ANALYZING POLITICAL AND SYSTEMIC DETERMINANTS OF FINANCIAL RISK IN LOCAL GOVERNMENTS.

AuthorNavarro-Galera, Andres
  1. Introduction

    The economic recession that began in 2008 led to high levels of bank debt and budget deficits in the public sector, reducing solvency and restricting access to the credit market, as well as jeopardizing the sustainability of public services. The debt crisis produced great concern about credit risks, among policymakers, financial regulators, and fiscal authorities. Researchers concluded that it was necessary to study the causes of high levels of local government (LG) default in order to design and implement corrective and preventive policies and thus put government finances on a sound footing and in a position to meet debt and deficit targets (Aldasoro and Seiferling, 2014; World Bank Group, 2015; European Commission, 2012; Council of the European Union, 2011; Federal Accounting Standards Advisory Board, 2018; Navarro-Galera et al., 2015; Balaguer-Coll, Prior and Tortosa-Ausina, 2016; Beetsma and Vermeylen, 2007).

    The debt crisis has been particularly worrying in countries such as Italy, Ireland, Portugal, Greece, and Spain. International organizations have concurred with academic studies that public debt in these countries is of such a magnitude that major problems of repayment may arise and that there are worrying differences between public revenue and expenditure (European Commission, 2012; Aldasoro and Seiferling, 2014; Balaguer-Coll, Prior and Tortosa-Ausina, 2016; Navarro-Galera et al., 2015).

    In this context, taking into account that financial institutions are the main creditors of LGs, researchers have considered it especially interesting to study the risk of loan default (Arbatli and Escolano, 2015; Balaguer-Coll, Prior and Tortosa-Ausina, 2016; Elgin and Uras, 2013; Guillamon, Bastida and Benito, 2011). Various studies, including Balaguer-Coll, Prior and Tortosa-Ausina (2016), Elgin and Uras (2013) and Geys and Revelli (2011), have analyzed the influence of variables such as absolute majority government and political fragmentation on the volume of bank debt and on sustainability. However, these papers examined only individual factors pertaining to each LG and did not address the causes of default risk.

    Despite the valuable conclusions presented in the above papers, many organizations in the field of public finances argue that a comprehensive analysis of government credit risk should include, besides political factors (which are specific to each entity and may be dependent on election outcomes), systemic ones, such as the macroeconomic cycle, fiscal policy, and the electoral cycle, in view of the vulnerability of individual government entities to macro economic changes and the volatility of non-controllable variables (Aldasoro and Seiferling, 2014; Local Government Association of South Australia, 2019; Federal Accounting Standards Advisory Board, 2014; World Bank Group, 2015; US Department of the Treasury, 2013).

    In view of the above considerations, the aim of this paper is to advance our understanding of the factors that influence LG credit risk. Assuming an interconnection between political decisions and economic management, we study the joint effects of political and systemic variables on the probability of bank loan default, following the Basel II rules (BCBS, 2006). To do so, we analyze 148 large Spanish LGs during the period 2006-2011, using a logit model with panel data and an artificial neural network. From the results obtained, we identify political and systemic factors underlying the risk of LG insolvency. The conclusions drawn provide useful new knowledge for policymakers, managers, financial analysts, regulators, national and international tax authorities, voters, users of public services, citizens at large, and other stakeholders.

  2. The impact of political and systemic variables on credit risk under Basel II rules

    We study the causes of credit risk by considering the probability of default (PD) as a financial indicator. In line with previous research in this field, our analysis takes into account the definition of default, or breach of bank payment commitments, established by the Basel II Banking Supervision Committee (Castren, Dees and Zaher, 2010; Bluhm and Overbeck, 2003; Gordy, 2003), according to which a higher probability of default is associated with a greater expected loss, a greater need for capital and, therefore, a higher risk-adjusted rate of interest.

    Considering the different default scenarios considered under Basel II, our study incorporates a dependent variable addressing these possibilities through an ability-to-pay process (APP), which measures LGs' capacity to meet their credit liabilities (Bluhm and Overbeck, 2003). This APP depends on the quality of LG assets and financial resources and is a latent random variable that is not directly observable, but which can be estimated using a nonlinear discrete-choice approach, namely the logit panel data model, which is an appropriate means of considering the factors that contribute to the likelihood of debt default (Bonfim, 2009; Jacobson, Linde and Roszbach, 2013). In addition, to study the phenomenon of government insolvency, we construct an artificial neural network (ANN), in the knowledge that previous researchers have used ANNs as a complement and an advance on parametric techniques. In most cases, this approach enhances the analytical process.

    In this respect, the standard known as Basel II (BCBS, 2006) is a highly significant advance in the international financial system as this model helps ensure the soundness and stability of credit institutions, making it possible to assess the financial risks associated with the institutions, such as governments, to which these entities make loans. In particular, the Basel II model seeks to ensure that banks implement new tools to strengthen the capital requirements arising from their credit risk operations, focusing both on the market and on operational aspects. Accordingly, the model proposes tools with which financial institutions can estimate the default risk on the loans made to their customers.

    According to Gordy (2003), a local government (LGi) is in default if its ability to pay at any given time AP[P.sub.it] is below a certain level of credit liability ([C.sub.it]). Under this approach, default by L[G.sub.i] in the period t is a random dichotomous variable [Y.sub.it] such that:

    [mathematical expression not reproducible] (i)

    where the probability of LG. default at time t is equal to

    P[D.sub.it]=P([Y.sub.it]=l) = P{AP[P.sub.it][less than or equal to][c.sub.it]) (2)

    Following the methods used in previous studies of credit risk in the business sector (Castro, 2013; Mileris, 2012), we analyze two types of credit risk factor, as variables expected to influence the probability of default: political factors and systemic factors. Political factors impacting on credit risk ([Z.sub.t]) are specific to each local government. In contrast, systemic credit risk factors ([X.sub.t]) include aspects of the macroeconomic cycle, fiscal policy, and the electoral cycle, with respect to the country.

    In this paper, the following initial premises are assumed: (1) local governments form a homogeneous segment within the public sector; (2) the systemic factors [X.sub.t] that influence credit risk affect all local governments at time t (t = 1,..., T); (3) the idiosyncratic political factors [Z.sub.it] (i = 1,..., Nt, t = 1,..., T) that influence credit risk individually affect each L[G.sub.i]; (4) the idiosyncratic LG factors (individual or LG-specific factors) are not entirely independent of systemic effects, an aspect that is particularly significant in economic recessions (Bonfim, 2009). Thus, the AP[P.sub.it] variable of the i-th LG at time t is a function of the political and systemic variables, as in expression (3):

    AP[P.sub.it]=[alpha]+[[beta].sub.j][X.sub.l]+[[delta].sub.k][Z.sub.it]+[u.sub.it] (3)

    where [[beta].sub.j] and [[delta].sub.k]are the parameter vectors estimated by a linear panel data model and [u.sub.it] is the random perturbation. Then, following Rosch (2003) and Bonfim (2009) a borrower [L.sub.g] is considered to be unpaid if its AP[P.sub.it] falls below the level [c.sub.it] (credit obligations). Although the variable AP[P.sub.it] is latent and not directly observable, the explanatory variables [X.sub.t] and Z. and the systemic and political factors, together with the independent binary variable [Y.sub.it], the indicator of default, are directly observable, from the sample data.

    In our study, credit risk was determined via the estimation of PD, using a logistic regression model and an ANN. This procedure was adopted for several reasons. First, discrete-choice models are held to be appropriate when the research aim is to analyze the determinants of the probability of an individual economic agent (Jacobson, Linde and Roszbach, 2013). Second, the two models we use meet all the statistical requirements specified in the Basel II rules (BCBS, 2006) and in Bank of Spain Circular 3/2008 for calculating PD. Third, the European Commission (2015), Aldasoro and Seiferling (2014), the World Bank Group (2015) and the US Department of the Treasury (2013) have all recognized the need to study the joint effect of idiosyncratic factors (or individual ones, for a single entity) and systemic factors (such as the macroeconomic cycle, fiscal policy, and the electoral cycle) in the measurement of government credit...

To continue reading

Request your trial