Testing the Reliability of Financial Sustainability. The Case of Spanish...

[Full title: Testing the Reliability of Financial Sustainability. The Case of Spanish Local Governments]

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sustainability Article Testing the Reliability of Financial Sustainability. The Case of Spanish Local Governments Vicente Pina , Patricia Bachiller * and Lara Ripoll Department of Accounting and Finance, Faculty of Economics and Business Administration, University of Zaragoza, Gran V í a, 2, 50005 Zaragoza, Spain; vpina@unizar.es (V.P.); lripoll@unizar.es (L.R.) * Correspondence: pbachiller@unizar.es; Tel.: + 34-876-554725 Received: 14 July 2020; Accepted: 20 August 2020; Published: 24 August 2020 Abstract: Local Governments (LGs) have strengthened the financial control as a consequence of mandatory requirements to ensure financial sustainability in their management. The aim of this study is to determine whether financial indicators about financial conditions defined in Spanish regulation are backed by worldwide generally accepted financial benchmarking indicators. For this purpose, we analyze the relationship between Spanish indicators of financial sustainability based on European Union (EU) regulations and Financial Trends Monitoring System Indicators (FTMS) of the International City / County Management Association (ICMA). For this purpose, two methodologies are applied: discriminant analysis and logistic regression, where the dependent variables are each of the Spanish financial indicators and the independent variables are ICMA indicators. The evidence supports that variables that are related to the control of expenditures, debt and the revenues show a greater explanatory power of financial sustainability, being the most important elements which o ff er relevant information about the financial sustainability measurement of LGs Keywords: financial sustainability; local governments; financial condition; benchmarking; financial indicators 1. Introduction The decline of public finances due to the global crisis in 2008 highlighted the lack of fiscal discipline of di ff erent levels of administration, emerging situations of financial instability. The financial crisis provided an opportunity to define the bases of financial sustainability good practice guidelines in order to control the use of public funds and indebtedness of governments around the world. Sustainability management is introduced to transform how governments implement public policies and deliver public services [ 1 ]. It opens a new financial scenario for all administration layers including Local Governments (LGs) based on a universally accepted principle: financial sustainability, which is related to the likelihood of failure for LGs with liabilities and debts. The mechanism, applied by countries at the international level to control LGs’ financial health, is a benchmark, distinguishing between a voluntary local self-management , without intervention from the state, compulsory hierarchical management , in which the design of the performance indicators takes place under the supervision of the central government and a vertically co-ordinated management with a co-operating between central and local governments [ 2 ]. The mandatory requirements seek the reduction of public sector costs and debt by achieving responsible management through the periodical assessment of the financial position. Nowadays, a challenge for governments is to define indicators that can provide a reliable measurement of the financial condition to be calculated, disclosed and reported to the central government by municipal managers. Next to the evaluation of fiscal health, governments also establish corrective actions to redirect financial situations The International City / County Management Association (ICMA) produced a prestigious publication in the 1980 s [ 3 ] about the evaluation of financial condition for LGs of the United States Sustainability 2020 , 12 , 6880; doi:10.3390 / su 12176880 www.mdpi.com / journal / sustainability

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Sustainability 2020 , 12 , 6880 2 of 20 (US), based on the financial condition defined by Groves et al. [ 4 ], which refers to the capacity of a government to provide the level and quality of services required for the welfare of a community. In the fourth edition (in 2003) of Evaluating Financial Condition: A Handbook for Local Government [ 5 ], the publication revised the Financial Trend Monitory System (FTMS), o ff ering a tool composed of 42 indicators to be considered for a comprehensive evaluation of the financial condition in LGs that includes the calculation of indicators and how to read them, providing interesting information to local administration managers, which have become a worldwide benchmark applied on most relevant empirical works to measure financial condition European Union (EU) policymakers have also chosen financial sustainability as a tool to track the fiscal condition of countries that belong to the Eurozone, establishing requirements for setting up the limits for governments’ deficit and debt, by the reform of the agreement of Stability and Growth Pact (SGP) in 2011, in order to ensure the stability of the Economic and Monetary Union (EMU). The SGP establishes the due process for the monitoring of the fiscal conditions of State members, which includes the procedure to be followed when a member breaches the SGP, with the adoption of an Excessive Deficit Procedure (EDP) that requires corrective actions in the case of exceeding the budget deficit allowed by the EU In Spain, LGs have full fiscal autonomy to approve their own budget of expenditures and revenues, establishing and collecting taxes, and borrowing from banks and markets. Therefore, in order to maintain LGs finance within the framework of SGP-EU requirements, the Spanish central government has transposed the financial sustainability requirements established by the EU to the Spanish LG arena (The Organic Law on Budgetary Stability and Financial Sustainability, 2012) with the purpose of monitoring how each LG performs across several financial indicators. Regulatory requirements establish how and when to evaluate the situation. The Spanish Ministry of Finance website publishes a set of financial indicators for each LG, and makes them available in an online database, as a category of Open Government Data (OGD) The objective of this article is to evaluate whether the way to measure the financial condition of LGs in Spain by transposing the SGP of the EU requirements is a fair and reliable tool for measuring the LGs’ financial condition. For this purpose, the relationship between the Spanish financial sustainability indicators for LGs and the ICMA worldwide generally accepted benchmarking indicators is analyzed. For this reason, we identify what common concepts are evaluated by generally accepted benchmarking in order to measure the financial sustainability and the research question of the analysis is to test if Spanish financial indicators are in line with worldwide definitions of financial sustainability Discriminant analysis and logistic regression methodology are applied to identify the ICMA indicators which have a higher discriminant power to define the financial condition of Spanish municipalities in a database which consists of four Spanish indicators and 18 ICMA financial indicators produced annually by 143 local governments from 2010 to 2017 This paper can contribute to country policymakers not only to assist managers in the design of indicators that measure financial sustainability, but also to allow the comparison at the international level using benchmarking The article is organized as follows: Firstly, the background regarding the assessment of LG financial risks. Secondly, the variables and methodology are described. Thirdly, the analysis of the results is shown. Finally, discussion and conclusions are drawn 2. Literature Review For L ó pez-Subir é s et al. [ 6 ], the financial sustainability is a key dimension in the management of governmental organizations in many parts of the world, the monitoring of financial risk being one of the most important challenges to promote the transparency of local administration. Therefore, LG’s managers and / or governments aim to find a fair and reliable model based on indicators to be able to measure the financial condition in order to achieve an alert system tool. They are also interested in determining the factors that most influence disclosure about sustainability because this

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Sustainability 2020 , 12 , 6880 3 of 20 information would help them to design measures to improve their management and communication of sustainability [ 7 ]. Academic literature shows multiple approaches to define financial risk in LGs such as: financial condition [ 8 ], fiscal health [ 9 ], or fiscal distress [ 10 ]. The common factor in all definitions of financial risk is that when LGs have liabilities and debts, a likelihood of failure exists. To face that likelihood, supranational institutions, such as Eurostat for the European Union (EU) countries, have set up deficit and indebtedness limits to EU countries. Consequently, some EU central governments have issued domestic legal requirements and / or transposed the Eurostat requirements to their own domestic legal framework in order to limit the indebtedness of LG based on the European System of Accounts’ (ESA) concept of net lending / net borrowing, and other broader concepts such as solvency or liquidity of LGs There are several countries that have developed Performance Measurement Systems (PMS) The most used is the Financial Trends Monitoring System (FTMS) of the International City / County Management Association (ICMA) from the United States, which explains the financial condition as the ability to maintain existing service levels, resistance to local and regional disruptions, and meeting the demands of natural growth, decline, and change. FTMS classifies indicators in six groups: revenues, expenditures, operating position, debt, unfunded liabilities and capital plant Other accepted benchmarking tools are the alert system of the Canadian Institute of Chartered Accountants, next to the Ministry of Municipal A ff airs and Housing of Canada which produced the Financial Information Returns (FIR) to measure the financial condition through a schedule of reporting requirement or the ratios included in the Comprehensive Annual Financial Report of the GASBS 34 (Governmental Accounting Standards Board), which measures financial assets, debt limit, surplus and relationship between expenses and revenues Several authors seek to explain what variables reveal useful information about the financial condition in LGs. Ryan et al. [ 11 ] analyze the case of Australia, where the financial framework of local governments is mainly composed of the Australian Accounting Standard 27 (AAS 27) Financial Reporting by Local Governments, and conclude that the key financial performance indicators about fiscal sustainability should encompass four dimensions: own source revenue reliance, revenue flexibility / intensity, indebtedness and liquidity Andrews [ 12 ] studies the amalgamations the case of England and Wales, defining the indicators of financial sustainability such as expenditures per capita, fiscal risk: analyzing the proportion of the overall expenditure that is funded via local property tax rather than central government transfers, or the “self-income ratio” [ 13 ], fiscal slack: absorbed or unabsorbed resources that can be appropriated by senior managers to meet new demands of the organization [ 14 ] and fiscal balance In the case of local councils in Ireland, Turley et al. [ 15 , 16 ] apply the Brown’s assessment tool [ 17 ] used to measure the financial condition of small cities in the US composed of 14 financial indicators, which measure: liquidity, autonomy, operating performance, collection e ffi ciency and solvency, obtaining a classification of the financial performance of councils, providing interesting results as some entities considered as “good” performers in the media appear as those in the best-performing group overall Table 1 shows a collection of studies aimed at determining the variables which better explain the financial condition in LGs. On one hand, Blore et al. [ 18 ], Kioko [ 19 ], Navarro-Galera et al. [ 20 ] and Gorina et al. [ 21 ], conclude that indicators which relate revenues and expenditures provide better predictive power of the financial condition. Cabaleiro et al. [ 22 ] find that the function that best allows for the classification of municipalities according to their financial health includes those indicators related to debt and revenues, while Cabaleiro and Buch [ 23 ] reveal the relationship between the tax e ff ort and financial condition. Trussel and Patrick [ 24 ] support that financial risk is related to debt service, and other authors such as Bulai et al. [ 25 ], suggest that the level of a ffl uence can be an essential component of a measure of financial sustainability. The literature also shows a solvency approach as a good instrument for evaluating financial conditions, as Zafra et al. [ 26 ] support, applying short-run solvency, budgetary flexibility solvency and service-level solvency as elements of the financial

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Sustainability 2020 , 12 , 6880 4 of 20 condition. Another way of measurement is developed by Navarro-Galera et al. [ 27 , 28 ], who propose a system based on Basel II criteria, establishing four aspects to measure the probability default (PD) of LGs: cash surplus for overheads, legal borrowing limit, solvency (current assets / current liabilities) and gross budget savings (current revenue / current liabilities). However, Clark [ 29 ] asserts that the Financial Condition Index (FCI), which is a framework for evaluating financial condition developed by Groves et al. [ 4 ] based on cash solvency, budget solvency, long-run solvency and service solvency, is not the most appropriate tool for measuring financial condition at the local level Table 1. Significant variables included in the conclusion of financial condition study by author Author Significant Variables Bulai et al. (2019) level of a ffl uence: entities that are more fluent may be better equipped to handle a potential downturn in local government finances Blore et al. (2012) revenues mobilisation, or how mobilise more money (enhancing tax revenues and exploiting charges better) expenditure management through budgeting and expenditure management and cost management and control Cabaleiro et al. (2012) long-term debt, net current budgetary revenues divided by budget obligations from nonfinancial current expenditures minus debt service, net current budgetary revenues divided by net budget obligations, direct and indirect taxes and fees divided by net budget obligations from current expenditures, direct and indirect taxes and fees divided by net budgetary revenues from current operations Cabaleiro and Buch (2014) tax e ff ort Gorina et al. (2018) cash solvency, long-term solvency, revenue structure Groves et al. (1981) cash solvency budget solvency long-run solvency service solvency Kioko (2013) revenues, expenses, assets and liabilities Navarro-Galera et al (2016) income statement Navarro-Galera et al (2017, 2020) Default 1: cash surplus for overheads Default 2: legal borrowing limit (capital or current debt) Default 3: solvency (current assets / current liabilities) Default 4: gross budget savings (current revenue / current liabilities) Trussel and Patrick (2017) debt service Zafra et al. (2009) short-run solvency budgetary flexibility solvency service-level solvency The revision of previous literature allows us to conclude that there are similarities in the di ff erent financial measurement systems, because the indicators studied, strive to measure the same concepts The di ff erent ways to measure financial sustainability, distinguish four main groups of indicators: evaluation of expenditures, evaluation of revenues, evaluation of debt and evaluation of cash, which is in line with the main groups of evaluation that ICMA establishes in the definition of the financial indicators applied on LGs. Table 2 shows a summary of four elements that can be defined, as a conclusion from the previous review, that are applied by authors and are accepted worldwide financial sustainability tools.

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Sustainability 2020 , 12 , 6880 5 of 20 Table 2. Main groups of indicators to evaluate financial sustainability by author and worldwide systems Expenditures Revenues Debt Cash Cabaleiro et al. (2012) Kioko (2013) Blore (2012) Cabaleiro et al. (2012) Kioko (2013) Cabaleiro and Buch (2014) Cabaleiro et al. (2012) Trussel and Patrick (2017) Navarro-Galera et al (2017, 2020) Groves et al. (1981) Gorina et al. (2018) Zafra et al. (2009) Navarro-Galera et al (2017, 2020) ICMA indicators (US) FIR indicators (Canada) ICMA indicators (US) FIR indicators (Canada) AAS 27 indicators (Australia) ICMA indicators (US) FIR indicators (Canada) AAS 27 indicators (Australia) ICMA indicators (US) AAS 27 indicators (Australia) 3. The Spanish Legal Framework The legal financial framework of Spanish LGs is made up of a set of regulations that have developed requirements from di ff erent perspectives to control the financial sustainability of LGs It consists of a package of actions introduced after the 2008 financial crisis in order to curb public expenditure and to reduce the annual deficit and debt. As a Eurozone member, Spain had to approve a regulatory framework consistent with EU requirements to achieve specific commitments towards getting back on the road to growth. As a consequence of pressure from the EU, Article 135 of the Spanish Constitution was modified by socialist president Zapatero and the Organic Law on Budgetary Stability and Financial Sustainability was enacted in 2012. This act establishes the requirements to be met by LGs in order to ensure their financial sustainability. It provides important requirements based on a set of principles about budgetary stability and financial sustainability, and establishes a legal basis applicable to the di ff erent layers of the public administration. According to the Organic Law on Budgetary Stability and Financial Sustainability, all Spanish public sector entities have to meet the following principles: budgetary stability, financial sustainability, multi-annuity investments, transparency, e ffi ciency in allocation and use of public resources, responsibility, institutional loyalty, and the development of mechanisms for the coordination and application of the law Budgetary stability is linked to the present control of financial risks which arose in the context of the 2008 financial crisis as an important requirement for LGs, assuming that is a challenge to LGs, which are introducing reforms in order to better manage [ 30 ]. The Organic Law on Budgetary Stability and Financial Sustainability establishes a legal mechanism called reporting requirements which provides a schedule for di ff erent analyses of the financial position of LGs for monitoring their financial health. LGs have to report about budgetary stability and financial sustainability over the year. LGs must upload the information shown onto the Ministry of Finance’s website entitled “Virtual o ffi ce of financial coordination of local entities” using the XML taxonomy Therefore, budgetary stability o ff ers another scenario composed of three indicators: Budgetary Stability , Expenditure Rule and Public Debt Budgetary Stability is defined as the net lending or net borrowing adjusted, i.e., the higher the surplus, the better the issuer can cope with debt payments [ 31 ]. Expenditure Rule measures the growth of the expenditure of public administrations cannot exceed the reference rate of growth of the medium-term GDP of the Spanish economy Public Debt represents the nominal value of outstanding liabilities of public administrations at the end of the fiscal year which is made up of: deposits, debt bonds and loans, according to ESA 2010 definitions. When a breach occurs, the law imposes corrective actions to avoid a relapse into financial instability. In these cases, the entities which fail to meet the cap limit of each indicator must elaborate an Eco-financial Plan aimed at recovering financial stability over the next two fiscal years to be approved by a fiscal authority 4. Variables According to the Organic Law on Budgetary Stability and Financial Sustainability, the analysis is focused on Budgetary Stability , Expenditure Rule , Public Debt and Eco-financial Plan indicators.

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Sustainability 2020 , 12 , 6880 6 of 20 These indicators will be the variable dependents, which are dummy variables with value 1 if the variable indicates that the LG fails to meet the limits of the indicators, and 0 otherwise The selection of independent variables was approached with the indicators developed by the International City / County Management Association (ICMA) with the Financial Trends Monitoring System (FTMS). ICMA defines the financial condition as the ability to maintain existing service levels, resistance to local and regional disruptions, and meeting the demands of natural growth, decline, and change. The set of ICMA indicators is a procedure recommended to monitor the financial trends in LGs being a tool to help decision-making processes. ICMA’s tool consists of a total of 42 quantifiable indicators (Table 3 ) used to evaluate the financial condition, categorized into di ff erent areas: financial, environmental and organizational factors Table 3. Financial Trends Monitoring System Indicators of International City / County Management Association (ICMA) Area Factors Indicator Financial Indicators Factor 1 Revenue Indicators Indicator 1 Revenues per Capita Indicator 2 Restricted Revenues Indicator 3 Intergovernmental Revenues Indicator 4 Elastic Revenues Indicator 5 One-Time Revenues Indicator 6 Tax Revenues Indicator 7 Uncollected Property Taxes Indicator 8 User Charge Coverage Indicator 9 Revenue Shortfalls or Surpluses Factor 2 Expenditure Indicators Indicator 10 Expenditures per Capita Indicator 11 Expenditures by Function Indicator 12 Employees per Capita Indicator 13 Fixed Cost Factor 3 Operating Position Indicators Indicator 14 Fringe Benefits Indicator 15 Operating Deficit or Surplus Indicator 16 Enterprise Operating Position Indicator 17 Fund Balances Indicator 18 Liquidity Factor 4 Debt Indicators Indicator 19 Current Liabilities Indicator 20 Long-Term Debt Indicator 21 Debt Service Indicator 22 Overlapping Debt Factor 5 Unfunded Liability Indicators Indicator 23 Pension Obligations Indicator 24 Pension Assets Indicator 25 Post Employment Benefits Factor 6 Capital Plant Indicators Indicator 26 Maintenance E ff ort Indicator 27 Capital Outlay Environmental Indicators Factor 7 Community Needs and Resources Indicators Indicator 28 Population Indicator 29 Population Density Indicator 30 Population under 18 and over 64 Indicator 31 Personal Income per Capita Indicator 32 Poverty Households or Public Assistance Recipients Indicator 33 Property Value Indicator 34 Top Five Taxpayers Indicator 35 Home Ownership Indicator 36 Vacancy Rates Indicator 37 Crime Rate Indicator 38 Employment Base Indicator 39 Business Activity

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Sustainability 2020 , 12 , 6880 7 of 20 Table 3. Cont Area Factors Indicator Environmental Indicators Factor 8 Intergovernmental Constraints Indicator 40 Mandated Activities Indicator 41 Restrictions on Fiscal Powers Factor 9 Disaster Risk Indicator 42 Disaster Risk Factor 10 Political Culture Factor 11 External Economic Conditions Financial factors show di ff erent sections defining a set of indicators aim at capturing from di ff erent perspectives the concepts of revenues, expenditures, operating position, debt structure, unfunded liabilities or condition of capital plant. The relevance of each indicator may be di ff erent according to the legal and economic framework of LGS. Environmental factors provide us with information about community needs and resources, intergovernmental constraints, disaster risk, political culture and external economic conditions. Financial and environmental factors are linked to management practices and legislative policies ICMA debt indicators are similar to the Spanish Public Debt indicator, particularly Indicator 21 Debt Service, which relates debt with revenues. ICMA indicators study revenues, on the one side, and expenditures, on the other side, but do not have an indicator that connects the di ff erence between them, so it is not possible to find an indicator similar to Budgetary Stability . As for the Expenditure Rule indicator , there is not an ICMA indicator that links the expenditure of the current year with the expenditure in the previous year In the study, we use the ICMA indicators for Spanish LGs by using the formula provided by ICMA’s book— Evaluating financial condition: A Handbook for Local Government [ 5 ]. It is not possible to calculate the totality of 42 ICMA indicators because, in the case of some indicators, there is not an equivalence of the indicator in Spanish financial reports; consequently, 22 ICMA indicators were estimated. Table 3 show in italics the 18 ICMA indicators which were applied in the analysis, after removing four indicators because of high multicollinearity. Descriptive statistics (see Table 4 ) were calculated for every indicator, where we can appreciate on average that Revenues per Capita is 957,372 Euros and Expenditures per Capita is 278,906 Euros Table 4. Descriptive Statistics N Mean Std. Dev. Min. Max. Revenues Per Capita 996 957.372 1142.672 0.000 36200.764 Restricted Revenues 996 0.079 0.089 − 0.028 0.523 One-Time Revenues 996 0.027 0.056 − 0.071 0.468 Uncollected Property Taxes 996 0.324 4.800 0.000 144.498 Revenue Shortfalls or Surpluses 996 1.407 0.881 0.000 27.054 Expenditures Per Capita 996 278.906 1445.815 − 1715.464 37517.232 Fixed Cost 995 0.410 0.086 0.000 0.907 Operating Deficit or Surplus 996 0.144 0.099 − 0.529 0.889 Liquidity 993 0.703 2.024 − 2.675 45.702 Current Liabilities 996 0.498 0.629 − 0.925 6.469 Long-Term Debt 996 635.377 921.917 0.000 25489.469 Debt Service 996 0.106 0.083 0.000 1.807 Population 996 1252.710 2466.863 0.000 18894.934 Population Density 996 64158.552 119889.214 0.000 1314474.000 Population Under 18 and Over 64 996 10554.222 1807.597 0.000 13436.000 Personal Income Per Capita 996 0.170 0.081 0.020 0.499 Vacancy Rates 996 46.232 11.946 0.000 68.000 Crime Rate 996 957.372 1142.672 0.000 36200.764

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Sustainability 2020 , 12 , 6880 8 of 20 As a result, Table 5 includes a summary of the dependent and independent variables which were determined for the models Table 5. Variables included in the models Variables Model-Indicator Number Indicator Abbreviation Dependent variables Model 1 Budgetary Stability BudStab Model 2 Expenditure Rule ExpRule Model 3 Public Debt PubDebt Model 4 Eco-financial Plan EFP Independent variables Indicator 1 Revenues per Capita RevCap Indicator 2 Restricted Revenues RestRev Indicator 5 One-Time Revenues OneTRev Indicator 7 Uncollected Property Taxes UncollPropTax Indicator 9 Revenue Shortfalls or Surpluses RevShortSurp Indicator 10 Expenditures per Capita ExpCap Indicator 13 Fixed Cost FixedCost Indicator 15 Operating Deficit or Surplus OpDefSurp Indicator 18 Liquidity Liq Indicator 19 Current Liabilities CurrLiab Indicator 20 Long-Term Debt LTDebt Indicator 21 Debt Service DebtServ Indicator 28 Population Pop Indicator 29 Population Density PopDens Indicator 30 Population under 18 and over 64 Pop 1864 Indicator 31 Personal Income per Capita PersIncomCap Indicator 36 Vacancy Rates VacRates Indicator 37 Crime Rate CrimeRate In this way, the models which link Spanish and ICMA indicators are: M 1 : BudStab i = β 0 + β 1 RevCap + β 2 RestRev + β 3 OneTRev + β 4 UncollPropTax + β 5 RevShortSurp + β 6 ExpCap + β 7 FixedCost + β 8 OpDe f Surp + β 9 Liq + β 10 CurrLiab + β 11 LTDebt + β 12 DebtServ + β 13 Pop + β 14 Pop Dens + β 15 Pop 1864 + β 16 PersIncomCap + β 17 VacRates + β 18 CrimeRate M 2 : ExpRule i = β 0 + β 1 RevCap + β 2 RestRev + β 3 OneTRev + β 4 UncollPropTax + β 5 RevShortSurp + β 6 ExpCap + β 7 FixedCost + β 8 OpDe f Surp + β 9 Liq + β 10 CurrLiab + β 11 LTDebt + β 12 DebtServ + β 13 Pop + β 14 Pop Dens + β 15 Pop 1864 + β 16 PersIncomCap + β 17 VacRates + β 18 CrimeRate M 3 : PubDebt i = β 0 + β 1 RevCap + β 2 RestRev + β 3 OneTRev + β 4 UncollPropTax + β 5 RevShortSurp + β 6 ExpCap + β 7 FixedCost + β 8 OpDe f Surp + β 9 Liq + β 10 CurrLiab + β 11 LTDebt + β 12 DebtServ + β 13 Pop + β 14 Pop Dens + β 15 Pop 1864 + β 16 PersIncomCap + β 17 VacRates + β 18 CrimeRate M 4 : EFP i = β 0 + β 1 RevCap + β 2 RestRev + β 3 OneTRev + β 4 UncollPropTax + β 5 RevShortSurp + β 6 ExpCap + β 7 FixedCost + β 8 OpDe f Surp + β 9 Liq + β 10 CurrLiab + β 11 LTDebt + β 12 DebtServ + β 13 Pop + β 14 Pop Dens + β 15 Pop 1864 + β 16 PersIncomCap + β 17 VacRates + β 18 CrimeRate The sample contains Spanish LGs with a population greater than 50,000, a total of 143 local entities with information from 2010 to 2017; the main sources of information were the “Virtual o ffi ce of financial coordination of local entities” website and the Spanish National Audit O ffi ce website For each LG, ICMA indicators were calculated and Spanish indicators were gathered from 2010 to 2017. The statistical software used in the empirical research was SPSS Statistics 24.

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Sustainability 2020 , 12 , 6880 9 of 20 Hypotheses Tested From the main conclusion of the revision of previous literature, we identify di ff erent ways to measure financial sustainability and distinguish four main groups of indicators, which allows us to base the study on the following hypotheses which are verified by empirical analysis: Hypothesis 1 (H 1). Financial sustainability of LGs may be measured by the evaluation of expenditures Hypothesis 2 (H 2). Financial sustainability of LGs may be measured by the evaluation of revenues Hypothesis 3 (H 3). Financial sustainability of LGs may be measured by the evaluation of debt Hypothesis 4 (H 4). Financial sustainability of LGs may be measured by the evaluation of cash 5. Methods The methodology applied is discriminant analysis to test the discriminant power of indicators, because this methodology is recommended for models which have categorical character in their dependent variables and allow us to analyze the di ff erences between groups and classify the LGs The discriminant analysis aims to explain the belonging of each entity to one pre-established group or another. The concept of discrimination is established by Fisher [ 32 ], although the origin begins with Pearson [ 33 ] and Mahalanobis [ 34 ]. Discriminant analysis is useful to obtain classifiers to distinguish groups using variances and co-variances, already having predefined categories of response in order to build a model that helps in predicting the category or group, existing as a multivariate technique that studies the di ff erences of categories established a priori, which allows a user to analyze the variables that contribute to discriminate subjects in the di ff erent groups. The model is composed of a discriminant function based on linear combinations of predictor variables The discriminant function is: D = β 0 + β 1 RevCap + β 2 RestRev + β 3 OneTRev + β 4 UncollPropTax + β 5 RevShortSurp + β 6 ExpCap + β 7 FixedCost + β 8 OpDe f Surp + β 9 Liq + β 10 CurrLiab + β 11 LTDebt + β 12 DebtServ + β 13 Pop + β 14 Pop Dens + β 15 Pop 1864 + β 16 PersIncomCap + β 17 VacRates + β 18 CrimeRate where β 0 . . . β 18 are the discriminant coe ffi cients The requirements of discriminant analysis are that the grouping variables (dependent variables) should be categorical variables with two values at least—in our study, default or non-default— while the independent variables should be continuous. This study seeks the relationship between the categorical variables: Budgetary Stability , Expenditure Rule , Public Debt and Eco-financial Plan , and the ICMA indicators (independent variables). In the analysis, we introduced all independent variables and applied the stepwise procedure in the discriminant analysis which shows only important variables selected based on Wilk’s lambda, while redundant variables are discarded The previous assumptions of discriminant analysis to apply this methodology are: normality in the independent variables, linearity, no multicollinearity and equal variances. We assume, as a limitation, that financial variables are more likely to be highly skewed, and for these reasons, the variables would be transformed in order to achieve the previous assumptions. Although discriminant analysis is considered a robust technique that is not altered if any of the previous assumptions are not applicable, we also apply the methodology of logistic regression with panel data in order to complement the analysis of the variables. This methodology is less stringent than discriminant analysis and it is not necessary that independent variables are normally distributed or equal variances are assumed. In this way, in binary logistic regression, the dependent variable can only take two values: 1 if the LG defaults, 0 otherwise.

[Find the meaning and references behind the names: Range, Log, Normal, Smirnov, Null, Close, Box]

Sustainability 2020 , 12 , 6880 10 of 20 The formula of the linear function of the logistic regression model is: Y = β 0 + β 1 RevCap + β 2 RestRev + β 3 OneTRev + β 4 UncollPropTax + β 5 RevShortSurp + β 6 ExpCap + β 7 FixedCost + β 8 OpDe f Surp + β 9 Liq + β 10 CurrLiab + β 11 LTDebt + β 12 DebtServ + β 13 Pop + β 14 Pop Dens + β 15 Pop 1864 + β 16 PersIncomCap + β 17 VacRates + β 18 CrimeRate where Y is each one of the dependent variables, and β 0 . . . β 18 are the estimated coe ffi cients and the logistic function is: p = 1 1 + e − Y where Y is the lineal function of the logistic regression model and e is the base of the Napierian logarithms (2.718) 6. Results 6.1. Robustness Test The previous assumptions of discriminant analysis to apply this methodology are: normality in the independent variables, linearity, no multicollinearity and equal variances. This means that these requirements must be checked in order to analyze the appropriateness of the sample in the application of methodology. To verify the normality for independent variables, we apply the Kolmogorov–Smirnov test. The result indicates a rejection of the null hypothesis, which means that the independent variables of our study do not follow a normal distribution, which is corrected with the log transformation of the variables that allow us to obtain a normal distribution. As for multicollinearity in the independent variables, we removed the indicators that show significant correlations: Tax Revenues , Poverty Households or Public Assistance Recipients , Employment Base and Business Activity . Furthermore, we run Box’s M test in order to observe the covariance matrices; the null hypothesis being the equality in the variance–covariance matrix, this test is sensitive in the absence of normality. The results confirm that the variance–covariance matrices are di ff erent, which indicates that this condition is not met (Table 6 ). Table 6. Box’s M test Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan Box’s M 215.137 88.140 202.078 64.349 F-value 2.314 8.775 7.096 10.504 Significance 0.000 0.000 0.000 0.000 Having made this verification we have obtained a satisfactory sample which meets the previous assumptions of discriminant analysis 6.2. Analysis of Results Discriminant analysis is a statistical procedure that o ff ers several interesting outputs to study Firstly, the Eigenvalue value indicates how well the function di ff erentiates the groups, where the greater the value, the more e ff ective the power of classifying the groups. Table 7 shows the results of this parameter for each model in which the highest value is for Model 3 Public Debt with 0.538, being also the highest value of canonical correlation which ranges from 0 to 1, with a value of 0.591 Secondly, the main parameter which characterizes the study of the database in the discriminant analysis is represented in Table 8 with Wilk’s Lambda test that measures the discriminative power independent variables. The range of plausible values is between 0 and 1. A value close to 0 would mean that groups are di ff erent and the discriminant function based on the ICMA variables can adequately predict financial health defined by financial indicators based on the Spanish legislation.

[Find the meaning and references behind the names: Square, Wilks, View, Idea, Label, Chi]

Sustainability 2020 , 12 , 6880 11 of 20 Of the four models, Model 3 of Public Debt shows the value closest to 0, Chi-square reveals that it is statistically significant Table 7. Eigenvalues Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan Eigenvalue 0.160 0.097 0.538 0.128 % of Variance 100 100 100 100 Cumulative % 100 100 100 100 Canonical correlation 0.371 0.297 0.591 0.337 Table 8. Wilk’s Lambda Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan Wilks’ Lambda 0.862 0.912 0.650 0.887 Chi-Squared 143.702 90.154 416.102 117.473 Significance 0.00 0.00 0.00 0.00 Standardized Canonical Discriminant Function Coe ffi cients (see Table 9 ) show the ICMA indicators with a higher power for each model. The biggest recurring ICMA indicator in di ff erent models is Indicator 1 Revenues per Capita , Indicator 9 Revenue Shortfalls or Surpluses , Indicator 10 Expenditures per Capita , Indicator 15 Operating Deficit or Surplus , Indicator 19 Current Liabilities , Indicator 20 Long-Term Debt and those related to population. This means that there is a powerful relationship between Spanish indicators and these indicators of ICMA, which could be pooled into three main groups of indicators: that link revenues ( Indicator 1 and Indicator 9 ), that link expenditures ( Indicator 10 and Indicator 15 ) and that link debt ( Indicator 19 and Indicator 20 ), which means that Hypothesis 1, 2 and 3 are accepted This result is consistent with the previous reviewing of the comparison of the meaning of both kinds of indicators as Cabaleiro et al. [ 22 ], Kioko [ 19 ], Navarro-Galera [ 27 , 28 ], Trussel and Patrick [ 24 ] and Gorina et al. [ 21 ] support in their analysis. ICMA indicators with a higher power of discrimination are those whose definition is in line with Spanish indicators, which supports the idea that the default or non-default concept of Spanish indicators is supported by the ICMA indicator system, i.e., the definition of financial condition by the Spanish legislation is consistent with the empirical evidence and with the financial condition standards at the international level. Both the Spanish legislation and the ICMA show common components in their own formulas, therefore, although ICMA and Spanish indicators do not have the same label, the study reveals that the informational content is similar and shares a common view about the representation of financial risk Table 9. Standardized Canonical Discriminant Function Coe ffi cients Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan Indicator 1 Revenues per Capita − 0.318 − 0.080 − 0.211 − 0.413 Indicator 2 Restricted Revenues 0.284 Indicator 5 One-Time Revenues − 0.375 0.407 Indicator 7 Uncollected Property Taxes 0.245 − 0.035 Indicator 9 Revenue Shortfalls or Surpluses 0.374 0.733 − 0.371 Indicator 10 Expenditures per Capita 0.903 0.042 0.881 Indicator 13 Fixed Cost − 0.261 0.103 Indicator 15 Operating Deficit or Surplus − 0.170 − 0.407 Indicator 18 Liquidity − 0.001 Indicator 19 Current Liabilities 0.363 0.074 0.355 Indicator 20 Long-Term Debt 0.347 − 0.143 0.837 Indicator 21 Debt Service − 0.217 − 0.146

[Find the meaning and references behind the names: Seven]

Sustainability 2020 , 12 , 6880 12 of 20 Table 9. Cont Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan Indicator 28 Population − 0.529 0.028 0.636 Indicator 29 Population Density 0.423 0427 0.134 Indicator 30 Population under 18 and over 64 0.016 − 0.815 − 0.286 Indicator 31 Personal Income per Capita 0.494 0.154 Indicator 36 Vacancy Rates Indicator 37 Crime Rate − 0.356 0.094 The Standardized Canonical Discriminant Function Coe ffi cients also provide the discriminant functions: D BudStab = − 0.318 RevCap − 0.375 OneTRev + 0.245 UncollPropTax + 0.374 RevShortSurp + 0.903 ExpCap − 0.261 FixedCost − 0.170 OpDe f Surp + 0.363 CurrLiab + 0.347 LTDebt − 0.217 DebtServ − 0.529 Pop + 0.423 PopDens + 0.494 PersIncomCap − 0.356 CrimeRate D ExpRule = − 0.080 RevCap + 0.284 RestRev + 0.407 OneTRev − 0.035 UncollPropTax + 0.733 RevShortSurp + 0.042 ExpCap + 0.103 FixedCost − 0.407 OpDe f Surp − 0.001 Liq + 0.074 CurrLiab − 0.143 LTDebt − 0.146 DebtServ + 0.028 Pop + 0.427 PopDens + 0.016 Pop 1864 + 0.154 PersIncomCap + 0.094 CrimeRate D PubDebt = − 0.211 RevCap − 0.371 RevShorSurpluses + 0.355 Current Liabilities + 0.837 LongTerm Debt + 0.636 Population + 0.134 Population Density − 0.815 Population under 18 and over 65 D EFP = − 0.413 RevCap + 0.881 ExpCap − 0.286 Pop 1864 After analyzing all independent variables, we applied the stepwise procedure which shows the number of steps and the variables introduced in the regressions with the value of Wilk’s Lambda in brackets (see Table 10 ). In this technique, the variables are incorporated one by one to the discriminant function in order to build a function using only the useful variables for the classification, also being possible to evaluate the individual contribution of each variable to the discriminant model. In the case of Budgetary Stability , there are thirteen steps, in Expenditure Rule there are four steps, with seven steps in Public Debt and three in Eco-financial Plan model. The more steps the model has, the higher the number of significant variables are included. The conclusion of this table is the same as in the previous analysis with all independent variables (Table 9 ) because the variables with a higher discriminant power are the same Table 10. Stepwise Procedure of Discriminant Analysis Steps Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan 1 Revenue Shortfalls or Surpluses Revenue Shortfalls or Surpluses Long Term Debt Expenditures per Capita 2 Revenue Shortfalls or Surpluses (0.972) Revenue Shortfalls or Surpluses (0.983) Long Term Debt (0.890) Expenditures per Capita (0.984) Current Liabilities (0.969) Population Density (0.951) Current Liabilities (0.722) Revenues per Capita (0.913) 3 Revenue Shortfalls or Surpluses (0.963) Revenue Shortfalls or Surpluses (0.959) Long Term Debt (0.828) Expenditures per Capita (0.972) Current Liabilities (0.954) Population Density (0.945) Current Liabilities (0.712) Revenues per Capita (0.904) One-Time Revenues (0.952) Operating Deficit or Surplus (0.931) Revenue Shortfalls or Surpluses (0.700) Population under 18 and over 64 (0.895)

Sustainability 2020 , 12 , 6880 13 of 20 Table 10. Cont Steps Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan 4 Revenue Shortfalls or Surpluses (0.943) Revenue Shortfalls or Surpluses (0.941) Long Term Debt (0.827) Current Liabilities (0.940) Population Density (0.940) Current Liabilities (0.702) One-Time Revenues (0.937) Operating Deficit or Surplus (0.921) Revenue Shortfalls or Surpluses (0.695) Population (0.936) One-Time Revenues (0.920) Population under 18 and over 64 (0.677) 5 Revenue Shortfalls or Surpluses (0.932) Long Term Debt (0.815) Current Liabilities (0.926) Current Liabilities (0.690) One- Time Revenues (0.930) Revenue Shortfalls or Surpluses (0.686) Population (0.924) Population under 18 and over 64 (0.675) Personal Income per Capita (0.921) Population (0.670) 6 Revenue Shortfalls or Surpluses (0.921) Long Term Debt (0.815) Current Liabilities (0.922) Current Liabilities (0.677) One-Time Revenues (0.916) Revenue Shortfalls or Surpluses (0.682) Population (0.911) Population under 18 and over 64 (0.669) Personal Income per Capita (0.920) Population (0.663) Crime Rate (0.909) Revenues per Capita (0.661) 7 Revenue Shortfalls or Surpluses (0.910) Long Term Debt (0.815) Current Liabilities (0.918) Current Liabilities (0.674) One-Time Revenues (0.913) Revenue Shortfalls or Surpluses (0.678) Population (0.908) Population under 18 and over 64 (0.665) Personal Income per Capita (0.917) Population (0.659) Crime Rate (0.904) Revenues per Capita 0.659) Operating Deficit or Surplus (0.897) Population Density (0.654) 8 Revenue Shortfalls or Surpluses (0.903) Current Liabilities (0.916) One-Time Revenues (0.903) Population (0.905) Personal Income per Capita (0.905) Crime Rate (0.897) Operating Deficit or Surplus (0.894) Population Density (0.892) 9 Revenue Shortfalls or Surpluses (0.894) Current Liabilities (0.903) One-Time Revenues (0.894) Population (0.901) Personal Income per Capita (0.903) Crime Rate (0.893) Operating Deficit or Surplus (0.888) Population Density (0.889) Uncollected Property Taxes (0.887) 10 Revenue Shortfalls or Surpluses (0888) Current Liabilities (0.895) One-Time Revenues (0.890) Population (0.897) Personal Income per Capita (0.896) Crime Rate (0.886) Operating Deficit or Surplus (0.883) Population Density (0.887) Uncollected Property Taxes (0.883) Revenues per Capita (0.882)

[Find the meaning and references behind the names: Rms, Rho, Part, Success, Fit, Goodness, Rea]

Sustainability 2020 , 12 , 6880 14 of 20 Table 10. Cont Steps Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan 11 Revenue Shortfalls or Surpluses (0.883) Current Liabilities (0.892) One-Time Revenues (0.885) Population (0.896) Personal Income per Capita (0.888) Crime Rate (0.879) Operating Deficit or Surplus (0.877) Population Density (0.884) Uncollected Property Taxes (0.878) Revenues per Capita (0.878) Fixed Cost (0.877) 12 Revenue Shortfalls or Surpluses (0.880) Current Liabilities (0.877) One-Time Revenues (0.880) Population (0.894) Personal Income per Capita (0.885) Crime Rate (0.875) Operating Deficit or Surplus (0.871) Population Density (0.881) Uncollected Property Taxes (0.873) Revenues per Capita (0.875) Fixed Cost (0.874) Long Term Debt (0.872) 13 Revenue Shortfalls or Surpluses (0.875) Current Liabilities (0.873) One-Time Revenues (0.876) Population (0.891) Personal Income per Capita (0.879) Crime Rate (0.871) Operating Deficit or Surplus (0.865) Population Density (0.878) Uncollected Property Taxes (0.868) Revenues per Capita (0.872) Fixed Cost (0.869) Long Term Debt (0.871) Debt Service (0.867) As the previous assumptions of discriminant analysis are not flexible to analyze the adequacy of the model and considering that is insu ffi cient to study the behavior of dependent and independent variables, we complement the analysis with the application of logistic regression models with panel data whose main results are in Table 11 . Table 11. Logistic Regressions Model 1 Budgetary Stability Model 2 Expenditure Rule Model 3 Public Debt Model 4 Eco-Financial Plan LR of rho 0.49 1 0.00 0.00 Classification matrix 75.91 63.21 92.49 91.95 We focused this part of the analysis in a set of magnitudes of di ff erent tests that are usually applied in logistic regression models. The likelihood-ratio test of rho = 0 (LR of rho), which explains the independence of equations is statistically significant in Model 3 and Model 4, so the null hypothesis is rejected, which means that estimated panel data explain an important proportion of the total variance The matrix classification represents the correct classification, providing us with the percentage of the level of success: Model 1 : 75.91%, Model 2 : 63.21%, Model 3 : 92.49% and Model 4 : 91.95%, which rea ffi rms the goodness of fit of models, particularly in Models 3 and 4, and the higher discriminant power of independent variables.

[Find the meaning and references behind the names: Wald, Sig, Forward, Constant]

Sustainability 2020 , 12 , 6880 15 of 20 We also applied a stepwise logistic regression method (forward LR) to compare results between discriminant analysis and logistic regression, obtaining the independent variables with a higher discriminant power (Table 12 ). In this way, we check if the application of a di ff erent methodology shows similar conclusions, identifying the significant independent variables in the models and helping to study if Spanish indicators respond to the default classification according to ICMA indicators Table 12. Variables in the equation in logistic regression. Forward LR Method Steps Model 1 Budgetary Stability Model 2 Expenditure Rule Indicators B S.E. Wald Sig. Exp (B) Indicators B S.E. Wald Sig. Exp (B) 1 Current Liabilities 0.00 0.00 27.45 0.00 1.00 One- Time Revenues 0.00 0.00 38.63 0.00 1.00 Constant − 1.55 0.10 224.42 0.00 0.21 Constant − 0.32 0.07 19.18 0.00 0.72 2 Expenditures per Capita 0.00 0.00 16.39 0.00 1.00 One- Time Revenues 0.00 0.00 26.51 0.00 1.00 Current Liabilities 0.00 0.00 31.28 0.00 1.00 Revenue Shortfalls 0.00 0.00 18.72 0.00 1.00 Constant − 1.54 0.10 210.50 0.00 0.21 Constant − 1.86 0.36 26.28 0.00 0.15 3 Expenditures per Capita 0.00 0.00 22.12 0.00 1.00 One- Time Revenues 0.00 0.00 32.01 0.00 1.00 Operating Deficit or Surplus 0.00 0.00 11.17 0.00 1.00 Revenue Shortfalls 0.00 0.00 21.67 0.00 1.00 Current Liabilities 0.00 0.00 33.69 0.00 1.00 Expenditures per Capita 0.00 0.00 9.31 0.00 1.00 Constant − 1.16 0.15 57.98 0.00 0.31 Constant − 2.00 0.37 28.92 0.00 0.13 4 Expenditures per Capita 0.00 0.00 23.50 0.00 1.00 One time revenues 0.00 0.00 31.71 0.00 1.00 Operating Deficit 0.00 0.00 10.59 0.00 1.00 Revenue Shortfalls 0.00 0.00 18.72 0.00 1.00 Current Liabilities 0.00 0.00 34.32 0.00 1.00 Expenditures per Capita 0.00 0.00 11.77 0.00 1.00 Debt Service 0.00 0.00 4.989 0.02 1.00 Operating Deficit 0.00 0.00 5.50 0.01 1.00 Constant − 0.86 0.20 18.45 0.00 0.42 Constant − 1.62 0.40 16.31 0.00 0.19 5 Revenues per Capita 0.00 0.00 4.46 0.03 1.00 Expenditures per Capita 0.00 0.00 25.02 0.00 1.00 Operating Deficit 0.00 0.00 9.04 0.00 1.00 Current Liabilities 0.000 0.000 30.30 0.00 1.000 Debt Service 0.00 0.00 5.00 0.025 1.000 Constant − 0.12 0.40 0.09 0.72 0.88 6 Revenues per Capita 0.00 0.00 7.25 0.00 1.00 Expenditures per Capita 0.00 0.00 27.57 0.00 1.00 Operating Deficit or Surplus 0.00 0.00 6.63 0.01 1.00 Current Liabilities 0.00 0.00 24.73 0.00 1.00 Long- Term Debt 0.00 0.00 5.93 0.01 1.00 Debt Service 0.00 0.00 8.19 0.00 1.00 Constant 0.00 0.40 0.00 0.99 1.00 Steps Model 3 Public Debt Model 4 Eco-Financial Plan Indicators B S.E. Wald Sig. Exp (B) Indicators B S.E. Wald Sig. Exp (B) 1 Long- Term Debt 0.00 0.00 117.54 0.00 1.00 Expenditures per Capita 0.00 0.00 26.88 0.00 1.00 Constant − 3.29 0.19 289.89 0.00 0.03 Constant − 3.55 0.37 92.25 0.00 0.03 2 Revenues per Capita 0.00 0.00 49.29 0.00 1.00 Expenditures per Capita 0.00 0.00 27.81 0.00 1.00 Long- Term Debt 0.00 0.00 173.07 0.13 1.00 Population under 18 and over 64 0.00 0.00 5.25 0.02 1.00 Constant − 0.78 0.52 2.28 0.00 0.45 Constant − 3.65 0.36 104.63 0.00 0.03

[Find the meaning and references behind the names: Step, Ten, General]

Sustainability 2020 , 12 , 6880 16 of 20 Table 12. Cont Steps Model 3 Public Debt Model 4 Eco-Financial Plan Indicators B S.E. Wald Sig. Exp (B) Indicators B S.E. Wald Sig. Exp (B) 3 Revenues per Capita 0.00 0.00 34.94 0.00 1.00 Expenditures per Capita 0.00 0.00 26.21 0.00 1.00 Current Liabilities 0.00 0.00 20.09 0.00 1.00 Fixed Cost 0.00 0.00 5.87 0.02 1.00 Long- Term Debt 0.00 0.00 161.21 0.00 1.00 Population under 18 and over 64 0.00 0.00 7.32 0.01 1.00 Constant − 1.75 0.57 9.38 0.00 0.17 Constant − 5.69 0.96 34.77 0.00 0.00 4 Revenues per Capita 0.00 0.00 35.89 0.00 1.00 Current Liabilities 0.00 0.00 14.81 0.00 1.00 Long- Term Debt 0.00 0.00 156.82 0.00 1.00 Crime Rate 0.00 0.00 9.94 0.00 1.00 Constant In Model 1 Budgetary Stability , the Indicator 19 Current Liabilities is included in the first step, while in the discriminant analysis, it is included in the second step Indicator 10 Expenditures per Capita is included in the second step, while in the discriminant analysis, it is not included in any Indicator 1 Revenues per Capita is included in the fifth step, while in the discriminant, it is in step number ten Model 2 Expenditure Rule shares the same number of steps in logistic regression and discriminant analysis, coinciding also with a higher discriminant power the same independent variables: Indicator 5 One-Time Revenues , Indicator 9 Revenue Shortfalls or Surpluses and Indicator 15 Operating Deficit or Surplus In Model 3 Public Debt , logistic regression shows four steps, while in the discriminant analysis, there are seven, including the Indicator 20 Long-Term Debt , the independent variable with a higher discriminant power included in the first step in both analyses. Finally, in Model 4 Eco-financial Plan , three steps in both analyses are observed, showing Indicator 10 Expenditures per Capita and Indicator 30 Population under 18 and over 64 as independent variables with more discriminant power The independent variables that discriminate better are Indicator 1 Revenues per Capita, Indicator 10 Expenditures per Capita, Indicator 9 Revenue Shortfalls or Surpluses, Indicator 19 Current Liabilities and Indicator 20 Long-Term Debt, in other words, we obtain the same conclusion of discriminant analysis: the independent variables with a higher discriminant power are those indicators that have a similar meaning to Spanish indicators. The similarity of results provides robustness to our study Both analyses conclude that the indicators that better explain the default of Spanish LGs are those related to expenditures, revenues and debt. Furthermore, in logistic regression, the percentage of success is very high for the models of Public Debt and Eco-financial Plan , which means that the classification about default and non-default is correct in almost 90% of cases. ICMA indicators that measure the revenues, expenditures and debt classify correctly almost all Spanish LGs in default according to the Spanish legislation, based on transposing Eurostat requirements. That is, there is a direct relationship about the concept of default in Spanish legislation and the ICMA model / system 7. Discussion The main objective of this article was to analyze whether the way to measure the financial condition of LGs in Spain is a fair representation and a reliable tool for the measurement of the LG financial condition under international standards. After the reform of the Spanish Constitution in 2011 as a consequence of the Stability and Growth Pact (SGP) requirements of the EU, the financial control of LGs has increased by fixing debt and deficit limits. In absence of general patterns of the definition of financial indicators for LG financial sustainability, our research is focused on verifying if Spanish LGs’ financial indicators show common factors of the definitions of financial sustainability which are universally accepted. For this reason, we analyze the previous literature about di ff erent ways of measuring financial sustainability (such as ICMA, FIR, or AAS 27 indicators), that are also

[Find the meaning and references behind the names: Delivery, Enough, Try, Give, Freedom, Progress, Still, Wonder]

Sustainability 2020 , 12 , 6880 17 of 20 used by authors who try to explain the best way to derive useful information and evaluate the financial condition, obtaining common factors which are evaluated in order to achieve a good tool which allows us to test the financial sustainability of LGs. From this study, we conclude that four common factors are evaluated in financial sustainability, which reveals the application of four hypotheses in the analysis: evaluation of revenues, evaluation of expenditures, evaluation of debt and evaluation of cash The methodology applied aims at providing a model to test if financial ratios adopted by countries to control financial sustainability are backed by the generally accepted benchmarking international standards. In particular, we apply ICMA financial indicators because they represent a consistent tool of benchmarking, defining them as independent variables in the models, whose dependent variables are the indicators that we want to test the reliability of (each one the Spanish LGs financial indicators). Our results are consistent with previous literature because the indicators are associated with the control of expenditures and debt, and the revenue development is the variables that better explain the financial sustainability of LGs that may also support evaluations of the credibility of financial indicators Access to public information is crucial to develop a robust study; unfortunately, there are still obstacles in order to obtain all the information that a researcher would like to obtain, and it has become extremely complex to gather the information of worldwide LGs. Because of this, the progress in transparency of LG information must be a tool in order to be enhanced by governments which would allow for the identification of synergies among di ff erent ways to measure financial sustainability in the search for the most reliable financial indicators. It would be desirable not only to know the financial sustainability or instability of a local entity, but also to know that financial indicators would give enough information about the degree of instability of the LG 8. Conclusions LGs in Spain have the autonomy to manage the delivery of public services under their responsibility, collecting their own taxes, borrowing from banks and markets, and receiving transferences and grants from the central government, regional governments, and / or supranational organizations The EU has established a set of financial requirements to be met by the Eurozone countries in order to ensure the sustainability of public sector finances. Those requirements are monitored by Eurostat, which controls the financial position of Eurozone countries. Some Eurozone countries have transposed the binding EU regulation to their own domestic framework. In the Spanish case, the freedom of LGs to borrow from banks and markets and the introduction of new taxes have led the central government to transpose the EU regulations at its domestic local level, in order to ensure that LGs stay within the EU financial requirements related to the sustainability of public services delivered. Notwithstanding, we wonder to what extent these EU financial requirements and the indicators designed in Spain to transpose EU financial requirements are able to faithfully represent the actual financial condition of local The aim of this study is to determine whether financial indicators about financial conditions defined in Spanish regulation are backed by worldwide generally accepted financial benchmarking indicators. For this purpose, we analyze the relationship between Spanish indicators of financial sustainability based on EU regulations and Financial Trends Monitoring System Indicators of the ICMA. In this study, two methodologies are applied: discriminant analysis and logistic regression, where the dependent variables are each of the Spanish financial indicators and the independent variables are ICMA indicators The similar results of both analyses allow us to conclude that the ICMA variables, which endorse Spanish financial requirements, are those related to the financial indicators categories of: revenues, expenditures, operating position indicators and debt indicators, which is consistent with previous literature. The unfunded liability indicator category is not applicable to the Spanish case because pension plans and other retirement liabilities are centralized at the central government level for the whole Spanish public administration. Capital Plant indicators are also not applicable because Spanish

[Find the meaning and references behind the names: Adm, Soc, Europe, Ahead, Feder, Parent, Might, Original, Future, Kuhlmann, Council, Role, Orts, Positive]

Sustainability 2020 , 12 , 6880 18 of 20 LG are sovereign entities with democratic elections of the council of the city and the Mayor and, therefore, they do not contain contributed capital from parent entities Within each category, the ICMA defines a set of indicators and ratios. In the Spanish LG case, the indicators that better capture and summarize the substance of the transposition of the EU financial requirements to the Spanish LG legislation are Revenues per Capita and Revenues Shortfalls or Surpluses , Expenditure per Capita , Operating Deficit or Surplus , Current Liabilities and Long-Term Debt The measurement of financial condition is related to revenues ( Budgetary Stability ), expenditures ( Expenditure Rule ) and debt ( Public Debt ), which is aligned with the ICMA system and previous literature At present, e ff orts of municipal managers must be focused on ensuring financial sustainability; otherwise, the liquidity and the solvency of LG would be a ff ected. To avoid a situation where LGs are not able to meet their future financial obligations, robust quality tools of financial indicators are necessary not only to give information to policymakers, but also to be able to predict instability situations and provide a classification of the financial performance of local administrations. Therefore, the consistency of Spanish transposition of Eurozone requirements with international standards is positive evidence that gives reliability to all economic players and provides additional tools to managers for benchmarking purposes. Each country might adopt financial thresholds in accordance with its own administrative and legal framework, but the di ff erent forms of transposing Eurozone financial sustainability requirements should represent the same generally accepted concepts of financial sustainability, solvency and liquidity. The congruence between Spanish financial indicators and worldwide generally accepted financial benchmarking indicators enable us to provide an interesting contribution: these conclusions allow others countries to test the reliability of their own domestic regulation, providing a model that allows them to test their own domestic measurement of financial condition against worldwide generally accepted benchmarking standards. Moreover, the ICMA indicator system may become a benchmark reference to compare the financial sustainability of LGs at the EUand international level which entails a reference framework for the financial controllers in LGs As a result, this article provides two contributions to the financial sustainability arena: on one hand, Spanish financial indicators are in line with worldwide accepted benchmarking, and on the other hand, we suggest a model to test the reliability of financial sustainability indicators of LGs The control process of financial condition in LG and the demands for transparency after the global financial 2008 and Covid-19 crises is defining a new paradigm in LG management, which is powering ahead in Spain with the launch of regulation based on EU standards that establishes a schedule of reports concerning the financial situation of LGs. This achieves more responsible management in local administration, providing public services with quality Author Contributions: Conceptualisation, V.P.; methodology, L.R. and P.B.; software, L.R. and P.B.; analysis of results, V.P. and L.R.; writing—original draft preparation, L.R. and V.P.; writing—review and editing, V.P., L.R. and P.B. All authors have read and agreed to the published version of the manuscript Funding: This study has been carried out with the financial support of the Spanish National Research and Development Plan (project PID 2019-106857 GB-I 00 MINECO / FEDER), the Regional Government of Arag ó n / FEDER 2014-2020 “Building Europe from Arag ó n” (project S 56-17 R) and the University of Zaragoza through Research Projects JIUZ-2018-SOC-01 and UZ 2019-SOC-05 Acknowledgments: We would like to appreciate the thoughtful and constructive advice provided by the reviewers Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results References 1 Zeemering, E.S. Sustainability management, strategy and reform in local government Public Adm. Rev 2018 , 20 , 136–153. [ CrossRef ] 2 Kuhlmann, S.; Jäkel, T. Competing, collaborating or controlling? Comparing benchmarking in European local government Public Money Manag 2013 , 33 , 269–276. [ CrossRef ]

[Find the meaning and references behind the names: Ortiz, Hendrick, Ranking, Alcaide, Singh, Israel, Mead, Cant, Urban, Int, Var, Sci, Gov, Slater, Thames, Large, Shulman, Rayo, Point, Hern, Routledge, Carmeli, Guide, Alcaraz, Robbins, Grigg, Maher, Rubio, Bastida, Guez, Carrillo, Romania, Fin, Socio, Washington, Quiles, Bol, Focus, Robinson, Valente]

Sustainability 2020 , 12 , 6880 19 of 20 3 Groves, S.M.; Valente, M.G Evaluating Financial Condition: A Handbook for Local Government , 1 st ed.; International City / County Management: Washington, DC, USA, 1986 4 Groves, S.M.; Godsey, W.M.; Shulman, M.A. Financial indicators for local government Public Budg. Financ 1981 , 1 , 5–19. [ CrossRef ] 5 Nollenberger, K.; Groves, S.M.; Valente, M.G Evaluating Financial Condition: A Handbook for Local Government , 4 th ed.; International City / County Management: Washington, DC, USA, 2003 6 L ó pez-Subires, M.D.; Alcaide-Muñoz, L.; Navarro-Galera, A.; Rodr í guez-Bol í var, M.P. The influence of socio-demographic factors on financial sustainability of public services: A comparative analysis in regional governments and local governments Sustainability 2019 , 11 , 6008. [ CrossRef ] 7 Navarro-Galera, A.; Alcaraz-Quiles, F.; Ortiz-Rodr í guez, D. Enhancing sustainability transparency in local governments—An empirical research in Europe Sustainability 2018 , 10 , 2161. [ CrossRef ] 8 Mead, D.M An Analyst’s Guide to Governmental Financial Statements ; Governmental Accounting Standards Board: Norwalk, CT, USA, 2001 9 Hendrick, R. Assessing and measuring the fiscal health of local government: Focus on Chicago suburban municipalities Urban. A ff . Rev 2004 , 40 , 78–114. [ CrossRef ] 10 Kloha, P.; Weissert, C.S.; Kleine, R. Developing and testing a composite model to predict local fiscal distress Public Adm. Rev 2005 , 65 , 313–323. [ CrossRef ] 11 Ryan, C.; Robinson, M.; Grigg, T. Financial performance indicators for Australian local governments Acc. Perform 2000 , 6 , 89–106 12 Andrews, R. Local government amalgamation and financial sustainability: The case of England and Wales Public Fin. Manag 2013 , 13 , 8–10 13 Carmeli, A. A conceptual and practical framework of measuring performance of local authorities in financial terms: Analysing the case of Israel Local Gov. Stud 2002 , 28 , 21–36. [ CrossRef ] 14 Singh, J.V. Performance, slack, and risk taking in organizational decision making Acad. Manag. J 1986 , 29 , 562–585. [ CrossRef ] 15 Turley, G.; Robbins, G.; McNena, S. A framework to measure the financial performance of local governments Local Gov. Stud 2015 , 41 , 1–20. [ CrossRef ] 16 Turley, G.; Robbins, G.; McNena, S. Benchmarking the financial performance of local councils in Ireland Administration 2016 , 64 , 1–27. [ CrossRef ] 17 Brown, K.W. The 10-point test of financial condition: Toward an easy-to-use assessment tool for smaller cities Gov. Fin. Rev 1993 , 9 , 61–66 18 Blore, I.; Devas, N.; Slater, R Municipalities and Finance: A Sourcebook for Capacity Building ; Routledge: Abingdon-on-Thames, UK, 2012; pp. 1–179 19 Kioko, S.N. Reporting on the financial condition of the States: 2002-2010 J. Public Budg. Acc. Fin. Manag 2013 , 25 , 165–198 20 Navarro-Galera, A.; Rodr í guez-Bol í var, M.P.; Alcaide-Muñoz, L.; L ó pez-Subires, M.D. Measuring the financial sustainability and its influential factors in local governments Appl. Econ 2016 , 48 , 3961–3975 [ CrossRef ] 21 Gorina, E.; Maher, C.; Jo ff e, M. Local fiscal distress: Measurement and prediction Public Budg. Financ 2018 , 38 , 72–94. [ CrossRef ] 22 Cabaleiro, R.; Buch, E.; Vaamonde, A. Developing a method to assesing the municipal financial health Am. Rev. Public Adm 2012 , 43 , 729–751. [ CrossRef ] 23 Cabaleiro, R.; Buch, E. Is the tax collection e ff ort an indicator of the financial condition of spanish municipalities? Int. Public Manag. J 2014 , 17 , 564–592. [ CrossRef ] 24 Trussel, J.; Patrick, P.A. Assessing and ranking the financial risk of municipal governments: The case of Pennsylvania J. Appl. Acc. Res 2018 , 19 . [ CrossRef ] 25 Bulai, V.C.; Horobet , , A.; Belascu, L. Improving local government’s financial sustainability by using open government data: An application of high-granularity estimates of personal income levels in Romania Sustainability 2019 , 11 , 5632. [ CrossRef ] 26 Zafra, J.; L ó pez-Hern á ndez, A.; Hern á ndez-Bastida, A. Evaluating financial performance in local government: Maximizing the benchmarking value Int. Rev. Adm Sci 2009 , 75 , 151–167. [ CrossRef ] 27 Navarro-Galera, A.; Buend í a-Carrillo, D.; Lara-Rubio, J.; Rayo-Cant ó n, S. Analysing credit risk in large local governments: An empirical study in Spain Local Gov. Stud 2017 , 43 , 194–217. [ CrossRef ]

[Find the meaning and references behind the names: Rescigno, Ann, Eugen, Meneguzzo, Fiorani, Size, Market, Bond, Oxford, Padovani]

Sustainability 2020 , 12 , 6880 20 of 20 28 Navarro-Galera, A.; Buend í a-Carrillo, D.; Lara-Rubio, J.; Rayo-Cant ó n, S. The impact of population size on the risk of local government default Int. Tax Public Financ 2020 , 1–23. [ CrossRef ] 29 Clark, B. Evaluating the validity and reliability of the financial condition index for local governments Public Budg. Financ 2015 , 35 , 66–88. [ CrossRef ] 30 Meneguzzo, M.; Sancino, A.; Guenon, M.; Fiorani, G. New development: The crisis and European local government reforms Public Money Manag 2013 , 33 , 459–462. [ CrossRef ] 31 Padovani, E.; Rescigno, L.; Ceccatelli, J. Municipal bond debt and sustainability in a non-mature financial market: The case of Italy Sustainability 2018 , 10 , 3249. [ CrossRef ] 32 Fisher, R.A. The use of multiple measurements in taxonomic problems Ann. Eugen 1936 , 7 , 179–188 [ CrossRef ] 33 Pearson, K On the Coe ffi cient of Racial Likeness , 1 2 ed.; Biometrika: Oxford, UK, 1926; Volume 18, pp. 105–117 [ CrossRef ] 34 Mahalanobis, P.C. On tests and measures of group divergence J. Asiat. Soc. Bengal 1930 , 26 , 541–588 © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http: // creativecommons.org / licenses / by / 4.0 / ).

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