A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France

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Innovative Applications of O.R.

A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France Emilios Galariotis a, Alexis Guyot a, Michael Doumpos b, Constantin Zopounidis a,b,∗ a b

Audencia Nantes School of Management, Centre for Financial and Risk Management, Nantes, France Technical University of Crete, School of Production Engineering & Management, Financial Engineering Laboratory, University Campus, 73100 Chania, Greece

a r t i c l e

i n f o

Article history: Received 9 September 2014 Accepted 16 June 2015 Available online xxx Keywords: Multiple criteria analysis Local governments Financial performance Benchmarking

a b s t r a c t The financial health of local governments has attracted considerable interest over the past couple of decades. In this study, we follow a benchmarking perspective and introduce a multi-attribute financial evaluation model that allows peer assessments to be made, including comparisons over time, while differentiating between managerial performance and the effect of the external environment. The model is applied to a large database involving the entire population of French municipalities over the period 2000–2012 to assess how the reforms implemented over the past decade (taxation and decentralization) have affected the financial performance of local governments in France. The findings are of particular interest to both the academia and policymakers including local public authorities and central governments. © 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

1. Introduction Since the 1990s, a wave of fiscal and competencies decentralization and amalgamations of local municipalities has emerged all over the world. The aim of central governments has been to seek greater efficiency by reducing expenditures mainly through the achievement of economies of scale (Reingewertz, 2012), and through a better allocation of resources, bringing government functions closer to citizens. The latter, as Borge, Brueckner, and Rattso (2014) explain, can be better achieved by allowing greater degrees of freedom to localities as regards spending. Actually the literature supports that decentralization of expenditures helps improve overall spending (Ashworth, Galli, & Padovano, 2013). At the same time the amalgamations of municipalities have been accused of creating opportunities for free riding and to overall lead to the worsening of the financial situation of the new entity and its members (Tyrefors, 2009). Naturally, this raises the question of the impact that such changes have had on the financial conditions of local governments (LGs); and of what kind of corrective actions and state policies can be implemented to overcome the existing challenges and prevent future failures. This becomes even more important in the current economic setting where, ∗ Corresponding author at: Technical University of Crete, School of Production Engineering and Management, Financial Engineering Laboratory, Chania, Greece. Tel.: +30 28210 37236; fax: +30 28210 69410. E-mail address: [email protected] (C. Zopounidis).

especially after the financial crisis, the tightening on central government budgets is prevailing, putting on additional pressure for fiscal consolidation at both the central and the LG levels. Past studies on the performance and efficiency of LGs can be classified into three major strands. The first one focuses on the provision of services by municipalities using frontier models (usually data envelopment analysis) to assess LGs’ technical and cost efficiency (Afonso & Fernandes, 2006; Cruz & Marques, 2014; Lin, Lee, & Ho, 2011; Rogge & De Jaeger, 2013), evaluate specific services provided by LGs (Chang, Liu, & Yeh, 2013), and develop budget plans using optimization models and multi-criteria techniques (Doumpos & Cohen, 2014; Gomez, Rios Insua, Lavin, & Alfaro, 2013). The second research strand is about predicting instances of financial distress of LGs (Garcia-Sanchez, Cuadrado-Ballesteros, Frias-Aceituno, & Mordan, 2012; Kloha, Weissert, & Kleine, 2005; Zafra-Gómez, Lopez-Hernández, & Hernández-Bastida, 2009b). Even though LGs in many countries cannot go bankrupt, their financial difficulties impair their capacity to maintain expenditures on infrastructures and investments (Bumgarner, Martinez-Vazquez, & Sjoquist, 1991). Consequently, the financial health of local municipalities and budget balancing/sustainability are scrutinized in order to give policymakers tools for monitoring the financial condition of LGs’ that can be used as early warning systems of financial distress. The third category of studies focuses on the identification of the factors that drive the financial performance of LGs: intergovernmental relations (Honadle, 2003); lack of organizational resources and

http://dx.doi.org/10.1016/j.ejor.2015.06.042 0377-2217/© 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

Please cite this article as: E. Galariotis et al., A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France, European Journal of Operational Research (2015), http://dx.doi.org/10.1016/j.ejor.2015.06.042

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managerial skills (Carmeli & Cohen, 2001); changes in the socioeconomic environment (Hendrick, 1989; Morgan & England, 1983; Stonecash & McAfee, 1981); demographic features (Bunce & Neal, 1984); cities’ size and citizen wealth (Jones & Walker, 2007); unfunded mandates from the shift of responsibilities from central governments to local ones (Beckett-Camarata, 2004); changes in the fiscal environment; and mergers and amalgamations into financial districts (Jones & Walker, 2007). Empirical investigations have been conducted on a range of countries such as the US (Kloha et al., 2005), Australia (Jones & Walker, 2007; Murray & Dollery, 2005), Israel (Carmeli & Cohen, 2001), Greece (Cohen, Doumpos, Neofytou, & Zopounidis, 2012), and Spain (Zafra-Gómez, Lopez-Hernández, & Hernández-Bastida, 2009a,2009b,2009c), among others. This study is related to the above described second and third streams of the literature. In contrast to previous studies that have relied on simple scorecards or statistical models for assessing the financial health of LGs, we employ a multi-criteria decision aid (MCDA) methodology. In particular, first a MCDA modeling approach is used to construct a multi-dimensional index for benchmarking the financial health of LGs, in a peer assessment context over time. The results of the multi-criteria performance index are further analyzed in a second (explanatory) stage to get insights on structural relationships between the financial performance of LGs and several important attributes involving the environment in which LGs operate, particularly related to the global crisis as well as decentralization and amalgamation policies implemented by central governments. This second stage of the analysis contributes through the identification of external factors that affect (implicitly) the global financial health of LGs, thus facilitating the formulation of more informed recommendations. The proposed multi-criteria model is a peer assessment tool that combines information related to the revenues of LGs, their expenses, debt burden, and taxation policies, while segmenting LGs in proper peer categories. Such a modeling tool can be used internally by the administrations of LGs to support decisions related to financial planning, budgeting, and control, or externally by authorities at the central government level that are responsible for designing and implementing policies related to LGs. Similar peer assessments are relevant in many other application domains. In corporate management, for instance, firms assess and monitor the performance of their operations over time (financial, human resources, customer relationship management, marketing strategies, supply chain, etc.), often in comparison to competitors and current best practices. Other areas include services in healthcare, education, tourism, and public safety, among others, as well as other fields such as energy management (e.g., energy efficiency analysis), and environmental assessments. Implementing performance evaluation systems in such areas often requires the consideration of panel data that span multiple time periods. The multi-criteria approach introduced in this study contributes in this context by providing a formal framework for performance benchmarking based on peer assessments under multiple appraisal criteria. The methodology extends existing static MCDA models to a panel data context and enables the explicit modeling of the changes of the performance assessment results along two distinct main dimensions: the internal characteristics of the evaluated alternatives and their external environment. In particular, when dealing with performance evaluation problems over time, a number of issues arise. First, the parameters of MCDA models should be specified in a dynamic rather than a static context, to accommodate changes in the system of preferences and the judgment policy of the decision makers, which naturally follow the dynamics of the decision environment (e.g., changes in socioeconomic and technological factors). To address this issue, the parameters of the evaluation model introduced in this paper are adjusted over time, to account for the dynamic nature of the evaluation process. Furthermore, when analyzing and interpreting the changes

in evaluation results over time, one must also take into consideration the developments observed in the decision environment. In particular, it is important to distinguish performance changes due to the internal characteristics of the alternatives under consideration (in our case, the competence of the LGs’ administration), from changes due to the dynamics of the external environment. Such a distinction provides a more accurate view of the evaluation results and facilitates decision makers in the identification of the true strengths and weaknesses of the alternatives, thus enabling the formulation of more informed decisions about proper corrective actions. In efficiency and productivity measurement, panel data are analyzed through the Malmquist index, but such an approach has not been previously considered in a multi-criteria performance evaluation framework. With the methodology introduced in this study we illustrate how such analyses can be performed in a MCDA framework, using an additive evaluation model, whereas the principles of the methodology are also applicable to other types MCDA of modes, such as outranking models. The proposed MCDA approach is applied to a unique large-scale panel data set that covers (with very minor exceptions) the entire population of French municipalities. The empirical data and the French case have several distinctive features. Over the past three decades, France has implemented decentralization policies that have led to the hierarchical structuring of LGs at multiple levels. In this study we focus on municipalities, which are the fundamental institutional and territorial organizations that directly provide services to citizens. The sample spans the period from 2000 to 2012, during which time the French local administration system consisted at first of more than 35,000 municipalities. To the best of our knowledge, this is the first study to employ such a comprehensive data set (more than 450,000 municipality-year observations), while past studies have either used rather small samples or have focused on specific regions of a country. The examined time period also has some interesting features. In particular, during the period under consideration, France implemented major tax policy reforms together with a decentralization process that focused on transferring competencies to the local level. At the same time, the amalgamation of nearby local municipalities into different types of public bodies of cooperation – all the way up to structures with their own fiscal status (financial districts) – was highly encouraged at first and imposed by law later on for the creation of economies of scale and the achievement of efficiency. We analyze the impact that these changes in taxation policies as well as the decentralization and the amalgamation processes had on the financial position of French municipalities. To this end, a second stage panel regression analysis is performed to examine how these, as well as other issues, relate to the findings of the multi-attribute evaluation model. The results obtained lead to a number of interesting findings with practical implications that are also generalizable. Firstly, even though there was a positive change relating to the enhancement of managerial competences, the external environment had a consistent and higher negative effect, mainly due to the tax reforms. The global financial crisis also had a negative effect, but this is found to be weaker compared to the reform in taxation. Secondly, in terms of their size, medium-sized municipalities are found to be in a better financial condition, but larger ones are more resilient to adverse external conditions. Thirdly, membership to a financial district proved to be particularly helpful after the 2010 taxation reform as well as during the crisis, as regards the vulnerability of the financial condition of municipalities to adverse conditions in the external environment. The rest of the paper is organized as follows. Section 2 presents the analytic approach used to assess the financial health of LGs. Section 3 provides an overview of the LG administrative system in France focusing on the recent tax and organizational reforms that are relevant to this study. Section 4 describes the data used in the analysis and the financial performance attributes, whereas Section 5 is devoted to the presentation and discussion of the results. Finally,

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Section 6 concludes the paper and outlines some future research directions. 2. Multi-attribute benchmarking methodology 2.1. Modeling setting The proposed methodology for evaluating the financial health of municipalities is based on a multi-criteria benchmarking approach (Zafra-Gómez et al., 2009a), which aims toward providing results that can be used by local public authorities and policymakers to perform peer assessments, identify the strengths and weaknesses of the municipalities, monitor their financial condition over time, and differentiate between changes due to management competence and the external environment. Previously developed models for assessing the financial performance of LGs have adopted statistical data analysis techniques (e.g., regression analysis and principal components analysis; Hendrick, 2004; Zafra-Gómez et al., 2009c) and scorecard approaches (see for example, Brown, 1993; Zafra-Gómez et al., 2009a,2009b). Statistical methods are limited to larger data samples and they are subject to statistical issues and assumptions (e.g., selection of dependent variables, casual relationships between the variables, model estimation issues), which may be irrelevant in a benchmarking context (although being relevant in an explanatory/descriptive framework). Scorecard approaches, on the other hand, are easier to implement and they provide easy to use composite performance indicators. Past studies adopting such approaches, have mostly relied on simple schemes, under which each municipality is assigned a single score on each performance attribute, usually defined by binning the data using the attributes’ quartiles on the basis of an available sample, and the summing up the scores across all attributes to obtain an overall performance estimate. Such an approach, however, has some shortcomings, as it assumes that the municipalities’ performance is uniformly distributed at the attributes’ level (due to the use of quartiles to bin the data), and may have low discriminatory power as all municipalities falling into the same quartile of an attribute receive the same score. Cohen et al. (2012) presented an alternative multi-criteria evaluation approach that addresses some of the shortcomings of scorecard approaches. Yet this approach was applied to a rather homogeneous set of Greek municipalities in a static setting. However, when dealing with LGs with very diverse characteristics, meaningful results can only be derived if the special features of the LGs are taken into consideration, particularly those related to the socio-economic and operating characteristics of the LGs (Brown, 1993). To address this issue in the proposed peer assessment approach, we consider a segmentation of LGs into properly defined groups consisting of LGs with similar socio-economic and operating characteristics. Furthermore, when the data cover multiple time periods, the derivation of conclusions on time trends should account for the effect of the changes in the external environment, which naturally lead to population drift. This may have a general positive (in times of growth) or negative effect (during recessions), which must be distinguished from the effect due to the actions taken by the administrations of the LGs, in order to derive meaningful conclusions and formulate targeted policy recommendations. Previous studies that have used panel data have not taken this point into consideration. Based on the framework set above Fig. 1 outlines the methodological context of the analysis, which consists of two main stages. The first involves the assessment of the LGs’ financial health using a multi-criteria benchmarking approach. The data used in this stage are taken from the main budgets of LGs, which are available to their administration and the central government. It is worth noting that most European Union countries have implemented financial reporting frameworks based on accrual accounting (Cohen et al., 2012; Pina,

3

Torres, & Yetano, 2009). So the financial data used in this analysis can be obtained through existing financial reporting systems. The raw data collected from LGs’ budgets are used to construct meaningful financial performance assessment attributes. These involve financial ratios related to the revenues of the LGs, their costs and expenditures, their financing ability and debt burden, as well as their taxation policies. Before the actual evaluation is implemented, proper peer groups of LGs are specified, thus defining a segmentation that ensures that LGs in each group have similar characteristics and their comparison in a benchmarking context is meaningful. The next step involves the specification of the evaluation parameters (which are dependent on the selected type of evaluation model), and finally the results are formulated regarding the overall financial performance of the LGs as well as its changes over time. The details of the MCDA modeling approach are presented in the subsequent sections. The evaluation results obtained from the MCDA evaluation phase, serve as the basis for the second stage explanatory regression analysis. The financial data analyzed in the first stage are the outcomes of administrative and policy choices made at the LG and central government levels. In that sense, the assessment of the LGs’ financial status should be complemented with an analysis of the driving factors that explicitly or implicitly affect the financial health of LGs. Such factors are related to the operating characteristics of the LGs under consideration as well as to the external environment in which they operate. Through regression models, the second part of the analysis focuses on the identification of structural relationships between the financial performance assessment results and their driving factors, thus facilitating the formulation of recommendations that can be used by the administration of the LGs and the central government. 2.2. Multi-criteria model The basis for the benchmarking and peer assessment context outlined in the previous section is a multi-criteria additive evaluation model. In particular, considering the above mentioned segmented and panel data setting, we assume that the financial performance of municipality i from peer group p in year t, is assessed through the following model:

 

Vpt xti =

n 





wtkp vkp xtik − btkp ,

(1)

k=1

where: •

•

•

wt1p , wt2p , . . . , wtnp are (non-negative) trade-off constants of the n performance attributes for peer group p in year t (normalized such that wt1p + wt2p + · · · + wtnp = 100), vkp ( · ) is the partial performance function of attribute k for peer group p, and bt1p , bt2p , . . . , btnp are benchmarks defined on the scale of the performance attributes.

Under the above model, the performance of a municipality in a specific year is based on the differences between its financial characteristics compared to a benchmark. In this study, we define the benchmark vector by the averages of the performance attributes in year t for the municipalities from peer group p. The overall financial performance index Vpt (xti ) of municipality i from peer group p in year t is an aggregate assessment (ranging between 0 and 100) of the financial condition of the municipality compared to the annual peer group benchmark; the higher it is, the higher is the overall financial strength of the municipality relative to its peers. The partial performance functions decompose the aggregate result by providing a list of partial performances of a municipality on each performance attribute. The higher the partial performance

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MCDA EVALUATION PHASE

EXPLANATORY ANALYSIS

Data collection

Performance drivers

a) LG’s operating budgets b) LG’s investing budgets c) Local taxes data

Financial performance attributes a) Revenues composition b) Costs and expenditures c) Financing ability and debt burden d) Tax rates

Segmentation into peer groups

a) Operating characteristics Size, administrative structure, assets & services mix, etc. b) External environment Socio-economic conditions Legislation & policies imposed by the central government (fiscal measures, taxation, mergers & amalgamations, etc.)

Impact of performance drivers on overall performance & performance dynamics

Evaluation parameters

a) Specification of benchmarks b) Trade-offs of financial attributes c) Performance evaluation functions

Results a) Overall evaluation b) Performance changes over time Administrative competence Changes due to external conditions Fig. 1. Outline of the methodological approach.

for a municipality, the better is its condition on the corresponding attribute compared to its peers. The partial performance functions have an increasing form for attributes in maximization form, which are positively related to the performance of the municipalities (e.g., own revenues/total revenues) and decreasing form for minimization indicators, which have a negative performance effect (e.g., expenses/revenues). For a given size-group p, the partial scores for performance attribute k are scaled such that the worst level Wkp = mini,t {xtik − btkp } < 0 of the attribute (over all years)

is assigned a partial score vkp (Wkp ) = 0 (worst performance), the best level Bkp = maxi,t {xtik − btkp } > 0 is assigned a partial score of

vkp (Bkp ) = 100 (best performance), whereas the mean level is assigned a score vkp (0) = 50. For this purpose, a logarithmic function is employed for attributes with Wkp + Bkp > 0, an exponential one for attributes with Wkp + Bkp < 0, whereas a linear function is used when Wkp + Bkp = 0:

⎧ t ln (akp dik − akpWkp +1) ⎪ 100 ⎪ ⎪ ln[a B ( kp kp − Wkp )+1] ⎪ ⎨   (akp dikt ) − exp (akpWkp ) vkp dikt = 100 exp exp (akp Bkp ) − exp (akpWkp ) ⎪ ⎪ ⎪ ⎪ ⎩100 dikt − Wkp Bkp − Wkp

if Wkp + Bkp > 0 if Wkp + Bkp < 0 if Wkp + Bkp = 0

t = xt − bt where dik and the parameter akp is chosen such that ik kp

vkp (0) = 50. Under this setting, the benchmark has an overall performance score of 50 points and municipalities with performance scores Vpt (xti ) > 50 are the ones that outperform the benchmark (i.e., their financial performance is better than their average peers). From

a statistical point of view, the linear function is used for attributes in which the municipalities are symmetrically distributed around the benchmark, the logarithmic function is employed for attributes with positive skewness, whereas the exponential function is used for negatively skewed attributes (for attributes approximately normally distributed, these cases reduce to linear partials). Fig. 2 provides sample illustrations of partial performance functions for two attributes used in the empirical analysis in this study (cf. Section 4). In these examples, the performance of municipalities decreases as their reliance to government financing increases, and improves as their surplus to operating revenues increases. Except for the partial performance functions, the multi-criteria evaluation model (1) also requires the specification of the attributes’ trade-off constants wt1p , wt2p , . . . , wtnp . Given the additive and compensatory structure of the evaluation model, a low performance in an attribute with a high trade-off constant is not easily compensated by high performance in other attributes with low trade-offs. In contrast to the evaluation schemes used in previous studies, we assume that the trade-offs of the performance attributes vary over time, in accordance with the changing operating and economic conditions that municipalities face, which naturally lead to adjustments in the priorities of the LGs’ administration. In addition, the attributes’ trade-offs vary for municipalities belonging in different peer groups, as these consist of LGs with different financial and operating characteristics. In order to take these issues into consideration, we follow the information theoretic approach first introduced by Zeleny (1982). Under this scheme, the trade-offs of the performance attributes are assessed in terms of their information content in analyzing the performance of the municipalities. In particular, the trade-off constant for attribute k

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80

80

Partial score

100

Partial score

100

60

40

20

5

60

40

20

0 -200

-100

0 100 200 300 400 Government financing / Population

500

600

0 -0.8

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 Internal financing capacity (surplus) / Operating revenues

Fig. 2. Examples of partial performance functions (French municipalities with population less than 250 inhabitants).

in year t and peer group p is specified as:

wtkp = 100

t 1 − Ekp n

n−

k=1

where t Ekp =

t Ekp

−

,

(2)

t Ekp

is the entropy indicator:

1



mt

 t

ln m p

p 

stik

t

ln sik , with

stik

i=1

  vkp xtik − btkp = t , mp t  t vkp xik − bkp

(3)

i=1

where mtp denotes the number of municipalities from peer group p in year t. In cases where all municipalities have similar performances in a specific attribute k, i.e., vkp (xtik − btkp ) ≈ vkp (xtjk − btkp ) for all mut ≈ 1 and wt ≈ 0, thus innicipalities i and j from group p, then Ekp kp

dicating that attribute k provides only limited information in differentiating between the municipalities. On the contrary, an attribute’s trade-off constant increases as the discrepancies in the performances of the municipalities on that attribute increase. 2.3. Decomposition of performance changes over time The evaluation model introduced in the previous section focuses on providing peer assessments for the municipalities in each year. However, given that the evaluations in each year are based on different benchmarks, direct comparisons over time are not immediately evident. Nevertheless, the performance evaluation results do provide the basis for deriving conclusions on time trends. In particular, information on the change in the performance of a particular municipality i between years t − 1 and t can be obtained by the following Malmquist-like index:

t t

Pit =



Vp xi − Vpt xt−1 i





 



+ Vpt−1 xti − Vpt−1 xt−1 i 2



(4)

This is an average of two comparisons performed on the basis of the benchmarks of years t and t − 1.1 In particular, the first difference in the nominator involves the comparison of the performances of municipality i in the 2 years, when both assessments are performed against the same benchmark of year t. On the other hand, the second difference considers the performances of the municipality in years t and t − 1, evaluated on the basis of the benchmark of year t − 1. Taking the average of the two differences eliminates the ambiguity due 1 In each year, a municipality may belong to different peer groups. In such cases it is assumed that both benchmarks refer to the peer group of year t.

to the selection of the benchmark against which the performance of a municipality is compared over the 2 years. A positive index Pit indicates that the performance of municipality i improved in year t compared to the previous year. However, the change (improvement or deterioration) can be due to internal (managerial performance, MP) and/or external factors (external conditions, EC). To separate between these two effects, the above index can be decomposed as follows:

    Pit = Vpt xti − Vpt−1 xt−1 i

  MP

t−1  t    t t +

Vp



xi − V p xi









+ Vpt−1 xt−1 − Vpt xt−1 i i 2



 

(5)

EC

The first term (MP) compares the global performance Vpt (xti ) of

) in year municipality i in year t, against its performance Vpt−1 (xt−1 i t − 1. Given that both evaluations are performed in comparison to the corresponding annual benchmarks, this term is positive for municipalities with successful administration and negative for municipalities with unsuccessful management. In that regard, the administration of a municipality is considered to be successful in year t, if the municipality’s financial performance relative to its peers, improved in year t in comparison to the previous year. On the other hand, the second term (EC) is an average of two comparisons. The first involves the difference Vpt−1 (xti ) − Vpt (xti ), which is related to the performance of municipality i in year t in comparison to the benchmarks from two successive years. The second difference Vpt−1 (xt−1 ) − Vpt (xt−1 ) is interpreted in the same manner, but now the i i comparison is made using the data of municipality i for year t − 1. In both cases, the results are related to the difference between the benchmarks corresponding to the two successive years. In that regard, the case EC > 0 indicates that the financial performance of the benchmark in year t improved compared to year t − 1, which can be interpreted as an improvement of the external environment. Similarly, the case EC < 0 indicates that the external conditions that affect all municipalities in a peer group have deteriorated, thus leading the benchmark point in year t (“average” municipality) to perform worse than the previous year. It is worth noting that Eq. (5) does not assume that the effect of the external environment is common to all municipalities. Instead, municipalities with different financial and operating characteristics may be affected differently (others positively and others negatively) by changes in the environment in which they operate. The above decomposition provides useful insights as it allows one to distinguish between municipalities that follow the general trend from those that move ahead or stay behind their peers.

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3. The French system of local governments As stated by lo Storto (2013), central governments all over the world engaged into similar trends to increase administrative efficiency: (1) transferring competencies and fiscal responsibilities to LGs, and (2) merging local municipalities in order to benefit from achieving economies of scale. The French government was no exception to this, targeting objectives such as the reduction of expenditures alongside the improvement on the quality of services to meet the needs of citizens, and a better allocation of funds from the central government. In the following section we briefly outline the main features of the French LGs administrative model, focusing on the key issues that are of interest to this study, namely the competencies and resources of French LGs as well as the reforms aiming at decentralization. 3.1. French municipalities’ competencies and resources According to the French Constitution, LGs have the necessary competencies on their scale of operations. Municipalities organize and provide numerous public services assigned by law: public law and order (security of citizens and property, road traffic and parking), social care support, housing and funding, job training and integration assistance, constructing and operating elementary schools, child care facilities, sporting and cultural infrastructures and education, libraries and museum functioning, tourism development, urban planning, natural areas and cultural heritage, sanitation and waste collection and treatment. Local municipalities have the right to raise and collect numerous taxes, directly or indirectly. The main source of financing for most of the municipalities comes from direct taxes such as housing tax, property tax on developed and undeveloped property, as well as corporate property tax and corporate value-added contribution. The latter were introduced with the 2010 finance law to replace the business tax based on the rental value of companies’ tangible assets, and a part of the wage bill until 2003. The central government tried to make this reform neutral by implementing an equalization program financed by a specific fund. There are two types of equalization: vertical equalization and horizontal equalization. Until 2010, at the municipality level, equalization was only vertical, through the general operating grant from the State. Since 2010, the central government engaged horizontal equalization at the municipality level in order to: (1) correct inequalities between municipalities arising because of the new taxable bases; (2) correct wealth inequalities between municipalities. This horizontal equalization program was financed by a specific fund that was operational only in 2012. The question is whether the tax reform was really neutral or not as regards its impact on the financial situation of municipalities. The 2010 legislation reform also introduced three new marginal taxes, an additional tax to the one on undeveloped property, a flatrate tax on information technology and communications companies, and a tax on commercial premises. Aside from these taxes, municipalities have the right to collect tax on transfer duties, for public services (e.g., household waste tax), occupancy tax on visitors, gambling tax, and other taxes depending on the status and characteristics of each municipality. Tax rates are set up by the municipalities’ councils within certain limits that are imposed by existing legislation. 3.2. Territorial decentralization movement The French government engaged deeply in territorial decentralization in 1982, by transferring state competencies to the territorial level: region, department and municipality. This reform is considered as “act I” of decentralization. This has been a major move in giving territorial governments more administrative rights and in transferring executive responsibilities to local authorities. In order to accompany

and support the urban development of local authorities, public establishments for the cooperation between local authorities (EPCI) were created in 1992 and evolved in 1999. The main objective was to pool resources in order to provide public services that small municipalities would be unable to afford alone. Two main forms of cooperation were allowed to nearby municipalities willing to voluntarily regroup: EPCI with their own taxation, and EPCI without a separate tax status. The former were given the right to raise taxes, usually in the form of an additional tax rate applied to local fiscal taxes, whereas the latter obtained resources through the contributions of member municipalities. “Act II” of decentralization was initiated in 2002 and transferred additional competencies (since 2005) to LGs while introducing the principle of financial autonomy. The 2010 reform of LGs is considered as “act III” of decentralization. It forced municipalities to adhere to an intercommunal structure by January 1, 2014. New public bodies of cooperation have been created, such as: “metropolises” for large urban areas (with more than 500,000 inhabitants, Nice Cote d’Azur gained this status on January 1, 2012 and is actually the first and only metropolis until 2015); or again the grouping of EPCI into urban hubs characterized as “metropolitan areas” (with more than 300,000 inhabitants). The main objective of this reform was to cut expenses induced by the existence of multiple levels of LGs, exerting the same competencies, essentially by a clarification and a better allocation of competencies among different levels of administration. This was a response to criticisms of the decentralization process: a significant increase in local tax rates; an increase in legal uncertainty; the continuation of redundant posts or new posts both at the local and intercommunal level and other sumptuary expenses or staff additional costs, without the benefit of better achievements in provided services; lack of clear definition of competencies sharing; and competition between local municipalities to take over the control of the intercommunal structure. The cooperation process was successful in quantitative terms but one can question the outcomes in qualitative terms. The last step has been spurred in 2013: the role of metropolises has been reinforced by giving them more rights while relaxing the regime of metropolitan areas, and giving local municipalities a leading role in monitoring the quality of air and enhancing sustainable mobility. 4. Data and financial performance attributes 4.1. Data description The dataset consists of compiled reports on local municipalities’ annual financial situation centralized by the Public Finances General Directorate (DGFiP). According to the National Institute of Statistics and Economic Studies (INSEE), on the January 1, 2012, France had 36,571 municipalities in addition to 129 in overseas departments and regions. The original sample contained a panel data set consisting of 476,683 municipality-year observations spanning the period 2000– 2012, but we decided to discard municipalities with less than 50 inhabitants as these are small villages and hamlets with very specific characteristics (that for example have a small taxable base and few services to provide). The final dataset includes 462,652 municipalityyear observations, corresponding to more than 97 percent of the initial database. The municipalities in the sample range from small ones with population of at least 50 inhabitants to large municipalities with more than 100,000 inhabitants. Given the diverse characteristics of the municipalities in the data set we segment the municipalities in proper groups. Zafra-Gómez et al. (2009b) used statistical clustering to define a segmentation of LGs, but in this case we employ a qualitative approach similar to that of Brown (1993) who defined population size categories. However, instead of solely focusing on population groups we also consider

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E. Galariotis et al. / European Journal of Operational Research 000 (2015) 1–17 Table 1 Definition of size groups.

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Table 2 Financial performance attributes.

Size groups

Population

Percent of sample

1 2 3 4 5 6 7 8 9

50–249 250–499 500–1,999 2,000–3,499 3,500–4,999 5,000–9,999 10,000–19,999 20,000–49,999 ≥50,000

32.8 22.3 31.2 5.8 2.4 2.9 1.4 0.9 0.3

whether the municipalities belong or not to financial districts. As explained earlier, this issue is of particular interest for the organization of the French LG system. On the basis of this approach 18 peer groups are considered, corresponding to the combinations between the nine groups defined in Table 1 (based on the size criteria used by INSEE to classify municipalities) with whether the municipalities belong or not in a financial district. 4.2. Performance attributes Jones and Walker (2007) consider the financial health of LGs in the context of financial distress, defined as the inability to provide services at pre-existing levels and measure it as the cost of restoring infrastructure. Groves et al. (2003) measure financial condition by means of four elements related to cash solvency (the capacity to pay short term debts), budget solvency (the capacity to generate sufficient revenues to cover expenses), long-run solvency (the capacity to face long term obligations), and service level-solvency (the capacity to provide the level of services needed). Zafra-Gómez et al. (2009b) consider that the financial health of LGs should be measured using indicators related to sustainability (the ability to maintain the long term citizens’ welfare), flexibility (the ability for a municipality to adapt its fiscal policy or budget to changes in its economic or financial environment), and vulnerability (the municipality’s dependence on external financing). The main factors that impact municipalities’ financial situation can be grouped into three different types (Carmeli, 2008; Carmeli & Cohen, 2001). First, LGs’ financial distress could be due to internal factors: their financial situation and fiscal structure, their size, the level of services they provide, the importance of the fiscal slack that permits municipalities to buffer the impact of environmental changes, the composition of revenues and expenses, liquidity, and future financial obligations (debt). Carmeli and Cohen (2001) highlight that LGs’ financial difficulties occur because of a lack of organizational resources and managerial skills in order to be efficient or to adapt to changing conditions. Second, environmental factors can affect considerably the financial health of a LG. Effectively, revenue wealth and spending needs depend on socioeconomic, political and demographic features (Hendrick, 2004). So, changes in the fiscal environment and fiscal structure could induce fiscal stress as fiscal sources of revenues and budgets for long term capital expenditures relying on past fiscal structures could not be balanced anymore. Finally, hybrid factors like politics and intergovernmental relations also affect the financial condition of LGs. In that regard, over the past decade, the French central government encouraged municipalities to merge, but merging two or more LGs that face financial difficulties does not create necessarily one healthier municipality (Jones & Walker, 2007). Decentralization processes engaged by central governments transfer competencies to local municipalities, changing the structure of their budget both in terms of revenues, expenditures, investment and financing decisions. In this study, we define financial performance attributes following the financial ratio approach used by Brown (1993) who devised an evaluation model that has been successfully used in the USA.

Revenues composition x1 x2

Own revenues/operating revenues Government financing/population

Costs and expenditures x3 x4 x5

Staff costs/operating expenses Other purchases and external expenses/operating expenses Investment expenditures/operating revenues

Financing ability and debt burden x6 x7

Internal financing capacitya /operating revenues Outstanding debt/operating revenues

Tax rates x8 x9 x10

Housing tax rate Property tax rate on developed land Property tax rate on undeveloped land

a Internal financing capacity is defined as the difference between operating revenues and expenses (in terms of cash flows).

Despite their limitations (mostly with regard to the accounting reporting standards used; Pina et al., 2009), financial ratios are easily available from the financial statements of the municipalities and they are easy to understand. Brown (1993) used a set of 10 key financial ratios related to the municipalities’ revenue generating ability, the composition of their revenues, their expenditures, as well as their debt burden and debt service capability. Following this setting we employ a similar set of 10 financial ratios and further extend Brown’s framework by introducing a specific category of indicators related to the taxation policy that the municipalities follow. All the necessary data for calculating the financial ratios are obtained from a publically available source that has been created in April 2013.2 To the best of our knowledge, this is the first study to use individual data at the LG level for France, as previous studies used data at a departmental or regional level. Our paper uses this data and proposes an operational tool that local and central governments can employ in order to assess not only LG performance, but also the impact that reforms have on the operating and budgetary environment and constraints of LGs (such as tax reforms, amalgamations, decentralization, central funding). As such, our paper has both research and policy implications, to the extent that it proposes a novel methodological approach for researchers in this area, and at the same time provides a guide for policymakers on how to better assess the implications of policies and better anticipate the impact of future ones. As explained, the financial ratios are built from the operating and investing budget as well as from local tax data. The operating budget refers to the LG’s revenues and operating expenses. Revenues come mainly from local taxes, central state grants, or revenues from services or facilities provided by the communality. Expenses are mainly staff costs, interest expense or subsidies given to local associations. The investing budget relates to capital expenditures and loan reimbursements for the investment part, financed by cash flows from operating activities, loan issues or investing subsidies given by the department or the region. Local tax data comprises housing, property and other business taxes. As shown in Table 2 the selected ratios are classified into four main categories and they are easy to calculate in order to be operational for LGs. For a critical review on the ratios see Hendrick, 2004; Zafra-Gómez et al., 2009b; that look into the study of LGs’ financial sustainability and identify different groupings of

2 The data (see: http://www.collectivites-locales.gouv.fr/) is made available by the Public Finances General Directorate and the General Division of Local Communities (DGFiP and DGCL) and is formed from detailed budgets (operating budget, investing budget and local tax data) that LGs are legally required to report to the DGFiP. The original intention for this web portal was to help central and local governments on policy decisions pertinent to their financial situation.

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ratios that are pertinent. The ratios in our paper cover all different dimensions identified in the above articles. More specifically, the first dimension is financial independence (the ability to meet obligations using resources that are not derived from grants or subsidies), in our paper this is named “revenues composition”. The second dimension is sustainability (capacity to meet budgetary obligations without relying on debt), in our paper it is grouped under “cost and expenditures”. The third dimension (flexibility to meet debt repayment obligations) is depicted here as “financing ability and debt burden”. The fourth category relates to fiscal slack and considers local taxation policy and the capacity of LGs to manage fiscal stress, and in our paper it is called “tax rates”. •

•

•

Revenues composition: The ratios in this category are used to analyze the municipalities’ revenues makeup, focusing on assessing their level of financial independence. Maher and Nollenberger (2009) note that high reliance on intergovernmental revenues should be considered as a warning sign, as it makes municipalities vulnerable to external decisions. To take this issue into account, two ratios are employed. Ratio x1 (own revenues to operating revenues ratio) represents the contribution of the municipalities’ own revenues to their total operating income (revenues). The municipalities’ total operating revenues comprise of local taxes collected by the municipalities and financing (operating grants) received from the central government. Ratio x1 compares these two sources of income. The ratio ranges between 0 and 1, with higher values indicating higher financial autonomy (i.e., a municipality relies mostly on income it collects on its own in the form of taxes, rather than on external financing received from the government). Additionally, the ratio of the operating grants from the government to population (ratio x2 , measured in euros per inhabitant) is used to assess the dependence of the municipalities to financing obtained from the central government, compared to their size (as measured by population). Costs and expenditures: This category of ratios is employed to analyze the cost structure of the municipalities. Three ratios are used for this purpose, involving staff costs (ratio x3 ), purchases and external expenses (i.e., non-financial administrative expenses other than staff costs; ratio x4 ), as well as investment expenditures (ratio x5 ). Ratios x3 and x4 assess the significance of staff costs and other administrative expenses to the overall operating expenses of the municipalities (both ratios are measured on a 0–1 scale), whereas ratio x5 represents the share of a municipality’s total operating revenue used to support its investment activities. More specifically, x3 is the ratio of staff costs to operating expenses, while x4 is the ratio of other purchases and external expenses to operating expenses. Ratio x5 is created by dividing investment expenditure by operating revenues. Therefore ratios x3 and x4 assess the significance of staff costs and other administrative expenses to the overall operating expenses of the municipalities, whereas ratio x5 represents the share of a municipality’s total operating revenue used to support its investment activities. Financing ability and debt burden: The third category of financial performance measures includes two ratios related to the internal financing capacity of the municipalities (ratio x6 : internal financing capacity to operating revenues) and their debt burden (ratio x7 : outstanding debt divided by operating revenues). Internal financing capacity (net of loan repayments) is defined as the difference between operating revenues and operating expenses (in terms of cash flows) and indicates the ability of the municipalities to finance their operation without having to resort to external debt. Thus, the higher the ratio x6 is, the higher is the fiscal surplus of a municipality, which can be used to cover its future financing needs (i.e., operating expenses, investments, and debt service payments). On the other hand, ratio x7 measures the debt burden of the municipalities. Higher values for ratio x7 correspond to

•

heavily indebted municipalities, which may have difficulties meeting their debt obligations through their operating revenues. Tax rates: This last category of performance indicators is used to assess the tax rate levels of the municipalities. Taxes are the main source of the municipalities’ income, but they place a burden to citizens and they may have a long-term negative effect on economic activity, posing risks for the future financial position of municipalities. The taxation level depends on the municipalities’ policy as well as the legislation imposed by the central government. Municipalities with low taxation rates have more opportunities for managing fiscal stress (Jones & Walker, 2007). On the other hand, LGs that rely heavily on tax revenues would find it difficult to reduce taxes in adverse financial conditions without affecting negatively the level (volume and quality) of the services they provide (Maher & Nollenberger, 2009). On the basis of data availability, the housing tax rate is employed (ratio x8 ) together with property taxes (on developed and undeveloped land; indicators x9 and x10 ).3

Drew and Dollery (2014), study amalgamations in Sydney and stress the importance of considering various population characteristics/demographics. In order to take into consideration for demographics and interactions with LG specificities that can moderate the financial variables (e.g., staff expenses, debt level, etc.), we consider size groups as well as the membership to financial districts (defined as membership to a public body of cooperation with its own fiscal status) for the purposes of the analysis. As noted earlier, French municipalities are facing an increasing trend in the number of financial districts. In 2000 about 59 percent of the municipalities (20,839 out of 35,568) belonged to financial districts. Over the years, this percentage has gradually increased, exceeding 96 percent in 2012. Table 3 presents some descriptive statistics (averages and coefficient of variation in parentheses) for the financial performance attributes for each size group and financial district membership, whereas Table 4 summarizes their evolution over the time period of the analysis (overall averages for all municipalities in the sample). We can distinguish different patterns of financial characteristics between municipalities, according to their size. Considering revenue composition, it is clearly evident that bigger municipalities rely to a larger extent on their capacity to generate their own revenues compared to smaller ones. The contribution of own revenues to total operating revenues (ratio x1 ) is an increasing function of size for smaller municipalities (with less than 2000 inhabitants, i.e., groups 1–3), but for larger municipalities it fluctuates around 45 percent. It is interesting to note that in relative terms, bigger cities benefit more from state subsidies as the government financing per citizen (ratio x2 ) is also an increasing function of size, with the exception of the smallest municipalities because of a lack of internal resources. In terms of operating costs there is also a different pattern depending on the municipalities’ size. Staff costs (ratio x3 ), expressed as a percentage of operating expenses, are increasing for larger municipalities, whereas other purchases and external expenditures (ratio x4 ) are higher for smaller municipalities. The overall expenses burden appears higher for larger municipalities, which have to bear higher internal costs of services as needs and infrastructure costs increase with the number of citizens. On the other hand, smaller municipalities outsource the provision of these services as they lack skilled personnel and their insufficient size makes them less competitive to external suppliers. Considering investment expenditure (ratio x5 ), it represents two thirds of operating revenues for smallest 3 Housing tax is payable by any person who has residential premises at their disposal, whatever their status. Property taxes are payable by owners of developed or undeveloped land. Taxable properties include constructions attached to the ground (residential properties or business assets), or lands that are currently unused for any purpose, respectively. All taxes are levied annually on basis of the taxpayers’ situation on January 1 of the taxation year.

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Table 3 Statistics (averages and coefficient of variation in parentheses) by size and district group.

Size groups

1 2 3 4 5 6 7 8 9

Districts

Yes No

x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

0.35 (0.41) 0.39 (0.33) 0.42 (0.29) 0.45 (0.27) 0.45 (0.28) 0.46 (0.28) 0.45 (0.28) 0.47 (0.24) 0.46 (0.23)

217.45 (0.63) 176.61 (0.54) 174.95 (0.59) 187.34 (0.56) 191.45 (0.49) 205.22 (0.46) 233.24 (0.41) 266.17 (0.37) 281.59 (0.34)

0.26 (0.47) 0.33 (0.33) 0.39 (0.24) 0.43 (0.19) 0.45 (0.16) 0.48 (0.15) 0.51 (0.14) 0.53 (0.13) 0.51 (0.13)

0.31 (0.34) 0.30 (0.29) 0.30 (0.24) 0.29 (0.21) 0.27 (0.20) 0.26 (0.20) 0.24 (0.20) 0.23 (0.21) 0.21 (0.26)

0.66 (1.14) 0.61 (1.02) 0.59 (0.85) 0.54 (0.66) 0.49 (0.61) 0.44 (0.57) 0.40 (0.58) 0.38 (0.49) 0.43 (0.47)

0.18 (1.10) 0.14 (1.16) 0.12 (1.21) 0.11 (1.10) 0.09 (1.25) 0.07 (1.32) 0.05 (1.59) 0.03 (2.55) 0.02 (3.46)

0.54 (1.17) 0.64 (0.92) 0.79 (0.74) 0.86 (0.59) 0.85 (0.55) 0.83 (0.53) 0.76 (0.53) 0.78 (0.47) 0.81 (0.49)

7.96 (0.53) 9.00 (0.45) 10.39 (0.39) 11.99 (0.35) 12.98 (0.35) 13.89 (0.33) 15.42 (0.34) 17.37 (0.30) 18.02 (0.30)

10.46 (0.52) 12.33 (0.43) 14.49 (0.39) 16.95 (0.36) 18.23 (0.33) 19.89 (0.34) 21.83 (0.35) 22.74 (0.34) 22.12 (0.32)

35.43 (0.71) 39.61 (0.59) 44.77 (0.50) 50.85 (0.45) 52.90 (0.45) 56.70 (0.44) 58.55 (0.46) 57.37 (0.51) 44.37 (0.58)

0.39 (0.34) 0.44 (0.35)

192.49 (0.56) 197.51 (0.79)

0.35 (0.36) 0.31 (0.40)

0.30 (0.29) 0.30 (0.31)

0.61 (1.00) 0.58 (0.99)

0.14 (1.20) 0.14 (1.23)

0.69 (0.87) 0.60 (0.92)

9.77 (0.47) 9.33 (0.46)

13.32 (0.48) 13.04 (0.45)

41.36 (0.58) 44.00 (0.59)

Table 4 Financial performance indicators over time (overall averages).

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

0.398 0.395 0.389 0.386 0.391 0.394 0.386 0.386 0.396 0.397 0.396 0.407 0.415

159.798 162.558 167.473 168.577 193.441 200.507 204.017 208.627 212.639 208.406 208.086 208.828 209.363

0.310 0.317 0.328 0.337 0.341 0.343 0.348 0.351 0.348 0.348 0.348 0.348 0.349

0.299 0.289 0.290 0.290 0.292 0.297 0.302 0.300 0.307 0.307 0.308 0.310 0.312

0.586 0.644 0.657 0.681 0.657 0.645 0.581 0.598 0.565 0.579 0.560 0.540 0.527

0.144 0.134 0.116 0.124 0.128 0.135 0.147 0.149 0.140 0.138 0.135 0.157 0.165

0.691 0.681 0.660 0.669 0.669 0.668 0.674 0.684 0.687 0.680 0.692 0.683 0.679

8.399 8.502 8.741 8.907 9.035 9.154 9.261 9.319 9.413 9.534 9.656 13.119 13.140

12.281 12.399 12.678 12.879 13.049 13.206 13.359 13.442 13.576 13.748 13.927 14.001 14.109

39.428 39.648 40.259 40.724 41.137 41.491 41.855 42.024 42.325 42.656 43.083 44.002 44.263

municipalities and it decreases with size down to less than one half. Transfer of competencies is a real constraint for small municipalities’ budgets. Smaller municipalities cannot reach larger cities’ optimal levels of efficiency because of their limited resources and complex challenges in the provision of new services (Carmeli, 2008). The process of decentralization also impacts taxation policies of municipalities. Overall, taxation rates (ratios x8 , x9 and x10 ) increase with size up to almost a 2:1 ratio between larger municipalities and the smallest ones. Metropolises seem to benefit from economies of scale, as staff costs stop increasing, but have to dedicate a relative larger part of their revenues to investment expenditures compared to cities in the smaller size classes. The bigger the cities are, the more services they have to provide, the higher the taxation rates are. As a matter of fact, metropolises that are prosperous do provide better services to their citizens, who are willing to pay a premium to enjoy such privileges as better schooling for example (Boustan, 2013). Again, metropolises are exceptions in the way that land property taxes (ratios x9 and x10 ) stop increasing, and even decrease notably for the property tax on undeveloped land (ratio x10 ) because as urbanization restricts the availability of farmland and undeveloped land, it would make no sense to create a tax disincentive. Finally, considering the sources of financing (expressed in terms of percentages of operating revenues), it is interesting to note that internal financing capacity (ratio x6 ) decreases with size, whereas debt burden (ratio x7 ) increases mostly for municipalities with up to 3500

inhabitants. As small municipalities have limited access to debt financing, they have to rely on self-financing. Financial institutions are more willing to grant loans to bigger municipalities with larger taxable bases, so bank loans represent a greater part of financing for investment purposes. This feature stops for medium-sized municipalities (larger than 3500 citizens): as self-financing capacity is dampened, borrowing capacity is too. Again, the largest municipalities are in a better position to develop their infrastructure needs through loans. Myazaki (2014), considers size, as measured by population, to be a prominent factor in a study that looks into the motivation of LGs to merge or consolidate. He finds that municipalities that could enjoy large economies of scale prefer consolidation, while large and small municipalities are likely to merge, especially municipalities receiving large unconditional grants from the central government. Our data are in line with this. Specifically, size is an important determinant of the financial and fiscal structures: small municipalities have lower own revenues compared to larger ones, but not necessarily higher state’s subsidies per capita. Furthermore, they exhibit lower staff costs but higher outsourcing expenses, spend a higher share of their revenues on investments, are self-financed for a greater part and rely less on bank financing. In, small municipalities’ taxation policy is much more stringent. On the other hand, one can see the metropolises’ uniqueness. They benefit both from economies of scale and government subsidizing: the larger the city, the higher the subsidies per capita, the

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E. Galariotis et al. / European Journal of Operational Research 000 (2015) 1–17 Table 5 Trade-offs of performance attributes for each peer group (averages over all years). Size groups

x1

x2

x3

x4

x5

x6

x7

9.6 8.2 7.8 8.5 9.1 9.4 10.6 8.8 4.0 8.6

10.0 11.5 11.5 11.2 10.4 10.2 10.6 9.2 13.0 10.9

10.0 9.9 9.8 10.1 9.4 9.3 9.5 11.3 9.8 9.9

12.0 13.1 12.4 11.5 10.9 10.4 10.3 10.0 9.4 12.2

7.2 7.1 7.1 7.4 7.1 7.1 7.0 6.7 6.7 7.2

12.5 12.0 11.5 11.2 11.0 11.0 8.9 9.7 10.9 11.8

10.9 10.0 7.1 7.2 8.5 8.2 8.4 8.8 9.7 9.3

9.5 11.3 13.4 12.1 11.0 11.2 13.2 10.9 16.6 11.3

10.1 10.5 10.5 10.9 12.0 10.5 8.0 9.2 10.9 10.4

10.5 11.1 11.1 10.9 11.9 11.6 12.0 8.7 11.3 10.9

7.1 6.8 7.3 8.7 6.9 7.5 6.8 7.1 5.5 7.2

10.8 8.9 10.3 9.4 9.3 10.0 7.5 11.4 6.1 10.1

x8

x9

x10

8.8 9.5 9.9 9.6 9.1 9.2 8.3 8.8 9.0 9.4

8.7 9.1 9.5 9.2 10.5 9.4 10.6 10.7 11.8 9.2

10.4 9.9 10.0 10.5 11.4 12.5 13.1 15.7 12.2 10.3

9.5 9.6 9.5 8.7 7.8 11.1 9.6 9.1 8.9 9.5

8.6 8.5 8.4 9.2 11.2 10.5 10.0 8.3 11.1 8.7

10.3 12.1 11.5 11.3 11.8 10.7 14.4 16.7 10.4 11.3

Financial districts 1 2 3 4 5 6 7 8 9 Average

10.7 9.8 10.3 10.7 11.3 11.4 11.0 9.1 13.2 10.4

Non-financial districts 1 2 3 4 5 6 7 8 9 Average

12.6 11.3 10.9 11.6 9.5 8.8 10.0 9.8 9.6 11.5

lower the operating costs, and they rely massively on loans to finance their development because of very little self-financing resources. Finally, municipalities that do not belong to a financial district seem to be better off than the ones that merge into an intercommunal structure, but it is only due to the fact that most of the municipalities that merge are small ones as they have a higher interest in doing so. We can also notice some time trends in the financial performance attributes. We can roughly break the sample period into two subperiods, with a turning point in 2005 and subsequent years. The first sub-period, driven by “act II” of decentralization, is characterized by an increase in staff costs, investment expenditures and other expenses, which is a consequence of the transfer of additional competencies from the central government to LGs. The decentralization process is mainly accompanied by an increase in central state subsidies, that balance out a decrease in LGs’ reliance on debt and on their capacity to self-finance the new services they are in charge of, despite of an increasing trend in local taxation rates. The second sub-period highlights the central government disengagement: LGs restore their financial health by increasing their own revenues thanks to a steadily increase in local taxation (notably the housing tax since 2011) and by cutting staff costs and investment expenditures. On the all period, external expenditures show an increasing trend, presumably due to the outsourcing of the provision of the new services incumbent to LGs. 5. Results

property are also important for evaluating the performance of municipalities not belonging in financial districts, but in this case there are no noticeable trends in relation to the size of the LGs. Furthermore, the relative information power of the outstanding debt/operating revenues ratio is weaker (for almost all size groups) compared to the municipalities belonging in financial districts, whereas the converse is observed for staff costs (ratio x3 ). On the other hand, the trade-off of the own revenues/operating revenues ratio appears higher for small municipalities (groups 1–5) not belonging in financial districts compared to the LGs of the same size that belong in financial districts, whereas the converse is observed for larger municipalities. Fig. 3 illustrates the changes of the trade-offs of selected attributes over time (averages for all size groups with one standard deviation band). The results indicate that the relative importance of government financing has increased, particularly after 2003. Furthermore, the information value of this ratio has been almost consistently higher for municipalities not belonging to financial districts. The relative importance of investment expenditures has followed a decreasing trend after its peak in 2003, whereas the trade-off of the debt ratio has been consistently lower for municipalities not belonging to financial districts. As far as taxes are concerned, the relative importance of the housing tax rate has followed a similar time trend for all municipalities that peaked up after 2010. On the other hand, the trade-off of the tax rate on undeveloped property has remained almost constant for municipalities not belonging to financial districts, but it has increased for municipalities in financial districts.

5.1. The relative importance of the performance attributes 5.2. Performance assessment Table 5 presents the attributes’ trade-offs for all peer groups estimated according to Eqs. (2) and (3). The results indicate that for municipalities belonging to financial districts, investment expenditures (ratio x5 ) is the most informative attribute for the evaluation of their performance, particularly for smaller municipalities. The outstanding debt/operating revenues ratio (x7 ) also appears to be an important aspect for the evaluation of municipalities belonging in financial districts and its trade-off constant is also slightly higher for small municipalities. This result confirms the importance of debt not only for economic performance as in da Cruz and Marques (2014) but also for financial performance. On the other hand, the relative information power of property taxes (mainly for undeveloped land, ratio x10 ) appears higher for larger municipalities. Property taxes for undeveloped

Table 6 summarizes the overall performance assessment results for all peer groups throughout the period of the analysis. The results for small municipalities belonging to financial districts (size groups 1–2 with population less than 500 inhabitants) indicate an increasing trend, particularly after 2002–2003, which is indicative of good management practices. This increasing trend becomes weaker for larger municipalities. On the other hand, the results for the municipalities that do not belong to financial districts exhibit larger fluctuations over time, as the number of such municipalities has drastically decreased. Furthermore, the overall improvements over time (2000– 2012) are almost consistently higher for municipalities that belonged to financial districts.

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Government financing / Population

14

10

8

01

02

03

04

05

06

07

08

09

10

11

10

0 00

12

Outstanding debt / Operating revenues

8

01

02

03

04

05

06

07

08

09

10

11

12

08

09

10

11

12

Housing tax rate

15

Districts Non-districts

Districts Non-districts

14

13

13

Trade-off constant

Trade-off constant

15

5

6

4 00

Districts Non-districts

20 Trade-off constant

Trade-off constant

12

Investment expenditures / Operating revenues

25

Districts Non-districts

11

12 11 10

11

9

9 8 00

01

02

03

04

05

06

07

08

09

10

11

7 00

12

01

02

03

04

05

06

07

Property tax rate on undeveloped property

13

Trade-off constant

12

11

10

9

8 00

Districts Non-districts 01

02

03

04

05

06

07

08

09

10

11

12

Fig. 3. Trade-offs of selected performance attributes over time (solid lines: averages over all size groups, dotted lines: one standard deviation band).

Table 7 provides some details on the relationship between the evaluation attributes and the overall performance scores of the municipalities. In order for the proposed composite performance measure to be a meaningful indicator, it must be monotonically related to the evaluation attributes (i.e., positive relationship with attributes expressed in maximization form such as x1 , x5 , x6 and negative for the others). To verify this issue, Table 7 reports the overall Pearson correlation coefficient between the multicriteria evaluation scores of all municipalities and the 10 financial attributes. It is evident that all correlations have the expected sign. All correlations are significant at the 1 percent level. The strongest correlations are observed for the three tax rate attributes, the two ratios related to financing ability and debt burden (x6 , x7 ), as well as the staff costs and the government financing ratios. Furthermore, we also test the means of the attributes for

five quantile ranges of the performance scores: 0–20 percent corresponding to very poor performing municipalities, 20–40 percent for poor performance, 40–60 percent for medium performance, 60–80 percent for good performance, and 80–100 percent for the top performing municipalities. The results reported in Table 7 demonstrate that there is a monotonic relationship between all attributes and the classification of the municipalities in these five global performance categories. In order to further assess the validity of the proposed evaluation approach we compared it to a simple scorecard model, similar to the ones used in previous studies such as the ones of Brown (1993) and Zafra-Gómez et al. (2009a,2009b). In particular, for each of the 18 peer groups, the data over all years were first used to bin the financial performance attributes into four rating grades with

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E. Galariotis et al. / European Journal of Operational Research 000 (2015) 1–17 Table 6 Average performance scores. Size groups

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

52.0 51.0 50.2 49.8 50.3 50.1 50.0 50.3 45.9 50.9

50.8 50.0 49.5 49.4 49.4 49.6 49.2 49.6 39.6 50.0

49.4 49.7 50.1 49.9 50.5 50.1 50.2 50.1 47.8 49.8

50.2 49.5 49.6 49.2 49.9 50.2 49.4 49.5 47.3 49.8

51.4 50.2 49.7 49.8 50.2 50.4 50.4 50.5 47.7 50.4

51.3 50.4 49.8 49.8 50.1 49.9 50.3 50.3 47.5 50.4

52.8 51.5 50.8 50.5 50.7 50.8 50.8 50.9 47.1 51.6

52.8 51.3 50.7 50.3 50.4 50.9 50.7 50.9 47.4 51.5

53.0 51.6 51.0 50.5 50.9 51.0 50.9 50.9 47.9 51.8

53.1 51.9 51.3 50.9 51.0 51.1 51.1 50.9 47.7 52.0

53.4 52.1 51.3 50.9 51.1 51.1 51.1 51.2 47.6 52.1

53.5 52.2 51.4 50.9 50.9 51.1 50.8 50.9 47.8 52.1

53.6 52.3 51.5 51.0 50.8 50.9 50.9 51.0 48.3 52.2

52.4 51.7 50.4 50.6 50.6 51.0 50.9 49.9 49.7 51.4

51.4 51.5 50.6 50.3 50.6 51.0 50.6 50.3 49.7 51.1

51.5 50.8 50.4 50.8 51.0 50.8 50.9 50.3 50.3 50.9

51.2 50.9 50.2 50.5 51.1 50.9 48.9 50.5 50.3 50.8

51.9 51.8 50.5 49.9 50.7 50.0 50.9 50.9 50.1 51.3

53.0 50.0 50.6 49.9 50.2 51.3 50.8 51.3 48.1 51.3

53.0 52.5 50.9 50.6 50.6 51.4 50.7 51.4 48.8 52.0

53.2 52.2 49.0 50.4 50.7 50.7 50.3 51.8 49.1 51.5

53.5 52.2 50.8 50.8 51.0 51.2 50.2 51.2 50.8 52.1

52.9 51.5 50.9 50.7 51.1 51.7 51.0 50.6 45.6 51.7

53.4 51.9 50.7 50.4 50.9 51.3 51.3 51.2 49.5 51.9

52.8 52.2 51.3 50.2 51.9 50.6 49.9 51.9 48.8 51.9

53.6 52.1 46.9 51.5 51.2 51.2 51.1 51.7 48.4 51.0

Financial districts 1 2 3 4 5 6 7 8 9 Average Non-districts 1 2 3 4 5 6 7 8 9 Average

Table 7 Relationship of the performance scores with the attributes (Pearson correlations). Score quantiles

x1

x2

x3

0–20 percent 20–40 percent 40–60 percent 60–80 percent 80–100 percent

0.38 0.39 0.39 0.40 0.42

256.82 206.32 184.57 168.07 150.41

0.40 0.37 0.34 0.32 0.28

Correlation

0.11

−0.33

−0.35

x4

x5

x6

0.31 0.31 0.30 0.30 0.29

0.47 0.53 0.58 0.65 0.77

0.08 0.11 0.14 0.16 0.21

−0.08

0.18

0.28

cut-offs defined by the 25th, 50th, and 75th percentiles. Municipalities falling in the first quartile (low performance) were assigned zero points, whereas municipalities in the top performance quartile were given four points. The results were then summarized over all attributes to calculate the overall performance scores ranging in [0, 40] (since 10 attributes are used in the analysis), with higher values corresponding to municipalities in a strong financial position. In addition, we test two variants of the multiattribute performance index, which incorporate non-compensatory aspects. The first is a multiplicative version of the basic model  wt (1) expressed as Vpt (xti ) = nk=1 [vkp (xtik − btkp )] kp . The second variant extends model (1) by introducing a penalty for the worst per formance dimension as follows Vpt (xti ) = λ nk=1 wtkp vkp (xtik − btkp ) +

(1 − λ)mink {vkp (xtik − btkp )}, where 0 < λ < 1 is a parameter that de-

fines the trade-off between the compensatory (additive) and noncompensatory part of the model. In this analysis we set λ = 0.7. The Spearman rank correlation between the proposed multiattribute evaluation approach and the scorecard assessments (averaged over all peer categories) was found to be 88 percent, whereas the corresponding correlations compared to the above mentioned multiplicative and the penalized additive variants were 82 percent and 92 percent, respectively. Furthermore, in order to test the relationship of the different evaluation approaches with the available data, we fitted regression models having as dependent variables the multi-attribute performance assessments (from each evaluation model) whereas the 10 performance attributes were employed as independent variables together with dummy variables to control for differences between the peer groups and over the years. The adjusted R2 for the regression with the multi-attribute model was 89.5 percent versus 80 percent for the penalized additive variant and 78.8 percent for the scorecard

x7

x8

x9

x10

0.94 0.80 0.69 0.58 0.38

12.76 10.66 9.59 8.50 6.98

17.99 14.62 13.04 11.50 9.25

63.59 47.19 39.45 33.26 25.31

−0.33

−0.45

−0.50

−0.55

assessments and the results of the multiplicative aggregation model. The results of this regression exercise indicate that the aggregation through the multi-attribute evaluation model is in higher accordance with the input raw data, compared to the scorecard approach and the two alternative aggregation variants. However, the performance results of Table 6 are peer assessments and, as such, direct comparisons over time and between different groups are not straightforward. To address this issue, we model the overall performance change through its decomposition (5) that distinguishes between the effect due to management competence and the effect due to the external environment. Fig. 4 illustrates the cumulative average change over time for the overall performance and its two components, distinguishing the municipalities that belong in financial districts from the rest. The results clearly show, that overall the performance of the municipalities has followed a consistent declining trend. The decrease was stronger in the period up to 2006 and it intensified again in 2011. This decrease has been primarily driven by the adverse external environment. On the other hand, consistent with the results reported in Table 6, changes due to managerial competence had a positive effect after 2003, particularly for municipalities belonging to financial districts. Municipalities not belonging to financial districts have followed a different performance path (in terms of managerial performance) after 2008 and they have been particularly affected by the changes imposed on the taxation legislation by the French central government in 2010. 5.3. Explanatory analysis As described in Section 2.1, from a policymaking and managerial perspective the benchmarking results presented in the previous section require further analysis in relation to the driving factors that have

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1.0

Non-districts

0.5

0.0

-0.5

-1.0

-1.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Change due to external conditions (EC)

2 Districts

Change due to managment (MP)

13

Districts

Non-districts

0

-2

-4

-6

-8 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

2

Overall performance change (ΔP)

Districts

Non-districts

0

-2 -4 -6 -8

-10 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Fig. 4. Cumulative average change in overall performance and its decomposition.

an explicit or implicit effect on the financial data of the municipalities. Given that the financial data of the municipalities are naturally affected by several other internal and external factors related to the operating characteristics of the LGs and the socio-economic environment, it is important to examine how these factors relate to the estimates of the financial health of LGs derived from the multi-criteria benchmarking approach. For example, Kimhi (2008) highlights that there exist two major approaches in the literature to explain financial failure of LGs: the (external) socio-economic decline approach and the (internal) local management approach. Regarding the first approach, it refers to the national economic conditions and has various aspects. One aspect is that in an economic downturns, tax related revenues decline while municipal expenditures may keep on increasing especially for social purposes. Two other aspects of the same approach are demographic and suburbanization changes, as well as inter-governmental policies. The second approach refers to the inability of local officials to manage resources efficiently. In this study, this second part of the analysis is implemented by constructing two panel regression models, through which we consider the above mentioned two main dimensions that describe the performance of the municipalities. In the first model (henceforth referred to as model P), the dependent variable corresponds to the peer assessments of the municipalities’ financial performance according to the additive evaluation model (1) for each year t during the time period of the analysis. As explained above, the time trends in these assessments are indicative of the managerial competence of a municipality’s administration. On the other hand, the second model (henceforth referred to as model EC) analyzes the performance changes due to the external environment in subsequent years (i.e., year t versus t − 1, as defined in Eq. (5)). The two models consider a number of explanatory variables. As noted earlier, in this study we are particularly interested on three main issues: (a) the effect of the global crisis, (b) the 2010 taxation

reform in France, and (c) the trend toward economic amalgamations of French municipalities, which led the vast majority to cooperate through the formation of financial districts. To this end, we first use annual dummy variables (denoted as Y2002 , Y2003 , . . . , Y2012 ) to control for annual effects (which include the years during the global crisis). Additional dummy variables DYi1 , DYi2 , and are also used to consider the effect of the municipalities’ membership in financial districts over different time periods: (a) DYi1 = 1 if municipality i belonged to a financial district during the period 2010–2012 (i.e., new taxation environment), (b) DYi2 = 1 if municipality i belonged to a financial district in 2008 (i.e., outbreak of the global crisis), and (c) DYi3 = 1 if municipality i belonged in a financial district in the remaining years. Finally, we control for changes in the membership of the municipalities in financial districts. To this end we include dummy variables EXTit such that EXTit = 0 when a municipality i did not belong to a financial district in year t, but it belonged to one in year t − 1, and ENT Rit = 1 if municipality i became a member of a financial district in year t (while not belonging to one in year t − 1). Except for the above factors, additional independent variables are considered involving the past performance of the municipalities (one year lag) to control for performance persistence effects, the logarithm of population (POP) and its square (given that size effects are not linear as they are linked to economics and diseconomies of scale, see for example Myazaki, 2014); are used to account for the size of the municipalities (see among others Skidmore & Scorsone, 2011; and Drew & Dollery, 2014), and the GDP per capita (GDPC) (see among others Honadle, 2003) of the departments in which each municipality belongs, so as to control for the effect due to regional economic status. Using departmental level GDPC is rather important as it is directly linked to socioeconomic features as a “weak regional environment [is] expected to contribute to producing a high level of local fiscal stress, possibly resulting in financial destabilization, because it would affect both the LG’s revenue generating capacity and the level of local

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E. Galariotis et al. / European Journal of Operational Research 000 (2015) 1–17 Table 8 Regression results on performance changes. P Pt−1 ln POPt ( ln POPt )2 GDPCt After tax reform indicator (Aftertaxreform ) × ln POPt (Aftertaxreform ) × ( ln POPt )2 (Aftertaxreform ) × GDPCt Year 2002 Year 2003 Year 2004 Year 2005 Year 2006 Year 2007 Year 2008 Year 2009 Year 2010 Year 2011 Districts in 2010–2012 Districts in 2008 Districts in other years Exit district Enter district Constant

EC

0.348 6.989 −0.544 −0.055 0.789 −0.700 0.034 0.048 0.030 0.055 0.774 0.623 1.722 1.266 1.861 1.501 −0.071 −0.085 0.367 −0.769 −0.497 0.124 0.213 32.933

Observations F-test for fixed effects Breusch–Pagan test Adjusted R2

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.370) (0.088) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.676) (0.000) (0.000) 421,506 309.25 (0.000) 0 (1.000) 0.81

0.042 −1.288 0.122 0.046 −2.175 0.287 0.003 −0.011 −1.430 −0.876 −2.183 −1.353 −2.685 −0.973 −2.429 −0.968 −0.295 −1.787 1.015 0.666 −0.109 −0.313 0.101 −1.183

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.095) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.003) (0.000) (0.000) 421,506 12608.87 (0.000) 0 (1.000) 0.35

p-values in parentheses (based on robust standard errors clustered at the municipality level).

expenses” (Capalbo & Grossi, 2014, p. 110).4 To account for possible structural changes in the relationship between these characteristics of the municipalities after the tax reform, we also consider their interactions with a dummy variable (ATR), which is set equal to one for the years 2010–2012 and zero for the other years. The analytic forms of the two explanatory (fixed effects) regression models are expressed as follows:

Pit =

αiP + β P Pit−1 + γ1P ln POPit + γ2P (ln POPit )2 + γ3P GDPCt + δ1P AT R × ln POPit + δ2P AT R × (ln POPit ) + δ2P AT R × GDPCt 3 + λP AT R + ζtP Yt + θ jP DYi j + η1P EXTit 2

j=1

+ η2P ENT Rit + εitP ECit =

(6)

αiEC + β EC Pit−1 + γ1EC ln POPit + γ2EC (ln POPit )2 + γ3EC GDPCt + δ1EC AT R × ln POPit + δ2EC AT R × (ln POPit ) 3 + δ2EC AT R × GDPCt + λEC AT R + ζtEC Yt + 2

j=1

+ η1EC EXTit + η2EC ENT Rit + εitEC

θ jEC DYi j (7)

where Pit = Vpt (xti ) is the performance score of municipality i in year t, ECit is the performance change for municipality i in year t (compared to year t − 1) attributed to external conditions, εitP , εitEC represent the error terms, whereas αi , β , γ1 , γ2 , γ3 , δ1 , δ2 , δ3 , λ, ζt , θ j , η1 , η2 correspond to the parameters of the models (constant terms and coefficients of the independent variables). The estimation of the two regression models was done in a fixed effects setting. The fixed effects specification allows (unobserved/ 4 French departments have competency in social, education, cultural, territorial planning and economic development matters. Departments could grant direct and indirect subsidies, hence could affect local municipalities’ financial structure at different levels: operating section (grants), investing section (subsidies), and the level of local taxes for citizens.

omitted) time-invariant characteristics of the municipalities to be correlated with the dependent variables used in the models, thus controlling for one form of endogeneity that may result when such independent variables are missing from the specification of the models (Wooldridge, 2010). The choice of fixed effects over random effects was verified with the F-test for fixed effects and the Breusch–Pagan Lagrange multiplier test (in addition, the random effects model was found degenerate and equivalent to a pooled OLS model). The data were confirmed to be stationary with the Fisher-type unit-root test (Choi, 2001), which is based on augmented Dickey–Fuller tests and is suitable for unbalanced panel data (the modified inverse χ 2 was found 406.36 for model P defined by Eq. (6) and 597.43 for model EC defined by Eq. (7), both significant at the 1 percent level). The estimation results are summarized in Table 8, together with all model fitting tests and statistics.5 The obtained results provide evidence of performance persistence as past performance has a positive effect on the future peer assessment of the municipalities (the coefficient of Pt−1 is 0.348 in model P; p-value < 1 percent). Furthermore, municipalities with strong past performance are also less exposed to adverse external conditions, as indicated by the positive and statistically significant coefficient of in the EC model. On the other hand, the effect of the size of the municipalities is different in the two models. In terms of managerial competence, the positive coefficient of ln POPt in model P indicates that medium-size municipalities performed better, whereas very large size has a negative effect (as evident by the negative coefficient of the squared population variable). This is consistent with Myazaki (2014) who assumes a U-shaped curve with regard to population size when explaining the motivations of LGs to consolidate or merge. For the EC model to the contrary, very large municipalities exhibit higher resistance to adverse external conditions compared to small and medium-sized

5 The presented p-values are based on Rogers standard errors clustered at the municipality level, which are robust to heteroskedasticity and autocorrelation (Petersen, 2009; Wooldridge, 2010).

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ones. As a consequence, in terms of managerial performance, large municipalities are less efficient compared to smaller ones, but this is counterbalanced by their better resilience to external conditions (the larger the more resilient). Smaller municipalities exhibit better managerial performance, but the limited size of their taxable base does not give them protection against a deteriorating environment. However, the obtained results from the interaction of the population variables with the dummy corresponding to the period after the tax reform of 2010, indicate that the reform ameliorated some of the scale effects. For instance, in terms of model P, the negative coefficient for the interaction of the after the reform dummy with the population variable, implies that the performance differences between large and small municipalities were reduced after the reform. Similarly, for the EC model the positive coefficient for this interaction, shows that after the reform the previously mentioned higher vulnerability of smaller municipalities to adverse external conditions was partially improved. Similarly to size, GDP per capita is also found to contribute differently in the two models. On the one hand, municipalities in poorer departments perform better in terms of their peer assessment (due to better management that more than counterbalances their economic weaknesses), but on the other hand in terms of the resilience to the external environment, municipalities in wealthier departments perform significantly better. This last result, strengthens the arguments of Capalbo and Grossi, (2014) that higher regional GDP is associated with less social costs and at the same time with an increased ability to of LGs to raise revenues through their taxes (i.e. citizens can afford to pay more taxes and are wealthier, thus imposing lower social costs). But at the same time that wealthier departments help local municipalities in times of financial stress, this could also be a source of inefficiency that results to an increase in their spending. However, it is interesting to observe that the interactions of the GDP per capita variable with the tax reform dummy have coefficients with opposite signs compared to the ones of the GDP per capita variable itself. This implies that some of the abovementioned imbalances between municipalities located at departments (regions) of different wealth levels have been partially ameliorated after the reform. This result implies that the horizontal equalization program that the French central government initiated in 2010 (see Section 3.1) has achieved some positive results in the first years of its implementation. The time dummies all have positive coefficients in model P, which is in accordance with the results reported earlier (cf. Table 6 and Fig. 4). The coefficients are larger for the dummies corresponding to the period 2006–2009, whereas they are much lower for the years 2010–2012 when the new taxation reform was implemented. The dummy variable corresponding to the whole period after the tax reform is also found to have a significantly positive effect. Nevertheless, municipalities belonging to financial districts managed to cope better with this reform as it is evident by the positive coefficient (0.367, p-value < 1 percent) of the dummy variable used to indicate municipalities that belonged in financial districts during the period 2010– 2012. In the previous years (including the 2008 crisis), however, the membership in financial districts had a negative effect on the peer assessment of the municipalities. On the other hand, all annual dummy variables have significant negative coefficients in the external conditions model. Therefore, compared to the year 2001, even though the administration of the municipalities has been more successful over the next years, the environment has deteriorated. In other terms, municipalities dealt with a tougher environment by managing more efficiently the organization. The global crisis had a significant negative effect, but this does not appear to be much stronger than previous bad years, such as 2004 and 2006. It is clear, however, that the sub-period with the most significant adverse effect was 2010–2012 (also confirmed by the negative coefficient of the reform dummy), with the first year in the implementation of the taxation reform in 2011 being the most challenging one. So, the changes in the tax environment and budget balancing

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have strongly affected the performance of French municipalities: the cancellation of the business tax in 2003, the transfer of competencies from the central government to LGs (“act II” of decentralization) in 2005 and the 2010 finance law reform. The explanation could lie in the way the adopted equalization program works. Since 2003, the State has to financially compensate any transfer of competencies. The compensation scheme is calculated on the cost of services bared by the central government the year before the competencies are transferred, and are not adjusted after. The same applies in case of a taxation reform. Therefore, municipalities could face higher costs or less fiscal resources in the near future, especially in case of transferring social duties and in a slump economy. These results are in line with the findings of da Cruz and Marques (2014) that the performance of LGs is sensitive to exogenous factors. More specifically, our results support that decentralization affects the municipalities’ financial health in terms of their resilience to the external environment. This has important implications for central government policy makers, and provides them a measure of the impact of such policies. According to Pagano and Hoene (2003), the financial crisis comes with the creation of a number of financial issues leading to difficult situations for LGs’ budgets: (1) a decrease in local fiscal government revenues and central government transfers (decline or delay); (2) the increasing expenditures to dampen the impact of the slowdown of the activity and the subsequent increase in unemployment and social welfare subsidies, associated with a decrease in revenues, lead LGs to drastic reductions of their operating expenses; (3) an increase in the difficulty to obtain loans and consequently the cost of borrowing, especially for local municipalities that experience difficulties. As a result, recession comes with a reduction in state subsidies and nonproperty tax revenues. The crisis impact depends on the institutional context, depending on the fiscal sources of revenues and the level of reliance on state transfers, in countries where specialized semipublic financial institutions provide the major part of borrowings instead of the market (France belongs to this category). The financial crisis should come with a sharp restriction in the supply of credit because of risk constraints for large financial institutions and the failure of specialized banks (like Dexia). Our results indicate that the year 2008 had a significant impact (compared to other years), related to a decrease/stagnation in central governments transfers (as is evident by the steady increase in ratio x2 up to 2008), as well as a stagnation in staff costs and investment expenditures (cf. Table 4). Nevertheless, similarly to the peer assessment model P, municipalities belonging to financial districts managed to cope significantly better with the challenges that came about as a result to the taxation reform and also the global crisis of 2008. Contrary to the Drew and Dollery (2014) municipalities can benefit from mergers and amalgamations by being more able to cope with changes in the environment. The overall effect due to the membership to financial districts is positive and this is further evident through the two variables related to municipalities exiting and entering a financial district, and support the increasing trend of French municipalities to cooperate in financial districts. In model P, both variables have a positive coefficient, but only the one associated with municipalities that enter a financial district is statistically significant. On the other hand, the coefficients in the EC model imply that municipalities that exit a financial district are significantly more vulnerable to adverse external conditions, whereas municipalities that enter a financial district appear to be significantly more protected. The evolution of the coefficients of the dummy variables that take into account the membership of the municipalities in financial districts over different time periods, further highlight that the main benefit of belonging to a financial district involves the potential to better cope with a deteriorating environment (especially in times of economic downturn), alongside better management efficiency (cf. Fig. 4), particularly over the period 2010–2012 when the new tax reform was implemented.

Please cite this article as: E. Galariotis et al., A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France, European Journal of Operational Research (2015), http://dx.doi.org/10.1016/j.ejor.2015.06.042

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6. Conclusions In this study we introduced a novel approach for assessing the financial health of LGs, combining financial ratios that can be calculated from publicly available data and easily replicated. The proposed multi-attribute model can be used by policymakers and local public authorities as a benchmarking tool that enables the evaluation of the LGs financial performance in a peer assessment context and its monitoring over time. Similar performance assessment problems where the analysis of panel data is of interest arise in many areas in management, economics/finance, and engineering. For instance, the proposed MCDA methodology can be used at the firm level to assess the performance of different operations inside a firm over time, perform comparisons against competitors, and examine the relation of the performance assessment results to internal and external factors. Other areas include a variety of services in the public and private sector (e.g., healthcare, education, tourism, public safety, etc.), energy management and environmental assessments, as well as performance evaluations at regional and country levels. In all such areas, useful insights can be gained by examining the evaluation results over a different time periods in order to identify dynamic trends in the results. The methodology introduced in this paper, shows how MCDA models can be adapted in such a setting. The methodology further allows the distinction between the effect due to managerial competence and the one due to the external environment. This aspect of the proposed methodology extends existing MCDA evaluation schemes, which have been mostly focused on static assessments, without explicitly considering performance changes over time and the disaggregation of time trends in terms of internal factors related to the alternatives under consideration (managerial competence) and the external conditions. The model was employed to assess and analyze the financial performance of French municipalities, over the period 2000–2012, during which a number of reforms and policy interventions changed the context in which French LGs operate. These included tax reforms, transfer of competencies, and a trend toward economic amalgamations. Even though the proposed modeling approach is more involved compared to other simpler scorecard models that have been presented in the literature on the financial health of LGs, it can still be easily used as all calculations can be done in a straightforward manner even with a simple spreadsheet.6 Together with the multiattribute assessment, a second stage regression analysis was employed to get further insights into how these changes and the recent global crisis affected the French municipalities. The results lead to a number of useful conclusions with policy implications regarding the reforms made in the French context. First, it is clear that the changes in taxation together with the decentralization process have been major steps, but they had a negative impact of the financial health of French municipalities. The decentralization process has given municipalities more competencies, which increased their expenses (staff and other costs) without more resources, thus leading municipalities to gradually increase tax rates. Horizontal equalization, which has been implemented recently (2011) could be a means to address the existing difficulties, if intergovernmental financing is designed with clear targets set on the basis of the municipalities’ characteristics. For example, our results show that municipalities in the wealthier departments are less efficient in terms of their managerial competence (but more resilient to adverse changes in the external environment), whereas medium-sized municipalities perform better. Furthermore, the economic amalgamations and the cooperation between municipalities organized into financial districts helped them face more successfully the changing environment such as the challenges of the decentralization process, 6

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A simple example spreadsheet is available at: http://www.fel.tuc.gr/MCDALG.xlsx.

particularly after the taxation reform was enforced (2010–2012) but also during the global economic crisis. The crisis itself had a negative effect, but this was weaker compared to the taxation reform. Such policy conclusions could be of interest to other countries in Europe and elsewhere, as such reforms (taxation, mergers, and amalgamations) are not uniquely linked to France. Therefore, the results of this study could serve as the basis for further empirical investigation in other countries as well as through cross-country analyses. Of course, the analysis in this study is also subject to limitations. Firstly, the proposed MCDA financial performance evaluation model needs further analytical and qualitative validation in cooperation with policymakers and the administrations of LGs, using additional data for the internal operation of LGs, their socio-economic characteristics, and the macroeconomic environment. The ability of the proposed approach to act as an early warning system of financial problems and crises in LGs, should also be further analyzed using data from multiple countries and regions, focusing specifically on financially distressed LGs. Unfortunately, such information on the financial distress status of the French LGs was not available in this study. Additionally, the financial performance evaluation results could be considered in an integrated performance context that considers both the financial aspects of LGs operation as well as qualitative elements involving the services they provide to citizens. On the methodological side, the MCDA methodology introduced in this paper could be extended to take advantage of recent developments on robust MCDA methods, particularly related to robust ordinal regression (ROR) techniques and simulation approaches (Greco, ´ Słowinski, Figueira, & Mousseau, 2010; Lahdelma & Salminen, 2001). ROR could facilitate the construction and revision of the multi-criteria financial performance index for LGs, by providing the means to infer the parameters of the model (e.g., the trade-off constants) through information provided by policymakers and the administrations of LGs. This could eliminate some ambiguity that naturally exists when defining the evaluation parameters from the data (e.g., as in this study) without additional expert domain knowledge. On the other hand, simulation approaches provide additional capabilities for examining the robustness and sensitivity of the results to changes in the parameters of the financial performance evaluation model and the available data. It would also be interesting to examine extensions of the proposed modeling approach for performing multi-criteria evaluations in panel data settings, with different types of MCDA models, other than the additive scheme used in this study. These may include, for example, outranking models and rule-based techniques, which provide the capability of constructing non-compensatory evaluation schemes. However, in implementing such alternative approaches (ROR, simulation techniques, sensitivity analyses, non-compensatory models, etc.), particular care should be given to the development of computational procedures that scale up well with the dimensionality of the data, as the volume of information is much larger than the one usually assumed by existing MCDA methods in other evaluation settings. Finally, the connections of the proposed MCDA benchmarking framework with efficiency and productivity analysis techniques (e.g., data envelopment analysis and stochastic frontier methods) could also be explored to further analyze the ability of the LGs to provide quality services to citizens by exploiting their financial and productive resources (personnel and infrastructure) in an efficient manner. References Afonso, A., & Fernandes, S. (2006). Measuring local government spending efficiency: Evidence for the Lisbon region. Regional Studies, 40, 39–53. Ashworth, J., Galli, E., & Padovano, F. (2013). Decentralization as a constraint to Leviathan: A panel cointegration analysis. Public Choice, 156(3/4), 491–516. Beckett-Camarata, J. (2004). Identifying and coping with fiscal emergencies in Ohio local governments. International Journal of Public Administration, 27(8–9), 615–630. Borge, L.-E., Brueckner, J. K., & Rattso, J. (2014). Partial fiscal decentralization and demand responsiveness of the local public sector: Theory and evidence from Norway. Journal of Urban Economics, 80, 153–163.

Please cite this article as: E. Galariotis et al., A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France, European Journal of Operational Research (2015), http://dx.doi.org/10.1016/j.ejor.2015.06.042

JID: EOR

ARTICLE IN PRESS E. Galariotis et al. / European Journal of Operational Research 000 (2015) 1–17

Boustan, L. P. (2013). Local public goods and the demand for high-income municipalities. Journal of Urban Economics, 76, 71–82. Brown, K. W. (1993). The 10-point test of financial condition: Toward an easy-to-use assessment tool for smaller cities. Government Finance Review, 9(6), 21–26. Bumgarner, M., Martinez-Vazquez, J., & Sjoquist, D. L. (1991). Municipal capital maintenance and fiscal distress. Review of Economics and Statistics, 73(1), 33–39. Bunce, H. L., & Neal, S. G. (1984). Trends in city conditions during the 1970’s: A survey of demographic and socioeconomic changes. Publius, 14(2), 7–19. Capalbo, E., & Grossi„ G. (2014). Assessing the influence of socioeconomic drivers on Italian municipal financial destabilization. Public Money & Management, 34(2), 107–114. Carmeli, A. (2008). The fiscal distress of local governments in Israel: Sources and coping strategies. Administration & Society, 39(8), 984–1007. Carmeli, A., & Cohen, A. (2001). The financial crisis of the local authorities in Israel: A resource-based analysis. Public Administration, 79(4), 893–913. Chang, D.-S., Liu, W., & Yeh, L.-T. (2013). Incorporating the learning effect into data envelopment analysis to measure MSW recycling performance. European Journal of Operational Research, 229(1), 496–504. Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249–272. Cohen, S., Doumpos, M., Neofytou, E., & Zopounidis, C. (2012). Assessing financial distress where bankruptcy is not an option: An alternative approach for local municipalities. European Journal of Operational Research, 218(1), 270–279. Cruz, N. F., & Marques, R. C. (2014). Revisiting the determinants of local government performance. Omega, 44, 91–103. da Cruz, N. F., & Marques, R. C. (2014). Revisiting the determinants of local government performance. Omega, 44, 91–103. Doumpos, M., & Cohen, S. (2014). Applying data envelopment analysis on accounting data to assess and optimize the efficiency of Greek local governments. Omega, 46, 74–85. Drew, J., & Dollery, B. (2014). The impact of metropolitan amalgamations in Sydney on municipal financial sustainability. Public Money and Management, 34(4), 281–288. Garcia-Sanchez, I. M., Cuadrado-Ballesteros, B., Frias-Aceituno, J. V., & Mordan, N. (2012). A new predictor of local financial distress. International Journal of Public Administration, 35(11), 739–748. Gomez, J., Rios Insua, D., Lavin, J. M., & Alfaro, M. (2013). On deciding how to decide: Designing participatory budget processes. European Journal of Operational Research, 229(3), 743–750. ´ Greco, S., Słowinski, R., Figueira, J. R., & Mousseau, V. (2010). Robust ordinal regression. In M. Ehrgott, J. R. Figueira, & S. Greco (Eds.), Trends in multiple criteria decision analysis (pp. 241–283). Berlin: Springer. Groves, M., Godsey, W., & Shulman, M. (2003). Evaluating Financial Condition: A Handbook of Local Government. Washington, D.C.: ICMA. Hendrick, R. (1989). Top-down budgeting, fiscal stress and budgeting theory. American Review of Public Administration, 19(1), 29–48. Hendrick, R. (2004). Assessing and measuring the fiscal health of local governments: Focus on Chicago suburban municipalities. Urban Affairs Review, 40, 78–114. Honadle, B. W. (2003). The states’ role in U.S. local government financial crises. International Journal of Public Administration, 26(13), 1431–1472. Jones, S., & Walker, R. G. (2007). Explanators of local government distress. Abacus, 43(3), 396–418. Kloha, P., Weissert, C. S., & Kleine, R. (2005). Developing and testing a composite model to predict local fiscal distress. Public Administration Review, 65(3), 313–323.

[m5G;July 7, 2015;20:13] 17

Kimhi, O. (2008). Reviving cities: Legal remedies to municipal financial crises. Boston University Law Review, 88, 663–684. Lahdelma, R., & Salminen, P. (2001). SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research, 49(3), 444–454. Lin, M.-I., Lee, Y.-D., & Ho, T.-N. (2011). Applying integrated DEA/AHP to evaluate the economic performance of local governments in China. European Journal of Operational Research, 209(2), 129–140. Maher, C. S., & Nollenberger, K. (2009). Revisiting Kenneth Brown’s 10-point test. Government Finance Review, 25(5), 61–66. Miyazaki, T. (2014). Municipal consolidations and local government behavior: Evidence from Japanese voting data on merger referenda. Economics of Governance, 15(4), 387–410. Morgan, D. R., & England, R. E. (1983). Explaining fiscal stress among large U.S. cities: Toward an integrative model. Policy Studies Review, 3(1), 73–78. Murray, D., & Dollery, B. (2005). Local council performance monitoring in New South Wales: Are ‘at risk’ councils really at risk? Economic Papers, 24(4), 332– 345. Pagano, M. A., & Hoene, W. C. (2003). Cities confront tough choices as fiscal conditions decline: Economic recovery threatened. Research Briefs on America’s Cities, 2, 1–8. Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. The Review of Financial Studies, 22(1), 435–480. Pina, V., Torres, L., & Yetano, A. (2009). Accrual accounting in EU local governments: One method, several approaches. European Accounting Review, 18(4), 765– 807. Reingewertz, Y. (2012). Do municipal amalgamations work? Evidence from municipalities in Israel. Journal of Urban Economics, 72(2/3), 240–251. Rogge, N., & De Jaeger, S. (2013). Measuring and explaining the cost efficiency of municipal solid waste collection and processing services. Omega, 41(4), 653– 664. Skidmore, M., & Scorsone, E. (2011). Causes and consequences of fiscal stress in Michigan cities. Regional Science and Urban Economics, 41(4), 360–371. Stonecash, J., & McAfee, P. (1981). Ambiguities and limits of fiscal strain indicators. Policy Studies Journal, 10(4), 379–395. lo Storto, C. (2013). Evaluating technical efficiency of Italian major municipalities: A data envelopment analysis model. Procedia – Social and Behavioral Sciences, 81, 346–350. Tyrefors Hinnerich, B. (2009). Do merging local governments free ride on their counterparts when facing boundary reform? Journal of Public Economics, 93(5/6), 721– 728. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge, Massachusetts: MIT Press. Zafra-Gómez, J. L., Lopez-Hernández, A. M., & Hernández-Bastida, A. (2009a). Evaluating financial performance in local government: Maximizing the benchmarking value. International Review of Administrative Sciences, 75(1), 151–167. Zafra-Gómez, J. L., Lopez-Hernández, A. M., & Hernández-Bastida, A. (2009b). Developing an alert system for local governments in financial crisis. Public Money & Management, 29(3), 175–181. Zafra-Gómez, J. L., Lopez-Hernández, A. M., & Hernández-Bastida, A. (2009c). Developing a model to measure financial condition in local government: Evaluating service quality and minimizing the effects of the socioeconomic environment: An application to Spanish municipalities. American Review of Public Administration, 39(4), 425–449. Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill.

Please cite this article as: E. Galariotis et al., A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France, European Journal of Operational Research (2015), http://dx.doi.org/10.1016/j.ejor.2015.06.042