WP/15/13

Governments’ Payment Discipline: The Macroeconomic Impact of Public Payment

Delays and Arrears

Cristina Checherita-Westphal, Alexander Klemm, and Paul Viefers

© 2015 International Monetary Fund WP/15/13

IMF Working Paper

Western Hemisphere Department

Governments’ Payment Discipline: The Macroeconomic Impact of Public Payment Delays and Arrears

Prepared by Cristina Checherita-Westphal, Alexander Klemm, and Paul Viefers

Authorized for distribution by Roberto Cardarelli

January 2015

Abstract

This paper considers the impact of changes in the payment discipline of governments on the private sector. We argue that increased delays in public payments can affect private sector liquidity and profits and hence ultimately economic growth. We test this prediction empirically for European Union countries using two complementary approaches. First, we use annual panel data, including a newly constructed proxy for government arrears. We find that payment delays and to some extent estimated arrears lead to a higher likelihood of bankruptcy, lower profits, and lower economic growth. However, while this approach allows a broad set of variables to be included, it restricts the number of time periods. We therefore complement it with a Bayesian VAR approach on quarterly data for selected countries faced with significant payment delays. We again find that the likelihood of bankruptcies rises when governments increase the average payment period.

JEL Classification Numbers: E6, H6, H8

Keywords: Public payment delays, government arrears, government spending

Authors’ E-Mail Addresses: Cristina.Checherita-Westphal@ecb.europa.eu, aklemm@imf.org, pviefers@diw.de.

Checherita-Westphal is with the European Central Bank; Klemm with the IMF; and Viefers with DIW Berlin and worked on this paper during an ECB internship. We thank Pedro Hinojo and seminar participants at the 16th Banca d'Italia Workshop on Public Finance in Perugia and an ECB seminar for helpful comments and discussion and Madeleine Bosch of Intrum Justitia AB, Sweden, for her valuable help with European Payment Index data. Views are those of the authors and do not necessarily represent those of the ECB, the IMF, or DIW Berlin.

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the authors and are published to elicit comments and to further debate.

Contents

Page

I Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

II Definitions and Data Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5A Official data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5B A proxy for fiscal arrears . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

III The Aggregate Effects of Payment Arrears—Evidence from Panel Regressions . . . . . 13A Growth regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13B Impact on profit growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16C Impact on likelihood of bankruptcy . . . . . . . . . . . . . . . . . . . . . . . . . . 16

IV The Aggregate Effect of Payment Delays—Evidence from Bayesian VARs . . . . . . . . 21A Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22B Non-recursive identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22C Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

V Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

VI Mathematical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

List of Tables

1 Panel Regressions of Real GDP Growth on Payment Delays. . . . . . . . . . . . . . . 152 Panel Regressions of Real GDP Growth on Estimated Arrears. . . . . . . . . . . . . . 173 Panel Regressions of the Growth of the Gross Operating Surplus on Payment Delays. . 184 Panel Regressions of the Distance to Default on Payment Delays. . . . . . . . . . . . . 195 Panel Regressions of the Distance to Default on Estimated Arrears. . . . . . . . . . . . 206 Quarterly Structural Repsonses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

List of Figures

1 Accounts Payable (AF.7) in EU Countries (percent of GDP). . . . . . . . . . . . . . . 102 Average Reported Payment Duration of the Public Sector in 2012 . . . . . . . . . . . . 113 Duration Density of Public Payments: Illustration . . . . . . . . . . . . . . . . . . . . 114 Actual and Estimated Payment Arrears of the Public Sector by Country. . . . . . . . . 125 Average Reported Payment Duration by the Public Sector . . . . . . . . . . . . . . . . 246 Structural Impulse Responses of Selected Variables . . . . . . . . . . . . . . . . . . . 26

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I. INTRODUCTION

The issue of government arrears has gained prominence during the European sovereign debtcrisis. Particularly in EU/IMF program countries—both in and outside the euro area—but also inother fiscally vulnerable economies, such as Italy and Spain, the identified amounts wereconsiderable, and measures to reduce the stock of arrears featured prominently in governmentstrategies and as program targets. At the same time, the European Commission took initiatives atthe EU level to reduce payment delays, such as the 2011 Directive on combating late payment incommercial transactions, which also covers transactions between undertakings and publicauthorities.1

To the best of our knowledge, this is the first paper to systematically address and econometricallyestimate the economic impact of public spending delays and arrears. The existing literature onarrears is mostly concerned with the measurement of spending arrears in different systems ofnational accounts, especially in developing countries (Diamond and Schiller (1993)), and ways toreduce the stock of arrears, for example through restructuring and/or securitization (Ramos(1998)). Flynn and Pessoa (2014) contains a recent overview of the issues arising in preventingand managing government arrears. The likely macroeconomic effects are only discussed viaintuition and example, but are not modeled in a theoretical setup or estimated using econometrictechniques.2 Our key contributions therefore are (i) to put forward a statistical measure that mayserve as a proxy for arrears, and (ii) to use this and other measures of payment delays to gauge theeffect on some key economic variables, such as growth and profitability.

Like private agents, governments have some discretion on when to pay their bills and otherobligations. The outstanding payments of governments are, however, different in various respectsfrom trade credit among private sector agents. First, within the private sector, paying a bill shiftsliquidity across firms, but does not affect aggregate private sector liquidity. Second, given the sizeof the government, particularly in European countries, its payment policies are important to alarge base of suppliers. Third, the government is at the same time a debtor and creditor, but in avery distinct way, as most of the funds owed to the government are taxes, i.e., unrequitedpayments, whose payment terms are set by the government.

The discretion governments have in choosing when to pay may be foreseen already in contractsthat include explicit or customary trade credit, but it can also go beyond that if governments missdue dates and fall into arrears. Payment traditions and expectations vary across countries andsectors, but as long as the situation is static the impact should be limited. If a government has atradition of taking a long time to pay bills, then suppliers will price the cost of such credit into the

1The directive, which entered into force in March 2013, imposes a maximum delay for new government payments of30 days (60 days for a limited set of exceptions, such as in the health sector) and an 8 percent surcharge forinfringement.

2Bank of Italy (2013) estimated the impact of the Italian initiative to clear arrears on growth to be close to unity ifpayments are used to finance investment, roughly 0.3 if used for firms’ wage arrears and close to zero if kept forprecautionary saving. Overall they estimate a positive impact on the economic growth rate of between 0.5 and 0.7percentage points. They do not provide a description of how exactly these numbers were estimated or whicheconometric model or rationale was used to obtain them.

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goods supplied. There could still be some limited impact, though, as firms with extreme creditconstraints may then not be able to do business with the government.

In times of economic crisis, however, payment delays could change in unexpected ways. Mostobviously, a government facing a funding constraint could delay payments. This could be bills forgood supplied, thus increasing its trade credit or it could be a delay in wage or pension payments.Typically, delaying payments for interest and amortization on public debt occurs only as a lastresult to avoid being officially in default. Even governments with full access to cheap financing,however, sometimes delay payments. Depending on the accounting framework used, this can leadto lower public debt or deficit figures. Whether debt turns out to be lower, depends on whethertrade credit and arrears are counted as government debt. Under the Excessive Deficit Procedure(EDP) definition, this is not the case, but EDP submissions include separate reporting of accountspayable.3 The deficit in selected years would also be reduced if measured on cash basis, but notnormally if an accrual definition is used (although in practice, some transactions may be misseduntil payment takes place).

Governments could also decide to accelerate payments to suppliers or previously accumulatedarrears, at some stage in a crisis, in particular to support a liquidity-constrained private sector. Inthis spirit, the Italian government announced in April 2012 a major program (EUR 40 billion) toclear arrears over two years. This program was later augmented (reaching EUR 66 billion), andby October 2014 already EUR 32.5 billion had been paid out. Similarly, Spain announced in May2012 a mechanism in the form of a government guaranteed syndicated loan worth EUR 30 billionby which the central government helps regional and local governments clear their arrears.

Payment delays may also have purely administrative reasons. To address these, the Italiangovernment, for example, introduced compulsory electronic invoices for central governmentadministrations in mid-2014 and plans to extend them to local governments by spring 2015.Moreover, to increase transparency the related data will be published on the web.

Changes in payment lags can be expected to have implications on the macroeconomic situationthrough various channels:

• Corporate profits can be affected, because unexpected delays change the present discountedvalue of payments. If no or a low interest rate applies, this reduces suppliers profitability.

• The size of the corporate sector can be affected if liquidity-constrained firms, in particularsmall and medium-sized enterprises (SMEs), go bankrupt. This will also have knock-oneffects on creditors of such firms. Various second-round effects are also likely, e.g., a higherbankruptcy rate could increase the cost of capital even to firms with access to credit; thecost of future orders of goods and services to the government could rise, as suppliers builtthe anticipated financing costs, including the uncertainty, into offers.

3It is less clear whether it is included in the national accounts measures of debt. The Eurostat Manual onGovernment Deficit and Debt (Eurostat (2010)) notes that there is no specific definition of government debt inESA-1995, but that the stock of government debt would cover the sum of all general government liabilities, includingaccounts payable (page 305). Elsewhere (page 303), the manual notes that mainly for practical reasons, accountspayable are not accounted for in government debt.

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• Business investment can be affected in liquidity-constrained firms. These may not only bethose directly dependent on government payments, but also their own suppliers as paymentdelays trickle on. Aggregate demand, and finally output and growth, could thus benegatively impacted.

To lay the foundation of our analysis, Section II discusses the various forms of payment delaysand the extent to which they form arrears. It also describes the available data and explains theconstruction of our measures of arrears and delayed payments. Section III provides an analysis ofthe impact of payment delays on profits, bankruptcies and growth, using dynamic panel datatechniques. Section IV complements the previous analysis by using a Bayesian VAR on quarterlydata for Italy, Spain and Portugal. Section V brings together the findings and concludes.

II. DEFINITIONS AND DATA AVAILABILITY

A. Official data

According to the IMF Government Finance Statistics Manual (IMF (2001)), “arrears arise whenan obligatory payment is not made by its due-for-payment date.” The term arrear should not beconfused with general unpaid government bills or other obligations. A true arrear only occurs if abill is not paid by the due date, whether this is based on a contractual agreement, commercial lawor custom (e.g., 60 days after the invoice date). A government may therefore have large amountsof unpaid bills without falling into arrears. Conversely, it is also possible that overall unpaid billsare small, but some of them are in arrears, maybe because of some administrative glitch.Nevertheless, an increase in unpaid bills could be indicative of potential arrears. Arrears may alsooccur in expenditure categories where there are no bills, such as pensions, transfers or wages. Inthat case, the definition is less clear, especially as the government could define the payment terms.However, a payment that occurs much later than the month to which it refers would probably beseen as an arrear.

Public accounts typically do not track true arrears, except following ad hoc audits to identify them(as sometimes required in IMF programs). Alternative sources from international datasets do notreport fiscal arrears either. For example, in the IMF’s Government Finance Statistics, publicpayment arrears are a memorandum item that member countries are free to report, but rarely do.Instead, depending on the public accounting system in place, there could be data on spendingcommitments, payment orders and actual payments (check or transfer). Differences betweenthese stages can provide indications of the development of payment lags.

• The difference between commitments and payment orders can reflect late supply by privateparties or delays by the government in issuing payment orders.

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• The difference between payment orders and actual payments (accounts payable) isnecessarily due to government procedures. An increase in this figure could, however, stilltake place without the government breaching due dates. 4

• Finally, if checks are used, there is a float as a result of uncashed checks. This would notlead to arrears, as companies would consider a debt cleared on receipt of a check, unless thecheck bounces.

An unusual increase in any of these measures would indicate a potential problem, but would notbe proof for the presence of arrears. Conversely, small or stable differences are not proof for theabsence of arrears either, as these aggregated figures could hide individual payments withexcessive delays. Moreover, if only some steps are observed, arrears can be missed. For example,if only accounts payable are known, arrears could occur because of the delayed issue of paymentorders (or more generally recognition of liabilities). Finally, irregular payments, made withoutrecording a commitment could still be potentially legally valid, but would not be known untilregularized.

While it may not be possible to cleanly identify arrears in a legal sense, from an economic pointof view, it may be more important to identify payment delays that go beyond what is expected bysuppliers. Accounts payable, possibly as a share of total spending, would be a proxy for theaverage payment duration, even if an imperfect one as governments may delay or avoidrecognizing valid liabilities. In this paper we mainly use Eurostat’s Sector Accounts data onaccounts payable (ESA-1995 code AF.7)5 as a basis for further data construction. For a fewcountries, we also have direct estimates of arrears that allow us to make comparisons.

In an accrual accounting system, such as ESA-1995, the timing of payments should not affectreported spending (with a few exceptions), as spending is registered at the time of good supply orservice provision. If payment is not made at the same time—be it an arrear or a delay withinpermissible payment terms—then it shows up under the category “other accounts payable” (AF.7)in the national accounts. This category comprises any financial liabilities “which are created as acounterpart of a financial or a non-financial transaction in cases where there is a timing differencebetween this transaction and the corresponding payment. It includes trade credits and advancesand any other receivables and payables. Trade credits and advances are financial assets/liabilitiesarising from the direct extension of credit by suppliers and buyers for goods and servicestransactions and advance payments for work that is in progress or to be undertaken and associatedwith such transactions” (Eurostat (1996)). The variable AF.7 is generally available for differentparts of the public sector. It is important to use data at the general government level, becauseliabilities between different government levels can be substantial, but are not part of theoutstanding payments from the public to the private sector. We therefore consistently use thevariable AF.7 at the general government level on a consolidated basis. See developments by

4Typically only the amount is known, but even if the delay is known, it is not possible to be certain about the absenceof arrears. If the delay is shorter than payment terms, there could still be an arrear if the payment order was issuedlater than the invoice date. For a delay that exceed payment terms, however, arrears are very likely to be present, aspayment orders are unlikely to be issued in advance of invoice dates.

5The ESA-2010 code changes to AF.8 and AF.7 then refers to financial derivatives and employee stock options.

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country over the period 2005-2012 for the AF.7-to-GDP ratios in Figure 1. In principle, there is afurther breakdown into two sub-categories: trade credits and advances (AF.71), and otheraccounts payable excluding trade credit and advances (AF.79). In practice, however, thebreakdown into the two subcategories suffers from rather severe measurement and reportingissues (European Commision (2012)), or are unavailable altogether, e.g., for Greece. Moreover,AF.71 is regularly reported to be only very small relative to GDP, even for countries where recentad hoc audits have revealed substantial spending arrears, as for instance in Spain.

B. A proxy for fiscal arrears

As the exact amount of payments in arrears is not available from ESA-1995 national accountsdata, we put forward a method to construct a proxy for the amount of payments in arrears. We dothis by combining the national accounts data on accounts payable with survey data from a privatecredit management company (Intrum Justitia) on payment durations. This combination ofinformation allows us to estimate the share of accounts payable that are within or beyond thedue-for-payment date. The accuracy of the resulting estimate depends on the validity of ourassumptions, which are described below, but also on the quality of the data. Specifically for thesurvey data, we should keep in mind that the likelihood of responding to the survey may not beindependent of being paid on time. Moreover, there could be differences in opinion betweenpayor and payee, and for some transactions that businesses consider overdue the government maynot agree, for example, if payment is withheld for incomplete delivery.

To illustrate how we construct our proxy, first suppose we had full information. In this idealsituation, we could on a given day retrieve the full payment record of the public sector (ESA-1995sector code S.13) from the national accounts. That is, on a given day of a fiscal year τ and forevery invoice i, we would have information on: (i) the amount $xi to be paid, (ii) the contractualpayment period T̄i and (iii) the payment duration Ti. We then say that invoice i is in arrears, ifTi > T̄i. For example, if the contractual payment period T̄i is 30 days and we are 45 days behindthe invoice date, the payment has been in arrears for 15 days. For any date τ , one could thenimmediately determine the amount of payments in arrears, but also construct the full durationdistribution FT (c) = Pr [T ≤ c] of public payments. Hence, 1− FT (T̄ ) represents the share ofpayments beyond the due-for-payment date. The duration distribution of payments can thereforebe used, e.g., to compute the amount of arrears.

In our less ideal case, the ESA-1995 accounts only provide the total amount of other accountspayable (AF.7) for each country. In order to estimate the share of AF.7 that is in arrears, we firstreconstruct the duration distribution of public payments. One might argue that it is more adequateto calculate a proxy for arrears not as a share of AF.7, but rather AF.71, as this has the advantageof avoiding biases due to certain liabilities that fall under AF.79, such as pending tax settlements,but which are not our main interest. At the same time, this would then also exclude delayedpayments of salaries, in which we are interested. Moreover, the data quality of the subcategoriesis less reliable than that of the aggregate (see above). We therefore prefer to use exclusively theaggregate figure.

Because administrative data on the duration of public payments are not available, we use surveydata on the average payment duration and the average contractual payment period of public

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authorities. These data are provided by Intrum Justitia, a private credit management firm, whichconducts an annual written survey among several thousand firms in 27 countries. The results fromthis survey are published in an annual European Payment Index Report (Intrum Justitia (2013)and previous editions). Among several other payment statistics, the survey reports (i) the averageannual payment duration and (ii) the average annual contractual payment period. Both numbersare further disaggregated into consumer, business-to-business and public sector debtors. We haveplotted the reported data for the public sector from the 2013 report in Figure 2. 6

In order to estimate the duration distribution we assume that the duration distribution of publicpayments is exponential, i.e., its c.d.f. is given by

FT (t) =

{1− exp(−λt) for t ≥ 0

0 for x < 0 ,

where λ > 0 is the parameter of the distribution and is often called the rate or intensity of thedistribution. The duration T decreases in λ in the sense of first order stochastic dominance, i.e.,higher values for T become uniformly less probable. The exponential distribution is often used tomodel time-to-event data, such as waiting times, queuing times or the time until default in creditrisk modelling. This is therefore comparable to invoices that remain outstanding until paid, but itrequires the assumption that the size of invoices is independent of the duration distribution, i.e.,that the government does not systematically delay payments of particularly large invoices. One ofits key features that motivates its use in our case is the fact that we may estimate the keyparameter λ via simple methods of moments (MM). Let the reported average payment durationfor country j be denoted by T̃j . Under weak regularity conditions, the sample average provides aconsistent estimator for the mean duration of payments, and hence we would estimate λj in thefollowing way

E [Tj] = λ−1j ⇒ T̃j = λ̂−1 ⇒ λ̂ = T̃−1

j . (1)

This immediately leads to the estimated duration distribution

F̂T (t) =

{1− exp

(−λ̂t

)for t ≥ 0

0 for t < 0 ,. (2)

Hence, with information on the average payment duration, an exponential distribution of paymentdurations is fully identified.7 If we do not allow for any grace period, the estimated share ofpayments in arrears equals (Figure 3):

Other accounts payable in arrears = AF.7× (1− F̂T (T̄ )) . (3)

6Given the entry into force of the EU Directive on Combating Late Payments, it is likely that delays have improvedsubsequently in various countries

7More flexible distributions that seem pertinent for our use, e.g., a Gamma distribution, feature two parameters andhence need more information than only the sample average to be identified.

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In the existing literature on the measurement of arrears, there is no general consensus which valueto take for T̄ . An exact notion of payment arrears would define them to be any amounts that arepast due for payment and are unpaid. Hence, any payment for which T > T̄ would be in arrearsunder this definition. In practice, however, this strict notion of arrears is often loosened to allowfor the fact that the exact limit may vary for each bill and this precise information is not available.

In a similar vein, the IMF’s Compilation Guide on Financial Soundness Indicators (IMF (2006),section 4.84) defines loans to be in arrears once “payments of principal and interest are past dueby three months (90 days) or more” and goes on to note that “the 90-day criterion is the timeperiod that is most widely used by countries to determine whether a loan is nonperforming.”Since trade credit granted by the private sector to the public sector is a form of a loan, thiscriterion is equally applicable and provides another way to define an“acceptable grace period.”

We follow this approach and set T̄j equal 90 days or the contractual payment period—whatever islonger—in order to consider the possibility that some variation in terms between different bills isallowed for.

Hence, in a first step we use (1) to estimate λ̂ and thus F̂T (·) using the average reported paymentduration in the European Payment Index survey for a given year. In the second step, we compute1− F̂T (c) where

c = max{

90, T̄j}.

In the final step, we take the share 1− F̂T (c) and calculate the total amount of payments in arrearsusing (3).

To make figures comparable across countries, we plotted our estimates as a share of GDP inFigure 4. We also included available administrative data on actual payment arrears, as obtainedfor example during a financial assitance program. There are several features worth mentioning.First, several European countries e.g., Finland, Denmark, Sweden and Bulgaria tend to haverelatively large AF.7-to-GDP ratios. While this may be indicative of payment arrears, especiallyScandinavian countries are known to roll over their debt in a timely manner and should have onlyvery little payment in arrears, if any. Our measure incorporates this explicitly via the averagepayment duration in these countries. As a result, our estimates of arrears for these countries isattenuated by their high payment discipline. Second, the individual time series for the differentcountries show fairly little variation over time and thus appear to be very persistent. Third, thetime series variation is higher for countries with relatively high arrears-to-GDP ratios, being thehighest in Greece and Spain. Fourth, in terms of matching official numbers, our estimates comesurprisingly close in most cases, but may still deviate substantially in individual country-years, asfor example the estimate for 2012 arrears in Greece. This deviation in some cases, however, isalso very likely to stem from conflicting definitions of what is subsumed under the term paymentarrears. For example, official figures from Bulgaria do not comprise outstanding hospital billsfrom state-owned hospitals.

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Figure 1. Accounts Payable (AF.7) in EU Countries (percent of GDP).AUT BEL BGR CYP

CZE DNK EST FIN

FRA DEU GRC HUN

IRL ITA LVA LTU

LUX MLT NLD POL

PRT ROM SVK SVN

ESP SWE GBR

0%

5%

10%

15%

0%

5%

10%

15%

0%

5%

10%

15%

0%

5%

10%

15%

0%

5%

10%

15%

0%

5%

10%

15%

0%

5%

10%

15%

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

year

AF.

7 as

% o

f GD

P

Source: Eurostat.

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Figure 2. Average Reported Payment Duration of the Public Sector in 2012 (measured in numberof days).

24

35

39.4

47.9

53.7

61.7

131

180

Source: Intrum Justitia (2013).

Figure 3. Duration Density of Public Payments. Area A shows share of obligations within con-tractual payment period T̂ , area B shows share of obligations beyond the contractual period.

A

B

T̂

Time (t)

Dura

tion

density

(fT(t))

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Figure 4. Actual and Estimated Payment Arrears of the Public Sector by Country.

AUT BEL BGR CYP CZE

DNK EST FIN FRA DEU

GRC HUN IRL ITA LVA

LTU NLD POL PRT ROM

SVK ESP SWE GBR

0%

2%

4%

6%

0%

2%

4%

6%

0%

2%

4%

6%

0%

2%

4%

6%

0%

2%

4%

6%

2006

2008

2010

2012

2006

2008

2010

2012

2006

2008

2010

2012

2006

2008

2010

2012

year

Arr

ears

in p

erce

nt o

f GD

P

Legend actual estimated

Source: Eurostat, Intrum Justitia, IMF staff reports, and authors’ calculation.

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III. THE AGGREGATE EFFECTS OF PAYMENT ARREARS—EVIDENCE FROM PANELREGRESSIONS

In a first step we estimate the macroeconomic impact of government delayed payments, bothestimated arrears and total accounts payable in a panel setting, exploiting both the country andtime variation in data. In line with the theoretical insights on the potential channels that delayedpayments may have on the economy, we investigate the short-term impact on real GDP growth,on profitability as proxied by the economy-wide gross operating surplus, and on liquidity asproxied by the probability of default (using Moody’s measure of distance to default, DTD).8

Given the large potential for endogeneity of government delayed payments and arrears, we uselagged variables and additionally use the system GMM (Blundell and Bond (1998)) estimator fordynamic panel models. This is particularly suitable for the regressions with variables constructedbased on the Eurpean Payment Index dataset, which has a rather short time dimension (maximumT = 7, i.e. the period 2005-2012) and larger cross-section dimension (the number of EUcountries with sufficient observations to be kept in the regressions being 24).9 We also correct forheteroskedasticity and autocorrelation that may be present in the error structure by using theconsistent estimator.

Our macroeconomic data are taken from the European Commission’s AMECO database, exceptfor GDP in purchasing power parity-adjusted terms, which is from World DevelopmentIndicators.

A. Growth regressions

In this subsection, we investigate the short-term impact of government payment delays on realGDP growth using three measures. First, we use a broad measures of delays, constructed as aninteraction term between the variable ”other accounts payable” of the general government (AF.7)as a share of GDP and the surveyed number of days public contracts are in delay, as availablefrom Intrum Justitia (2013) (Table 1). Second, we employ our estimated measure of arrearsoverdue more than 90 days (or the legal limit if greater) as a share of GDP (Table 2).10 Third, weconsider simply the total amount of accounts payable as a share of GDP. As this final variable ismostly not statistically significant when the GMM estimator is used (it is, though, significantlynegative with the fixed effect estimator), results are not shown.

In Table 1, we show the estimation results for various regressions starting with the simplest one inwhich we only add delayed government payments in addition to country and year fixed-effects

8The distance to default measures the number of standard deviations it takes a shock to be large enough to render afirm’s asset value lower than the value of the firm’s debt. The country average is weighted by firm assets (seehttp://www.moodysanalytics.com/).

9The results remain robust if the difference GMM (Arellano and Bond (1991)) estimator is used instead. The sameholds if the forward orthogonal transformation is used instead of differencing.

10Checks performed with other measures (estimated arrears overdue more than 30 or 60 days) showed less robustresults.

14

and two lags of the dependent variable (using only the first lagged GDP growth does not eliminateauto-correlation as indicated by the rejection of the AR(2) test null hypothesis). In the nextcolumns (2) to (9), one potentially relevant variable is added at a time, as follows (by category):(i) fiscal variables: we first control for a base effect of our variable of interest by adding thegovernment spending-to-GDP ratio (column 2) in order to capture the possibility of higherdelayed payments accumulating only as a result of higher total spending. We then aim to capturethe impact of the discretionary fiscal policy on the economy through the change of the structuralprimary balance ratio (column 3); (ii) credit to the private sector as captured by the GDP share ofloans to private entities (column 4); (iii) position in the business cycle as captured by: the outputgap (column 5) or the unemployment rate (column 6); (iv) basic determinants of growth in aconditional convergence model, that is labor force (population) growth rate (column 7), the saving(investment) ratio to GDP (column 8) and the initial level of GDP per capita (column 9). Column10 includes all the three variables of the convergence growth model together with our variable ofinterest. Overall, the results presented in Table 1 show pretty robust evidence that delayedpayments have a negative impact on growth. The impact is also economically significant, ascoefficients between -0.005 and -0.009 mean that a one standard deviation change in delayedpayments reduces the growth rate by 0.8 to 1.5 percentage points.

The findings with estimated arrears (Table 2) are more variable, but a significant result is obtainedin many of the specifications, and particularly those that control for the economic cycle. Again,the coefficients suggest an economically very significant impact on growth, as the coefficientsindicate the impact of an increase in arrears by 1 percent of GDP, which would reduce growth by0.6 to 0.9 percentage points, depending on the specification. The insignificant results with totalaccounts payable, support the idea that large amounts that are rolled over regularly may not be aproblem.

15

Tabl

e1.

Pane

lReg

ress

ions

ofR

ealG

DP

Gro

wth

onPa

ymen

tDel

ays.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Gro

wtht−

10.

603*

**0.

598*

**0.

469*

**0.

586*

**0.

737*

**0.

607*

**0.

627*

**0.

599*

**0.

587*

**0.

580*

**(0

.102

)(0

.105

)(0

.087

)(0

.089

)(0

.112

)(0

.093

)(0

.117

)(0

.094

)(0

.100

)(0

.075

)G

row

tht−

2-0

.351

***

-0.3

55**

*-0

.365

***

-0.4

03**

*-0

.161

*-0

.317

**-0

.332

**-0

.370

***

-0.3

69**

*-0

.414

***

(0.1

00)

(0.0

92)

(0.0

79)

(0.1

21)

(0.0

85)

(0.1

29)

(0.1

39)

(0.1

14)

(0.1

04)

(0.1

43)

AF.

7×

Del

ay-0

.007

***

-0.0

07**

*-0

.005

**-0

.008

***

-0.0

08**

*-0

.008

***

-0.0

07**

*-0

.007

**-0

.007

***

-0.0

09**

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

03)

(0.0

01)

(0.0

03)

Exp

endi

ture

ratio

-0.0

0484

(0.0

66)

∆St

r.Pr

imar

yB

alan

ce-0

.767

**(0

.306

)Pr

ivat

ecr

edit

0.00

4(0

.011

)O

utpu

tgap

-0.5

5***

(0.1

26)

Une

mpl

oym

entr

ate

0.10

4(0

.163

)G

row

thof

labo

rfor

ce-0

.290

0.10

6(0

.498

)(0

.281

)Sa

ving

sra

te-0

.034

0.09

2(0

.093

)(0

.093

)G

DP

perc

apita

-0.0

08-0

.978

**(0

.045

)(0

.034

)O

bser

vatio

ns14

414

414

414

114

414

414

414

414

414

4N

umbe

rofc

ount

ries

2424

2424

2424

2424

2424

No.

ofin

stru

men

ts17

2222

2222

2222

2222

32A

R(1

)p0.

004

0.00

40.

016

0.00

40.

006

0.00

40.

008

0.00

20.

003

0.00

3A

R(2

)p0.

237

0.28

80.

401

0.31

50.

290

0.20

50.

398

0.27

00.

455

0.59

8H

anse

np

0.47

40.

414

0.36

10.

299

0.43

40.

156

0.66

70.

370

0.74

90.

921

Not

es:

All

expl

anat

ory

vari

able

sla

gged

byon

eye

arex

cept

the

chan

gein

perc

ento

fth

est

ruct

ural

prim

ary

bala

nce

and

the

grow

thra

teof

the

labo

rfo

rce.

Acc

ount

spa

yabl

e,ex

pend

iture

(gen

eral

gove

rnm

ent)

,priv

ate

cred

it,an

dsa

ving

sra

tear

ein

perc

ento

fGD

P,pe

rcap

itaG

DP

inth

ousa

nds

of20

11PP

PU

SD.A

llre

gres

sion

sar

ees

timat

edw

ithSy

stem

GM

Man

dus

eth

ese

cond

tofif

thla

g,co

llaps

ed,a

sin

stru

men

ts.

Reg

ress

ions

incl

ude

time

and

coun

try

fixed

effe

cts.

Rob

usts

tand

ard

erro

rsin

pare

nthe

ses.

***

p<0.

01,*

*p<

0.05

,*p<

0.1.

16

B. Impact on profit growth

We also investigate the impact of delayed payments (Table 3), estimated arrears and accountspayable on profit growth, using the economy-wide gross operating surplus as an indicator ofprofits. We find a statistically significant, robust impact only in the case of delayed payments,which are associated with a reduction in the growth rate of the operating surplus. Thisrelationship holds across various specifications, including when controlling for the economiccycle, such as by adding the unemployment rate or output gap. A one-standard deviation increasein delayed payments reduces profit growth by 1.5 to 3.4 percentage points. Results for the othertwo variables of interest are, however, mostly not significant and therefore not reported.

C. Impact on likelihood of bankruptcy

Finally, we consider the impact of payment delays and arrears on the likelihood of bankruptciesusing the distance-to-default measure. The distance-to-default variable is available for fewercountries. Morevoer, it is less persistent and the likelihood of endogeneity of the public paymentarrears is lower. We therefore present specifications with one or no lagged dependent variable,and we use fixed effects estimates using Newey standard errors in some specifications. We findthat delayed payments (Table 4) and estimated arrears (Table 5)—but again not total accountspayable—reduce the distance to default. That is, the larger such delayed payments, the smallerthe distance to default or the higher the probability of default among private companies thoughonly publicly listed companies are hereby captured.

17

Tabl

e2.

Pane

lReg

ress

ions

ofR

ealG

DP

Gro

wth

onE

stim

ated

Arr

ears

.(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)G

row

tht−

10.

624*

**0.

619*

**0.

468*

**0.

604*

**0.

766*

**0.

622*

**0.

635*

**0.

618*

**0.

614*

**0.

597*

**(0

.105

)(0

.112

)(0

.091

)(0

.093

)(0

.119

)(0

.104

)(0

.100

)(0

.097

)(0

.106

)(0

.082

)G

row

tht−

2-0

.354

***

-0.3

66**

*-0

.367

***

-0.3

95**

*-0

.147

*-0

.342

**-0

.346

**-0

.364

***

-0.3

68**

*-0

.413

***

(0.1

06)

(0.0

99)

(0.0

81)

(0.1

22)

(0.0

85)

(0.1

46)

(0.1

43)

(0.1

14)

(0.1

05)

(0.1

33)

Est

imat

edar

rear

s-0

.673

-0.6

21-0

.607

*-0

.948

*-0

.869

***

-0.7

30**

-0.6

67-0

.732

-0.0

637

-0.8

57(0

.468

)(0

.478

)(0

.349

)(0

.484

)(0

.245

)(0

.337

)(0

.466

)(0

.740

)(0

.400

)(0

.653

)E

xpen

ditu

re0.

009

(0.0

72)

∆St

r.Pr

imar

yB

alan

ce-0

.795

***

(0.2

72)

Priv

ate

cred

it0.

002

(0.0

11)

Out

putg

ap-0

.572

***

(0.1

27)

Une

mpl

oym

entr

ate

0.04

67(0

.100

)G

row

thof

labo

rfor

ce-0

.240

-0.0

70(0

.454

)(0

.255

)Sa

ving

sra

te-0

.015

-0.0

66(0

.119

)(0

.093

)G

DP

perc

apita

-0.0

08-0

.098

***

(0.0

56)

(0.0

34)

Obs

erva

tions

144

144

144

141

144

144

144

144

144

144

Num

bero

fcou

ntri

es24

2424

2424

2424

2424

24N

o.of

inst

rum

ents

1722

2222

2222

2222

2232

AR

(1)p

0.00

350

0.00

355

0.01

720.

0041

50.

0039

90.

0036

80.

0052

40.

0024

50.

0026

70.

0023

9A

R(2

)p0.

337

0.42

30.

452

0.46

00.

459

0.33

10.

580

0.37

40.

744

0.66

0H

anse

np

0.25

10.

327

0.34

60.

165

0.31

60.

161

0.41

30.

364

0.57

70.

647

Not

es:

All

expl

anat

ory

vari

able

sla

gged

byon

eye

arex

cept

the

chan

gein

perc

ento

fth

est

ruct

ural

prim

ary

bala

nce

and

the

grow

thra

teof

the

labo

rfo

rce.

Acc

ount

spa

yabl

e,ex

pend

iture

(gen

eral

gove

rnm

ent)

,priv

ate

cred

it,an

dsa

ving

sra

tear

ein

perc

ento

fGD

P,pe

rcap

itaG

DP

inth

ousa

nds

of20

11PP

PU

SD.A

llre

gres

sion

sar

ees

timat

edw

ithSy

stem

GM

Man

dus

eth

ese

cond

tofif

thla

g,co

llaps

ed,a

sin

stru

men

ts.R

egre

ssio

nsin

clud

etim

ean

dco

untr

yfix

edef

fect

s.R

obus

tsta

ndar

der

rors

inpa

rent

hese

s.**

*p<

0.01

,**

p<0.

05,*

p<0.

1.

18

Tabl

e3.

Pane

lReg

ress

ions

ofth

eG

row

thof

the

Gro

ssO

pera

ting

Surp

lus

onPa

ymen

tDel

ays.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Ope

ratin

gsu

rplu

sgr

owth

0.26

0***

0.25

0**

0.23

3***

0.32

5***

0.23

3**

0.36

7***

0.25

9***

0.10

80.

337*

**(0

.089

1)(0

.098

1)(0

.081

9)(0

.101

)(0

.092

2)(0

.105

)(0

.091

6)(0

.121

)(0

.111

)A

F.7×

Del

ay-0

.013

0***

-0.0

131*

*-0

.013

6***

-0.0

117*

*-0

.019

8***

-0.0

108*

*-0

.013

2*-0

.009

25**

-0.0

114*

(0.0

0455

)(0

.004

68)

(0.0

0480

)(0

.004

25)

(0.0

0472

)(0

.004

75)

(0.0

0761

)(0

.004

20)

(0.0

0598

)E

xpen

ditu

rera

tio0.

133

0.14

9(0

.298

)(0

.226

)Pr

ivat

ecr

edit

-0.0

0697

0.00

770

(0.0

155)

(0.0

149)

Out

putg

ap-0

.702

**-0

.576

(0.2

82)

(0.4

62)

Une

mpl

oym

entr

ate

0.71

3**

(0.2

97)

Gro

wth

ofla

borf

orce

-1.7

62**

-0.3

88(0

.679

)(0

.906

)Sa

ving

sra

te-0

.128

(0.3

03)

Gro

wth

0.35

3(0

.273

)O

bser

vatio

ns14

314

314

014

314

314

314

314

314

0N

umbe

rofc

ount

ry24

2424

2424

2424

2424

No.

ofin

stru

men

ts17

2222

2222

2222

2237

AR

(1)p

0.00

0839

0.00

111

0.00

0648

0.00

121

0.00

0965

0.00

144

0.00

0811

0.00

102

0.00

153

AR

(2)p

0.25

10.

211

0.24

40.

584

0.35

10.

242

0.22

00.

147

0.39

4H

anse

np

0.53

20.

690

0.31

90.

296

0.23

30.

441

0.15

20.

348

0.95

5

Not

es:

All

expl

anat

ory

vari

able

sla

gged

byon

eye

arex

cept

the

labo

rfo

rce

grow

thra

te.

Acc

ount

spa

yabl

e,sp

endi

ng,p

rivat

ecr

edit

and

savi

ngs

rate

are

inpe

rcen

tof

GD

P.A

llre

gres

sion

sar

ees

timat

edw

ithSy

stem

GM

Man

dus

eth

ese

cond

tofif

thla

g,co

llaps

ed,a

sin

stru

men

ts.

Reg

ress

ions

incl

ude

time

and

coun

try

fixed

effe

cts.

Rob

usts

tand

ard

erro

rsin

pare

nthe

ses

.**

*p<

0.01

,**

p<0.

05,*

p<0.

1.

19

Tabl

e4.

Pane

lReg

ress

ions

ofth

eD

ista

nce

toD

efau

lton

Paym

entD

elay

s.(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)D

ista

nce

tode

faul

t t−1

0.75

5***

0.80

9***

0.88

0***

0.77

3***

0.85

4***

0.80

8***

(0.0

935)

(0.0

948)

(0.0

947)

(0.0

858)

(0.0

763)

(0.1

04)

AF.

7×

Del

ay-0

.000

655*

**-0

.003

09**

*-0

.000

578*

**-0

.000

529*

**-0

.000

745*

*-0

.000

810*

**-0

.000

735*

**-0

.001

86*

(0.0

0014

9)(0

.000

859)

(0.0

0019

2)(0

.000

135)

(0.0

0030

2)(0

.000

172)

(0.0

0024

6)(0

.001

05)

Exp

endi

ture

ratio

0.01

620.

0215

0.04

55**

(0.0

161)

(0.0

167)

(0.0

222)

Priv

ate

cred

it-0

.000

278

-8.2

1e-0

5-0

.011

6*(0

.000

961)

(0.0

0079

5)(0

.006

06)

Une

mpl

oym

entr

ate

0.00

933

-0.0

0604

-0.0

345

(0.0

245)

(0.0

198)

(0.0

291)

Gro

wth

-0.0

303*

-0.0

110

-0.0

412*

(0.0

162)

(0.0

147)

(0.0

214)

Obs

erva

tions

116

119

116

113

116

116

113

116

Num

bero

fcou

ntry

2020

1920

2019

No.

ofin

stru

men

ts17

.22

2222

2237

.A

R(1

)p0.

0215

.0.

0155

0.02

550.

0136

0.01

680.

0169

.A

R(2

)p0.

433

.0.

427

0.44

70.

382

0.61

10.

427

.H

anse

np

0.36

0.

0.40

50.

286

0.57

40.

592

1.00

0.

Not

es:

All

expl

anat

ory

vari

able

sla

gged

byon

eye

arex

cept

the

labo

rfo

rce

grow

thra

te.

Acc

ount

spa

yabl

e,sp

endi

ng,p

rivat

ecr

edit

are

in%

ofG

DP.

All

regr

essi

ons

are

estim

ated

with

Syst

emG

MM

(usi

ngth

ese

cond

tofif

thla

g,co

llaps

ed,

asin

stru

men

ts),

exce

ptm

odel

s2

and

8,in

whi

chth

efix

edef

fect

estim

ator

isus

ed.R

egre

ssio

nsin

clud

etim

ean

dco

untr

yfix

edef

fect

s.R

obus

tsta

ndar

der

rors

inpa

rent

hese

s(m

odel

s2

and

8us

eN

ewey

stan

dard

erro

rs).

***

p<0.

01,*

*p<

0.05

,*p<

0.1.

20

Tabl

e5.

Pane

lReg

ress

ions

ofth

eD

ista

nce

toD

efau

lton

Est

imat

edA

rrea

rs.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Dis

tanc

eto

defa

ult t−

10.

767*

**0.

846*

**0.

884*

**0.

766*

**0.

890*

**0.

904*

**(0

.097

6)(0

.064

1)(0

.083

3)(0

.073

4)(0

.068

3)(0

.079

5)E

stim

ated

arre

ars

-0.0

959*

**-0

.672

***

-0.0

574

-0.0

689

-0.1

02**

-0.1

18**

*-0

.129

***

-0.4

89**

*(0

.025

2)(0

.121

)(0

.047

6)(0

.059

0)(0

.037

5)(0

.028

5)(0

.034

9)(0

.152

)E

xpen

ditu

rera

tio0.

0130

0.01

590.

0470

**(0

.015

2)(0

.015

7)(0

.021

8)Pr

ivat

ecr

edit

-0.0

0058

60.

0004

20-0

.011

6**

(0.0

0147

)(0

.000

731)

(0.0

0549

)U

nem

ploy

men

trat

e0.

0071

30.

0030

1-0

.026

1(0

.017

5)(0

.016

1)(0

.027

4)G

row

th-0

.027

9*-0

.014

8-0

.024

9(0

.014

3)(0

.014

7)(0

.022

7)O

bser

vatio

ns11

611

911

611

311

611

611

311

6N

umbe

rofc

ount

ry20

2019

2020

19N

o.of

inst

rum

ents

17.

2222

2222

37.

AR

(1)p

0.01

78.

0.01

250.

0247

0.01

900.

0205

0.02

16.

AR

(2)p

0.41

7.

0.44

10.

453

0.38

40.

603

0.45

0.

Han

sen

p0.

306

.0.

590

0.43

00.

610

0.48

50.

999

.

Not

es:

All

expl

anat

ory

vari

able

sla

gged

byon

eye

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21

IV. THE AGGREGATE EFFECT OF PAYMENT DELAYS—EVIDENCE FROM BAYESIANVARS

As noted the approach in the previous section using annual panel data has its pros and cons. Oneof the major shortcomings is the difficulty of dealing with endogeneity. While we usedSystem-GMM to address this, it could be argued that a more systematic approach would be tomove to a system of equations that takes each variable to be endogenous with respect to oneanother. This simultaneous equations framework is accommodated in a structural Bayesian VAR.Contrary to classical reduced-form VARs which identify shocks using a recursive identificationscheme, we wanted to allow for a less restrictive identification scheme and move towardnon-recursive identification as in Waggoner and Zha (2003).

Bayesian VARs seem a natural alternative to the single equation framework we considered in theprevious sections. First, they provide a well-established way to take into account the complexinterdependencies among the variables under consideration and thus control for their mutualfeedback. Second, by imposing prior restrictions on the parameters in the model we are able toaddress (i) the proliferation of the parameter space and (ii) the relatively small sample size, whichmakes it likely that an unrestricted VAR would mistake much of the sample variation to besystematic instead of unsystematic. Using prior restrictions we are able to provide conservativeestimates of cross-variable effects, because we ”shrink” them toward a zero prior mean (Koop andKorobilis (2010)). Third, the cross-variable effects from a shock in variable j to variable i, maybe easily gauged by computing the dynamic multipliers

∂yi,t+k∂εj,t

, k = 0, . . . (4)

which at the same time control for shocks to the other variables in the system.

The change in methodology requires various changes to our approach. First, we are morerestricted in our choice of control variables. Given that we need longer time series, we move toquarterly data, for which many variables are not available. In any case, given the inclusion ofmany more lagged variables, we cannot overburden the equations with excessive explanatoryvariables. Hence we undertake a number of simplifications. First, instead of our measures ofarrears or delayed payments, we now simply focus on accounts payable. Their movement overtime, especially at the quarterly level, should be indicative of underlying payment delay or arrearissues. Second, instead of dividing AF.7 by GDP and separately controlling for the share ofgovernment expenditure in GDP, we now use directly the ratio of AF.7 to total expenditure. Thissaves one variable (more if lags are counted), but still allows to control for the purely mechanicalpositive relationship between expenditure and the amount of outstanding payments. After all, itseems natural to assume that AF.7 rises when spending increases. If the general government rollsover these additional obligations with the same efficiency, our measure of payment efficiencyshould not be affected. This, however, could be the case with the AF.7-to-GDP ratio. This way wealso control for expenditure shocks. Much like the debt-to-GDP ratio, the AF.7-to-expenditure isin units of time and measures how many quarters on average the general government needs to payits obligations, for every euro it committed to pay. The smaller this ratio, the more efficient thegeneral government is in a given quarter in paying its obligations.

22

A. Data

We use a similar, but reduced, set of variables as in the single equation regression analysis. First,we include the standard set of macroeconomic variables, i.e., quarterly real GDP (seasonallyadjusted, national currency) in log-levels, inflation as measured by the GDP deflator (2005=100),the 3-month Euribor money market rate and the AF.7-to-Expenditure ratio. In particular, we takethe AF.7 as a ratio of total expenditure, i.e., including wages and transfers, as AF.7, unlike the lessoften available AF.71 (trade credit), also includes accounts payable in these categories. Theliquidity channel through which we suspect the AF.7-to-Expenditure ratio to affect the privatesector is proxied by the distance-to-default measure that was used earlier, too.

The sample ranges are unbalanced across countries, but mostly go from 1999Q3 until 2012Q4.We discard countries from our analysis for which (i) the data are not available before 2002Q1, (ii)an entire series contains only missing values or (iii) one or more series contain gaps. This leaves16 countries in our sample. Further, the empirical analysis on quarterly data will be selectivelyperformed for Italy, Spain, and Portugal, as explained below.

B. Non-recursive identification

In this subsection we estimate a structural VAR, i.e., a model that is not generically identifiedusing a Cholesky ordering among variables. Instead, we will follow the approach put forward bySims (1986) and Waggoner and Zha (2003) and identify shocks directly via restrictions on thecontemporaneous impact matrix. This approach is more flexible than recursive identification,because (i) it allows for non-recursive causation and (ii) restrictions can interpreted asrepresenting behavioral equations in the sense of simultaneous equations models (SEMs). Thefirst point plays an important role in our case, because we can implement the restriction thatshocks to the AF.7-to-Expenditure ratio do not directly enter the equation for GDP and DTD,without having to put both to the top of the vector yt as in a Cholesky ordering.

Our point of departure is the standard structural BVAR model, i.e., let yt be an n-dimensionalrandom vector, following the structural VAR model

y′tA0 = c +

p∑i=1

y′t−iAi + ε′t , t = 1, . . . , T , (5)

where Ak ∈ Rn×n are matrices of parameters, c is an intercept and εt ∈ Rn denotes the vector ofstructural shocks or disturbances in the system. We assume that εt is the standard zero-meanspherical disturbance.

Letting x′t = [yt, . . . ,yt−p, 1] and

Y = [y′t, . . . ,y′1]′

; X = [x′t, . . . ,x′1]′

; E = [ε′t, . . . , ε′1]′

; F = [A1, . . . ,Ap, c]′

23

we may write the whole system more compactly as

YT×n

A0n×n

= XT×k

Fk×n

+ ET×n

, (6)

where k = np+ 1. In this form, it becomes apparent that the structural VAR may be viewed as asystem of linear simultaneous equations with endogenous variables Y and exogenous (orpredetermined) variables X. The system is identified imposing exclusion restrictions on thematrix A0.

The key behavioral assumption in the non-recursive scheme will be that shocks to theAF.7-to-Expenditure ratio do not directly affect GDP and DTD contemporaneously. We base thisassumption on the European Payment Index Report and the average payment duration ofcountries. Note that for countries where the average payment duration of one quarter (90 days),the private sector is very likely to anticipate no payment within the same quarter. That is, for anyinvoice dated in a given quarter, payment is expected not before the next quarter. If this holdstrue, then any shock to public payment durations will not by itself affect GDP or the DTDimmediately, but either (i) only indirectly via affecting other variables in the system or (ii) onlywith a lag. We are thus not assuming there is no contemporaneous effect, but merely preclude it isa direct effect. A shock to the average payment duration, for example, may have an immediatedirect effect on interest rates, due to the effect it has on credit demand, which in turn can have aneffect on GDP within the same quarter.

However, the European Payment Index report shows that this assumption is only warranted forthree countries in our sample. At the same time, the three countries—Italy, Spain andPortugal—which exhibit an average payment duration of at least 90 days are those that have beenin the focus in terms of their payment discipline (Figure 5).11

We implement the identification scheme through the matrix A0. In our case, it will be given asGDPtπt

AF.7 ratiotDTDt

it

′ a11 0 a31 a41 a51

0 a22 a32 a42 a52

0 0 a33 0 a53

0 0 0 a44 a54

0 0 0 a45 a55

= c +

p∑i=1

y′t−iAi + ε′t (7)

where AF.7-ratio means the AF.7-to-expenditure ratio. The first column of A0 represents theassumption that any contemporaneous shocks to aggregate growth are pure TFP shocks and thatany feedback from the other endogenous variables affects GDP only with a lag. Hence, ε1,t maybe viewed as the TFP shock. The second column states that prices are sticky in the short run. Thethird column serves to identify the shock from the AF.7-to-expenditure ratio, in particular to set itapart from the TFP shock. It states that shocks to GDP affect the average payment duration in thepublic sector, but not vice versa. In principle, this scheme stems from the observation that for thethree countries under consideration, the average payment delay is 90 days or at least very close to90 days. Thus, private suppliers are thought to anticipate this average delay and to adjust their

11For Greece the average payment duration also exceeds 90 days, but the country drops out owing to insufficient data,according to our criteria data section.

24

Figure 5. Average Reported Payment Duration by the Public Sector (number of days).

0

50

100

150

ESP ITA PRTcountry

dura

tion

year20082009201020112012

Source: Intrum Justitia (2013).

businesses accordingly. Only once an entire quarter goes and payments still do not arrive, privatesuppliers realize that they had underestimated the public payment delay. Column four says thatDTD is affected directly and immediately by all variables, but the AF.7-ratio. Finally, column fivestates that the interest rate as a fast-moving variable reacts to all shocks immediately.

We set the following hyperparameters for the model: λ0 = 0.5, λ1 = 0.1, λ3 = 2 and λ4 = 1.12

Further details on the prior and the posterior simulation via Gibbs sampling can be found in in theMathematical Appendix.

C. Empirical results

The impulse responses that derive from the structural model are depicted in Figure 6 and theassociated cumulative responses are reported in Table 6. We restrict ourselves to report onlyimpulse responses of interest, i.e., the impulse response of the DTD, GDP and the short-terminterest rate to a 10 percent expenditure shock. The solid black lines show the median impulseresponse drawn from 3,000 Monte Carlo draws from equation (16). Additionally, we have plottedclassical pointwise 68th percentile error bands.

The model yields fairly rich dynamics in terms of the impulse responses. For the three countriesunder consideration, we find that private sector solvency as measured by the distance to defaultcontracts as the average payment period of the general government increases. Signs for the

12Please refer to the Mathematical Appendix for a detailed discussion of the prior hyperparameters. We use the Rpackage MSBVAR by Brandt (2014) for estimation. We also did a prior specification search, but the marginallikelihood criterion suggested only very little shrinkage. We believe that given the small sample size, it is appropriateto be more conservative than is suggested by the prior search.

25

Table 6. Quarterly Structural Repsonses.Impulse response Cumulative

No. of quarters ahead annual responseCountry Variable 1 2 4 8 Lower Median UpperITA GDP 0.00 -0.00 -0.00 -0.00 -0.00 -0.00 0.00

DTD -0.04 -0.06 -0.07 -0.07 -0.35 -0.29 -0.23i 0.05 -0.06 -0.16 -0.30 -0.38 -0.28 -0.17

ESP GDP 0.00 -0.00 -0.00 -0.00 -0.01 -0.00 -0.00DTD -0.19 -0.15 -0.13 -0.10 -0.87 -0.78 -0.64

i 0.27 0.29 0.31 0.37 1.21 1.44 1.73PRT GDP 0.00 -0.00 -0.00 -0.01 -0.01 -0.01 -0.01

DTD -0.06 -0.10 -0.13 -0.18 -0.56 -0.48 -0.40i 0.22 0.17 0.11 -0.02 0.64 0.82 0.96

Source: Authors’ calculation

responses of interest are as expected. For all three countries we find that an increase in theAF.7-to-Expenditure ratio results in a negative shock to the distance to default in the privatesector. The cumulative response of the distance to default to a shock in the AF.7-to-expenditureratio is sizable after just 4 quarters, e.g., for Spain the annual response is such that the mediandistance to default is roughly 0.8 standard deviations smaller. For the direct impact on aggregategrowth we find almost no significant impact. Only in Portugal, the response is significantlynegative, albeit small in the short run. The response of the interest rates to an increase in publicpayment delays is ambiguous. While for example the initial response is positive in all countries,the pattern quickly reverses for Italy and Portugal and interest rates make up for the initialincrease. For Italy, this renders the cumulative response even negative over the course of a year.For the other two countries, the annual response is significantly positive, economically sizableand persistent.

The overall results for the subset of countries in this section suggest that public payment delaysaffect the economy through a liquidity channel. While in aggregate terms, growth is notimmediately affected (and we would arguably not expect it to do so significantly), the resilienceof private sector entities—here publicly listed firms—is negatively affected. Moreover, theamount of liquidity absorbed by the central government also affects interest rates in the very shortterm. The three-month Euribor rate reacts with a mild increase over the first few quarters.

26

Figure 6. Structural Impulse Responses of Selected Variables to a 10 Percent of Expenditure Shockto AF.7 (denoted as ”Credit” in the figure).

Response of DTD to shock from Credit Response of GDP to shock from Credit Response of i to shock from Credit

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

−0.015

−0.010

−0.005

0.000

0.005

0.010

0.015

−1.0

−0.5

0.0

0.5

0 5 10 15 20 0 5 10 15 20 0 5 10 15 20quarters

uppe

r

Italy

Response of DTD to shock from Credit Response of GDP to shock from Credit Response of i to shock from Credit

−1.0

−0.5

0.0

0.5

−0.050

−0.025

0.000

0.025

−2

−1

0

1

2

0 5 10 15 20 0 5 10 15 20 0 5 10 15 20quarters

Res

pons

e to

10%

AF

7−to

−E

xpen

ditu

re s

hock

Portugal

Response of DTD to shock from Credit Response of GDP to shock from Credit Response of i to shock from Credit

−0.5

0.0

0.5

−0.03

0.00

0.03

−1

0

1

2

0 5 10 15 20 0 5 10 15 20 0 5 10 15 20quarters

uppe

r

Spain

Source: Authors’ calculation.

27

V. CONCLUSION

This paper has considered the impact of the government’s payment discipline on the privatesector. The overall conclusion is that government decisions on the speed of effecting paymentshave important repercussions for the economy. Interestingly, the crucial aspect appears to be thetotal amount of outstanding payments and their average delay, rather than whether or notpayments are arrears in a legal or accounting sense.

Our empirical results from panel data have shown that payment delays appear to reduce profits,increase the likelihood of bankruptcies, and even reduce economic growth. While the exact sizeof the impact is hard to pin down given variable results across specifications, results aresignificant in most specifications. Findings using estimated arrears are qualitatively similar, butare less often significant. This could either be interpreted as meaning that whether a payment is inarrear in a formal sense is less important than the size and average delay of payments, or it couldbe due to our estimation. If data on actual arrears were available, this aspect could be investigatedfurther. Finally, on average for the European Union sample, the total amount of outstandingpayments does not appear to play a role, suggesting that predictable and regularly clearedpayment delays are not necessarily a problem, but rather changes in their duration.

Our results from Bayesian VARs performed on available quarterly data for Spain, Italy andPortugal, show that an increase in the average payment duration leads to (i) an increase in thelikelihood of private sector defaults and (ii) in some cases a transitory increase in the short-terminterest rate, i.e., it acts like a liquidity shock.

Our analysis and results have several implications for policy makers. Based on the findings in thispaper it would appear that delaying payments to deal with a funding issue or a debt limit is acostly way of achieving these aims. Quite to the contrary, efforts to accelerate payments andreduce existing stocks of arrears could be a helpful way of boosting the economy and typicallywould not increase deficits as long as all spending was properly accounted when it accrued.13

Having established that there is an inverse relationship between public payment delays andoverall economic performance or growth, the first policy recommendation is to closely monitorthe amount of arrears and payment practices in a given country to foster economic performance.A second policy implication would be to address the prevailing measurement issues associatedwith variables such as other accounts payable (AF.7) and install a comprehensive and frequentmeasurement and accounting system for public payment practices. Ideally this would aim torecord the entire payment history for each individual invoice, i.e., the outstanding amount, theinvoice date, the contractual payment period, and the payment duration for the both the centraland the local/regional levels of the general government on a consolidated and unconsolidatedbasis. Third, given their impact on economic performance delayed payments or arrears could beincluded in economic surveillance. For that purpose it would also be useful to improve thatavailability of data on accounts payable and their breakdown, as well as on arrears proper.

13In countries where the issue is lack of funding, speeding up of payments would have to be weighed against otherspending though, as this would also have a positive impact on the economy. The benefits of other spending may bereduced, though, if suppliers cannot be sure about when they will be paid.

28

VI. MATHEMATICAL APPENDIX

We follow Sims and Zha (1998) and Waggoner and Zha (2003) in estimating the model. Towardthat end, note that the (conditional) likelihood function of the data is given as

p(y1, . . . ,yT | A) ∝ |A0| exp

{−1

2[E′E]

}Conditional on A0 the above likelihood is quadratic in F and thus together with an appropriateprior F | A0 is matricvariate normal. The posterior for A0 however turns out to be non-standardand requires further processing. The exclusion restrictions we impose on each of the columns ofA0, may be represented by the restriction matrices Qi of rank qi

Qiai = 0 . (8)

Elements of F may be restricted in a similar way via a matrix Ri that has rank ri. As has beendemonstrated by Waggoner and Zha (2003), ai anf fi will satisfy the above restrictions, iff thereexists a n× qi matrix Ui and n× ri matrix Vi, such that

ai = Uibi (9)fi = Vigi . (10)

The matrix Ui may be found via a singular value decomposition, that takes Ui to be the matrix ofright-singular vectors that lie in the Null space of diag(ai). The set of parameters given by bi anddenotes gi is the set of parameters that is free to estimate.

Our prior on (ai, fi) is of the form

p(A0)p(F | A0) . (11)

where

ai ∼ N(0,Si) ; Si = diag(λ2

0

σ2i

), (12)

fi | ai ∼ N(Piai,Hi) ; Pi =[In,0n(p−1)+1×n

]; Hi =

[(λ0λ1lλ3σi

)2

Ik 0k−1

01×k−1 λ20λ

24

](13)

This prior on (ai, fi) is then mapped to a prior on (bi,gi) (we refer the reader to Waggoner andZha, 2003 for any details):

bi ∼ N(0, S̃i) ; S̃i = (U′iSiUi)−1 (14)

gi | bi ∼ N(P̃ibi, H̃i) ; P̃i = PiUi ; H̃i = Hi . (15)

29

Combining this prior with the likelihood, the posterior is found to be

p(bi,gi | Y) = p(b1, . . . ,bn | Y)n∏i=1

p(gi | bi,Y) (16)

where

p(gi | bi,Y) = N(Pibi,Hi) ; Pi = Hi

(V′iX

′YUi + H̃−1i P̃i

)(17)

Hi =(V′iX

′XVi + H̃−1i

)−1

(18)

and

p(b1, . . . ,bn | Y) = |[U1b1, . . . ,Unbn]|′ exp

{−T

2

n∑i=1

b′iSibi

}(19)

with

Si =

(1

T

[U′iY

′YUi + S̃i + P̃′iH̃−1i P̃i −P′iH

−1i Pi

])−1

. (20)

In order to estimate the model, we use the Waggoner-Zha Gibbs sampler, because there is nostraightforward way to sample from (16). Especially the fact that bi appears in the determinant of(19) makes the posterior of bi non-normal. Waggoner and Zha (2003) show that alternatively onemay sample from

p(β1, . . . ,βqi | b−i,Y) ∝ |β1|′ exp

{−T

2

qi∑i=1

β2i

}(21)

where

bi = Ti

qi∑i=1

βiwi (22)

denotes the set of bjsuch that j 6= i,

TiT′i = Si (23)

and w1, . . . ,wqi form an orthonormal basis of Rqi .14

We use the orthogonalization approach of Waggoner and Zha (2003) to devise the followingGibbs sampler:

1. Choose a starting value A(0)0 satisfying (8).15

14Use for example the Gram-Schmidt method to find them.

15We take the posterior mode of the marginal posterior of A0, which we find by numerical maximization

30

2. Draw A(s)0 conditional on (F(s−1),Y): for i = 1, . . . , n draw β1, . . . ,βqi from (21)

conditional on b(s)1 , . . . ,b

(s)i−1,b

(s−1)i+1 , . . . ,b

(s)n , and let b(s)

i be defined by (22) and takeai = Uib

(s)i .

3. Draw F(s) conditional on (A(s)0 ,Y) from (17).

(Nelder-Mead then BFGS).

31

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Flynn, Suzann, and Mario Pessoa, 2014, “Prevention and Management or GovernmentExpenditure Arrears,” IMF Technical Notes and Manuals.

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, 2006, Financial Soundness Indicators – Compilation Guide (International MonetaryFund).

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Ramos, Albert, 1998, “Government Expenditure Arrears: Securitization and Other Solutions,”IMF Working Papers, No. 98/70.

Sims, Christopher A, 1986, “Are Forecasting Models Usable for Policy Analysis?,” FederalReserve Bank of Minneapolis Quarterly Review, Vol. 10, No. 1, pp. 2–16.

Waggoner, Daniel F, and Tao Zha, 2003, “A Gibbs Sampler for Structural VectorAutoregressions,” Journal of Economic Dynamics and Control, Vol. 28, No. 2, pp. 349–366.