22
Does Government Debt Affect Bank Credit? Riccardo De Bonis and Massimiliano Stacchini Bank of Italy, Economics, Research and International Relations Abstract This paper analyses 43 countries from 1970 to 2010 to investigate the effect that public debt has on bank loans. The study is motivated by the need to understand the consequences of high public debt, a condition many countries are already experiencing or will experience in the next few years. Looking at the 40year period, we nd that the ratio of government debt to GDP has a negative association with the subsequent growth of bank credit. Our results hold using different econometric methods and controlling for variables such as the lagged value of bank loans, the occurrence of banking crises, per capita GDP, the ination rate, trade openness, international investment position, stock market capitalization and countrieslegal origin. We express our special thanks to the editor and two anonymous referees for very useful suggestions that greatly improved the quality of the paper. We are also grateful to Mauro Costantini, Giovanni Ferri, Sandro Momigliano, Franco Peracchi, Enrico Perotti, Federico Signorini and Robert Waldmann for helpful discussions and comments on previous versions of this paper. We also thank the participants at seminars held at the International Tor VergataConference on Banking and Finance, held in Rome, and at the Annual Conference of the Money Macro and Finance Research Group, held at the University of Birmingham. Andrew Henderson provided outstanding editorial support. The views are those of the authors and do not necessarily reect those of the Bank of Italy and the Eurosystem. International Finance 16:3, 2013: pp. 289310 DOI: 10.1111/j.1468-2362.2013.12037.x © 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit?

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Page 1: Does Government Debt Affect Bank Credit?

Does Government DebtAffect Bank Credit?�

Riccardo De Bonis and Massimiliano StacchiniBank of Italy, Economics, Research and International Relations

Abstract

This paper analyses 43 countries from 1970 to 2010 to investigate theeffect that public debt has on bank loans. The study is motivated by theneed to understand the consequences of high public debt, a conditionmany countries are already experiencing or will experience in the nextfew years. Looking at the 40‐year period, we find that the ratio ofgovernment debt to GDP has a negative association with the subsequentgrowth of bank credit. Our results hold using different econometricmethods and controlling for variables such as the lagged value of bankloans, the occurrence of banking crises, per capita GDP, the inflationrate, trade openness, international investment position, stock marketcapitalization and countries’ legal origin.

�We express our special thanks to the editor and two anonymous referees for very usefulsuggestions that greatly improved the quality of the paper. We are also grateful to MauroCostantini, Giovanni Ferri, Sandro Momigliano, Franco Peracchi, Enrico Perotti, FedericoSignorini and Robert Waldmann for helpful discussions and comments on previous versionsof this paper. We also thank the participants at seminars held at the International ‘TorVergata’ Conference on Banking and Finance, held in Rome, and at the Annual Conferenceof the Money Macro and Finance Research Group, held at the University of Birmingham.Andrew Henderson provided outstanding editorial support. The views are those of theauthors and do not necessarily reflect those of the Bank of Italy and the Eurosystem.

International Finance 16:3, 2013: pp. 289–310

DOI: 10.1111/j.1468-2362.2013.12037.x

© 2013 John Wiley & Sons Ltd

Page 2: Does Government Debt Affect Bank Credit?

I. Introduction

One effect of the financial crisis of 2007–09 was that public debt in industrialcountries reached levels not recorded since the end of World War II. Theincrease can be explained by the cost of bank bailouts and the GreatRecession that hit the economies after the financial crisis (IMF 2012).

The goal of this paper is to investigate how public debt influences thegrowth of bank credit.1 Our work may be seen as complementary to theliterature on the nexus between economic growth and the debt‐to‐GDPratio. The effects of large government debt on GDP growth have alreadybeen studied and debated in policy circles without a clear consensus (Kumarand Woo 2010; Reinhart and Rogoff 2010; Panizza and Presbitero 2012;Herndon et al. 2013). On the other hand, only scant attention has been paidto the effects of public debt on bank credit, notwithstanding the importanceof loans for the stability of financial systems, the business cycle, and thetransmission effects of monetary policy. There is a need to understandthe mechanism through which large public debts may affect the economy.As underlined by the IMF (2012), many industrial countries will experiencehigh government debts in the next decade. Reducing public debt takestime, and therefore the current environment will persist in the years tocome. Public debt is an overhang that countries must manage, whichincludes understanding its potential consequences.

Our finding – based on the analysis of 43 countries studied between 1970and 2010 – is that government debt has a negative effect on the subsequentgrowth of bank lending. These results are obtained with a dynamic paneldata model and are robust to a number of different estimators.

There are several (not mutually exclusive) channels through which publicdebt can influence bank credit. First, there is a typical crowding‐out effect. Incountries with high debt‐to‐GDP ratios, banks may become importantunderwriters of state securities. These economies might also have a largenumber of state‐owned firms. Therefore, general government may absorb alot of saving; hence, credit to the private sector – our dependent variable –

may be negatively affected. Second, high public debt may signal financiallyrepressed economies in which banks are forced to invest in public debtsecurities, possibly because there are credit ceilings and/or portfolio con-straints. Third, the euro‐area debt sovereign crisis showed a causal linkbetween growing public debts and banks’ funding and lending conditions(Committee on the Global Financial System 2011). The interest rates ongeneral government securities are the benchmark for the cost of credit not

1The terms public debt, government debt and debt are used interchangeably. The sameapplies to the words credit, loans and lending.

290 Riccardo De Bonis and Massimiliano Stacchini

© 2013 John Wiley & Sons Ltd

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only for the state but also for banks. An increase in the interest rates ongovernment bonds may be followed by an increase in the cost of banks’funding, thus leading to a higher cost of bank loans and to a creditdeceleration (Albertazzi et al. 2012). We conventionally label these threechannels as the classic crowding‐out channel, the financial repression chan-nel and the risk channel of public debt, respectively.

In theory, a high debt‐to‐GDP ratio might be the result of low creditgrowth with negative consequences for output. We manage this reversecausality issue by looking at the behaviour of credit at five‐year intervalsusing the debt‐to‐GDP ratio at the beginning of the window as the indepen-dent variable. We also include among our covariates the level of credit‐to‐GDP ratio at the beginning of each five‐year period.

There may be another factor that increases public debt and, at the sametime, reduces credit: the occurrence of a banking crisis. There is evidencethat banking crises are associated with a rise in government debt (Reinhartand Rogoff 2011; Furceri and Zdzienicka 2012). We accordingly consider theeffect that previous bank crises might have on the dynamics of lending.

In the econometric exercises we control for a number of determinants ofcredit growth – other than the debt‐to‐GDP ratio – discussed in theliterature. Inflation may negatively affect bank behaviour because it causeslower real rates of return on loans (Boyd et al. 2001). According to a large,though still controversial, body of literature, there is a link between econom-ic growth and finance, even if the direction of causality is difficult toascertain. We do not address this challenging issue of causality here.2 Wesimply include predetermined per capita GDP among our control variables.Some scholars have argued that financial development – and therefore alsocredit growth – may be influenced by trade openness and capital accountliberalization (Rajan and Zingales 2003; Chinn and Ito 2006) while otherstudies emphasize the differences between bank‐based and market‐basedcountries (see Levine and Zervos 1998). Consequently, in studying howpublic debt influences credit growth, we take into account the role of tradeopenness and stock market capitalization. We also control for the influenceof the net foreign position on the dynamics of credit because the latter maybe affected differently if countries are net foreign debtors or net foreigncreditors (see Lane and Milesi‐Ferretti 2007).

The paper is divided into five sections. Section II presents the data andillustrates the empirical specification. Section III comments on the baselineeconometric exercise. Section IV contains robustness checks. Section V

2Before the crisis of 2007–09 the predominant empirical finding was that financial develop-ment produces economic growth (Levine 2003). The crisis suggests that the role of finance inreal growth needs rethinking (Cecchetti and Kharroubi 2012).

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 291

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concludes by summarizing the various channels through which public debtmay negatively impact on credit growth.

II. The Data and the Empirical Specification

Our sample includes 43 countries: 23 are members of the OECD while 20 areclassified as upper income non‐OECD countries (see Table 1). The choice ofcountries was dictated by the availability of statistics. The dataset is unbal-anced as some countries do not provide statistics for the entire time spanunder scrutiny. The analysis covers the period 1970–2010, and the data wereoriginally recorded annually. As we are interested in the long‐term conse-quences of public debt levels, short‐term fluctuations were smoothed bygenerating five‐year non‐overlapping windows. The final dataset has a short‐time dimension and a larger country size as it is made up of eight observa-tions for 43 countries.

The definition of credit adopted by Levine and Zervos (1998) is used inthis paper. CREDIT is the credit granted by the banking system to the privatesector as a percentage of GDP. GOVDEBT is measured by the gross generalgovernment debt as a percentage of GDP.

Private credit growth may be influenced by the occurrence of bank crises.These events are considered by using the dataset built by Reinhart andRogoff (2009) covering data from 1800 to 2010 for a variety of financialcrises – banking, currency, domestic and external default or restructuring,and inflation – for many countries. We take into account only banking crises.The term capturing crisis events is based on year‐country specific binarydummy variables which are equal to 1 if there is a bank run or a closure/merger/large‐scale government assistance of an important financial institu-tion. As our panel spans over eight non‐overlapping five‐year periods, weaggregate annual data into an indicator (CRISIS) measuring the totalnumber of bank crisis events during the five‐year windows in each country.

Per capita income (PERCAPITAGDP) is measured in terms of purchasingpower parity. Inflation rates (INFL) are measured as annual growth rates ofthe consumer price index. Trade openness (OPENNESS) is measuredaccording to the index suggested by Rajan and Zingales (2003), that is thesum of exports and imports of goods and services as a percentage of GDP.The size of the equity markets is captured through the term STOCK, whichmeasures the stock market capitalization as a percentage of GDP. Table 2reports the data definitions and sources.

Both in the 1970s and the 2000s, public debt was greater in the OECDcountries than in non‐OECD countries. One may argue that the weight ofthe state, financial deepening and creditor protection are generally more

© 2013 John Wiley & Sons Ltd

292 Riccardo De Bonis and Massimiliano Stacchini

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intense in OECD countries. The access to financial markets remains moredifficult for emerging economies than for industrial countries, even if thisgap was larger in the past than today. In 2010 public debt was >100% inJapan, Greece, Italy and Belgium but only in Jamaica among the non‐OECDcountries. In recent years credit growth was intense in Ireland, Spain, theUK, Portugal and many other industrial nations. The increase in loans wason average slower in non‐OECD countries. Table 3 presents some descriptivestatistics.

Figure 1 shows the debt‐to‐GDP ratios on the x‐axis and credit growth inthe subsequent five years on the y‐axis. Countries where public debt waslower than 30% had greater credit growth than those countries where thepublic debt/GDP ratio was higher than 90% (the same evidence is obtainedusing a threshold of 80%). This is true in both OECD countries and non‐OECD countries. The evidence is very strong in the two decades 1990–2010,while it is weaker over the time span 1970–90, when public debt levels werestill smaller than in the following years.

Turning to multivariate analysis, we exploit the panel nature of the datasetthrough a model specified as:

Table 1: Country List

23 OECD countries 20 Non‐OECD countriesAustralia AlgeriaAustria ArgentinaBelgium BotswanaCanada BrazilDenmark ChinaFinland ColombiaFrance Costa RicaGermany Dominican RepublicGreece EcuadorHungary JamaicaIreland JordanIsrael MalaysiaItaly MexicoJapan PanamaNetherlands PeruNew Zealand South AfricaNorway ThailandPortugal TunisiaSpain TurkeySweden UruguaySwitzerlandUnited KingdomUnited States

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 293

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CREDITi;t � CREDITi;t�5 ¼ gGOVDEBTi;t�5 þ bCREDITi;t�5

þ Xi;t�5fþ hi þ nt þ ei;t ð1Þ

where i stands for a country, t indicates the end of each of the five‐yearwindows and t� 5 is the beginning of the same window. The terms CREDITand GOVDEBT are explained earlier in this section. The vector Xi;t�5

contains country‐specific time‐varying covariates (such as per capita GDPand inflation), while the terms hi and nt denote time‐invariant countrycomponents and time‐level fixed effects, respectively.

We address the reverse causality problem potentially running from creditgrowth to government debt using values which are pre‐determined withrespect to our dependent variable. Therefore, we adopt the level of the debt‐to‐GDP ratio at the beginning of each window as the main independentvariable (see Kumar and Woo 2010 for an analogous treatment of therelationship between GDP growth and the debt‐to‐GDP ratio). We also

Table 2: Data Definition and Sources

Sources of the statistics (key to abbreviations)BDL: Beck, Demirgüç‐Kunt and Levine (2012)PENN: Penn World Table, Centre for International Comparisons,University of PennsylvaniaWDI: World Bank, World Development IndicatorsCPS: Giulia Catini, Ugo Panizza and Carol Saade (2010), ‘MacroData 4 STATA’, http://graduateinstitute.ch/md4stataLLSV: La Porta, Lopez‐de‐Silanes, Shleifer, and Vishny (1997)

VariablesCredit Claims on private sector by deposit money banks as a percentage

of GDP at the beginning of each five‐year window, BDLCredit_growth Difference between the private credit‐to‐GDP ratios at the end

and at the beginning of each five‐year windowsGovdebt Gross government debt as a percentage of GDP at the beginning

of each five‐year window, CPSBanking crisis Number of incidents over each of the five‐year windows, based

on the archive available in C. Reinhart’s website at http://www.carmenreinhart.com/data/browse‐by‐topic/topics/7

Per capita GDP PPP Converted GDP Per Capita, at current prices (in $) at thebeginning of each five‐year window, PENN

Openness Sum of exports and imports of goods and services as a percentageof GDP at the beginning of each five‐year window, PENN

Infl Inflation rate calculated as annual growth rate of CPI at thebeginning of each five‐year window, WDI

Stock Stock market capitalisation as a percentage of GDP at thebeginning of each five‐year window, BDL

Legal origin_x Legal origin of countries (Anglo‐Saxon, French, Scandinavian,German), LLSV

© 2013 John Wiley & Sons Ltd

294 Riccardo De Bonis and Massimiliano Stacchini

Page 7: Does Government Debt Affect Bank Credit?

regress the growth of credit over a five‐year horizon on the initial value (thatis five years before) for the other independent variables.

We use different econometric methods to estimate the model (1) as eachhas its pros and cons. In the OLS estimation, simultaneity bias may occur asomitted country time‐invariant components might be included both in theinitial value of outstanding loans (CREDIT) and in the error term. Further-more, simultaneity bias might arise given the simultaneous inclusion ofpotential measurement errors in both the covariates and the error term. Totackle these drawbacks, we start with the (pooled) OLS estimator and then

Table 3: Summary Statistics

Variable Mean SD Min. Max. Observations

Credit Overall 59.5 43.6 3.3 228.2 N¼ 293Between 37.0 13.0 156.6 n¼ 43Within 26.0 �5.5 201.1 T‐bar¼ 6.8

Credit_growth Overall 7.8 17.5 �96.6 87.2 N¼ 293Between 8.4 �4.2 36.0 n¼ 43Within 15.7 �91.1 70.0 T‐bar¼ 6.8

Govdebt Overall 55.2 32.3 2.0 225.9 N¼ 293Between 21.6 14.9 108.1 n¼ 43Within 24.0 �29.0 173.0 T‐bar¼ 6.8

Stock market Overall 59.7 55.5 0.4 281.9 N¼ 182Between 45.0 0.6 187.6 n¼ 41Within 31.8 �51.7 216.8 T‐bar¼ 4.4

Openness Overall 65.2 40.4 10.4 212.2 N¼ 293Between 34.3 19.1 160.5 n¼ 43Within 20.0 �4.9 135.3 T‐bar¼ 6.8

Per capita GDP Overall 13831.5 11353.0 691.8 49727.9 N¼ 293Between 8934.4 3728.1 32400.5 n¼ 43Within 7511.7 �5149.9 39889.3 T‐bar¼ 6.8

Infl Overall 59.2 488.7 �0.9 7481.7 N¼ 293Between 179.2 1.4 968.2 n¼ 43Within 453.2 �907.5 6572.7 T‐bar¼ 6.8

Banking crisis Overall 0.8 1.4 0.0 5.0 N¼ 293Between 0.6 0.0 2.0 n¼ 43Within 1.3 �1.2 5.1 T‐bar¼ 6.8

Time periods refer to the intervals 1971–75, 1976–80, 1981–85, 1986–90, 1991–95, 1996–2000,2001–05 and 2006–10. Credit is the amount of credit granted by the banking system to the privatesector as a percentage of GDP measured at the beginning of each five‐year window; Credit_growthis the difference between the private credit‐to‐GDP ratios at the end and at the beginning of eachfive‐year window; Govdebt is the gross government debt as a percentage of GDP at the beginningof each five‐year window; Stock market is the stock market capitalization as a percentage of GDP atthe beginning of each five‐year window; Openness is the sum of exports and imports of goods andservices as a percentage of GDP at the beginning of each five‐year window; Per capita GDP is theper capita GDP at current prices at the beginning of each five‐year window; Infl is the annualchange in consumer prices at the beginning of each five‐year window; Banking crisis is thecumulated number of incidents over each of the five‐year periods.

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 295

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adopt three different techniques – the fixed effects (FE) estimator, thebetween (BE) estimator and the system GMM (sGMM) estimator – toaddress the potential sources of bias affecting the coefficients. The BEestimator copes with the inconsistency of the estimates generated by mea-surement errors in the covariates (Hauk and Wacziag 2009). However, thisestimator gives rise to biased parameters when explanatory variables areomitted. The FE estimator mitigates the omitted variable problems bydrawing fixed effects out of the error term. However, it does not eliminatedynamic panel bias as it introduces a correlation between the transformederror and the transformed lagged independent variable in contexts of large Nand small T datasets (Bond 2002). The sGMM estimator deals with severaldifficulties encountered in estimating dynamic panel models, such as endo-geneity, measurement errors and omitted variable problems,3 but its

0

2

4

6

8

10

12

14

govd

ebt<

30

govd

ebt>

90

govd

ebt<

30

govd

ebt>

90

govd

ebt<

30

govd

ebt>

90

govd

ebt<

30

govd

ebt>

90

govd

ebt<

30

govd

ebt>

90

All countries (1971-2010)

All countries Non-OECDOECD(1971-1990)

All countries (1991-2010)

Cre

dit g

row

th

Figure 1: Credit growth and government debt across countries and periods

3The strategy is to take first differences to remove unobserved time‐invariant country‐specific effects, and instrument the right‐hand variables in the first differenced equationsusing levels of the series lagged two periods or more. The sGMM combines this set ofequations with an additional set of equations in levels with lagged first‐differences asinstruments. We use the two‐step estimator GMM in most cases and correct the estimatedasymptotic variance with the finite sample correction that Windmeijer (2005) obtainsthrough asymptotic expansion techniques. In the ‘first difference’ equation, the endogenousterm is instrumented with lagged values of the variable in levels. In the ‘level equation’ theendogenous variable in level is instrumented with its own first differences. Lag limits –

restricting the number of instruments – have been fixed to avoid the risk of the weakinstrument problems (Roodman 2009).

© 2013 John Wiley & Sons Ltd

296 Riccardo De Bonis and Massimiliano Stacchini

Page 9: Does Government Debt Affect Bank Credit?

consistency is conditioned to the quality of the instruments (Bond andWindmeijer 2005). For this reason, we carry out specific tests on the weakinstrument problem and on the serial correlation of error terms.

In the following section we turn to multivariate regressions.

III. The Baseline Econometric Results

We study the impact of public debt on subsequent credit growth usingdifferent econometric methods. We start with four regressions that includetime dummies (see the left‐hand part of Table 4). In all four regressionspublic debt has a negative and significant coefficient. This is true using eitherthe between estimator or the pooled estimator. Fixed effects (the withinestimator) and the sGMM estimator confirm the negative influence of publicdebt on private credit growth. The sGMM estimate (�0.36) indicates that a10% increase in the debt‐to‐GDP ratio is followed by a deceleration of 0.7%points in the annual change of the credit‐to‐GDP ratio.

With regard to the coefficient of CREDITi;t�5, OLS estimators are expectedto provide an upwards‐biased estimate whilst the FE should give a down-wards‐biased estimate in a panel with a short timespan (Blundell et al. 2000).A consistent sGMM estimate should fall between the two (Bond 2002). Theparameters presented in Table 4 are strictly in line with these expectations.The negative sign of CREDITi;t�5 implies that loan growth is greater whenthe initial value of the credit‐to‐GDP ratio is smaller.

Looking at control variables, initial per capita income has a prevalentlypositive effect on credit growth. We find the same positive effect for tradeopenness. By contrast, inflation is not significant in most cases.

The regressions control for previous episodes of banking crises. Thesign of a banking crisis’s influence on credit growth is not easy to assessex‐ante (see Takats and Upper 2013 for an application). On the one hand,failures of intermediaries might be followed by a resurgence of creditwhile, on the other hand, the collapse of banks might be followed by aslowdown in lending. In our regressions, the dummy measuring bankcrisis is generally not statistically significant. Only in the within‐estimatormodel do banking crises have a statistically significant negative effect oncredit growth.

The validity of the instruments adopted in the sGMM model is a keyfactor for the consistency of this estimator. Table 4 presents the Hansen(1982) J‐test statistics for overidentifying restrictions and the Arellano–Bondtests for autocorrelation of the errors. The Hansen statistics indicate that wecannot reject the null hypothesis of the appropriateness of the full set of

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 297

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Table

4:TheEffectof

Debt‐to‐G

DPRatio

onCreditGrowth:BaselineRegressions

Covariates

Dependentvariable:creditgrow

th¼credit(t)�credit(t�5)

Withtimedu

mmies

Withou

ttimedu

mmies

BE

Pooled

OLS

FEsG

MM

Pooled

OLS

FEsG

MM

Govdebt

(t�5)

�0.154

��(�

2.66)

�0.118

��(�

2.37)

�0.152

���(�

3.40)

�0.360

���(�

3.30)

�0.109

��(�

2.49)

�0.153

���

(�3.81)

�0.285

��(�

4.51)

Credit(t�5)

0.0938

�(1.79)

�0.0448

(�1.24)

�0.304

���(�

3.71)

�0.196

���(�

3.74)

�0.05

(�1.33)

�0.321

���

(�5.10)

�0.177

���(�

9.48)

Percapita

GDP(t�5)

�0.000477(�

1.37)

0.000545

�(1.79)

0.00149�

��(3.76)

0.000894

(0.57)

0.000671

���(3.14)

0.00135�

��(6.41)

0.00104�

��(2.98)

Openness(t�5)

0.126�

�(2.71)

0.0949

��(2.69)

0.177�

��(2.68)

0.199

(0.93)

0.0922

���

(2.71)

0.160�

�(2.46)

0.152�

��(2.92)

lninfl(t�5)

3.856

(1.61)

1.315

(1.18)

1.968�

(1.80)

0.332

(0.06)

0.919

(1.12)

1.725�

(1.78)

0.855

(0.84)

Bankingcrisis

3.486

(1.52)

�1.093

(�1.46)

�1.553

��(�

1.99)

�1.619

(�0.70)

�0.925

(�1.30)

�1.281

�(�

1.68)

0.811

(0.48)

Arellano–Bon

dAR(1)test

(P‐value)

0.020

0.004

Arellano–Bon

dAR(2)test

(P‐value)

0.864

0.866

HansenJ‐statistics

(P‐value)

0.60

0.307

Tim

e‐fixedeffects

–Yes

Yes

Yes

No

No

No

Numberof

observations

293

293

293

293

293

293

293

Tim

eperiods

referto

theintervals1971–75,1976–80,1981–85,1986–90,1991–95,1996–2000,2001–05

and2006–10.Thedependentvariableiscredit_grow

th,that

isthedifference

betw

een

theprivatecredit‐to‐G

DPratios

attheendandat

thebeginningof

each

five‐yearwindo

w;Creditistheam

ountof

creditgrantedby

thebankingsystem

totheprivatesector

asapercentage

ofGDPmeasuredat

thebeginningof

each

five‐yearwindo

w;Govdebtisthegrossgovernmentdebt

asapercentage

ofGDPat

thebeginningof

each

five‐yearwindow

;Opennessisthesum

ofexpo

rtsandim

portsof

good

sandservices

asapercentage

ofGDPat

thebeginningof

each

five‐yearwindo

w;Per

capitaGDPisthepercapita

GDPat

currentprices

atthebeginningof

each

five‐yearwindo

w;lninflisthenaturallogof

1plustheannualchange

inconsumer

prices

atthebeginningof

each

five‐yearwindo

w;B

ankingcrisisisthecumulative

number

ofincidentsover

each

ofthefive‐yearperiods.BEdenotesBetweenEstim

ator;FE

denotesWithin

Estim

ator;sG

MM

indicatesSystem

Generalized

Methodof

Mom

ents,estimated

withtheWindm

eijer’sfinite

samplecorrection

.Adu

mmyvariableequal

to1forthegrou

pof

advancedOECD

countriesisincludedin

regression

s.T‐statistics

inparentheses.

� P<0.10.

��P<0.05.

��� P

<0.01.

© 2013 John Wiley & Sons Ltd

298 Riccardo De Bonis and Massimiliano Stacchini

Page 11: Does Government Debt Affect Bank Credit?

instruments, nor can we reject the null hypothesis of no second‐order serialcorrelation in the first‐differenced errors.

We confirm the negative sign of GOVDEBT when we drop time dummiesin the regressions (see the right‐hand part of Table 4). Moreover, thecoefficients for the other variables are broadly similar to those reported inthe first part of the table. The Hansen J‐statistics – aimed at testing therobustness of the instruments for the lagged endogenous variable – confirmthe adequacy of the instruments used in the sGMM estimation.

To summarize, whatever specification we adopt, government debt has anegative and statistically significant influence on loan growth. As antici-pated in the introduction, our results might reflect three linked phenome-na. The first interpretation is a typical crowding‐out effect: in countrieswhere the government has a large involvement in the economy, greatershares of bank assets may flow towards government securities and state‐owned firms, reducing loans to the private sector. Secondly, according tothe public finance approach to financial repression (Giovannini and deMelo 1991; Roubini and Sala‐i‐Martin 1995), issuing government securi-ties is a way for the state to collect revenues, especially when the proceedsfrom legal taxation are difficult or costly to obtain and banks are forced toinvest in government securities. Thirdly, the recent tensions in the euroarea have confirmed that the direction of causality may run from thecondition of the public finances to banks’ financial position. In manyEuropean countries the increase in the yields on public debt securitiesimplied a higher cost and a deceleration of bank funding: a slowdown ofcredit followed. Our results are consistent with those obtained by Hauner(2009), who found a negative link between public debt held by banks andfinancial development in middle‐income countries.

Now we turn to some robustness checks of our econometricexercises.

IV. Robustness Checks

A. Splitting the Time Span into Two Sub‐Periods

One might argue that our results derive from the choice of a very long timespan (40 years) which could compound contrasting patterns specific to sub‐periods. For this reason we distinguished between the interval 1971–90 andthe interval 1991–2010 (see Table 5). The split is conventional (see Kumarand Woo 2010 for a similar choice): liberalization of capital movements inmany countries started in the early 1990s, and financial globalization alsointensified in those years. Irrespective of the econometric method used, we

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 299

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Table

5:TheEffectof

Debt‐to‐G

DPRatio

onCreditGrowth:SeparateEviden

ceforSu

b‐Periods

Dependentvariable:creditgrow

th¼credit(t)�credit(t�5)

1971–90

1991–2010

BE

Pooled

OLS

FEsG

MM

BE

Pooled

OLS

FEsG

MM

Govdebt

(t�5)

0.103

(1.36)

�0.0553(�

1.19)

�0.082

(�1.21)

�0.197

�(�

1.74)

�0.135

��(�

2.08)

�0.131

��(�

2.20)

�0.205

��(�

2.50)

�0.341

���(�

3.51)

Credit(t�5)

0.0844

(1.4)

0.00857

(0.12)

�0.491

���(�

4.61)

�0.106

���(�

5.24)

0.0134

(0.19)

�0.0599

(�1.03)

�0.386

���(�

3.42)

�0.176

���(�

6.15)

Per

capita

GDP(t�5)

0.000654

(0.97)

0.00074

(1.48)

0.00168�

�(2.42)

0.000963

(1.15)

�0.00041

(�0.92)

0.000388

(0.79)

0.00353�

��(5.64)

0.00156

(1.23)

Openness(t�5)

�0.0209

(�0.49)

0.0336

(1.13)

0.211

(1.45)

0.437�

(1.7)

0.129�

�(2.39)

0.125�

�(2.34)

0.0625

(0.49)

0.202�

�(2.50)

lninfl(t�5)

�2.013

(�1.17)

�1.204

(�1.10)

0.602

(0.32)

2.848

(0.83)

3.949

(1.14)

2.583�

�(2.23)

0.638

(0.41)

3.028�

�(2.27)

Bankingcrisis

0.068

(0.04)

0.166

(0.13)

0.791

(0.86)

1.656

(0.74)

0.305

(0.11)

�1.676

(�1.42)

�2.248

�(�

1.80)

0.0705

(0.03)

Arellano–

Bon

dAR(1)test

(P‐value)

0.008

0.062

Arellano–

Bon

dAR(2)test

(P‐value)

0.509

0.409

HansenJ‐statistics

(P‐value)

0.651

0.564

Tim

e‐fixedeffects

–Yes

Yes

Yes

–Yes

Yes

Yes

Numberof

observations

129

129

129

129

164

164

164

164

Regressionslabeled‘1971–

90’referto

thewindo

ws1971–75,1976–80,1981–85

and1986–90;regression

slabelled‘1991–

2010’referto

thewindow

s1991–95,1996–2000,2001–05

and2006–

10.SeeTable4foradescription

ofvariablesandestimators.T‐statistics

inparentheses.

� P<0.10.

��P<0.05.

��� P

<0.01.

© 2013 John Wiley & Sons Ltd

300 Riccardo De Bonis and Massimiliano Stacchini

Page 13: Does Government Debt Affect Bank Credit?

find that public debt has a negative impact on bank loans in the time span1991–2010. In the time interval 1971–90 the term GOVDEBT is significantonly when the sGMM estimator is used. This result probably reflects the factthat public debt levels were higher in the 1990s and the 2000s than in theprevious 20 years. The magnitude of the coefficients of the debt‐to‐GDPratio is also greater in the time span 1991–2010 than in the years 1971–90.

B. Splitting the Sample Between OECD and Non‐OECD Countries

Notwithstanding real and financial globalization and convergence, it isreasonable to assert that industrial countries continue to differ fromemerging economies in access to international markets and the character-istics of their institutional frameworks. For instance, in the past financialrepression was stronger in non‐OECD countries than in OECD ones. Also,in growth regressions a distinction is frequently made between industrialand emerging economies, since the two groups of countries are on differentsteady‐state paths (Barro and Sala‐i‐Martin 2004). Another difference is thatspending on public infrastructure is important in non‐OECD nations, whileother components of public expenditure, such as social welfare spending,are more significant in OECD countries: these differences may impact onloan growth.

Therefore, we run different regressions for the 23 OECD countries and the20 non‐OECD countries. The results show that in OECD countries generalgovernment debt has a negative impact on credit growth (Table 6). This istrue for all the regression methods. In non‐OECD countries the effect ofpublic debt on loan growth is always negative but is only statisticallysignificant when using the BE and the sGMM estimators (right‐hand panelof Table 6).

C. Controlling for the Ability to Access Foreign Resources

One might suppose that the ability to resort to net foreign savings as a sourceof funding beyond national savings might alleviate borrowing constraintsfaced by countries. In other words, the adverse effects of public debt onprivate credit growth might be reduced if countries can raise funds abroad(see Lane and McQuade 2013 for an application to European countries forthe 2003–08 boom period).

A country’s ability to access foreign resources can be measured by itsinternational investment position (see Zucman 2013 for a recent account ofthe statistical issues). We split the countries into two groups. In the first

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 301

Page 14: Does Government Debt Affect Bank Credit?

Table

6:TheEffectof

Debt‐to‐G

DPRatio

onCreditGrowth:Are

ThereDifferencesBetweenOECD

andNon

‐OECD

Cou

ntries?

Dependentvariables:creditgrow

th¼credit(t)�credit(t�5)

OECD

countries

Non

‐OECD

countries

BE

Pooled

OLS

FEsG

MM

BE

Pooled

OLS

FEsG

MM

Govdebt

(t�5)

�0.275

��(�

2.58)�0

.159

��(�

2.48)�0

.197

��(�

2.54)�0

.337

���(�

3.24)�0

.139

���(�

3.83)�0

.067

(�1.64)�0

.0749

(�1.58)�0

.388

��(�

2.17)

Credit(t�5)

0.0933

(1.17)

�0.0266

(�0.59)�0

.230

��(�

2.46)�0

.10�

��(�

7.70)

0.142�

�(2.33)

�0.068

(�1.20)�0

.401

���(�

5.59)�0

.04�

��(�

8.33)

Per

capita

GDP(t�5)

�0.00148

�(�

2.00)�0

.00016

(�0.28)

0.00102

(1.43)

�0.00137

(�0.66)

0.000206

(0.42)

�0.00111

�(�

1.89)�0

.00042

(�0.30)�0

.000522(�

0.54)

Openness(t�5)

0.254�

�(2.45)

0.167�

(2.03)

0.525�

��(3.38)

0.288�

�(1.14)

0.0939

���

(3.31)

0.0382

(1.18)

0.101

(1.59)

0.010

(0.12)

lninfl(t�5)

�4.457

(�0.69)

2.169

(0.66)

3.297

(1.06)

�7.204

(�0.63)

4.045

(1.75)

�0.458

(�0.45)

0.309

(0.35)

�3.884

(�0.97)

Bankingcrisis

7.668

(1.55)

�1.03

(�0.89)�1

.558

(�1.22)�2

.383

(�0.65)

0.37

(0.29)

�1.212

(�1.59)�2

.302

��(�

2.62)�8

.413

���(�

3.04)

Arellano–

Bon

dAR(1)test

(P‐value)

0.107

0.035

Arellano–

Bon

dAR(2)test

(P‐value)

0.363

0.498

HansenJ‐statistics

(P‐value)

0.893

0.907

Tim

e‐fixedeffects

–Yes

Yes

Yes

–Yes

Yes

Yes

Numberof

observations

154

154

154

154

139

139

139

139

Thelistof

OECD

andnon‐O

ECD

countriesisreportedin

Table1.

SeeTable4foradescription

ofvariablesandestimators.T‐statistics

inparentheses.

� P<0.10.

��P<0.05.

��� P

<0.01.

© 2013 John Wiley & Sons Ltd

302 Riccardo De Bonis and Massimiliano Stacchini

Page 15: Does Government Debt Affect Bank Credit?

group foreign liabilities are greater than foreign assets, that is the countriesare net borrowers. In the second group foreign assets are greater than foreignliabilities, that is the countries are net creditors. We interact the public debt‐to‐GDP ratio with two dummy variables for the two groups of countries.

The estimates indicate that the effect of public debt on credit growth isnegative and significant for countries that are either net foreign borrowers ornet foreign creditors (Table 7). More strikingly, the strength of the linkappears higher for countries with a positive net foreign position. However,the difference between the two groups of countries is negligible when thesGMM estimator is adopted. Our evidence confirms that the negative impactof public debt on credit growth is robust to cross‐country heterogeneities asfar as the ability to draw financial resources from abroad is concerned.

D. Non‐Linear Effects

The influence of public debt on credit growth might be non‐linear inprinciple: the magnitude of the crowding‐out could depend on the size ofthe public debt (as a share of GDP). We perform a sensitivity analysis ofnon‐linear effects of public debt on credit growth in a step‐by‐step exerciseby creating a granular grouping of our countries according to the value ofGOVDEBT.4 In order to insulate non‐linear effects and avoid misinter-pretations due to different results obtained by alternative estimators, weanalyse the issue by focusing only on the sGMM estimator which, as wenoted earlier, is robust to endogeneity issues.

Table 8 reports our results. In column (a), we start by grouping ourcountries into four sets: those with a public debt of <20% of GDP, between20% and 80% of GDP, between 80 and 90% of GDP, and >90% of GDP. Wefind a negative and significant influence of public debt on credit growth onlywhen the former exceeds the level of 80%. In order to improve the sensitivityof our estimates below the 80% level, regression (b) increases the granularityof our grouping for countries with a public debt lower than this value. Theresults confirm that the impact of public debt is not significant below the80% level of public debt.

In columns (c–f), we study non‐linear effects in the higher part of thedistribution of the public debt‐to‐GDP ratio, that is for values >80%. Weprogressively shift the thresholds, for instance by taking into account publicdebt‐to‐GDP ratios higher than 100%, 110%, 120% and 130% in separate

4Technically, we built several interaction terms. Each term takes into account the level ofgovernment debt and a dummy variable specific to the groups of countries.

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 303

Page 16: Does Government Debt Affect Bank Credit?

Table

7:Does

theNet

Intern

ational

Investmen

tPositionMatter?

Covariates

Dependentvariable:creditgrow

th¼credit(t)�credit(t�5)

BE

Pooled

Within

sGMM

Govdebt

(t�5)

� Dummy_NegativeNIP

�0.0916

(�1.48)

�0.0674�

(�1.89)

�0.101

��(�

2.26)

�0.358

���(�

2.88)

Govdebt

(t�5)

� Dummy_Po

sitive

NIP

�0.229

���(�

3.54)

�0.235

���

(�5.11)

�0.385

���(�

5.67)

�0.384

�(�

1.81)

Credit(t�5)

0.116�

�(2.28)

�0.0115

(�0.33)

�0.312

���(�

5.14)

�0.242

���

(3.67)

Percapita

GDP(t�5)

�0.00025

(�0.71)

0.000692

��(2.26)

0.0017

���

(6.68)

0.000643

(0.42)

Openness(t�5)

0.103�

�(2.29)

0.0768

��(2.63)

0.153�

�(2.4)

0.245

(0.92)

lninfl(t�5)

3.028

(1.31)

0.892

(0.87)

1.709

(1.62)

�1.732

(�0.27)

Bankingcrisis

3.112

(1.42)

�1.265

�(�

1.68)

�1.657

��(�

2.20)

�1.905

(�0.88)

Arellano–

Bon

dAR(1)test(P‐value)

0.026

Arellano–

Bon

dAR(2)test(P‐value)

0.971

HansenJ‐statistics

(P‐value)

0.590

Tim

efixedeffects

Yes

Yes

Yes

Yes

Numberof

observations

293

293

293

293

SeeTable

4foradescriptionof

variablesandestimators.Dummy_Negative(Positive)

NIP

isadu

mmyvariable

equal

to1when

thecountry’slevelof

‘Net

financial

assets’islower

(higher)than

zero.T‐statistics

inparentheses.

� P<0.10.

��P<0.05.

��� P

<0.01.

© 2013 John Wiley & Sons Ltd

304 Riccardo De Bonis and Massimiliano Stacchini

Page 17: Does Government Debt Affect Bank Credit?

Table

8:Sensitivity

Analysisof

Non

‐LinearEffects

Covariates

Dependentvariable:creditgrow

th¼credit(t)�credit(t�5)

sGMM

(a)

sGMM

(b)

sGMM

(c)

sGMM

(d)

sGMM

(e)

sGMM

(f)

Govdebt(t�5)

� Dummy0_20

2.041

(1.33)

1.280

(0.80)

1.511

(1.05)

2.009

(1.41)

1.542

(0.99)

1.584

(1.10)

Govdebt(t�5)

� Dummy20_60

�0.311

(�0.67)

�0.339

(�1.08)

�0.278

(�0.91)

�0.340

(�1.05)

�0.326

(�0.97)

Govdebt(t�5)

� Dummy60_70

�0.121

(�0.32)

Govdebt(t�5)

� Dummy70_80

�0.104

(�0.32)

Govdebt(t�5)

� Dummy20_80

�0.129

(�0.37)

Govdebt(t�5)

� Dummy80_90

�0.411

�(�

1.97)

�0.502

��(�

2.12)

Govdebt(t�5)

� Dummy_over90

�0.381

��(�

2.33)

�0.398

�(�

1.72)

Govdebt(t�5)

� Dummy60_80

�0.156

(�0.73)

�0.126

(�0.62)

�0.156

(�0.63)

�0.155

(�0.57)

Govdebt(t�5)

� Dummy80_100

�0.434

��(�

2.21)

Govdebt(t�5)

� Dummy_over100

�0.429

��(�

2.27)

Govdebt(t�5)

� Dummy80_110

�0.343

��(�

2.13)

Govdebt(t�5)

� Dummy_over110

�0.437

��(�

2.05)

Govdebt(t�5)

� Dummy80_120

�0.429

��(�

2.11)

Govdebt(t�5)

� Dummy_over120

�0.423

��(�

2.32)

Govdebt(t�5)

� Dummy80_130

�0.430

�(�

1.84)

Govdebt(t�5)

� Dummy_over130

�0.398

��(�

2.09)

Credit(t�5)

�0.32�

��(�

3.29)

�0.26�

��(�

4.32)

�0.31�

��(�

4.80)

�0.31�

��(�

4.14)

�0.31�

��(�

4.61)

�0.31�

��(�

4.99)

Per

capita

GDP(t�5)

0.00182

(1.33)

0.00139

(1.25)

0.00163

(1.31)

0.00126

(1.14)

0.00162

(1.32)

0.00158

(1.32)

Openness(t�5)

0.238

(0.95)

0.220

(1.04)

0.271

(1.31)

0.261

(1.07)

0.274

(1.30)

0.272

(1.07)

lninfl(t�5)

7.230

(1.28)

6.079

(0.94)

6.342

(1.06)

5.805

(0.86)

6.402

(1.03)

6.610

(1.07)

Bankingcrisis

�1.125

(�0.62)

�1.318

(�0.61)

�1.041

(�0.52)

�0.746

(�0.32)

�1.007

(�0.46)

�0.811

(�0.40)

Arellano–

Bon

dAR(1)test(P‐value)

0.033

0.050

0.027

0.017

0.029

0.027

Arellano–

Bon

dAR(2)test(P‐value)

0.617

0.966

0.956

0.908

0.950

0.929

HansenJ‐statistics

(P‐value)

0.881

0.867

0.926

0.916

0.927

0.934

Numberof

observations

293

293

293

293

293

293

SeeTable4foradescriptionof

variablesandestimators.DummyX

_YandDummy_overXaredu

mmyvariablesequalto

1when

thecountry’slevelof

governmentdebt

(asapercentage

ofGDP)isbetw

eenX

andYpercentandstrictly

higher

than

Xpercent,respectively.Regressionsincludetime‐fixedeffects.T‐statistics

inparentheses.

� P<0.10.

��P<0.05.

��� P

<0.01.

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 305

Page 18: Does Government Debt Affect Bank Credit?

regressions. The results show that the estimates of the impact are broadlysimilar in these upper values of the distribution of public debt.

To conclude, the results seem to suggest some non‐linearity in therelationship between government debt and credit growth, as the negativeassociation appears insignificant for countries with a moderate ratio ofpublic debt to GDP.

E. Other Robustness Checks

First, we checked for the effect of stock market capitalization. More highlydeveloped financial markets might allow firms to issue more shares, whichcould affect the dynamics of bank credit. We introduced stock marketcapitalization in the regressions for a set of 41 countries. Using the sGMMtechnique confirms the negative effect of the debt‐to‐GDP ratio on creditgrowth and estimates a negative and significant coefficient of stock marketcapitalization.5

Second, path dependency theories of financial development stress the roleof legal origin as the central driver of the cross‐country differences inbanking and finance we observe today. Therefore, legal origin might explainthe behaviour of credit. Legal origin is traditionally considered using thedummies introduced by La Porta et al. (1997), in which legal systems areclassified into four categories of origin: Anglo‐Saxon, French, German andScandinavian. We follow the same approach. In our regression the coefficientof the public debt‐to‐GDP ratio remains negative and statistically significantwhile legal origin is rarely significant.6

V. Conclusions

This paper finds that between 1970 and 2010 the debt‐to‐GDP ratio has hada significant and negative effect on subsequent credit growth in a sample of43 countries. The econometric results are robust to the use of differentmethods. The negative effect of public debt is stronger in the 1990s and the2000s than in the 1970s and the 1980s. Splitting the sample, we confirm thenegative effect of the public debt‐to‐GDP ratio for the 23 industrial countrieswhile the effect is weaker in the 20 emerging economies, where the stock ofpublic debt is generally smaller. Looking at the control variables, lagged per

5Results available from the authors upon request.

6Results available from the authors upon request.

© 2013 John Wiley & Sons Ltd

306 Riccardo De Bonis and Massimiliano Stacchini

Page 19: Does Government Debt Affect Bank Credit?

capita income in most cases enters the regression with a positive sign. Thesame is true for trade openness, whereas inflation is not significant in mostof the cases. The regressions are robust to the inclusion of other variablessuch as previous episodes of banking crisis, countries’ international invest-ment position, stock market capitalization and legal origin.

As already anticipated, public debt may be associated with lower creditgrowth throughout three channels. The first is that a higher debt‐to‐GDP ratiomay induce banks to be regular holders of government securities. Greaterportions of credit are absorbed by government liabilities rather than by theprivate sector, partly because countries with a large debt might also have anextensive general government sector: we label this the ‘classic crowding outchannel’. One example is the Japanese experience in the last two decades: a vastportion of the huge Japanese public debt is held by banks while credit hasstagnated since the 1990s (see Hoshi and Ito 2012). The second – and perhapsinterdependent – explanation is that financially repressed economies artificiallyincrease their demand for government securities. Banks are forced to invest ingovernment bonds, so the granting of credit to the economy is reduced: we callthis the ‘financial repression channel’. Third, as shown by the recent euro‐areasovereign debt crisis, high government debt might have adverse effects onprivate credit by raising the costs and cutting the availability of bank funding.This is the ‘risk channel’ of high public debt.

Which channel was most likely to have been in action in our set ofcountries over the last four decades? Our tentative answer is that the firstchannel – the typical crowding out of loans to the private sector – wasperhaps the dominant one. As far as the second channel is concerned, afterWorld War II financial repression was common even in the richest coun-tries,7 but in the 1980s it disappeared or became weaker. In most of thecountries examined, capital movements were liberalized and credit ceilings,portfolio constraints and limits on loan interest rates were removed; bankreserve requirements were lowered; banks were also privatized, and thereforethe proprietary bond between governments and intermediaries became lessstringent. On the other hand, this financial repression channel is difficult toestimate as harmonized statistics on indicators like credit ceilings, portfolioconstraints and other administrative measures are difficult to collect for alarge set of countries and over a long time span.

The third potential channel – the risk channel – was probably alsoinfrequently in action during the 40 years analysed in our paper. In therecent European sovereign debt crisis, banks suffered because of the worsen-ing of public finance indicators in countries with a large debt‐to‐GDP ratio.

7See McKinnon (1973) and Shaw (1973). The IMF (2012) states that in the 1940s the UnitedStates was characterized by financial repression.

© 2013 John Wiley & Sons Ltd

Does Government Debt Affect Bank Credit? 307

Page 20: Does Government Debt Affect Bank Credit?

We feel that this situation – which we marginally cover in our dataset – wasnot frequent in the time span from 1970 to 2010. Choosing a threshold of90% of the debt‐to‐GDP ratio, Reinhart et al. (2012) identify 26 episodes ofpublic debt overhangs in advanced countries since 1800. But only six of theseepisodes – involving Japan, Belgium, Canada, Ireland, Greece and Italy –

took place after 1970. Moreover, in contrast to the recent euro‐area crisis,these scholars found that, since 1800, countries with a public debt overhanghave by no means always experienced ‘either a sharp rise in real interest rates ordifficulties in gaining access to capital markets’. Therefore, the risk channel of highdebt – operating through exceptionally high interest rates – was not frequentin the past.

While our empirical results appear complementary to a classic ‘crowding‐outexplanation’, we recognize that the analysis of the channels through whichpublic debt impacts on credit growth must remain on the research agenda.

Riccardo De Bonis and Massimiliano StacchiniEconomics, Research and International RelationsBank of [email protected];[email protected]

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