Brusca 2015 Drivers for the Financial Condition of Local Government-A Comparative Study Between Italy and Spain

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LEX LOCALIS - JOURNAL OF LOCAL SELF-GOVERNMENT

Vol. 13, No. 2, pp. 161 - 184, April 2015

Drivers for the Financial Condition of Local

Government: A Comparative Study Between

Italy and Spain

ISABEL BRUSCA, FRANCESCA MANES R OSSI & NATALIA AVERSANO 2

ABSTRACT This paper aims to analyse the influence of socio-

economic, political and financial factors on the financial conditionof Italian and Spanish local governments in a comparative approach.

The research is also aimed at understanding to what extent a modelfor the analysis of the financial condition can be generalized to

different contexts. We assume that the financial condition is a

multidimensional concept, with the results highlighting that while in

Spain there is a high correlation between the long term financial and

short term economic situations and an indicator can combine both

dimensions, in Italy both dimensions are differentiated. There arealso differences in the drivers of financial sustainability in both

countries.

K EYWORDS: • financial condition • financial sustainability •

financial health • financial indicators • local government • Spain •

Italy

CORRESPONDENCE ADDRESS: Isabel Brusca, University of Zaragoza, Department ofAccounting and Finance, Gran Ví, 50005 Zaragoza, Spain, email: [email protected] Manes Rossi, University of Salerno, Department of Management and

Information Technology, Via Giovanni Paolo II, 84084 Fisciano SA, Italy,[email protected]. Natalia Aversano, University of Salerno, Department ofManagement and Information Technology, Via Giovanni Paolo II, 84084 Fisciano SA,

Italy, [email protected].

DOI 10.4335/13.2.161-184(2015)

ISSN 1581-5374 Print/1855-363X Online © 2015 Lex localis (Maribor, Graz, Trieste, Split)Available online at http://journal.lex-localis.info.



162 LEX LOCALIS - JOURNAL OF LOCAL SELF-GOVERNMENT

I. Brusca, F. Manes Rossi & N. Aversano: Drivers for the Financial Condition of Local Government: A Comparative Study Between Italy and Spain

1 Introduction

One of the effects of the recent financial crisis has been to force local governments

(LG) to deal with financial difficulties. In fact, while the resources available have

decreased, citizens continue to ask for an increasing amount and better quality of

public services.

Scholars have discussed at length, the factors that can determine or contribute tocreating financial distress or financial crisis (Groves and Valente, 2003, Klein et

al., 2003, Carmeli, 2007, Jones and Walkers, 2007) and – especially in recent

years under the incumbent financial crisis – many models have been proposed

both by practitioners and standard setters (CICA, 1997, ICMA, 2003; UK Audit

Commission, 2007) as well as scholars (Zafra-Gómez et al., 2009; Cohen et al.,2012, Manes Rossi, 2011; García-Sánchez et al., 2012) to prevent financial

distress. In addition, the IPSASB recently released a Recommended Practice

Guideline (RGS, 2013) “Reporting on the Long-Term Sustainability of an Entity’s

Finances”, highlighting the need to monitor the ability of public ent ities to sustain

their projects in the long-term.

This study aims to discuss the interesting research theme in a comparative

approach, by analysing the situation of all the Italian and Spanish local

governments with more than 60.000 inhabitants and relating their financial

conditions to some economic and social factors. Italy and Spain have been chosen

because they are two Southern European countries characterised by a Napoleonic

administrative tradition (Peter, 2008). In addition, their financial situation iscontrolled by the European Union.

The research is also aimed at understanding to what extent the use of financial

indicators to monitor the financial health of a local government can be generalized

to different contexts and whether a model can have or not an international

application to support both managers and politicians in assuming timely decisionsto avoid financial distress.

A set of indicators has been identified in accordance with previous literature and

its relevance has been evaluated with principal component analysis. The results

highlight that in Spain there is a high correlation between the long term and shortterm economic situations, with an indicator combining both dimensions. However,

in Italy two different indicators are necessary to measure both dimensions.

Furthermore, the number of inhabitants and density of the population are two

drivers for fiscal stress in Italy, while in Spain only the number of inhabitants is

significant.

The paper is organized in five sections, excluding the present introduction. First,

the literature review discusses the definitions of financial condition elaborated by





163

scholars as well as the environmental, organizational and financial determinants

that can affect it. The third section describes the Italian and Spanish context, along

with the tools that have already been adopted to prevent financial distress. The

fourth section defines the objectives of the empirical research as well as theresearch design, while the fifth presents the results collected and discusses them in

accordance with the objectives. In the conclusion, the results achieved are

summarized, along with the limitations of the study and any possible future

developments.

2 The analysis of financial condition in local goverments: A literature

review

Since the first financial crisis experienced by large cities in the USA in the 1970-

80s, like New York and Cleveland, scholars have discussed the conditions andeffects of the so-called financial distress or financial crisis or fiscal strain(Honadle et al., 2004). As reported by Dollery and Crase (2006), there are

numerous definitions but no consensus about the concept of financial health and

financial condition. Rivenbark et al. (2010) revise the different concepts used and

define financial condition as “a local government’s ability to meet its ongoing

financial, service and capital obligations based on the status of resource flow and

stock as interpreted from annual financial statements”.

In some countries, the term ‘financial sustainability’ has become fashionable and

even several standard setters seem to prefer this expression. ThePriceWaterhouseCoopers (2006, 96) report states that “the financial sustainability

of a council is determined by its ability to manage expected financial requirementsand financial risks and shocks over the long term without the use of disruptive

revenue or expenditure measures”. Along the same lines, the RGS of the IPSASB

(2013) highlights the need to monitor the ability of public entities to sustain their

projects in the long-term. The RGS depicts three intertwined dimensions of long-

term financial sustainability: service dimension, revenue dimension and debit

dimension.

Assuming that there is a large variety of nuance inside the concept of financialsustainability, we consider that this can be analysed with reference to the financial

condition or financial health, which represents the ability to comply with current

and future obligations through a proper inflows by tax, transfers and services as

well as to sustain a certain service level, coherent with the citizens’ needs(Honadle et al., 2004). According to Groves and Valente (2003), from an internal

perspective, the financial condition can be synthesised in cash, budgetary, long run

and service solvency of the entity.

Several models have been developed with the aim of assessing the financial

condition, while preventing financial distress. The first group focuses on





analysing the financial equilibrium of a LG every year in order to assess if a

condition of distress occurred or not (i.e. Jones and Walkers, 2007). The secondgroup, which is larger, aims at forecasting the future ability of the LG in

maintaining a financial equilibrium (Kleine et al., 2003; Zafra-Gómez et al., 2009;

Cohen et al., 2012; García-Sánchez et al., 2012; Manes Rossi et al., 2012). These

models adopt a large variety of techniques, ranging from basic approaches such as

accounting information and financial reporting analysis (e.g. Kleine et al., 2003;

Manes Rossi, 2011), to more sophisticated statistical modelling approaches, evenwith the aim of discriminating a financial healthy LG from a distressed one (e.g.

Zafra-Gómez et al., 2009; Cohen et al., 2012; García-Sánchez et al., 2012; Manes

Rossi et al., 2012).

In addition, standard setters and regulators all over the word have developedseveral models to assess financial condition. It is well-known that central

governments need to monitor the financial health of a LG. In several countries, the

control agencies have defined some predictive models. For example, in France, the

Direction Générale des Collectivités Locales (1997) has provided an alert system

composed of 7 indicators, while in Australia and the UK, models have been

prepared by control agencies to discriminate a healthy LG from non-healthy ones.Similarly, in USA, the 10-point scale proposed by Brown (1993) or the model

proposed by the ICMA as developed by Groves and Valente (2003) have been

largely adopted (Zafra-Gómez et al., 2009).

In the European context, some studies focused on a LG credit ratings and

solvability assessment (Manes Rossi, 2011) for the analysis of the financialcondition. In fact, financial equilibrium is an essential prerequisite in order to gain

access to the capital market, by obtaining a good rating (Caperchione and

Salvatori, 2012).

Simultaneously, there is an increasing amount of literature on identifying the

determinants that can provoke or accelerate some detrimental financial conditions.Some scholars consider environment factors, such as socioeconomic forces

(Peterson 1976; Capalbo and Grossi, 2014), as the principal external determinants

that can affect the financial situation of a LG, assuming that they affect both the

citizens’ need and demand of public services as well as productive costs (Andrews

et al., 2005; Boyne et al., 2001). Empirical research has tried to analyse the effectsof population, density of the population or gross domestic product (Bahl and

Duncombe, 1993; Zafra-Gómez et al., 2009; Guillamón et al., 2011; García-

Sánchez et al., 2012; Vicente et. al., 2013).

Political factors have also been another issue considered in current literature as

influencing the financial condition of local governments, considering thatconservative parties have a lower level of debt (Kiewiet and Szalky, 1996),

although many studies have not found a relationship between political ideologies





165

and the level of debt (Vicente et. al., 2013). Furthermore, the incidence of political

budget cycles on debt has also been studied (Benito et al. , 2012).

In addition, organizational factors such as human resources and capabilities thatthe organization has acquired or developed, have been considered as pivotal for

the LG to have the ability to cope with the need of changes, which in turn support

the possibility of maintaining a financial equilibrium (Carmeli and Cohen, 2001;

Carmeli, 2007). Considering current literature, the study aims to contribute to this

field by creating a model for the analysis of the financial condition of Italian andSpanish LG, that combines all the above dimensions and tests to what extent

environment factors can influence the financial condition.

3 The Italian and Spanish scenario

3.1 The control of financial health in Italy

Italy belongs to those European countries characterized by a bureaucratic

approach in Public administration, where any reforms are mainly implemented

through the law and with a highly legalistic governance culture (Ongaro and

Vallotti, 2008). Italian Local Governments (ILG) manage most of the public

services and in the last decades, there has been an increasing use of partnershipswith other public or private operators to run services like transportation, waste,

water as well as many others. ILG obtain resources only partially by transfers and

grants from Central and Regional governments, while they have the power to bothincrease local taxes as well as manage their own assets.

The law, which also provides a complete set of rules for accounting, describes the

conditions of financial health and sets out ten parameters as indicators of distress

(Manes Rossi, 2011). According to D. Lgs. 267 (art.244) financial distress occurs

when the entity, municipality or province is no longer able to perform its essential

functions and deliver due services, or when it is no longer able to meet debt with

third parties through the ordinary means of restoring the fiscal balance or debtinstrument with an off balance-sheet. The accounting systems requires both the

preparation of a budget and reporting as well as a budgetary modified cash- andcommitment-based accounting systems for both, even if the reporting is integrated

by financial and income statements prepared in accordance with accrual principles

(Nasi and Steccolini, 2008). Three different institutions play the role of watchdogs

regarding the financial condition of ILG: the Ministry of Interior, the NationalAudit Court and the State General Accounting Department (which is a part of the

Ministry of Economy). In the case financial distress is declared by the mayor, an

Extraordinary Board is appointed by the central government, upon the suggestion

of the Ministry of the Interior, with the duty to prepare and manage a plan to

extinguish all the expired debts. Meanwhile, the city council has to reorganize the

whole activity, with the aim of removing what has provoked the financial





disequilibrium. The declaration of distress has a significant impact on the citizens:

local-tax increases, services are reduced to the mere essential and no personnelcan be hired until the financial equilibrium is restored.

In order to avoid this condition, since 2012 a new regime has been introduced –

the so-called financial re-equilibrium procedure – where the city council, with the

aim of facing an incumbent distress, prepares a plan (no longer than 10 years) to

cope with the financial equilibrium condition.

3.2 The control of financial sustainability in Spain

Spanish local governments have increased the services delivered since the

decentralisation process in force since the 1990s. The increase in services and theimportant expenditures in infrastructures and equipment have led to a continuous

increase in the level of debt of governments. With the financial crisis, the situation

has been aggravated, with the control of public expenditure and debt having

become one of the main objectives for public policies.

In this framework, Spanish Central Governments have always tried to control theincrease of debt of local governments and different laws limit the use of debt for

financing local government expenditures.

These controls have been highlighted by the Spanish financial crisis and different

initiatives have been carried out under the Stability and Growth Pact of the

Member States of the European Union.

With the aim of fulfilling the Treaty, the Spanish Law of Budgetary Stability and

Financial sustainability prohibits public entities from obtaining deficit, increasing

the expenditure over the increase in the Gross Domestic Product (GDP) and limits

public sector administrations debt to 60% of the GDP (44% for Central

Government, 13% Autonomous Communities or States and 3% for localgovernment). Those entities that do not fulfill the objectives of budgetary stability

must prepare an economic-financial plan that within a year will allow the entity to

fulfill these objectives. The entities that do not achieve the limit of debt must also

elaborate a plan to balance the situation.

In this framework, financial sustainability studies are particularly timely and

relevant both in Spain and Italy, where the control of financial health is currently a

priority of the Government.





167

4 Research design

4.1 Objectives and Methodology

In order to carry out the analysis, all the Italian and Spanish Local Governments

with more than 60,000 inhabitants were selected.

The analysis has two main objectives:

a) The construction of a model for the analysis of the financial condition thatidentifies what dimensions must be considered. Several studies (i.e. Groves and

Valente, 2003; Klein et al., 2003; Jones and Walkers, 2007; Zafra-Gómez et al.,

2009; Cohen et al., 2012; García-Sánchez et al., 2012; Manes Rossi et al., 2012)

have proposed a methodology for analysing the financial condition of a local

government but it does not coincide with what indicators must be used and how itmust be propounded in order to obtain a score determining the financial condition ofa local government. Furthermore, current literature argues that different indicators

can be suitable for different legal and economic environments (Carmeli, 2007) but

there is no empirical research that proves it.

In this framework, we have tried to obtain a model for the financial analysis of local

governments using both financial and economic indicators. Considering that theanalysis of the financial health of a government requires taking into account

different perspectives, we have used a principal component analysis to identify what

the most relevant indicators are. The variables included in the analysis are based onthe level of debt of the government, along with variables representing the economic

capacity of the entity.

The analysis was carried out in both Spain and Italy, with the aim of not only

comparing the two countries but also to study to what extent the same determinants

can affect the financial condition of Italian and Spanish LG. As previously

suggested by some scholars, the heterogeneity of legal structures and socioeconomic

framework must be controlled (Blejer and Cheasty, 1991). Thus, we intend to verifyto what extent this can influence the analysis of financial condition and whether it is

possible to apply the model on an international level.

The methodology used for this part is principal component analysis (PCA). This

method allows to reduce a data set based on observations of variables into a set of

values of new variables called principal components (PC), characterized by the factthat they are interrelated. The components are built as a linear combination of the

original variables and collect most of the information contained in the original

variables (Lattin et al. 2003). The first PC explains the maximum percentage of the

total variance of the data, the second the maximum percentage of the remaining

variance, and so on.





b) The identification of factors and drivers that can determine or at least affect the

financial condition. Most of the previous studies (Carmeli, 2007; Zafra-Gómez etal., 2009; Manes Rossi et al. 2012; Vicente et. al., 2013) focus on the analysis of

debt per capita to analyse what factors determine debt level. Our study focuses on

the financial indicators identified in the first part of the analysis.

The objective is to understand what variables affect or can be used to predict a

detrimental change of the financial condition in local governments in Italy andSpain. We use a linear regression approach to model the relationship between the

financial condition and the explanatory variables.

First, we carried out a simple linear regression analysis with the aim of analysing

whether there is or not the influence of each variable on the financial condition. Thedependent variable is the financial indicator obtained and the independent variables

are the different dimensions. We then considered the dimensions that affect the local

fiscal health according to Hendrick (2004) and grouped the variables into four

classes: size, socio-economic, political and financial structure.

FS= f (size, socioeconomic, political and financial factors).

Simple linear regression analysis allows to select the variables that are significant in

order to construct a model to determine the financial condition that includes the

independent variables through multiple linear regressions. We have used the

stepwise model in which the choice of predictive variables is carried out by an

automatic procedure.

4.2 Sample and Variables

The sample used for the analysis includes all the Italian and Spanish local

governments with more than 60,000 inhabitants: 102 Italian and 123 Spanish local

governments. In order to analyse the financial condition, the budgetary data for2012 and 2011 were collected, along with other information about the

socioeconomic and political variables used in the analysis, which refer to 2012.

We focus only on the largest cities since they have a high percentage of the total

debt of the local government of both countries, while the debt of the smaller citieshas relatively less importance and is also lower. Bigger cities also have a higher

percentage of the population in both countries, thus representing a higher percentage

of local government expenditure (Hulten and Peterson, 1984; Rivers and Yates,

1997; Vicente et. al., 2013). Furthermore, the data are more reliable for the bigger

entities and sometimes are not available for smaller entities.

Table 1 shows the variables used for the principal component analysis and how they

are defined as well as the descriptive statistics for Italy and Spain.





169

Table 1: Variables for the analysis of Principal Components

The variables expressing the long term situation, in accordance with some previousstudies (Carmeli, 2007; García-Sánchez et al., 2012; Vicente et. al., 2013) focus on

the level of debt and the capacity of the entity to repay that debt.

Whereas, the variables expressing the short term stability focus on the operating

revenues surplus (Brown, 1993; Cohen et al., 2012; Manes Rossi et al. 2012;

Vicente et. al., 2013).

VARIABLE Definition Country

Mean

Std.

Deviation

L O N

G T E R M F

I N A N C I A L

S I T U A T I O N

Debt per capita Total debt/Number ofinhabitants

ItalySpain

719.24765.23

471.60516.27

Debt over GDP Total debt/GDP Italy

Spain

4.65

3.71

3.70

2.82

Debt overOperative

revenues

Total debt/operativerevenues

ItalySpain

74.5985.30

45.7756.42

Variation of debtin the year per

capita

Debt 2012-debt2011/Number of

inhabitants

ItalySpain

-58.18210.03

102.41357.41

Debt service Payments fordebt+Payments for

interest of

debts/operatingrevenues

ItalySpain

10.6510.92

7.896.60

S H O R T T E R M S

T A B I L I T Y

Operative Surplus per capita

(Operative revenues-Operative

expenditures)/number

of inhabitants.

ItalySpain

115.00171.262

87.98423.81

Operative Surplus

over operative

revenues

(Operative revenues-

Operative

expenditures)/operative revenues.

Italy

Spain

9.74

10.55

5.08

12.29

Operative

expenditurecoverage

Operative

expenditure/operativerevenues

Italy

Spain

90.26

89.45

5.08

12.29

Debt Payments

coverage

(Operative revenues-

operativeexpenditure)/payments for

debt

Italy

Spain

2.25

22.08

2.56

222.34





The mean of the debt per capita of local governments is greater for Spanish LG than

for Italian LG, where the standard deviation is also higher. The percentage of debtover operative revenues is also higher for Spain, with it reaching 85.30. However,

the debt represents a higher percentage of GDP in Italy than in Spain. During 2012,

the data show that while in Italy the mean of the debt per capita decreased to €58.18

per capita, in Spain it increased to €210.03.

Using the indicators constructed with the principal component analysis as dependentvariables, in line with some previous studies, we tried to analyse the effect of size

(García-Sánchez et al., 2012; Vicente et. al., 2013), socio-economic (Zafra-Gómez

et al., 2009; Vicente et. al., 2013), financial (Skidmore and Scorsone, 2011; Zafra-

Gómez et al., 2009) and political characteristics (Vicente et. al., 2013). Furthermore,

we carried out the same analysis using the variable debt per capita, studied innumerous papers as a determinant of financial health in local government (Carmeli,

2007; Zafra-Gómez et al., 2009; Guillamón et al., 2011; Manes Rossi et al. 2012;

Vicente et. al., 2013).

Table 2 shows the independent variables used for the analysis of the factors that

influence the financial condition as well as the descriptive statistics for Italy andSpain.

Table 2: Independent Variables

VARIABLE Definition Country MeanStd.

Deviation

S I Z E

Population (INH.)

Number of

inhabitantsItalySpain

176.023190.464

306.928332.177

Density of population(DP)

Number ofinhabitants/Km2

ItalySpain

1,486.5

42,696.7

26

1,642.733,323.74

S O C I O E C O N O M

I C

Unemployment (U)% of unemployment

people.

Italy

Spain

11.83

15.29

5.40

3.79

Gross Domestic Product

(GDP)

Total GrossDomestic Product/n.inhabitants.

Italy

Spain

16,605.19

22,166.60

3,870.51

5,561.65

P O L Í T I C A L Political party in

government (PP)

Conservative party=0Progressive party=1

Italy

Spain

0.81

0.33

0.39

0.470

Gender of theMayor(G)

Man=0Woman=1

ItalySpain

0.060.23

0.240.421





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F I N A N C I A L S T R U C T U R E

Capital

expenditure(KE)

Total capitalexpenditure/n.

inhabitants

Italy

Spain

187.25

84.76

230.58

61.60

Personalexpenditure(PE)

Personalexpenditure/n.

operative revenues

ItalySpain

27.5836.44

8.426.80

Fiscal pressure(FP)Fiscal revenues/n.inhabitants

ItalySpain

691.00613.26

232.52183.90

Fiscal revenues over

GDP (FR)

Fiscal

revenues/GDP

Italy

Spain

0.043

0.029

0. 0174

0.012

Financial

independence(FI)

Fiscal

revenues/operativerevenues

Italy

Spain

62.71

66.45

14.72

9.92

Operative grants (OG)

Grants for current

expenditure/operative revenues

Italy

Spain

17.02

31.21

15.39

9.84

We have constructed three multiple linear regressions, one for each dependent

variable, as follows:Y= α + β 1 INH + β 2 DP + β 3 U + β 4 GDP+ β 5 PP+ + β 6 G + β 7 KE + β 8 PE+ β 9 FP + β 10 FR+β 11 FI + β 12 OG+ ε

Regarding the data, it is worth noting that the personal expenditure index is a little

higher in Spain. The fiscal pressure per capita is however higher in Italy, where it

reaches €691 in Italy and €613 in Spain, representing 4.3% of the GDP in Italy and2.09% in Spain. Nevertheless, the financial independence ratio of Italian local

governments reaches 62.71% compared to 66.45% in Spain. Whereas, relating to

the size, the mean of the number of inhabitants - as well as the density of

population - is slightly higher for Spain. There are also differences in the GDP per

capita, higher in Spain than in Italy but also with more variability. Nevertheless, the

unemployment index is also higher in Spain, with a mean of 15.29%, while in Italy,it is 11.83%. Regarding the political variables, in most of the Italian LG, there is a

mayor from a progressive party (only in 19% of the cities does a conservative party

govern) and in 94% of the cases, the mayor is a man. In Spain, 67.5% of the mayors

are from a conservative party and the percentage of women increases to 22.8%.

5 Analysis of the results

5.1 Principal Component Analysis

In order to carry out the PCA, first all the variables included in table 1 were

considered. However, considering both the problems of communality and

redundancy, some of them were removed, as highlighted in table 4. Tables 3 and 4show the results and Graph 1 compares the components for Italy and Spain.





Table 3: Matrix of the Principal Component

Country ComponentInitial Eigen values

Total % of Variance % Cumulative

Italy1 2.752 45.871 45.871

2 2.156 35.939 81.811

Spain1 3.860 64.342 64.342

2 1.045 17.409 81.750

Table 4: Extraction Method: Principal Component Analysis with two componentextracts

Italy SpainComponents Components

1 2 1 2

Debt per capita .943 .173 .900 .250

Debt/ GDP .913 .200 .884 .226

Debt/Op. revenues .863 .089 .948 .122

Op. Surplus/ Percapita -.062 .911 -.801 .409Op. Surplus /op. revenues -.050 .939 -.837 .404

Debt-payment -.527 .604 -.164 -.766

Graph 1: Principal components

The PCA shows that there are some differences in the dimensions for the analysis

of the financial condition in Italian and Spanish local governments.

In Italy, the results show that a first component includes the variables representing

the level of debt which is called long term financial situation. The first componentexplains a 45.87% of the variance.





173

Long term financial situation = 0.943*Debt Per capita+0.913*Debt/GDP+

0.863*Debt/operative revenues-0.062*Operative surplus percapita-

0.050*Operative surplus/operative revenues-0.527Debt payment.

The second component explains 35.94% of the variance and includes all the

variables representing a short term economic perspective (Table 3). It reveals that

in Italy there is no correlation between the variables representing the long term

financial situation (level of debt) and short term economic situation (operative

surplus). In fact, the correlation coefficients between both types of variable is verylow.

In Spain, the first component explains 64.34% of the variance but includes both

the long term debt as well as the short term economic dimension in the same

component, but with a negative sign. Those entities with a higher level of debt andlower surplus will have more financial problems. There is a high correlation

between both dimensions. Those entities with a higher level of debt also have less

operative surplus.

The equation of the first component (explains 64.34% of the variance) is the

following:

Financial situation = 0.900 * Debt Percapita + 0.884 * Debt/GDP + 0.948 *Debt/operative revenues-0,801*Operative surplus percapita-0,837*Operative

surplus/operative revenues-0,164*Debt payment.

The second component, in Spain, explains only 17.409% of the variance and the

variable with a higher weight is debt payment, since all the others have a highweight in the first component.

To summarize, while in Spain there is high correlation between long term debt and

the short term economic-financial situation and an indicator that combines both

can explain a high percentage of the variance, in Italy both dimensions have a

different behaviour. The indicators of the economic situation such as operativedeficit and the debt level are in two different components.

5.2 Drivers for financial condition

In the second part, we tried to analyse the factors that influence or can explain the

differences in the financial condition, carrying out a regression analysis with all thevariables so as to analyse whether the variables are significant or not over the

dependent variables. For the variables dummy, the T-test of the difference of means

was used.

Three alternative dependent variables were considered: component 1, component 2

and debt per capita.





The results in Tables 5, 6 and 7 show that for Italy the variables that have a

significant influence are: population, density of population and most of the variablesrepresenting the financial structure of expenditures and revenues of local

government. However, the political and socioeconomic variables, such as the

political party in power, the gender of the mayor, unemployment and GDP are not

significant in any of the models. In Spain, the situation is very different: size is

important when measuring the number of inhabitants, with there being less

relevance when considering the density of the population. The political and socio-economic factors are significant in component 1, that in the case of Spain has a high

explicatory power, with only the gender of the mayor not being significant in any of

the models. The financial structure of the revenues and expenses is also a driver for

financial stress.

Table 5: Factors that explain component 1 in Italian and Spanish LGs

Dependent: Component 1 Italy Spain

F/T Coef. Sig. F/T Coef. Sig

Population (INH.) 2.882 -0.169 0.093 0.114 0.031 0.736

Density of population (DP)3.329 -.181 .071 2.500 -.142 .116

Unemployment (U)1.518 -.124 .221 7.613 .243 .007

Gross Domestic Product (GDP)

.465 -.069 .497 7.002 -,234 .009Political party in government (PP)

.010 1.536 .132 4.388 2.753 .007

Gender of the Mayor (G).010 .289 .764 .032 .216 .829

Capital expenditure (KE).080 .028 .779 4.341 .186 .039

Personal expenditure (PE).305 .056 .582 8.170 .251 .005

Fiscal pressure (FP)1.393 .118 .241 .230 -.044 .633

Fiscal revenues over GDP4.097 .200 .046 1.801 .121 .182

Financial independence (FI)5.213 .225 .025 .580 .069 .448

Operative grants 5.950 -.239 .017 .001 -.003 .973





175

Table 6: Factors that explain component 2 in Italian and Spanish LGs

Dependent: Component 2 Italy Spain


Population (INH.) 1.244 0.112 0.093 2.820 0.151 0.096

Density of population (DP)7.196 .262 .009 .335 -.053 .564

Unemployment (U)2.255 .150 .136 .129 -.033 .720

Gross Domestic Product (GDP).341 -.059 .561 .420 -.059 .518

Political party in government (PP).514

-1.004 .252 .642 1.302 .195

Gender of the Mayor (G).882 .851 .248 5.171 -.158 .875

Capital expenditure (KE) .012 .011 .913 1.055 -.093 .306

Personal expenditure (PE)11.664 -.326 .001 1.383 -.106 .242

Fiscal pressure (FP)5.325 .227 .023 12.669 .308 .001

Fiscal revenues over GDP6.023 .241 .016 11.490 .294 .001

Financial independence (FI)8.962 -.289 .003 8.238 .252 .005

Operative grants1.524 .124 .220 10.079 -.277 .002

Table 7: Factors that explain debt per capita in Italian and Spanish LGs

Dependent: Debt Per capita Italy Spain


Population (INH.) 4.669 -.212 .033 4.669 -.212 .033

Density of population (DP) 4.953 -.218 .028 .801 -.081 .373

Unemployment (U) 1.923 -.138 .169 1.784 .121 .184

Gross Domestic Product (GDP) .433 -.066 .512 .871 -.085 .352

Political party in government (PP).370 1.118 .292 4.702 2.760 .007

Gender of the Mayor (G) .113 .393 .710 2.403 -.714 .477

Capital expenditure (KE) .570 .076 .452 7.023 .234 .009

Personal expenditure (PE) .177 -.042 .675 1.565 .113 .213

Fiscal pressure (FP) 1.611 .127 .207 4.366 .187 .039

Fiscal revenues over GDP 5.381 .227 .022 6.524 .226 .012

Financial independence (FI) 1.037 .102 .311 1.724 .119 .192

Operative grants 2.362 -.153 .128 .660 -.074 .418





In Italy, the entities with more inhabitants have less financial stress when

considering component 1, which represents the long term financial situation. In fact,the level of debt per capita is lower for bigger entities, since the coefficient of the

variable in table 7 is negative and significant. Similarly, these entities have a better

short term economic situation, with the variable in table 6 having a positive

coefficient and being significant. The density of the population has a similar

influence: entities with a higher density of the population have fewer problems in

the long term financial situation, debt per capita and a better situation whenconsidering the second component, which represents the short term economic and

financial situation.

In Spain, the results (contrasting Guillamón et al. (2011) and Vicente et al.

(2013),which found a positive relation as well as Benito and Bastida (2004) whichfound no significant relation in a sample including entities up to 50.000 inhabitants)

show that entities with more inhabitants have also a lower level of debt, since the

population variable has a negative coefficient and is significant in the regression

with the debt per capita. They show also a better situation in the short term

economic and financial situations. However, the variable is not significant in

explaining the whole financial condition (component 1) of Spanish localgovernments. In spite of the negative impact of the density of the population on the

per capita total expenditures found in Bastida et al. (2013), the results show that the

density of the population is not significant in explaining the differences in financial

health.

The political party is a significant variable in the case of Spain, showing that progressive governments have more debt per capita and that the financial condition

(component 1) is also higher, in line with the results of Guillamón et al. (2011) and

Vicente et al. (2013). The unemployment rate also has a positive impact on the

financial condition in the case of Spain, and those entities with higher

unemployment have more financial problems, which is in line with Zafra-Gomez et

al. (2009). Nevertheless, in this study, unemployment is not significant for the debt per capita, contrasting the results of Vicente et al. (2013) where it has a positive

relevance on the level of debt. On the other hand, the GDP is a good driver for

financial health and entities with a higher GDP have lower financial stress. This

confirms the results in Guillamón et al. (2011) but differs from those obtained in

Bastida et al. (2013).

With respect to the financial structure of the revenues and expenses, in Spain those

entities that have a higher capital expenditure per capita have more debt, since the

law requires that debt goes to financial long term investments. Thus, entities with a

higher capital expenditure per capita are more likely to have financial stress.

However, capital expenditure per capita is not relevant for Italian entities. The percentage of personal expenditure over operative revenues is also significant in

component 1 of Spanish local governments, which shows that the higher the





177

expenses are, the higher the probability to have financial problems is. In Italy, the

last variable is significant only in the short term economic and financial situation

(component 2), but does not influence the long term situation nor the level of debt.

Regarding the revenues, the fiscal revenues over GDP is the variable that has

significant power in explaining both the long term financial stress, short term

economic and financial situation as well as debt per capita in the case of Italy.

Although focusing on operative surplus entities with a higher percentage of

revenues over GDP, they have a better short term financial situation as well as moredebt per capita and more probability of having higher long term financial stress

indicators. Fiscal pressure is also positively correlated with the short term economic

and financial situation (component 2) as well as the financial independence. This

confirms that fiscal policies can influence the level of debt of governments

(Guillamón et al., 2011).

The operative transfer and grants (for financing current expenditures) have a

positive impact on the long term financial situation for Italy, since those entities

with a higher percentage of operative transfer and grants have lower values in

component 1.

In Spain, the percentage of fiscal revenues over GDP also has a positive impact onthe debt per capita and component 2, while it is not significant for component 1.

Fiscal pressure is significant in the debt per capita as well as component 2,

highlighting how those entities with a higher level of taxes per capita also have ahigher debt per capita. Operative transfer and grants are only relevant for

component 2.

With the aim of selecting a group of indicators suitable for predicting financial

distress, all the variables that are significant in the simple regression analysis were

considered in a multiple regression, using the stepwise method of the regression.

The results are shown in Tables 8, 9 and 10.

Table 8: Multiple Regression Analysis for Component 1

Dependent: Component 1 I taly Spain

Coef. Sig. Coef. Sig

Population (INH.)

Density of population (DP)-0.230 0.020

Unemployment (U)0.254 0.003


Political party in government (PP)-0.240 0.004

Gender of the Mayor (G)





Capital expenditure (KE)0.277 0.001

Personal expenditure (PE)0.234 0.005

Fiscal pressure (FP)

Fiscal revenues over GDP

Financial independence (FI)

Operative grants-0.286 0.004

R

F

0.096

6.191

0.204

8.83

Table 9: Multiple Regression Analysis for Component 2

Dependent: Component 2 I taly Spain


Population (INH.) 0.191 0.029

Density of population (DP)0.207 0.017

Unemployment (U)


Political party in government (PP)

Gender of the Mayor (G)Capital expenditure (KE)

Personal expenditure (PE)-0.212 0.20


Fiscal revenues over GDP0.323 0.001 0.318 0.000

Financial independence (FI)-0.411 0.000

Operative grants

R 2

F

0.291

11.178

0.108

8.38

Table 10: Multiple Regression Analysis for Debt Percapita

Dependent: Debt Per capita Italy Spain


Population (INH.) -0.257 0.000

Density of population (DP)-0.330 0.000

Unemployment (U)





179


Political party in government (PP)

Gender of the Mayor (G)Capital expenditure (KE)

Personal expenditure (PE)


Fiscal revenues over GDP0.167 0.006

Financial independence (FI)

Operative grants

Debt per capita t-10.739 0.000 0.726 0.000

R

F0.545

40.1290.567

80.846

For the first principal component, in the case of Italy, there are only two variables

that enter in the model (due to autocorrelation problems): the density of the

population and the operative grants index. However, the explanatory power of the

model is very low. For component 2, in addition to the density of the population, personal expenditure index, fiscal revenues over GDP and financial independence

are also significant. In this case, the explanatory power of the model increases to

29.1%.

In the case of the debt per capita, the level of debt of the previous year (lagged

variable) was also considered, taking into account that it is a variable thatmaintains a certain stability. For Italy, in addition to the debt of 2011, the

population and the density of the population are significant in the model in

explaining 73.9% of the variance of the debt per capita.

In Spain, there are four variables that enter into the multiple regression for thefinancial health indicator (component 1): unemployment, political party, capital

expenditure and personal expenditure. The R 2

of the model is 20.4%. For

component 2, the variables are population and fiscal revenues over GDP. In thedebt per capita regression, the significant variable is also the percentage of fiscal

revenues over GDP, in addition to the debt of the previous year. The model

explains 72.6% of the variance of the debt per capita.

6 Conclusions

The global financial crisis has had significant effects on the viability and financial

conditions of the public sector, including local governments. They are facing

financial difficulties, in part related to the need to increase public services





provided with a reduction of available resources and in part connected to the

financial crisis.

In this new era, it has become extremely relevant for the on-looking agencies,

politicians and citizens to detect the financial performance of local governments

(Cohen et al., 2012) as well as a more general concept of financial condition. In

current literature, several studies (i.e. Kleine et al., 2003; Jones and Walkers,

2007; Zafra-Gómez et al., 2009; Cohen et al., 2012; García-Sánchez et al., 2012;Manes Rossi et al., 2012) along with standard setters and regulators all over the

world (CICA, 1997, ICMA, 2003; UK Audit Commission, 2007) have developed

several model to assess and prevent financial distress.

The novelty of this study is threefold: it presents a comparison between twoEuropean countries affected by the financial crisis, attempting to discover the

dimensions of the financial condition of Italian and Spanish local governments

using principal component analysis, while studying how the economic and social

conditions of the local government affect their financial health.

The analysis of the financial condition shows relevant differences between Italyand Spain. In Italy, the results highlight how the long term financial situation and

the short term economic perspective are not correlated to each other and have a

different behaviour. Thus, the analysis of the financial health should be carried out

considering both dimensions separately. On the contrary, in Spain these two

dimensions are strongly negatively correlated. In other words, entities with a

higher level of debt and lower operative surplus will have more financial problems.

Moreover, the results also highlight how the factors that can explain the results

highlight how differences in the financial health are strongly different in Italy and

Spain. The number of inhabitants is an important element in both countries,

therefore the Italian and Spanish larger local governments have a lower level ofdebt and better situation in the short term economic and financial situation.

However, the density of the population is not a significant variable for Spanish

LG, while it has a negative effect on the level of debt of Italian LG.

The socioeconomic and political variables have no impact on the financial healthof the Italian municipalities but, for the Spanish LG, the results highlight how the

progressive political party and unemployment rate have a positive impact on the

financial stress.

Differences also emerge in relation to the influence of the financial variables on

the financial health of LG.





181

For example, capital expenditure per capita and the percentage of personal

expenditure over operative revenues influence the financial stress of the Spanish

municipalities, while they do not have a strong influence on the Italian ones.

On the other hand, the fiscal revenues over GDP, fiscal pressure and the operative

transfer and grants have a stronger influence on the financial health of Italian LG.

These results could support managers and politicians in understanding which

factors should be constantly monitored, with the aim of assuming timely decisions

so as to avoid financial distress.

In this framework, the law also plays an important role in the control of financial

health of the local governments. In fact, in both countries, it is expected that the

municipalities in financial difficulty have to prepare a financial rebalancing plan

in order to avoid financial distress.

This study highlights how, even if Italy and Spain have a similar economic, social

and administrative environments, the factors that have an impact on the financial

distress are different. For these reasons, it is difficult to create a single model to

prevent the financial crisis of LGs for all countries, while it is more correct to

develop a specific model that reflects the peculiarities of each country.

Nevertheless, the analysis presents several limitations. First, the data for only for

one year was analysed because we want to related the financial condition to

political variables, that can change in a three-five year period. Moreover, thesample focuses on entities with more than 60,000 inhabitants, with it being

necessary to extend the analysis to smaller entities. Further possible developmentsof the research could include the analysis and comparison with other countries

with a different socioeconomic framework, with the aim of highlighting the main

differences in the factors affecting the financial distress.

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