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Banks, Government Bonds, and Default: What do the Data Say?
Nicola Gennaioli, Alberto Martin, and Stefano Rossi*
We use data from Bankscope to analyze the holdings of public bonds by over 18,000 banks located in 185 countries and the role of these bonds in 18 sovereign debt crises over the period 1998‐2012. We find that: (i) banks hold a sizeable share of their assets in government bonds (about 9% on average), particularly in less financially developed countries; (ii) during sovereign crises, banks on average increase their bondholdings by 1% of their assets, but this increase is concentrated among larger and more profitable banks, and; (iii) the correlation between a bank’s holdings of public bonds and its future loans is positive in normal times, but turns negative during defaults. A 10% increase in bank bond‐holdings during default is associated with a 3.2% reduction in future loans, and bonds bought in normal times account for 75% of this effect. Our results are consistent with the view that there is a liquidity benefit for banks to hold public bonds in normal times, which is critical for understanding bank fragility during sovereign crises.
JEL classification: F34, F36, G15, H63
Keywords: Sovereign Risk, Sovereign Default, Government Bonds
*Bocconi University and CREI, E‐mail: [email protected]; CREI, UPF and Barcelona GSE, E‐mail:
[email protected]; and Purdue University, CEPR, and ECGI, E‐mail: [email protected]. We are grateful for helpful suggestions from seminar participants at the Norwegian School of Economics, the Stockholm School of Economics, Banque de France/Sciences Po/CEPR conference on “The Economics of Sovereign Debt and Default,” and the Barcelona GSE Summer Forum. We have received helpful comments from Mariassunta Giannetti, Sebnem Kalemli‐Ozcan (discussant), Paolo Pasquariello, and Hélène Rey (discussant). Jacopo Ponticelli provided excellent research assistantship. Gennaioli thanks the European Research Council for financial support and the Barcelona GSE Research Network. Martin acknowledges support from the Spanish Ministry of Science and Innovation (grant Ramon y Cajal RYC‐2009‐04624), the Spanish Ministry of Economy and Competitivity (grant ECO2011‐23192), the Generalitat de Catalunya‐AGAUR (grant 2009SGR1157) and the Barcelona GSE Research Network. Martin and Gennaioli acknowledge support from the International Growth Center, project RA‐2010‐03‐2006.
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1. Introduction
Current events in Europe vividly illustrate how government default can endanger domestic bank stability. Growing concerns of public insolvency have caused severe stress in the European banking sector, which is loaded with Euro‐area debt (Andritzky 2012). Problems are very severe for banks in troubled countries, which entered the crisis holding a sizeable share of their assets in their governments’ bonds: roughly 5% in Portugal and Spain, 7% in Italy and an astounding 16% in Greece (2010 Stress Test). Furthermore, since the onset of the crisis these banks appear to have substantially increased their exposure to the bonds of their financially distressed governments (Brutti and Sauré, 2012), leading to even greater fragility. As The Economist recently put it, “Europe’s troubled banks and broke governments are in a dangerous embrace.”1
This paper provides the first systematic empirical assessment of the link between sovereign default, bank bondholdings, and bank loans. We investigate two key questions:
These events are not unique to Europe: a similar relationship between sovereign defaults and the banking system has been at play also in other sovereign crises (IMF 2002).
• What are the patterns of banks’ holdings of government bonds? How do they vary across banks and countries? Do banks accumulate public bonds during sovereign defaults or outside of these events?
• Do bondholdings affect banks’ lending behavior in normal times and during sovereign defaults? Do the banks that hold more bonds cut their loans more during these crises? If so, is this due to bonds purchased outside of or during the crises themselves?
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To shed light on these questions, we study the patterns of bondholdings and the lending behavior of individual banks outside of and during sovereign defaults. Our aim is to uncover broad stylized facts regarding bondholdings and their relation with lending, not to identify specific causal mechanisms. We do however conduct extensive tests of alternative mechanisms to evaluate the robustness of the stylized facts that we document. We use the BANKSCOPE dataset, which provides the main source of information on the bondholdings and characteristics of banks, covering over 18,000 banks in 185 countries over the period 1998‐ 2012. These observations span 18 sovereign default episodes (we often refer to these as “crises”) involving 15 countries, 352 banks and 863 bank‐years observations. To study the role of country‐ versus bank‐level factors in determining the demand for bonds and the unfolding of crises, we combine the BANKSCOPE data with information from IMF and World Bank sources on the macroeconomic conditions faced by banks in each of the countries in sample.
We organize our empirical exercise around the threemainhypothesesconcerning banks’ holdings of government bonds and their effects during sovereign defaults. The first hypothesis, which we denote as the “liquidity view”, sustains that banks hold bonds on a regular basis to store liquidity and to post them as collateral in borrowing arrangements (Bolton and Jeanne 2012, Gennaioli, Martin, and Rossi 2012). 2
2 Of course, the general notion that government bonds provide liquidity to the private sector is not original to
these papers (see, for instance, Holmstrom and Tirole 1998).
In this view, government bonds allow banks to operate more effectively in normal times but they become costly during crises, when public default destroys the asset base of banks and thus their ability to borrow and lend. The second hypothesis, which we label the “risk‐taking” view, holds that banks demand public bonds in
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anticipation of, as well as during, sovereign crises in order to chase high returns, perhaps without fully internalizing the systemic consequences of doing so (Acharya and Steffen 2013). Finally, the “government intervention” view sustains that banks hold public bonds because the government induces them to do so, either through capital regulation in normal times or through creditor discrimination or moral suasion during crises (Livshits and Schoors 2009, Basu 2010, Broner et al. 2013). According to the latter two views, banks derive few benefits from holding bonds in normal times, and sovereign crises reduce bank lending both because they hurt bank assets and because they provide incentives for other banks to buy additional bonds, which crowds out new loans.
To evaluate the empirical content of these hypotheses, we split a bank’s holdings of public bonds into a time‐invariant and a time‐varying component. The time‐varying component includes changes in bondholdings before as well as during crises, while the time‐invariant component captures a bank’s stable demand for government bonds. To isolate the role of common factors, we further split each component into a country‐level determinant of bonds common to all banks, and an idiosyncratic bank‐level factor. We then document the following main facts:
• Bondholdings by banks are large. In countries that experience at least one sovereign default, banks hold on average 14.4% of their assets in public bonds. Most of the variation in bondholdings (80%) is cross sectional (i.e., time‐invariant). This component of the demand for bonds is larger in less financially developed countries and for banks that fund fewer loans, take less risk, and are more levered.
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• On average, during default events banks increase their holdings of public bonds from 14% to 15% of their assets. This increase is very heterogeneous, occurring primarily in larger and more profitable banks and in more financially developed countries. • Bondholdings matter for lending: higher current bondholdings by a bank are
associated to an increase in future loans during normal times, and to a decrease in future loans during defaults. A 10% increase in bondholdings during default is associated with a 3.2% reduction in a bank’s loans. Approximately 75% of this drop is attributable to the stable, time invariant, component. Bonds acquired during crises reduce loans only after controlling for the characteristics of banks buying them.
These facts have two interesting implications. First, the variation of bondholdings within the sample is largely driven by the behavior of their stable (cross‐sectional) component. Moreover, variation in this cross‐sectional component appears consistent with the liquidity view, whereby banks should hold the liquid and safe government bonds particularly when they have few investment opportunities and in less financially developed countries. The behavior of bank lending is also consistent with this view: government defaults hurt the liquidity of banks (via their stable bondholdings), leading to a reduction in future loans. This is true also when adjusting for any time‐varying country‐wide shocks: within the same defaulting country, it is the banks most loaded with public bonds that subsequently cut their lending the most.
Second,the time‐varying component also matters for understanding bondholdings and loans. It rises during sovereign defaults and is associated to a further decrease in bank lending in these episodes. This is in line with the risk‐taking and government intervention views, which
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suggests that they may play a role as well. Interestingly, these time‐varying effects are very heterogeneous, being stronger for banks that are more profitable and are located in more financially developed countries. As we will discuss in the paper, this feature has useful implications in the interpretation of our results.
This paper is related to two bodies of work. The first one studies the costs of sovereign default. In the conventional approach, these costs consist of market exclusion and external sanctions (see Eaton and Fernandez 1995). The problem is that, in reality, market exclusion is typically short‐lived (Gelos et al. 2011) and sanctions are seldom observed (Tomsz, 2007).3
Recent theories build on the idea that sovereign default is costly because it inflicts a “collateral damage” to the domestic economy. In these models, such damage arises because default is assumed to be nondiscriminatory, so that it hurts domestic bondholders as well as foreign ones and this has consequences for domestic financial markets.
In these theories, moreover, domestic agents hold no bonds or – if they do – they are perfectly shielded from the default (e.g., the government engineers a perfect bailout).
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3 Arellano (2008) shows that in a standard infinite horizon model an output costs of default is needed (on top of
market exclusion) in order to rationalize the observed low frequency of default events.
In previous work, for instance, we built a model where public defaults destroy the net worth of banks that hold public bonds, hindering financial intermediation (Gennaioli, Martin, and Rossi 2012). We also provided country‐level evidence showing that, after a public default, private credit falls more in those banking systems that are on aggregate more exposed to government bonds.
4 An exception to this is Sandleris (2012), who builds a model where defaults signal bad news about the economy
and are thus followed by lower output growth. Broner and Ventura (2011) study a model where the government strategically choosing not to enforce private contracts cannot discriminate among domestics and foreigners. Here non‐enforcement destroys both international and domestic risk sharing. Brutti (2011), Basu (2010) and Mengus (2012) build models where default is nondiscriminatory and reduces entrepreneurial wealth and investment.
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Other papers have recently examined whether the data are consistent with the “collateral damage” view of sovereign defaults, either by using aggregate data or by focusing on a specific crisis or country. Brutti and Saure (2013) study the demand for government bonds by European banks during the recent sovereign debt crisis. Acharya and Steffen (2013) examine German banks’ stock returns to infer the demand for bonds during the recent crisis. Both papers find support for the risk‐taking or government intervention views of bondholdings. Acharya, Drechsler, and Schnabl (2013) study the effects of public bailouts of banks, and of sovereign spreads spikes, on banks’ stock returns. Reinhart and Sbrancia (2011) study the increase in aggregate bondholdings around defaults, and attribute it to financial repression.5
By using bank‐level data for many countries, default episodes and time periods, we provide the first systematic analysis of the patterns of bondholdings at the bank level, and the effects of bondholdings on lending by individual banks outside of and during default events.
The second body of work studies the demand for government bonds. Krishnamurthy and Vissing‐Jorgensen (2012) analyze the impact of changes in the supply of U.S. treasury bonds on their yields between 1926 and 2008, identifying a liquidity and a safety component of the demand for government bonds (not necessarily by banks, though). Greenwood and Vayanos (2012) analyze the yields of U.S. public bonds of different maturities in light of a “preferred habitat” theory of demand. Rather than looking at yields, we study the holding of public bonds by banks and the association between these holdings and lending behavior during defaults.
5 Other work documents the association between public defaults and private credit. Arteta and Hale (2008) show
that sovereign defaults are accompanied by a decline in syndicated foreign credit to domestic firms. Using aggregate data, Borensztein and Panizza (2008) show that sovereign defaults are accompanied by larger contractions in GDP when they happen in tandem with banking crises.
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The paper proceeds as follows. Section 2 describes the data. Section 3 explains the basic methodology used to decompose banks’ bondholdings into a time‐invariant and a time‐varying component, lays out our empirical strategy, and discusses alternative hypotheses. Sections 4 and 5 contain the main results of the paper. Finally, Section 6 concludes.
2. The Data
We obtain bank‐level data from the BANKSCOPE dataset, which contains information on the holdings of public bonds for 18,776 banks in 185 countries over the period 1998‐2012 (84,900 bank‐year observations). This dataset, which is provided by Bureau van Dijk Electronic Publishing (BvD), provides information on a broad range of bank characteristics, such as size, leverage, risk taking, profitability, amount of loans outstanding, balances with the Central Bank and other interbank ratios. Most important for our purposes is that BANKSCOPE also reports banks’ holdings of public bonds. Bonds are reported at book value, and the nationality of the bonds is not reported. We shall return to this last issue later on. The information in BANKSCOPE is suitable for international comparisons because BvD harmonizes the data.
We start with the full sample of banks in BANKSCOPE. We filter out duplicate records, banks with negative values of all types of assets, banks with total assets smaller than $100,000, and years prior to 1997 when coverage is less systematic. This procedure results in 84,900 observations of the bondholdings variable at the bank‐year level over 1998‐2012. We impose two additionalrequirements on remaining banks: first, that we observe at least two consecutive years of data so that we can perform our decomposition; and second, that data is available on
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all of the other main variables such as leverage, profitability, cash and short term securities, exposure to Central Banks, and interbank balances. Our main sample then consists of 7,094 banks in 159 countries for a total 32,068 bank‐year observations. We take the location of banks to be the one reported in Bankscope, which coincides with the location of the Bank’s headquarters. Commercial banks account for 33.2% of our sample; cooperative banks for 38.2%; savings banks for 20.6%; investment banks for 1.6%; the rest includes holdings, real estate banks, and other credit institutions.
Data on the macroeconomic conditions of the different countries is obtained from the IMF’s International Financial Statistics (IFS) and the World Bank’s World Development Indicators (WDI).6
6 See Table AI in the Appendix for a description of variables.
To measure the size of financial markets we use the ratio of private credit provided by money deposit banks and other financial institutions to GDP, which is drawn from Beck et al. (2000). This widely used measure is an objective, continuous proxy for the size of the domestic credit markets. Finally, we follow the existing literature and proxy for sovereign default with a dummy variable based on Standard & Poor’s, which defines default as the failure of a debtor (government) to meet a principal or interest payment on the due date (or within the specified grace period) contained in the original terms of the debt issue. According to this definition, a debt restructuring under which the new debt contains less favorable terms to the creditors is coded as a default. The Greek bond swap that was launched in February of 2012, for instance, is identified as a default by Standard & Poor’s because the retroactive insertion of collective action clauses was deemed to materially change the original contract terms.
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In our robustness tests, we complement our analysis by using two alternative measures of sovereign default, namely: (i) a monetary measure of creditors’ losses given default, i.e., “haircuts”, from the work of Cruces and Trebesch (2013) and Zettelmeyer, Trebesch, and Gulati (2012), and; (ii) a market‐based definition based on whether sovereign bond spreads exceed a threshold identified with extreme value theory (approximately 1000 basis points), following the methodology of Pescatori and Sy (2007). These measures capture dimensions of sovereign risk that are not captured by the S&P default dummy, such as spikes in credit spreads and the economic magnitude of creditors’ losses. As we show in Tables AVI and AVII in the Appendix, and as we later discuss in the text, our results are very robust to these alternative measures. In our main analysis, however, we stick to the S&P default dummy. Indeed, measures of haircuts depend heavily on the assumptions one makes about counterfactuals (e.g., Sturzenegger and Zettelmeyer 2008); and measures based on sovereign bond spreads require observing reliable data on secondary market trading, which limits our sample size. Using the S&P dummy, we have 18 sovereign defaults of different duration in 15 countries, which are listed in the Table AII of the Appendix. Using the haircut measure we can only consider 12 defaults in 11 countries. Using the credit spreads measure we can only consider 12 defaults in 10 countries.
Table AII shows that there is a large variation in the size of defaulting countries and in the extent of bank involvement. A few countries such as Argentina, Russia, Nigeria, Kenya and Ecuador have the lion’s share of banks, while in other six defaulting countries our data contains fewer than five banks in each country. These features raise the concern that countries that are small and have few banks might drive our results. In our robustness tests (Tables AIV and AV in
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the Appendix) we re‐estimate our regressions focusing on the set of large defaulting countries and discarding countries with fewer than five banks during a default episode, respectively.
2.1 Data Comparability
The BANKSCOPE dataset is widely used and has an established track record,7
[Table I here]
but there is one important dimension along which its reliability has not been scrutinized: its measure of government bondholdings. To check the quality of this measure, we compare it to other data sources on bondholdings: the country‐level measure of “banks’ net claims on the government” from the IMF, and the bank‐level data from the recent European Stress Test.
Table I compares the BANKSCOPE data on bondholdings with the IMF measure. Panel A contains the mean, the median, and the standard deviation of bondholdings (as a share of total assets) in BANKSCOPE. Mean bondholdings are at 9%of assets, while median bondholdings are approximately half as high. The standard deviation of bondholdings in the sample is also high.8 Panel B reports the same information, but only for the subset of countries for which the IMF also reports banks’ bondholdings. Panel C displays the IMF measure of “financial institutions’ net claims to the government,” computed as a share to total assets.9
7 See, for instance, Classens and Laeven (2004), and Kalemli‐Ozcan et al. (2011).
Mean, median and
8 The highest bondholdings in the sample are above 65% for selected banks in Argentina, Nigeria, India, Jamaica
and Venezuela in 2003; the lowest bondholdings are 0% (e.g., several U.S. banks).
9 This variable reports the net positions of commercial banks, defined as holdings of securities plus direct lending
minus government deposits, and it can be interpreted as a proxy for the bondholdings of banks. Other papers using this measure are Gennaioli et al. (2012) and Kumhof and Tanner (2008).
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standard deviation of the IMF measure are very close to the BANKSCOPE data. The IMF data gives a slightly higher mean bondholdings, but measurement in the two datasets tends to converge towards the end of the sample. This discrepancy between IMF and BANKSCOPE data could be due to the fact that the former might also capture non‐bond finance and to the fact that the banks used to compute the IMF measure may differ from those in BANKSCOPE.
The IMF data cannot address the quality of the BANKSCOPE data on a bank‐by‐bank basis. We thus compare our measure of bondholdings to the one reported by the European stress test of 2010. This also allows us to evaluate the mismeasurement stemming from the fact that, contrary to the stress test, BANKSCOPE does not break down bonds by nationality.
Table II reports bondholdings from the European stress tests of 2010 and 2011. Panel A of the table reports bondholdings for the full sample contained in the stress test, whereas Panel B reports bondholdings for the subset of the banks in the stress test sample that is contained in BANKSCOPE. The bondholdings reported by BANKSCOPE are showed in Panel C. Clearly, the data from both sources are highly comparable. The bank‐by‐bank correlation between the bondholdings reported by BANKSCOPE and by the stress test is 80%. The small discrepancies between our measure and the stress test measure are thus most likely due to differences in the time at which the measurement itself took place.10
[Table II here]
10 While BANKSCOPE also counts non‐EU bonds, the bondholdings of European banks consist almost exclusively of
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The evidence is reassuring. Even in highly integrated European markets, where domestic and foreign bonds are in many cases treated symmetrically by the regulatory framework, more than 75% of bank bondholdings correspond to domestic bonds. This share is in all likelihood much larger in the subset of developing countries that provide most of our observations on sovereign defaults. In sum, the BANKSCOPE measure is a good proxy for the domestic public bonds held by banks around the world, and we use it as such in the rest of the paper.
2.2Summary Statistics
We consider the distribution of bank characteristics in BANKSCOPE, focusing on: (i) bank size as measured by total assets, (ii) risk taking as measured by the investment in assets other than cash and other liquid securities, (iii) leverage as measured by one minus shareholders’ equity as a share of assets, (iv) loans outstanding as a share of assets, (v) profitability as measured by operating income over assets, (vi) exposure to the Central Bank as measured by deposits in the Central Bank over assets, (vii) balances in the interbank market, and (viii) government owned, a dummy that equals one if the government owns more than 50% of the bank’s equity.11
[Table III here]
To neutralize the impact of outliers, all variables are winsorized at the 1st and 99th percentile. Table III provides descriptive statistics for these variables in our sample.
11 For robustness, we adopt two different definitions for risk taking. The first definition measures the share of
assets other than cash and bonds. This is our preferred definition, as it reflects the status of bonds as “safe assets” in normal times, not just from the viewpoint of banks’ preferences but also of risk weightingin capital regulation. The only problem of this definition is that it mechanically decreases with bondholdings, potentially generating spurious correlations. As a result, we perform robustness tests using a definition of risk taking equal to the share of non‐bond assets other than cash. Our main results do not change when we use this alternative measure.
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Panel A shows that there is a fairly large variation in bank characteristics within the BANKSCOPE sample. The average bank invests roughly 90% of its resources in risky assets (60% of which are loans, and the rest includes debentures and other non‐government securities), obtains 90% of its financing in the form of debt, which includes deposits (for an average leverage ratio assets/equity of about 10), and holds 3% of its assets in central bank reserves.12
[Table IV here]
Table IV reports the correlations between different bank characteristics in our sample.
All correlations, except for the one between size and loans, are statistically significant. Bank profitability is positively correlated with size, exposure to the central bank and interbank balances, while it is negatively correlated with risk taking, leverage, and loans outstanding.
3. Competing Hypotheses and Methodology
We organize our exercise around the three main hypotheses regarding the bank bondholdings‐ default link. The first hypothesis, which we call the “liquidity” view, holds that banks use public bonds as a way to store their funds in the short run, in order to finance future investments and outlays. According to this view, bondholdings are part of a bank’s normal business activity. They are accumulated when the bank has few current investment opportunities relative to those in the future, but they may be costly when a sovereign crisis breaks out (Bolton and Jeanne 2012, Gennaioli et al. 2012). The “risk‐taking” view holds instead that banks buy public
12 Panel B of Table III shows the characteristics of banks involved in the stress test. These banks are much larger
and extend more loans than the median BANKSCOPE bank.They also have lower exposure to the Central Bank and to other banks. Leverage and risk taking are instead of similar magnitude to those observed in BANKSCOPE.
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bonds when risk premia on sovereign bonds become large in anticipation of, as well as during, sovereign debt crises. In this view, banks choose to take on sovereign risk in order to chase high returns (Acharya and Steffen 2013). Banks’ risk seeking thus reduces loans not only because of the losses they suffer in the event of a sovereign default, as in the liquidity view, but also because investment in high yield bonds crowds out loans to households and business firms. Finally, the “government intervention” view sustains that banks hold public bonds because they are induced to do so by the government, through capital regulation in normal times, through creditor discrimination (e.g., promises of bank bailouts) and moral suasion during defaults (see Livshits and Schoors 2009, Hannoun 2011, and Broner et al. 2013). As in the risk‐taking view, bondholdings and bond purchases crowd out loans. Before moving on, it is useful to remark that in principle a different form of “intervention” is possible, according to which the government uses its bonds to compensate troubled banks during crises. This also predicts that banks pile up bonds during default but, in contrast with the previous hypotheses, bondholdings should, if anything, boost banks loans during default.
Before proceeding, we must acknowledge that our data set is ill‐suited to perfectly distinguish among these hypotheses. To do so, we would need data on the returns of bonds and of alternative assets, as well as a precise knowledge of bank regulation around the world. Our dataset cannot provide this depth of information, as it has been constructed instead to cover the largest possible sample of banks and countries on and off default. Even though our data makes it difficult to univocally identify the merits of alternative views, however, it still enables us to analyze some broad implications along which they differ.
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The first set of implications concerns the timing of bondholdings. According to the liquidity view, banks should if anything reduce their bondholdings during crises because risky bonds do not constitute a good store of liquidity. According to the risk‐taking and government intervention views, by contrast, banks should buy government bonds particularly at certain specific times, namely right before as well as during sovereign defaults.13
The second set of implications comes from variation across banks. According to the liquidity view, bondholdings should be larger: (i) in financially underdeveloped countries (where private liquid assets are few) and (ii) among banks that currently have few alternative investment opportunities. In line with this notion, we study the determinants of bondholdings by evaluating the role of country‐level measures of financial/economic development and of bank‐level measures of investment opportunities such as outstanding loans, profitability, risk taking, and central bank deposits. The risk‐taking and government intervention views also have cross sectional predictions that can be tested in the data. The risk‐taking view predicts, among other things, that during sovereign crises bonds should be demanded by banks that are more risk tolerant (e.g. because they are larger and thus more diversified), or by banks that have more resources to invest (e.g. because they are more profitable). According to the government intervention view, large banks that are too big to fail or banks that are particularly close to the government (e.g., because they are government owned) should be expected to buy more bonds during crises.
Evaluating the time variation of bondholdings thus provides a way to assess the role played by different views.
13 Governments may induce banks to hold bonds in normal times by using capital regulation. We later discuss this
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Guided by these considerations, we will formally test some of the (sometimes exclusive, sometimes non‐exclusive) implications of these different hypotheses. To do so, we employ the econometric methodology described below.
3.1 Decomposition of Bondholdings
As a first step, we decompose bondholdings into a time‐invariant and a time‐varying component. Let 𝑏𝑖,𝑐,𝑡 denote the ratio of government bonds over assets held at time 𝑡 by bank
𝑖 located in country𝑐. We regress 𝑏𝑖,𝑐,𝑡 on a set of bank dummies and obtain:
𝑏𝑖,𝑐,𝑡 = 𝑏𝑖 +𝑏�𝑖,𝑐,𝑡. (1) Here 𝑏𝑖 is the predicted time‐invariant component of bank i’s bonds, while 𝑏�𝑖,𝑐,𝑡 is the time varying residual. In this decomposition, 𝑏𝑖 is the average bonds/asset ratio of bank 𝑖over time; all time variation in bondholdings, including the one that takes place across normal and crisis times, is accounted for by 𝑏�𝑖,𝑐,𝑡. We can now go back to our different hypotheses. Because the risk‐taking and government intervention views especially rely on time variation, they should be captured by the time varying component 𝑏�𝑖,𝑐,𝑡. In particular, if we regress a bank’s lending behavior during default on the two components of bonds, the time varying part 𝑏�𝑖,𝑐,𝑡 would absorb the role of increases in bonds around the default window (be they due to risk taking or moral suasion) while 𝑏𝑖 would capture long term, stable determinants of the demand for bonds. As a result, we refer to the time invariant component 𝑏𝑖 as a bank’s “stable” bondholdings.
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To isolate the determinants of stable bondholdings that are common to all banks in a country (e.g., financial development) from those that are idiosyncratic to individual banks, we regress 𝑏𝑖 on a set of country dummies, obtaining:
𝑏𝑖 = 𝑏𝑐+𝑏�𝑖,𝑐, (2) where 𝑏𝑐 is the predicted country‐specific component of stable bondholdings, while 𝑏�𝑖,𝑐 is the residual from the same regression. By construction, 𝑏�𝑖,𝑐 absorbs all bank‐specific factors.
To assess whether changes in bondholdings over time reflect common country‐level shocks (e.g., business cycle fluctuations) or bank level factors, we regress the time‐varying residual 𝑏�𝑖,𝑐,𝑡 on the interaction of time and country dummies, obtaining:
𝑏�𝑖,𝑐,𝑡= 𝑏𝑐,𝑡+𝑏�𝑖,𝑐,𝑡. (3) Component 𝑏𝑐,𝑡 is the part of time‐varying bondholdings at time 𝑡 that is common to all banks in a country. The residual 𝑏�𝑖,𝑐,𝑡captures bank‐specific time‐varying bondholdings at time 𝑡.
Before performing our regressions, it is useful to quantify the share of the total variation of bondholding that is accounted for by each of these components. Table V performs this calculation in the full set of countries (Panel A) and in the sample of defaulters (Panel B).
[Table V here]
Three intriguing features stand out. First, in the full sample (Panel A), variation in stable bondholdings explains the lion’s share (80%) of the total variation of bonds. This is almost equally split between country‐ and bank‐specific subcomponents. Understanding why banks
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regularly hold bonds seems therefore important. Second, the picture changes once we focus on the subset of countries that experienced at least one sovereign default during the sample period. Now (see Panel B), the explanatory power of the time‐varying component increases from 20% to 40%, becoming roughly similar to variation in stable bondholdings.14 A plausible explanation is that default events are associated with significant time variation of bonds, perhaps consistent with the risk taking and government intervention views. Figure 1 illustrates this point by showing country‐specific time‐varying bondholdings for selected countries in default. Third, Panel C shows that the correlation between the stable and time varying bondholdings is negative at the bank‐level, suggesting that banks having large stable bondholdings may be different from those that load up more on public bonds during crises. Insofar as stable bondholdings reflect average bondholdings in normal times, this suggests that the demand for bonds may behave very differently during crises and outside of crisis periods.15
[Figure 1 here]
The importance of bank‐level variation in Table V indicates that bank heterogeneity is critical. Empirical analyses based only on country‐level aggregate data, which are the only ones performed so far, neglect the bulk of the variation in bonds, particularly during defaults.
14 This change may partly reflect lower cross country variation: any two countries picked from the subsample of
defaulters are likely to be more similar than any two countries picked randomly from the total sample. At the same time, the relative importance of country‐level factors in explaining time variation in bondholdings is almost unaffected, being equal to 24% in Panel A and to 22% in Panel B. Thus, there is not a drastic drop of country‐level heterogeneity, but rather this heterogeneity plays out in the time variation, presumably during default.
15 Panel C also confirms that the correlations between the orthogonal components of time varying and time
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3.2 Regression Strategy
We now ask: (i) what factors shape bank bondholdings?, and (ii) how do bondholdings shape the extent to which bank lending responds to sovereign defaults?
In the first part of the analysis we study the determinants of a bank’s holdings of public bonds, starting with stable bondholdings. We run the following regressions:
𝑏�𝑖,𝑐 = 𝛾+ 𝛽 ∙ 𝑋𝑖,𝑡−1+𝜖𝑖,𝑐, (4)
𝑏𝑐 = 𝛾+𝛽𝑐 ∙ 𝑋𝑐,𝑡−1 + 𝛽𝑖 ∙ 𝑋𝑖,𝑡−1+𝜖𝑖,𝑐, (5)
In light of previous definitions, Equation (4) estimates the bank‐level determinants of stable bondholdings, while Equation (5) estimates their country level determinants. In line with our previous discussion, 𝑋𝑖,𝑡−1 is a vector of bank characteristics such as risk taking, loans outstanding, exposure to central bank, interbank balances, profitability, size, and whether the bank is owned by the government. Accordingly, vector 𝑋𝑐,𝑡−1 includes country level factors such as a country’s financial development (as measured by Private Credit to GDP and banking crises), inflation and other macro variables that affect demand for public bonds.
We employ the same methodology to analyze the determinants of time variation in bondholdings. To do so, we run the following regressions:
𝑏�𝑖,𝑐,𝑡 = 𝛾+𝛼 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑋𝑖,𝑡−1+ 𝛾 ∙ 𝑋𝑖,𝑡−1+𝛿 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1+𝜖𝑖,𝑐,𝑡., (6)
𝑏𝑐,𝑡 = 𝛾+𝛼𝑐∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑋𝑐,𝑡−1+ 𝛾𝑐 ∙ 𝑋𝑐,𝑡−1+
21
where 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1 is a dummy variable taking value 1 if at t–1 country 𝑐 was in default and value zero otherwise. Equation (6) estimates bank‐specific time variation while Equation (7) estimates the country‐level time variation, with specific reference to default events. An increase in bondholdings during defaults (i.e., positive coefficients 𝛿𝑐 and 𝛿), for instance, would be consistent with the risk‐taking and government intervention views, particularly if this increase is concentrated in certain banks and countries as captured by the interaction between default and controls 𝑋𝑖,𝑡−1 and 𝑋𝑐,𝑡−1.
In the second part of our regression analysis, we study how the association between sovereign defaults and a bank’s lending behavior is affected by the bank’s bondholdings. Let
Λi,c,t denote the change in loans made by bank i in country c between time t–1 and t. We then run the following regression:
Λi,c,t =𝛾+𝜓𝑛∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑏𝑖 +𝜓𝑐𝑟∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑏�𝑖,𝑐,𝑡−1+
𝜁𝑛 ∙ 𝑏𝑖 +𝜁𝑐𝑟∙ 𝑏�𝑖,𝑐,𝑡−1 + 𝛿 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1+ 𝜖𝑖,𝑐,𝑡. (8) The regression separately estimates the impact, outside of and during default crises, of stable bondholdings 𝑏𝑖 and time‐varying bondholdings 𝑏�𝑖,𝑐,𝑡 on the change in bank loans. Coefficients 𝜁𝑛 and 𝜁𝑐𝑟 capture the role of bonds outside of default. If 𝜁𝑐𝑟 > 0, higher bondholdings in normal times associate with more future loans. This is consistent with the liquidity view, whereby banks accumulates bonds at times in which investment opportunities are few in order to tap investment opportunities arising in the future. If 𝜁𝑛 > 0, banks that on average hold more bonds on average make more loans in normal times. The sign of this coefficient is not fully pinned down by the liquidity view. If the banks holding many bonds on
22
average are those having few sporadic investment opportunities, they should be expected to also make fewer new loans, i.e. 𝜁𝑛 < 0. If instead the banks holding many bonds on average are those expecting a steady growth of future investment opportunities, they should also expect to make more new loans, i.e. 𝜁𝑛 > 0. Our empirical tests allow us to distinguish between these two possibilities.
On the other hand, coefficients 𝜓𝑛 and 𝜓𝑐𝑟 in Equation (8) capture the differential effect of bonds on loans during sovereign defaults. If a high stable demand for bonds is associated with fewer loans during defaults, we should observe 𝜓𝑛 < 0. If instead a high time‐varying component of bondholdings during default (due for instance to risk taking or moral suasion) is associated with fewer new loans during default, we should observe 𝜓𝑐𝑟 < 0.
In light of our previous analysis on the demand for bonds, we then estimate Equation (8) also by controlling for bank‐ and country‐ level variables 𝑋𝑖,𝑡−1 and 𝑋𝑐,𝑡−1 that may affect the demand for public bonds. We also include country dummies to control for unobserved time‐ invariantcountry‐level factors and time dummies to control for country‐level shocks.
Finally, to tease out the role of country level versus bank specific factors, we estimate:
Λi,c,t = 𝜓𝑛,𝑖 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑏�𝑖,𝑐 +𝜓𝑐𝑟,𝑖 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑏�𝑖,𝑐,𝑡+
+ 𝜓𝑛,𝑐 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑏𝑐 +𝜓𝑐𝑟,𝑐 ∙ 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑐,𝑡−1∙ 𝑏𝑐,𝑡+⋯ (9)
This regression has an intuitive interpretation. If the reduction in lending during crises is due to bank‐ rather than to country‐level variation in bonds, then coefficients 𝜓𝑛,𝑖 and 𝜓𝑐𝑟,𝑖 and should be negative while 𝜓𝑛,𝑐 and 𝜓𝑐𝑟,𝑐 should be zero. If instead it is country‐level common
23
factors that drive the effects of sovereign crises on loans, the coefficient attached to bank‐level variation in bonds should be zero. Separating country‐ versus bank‐level factors allows us to: (i) address the concern that some omitted country‐level factors drive our results, and (ii) evaluate the role of the distribution of individual bank characteristics in a country. Once again, the coefficients on non‐interacted bondholdings capture the role of bonds outside of default: also with respect to this latter dimension, Equation (9) separates country and bank level variation.
3.3 Interpretation and Causality
Regressions (4) to (7) assess the correlates of government bonds, allowing us to take a first look at whether observed bondholdings are more consistent with a liquidity, risk‐taking or government intervention view. The key step in our analysis, however, is the interpretation of Equations (8) and (9), which estimate the association between bondholdings and loans both in normal times and during default episodes. As previously stressed, our goal here is not to identify specific causal mechanisms. To interpret our results, however, it is useful to consider how our estimates may be affected by the most pressing endogeneity concerns.
Even if higher bondholdings are associated with more severe drops in loans after a default, it may not be government default itself that causes the drop in lending though the bank‐bondholding channel. It may simply be that government defaults occur precisely when lending is weak (e.g., in recessions), which also happens to be when banks hold many government bonds. In this case, the correlation documented by the regression would be spurious. Although we do not claim that our estimation strategy fully solves these causality
24
concerns, it is important to stress two important properties of our decomposition. First, the stable component of bonds is by construction uncorrelated with time variation around the default event. As a result, the value of stable bondholdings is predetermined relative to the timing of these events. This fact significantly ameliorates the concern that the interactive coefficients on stable bondholdings are spurious. It also ameliorates “selection at the treatment” type of concern, namely, concerns that banks may change their lending behavior in anticipation of a sovereign default: by construction, not only stable bondholdings are predetermined but also do not trend in the vicinity of default. Second, and related, our exercise allows us to separate – in Equation (9) – the effect of country‐level variation in bonds from that of idiosyncratic bank‐level variation in bonds. As long as common country‐level shocks affect banks in the country in a uniform way, the coefficient on the idiosyncratic bank‐level component (e.g., coefficient 𝜓𝑐𝑟,𝑐 in regression (9)) will reflect the impact of a bank’s bonds on its loans and not the general effect of common shocks.
Of course, there may be reasons to believe that not all banks are affected in the same way by country‐level shocks. It could be, for instance, that bonds are purchased precisely by those banks that anticipate the largest drop in credit demand during a sovereign debt crisis. In this case, the data would depict a negative correlation between bondholdings and loans but there would be no causal effect from the former to the latter. We attempt to deal with this concern by analyzing the characteristics of banks. To the extent that banks purchasing government bonds during default are the small, low‐profitability banks, it would seem plausible that their bondholdings are driven by an anticipation of a fall in demand. By contrast, this hypothesis seems less likely if during default bondholdings are accumulated among large and
25
profitable banks. Presumably, these banks have access to the best investment opportunities and are thus less subject to a drop in demand than their less profitable counterparts.
Finally, in different versions of our regressions we control for time dummies, country dummies, and for an extensive number of country‐ and bank‐specific time‐varying controls. We also control for country‐year interacted dummies. Our results are very robust to these tests.
4. Determinants of Banks’ Bondholdings
We now use our decomposition to study the determinants of bank bondholdings. Section 4.1 studies stable bondholdings, whereas Section 4.2 deals with time‐varying bondholdings.
4.1 Stable bondholdings
Table VI reports the estimation of Equation (4) in Panel A, and of Equation (5) in Panel B. To show the explanatory power of individual bank characteristics, we report a set of univariate regressions. In the last columns, we include all controls together.
[Table VI here]
Consider Panel A. The variables with most explanatory power are risk taking and outstanding loans, which in columns (2) and (4) respectively account for 23% and 15% of the variation of the dependent variable. Both variables have a negative impact on bonds. These results are consistent with the liquidity view: banks that are risk‐seeking and that on average
26
access many good investment opportunities do not need safe and liquid public bonds to ‘store’ their funds. Note that here a high level of outstanding loans means, ceteris paribus, that the bank has a plentiful of investment opportunities. As a result, it does not need to store many of its funds in order to tap investment opportunities in the future.
This message is confirmed when all variables are considered jointly, as in column (9) of Table VI Panel A. Other interesting observations are that bondholdings increase with bank leverage, consistent with the fact that public bonds are used as collateral to lever up, and decrease with banks’ use of substitute liquid assets, such as central bank reserves and interbank deposits. Bondholdings also increase with bank size, consistent with the finding that larger industrial firms use relatively less cash to manage their liquidity (Opler et al. 1999).
One concern with these results is that they might partially capture the effect of bank characteristics during sovereign crises. Indeed, the dependent variable averages also bonds held by banks during sovereign defaults in the sample. To avoid this problem,we re‐estimate the specification in column (9) within the subsample of countries that never experience a default, and we report the results in column (10). In this column, the stable demand for bonds can be interpreted as the average demand for bonds in normal times. Most coefficients remain of the predicted sign and of similar magnitude as in column (9) with the exception of leverage, which is now insignificant and profitability, which is negatively associated with bonds, most likely because profitable banks have more investment opportunities. Finally, bondholdings are
27
lower for government‐owned banks.16 Overall, the economic magnitude of these effects is large and our model can explain up to 34% of the idiosyncratic variation in stable bondholdings.17
Panel B studies the country level determinants of stable bondholdings. Crucially, these bondholdings decrease sharply in Private Credit to GDP. This is by far the most important variable, as it explains a staggering 67% of the variation alone. Financial underdevelopment seems to be a key driver of bondholdings, consistent with the liquidity view. Accordingly, a higher frequency of banking crises – another proxy for financial underdevelopment – is associated with higher bondholdings.18 The positive coefficient of sovereign default in Column (1) seems prima facie inconsistent with the liquidity view. The effect is however driven by the fact that sovereign defaults are more frequent in less financially developed countries. Once we include Private Credit to GDP in Column 6, the coefficient on sovereign default turns negative.19
Overall, Table VI is consistent with the notion that stable bondholdings are well explained by the liquidity view: banks’ holdings of public bonds are higher in countries that ceteris paribus experience fewer public defaults (so that public bonds are indeed a relatively
16 In univariate regressions the coefficient on profitability is positive while that on the government owned dummy
is positive. This is intuitive: profitability is positively correlated with size. However, after we control for the fact that large banks hold more bonds, the sign of profitability turns negative. Accordingly, government owned banks take fewer risks. After we control for risk taking, we find that government owned banks own fewer bonds.
17 A one‐standard deviation increase in risk‐taking is associated with 2.7% fewer bondholdings; a one‐standard
deviation increase in bank profitability is associated with a 0.28% increase in bondholdings; a one‐standard deviation increase in size is associated with a 0.2% increase in bondholdings. Another way to see this is to note that a bank in the bottom quartile of size, risk taking, and profitability has 4.2% of public bonds, while a bank in the top quartile of size, risk taking, leverage and profitability has 1.6%. The economic consequences of these differences are further amplified by the possibility that banks use public bonds to lever up and raise more funds.
18 As we are dealing with aggregate country‐level bondholdings, also supply‐side forces may play a role here.
Indeed, it could be that governments in countries prone to banking crises may engage more heavily in intermediation and in bailouts, and thus exhibit higher public debt levels.
19Bank characteristics do not matter in explaining country‐level bondholdings, with the exception of average risk‐
taking in the banking sector (see columns (7) and (8)). One interpretation is that higher average risk taking may capture laxer capital regulation, which reduces banks’ willingness to hold public bonds. Alternatively, average risk taking may reflect the existence of private insurance mechanisms which lead banks to hold less cash and bonds.
28
safe instrument) and that are less financially developed (so that private provision of safe assets is low). Within these countries, banks with few profitable investment opportunities are those that hold more bonds on average, consistent with the view that these banks have greater need to store liquidity to fund their sporadic investment projects.
4.2 Time-Varying Bondholdings
Table VII reports the estimation of Equation (6) in Panel A, and of Equation (7) in Panel B.
[Table VII here]
Panel A reports the time‐varying component of idiosyncratic bank‐level bonds. Variation in this component is harder to explain than variation in stable bondholdings. Risk taking and loans outstanding continue to be the most successful explanatory variables, but now they account only for 1.9% and 0.5% of the time variation, respectively. These variables continue to be negatively correlated with bonds, and the interactive effect with default is negative as well. Once again, consistent with the liquidity view, banks that currently fund many loans do not need to store their funds to invest in the future. Banks that enter sovereign crises with riskier balance sheets reduce their bondholdings. This could reflect deleveraging by these banks.
The most intriguing result here concerns bank profitability. More profitable banks buy more bonds during defaults (see columns (5) and (9)), in contrast with what we obtained in the case of stable bondholdings (see Table VI Panel A). It seems that, regardless of their superior investment opportunities, more profitable banks increase their exposure to public bonds during
29
defaults. This finding seems consistent with the “risk‐taking” view: more profitable banks are likely to have spare liquidity required to expand risky bondholdings. Thus, these banks may buy distressed public bonds to undertake a risky, albeit profitable, gamble. An alternative interpretation involves government intervention: moral suasion may be particularly effective for banks that have enough liquidity to invest. Both risk taking and moral suasion may also help explain why larger banks abnormally increase their exposure to government bonds during default episodes (see column (1) and (9)). These banks may have more capacity to gamble, and they may well be “too big to fail” and thus effective targets of moral suasion.20
The key finding here is that, be it through risk taking or moral suasion, relatively “better” banks – the larger and more profitable ones – buy many public bonds during sovereign crises. This has two implications for our analysis. First, during default crises, the banking sector increases its aggregate exposure to the government in a very unequal way. Figures 2 and 3 plot the default‐induced change in bank‐specific bondholdings predicted by our regression for banks differing in their size and profitability. Banks in the lowest size decile decrease their bondholdings by 0.7% of assets, while banks in the highest decile increase their bondholdings
Whatever the ultimate rationale for this behavior of time‐varying bondholdings, one thing is clear: it seems hard to account for through the liquidity view.
20 Two qualifications are in order here. Moral suasion works best if banks buy bonds at primary issues rather than
in secondary markets, because the government may be more interested in rising fresh funds rather than in controlling secondary market prices (there are of course several reasons why government may prefer to prop the price of bonds in secondary markets, for instance to avoid large fluctuations in the balance sheets of intermediaries holding public bonds).Second, the findings of Panel B may also be consistent with the following, distinct, hypothesis: large and profitable banks might be in a better position to bargain with the government over the repayment of defaulted bonds. As a result, they purchase bonds during crises (either from other banks or from foreigners) because their expected return from holding them is higher than that of other banks.
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by 1.1% of assets. Similarly, banks in the lowest profitability decile decrease their bondholdings by 0.9% of assets, while bank in the highest decile increase their bondholdings by 1% of assets.
[Figures 2 and 3 here]
A second and crucial implication of this finding is that the impact of bondholdings on bank lending behavior during crises may be systematically under‐estimated. In fact, more profitable and larger banks do not just buy more public bonds, they are also likely to make more loans relative to other banks. As a result, if bank characteristics are not properly controlled for, it may be spuriously concluded that larger bondholdings are not associated with stronger loan contractions during crises. Moreover, as we mentioned in Section 3, bank profitability allows us to partially address the endogeneity concerns arising from changes in the demand for credit. We shall return to this point in Section 5.
Consider finally Panel B, which focuses on country‐level time‐variation in bondholdings. Some interesting features stand out. First, the variables with highest explanatory power are Private Credit to GDP, Banking crises, and unemployment growth: interacted with default, they capture more than 30% of the total variation.
A higher frequency of banking crises increases bondholdings during defaults and, perhaps surprisingly, higher levels of Private Credit are associated with higher bank bondholdings during crises (see Columns (8) and (9)).21
21 When introduced alone, Private Credit is associated with a lower take up of bonds during default. This effect
suggests that increases in Private Credit to GDP increase the take up of bonds during crises only after the broad level of financial development is controlled for (See Panel C for further evidence of this notion).
This result stands in contrast with Table VI (Panel B), where the association between Private Credit and stable bondholdings was
31
negative. Thus, just as in the case of bank profitability, it seems that bondholdings behave very differently in normal times and during sovereign crises: in financially developed countries, banks hold few government bonds in normal times but buy many government bonds during crises.22 These latter aspects of the behavior of banks during crises is in turn inconsistent with the liquidity view (again, compare Panel A and B of Table VII), supporting the risk taking, or the moral suasion hypothesis, or both, also play a role.
5. The Effect of Default on Bank Loans
We now study the correlation between bondholdings and bank lending. Recall from Section 3 that the dependent variable is changes in loans outstanding between year t–1 and t.
Table VIII reports our estimates. Column (1) includes as explanatory variables the total bondholdings of a bank in year t–1, 𝑏𝑖,𝑐,𝑡−1, as well as their interaction with sovereign default. Columns (2)‐(4) implement Equation (8) by decomposing the total bondholdings of banks into the stable and time‐varying components, 𝑏𝑖 and 𝑏�𝑖,𝑐,𝑡−1. Relative to column (2), columns (3)‐(4) progressively include bank characteristics with their interaction with default, and country fixed effects. Column (5) then adds the interaction of country and year fixed effects, to identify the effects of within‐country across banks variation of bondholdings. Finally, columns (6)‐(8) implement Equation (9) by further decomposing total bondholdings into the four orthogonal
22To further distinguish whether the coefficient on Private Credit in columns (8) and (9) are due to temporary
credit booms or to larger volumes of intermediation in normal times, Panel C controls for the level of Private Credit/GDP at time t‐1 and its average level in the sample. Once country and bank‐level variables are controlled for (see column 4), it is the flow (not the average level) of Private Credit that increases bondholding.
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components. Our regression strategy thus seeks to control for aggregate shocks to the demand for credit in two ways. First, by controlling for country*year dummies we focus on the variation in bondholdings within a (defaulting) country. Second, in columns (6)‐(8) we control for several measures of country level demand (e.g., GDP growth) and credit supply (e.g., Private Credit to GDP ratio; banking crises) shocks, both alone and interacted with our default dummy. We also perform a large number of robustness tests, and we will discuss them later.
[Table VIII here]
Column (1) contains a number of interesting findings. First, it shows that higher bondholdings in any given year are associated with an increase in future loans, which is broadly consistent with the liquidity view. Indeed, according to this view, banks bought more bonds in the past precisely to tap future investments opportunities (which are captured by the increase in future loans made). Second, it shows that – on average – sovereign defaults have an insignificant effect on the loans extended by banks. Third, bank bondholdings do not seem to matter when interacted with default. These findings would seem to show, somewhat surprisingly, that bondholdings continue to boost lending even during sovereign debt crises.
When we split bondholdings into a stable and a time‐varying component, however, we see that the latter result is due to a composition effect (see Column 2). Stable bondholdings have a strong negative effect on the loans extended by a bank during a sovereign default. A ten percent increase in these bondholdings is associated with a 1.2% drop in loans during a sovereign default. By contrast, the bonds accumulated during the crisis itself do not seem to
33
impair lending: the interaction between the default dummy and the time‐varying component of bonds is in fact positive and significant.
Two considerations are in order here. First, the negative role of stable bondholdings during crises is an interesting finding because, as we previously stressed, this component of bonds is by construction orthogonal both to the default event and to country‐ (or bank‐) specific shocks occurring around it, which reduces endogeneity concerns. Second, the result on the role of time‐varying bondholdings is also interesting: perhaps banks buying more bonds during crises also extend more loans because – as we found in Section 4 – banks with better fundamentals self‐ select into buying government bonds during defaults. Without controlling for such self‐selection, then, the effect of default on credit might be underestimated.
To assess this possibility, in column (3) we control for bank characteristics and their interaction with default, in the spirit of Equation (7) and Table VII. In column (4) we also introduce country dummies, which control for the endogeneity of stable bondholdings to country‐level variables (see Table VII Panel A), and for country‐level time‐invariant omitted factors affecting the demand for loans. The results are consistent with our conjecture. The coefficient that captures the interaction between default and time‐varying bonds becomes negative (but insignificant) once bank characteristics are controlled for. This suggests that, as we had anticipated, the consolidation of bonds into more profitable and larger banks during crises creates a bias towards under‐estimating the adverse impact of bondholdings on the
34 default‐induced drop in loans.23
Our decomposition of bondholdings also allows us to undertake a more detailed analysis of the positive association between public bonds and new loans during normal times. As columns (3) and (4) show, a bank’s loans at time t are positively associated with its time‐varying bondholdings at time t–1. Once again, this is consistent with the liquidity view whereby public bonds allow banks to transfer current funds toward the financing of future investments. Interestingly, the effect of stable bondholdings is negative. As we discussed in Section 3.2, this finding probably reflects the fact that banks that hold more public bonds on average are those that also have fewer loans to fund on average. As a result, they should be expected to make fewer new loans, consistent with the results of Section 4 (Table VI, Panel A).
Moreover, the coefficient on the interaction between default and stable bondholdings remains negative, significant, and – as previously anticipated – increases in magnitude. This last result is important, because the choice of stable bondholdings also depends on bank characteristics. The results of columns (3) and (4) therefore ameliorate the concern of estimating a spurious interaction between default and bonds.
To better understand the role of bank heterogeneity in shaping the impact of defaults on lending behavior, and to further address endogeneity concerns, Column (5) adds the interaction of country and year fixed effects. After that, columns (6)‐(8) break down bondholdings into their bank‐ and country‐specific component. To further sharpen our test, we also introduce in our regressions various bank characteristics, country dummies, and country‐ level variables, both in levels and interacted with default. The result that emerges is clear: the
23 Also bank risk‐taking plays a role here. In fact, banks with already risky balance sheets take relatively fewer
bonds during crises. But then, because these banks are also those cutting loans the most, exclusion of risk taking from the regression can create the misleading impression that the banks having fewer bonds cut loans the most.
35
critical driver of post‐default drops in loans is stable bondholdings. In particular, it is the within‐ country bank heterogeneity in bondholdings that consistently matters, but also country‐wide factors common to all banks matter once we control for country fixed effects in column (8).
Notice that, in columns (7) and (8), even the bank‐specific time‐varying component matters during default: banks purchasing a large amount of bonds during defaults significantly contract their loans relative to other banks. This negative association between time‐varying component of bondholdings and lending during default episodes is interesting. Indeed, we already established that during default events these bonds are mostly bought by the large and more profitable banks. Presumably, these banks are also the ones with the best investment opportunities. As a result, the drop in their loans during default seems likely to be induced by the bonds they hold, and not by a drop in the demand for credit.24
Our exercise thus reveals two very robust effects that hold under all our specifications. First, banks holding more public bonds in normal times make more future loans. Second, banks that regularly hold more bonds outside of default events contract more their loans during default events. These results are consistent with the notion that there is a liquidity benefit for banks to hold government bonds in normal times, but this very benefit begets fragility during sovereign crises: default reduces the liquidity of banks holding many public bonds and thereby limiting their ability to lend.
24 To see this point formally, consider Table AIII, which reports the estimates for country and bank level controls.
In column (7) and (8) the interaction of bank profitability with the default dummy is insignificant. However, if we re‐estimate column (8) without the bondholdings terms, then the interaction of profitability and default is negative and significant. This implies that, during a default, lending decreases relatively more in more profitable banks, due to their greater bondholdings.
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Our estimates suggest that during sovereign defaults, after controlling for country and bank level variables, a ten percent increase in public bonds is associated with a subsequent drop in loans by 3.2%. Our analysis allows us to split this drop in terms of stable and time‐ varying bondholdings. Stable bondholdings account for 75% of the drop in loans during default. Time‐varying bondholdings account for 25% of the drop. Thus, stable bondholdings have three times as much explanatory power as time‐varying bondholdings to understand the drop in loans during a default. Another way to assess the magnitude of these effects is to see their strength in the context of the variation observed in the dataset. Accordingly, a one standard deviation increase in bondholdings in a defaulting country is associated with a 4.4% larger decrease in lending as a percentage of total assets, of which 3.2% is due to stable bondholdings and 1.2% to time‐varying bondholdings. These are large effects.
We perform a number of robustness tests. To begin, we are concerned that our results may be driven by relatively “unimportant” defaults. The reason is that about one half of the default episodes in our sample involve either small countries, or countries with a small banking sector, or both. We address this concern in two ways. First, in Table AIV we exclude the smaller defaulting countries in our sample and focus on the defaults in the largest countries as measured by GDP per capita, namely Argentina, Russia, Ukraine, and Greece. Second, in Table AV we exclude the defaulting countries with fewer than 5 banks in our sample. In both exercises, our results are confirmed: if anything, they are stronger both in terms of statistical and economic significance.
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We also repeat our analysis by including only our measures of stable bondholdings, as these are pre‐determined with respect to the sovereign default and therefore more easily interpretable as exogenous relative to the default event. Our results are virtually identical and we report them in Table AVIII in the Appendix.
After that, we re‐do our tests with the two alternative measures of default. In Table AVI we focus on the market‐based measure of Pescatori and Sy (2007), which captures whether sovereign credit spreads exceed a threshold identified with extreme value theory (identified in 1000 basis points with their methodology). Again, our results are broadly confirmed, both in terms of statistical and economic significance. In Table AVII we focus on the haircut measure of the severity of default from Cruces and Trebesch (2013) and Zettelmeyer et al. (2012). Our results are still confirmed in terms of statistical significance, but here the role of stable and time varying bondholdings is now more balanced: in some specifications time‐varying bondholdings explain close to 55% of the total drop in loans following default.
Finally, we re‐do all our tests of the demand for time‐varying bonds (Table 7) and determinants of changes in loans (Table 8) after de‐meaning our independent variables, to address concerns about the appropriate identification of “differences‐in‐differences” interaction effects in the presence of fixed effects (Ozer‐Balli and Sorensen, 2013). Our results confirm both qualitatively and quantitatively the earlier results of Tables 7 and Table 8. This is likely to be due to the fact that several of our bonds measures are already de‐meaned through our decomposition. We report these tests in Table AIX (Panels A and B report tests of the demand of time‐varying bonds; Panel C reports changes in loans regressions).