0.892 0.549 0.736
Elvas 0.583 0.647 0.902 0.703
0.575 0.617 0.931 0.626
Entroncamento 0.591 0.708 0.835 0.399
0.588 0.692 0.85 0.397
Espinho 0.48 0.505 0.95 0.310
0.514 0.552 0.931 0.324
Esposende 0.723 0.809 0.894 0.791
0.67 0.738 0.908 0.726
Estarreja 0.592 0.641 0.924 0.681
0.792 0.88 0.901 0.857
Estremoz 0.589 0.693 0.851 0.734
0.619 0.71 0.871 0.742
Évora 0.553 0.555 0.997 0.598
0.649 0.652 0.995 0.675
Fafe 0.636 0.658 0.966 0.728
0.657 0.691 0.952 0.739
Faro 0.728 0.734 0.992 0.780
0.676 0.694 0.975 0.738
Felgueiras 0.644 0.668 0.964 0.652
0.655 0.68 0.964 0.666
Ferreira do Alentejo 0.587 0.645 0.911 0.563
0.685 0.762 0.899 0.713
Ferreira do Zêzere 0.542 0.762 0.711 0.727
0.483 0.696 0.695 0.697
Figueira da Foz 0.736 0.763 0.966 0.788
0.774 0.787 0.984 0.797
Figueira Castelo Rodrigo 0.648 0.755 0.859 0.676
0.528 0.618 0.855 0.515
Figueiró dos Vinhos 0.455 0.728 0.626 0.630
0.47 0.733 0.642 0.681
Fornos de Algodres 0.214 0.385 0.556 0.217
0.487 0.941 0.518 0.748
Freixo de Espada à Cinta 0.407 0.629 0.648 0.463
0.412 0.671 0.614 0.527
Fronteira 0.573 0.906 0.633 0.726
0.504 0.852 0.591 0.676
Fundão 0.795 0.824 0.966 0.851
0.691 0.729 0.948 0.719
Gavião 0.646 0.91 0.71 0.650
0.606 0.868 0.698 0.637
Góis 0.502 0.745 0.673 0.618
0.415 0.632 0.657 0.538
Golegã 0.348 0.697 0.5 0.530
0.382 0.725 0.527 0.575
Gondomar 0.902 0.914 0.987 0.806
0.854 0.869 0.982 0.793
Gouveia 0.842 0.967 0.871 0.919
0.759 0.884 0.859 0.879
Grândola 0.458 0.48 0.954 0.388
0.493 0.519 0.949 0.406
Guarda 0.609 0.635 0.959 0.717
0.526 0.547 0.96 0.578
Guimarães 0.786 0.795 0.989 0.763
0.762 0.781 0.975 0.775
Idanha-a-Nova 1 1 1 0.701
0.813 0.93 0.874 0.533
Ílhavo 0.662 0.692 0.957 0.669
0.802 0.866 0.926 0.801
Lagoa 0.309 0.358 0.866 0.328
0.302 0.341 0.884 0.268
Lagos 0.304 0.327 0.93 0.319
0.258 0.28 0.922 0.160
Lamego 0.727 0.782 0.93 0.814
0.481 0.538 0.895 0.554
Leiria 0.838 0.881 0.951 0.895
1 1 1 1
Lisboa 0.427 1 0.427 0.563
0.438 1 0.438 0.573
Loulé 0.298 0.301 0.99 0.170
0.341 0.342 0.999 0.222
Loures 0.616 0.684 0.9 0.599
0.693 0.741 0.935 0.684
32
Lourinhã 0.489 0.542 0.901 0.595
0.494 0.555 0.889 0.593
Lousã 0.533 0.66 0.808 0.692
0.477 0.579 0.824 0.611
Lousada 0.675 0.732 0.921 0.655
0.737 0.767 0.96 0.686
Mação 0.552 0.694 0.795 0.594
0.558 0.684 0.816 0.613
Macedo de Cavaleiros 0.638 0.691 0.923 0.718
0.564 0.605 0.932 0.475
Mafra 0.527 0.535 0.986 0.621
0.645 0.647 0.997 0.716
Maia 0.819 0.82 0.999 0.685
0.847 0.879 0.964 0.782
Mangualde 0.572 0.638 0.896 0.696
0.597 0.687 0.869 0.720
Manteigas 0.36 0.825 0.436 0.506
0.201 0.484 0.415 0.277
Marco de Canaveses 0.845 0.903 0.936 0.898
0.848 0.875 0.969 0.879
Marinha Grande 0.659 0.678 0.971 0.738
0.746 0.792 0.943 0.814
Marvão 0.435 0.825 0.527 0.531
0.493 0.995 0.495 0.595
Matosinhos 0.669 0.687 0.972 0.538
0.675 0.702 0.962 0.602
Mealhada 0.623 0.697 0.893 0.697
0.643 0.742 0.867 0.703
Mêda 0.512 0.729 0.702 0.607
0.517 0.725 0.713 0.650
Melgaço 0.543 0.678 0.801 0.573
0.501 0.627 0.799 0.560
Mértola 1 1 1 0.722
1 1 1 0.677
Mesão Frio 0.248 0.553 0.448 0.312
0.341 0.957 0.356 0.548
Mira 0.471 0.603 0.782 0.554
0.489 0.645 0.758 0.578
Miranda do Corvo 0.61 0.804 0.758 0.767
0.581 0.744 0.781 0.702
Miranda do Douro 0.655 0.768 0.852 0.688
0.607 0.711 0.855 0.687
Mirandela 0.529 0.593 0.893 0.661
0.555 0.608 0.913 0.613
Mogadouro 0.803 0.856 0.938 0.769
0.727 0.753 0.966 0.651
Moimenta da Beira 0.573 0.826 0.694 0.807
0.499 0.702 0.71 0.733
Moita 0.725 0.73 0.993 0.643
0.724 0.738 0.981 0.660
Monção 0.702 0.742 0.947 0.717
0.711 0.808 0.88 0.774
Monchique 0.685 0.855 0.801 0.732
0.577 0.728 0.792 0.684
Mondim de Basto 0.28 0.468 0.599 0.402
0.428 0.694 0.616 0.653
Monforte 0.75 0.932 0.805 0.697
0.771 1 0.771 0.744
Montalegre 0.66 0.697 0.947 0.623
0.664 0.684 0.97 0.438
Montemor-o-Novo 0.775 0.78 0.993 0.695
0.81 0.814 0.996 0.700
Montemor-o-Velho 0.686 0.714 0.961 0.735
0.694 0.76 0.912 0.760
Montijo 0.569 0.581 0.978 0.665
0.724 0.744 0.974 0.810
Mora 0.763 0.923 0.827 0.784
0.713 0.891 0.8 0.765
Mortágua 0.591 0.825 0.717 0.776
0.647 0.864 0.749 0.855
Moura 0.835 0.86 0.971 0.802
0.852 0.879 0.969 0.807
Mourão 0.444 0.658 0.675 0.371
0.475 0.749 0.634 0.429
Murça 0.461 0.78 0.591 0.677
0.464 0.764 0.608 0.706
Murtosa 0.709 1 0.709 0.853
0.749 1 0.749 0.900
Nazaré 0.414 0.519 0.797 0.499
0.389 0.483 0.805 0.418
Nelas 0.462 0.563 0.821 0.590
0.584 0.716 0.816 0.704
Nisa 0.625 0.705 0.886 0.635
0.715 0.798 0.896 0.745
Óbidos 0.273 0.367 0.743 0.323
0.35 0.45 0.778 0.442
Odemira 0.754 1 0.754 0.766
0.755 1 0.755 0.773
Odivelas 0.786 0.811 0.97 0.638
0.803 0.841 0.955 0.672
Oeiras 0.588 0.695 0.846 0.531
0.684 0.759 0.902 0.617
33
Oleiros 0.804 0.954 0.843 0.713
0.657 0.782 0.839 0.551
Olhão 0.535 0.546 0.981 0.590
0.709 0.747 0.949 0.760
Oliveira de Azeméis 0.753 0.754 0.999 0.760
0.826 0.841 0.982 0.815
Oliveira de Frades 0.549 0.789 0.697 0.765
0.583 0.821 0.71 0.794
Oliveira do Bairro 0.619 0.676 0.915 0.678
0.669 0.757 0.884 0.733
Oliveira do Hospital 0.674 0.741 0.909 0.787
0.589 0.662 0.89 0.706
Ourém 0.666 0.677 0.984 0.751
0.701 0.724 0.968 0.782
Ourique 0.723 0.804 0.9 0.634
0.711 0.82 0.867 0.680
Ovar 0.654 0.672 0.973 0.712
0.717 0.75 0.955 0.763
Paços de Ferreira 0.84 0.896 0.938 0.765
0.885 0.91 0.973 0.775
Palmela 0.53 0.542 0.978 0.629
0.553 0.558 0.991 0.628
Pampilhosa da Serra 0.574 0.715 0.803 0.430
0.449 0.572 0.785 0.328
Paredes 0.834 0.849 0.983 0.805
0.742 0.744 0.997 0.730
Paredes de Coura 0.495 0.68 0.728 0.640
0.458 0.639 0.717 0.607
Pedrógão Grande 0.429 0.806 0.532 0.557
0.416 0.885 0.47 0.633
Penacova 0.736 0.894 0.823 0.857
0.642 0.78 0.823 0.769
Penafiel 0.902 0.942 0.958 0.924
0.92 0.928 0.991 0.916
Penalva do Castelo 0.655 0.95 0.689 0.810
0.611 0.887 0.689 0.796
Penamacor 0.874 0.992 0.881 0.761
0.614 0.693 0.885 0.422
Penedono 0.422 0.966 0.436 0.621
0.355 0.884 0.401 0.564
Penela 0.489 0.798 0.612 0.664
0.481 0.814 0.591 0.708
Peniche 0.58 0.62 0.936 0.629
0.647 0.714 0.907 0.681
Peso da Régua 0.541 0.654 0.827 0.673
0.536 0.655 0.818 0.631
Pinhel 0.579 0.68 0.851 0.684
0.645 0.75 0.86 0.736
Pombal 0.898 0.939 0.956 0.948
0.849 0.856 0.992 0.881
Ponte da Barca 0.406 0.537 0.757 0.541
0.434 0.576 0.755 0.583
Ponte de Lima 0.645 0.648 0.994 0.728
0.607 0.635 0.957 0.703
Ponte de Sor 0.741 0.776 0.956 0.793
0.676 0.703 0.962 0.687
Portalegre 0.588 0.636 0.925 0.722
0.684 0.744 0.919 0.816
Portel 0.681 0.765 0.89 0.655
0.638 0.741 0.862 0.666
Portimão 0.415 0.416 0.999 0.469
0.402 0.413 0.972 0.438
Porto 0.632 0.975 0.648 0.704
0.665 0.797 0.834 0.592
Porto de Mós 0.604 0.676 0.893 0.741
0.633 0.704 0.9 0.764
Póvoa de Lanhoso 0.645 0.769 0.839 0.794
0.61 0.716 0.852 0.740
Póvoa de Varzim 0.524 0.546 0.959 0.466
0.602 0.615 0.979 0.562
Proença-a-Nova 0.606 0.764 0.793 0.696
0.586 0.721 0.814 0.735
Redondo 0.484 0.626 0.774 0.591
0.486 0.642 0.757 0.628
Reguengos de Monsaraz 0.524 0.63 0.831 0.641
0.484 0.579 0.837 0.575
Resende 0.447 0.607 0.736 0.614
0.395 0.536 0.738 0.525
Ribeira de Pena 0.302 0.496 0.608 0.446
0.31 0.486 0.637 0.460
Rio Maior 0.442 0.515 0.858 0.589
0.528 0.596 0.887 0.674
Sabrosa 0.409 0.709 0.577 0.619
0.427 0.733 0.583 0.676
Sabugal 0.811 0.855 0.948 0.746
0.679 0.703 0.967 0.418
Salvaterra de Magos 0.762 0.852 0.895 0.876
0.779 0.867 0.899 0.884
Santa Comba Dão 0.576 0.747 0.771 0.721
0.552 0.74 0.746 0.677
Santa Maria da Feira 1 1 1 0.939
1 1 1 0.952
34
Santa Marta de Penaguião
0.457 0.723 0.633 0.544
0.42 0.721 0.582 0.494
Santarém 0.708 0.749 0.945 0.790
0.716 0.721 0.993 0.767
Santiago do Cacém 0.57 0.581 0.982 0.582
0.6 0.613 0.979 0.600
Santo Tirso 0.702 0.702 1 0.715
0.788 0.799 0.987 0.771
São Brás de Alportel 0.381 0.536 0.71 0.553
0.423 0.565 0.749 0.578
São João da Madeira 0.661 0.802 0.825 0.452
0.733 0.83 0.883 0.476
São João da Pesqueira 0.446 0.663 0.672 0.646
0.459 0.668 0.688 0.677
São Pedro do Sul 0.548 0.638 0.859 0.709
0.546 0.631 0.865 0.658
Sardoal 0.297 0.683 0.435 0.429
0.29 0.72 0.403 0.485
Sátão 0.67 0.91 0.736 0.872
0.667 0.877 0.76 0.862
Seia 0.57 0.611 0.933 0.649
0.534 0.581 0.919 0.546
Seixal 0.858 0.865 0.992 0.748
0.78 0.791 0.986 0.720
Sernancelhe 0.517 0.831 0.622 0.718
0.446 0.706 0.631 0.655
Serpa 0.816 0.829 0.984 0.780
0.757 0.771 0.982 0.684
Sertã 0.54 0.638 0.847 0.683
0.618 0.72 0.859 0.759
Sesimbra 0.433 0.447 0.968 0.499
0.451 0.468 0.964 0.519
Setúbal 0.711 0.763 0.932 0.758
0.629 0.637 0.989 0.654
Sever do Vouga 0.743 0.986 0.753 0.903
0.695 0.919 0.756 0.839
Silves 0.399 0.409 0.976 0.422
0.49 0.511 0.96 0.523
Sines 0.24 0.318 0.756 0.222
0.365 0.44 0.83 0.440
Sintra 0.886 1 0.886 0.828
0.941 1 0.941 0.909
Sobral de Monte Agraço 0.453 0.683 0.663 0.603
0.423 0.624 0.678 0.538
Soure 0.805 0.865 0.93 0.852
0.726 0.819 0.887 0.820
Sousel 0.594 0.858 0.692 0.750
0.532 0.78 0.682 0.720
Tábua 0.535 0.718 0.745 0.739
0.585 0.775 0.755 0.785
Tabuaço 0.232 0.428 0.541 0.303
0.42 0.761 0.551 0.665
Tarouca 0.3 0.494 0.607 0.430
0.358 0.596 0.601 0.521
Tavira 0.399 0.433 0.921 0.436
0.5 0.542 0.924 0.561
Terras de Bouro 0.429 0.629 0.682 0.611
0.395 0.573 0.689 0.589
Tomar 0.723 0.738 0.979 0.768
0.759 0.776 0.978 0.783
Tondela 0.688 0.69 0.997 0.705
0.754 0.795 0.949 0.771
Torre de Moncorvo 0.665 0.762 0.873 0.676
0.741 0.842 0.881 0.821
Torres Novas 0.585 0.595 0.983 0.652
0.755 0.787 0.959 0.815
Torres Vedras 0.744 0.771 0.964 0.819
0.687 0.697 0.986 0.758
Trancoso 0.628 0.819 0.767 0.812
0.641 0.8 0.8 0.838
Trofa 0.69 0.733 0.941 0.678
0.663 0.727 0.912 0.670
Vagos 0.668 0.738 0.906 0.765
0.671 0.765 0.877 0.775
Vale de Cambra 0.632 0.687 0.919 0.709
0.698 0.788 0.886 0.764
Valença 0.407 0.511 0.796 0.495
0.405 0.512 0.791 0.473
Valongo 0.977 0.994 0.983 0.854
1 1 1 0.896
Valpaços 0.739 0.84 0.88 0.842
0.724 0.808 0.896 0.736
Vendas Novas 0.518 0.689 0.752 0.725
0.62 0.786 0.788 0.797
Viana do Alentejo 0.693 0.871 0.796 0.780
0.654 0.851 0.768 0.757
Viana do Castelo 0.833 0.876 0.951 0.879
0.809 0.813 0.996 0.835
Vidigueira 0.675 0.934 0.723 0.842
0.567 0.791 0.717 0.738
35
Vieira do Minho 0.623 0.824 0.756 0.825
0.428 0.559 0.766 0.599
Vila de Rei 0.518 0.892 0.581 0.664
0.503 0.893 0.564 0.645
Vila do Bispo 0.236 0.428 0.552 0.276
0.238 0.422 0.563 0.284
Vila do Conde 0.56 0.571 0.981 0.578
0.636 0.643 0.988 0.660
Vila Flor 0.429 0.628 0.682 0.592
0.471 0.673 0.7 0.686
Vila Franca de Xira 0.829 0.83 0.999 0.843
0.926 0.97 0.955 0.944
Vila Nova da Barquinha 0.431 0.695 0.621 0.524
0.43 0.705 0.61 0.494
Vila Nova de Cerveira 0.319 0.486 0.656 0.428
0.361 0.522 0.692 0.477
Vila Nova de Famalicão 0.73 0.731 0.998 0.708
0.722 0.736 0.98 0.733
Vila Nova de Foz Côa 0.568 0.711 0.8 0.677
0.507 0.63 0.805 0.606
Vila Nova de Gaia 0.965 1 0.965 0.857
0.962 1 0.962 0.903
Vila Nova de Paiva 0.414 0.762 0.543 0.648
0.457 0.826 0.553 0.709
Vila Nova de Poiares 0.435 0.772 0.563 0.630
0.405 0.732 0.553 0.587
Vila Pouca de Aguiar 0.493 0.606 0.813 0.650
0.46 0.547 0.841 0.455
Vila Real 0.854 0.863 0.989 0.901
0.729 0.757 0.963 0.817
Vila Real de Santo António
0.262 0.31 0.846 0.175
0.304 0.357 0.853 0.221
Vila Velha de Ródão 0.626 0.839 0.745 0.473
0.55 0.763 0.722 0.437
Vila Verde 0.757 0.78 0.97 0.828
0.65 0.683 0.952 0.737
Vila Viçosa 0.429 0.714 0.602 0.677
0.502 0.771 0.65 0.751
Vimioso 0.797 0.946 0.843 0.693
0.625 0.757 0.825 0.560
Vinhais 0.849 0.92 0.922 0.768
0.763 0.812 0.94 0.618
Viseu 0.873 0.917 0.952 0.927
0.852 0.855 0.996 0.889
Vizela 0.72 0.848 0.848 0.610
0.602 0.725 0.831 0.468
Vouzela 0.55 0.722 0.763 0.715
0.632 0.829 0.762 0.805
Average 0.610 0.734 0.831 0.669 0.616 0.744 0.830 0.677
Maximum 1 1 1 1
1 1 1 1
Minimum 0.214 0.247 0.427 0.139 0.201 0.269 0.356 0.154
Note: OTE, PTE and SE obtained with standard DEA;
* Normalized Compound Efficiency Score: computed by the average of PTE and PTE*(obtained with Inverted DEA).