jones et al 2007 abacus

ABACUS, Vol. 43, No. 3, 2007

doi: 10.1111/j.1467-6281.2007.00238.x

396

© 2007 The AuthorsJournal compilation © 2007 Accounting Foundation, The University of Sydney

Blackwell Publishing AsiaMelbourne, AustraliaABACAbacus0001-3072© 2007 Accounting Foundation, Unviersity of SydneyXXX

ORIGINAL ARTICLES

EXPLANATORS OF LOCAL GOVERNMENT DISTRESSABACUS

STEWART JONES AND R. G. WALKER

Explanators of Local Government Distress

This article develops a statistical model to explain sources of distress inlocal government. Whereas ‘financial distress’ in the private sector hasbeen equated with a failure to meet financial commitments, here ‘distress’is interpreted as an inability to maintain pre-existing levels of services tothe community. Since the late 1990s local councils in an Australian state(New South Wales) have been required to estimate the cost of restoringinfrastructure assets to a satisfactory condition (a requirement whichpredates that form of reporting on infrastructure condition introduced asan option in U.S. GASB 34). Information regarding the cost of restoringinfrastructure is used in this study as a proxy for

levels

of distress (incontrast to the binary classification that characterizes much of prior privatesector financial distress research). Data regarding service levels for asample of 161 councils for 2001 and 2002 were used and a multipleregression model was estimated and interpreted. The main findings werethat the degree of distress in local councils is positively associated withthe size of the population they serve and the size and composition oftheir revenues. Road maintenance costs featured prominently in results,as higher road program costs were associated with higher levels of distress(particularly when interacted with other variables). However, the revenuegenerating capacity of councils had the strongest statistical impact onlocal government distress. Councils with lower percentages of rates revenueto total revenue and lower ordinary revenue levels to total assets weretypically identified as more distressed. However, no systematic evidencewas found that rural councils have higher distress levels than urban councils(i.e., both rural

and

urban councils serving larger populations were relativelymore distressed than councils serving smaller populations). It is suggestedthat the model (or modifications thereof) may serve as an early warningsystem for those monitoring the circumstances and performance of localgovernments.

Key words:

Distress prediction; Local government; Model; Satisfactorycondition.

Following the new millennium collapse of many major corporations, includingEnron, WorldCom, Global Crossing, Adelphia Communications, Vivendi and, inAustralia, HIH, One.Tel, Pasminco and Ansett (just to mention a few), there hasbeen a resurgence of interest in various aspects of corporate distress modelling(e.g., Altman, 2001). Distress predictions have been used widely for a variety ofscreening and monitoring purposes, such as going concern assessments by auditors,assessment of loan default by lenders and corporate health assessments by credit

S

tewart

J

ones

([email protected]) and R. G. W

alker

are Professors in the Discipline ofAccounting at The University of Sydney.

EXPLANATORS OF LOCAL GOVERNMENT DISTRESS

397


rating agencies and regulators. More recently, distress forecasts have been usedextensively in the measurement of portfolio risk, and in the pricing of corporateand sovereign bonds, including off-balance sheet financial instruments subject tocredit risk, such as collaterized debt obligations (e.g., Altman, 2001; Duffie andSingleton, 2003; Jones and Hensher, 2004).

However, the main focus has been on corporations in the private sector, and inparticular, on those corporations whose securities are publicly traded—possiblybecause financial and market data about these firms were readily available. In thiscontext, ‘distress’ has been variously interpreted as being evidenced by voluntaryor creditor-induced administration (bankruptcy), default on a loan repayment,failure to pay a preference dividend (or even a reduction in the amount of ordinarydividend payments), share issues specifically to meet shortfalls in working capital,financial reorganization where debt is forgiven or converted to equity, and a failureto pay listing fees (e.g., Foster, 1986; Lau, 1987; Bahnson and Bartley, 1992; Ward,1994; Jones and Hensher, 2004).

Here concern is with distress in the

public sector

, with a focus on local governmentin the state of New South Wales.

1

‘Distress’ is interpreted as an inability to provideservices at pre-existing levels. In order to provide services to the community, localgovernments are expected to invest in infrastructure and to maintain legacy infra-structure. Accordingly, the article uses the estimates developed by local govern-ments of the cost of restoring infrastructure to a satisfactory condition as ameasure of degrees of ‘distress’. As such, the study uses a quantitative measure ofdistress, as opposed to the more limited (and less relevant) binary classificationthat characterizes private sector distress research.

There have only been limited applications of financial distress models to thenot-for profit sector (e.g., Schipper’s 1977 analysis of financial distress in U.S. pri-vate colleges, and some others listed below). It is acknowledged that there hasbeen extensive analysis of fiscal and financial crises in the local government sector,particularly in the U.S.A. in the wake of the financial problems facing New Yorkcity and Cleveland during the 1970s (e.g., Gramlich, 1976; Clark and Ferguson, 1983;Wallace, 1985; Falconer, 1991) and a subsequent spate of major financial crises inthe early 1990s (e.g., Gramlich, 1991; Honadle, 2003). Commentators have alsoexamined the financial crises experienced by local governments in a range of othercountries (e.g., Carmeli and Cohen, 2001; Bach and Vesper, 2002; Carmeli, 2003).In this context, a financial crisis could involve bankruptcy or loan default (e.g.,the cases described in Cahill and James, 2002) but it has also been equated with aseries of operating deficits (Cahill and James, 1992; Bach and Vesper, 2002).

The main focus of prior studies has been to explore the

reasons

for fiscal crises—with some commentators attributing these problems to a lack of organizationalresources and managerial skills leading to an incapacity to deliver quality services

1

Australian local governments generally provide a more limited range of services than their counter-parts in North America or the U.K. Major responsibilities include provision of local roads, wasteremoval and maintenance of building controls—but not education, health services or policing.Local councils in rural areas may also maintain water treatment and sewerage facilities, and otherforms of transport infrastructure (such as regional airports).

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in an efficient manner or to adapt to changing conditions (Carmeli and Cohen,2001). Others have suggested that distress is a consequence of a failure to adapt toeconomic downturns in general, or to the financial impact of unfunded mandates,as state governments shifted responsibilities to cities or municipalities withoutfinancial compensation or while restricting the capacity of local governments toincrease revenues (Falconer, 1991; Beckett-Camarata, 2004). Still others sought toexplain local government behaviour in times of financial stress (for a review, seeCooper, 1996), or to describe state responses to municipal crises (see Cahill andJames, 1992;

Harvard Law Review

, 1997).While it appears there have only been limited attempts to predict local government

financial distress in the research literature, those with a responsibility to monitorthe performance of the local government sector have utilized a range of techniquesto identify municipalities that may be facing difficulties. However, one contributionnoted that while some jurisdictions have sought to establish early warning systems,‘they may not be functioning as planned’ (Cahill and James, 1992, p. 92). Anotherstudy reported the development of a simple index based on arbitrary weighting ofnine variables (Kleine

et al.

, 2003), and subsequently these authors reported theresults of applying this to a sample of Michigan local governments—suggestingthat it performed better than Michigan’s current system of identifying potentiallydistressed local councils, apparently via financial statement analysis (Kloha

et al.

,2005). The claim of superior performance was based on the suggestion that it had‘theoretical validity’, produced similar results to the assessments of a state agency,and was parsimonious. A fifty-state survey by Honadle (2003) revealed that justunder half of those states made some attempt to predict local government’s fiscalcrises, mainly through reviewing audit reports, local government reporting, orfrom information gleaned from discussions or regional workshops, with only someU.S. states using ‘financial analysis methods’ (p. 1454)—and apparently noneusing a statistical distress prediction model.

A recent Australian study proposed an econometric distress prediction modeland (as with the approach used by Kloha

et al.

, 2005) compared the findings witha ‘watch list’ compiled by a state government agency—in this case, concluding thatthe latter’s selection of ‘at risk’ councils did not accurately identify municipalitiesthat were (according to their model) in fact ‘at risk’ (Murray and Dollery, 2005).

A number of commentaries have acknowledged that reviews of financial statementanalysis alone may be a poor basis for predicting local government distress,because financial ratios may show up problems ‘too late’. Indeed, Clark andFerguson (1983) provided extensive evidence to support their contention that fiscalstrain reflects the degree to which governments fail to adapt to changes in theresources available to the taxpaying community. That observation in itselfhighlights the difficulties of applying distress prediction models to the public sector.It is well recognized that, in the private sector, a prediction of distress may not befulfilled if management takes corrective action. It is also recognized that a majorlimitation of the distress literature is that many studies have modelled failure as asimplistic binary classification of failure or non-failure (Jones, 1987). This methodologyhas been widely questioned, because the strict legal concept of bankruptcy (or


399


insolvency) may not always reflect the underlying economic reality of corporatefinancial distress. For instance, there is considerable documentary evidence thatcorporations have, from time to time, misused bankruptcy provisions for their ownstrategic purposes, such as staving off creditors (Lau, 1987; Delaney, 1991). Further,the two-state model can also conflict with underlying theoretical models of financialfailure and this can potentially limit the extent to which empirical results can begeneralized (Scott, 1981; Bahnson and Bartley, 1992; Lau, 1987; Hill

et al.

, 1996).

2

If the archetypal two-state failure model is of dubious relevance in the privatesector, its application may be even more severely limited in the public sector, whereentities that are financially distressed may respond to falls in revenues or increases incosts by reducing the range and quality of services they provide to the community.

Against this background, the objective of this study is to fill a gap in the distressliterature by developing a quantitative modelling approach to identify explanatorsof local council distress. As local councils typically do not fail in the sense of beingunable to pay their debts, the aim is not to

predict

financial failure

per se

butrather to identify factors that explain local government distress—interpreted hereas an inability of those entities to maintain standards of service.

A statistical modelling approach is arguably superior to more rudimentary andheuristic approaches (such as a financial statement analysis), because it allows thetesting of a formal hypothesis and an examination of the statistical and explanatoryimpact of a range of covariates in a multivariate setting. A quantitative modelmay have relevance in assisting state or commonwealth agencies overseeing theactivities of the local government sector to develop robust early warning systemsto identify potentially distressed councils. It may also assist in formulating policiesregarding local council mergers or amalgamations, and (for jurisdictions thatengage in rate pegging) in reviewing applications for special variations.

3

A quantitativemodelling approach can also assist regulators back-test their own ‘in house’ distressratings system (such as that undertaken by the N.S.W. Department of LocalGovernment in identifying councils that are ‘at risk’ of distress).

MEASURING DISTRESS IN LOCAL GOVERNMENT

Previous works have struggled to establish a satisfactory metric for local governmentdistress. For instance, Clark (1977) discussed four indicators of municipal fiscalstrain: (a) probability of default, where default is defined as not meeting bondrepayments; (b) ratio indicators, such as gross debt divided by the tax base or

2

Others have argued that the practical risk assessment decisions by lenders and other partiesnormally cannot be reduced to a simple pay-off space of failed and non-failed (see, e.g., the discussionin Ohlson, 1980; Ward, 1994; Jones and Hensher, 2004). For instance, firms can experience a rangeof distress conditions that do not necessarily result in bankruptcy.

3

Australian local governments are commonly subject to direction from state governments. NewSouth Wales (alone amongst Australian states) subjects local government to a regime of ratepegging, whereby percentage increases in general income are subject to an upper limit unless appli-cations for ‘special rate variations’ are approved by the Minister for Local Government (

LocalGovernment Act

, ss 505–13).

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short-term debt to long-term debt, (c) socio and economic based characteristics,such as population size and median family income, and (d) funds flow measures.However, these measures have certain intractable problems when operationalizedas a formal measure of local government distress (particularly in Australia). Asnoted by Clark, bond defaults may not be useful given that actual default rateshave historically been extremely low (see also Kaplan, 1977). Furthermore, thelink between bond ratings and financial condition presupposes that capital marketsare provided with adequate financial information by local governments (whichmay be unrealistic given the lack of consistent and transparent financial disclosurein many U.S. local government authorities). Ratio indicators (such as debt toequity) are unsuitable as dependent variables in the modelling of distress formany reasons, including (a) financial ratio indicators used as dependent variablesare likely to be correlated, directly or indirectly, with other financial indicatorsused as independent variables and (b) ratio metrics are not specifically related tothe broader non-financial dimensions of local council distress (if interpreted as aninability to maintain the quality of service delivery to the community). Similarly,socioeconomic factors (such as population size) are again more appropriatelyused as explanatory variables in modelling as these measures do not representdirect measures of local council distress.

Another dependent variable that can potentially indicate distress in local councilsis the incidence of mergers and amalgamations. The N.S.W. government hasencouraged voluntary local council mergers to encourage efficiencies. Forinstance, the

Local Government Amendment (Amalgamations and BoundaryChanges) Bill 1999

streamlines the procedures laid down in the

Local GovernmentAct 1993

for voluntary amalgamations of council areas.

4

Prima facie

, the incidenceof mergers and amalgamations represents a potentially attractive dependent variable,as merged councils can be readily identified

5

and typically such merger activity hasbeen motivated by concerns about the financial viability of some local councils.

6

For example, a proposal for creation of a New Capital City Regional council insouthern areas of the state would have seen the merger of five smaller councils.A major rationale was described as follows:

The substantial pool of funds [through consolidating the revenues of all adjacent councils]contrasts with the meagre financial bases that some of the existing Councils possess. It

4

Chapter 9 of the

Local Government Act 1993

, Part 1, Areas, and Part 3, Local GovernmentBoundaries Commission, contain information on the constitution, dissolution and alteration oflocal government areas.

5

Over the past ten years, around 13 per cent of councils have disappeared through mergers andamalgamations.

6

S. 263 of the NSW

Local Government Act

requires that the Boundaries Commission has regardto: (a) the financial advantages or disadvantages (including the economies or diseconomies of scale)of any relevant proposal to the residents and ratepayers of the areas concerned; (b) the impact ofany relevant proposal on the ability of the councils of the areas concerned to provide adequate,equitable and appropriate services and facilities; (c) the impact of any relevant proposal on theemployment of the staff by the councils of the areas concerned; and (d) the impact of any relevant pro-posal on rural communities in the areas concerned.


401


might be noted that two of the five Councils had deficits on ordinary activities in 2002.Gunning (southern half) had a small surplus in 2002, but a large deficit of $244,000 in2001. Tallaganda had a deficit of $322,000 in 2001. It is clear that four out of the fiveCouncils are operating on very slim margins between revenues and expenditure fromordinary activities. (p. 10 of application

7

)

However, there are a number of issues to consider if council mergers and amalga-mations are used as the distress metric. Public companies experiencing distresscan seek out merger partners in any number of locations, and typically merge withbusiness partners that are in a stronger financial position. However, mergers of localcouncils in N.S.W. (and elsewhere) are constrained by geographic considerations.Typically, distressed councils merge with adjacent councils that may only bemarginally better off in financial terms themselves. Merging two or more finan-cially fragile councils does not necessarily produce one larger ‘healthy’ council.

8

Mostmergers over the recent years in N.S.W. have involved smaller regional councils,and the numbers have been comparatively small in absolute terms (not more than13 per cent in the past ten years).

Service Delivery as the Dependent Variable

Given the difficulties in operationalizing an appropriate financial distress measurein local councils, this study focuses on constructing a proxy of distress linked tobasic operating objectives of local councils, which is to provide services to thecommunity. The major responsibilities of Australian local government are theprovision of local infrastructure (such as roads, bridges and community facilities)and waste collection. Local councils are responsible for administering buildingcontrols (though in some circumstances these may be overridden by state authorities).In major metropolitan areas the provision of water and sewerage services isundertaken by state agencies, but in rural and regional areas these functions aregenerally provided by local councils (sometimes through joint ventures). Whileindividual councils may provide some social welfare services, the provision ofhealth and education services is a responsibility of the states, with the Common-wealth government providing earmarked grants to support some services (such ashome care programs for the aged or persons with disabilities).

9

7

The application can be located at the following url: http://www.dlg.nsw.gov.au/Files/CommissionsTribunals/BC_Proposal_Capital_City.pdf#xml=http://www.dlg.nsw.gov.au/Scripts/dtSearch/dtisapi6.dll?cmd=getpdfhits&DocId=676&Index=c%3a%5cdtsearch%5cuserdata%5cAllDocuments&HitCount=4&hits=6c+70a+a64+1603+&.pdf

8

For example, consider the December 2000 proposal to create the new Gwydir Shire Council whichcombined the Yalloroi, Bingara and part of the Barraba shire councils (the new Gwydir Council wasofficially formed on 17 March 2004). All three councils were in a fragile financial position—withthe Bingara and Yalloroi Shire Councils both showing a deficit in 2002. See their original petition athttp://www.dlg.nsw.gov.au/Files/CommissionsTribunals/BC_Proposal_Gwydir_Shire_Council.pdf#xml=http://www.dlg.nsw.gov.au/Scripts/dtSearch.

9

For a detailed summary of local government functions in Australia, see the

Local GovernmentNational Report

,

2004–05 Report on the Local Government (Financial Assistance) Act, 1995

, 2006,available at http://www.dotars.gov.au.

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Service delivery can be considered in terms of both the

quantity

or

quality

ofservices provided. In this exploratory analysis, the focus is on

qualitative

aspectsof service delivery. A purely quantitative measure of service delivery can result inmisleading interpretations of local council distress and may not be, for variousreasons, strongly associated with explanators of distress (see Table 2 below). Forexample, road infrastructure can be provided and/or maintained by a local council, inspite of the fact that road quality itself can be steadily diminishing over time or leftin a poor state of repair. Similarly, sewerage infrastructure in a poor state of repairmay continue to operate, even though it threatens public health standards and the localenvironment. An example of why a quantitative measure of service delivery may not beappropriate are the circumstances of Windouran Shire Council in New South Wales. The‘distress’ of this council was discussed in the N.S.W. parliamentary debates as follows:

Windouran’s plan to get out of its financial distress was to significantly increase rates eachyear, rip up the bitumen on some roads and make them all gravel to save maintenancecosts, sell all but essential plant and equipment and delay the purchase of new equipment,and consider further reductions in staff. This response demonstrates clearly theseriousness of the situation of Windouran council. Windouran Shire Council has anoperating deficit amounting to $1,251 for each man, woman and child in the shire.(

Hansard

, NSW Legislative Assembly, 11 November 1999, p. 2786).

It has been suggested that local government fiscal stress may lead to a decline inmaintenance expenditure on infrastructure and a decrease in capital investmenton infrastructure in order to finance other expenditures (Bumgarner

et al.

, 1991).This article interprets a decline in expenditure on infrastructure—and a correspondingincrease in the funding required to restore the functionality of infrastructureassets—as a proxy for distress, as local government entities fail to allocate sufficientfunds to adequately maintain that infrastructure.

Information concerning the funding required to restore the functionality ofinfrastructure is available in N.S.W., since the 1993 legislation required localcouncils to assess and report to the Minister for Local Government whether theirinfrastructure (in four categories) was in a satisfactory condition, and if not, theestimated cost of bringing that infrastructure to a satisfactory condition. Further,councils were required to provide estimates of the (hypothetical) cost of maintainingthose assets in a satisfactory condition, together with particulars of the current-yearbudgetary allocations for maintenance.

Subsequently other standard setting bodies—including the U.S. Federal Account-ing Standards Advisory Board (FASAB, 1996) and the Governmental AccountingStandards Board (GASB, 1996)—have introduced accounting standards requiringsome form of reference to infrastructure condition. The FASAB required disclosuresrelated to the condition and the estimated cost to remedy deferred maintenance toproperty, plant and equipment, while prohibiting recognition of the dollar valuesof these items in the financial statements (FASAB, 1996). The GASB introducedoptions for accounting for infrastructure by American states and municipalities:These entities were either to value and then depreciate infrastructure assets, oralternatively, demonstrate that infrastructure assets were being managed using anasset management system, and document that the assets were being preserved


403


approximately at (or above) a condition level established by and disclosed by thatgovernment. Whereas the GASB required that state and municipal governmentsaccount for infrastructure either through asset recognition followed by depreci-ation, or through supplementary reporting on infrastructure condition, the N.S.W.local government requirements required

both

, through a combination of theapplication of Australian Accounting Standards, and supplementary reporting.

It is recognized that a potential limitation of this dependent variable constructis that, in the absence of a standard methodology for defining and measuring ‘satis-factory condition’, local councils may have widely different interpretations ofthat concept and use differing estmation methods. An earlier study found thatthe information reported by councils varied in quality and coverage, and in theinterpretations adopted for ‘satisfactory condition’ (see Walker

et al.

, 1999). How-ever, subsequently the N.S.W. Department of Local Government has providedclearer advice to councils. Further, telephone interviews with several local councilmanagers, accountants and engineers indicate that estimates have been refinedover time and (with some exceptions) a greater proportion were ‘evidence based’(e.g., underground drainage pipes in metropolitan areas had been inspected withthe use of television technology). Another study (Edwards and Walker, 2007, notreported here) suggests that this has led to more consistency in the way in whichestimates of the cost to bring assets to ‘satisfactory condition’ have been reportedby councils, as reflected in a reduction of the extent of variations reported from yearto year.

METHODOLOGY

Much of the traditional corporate distress prediction literature has employed avariety of discrete choice models, the most popular being linear discriminant analysisand binary logit and probit models (see e.g., Altman, 1968; Altman

et al.

, 1977;Ohlson, 1980; Zmijewski, 1984; Duffie and Singleton, 2003). There has also beena plethora of new modelling approaches in recent years, including structural (or‘distance to default’) models and intensity (or reduced form) models (see Duffieand Singleton, 2003, for a detailed review).

10

Furthermore, new research has beenconducted into the behavioural performance of advanced logit models, such as

10

Under a structural modelling approach (also known as the Black-Scholes or Merton model), theprobability of failure is framed around a stochastic model having two crucial inputs: (a) the marketvalue of the firm’s assets relative to external debt and (b) volatility of the market value of a firm’sassets relative to debt. A popular extension of this basic concept is the KMV (1993) and Kealhofer(1996) model. KMV is based on an empirically determined ‘distance from default’ which is meas-ured by the number of standard deviations a firm’s asset values are currently above/below its exter-nal debt level (this is compared to the percentage of firms which actually failed at a given asset todebt level). Reduced form models do not rely on accounting book data (such as debt), but asexplained by Duffie and Singleton (2003), these models assume an exogenously specified processfor default probabilities which are calibrated for past and current market data. They note thatstructural and intensity models have been popular in academic research and in practice.

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random parameters logit (or mixed logit), nested logit models, latent class MNLand kernel logit models (see Jones and Hensher, 2004).

11

Data Collection and Sample

The sample used in this study is based on the financial statements and infrastruc-ture report data of 172 local councils in New South Wales over a two year period2001–02. The data collected included local council characteristics (such as whetherthe council is large or small, or urban or rural, based on formal classifications usedby the DLG), service delivery outputs, condition of infrastructure and an extensiverange of financial variables (described below). Data were collected from severalsources. Infrastructure data were accessed from the 2002 infrastructure reports tothe minister on the condition of public infrastructure prepared by New SouthWales councils in accordance with the

Local Government Act

of 1993. Thesereports were provided by the New South Wales Department of Local Governmentfor the population of 172 councils then operating in the state in 2002.

Council background data were collected from a report of

Comparative Informationon New South Wales Local Government Councils

prepared by the Department ofLocal Government (2002). This report contains comparative data for all councils inNew South Wales across a series of key performance indicators for the years 1999/2000, 2000/01 and 2001/02. This report was also used to source many of the financialand non-financial variables used in this study. Other financial variables were obtainedfrom the 2001 and 2002 annual reports or financial statements of local councils.

The first stage in the annual report collection process involved searching thewebsites of the sampled local councils for downloadable copies of their 2001 and2002 annual reports. The reports of sixty-nine local councils were available online,and these reports were downloaded. The remaining 103 councils were contacted(either via email, telephone or mail) and requested to forward their 2001 and 2002annual reports. This procedure resulted in 161 useable annual reports. Table 1summarizes the sample of annual report data collected.

Data integrity checks of data relating to infrastructure condition that had beenfiled with the DLG identified some significant percentage variations betweenyears. Follow-up enquires to individual councils established that most reflected afailure to ‘round’ reported data to the nearest $1,000, while other amendmentscorrected typographical errors by those councils in one or other year.

Definition of Distress

As already described, the definition of distress used here incorporates a qualitativemeasure of service delivery. This definition is not linked to social service outputs

per se

but to the

condition

of infrastructure assets upon which the delivery of localcouncil services is critically dependent. Levels of distress are defined for this

11

In contrast to standard logit models, advanced logit structures, to varying degrees, relax the highlyrestrictive IID condition (identical and independently distributed errors) and the IIA condition(independence from irrelevant alternatives) assumption. Advanced models also incorporate a richvariety of observed and unobserved firm-specific heterogeneity into model estimation (bothbetween and within firms).


405


purpose as being represented by the estimated cost expected to be incurred bylocal councils to get infrastructure assets into a ‘satisfactory condition’. Specifically,the dependent variable in this study is a continuous variable defined as

the ratio ofexpected total costs to bring local council infrastructure assets to a satisfactory condition,scaled by total revenues

. Scaling (i.e., dividing) to total revenues is intended to controlfor size differences between local councils, and is appropriate as general revenue isthe primary source of funds available to a local councils to maintain infrastructurein satisfactory condition. Also noted above, many N.S.W. councils (mainly thoseoutside major metropolitan areas) received revenues from charges for water andsewerage services (and these charges are not subject to rate pegging). Accordingly,scaling involved use of total revenues (both excluding and including water andsewerage charges). It was found that scaling total costs to bring infrastructureassets to a satisfactory condition by other denominators (such as operating cashflows or total assets) was highly correlated with total revenues, suggesting that thismeasure is robust to choice of scale.

12

Explanatory Variables

A broad range of financial and non-financial measures were tested (see theAppendix for a list of variables and their definitions). The explanatory variablesare in four categories: (a) council characteristics; (b) local service delivery variables;(c) infrastructure variables; and (d) financial variables. A brief description andexplanation of the choice of variables follows:

Council Characteristics

Local government areas in N.S.W. vary considerably in terms of geography anddemography, and encompass areas as small as 5.8 square kilometres (the Sydneysuburb of Hunters Hill) to as large as 53,510 square kilometres (the local government

12

The total revenue measure and operating cash flow measure have a coefficient of .82, and the totalrevenue measure and total asset measure have a coefficient of .66 (both tests are based on a Pearson’s

r

two-tailed test and are significant at the .01 level).

Table 1

SUMMARY OF ANNUAL REPORT DATA COLLECTED

Annual report and financial statements received 161

Annual report received without financial statements 3

Annual report not received 8

Total 172

Complete data 146

Incomplete—missing cash flow statements only 13

Incomplete––missing cash flow statements and other data 2

Total 161

ABACUS

406


area of Central Darling in the far west of the state) (DLG, 2005). Population den-sities also vary considerably—from 0.11 per square kilometre in the rural areas ofBrewarrina and Cobar, to 6,697.85 per square kilometre in the

Sydney suburb ofWaverley. Such variations would affect both the volume of services to be providedto local communities, and the scale of revenues received by councils, mainly from‘rates’ (property taxes based on unimproved capital values). Because of a com-bination of a lower rate base, smaller staffing levels and the challenge of having toservice larger areas, it has been commonly suggested that the local governmentareas most exposed to financial stresses are in rural and regional areas (and,indeed, the Department of Local Government’s 2002 ‘watch list’ indicates thattwenty-four of the twenty-six ‘at risk’ councils are rural or regional).

Local councils were classified into eleven major categories in the DLG database:

1 = Capital city2 = Metro developed—small /medium3 = Metro developed—large/very large4 = Regional town/city—small /medium5 = Regional town/city—large/very large6 = Fringe—small /medium7 = Fringe—large/very large8 = Agricultural—small9 = Agricultural/Remote—medium10 = Agricultural/Remote—large11 = Agricultural—very large

The variables included in the model to reflect council characteristics were as follows:

• whether the council is large or small (a dummy variable describing whether acouncil is large/extra large or small /medium, based on the DLG classificationsummarized above, with large councils encompassing categories 1, 3, 5, 7, 10, 11and small councils encompassing 2, 4, 6, 8, 9);

• whether the council is rural or urban (a dummy variable coded whether council isurban or rural, with urban councils being categories 1–7, and rural councilscategories 8–11 in the DLG classification scheme);

• number of equivalent full-time council staff;• population serviced within local council boundaries;• number of rateable farmland properties; and• number of rateable business and rateable residential properties.

Service Delivery Variables

It is commonly suggested that the main issues facing Australian local councils arethe 3Rs—roads, rates and rubbish. That label summarizes some of the majorresponsibilities of the ‘third tier’ of Australian government—councils are responsiblefor local roads, and the collection and disposal of waste; unlike local governmentin some other countries, they are not responsible for health, education, policing orpublic housing. Australian local governments may also provide a range of other


407


services: Some maintain libraries, swimming pools and sporting facilities, andprovide care for the aged or disabled citizens (though the latter services areprimarily funded by Commonwealth and State governments). The provision ofwaste collection services constitutes a material expenditure for the sector. Otherservices (such as the provision of library facilities) are likely to be less significant.

Accordingly, service delivery variables (using data from the DLG database) were:

• domestic waste pickups per week;• number of residential properties receiving waste management services; and• total kilograms of recyclables collected.

Infrastructure Variables

Arguably the largest expenditures of local governments are directed towards pro-viding and maintaining infrastructure of varying kinds. While the maintenance oflocal roads, other transport infrastructure and drainage systems is a commonresponsibility, many local councils are also responsible for reticulation of potablewater and sewerage (the exceptions being those local government areas servicedby state-owned authorities operating in areas surrounding Sydney and Newcastle).

With this background, the infrastructure variables examined in the analysis were:

• the carrying values for buildings, roads, other transport, water, sewerage anddrainage infrastructure;

• estimated costs to bring buildings, roads, other transport, water, sewerage anddrainage infrastructure to a satisfactory condition; and

• budgeted maintenance expenditure for buildings, roads, water, sewerage anddrainage infrastructure.

Financial Variables

The financial measures used are based on a number of ratio measures examinedin the financial distress literature over the last three decades (examples includeAltman

et al.

, 1977; Ohlson, 1980; Zmijewski, 1984; Casey and Bartczak, 1985;Gentry

et al

., 1985; Jones, 1987; Altman, 2001). More recent research has establishedthe importance of operating cash flows in predicting corporate failure (see Jonesand Hensher, 2004). Previous U.S. research had only tested estimates of operatingcash flows and such proxies have been found to be associated with significantmeasurement error (see Rebar and Collins, 2002). As Australian accountingstandards since the early 1990s have required local councils to prepare detailedcash flow statements prepared using the direct method (see AAS 27,

FinancialReporting by Local Government

, paras 31–32), it was possible to use reported cashflow measures). Financial ratio categories tested here include: operating cashflows (e.g., operating cash flows to total assets); cash position (e.g., cash and short-term investments to total assets); liquidity and working capital (e.g., currentratio); rate of return (e.g., reported surplus to total assets); financial structure(e.g., total debt to total assets); and debt servicing capacity (e.g., operating cashflow to interest payments). Further, several possible interactions of financial andcouncil specific and demographic variables were examined. Testing interaction

ABACUS

408


effects sought to determine whether contextual factors (such as population size orcouncil type) have a moderating influence on the financial variables which enterthe model. For instance, it is possible that the size of a council has a moderatinginfluence on the level of indebtedness (larger councils might be more indebtedthan smaller councils or

vice versa

).

EMPIRICAL RESULTS

Two regression models based on a

quantitative

and

qualitative

measure of servicedelivery are compared. A quantitative measure of service delivery only considerschanges in the physical level of output or services provided to the community bycouncils (such as the physical number of residential properties which receive a wastemanagement service), whereas a qualitative measure of service delivery is basedon the adequacy of services provided, as proxied by the physical condition of assetson which service delivery is critically dependent. As indicated earlier, a quantitativemeasure of service delivery can lead to misleading interpretations as it may be spu-riously linked to financial aspects of council distress and service quality itself. Forexample, local councils are obliged to collect residential waste products

irrespective

of their financial position or performance. Also, while councils can be maintainingservices at current levels, a lack of investment in maintaining critical infrastructurecan (potentially) result in a catastrophic drop in service quality in a later period.

In order to examine whether council financial performance is linked to a quan-titative measure of service delivery, a service output measure was constructed, basedon the annual growth of services provided by local councils over a two-year period(2001–02). This measure of distress includes the following service output variables

13

:number of services (domestic waste pickups per week), total kilograms of recyclablescollected, total kilograms of domestic waste collected, and number of residentialproperties receiving waste management service. A composite of growth in servicedelivery across these key measures was calculated, and then regressed ontoa range of local council financial variables taken over the same period. Table 2reports the parameter estimates and

t

-values across a number of financial vari-ables, including ratios based on working capital, cash flow from operations, cashposition, interest cover, net surplus to total assets and total debt to total assets.Table 2 fails to reveal any statistically significant relationships between thesevariables and the quantitative measure of service delivery.14

In order to test the qualitative measure of distress, a multiple regression modelwas also estimated. Table 3 displays parameter estimates and significance levelsfor the final multiple regression model, while Table 4 provides descriptive statisticsfor the significant covariates in the final model. The final model was selectedbased on its overall explanatory power and statistical coherence. The regression

13 Data were collected from the Department of Local Government (2002).

14 The results were essentially the same when individual physical output measures (such as change inthe number of residential properties receiving waste management service) were used as thedependent variable.


409© 2007 The Authors

Journal compilation © 2007 Accounting Foundation, The University of Sydney

model in Table 3 has strong overall goodness of fit with an adjusted R2 of 0.70. Itcan be seen that all parameters are statistically significant and appear to have logicaland consistent signs.

Interpreting the parameter estimates in Table 3, it can be seen that distress inlocal councils is positively associated with the population serviced within localcouncil boundaries and also with the size of local councils (larger local councilsare relatively more distressed than smaller councils). The distinction between sizeof councils (as defined by the DLG classifications) was found to be greatestbetween large and small urban councils. In particular, larger urban councils werefound to be significantly more indebted (measured by total debt to total assets),but had relatively stronger operating cash flows, greater revenue generationcapacity and higher rates of return on assets (net surplus to total assets). Whenthe financial performance of small and large rural councils was compared, allexcept one variable (the current ratio) were found to be significant (albeit thesignificance level was marginal at p < .1), indicating that larger rural councils hadslightly greater working capital than smaller rural councils.

It was found that distress is negatively associated with measures based on revenuegeneration capacity. Councils with lower percentages of rates revenue to total

Table 2

REGRESSION ANALYSIS FOR QUANTITATIVE MEASURES (I.E., PHYSICAL OUTPUT LEVELS) OF SERVICE DELIVERY

Financial variables Unstandardized coefficients

Standardized coefficients

t-value p-value

Beta Std. Error Beta

Constant 5.572 5.171 1.078 .283

Current ratio .518 1.087 .046 .477 .634

Cash flow operations to total assets

.425 1.235 .043 .344 .731

Long term interest bearing debt to total assets

.000 .002 .047 .265 .791

Cash resources to total assets .100 .560 .018 .178 .859

Interest cover .000 .000 −.097 −.536 .593

Gross debt to operating cash flow

−.190 .290 −.060 −.655 .514

Operating cash flow to total infrastructure assets

−.022 .263 −.008 −.085 .932

Ordinary revenue (less waste and sewerage charges) to total assets

.727 .977 .232 .745 .458

Total expenditure by total assets

−.912 .965 −.310 −.945 .347

Surplus to total assets −.159 1.031 −.020 −.154 .878

ABACUS

410© 2007 The AuthorsJournal compilation © 2007 Accounting Foundation, The University of Sydney

revenue and lower ordinary revenue levels to total assets are typically more dis-tressed. Councils with lower staff numbers are also relatively more distressed.Councils with lower carrying values for infrastructure assets were associated withgreater distress (lower written down values could suggest that assets are older andpossibly in poorer condition). Also, road maintenance costs featured prominentlyin results—higher road program costs are associated with higher council distress(t = 3.38). It was expected that this variable could have important interactionaffects with other variables, such as total area (in square kilometres) serviced bythe local councils. The results indicated that the interaction of road maintenancecosts and area serviced by councils was positive and significant, suggesting that thelarger the area serviced by local councils, the greater the impact of road maintenancecosts on council distress (t = 2.21). Further, the interaction of road maintenancecosts and the estimated costs to bring road assets to a satisfactory condition wasalso positive and significant, suggesting that the poorer the physical condition ofroad assets (reflected by the higher costs to get assets into satisfactory conditions)the greater the impact of road maintenance costs on council distress (t = 2.01).15

15 Further, the interaction of road maintenance costs and whether a council is urban or rural wasfound to be positive and significant, suggesting that the impact of road maintenance costs on councildistress was greater for urban councils (t = 2.21).

Table 3

REGRESSION ANALYSIS FOR QUALITATIVE MEASURE OF SERVICE DELIVERY

Variables

Unstandardized coefficients

Standardizedcoefficients

t-value p-value Collinearity statistics

Beta Std. Error Beta Tolerance VIF

Constant 690.8 127.7 5.409 .000

Population within council boundaries

.001 .000 .565 2.658 .014 .221 4.522

Local council large or small

91.09 34.455 .391 2.644 .015 .456 2.194

Rates revenue to total ordinary revenue

−8.596 1.967 −.601 −4.371 .000 .529 1.890

Ordinary revenue (less waste and sewerage charges) to total assets

−12.297 3.622 −.447 −3.395 .002 .577 1.734

Road program costs over total assets

8026.680 2374.710 .537 3.380 .003 .396 2.525

Number of full-time (equivalent) staff

−.232 .095 −.564 −2.455 .022 .189 5.279

Carrying value — totalinfrastructure

.000 .000 −.488 −2.434 .023 .249 4.023




The highest t-values (indicating a stronger statistical influence on distress) werevariables associated with revenue generation, particularly the ratio of rates reve-nue to total revenue (t = −4.371) and ordinary revenue (less waste and seweragecharges) to total assets (t = −3.395). Again, this result could be directly related tolegislative requirements for rate pegging among local councils in N.S.W., whichcan restrict the capacity of some councils to raise revenues to meet greater servicedelivery demands as well as finance the maintenance of infrastructure assets sothat they remain in a satisfactory condition.

There are some noteworthy findings that were not found to be significant in theresults. For instance, the urban versus rural council classification did not yield anystatistically significant findings in the model. This is despite a widely held belief

Table 4

DESCRIPTIVE STATISTICS FOR SIGNIFICANT COVARIATES REPORTED IN TABLE 3

Variables N Minimum Maximum M SD

Population within council boundaries

342 1,337 266,072 38,034.06 50,740.5

Size of council (large/small)

65 .00 1.00 .4923 .50383

Ordinary revenue (less water and sewerage charges) tocarrying value of infrastructure assets

172 .02 2177.41 30.3511 166.9

Rates revenue to ordinary revenue

342 9.43 72.00 40.96 13.03

Carrying value of infrastructure assets ($000s)

172 1749 76181787 844822 6327203

Road program costs to total assets 159 .00 .53 .0173 .0428

Number of equivalent full-time staff 342 15 1177 232.15 235.72

Total cost to get infrastructure assets (total) into satisfactory condition by total ordinary revenues

172 .00 1330.16 135.5096 155.79047

Total cost to get building infrastructure assets into satisfactory condition by total ordinary revenues

172 .00 46.62 7.2604 9.75211

Total cost to get road infrastructure assets into satisfactory condition by total ordinary revenues

172 .00 1319.04 96.7363 139.94884

Total cost to get water infrastructure assets into satisfactory condition by total ordinary revenues

108 .00 87.56 13.6689 18.81730

Total cost to get sewerage infrastructure assets into satisfactory condition by total ordinary revenues

118 .00 137.14 16.2807 23.31940

Total cost to get drainage infrastructure assets into satisfactory condition by total ordinary revenues

162 .00 399.82 12.4868 35.37008

ABACUS


that Australian rural councils are experiencing a relatively higher degree of distress,partly because they are required to service larger geographical areas coupled withsmaller population sizes to generate rates revenue (as pointed out earlier, manycouncil mergers and amalgamations in recent years have involved smaller ruralcouncils). Table 5 provides a finer analysis of the financial performance of urban

Table 5

COMPARISON OF FINANCIAL PERFORMANCE OF URBAN VS RURAL COUNCILS

Financial variables N M SD Std error mean

Current ratio Rural 259 2.5978 1.64 .10Urban 65 2.4572 1.09 .13

Capital expenditure ratio Rural 259 .3676 11.59 .72Urban 65 .9602 1.28 .15

Debt ratio Rural 259 6.06*** 5.02 .31Urban 65 3.52 3.24 .40

Cash flow operations to total assets Rural 221 3.91* 6.72 .45Urban 50 2.27 3.08 .43

Ordinary revenue to total assets Rural 239 16.80* 42.67 2.76Urban 59 7.98 4.60 .60

Ordinary revenue (less waste and sewerage charges) to total asset

Rural 239 13.47* 38.46 2.48Urban 59 7.20 4.09 .53

Cash resources to total assets Rural 238 3.43* 6.02 .39Urban 60 1.877 2.11 .27

Cash flow cover Rural 98 54.93 38.01 38.85Urban 21 53.17 181.58 39.94

Rates to ordinary revenue Rural 259 37.06*** 10.83 .67Urban 65 57.73 8.24 1.02

Gross debt to operating cash flow Rural 221 1.60 5.91 .39Urban 50 1.23 2.66 .37

Operating cash flow to total infrastructure assets

Rural 105 5.2794 7.15 .69Urban 26 5.2142 4.49 .88

Ordinary revenue (less water and sewerage charges) to total infrastructure assets

Rural 121 17.6852* 31.91 2.90Urban 33 85.6761 375.58 65.38

Operating cash flow to revenues Rural 221 .3545* .16 .01078Urban 50 .2614 .19 .02816

Net surplus to total assets Rural 239 .4290 5.38 .34833Urban 59 .6366 1.13 .14819

Total costs to bring local council infrastructure assets into satisfactory condition scaled by total revenues (net of waste and sewerage charges)

Rural 121 144.8476 166.62048 15.14732Urban 33 114.7209 116.03305 20.19876

* Significant at .05 level, ** significant at .01 level, *** significant at .001 level.




versus rural councils in N.S.W. across a wide range of financial indicators, includingthe current ratio, capital expenditure ratio, debt to assets, cash flow from operations,cash resources to total assets and revenue generation capacity.

Also displayed are group differences in the distress construct used in this study(the costs to get infrastructure assets into satisfactory condition). It is noteworthythat neither the distress construct nor a range of financial performance variableswas found to be statistically significant between urban and rural councils. Table 5indicates that rural councils have a significantly higher level of indebtednessrelative to urban councils, however rural councils appear to have a strongerrevenue base relative to total assets (irrespective of whether waste and seweragecharges are included or excluded in revenues). Furthermore, rural councils have asignificantly stronger cash position and operating cash flow performance relativeto urban councils; as well as a lower proportion of rates to total revenue. Otherfinancial indicators, such as the current ratio, debt servicing capacity, net surplusto total assets and gross debt to operating cash flows, were not statisticallysignificant between rural and urban councils.16

A final comment needs to be made about the robustness of the regressionmodel reported in Table 3. Two common problems in multiple regression modelsare multicollinearity (a condition where the model’s independent variables arehighly correlated with each other) and heteroscedasticity (or unequal variance—which is a violation of the identical and independently distributed error [IID]condition which can seriously undermine the statistical power of parameterestimates). Variance inflation factors reported in Table 3 are quite low for allvariables, indicating that the presence of multicollinearity among explanatoryvariables is unlikely to be significant in the model. Furthermore, specific testscarried out on the sampled data indicated that the t-values reported in Table 3 donot need to be corrected for heteroscedasticity.17

CONCLUSIONS

This exploratory article develops a statistical model to identify factors mostclosely associated with local council distress in Australia. A major objective hasbeen to develop a pragmatic and meaningful measure of council distress that canbe readily operationalized for statistical modelling purposes.

The concept of distress is linked to the basic operating objective of localgovernment, which is to provide a basic range of services to the community. It hasbeen suggested that fiscal stress leads to underspending on infrastructure maintenance

16 Use of non-parametric tests (i.e., Mann-Whitney U for independent samples) did not alter thereported results materially.

17 A common test for heteroscedasticity is to regress the squared residuals of the model on theexplanatory variables. Following this procedure, it was found that none of the explanatory vari-ables was statistically significant, suggesting that the standard errors of the t-values do not requirecorrection.

ABACUS


and new capital projects. However, this article views estimates of the cost ofremedying deterioration in infrastructure as a proxy for a decline in service standards—and as such is a pertinent measure of local council distress. Specifically, thedependent variable construct is specified as a continuous variable, defined as theratio of expected total costs to bring local council infrastructure assets into satisfactorycondition scaled by total revenues (net of waste and sewerage charges). Thisdependent variable was modelled in a multiple regression framework using a widerange of performance indicators, including variables relating to council characteristicsand demographics (such as council size and population size), service delivery andinfrastructure variables and a wide range of financial indicators. The resultsrevealed that revenue generating capacity (measured by rates revenue to totalordinary revenue and ordinary revenue less waste and sewerage charges to totalassets) had the strongest overall statistical impact on council distress. Road main-tenance costs were also found to have a significant main effect in the model; butwere also associated with a number of significant interaction effects, particularlywhen this variable was interacted with the area (in square kilometres) serviced bycouncils; whether councils are classified as urban or rural; and with the expectedcosts of getting road assets into satisfactory condition. Surprisingly, some variableswidely believed to be associated with local council distress in Australia were notfound to be significant in the results, such as the distinction between rural andurban councils (using t-tests, the dependent variable used in this study was notfound to be statistically significant between rural and urban councils, nor were alarge number of financial performance variables).

There are several advantages for a using a statistical model in evaluating localcouncil distress, as opposed to a purely descriptive or heuristic approach (such asa ratio analysis of key variables). First, a multiple regression model allows anumber of complex relationships among variables (and associated interactions) tobe examined simultaneously and in terms of their joint contribution to explainingvariation in local council distress. Parameter estimates represent the statistical‘weights’ calibrated to the relative contribution or strength of each variable to thedistress outcome. Second, statistical models provide a formal basis for assessingthe significance of each variable’s explanatory power in the overall model system, andfor accepting or rejecting the importance of different variables in model estimation.

Finally, statistical models, once estimated, are relatively efficient and easy toapply in practice, particularly to large samples. A major benefit of a statisticallygrounded distress model is that it can be used as an effective screening deviceacross a large number of councils to identify emerging financial difficulties andpressures among councils. Such models can also have important policy applications,such as providing an objective basis for assessing the financial circumstances oflocal councils in various rate-pegging applications and/or in identifying distressedcouncils susceptible to merger and/or amalgamation activity (as well assessingwhether the merger activity itself has been effective in reducing distress). Possiblytriangulation of different distress measurement techniques (such as heuristic/descriptive and quantitative tests) may provide greater insights into the circumstancesof public sector agencies than the use of one approach in isolation.




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APPENDIX

DEFINITION OF EXPLANATORY VARIABLES

Variable Description

Council characteristics

Ccode Council and year code

Group N.S.W. Department of Local Government council group number

Exist Dummy variable describing whether council existed in a certain year

Urban Dummy variable describing whether council is urban or rural

Large Dummy variable describing whether council is large/extra large or small/medium based on the DLG’s classification

Popn Estimated resident population within council boundaries

Area Area (sq. km) at 30 June 2001

Norrp Number of rateable residential properties

Norfp Number of rateable farmland properties

Norbp Number of rateable business properties

Staff Number of equivalent full time council staff

Local service delivery variables

Dwpickup Number of services (domestic waste pickups per week)

Wmsprop Number of residential properties receiving waste management service

Recyc Total kilograms of recyclables collected

Wastekg Total kilograms of domestic waste collected

Infrastructure variables

Cbldg Carrying value of buildings infrastructure

Cvroad Carrying value of roads infrastructure

Cvwatr Carrying value of water infrastructure

Cvsewr Carrying value of sewerage infrastructure

Cvdrn Carrying value of drainage infrastructure

Cvtot Total carrying value of infrastructure assets

Satbldg Cost to bring buildings infrastructure to a satisfactory condition

Satroad Cost to bring roads infrastructure to a satisfactory condition

Satwatr Cost to bring water infrastructure to a satisfactory condition

Satsewr Cost to bring sewerage infrastructure to a satisfactory condition

Satdrn Cost to bring drainage infrastructure to a satisfactory condition

Sattot Total cost to bring infrastructure assets to a satisfactory condition

Pmbldg Program maintenance expenditure for buildings infrastructure

Pmroad Program maintenance expenditure for roads infrastructure

Pmwatr Program maintenance expenditure for water infrastructure

Pmsewr Program maintenance expenditure for sewerage infrastructure

Pmdran Program maintenance expenditure for drainage infrastructure

Pmtot Total program maintenance expenditure for infrastructure assets.

ABACUS


Financial Variables

Ordrev Total ordinary revenue (including water and sewerage charges)

Ornows Total ordinary revenue (excluding water and sewerage charges)

Raterev Rates revenue (ordinary and special) and annual charges

Rrrev Residential rates revenue

Frrev Farmland rates revenue

Brrev Business rates revenue

Racucf Rates and annual charges plus user charges and fees

Dwmchg Total domestic waste management charges

Ratesout Outstanding rates and annual charges plus user charges and fees

Ordexp Total ordinary expenditure

Oenows Total ordinary expenses (excluding water and sewerage)

Wsopcost Water supply operating costs including depreciation

Ssopcost Sewerage service operating costs including depreciation

Depn Depreciation

Borrcost Borrowing costs

Ndscost Net debt service cost

Debtrat Debt service ratio (Ndscost/revenue from ordinary activities)

Empcost Employee costs

Ta Total assets

Cash Cash assets

Ca Current assets (less external restrictions)

Cl Current liabilities (less specific purpose liabilities)

Invsec Investment securities

Cibl Current interest bearing liabilities

Ncibl Non-current interest bearing liabilities

Cfo Cash from operating activities

Netopta Net operating cash flow by total assets

Netoptr Net operating cash flow by sales revenue

Cpta Cash, deposits and marketable securities by total assets

Cpcl Cash, deposits and marketable securities by current liabilities

Debtta Total debt to total assets

Tlta Total liabilities to total assets

Roa Net income (surplus) to total assets

Current Current assets by current liabilities

Workcta Working capital (current assets––current liabilities) by total assets

Nicover Net income (surplus) by annual interest payments

Cdebtc Total debt by gross operating cash flow

Cfcover Net operating cash flow by annual interest payments

Variable Description