metadata: authors: 0: author: Calderon,Cesar
1: author: Schaeck,Klaus
count: World
docna: 0: docna: Bank bailouts, competition, and the
            disparate effects for borrower and depositor welfare
docty: Policy Research Working Paper
subtopic: Housing Finance,Capital Markets and Capital Flows,Non Bank Financial Institutions,Capital Flows,Economic Growth,Deposit Insurance,Financial Structures,Access to Finance
teratopic: Finance and Financial Sector Development
abstracts: cdata!: This paper investigates how government
            interventions into banking systems such as blanket
            guarantees, liquidity support, recapitalizations, and
            nationalizations affect banking competition. This debate is
            important because the pricing of banking products has
            implications for borrower and depositor welfare. Exploiting
            data for 124 countries that witnessed different policy
            responses to 41 banking crises, and using
            difference-in-difference estimations, the paper presents the
            following key results: (i) Government interventions reduce
            Lerner indices and net interest margins. This effect is
            robust to a battery of falsification and placebo tests, and
            the competitive response also cannot be explained by
            alternative forces. The competition-increasing effect on
            Lerner indices and net interest margins is also confirmed
            once the non-random assignment of interventions is accounted
            for using instrumental variable techniques that exploit
            exogenous variation in the electoral cycle and in the design
            of the regulatory architecture across countries. (ii)
            Consistent with theoretical predictions, the
            competition-increasing effect of government interventions is
            greater in more concentrated and less contestable banking
            sectors, but the effects are mitigated in more transparent
            banking systems. (iii) The competitive effects are
            economically substantial, remain in place for at least 5
            years, and the interventions also coincide with an increase
            in zombie banks. The results therefore offer direct evidence
            of the mechanism by which government interventions
            contribute to banks' risk-shifting behavior as reported
            in recent studies on bank level runs via competition. (iv)
            Government interventions disparately affect bank
            customers' welfare. While liquidity support,
            recapitalizations, and nationalizations improve borrower
            welfare by reducing loan rates, deposit rates decline. The
            empirical setup allows quantifying these disparate effects.
repnme: repnme: Bank bailouts, competition, and the
            disparate effects for borrower and depositor welfare
display_title: Bank bailouts, competition, and the
            disparate effects for borrower and depositor welfare
listing_relative_url: /research/2013/04/17580914/bank-bailouts-competition-disparate-effects-borrower-depositor-welfare
topicv3: Housing Mortgage Finance Products m1326719 2183,Deposit Insurance m1326722 322,Market Discipline m1326726 1248,Monetary and Financial Stability m1327169 1098,Access to Finance m1327464 1249
publishtoextweb_dt: 2013-04-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
no_of_pages: 51
author_keys: 0: author_key: 166739
1: author_key: 1181152
model_extractions: WPS6410 Policy Research Working Paper 6410 Background Paper to the 2014 World Development Report Bank Bailouts, Competition, and the Disparate Effects for Borrower and Depositor Welfare _Cesar Calderon Klaus Schaeck_ The World Bank Development Economics Office of the Senior Vice President and Chief Economist April 2013
Policy Research Working Paper 6410

# **Abstract**

This paper investigates how government interventions into banking systems such as blanket guarantees, liquidity support, recapitalizations, and nationalizations affect banking competition. This debate is important because the pricing of banking products has implications for borrower and depositor welfare. Exploiting data for 124 countries that witnessed different policy responses to 41 banking crises, and using difference-in-difference estimations, the paper presents the following key results: (i) Government interventions reduce Lerner indices and net interest margins. This effect is robust to a battery of falsification and placebo tests, and the competitive response also cannot be explained by alternative forces. The competition-increasing effect on Lerner indices and net interest margins is also confirmed once the non-random assignment of interventions is accounted for using instrumental variable techniques that exploit exogenous variation in the electoral cycle and in the design of the regulatory architecture across countries. (ii) Consistent with theoretical predictions, the competitionincreasing effect of government interventions is greater in more concentrated and less contestable banking sectors, but the effects are mitigated in more transparent banking systems. (iii) The competitive effects are economically substantial, remain in place for at least 5 years, and the interventions also coincide with an increase in zombie banks. The results therefore offer direct evidence of the mechanism by which government interventions contribute to banks’ risk-shifting behavior as reported in recent studies on bank level runs via competition. (iv) Government interventions disparately affect bank customers’ welfare. While liquidity support, recapitalizations, and nationalizations improve borrower welfare by reducing loan rates, deposit rates decline. The empirical setup allows quantifying these disparate effects.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2014: Managing Risk for Development_ —is a product of the Development Economics Vice Presidency. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ccalderon@worldbank. org and klaus.schaeck@bangor.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Bank bailouts, competition, and the disparate effects for borrower and depositor welfare**

Cesar Calderon _The World Bank_ Klaus Schaeck* _Bangor University_

**Keywords:** Banking competition; government interventions; bailouts; zombie banks; borrower and depositor welfare **JEL Classification:** G28, G21, C32

## **Sector Board:** FSE

* Corresponding author.

Cesar Calderon: The World Bank, 1818 H Street NW, Washington, DC, 20433, USA. Phone: ++1 202 473-1000. E-mail: ccalderon@worldbank.org Klaus Schaeck: Bangor Business School, Hen Goleg, College Road, Bangor LL57 2DG, U.K. Phone: ++44 1248 38 8540. E-mail: klaus.schaeck@bangor.ac.uk

### **Acknowledgments**

We thank Allen Berger, Lamont Black, Martin Brown, Richard Grossman, Asli Demirguç-Kunt, Olivier de Jonghe, Martin Goetz, Andre Guettler, Olena Havrylchyck, Thomas Kick, Paul Kupiec, Mrdjan Mladjan, Danny McGowan, Greg Nini, Enrico Onali, Benedikt Ruprecht, Isabell Schnabel, Paul Söderlind, Günseli Tümer-Alkan, Jouko Vilmunen, Xavier Vives, and conference and seminar participants at the Conference on Financial and Macroeconomic Stability: Challenges ahead, Istanbul, the VII Annual Seminar on Risk, Financial Stability and Banking at the Central Bank of Brazil, the Ifo/CESifo and Bundesbank Conference on “The Banking Sector and State”, Munich, the 12th FDIC Annual Bank Research Conference, the CAREFIN conference “The Effect of Tighter Regulatory Requirements on Bank Profitability and Risk-Taking Incentives”, Milan, at Bangor Business School, at the University of St Gallen, at European Business School, and at the Deutsche Bundesbank for helpful comments. Mauricio Pinzon Latorre and Piotr Danisewicz provided able research assistance. Klaus Schaeck acknowledges financial support from the Europlace Institute of Finance (EIF).
“[...] interventions to restore financial stability will lead to massive distortions of competition in the banking sector” _Beck, Coyle, Dewatripont, Freixas, and Seabright (2010, p. 2)_ “[... ]banks’ competitive conduct after the crisis may not be independent of government intervention during the crisis” _Gropp, Hakenes, and Schnabel (2011, p. 2086)_

# **1. Introduction**

Banking systems have been profoundly reshaped by the financial crisis and the concomitant policy responses. The two quotes above illustrate growing concerns by policymakers and academics about the effects on competition arising from massive interventions in banking markets.

In recent years, governments and other authorities designated with the regulation of financial institutions introduced blanket guarantees, extended liquidity support, injected capital, and nationalized banks on an unprecedented scale (Laeven and Valencia (2008, 2010, 2012); Hoshi and Kashyap (2010); Bayazitova and Shivdasani, (2012); Phillipon and Schnabl (2013)).<sup>1</sup> During tranquil periods, the competitive effects of rescue operations tend to be only relevant for a limited number of distressed institutions and their immediate competitors (Gropp, Hakenes and Schnabel (2011)). However, banking crises result in massive policy responses that affect large numbers of institutions with possible implications for banks’ competitive conduct over longer periods of time.<sup>2</sup> While a growing body of literature has started examining the unintended effects of government bailouts on risk-taking on the micro level (Cordella and Yeyati (2003); Berger, Bouwman, Kick and Schaeck (2010); Gropp, Gruendl, and Guettler (2011); Gropp, Hakenes, and Schnabel (2011); Duchin and Sosyura (2012a)), little effort has been devoted to their effects on competition. However, this question is critical because the role of the government in banking systems lies at the heart of the design of financial systems (Song and Thakor (forthcoming)). Our paper contributes to this debate.

We first raise the issue of how the responses to crises such as blanket guarantees, liquidity support, recapitalizations, and nationalizations affect competition in banking. How can such interventions affect competition? Distortions are primarily attributed to supplanted market discipline which reduces banks’ funding costs. Gropp, Hakenes, and Schabel (2011) focus on individual banks, and argue that bailouts and guarantees reduce protected banks’ refinancing costs vis-à-vis their competitors because bailouts undermine market participants’ monitoring incentives. In turn, the protected banks’ competitors

> 1  Acharya and Yorulmazer (2007) show that when the number of bank failures is large, regulators find it optimal to rescue distressed institutions.

> 2  Examples for responses to crises that affect all banks are blanket guarantees and the provision of central bank liquidity support. During the current crisis, such measures have been adopted. Laeven and Valencia (2008) show the global financial landscape has undergone major transformations. Banks from the U.S. and Europe that used to dominate global banking have become smaller in terms of market capitalization, and banks from countries unaffected during the crisis are now among the largest institutions in the world.

2
become more aggressive because their charter values are eroded. Another argument relates to moral hazard. Gropp, Gruendl, and Guettler (2011) and Berger et al. (2010) suggest bailouts increase moral hazard as monitoring incentives are distorted, resulting in loans being originated to risky borrowers, inflated loan volumes, and inadequate loan pricing, respectively. Duchin and Sosyura (2012a) offer further evidence: Loans originated by banks that participated in the Troubled Asset Relief Program (TARP) are riskier, and these banks shift assets into riskier categories.<sup>3</sup> However, other theories point out that bailouts increase charter values. Cordella and Yeyati (2003) emphasize that the charter-value effect of bailouts reduces risk-taking which suggests less aggressive conduct. These arguments highlight that it is far from clear how the competitive landscape changes in the aftermath of bailouts. Ultimately, the effect of bailouts on competition therefore remains an empirical question we address in this paper. To the extent that competition changes following government aid, our work helps understand the mechanism by which interventions contribute to risk-shifting behavior on the bank level.

The question of how competition is affected is also important for other reasons. Mispricing of products can have consequences for consumer welfare. Guarantees can entrench the institutions, assisted mergers of large banks increase concentration and signal that these banks are ‘ _too-big-to-fail’_ (Beck et al. (2010)).<sup>4</sup> Moreover, entry barriers have risen as a consequence of tighter regulation (Andresen (2011)). In turn, prices of services provided by bailed institutions may be affected. Competitors may benefit _indirectly_ because crises are stalled, or _directly_ because they are creditors of the rescued bank. Others suggest rescues influence credit supply and liquidity creation. These effects can spill over into the real economy (Norden, Rosenboom, and Wang (forthcoming)).

These considerations beg a second question: How do these policy responses impact consumer welfare? In particular, what are the effects on borrowers and depositors? We address this by investigating the effects of blanket guarantees, liquidity support, recapitalizations, and nationalizations separately on competition in loan and deposit markets. In addition, we then explore the ramifications for provision of credit and access to finance.

For our empirical tests, we exploit a vast array of information about interventions in banking systems. Our dataset contains 41 crises (29 systemic and 12 borderline cases) for the period 1996 to 2010. We account for two types of interventions in the containment phase (blanket guarantees and liquidity support), and two in the resolution phase (recapitalizations and nationalizations).

> 3  Policymakers share this concern. The former Fed Chairman Paul Volker argues that bailouts constitute an “unintended and unanticipated extension of the official safety net” (Testimony, House Financial Services Committee on October 1, 2009). 4  Hakenes and Schnabel (2010) note that the German government was criticized by the European Commission for the competitive effects that may arise from the rescue of Commerzbank.

3
To examine the effects of these measures, we exploit the variation across countries across time and use difference-in-difference estimation for our main analysis. The widespread use of blanket guarantees, liquidity support, recapitalizations, and nationalizations provides a natural setting to identify the effects of such interventions. While our illustration below shows that interventions are orthogonal with respect to the pre-existing competitive environment, banking systems that revert to such actions during crises subsequently display different competitive conduct. In contrast, countries in a control group that do not experience interventions display no changes in competition despite exhibiting similar trends. Therefore, difference-in-difference estimation allows establishing causal effects arising from interventions for two competition measures: Lerner indices and net interest margins.

Our key finding is that liquidity support, recapitalizations, and nationalizations increase competition, reflected in lower Lerner indices, and in declining net interest margins. A long-run analysis illustrates that both competition measures remain below the level they had in the announcement year of the interventions. Moreover, the effects remain invariant when high income economies and when emerging market economies are excluded from our tests. We can also rule out that the competitive response to these policy measures constitutes a response to systemic banking crises, and there is no reason to believe that compressed interest income during crises, low monetary policy rates, structural adjustment programs by the International Monetary Fund, and the recent crisis drive the results. Propensity score methods as well as instrumental variable techniques that account for the non-random assignment of interventions using exogenous variation in the electoral cycle and in the design of regulatory architecture also reinforce our results. In extensions, we first explore whether the effect of interventions depends on the initial conditions in the banking sector in terms of market structure, contestability, and moral hazard. The competition-increasing effect is larger in concentrated and less contestable banking systems. However, deposit insurance offsets the impact of interventions. Second, we test whether disclosure requirements and charter values amplify or mitigate the effects. While interventions have competition-increasing effects in countries with less transparency, charter values do not play a role. To examine the force behind the increase in competition, we also analyze whether bailouts correlate with the occurrence of economically unviable zombie banks. Liquidity support and recapitalizations indeed go hand in hand with zombie banks and their market shares.

The analysis of how interventions affect consumer welfare decomposes interest margins into loan and deposit rates. While deposit rates decline, suggesting that interventions supplant market discipline and harm depositors, borrowers gain because loan rates are reduced. These tests also allow quantifying the effects. Our final analysis demonstrates interventions are positively associated with credit provision but this effect comes at the cost of reduced access to finance.

4
We proceed as follows. Section 2 discusses the data, and Section describes 3 the identification strategy, and the main results. Section 4 explores the roles of transparency and charter values, and we also examine how interventions correlate with the presence of zombie banks. Section 5 reports on the effect of interventions on consumer welfare. Section 6 offers concluding remarks.

# **2. Data and overview of policy responses to banking crises**

Data for 41 banking crises are obtained from Laeven and Valencia (2008, 2010, 2012) for the period 1996-2010. Of those crises, 29 are classified as systemic crises and 12 are episodes of borderline crises.<sup>5</sup> A country is classified as having a crisis if the banking system exhibited signs of stress (bank runs, losses, and liquidations) and, additionally, if significant interventions can be observed. Interventions are considered significant if significant guarantees, liquidity support (5% of deposits and liabilities to nonresidents), recapitalizations with public funds (exceeding 3% of GDP), and significant nationalizations took place. The information for the policy responses is taken from Laeven and Valencia (2010, 2012). Appendix I presents details.<sup>6</sup> Crises responses consist of an initial phase concerned with containing liquidity strain, protecting liabilities, and limiting effects from fire sales. The containment phase results in wide-ranging liquidity support, and guarantees on banks’ liabilities, and, less frequently deposit freezes and bank holidays. Subsequently, balance sheet restructuring takes center stage: banks are resolved, recapitalized, nationalized, and unviable ones exit the market. While deposit freezes and bank holidays are short-lived with no prediction as to how they affect competition, other policy responses translate into precise predictions. We constrain our analysis to interventions where theory offers clear indications for competition: blanket guarantees, liquidity support, recapitalizations, and nationalizations.

The common theme that links these different manifestations of bailouts is moral hazard. The idea that bailouts are a source of risk-shifting opportunities is undisputed (Farhi and Tirole (2012)). The literature casts the effects of bailouts in terms of their effect on risk-taking. However, since Keeley (1990) has shown that competition drives bank’s risk-taking, the ideas put forward in this literature also apply in the context of our study. Acharya (2011) argues similarly. He shows that short-termist governments - thanks to the opaqueness of their balance sheet - exploit moral hazard opportunities in the financial system to boost activity and ignore the risks associated with these actions. Either by extending guarantees or promoting risky lending, the government allows competition at the expense of

> 5 Laeven and Valencia (2010) do not offer a definition for borderline cases. They classify countries that “ _almost met_ ” the definition of a systemic crisis as borderline cases.

> 6  G overnments also engage in other interventions, e.g., deposit freezes and bank holidays. As we discuss below, these measures do not translate into predictions for competitive effects.

5
instability of the financial system down the line. Thus, bailouts impose the costs associated with the “day of reckoning” on future generations.

Using deposit insurance, Merton (1977) and Demirguc-Kunt and Detragiache (2002) show that moral hazard increases risk-taking and the probability of banking crises, respectively. Gropp, Gruendl, and Guettler (2011), Gropp, Hakenes, and Schnabel (2011), and Dam and Koetter (2012) offer further support. The former two studies show that guarantees increase risky lending, and that bailouts increase risk-taking among protected banks’ competitors. Dam and Koetter (2012) highlight that higher bail-out probabilities increase risk-taking. Put simply, the expectation of bailouts reduces the anxiety from avoiding the bad state of having the charter revoked (Mailath and Mester (1994)).

_Blanket guarantees._ A common response to stall runs is blanket guarantees. Such guarantees go beyond deposit insurance coverage levels. Since bank runs can destabilize payment systems and trigger fire sales, guarantees are used to restore confidence during the containment phase.<sup>7</sup> Theory offers clear predictions for the effect of guarantees. Hakenes and Schnabel (2010) show that guarantees affect protected banks and their competitors. They stress that guarantees reduce margins and charter values of the protected banks’ competitors which arises from more aggressive competition from banks that refinance at subsidized rates. This pushes competitor banks to behave more competitively. Blanket guarantees have a similar effect. They reduce refinancing costs, but they simultaneously also increase moral hazard, resulting in more aggressive competition.

_Liquidity suppo_ rt. The provision of liquidity support plays an important role in the containment phase (Gorton and Huang (2004)). The premise is that extending loans to troubled banks is less costly than no intervention at all. Richardson and Troost (2009) illustrate that monetary intervention can be an effective tool to allow banks to survive stress: Emergency lending to distressed banks raises their probability for survival and helps sustain lending.

Theoretical work in this area relates to moral hazard and discusses whether the central bank should act as a lender of last resort. Freixas (1999) shows that the lender of last resort should not support all banks because rescues are costly. However, when large banks (the _too-big-to-fail_ phenomenon) or large numbers of banks are in distress (the _too-many-to-fail_ phenomenon), supporting the large bank or all distressed banks is the preferred action. Both the _too-big-to-fail_ and the _too-many-to-fail_ effects incentivize banks to invest in risky assets, suggesting moral hazard.

Unlike the two interventions during the containment phase, the interventions during the resolution phase are observed on the bank level. However, since we focus on large-scale interventions and effects

> 7 We only consider full blanket guarantees, i.e., blanket guarantees that affect liabilities at all banks. 6
for the industry as a whole, we continue to analyze the effects of recapitalizations and nationalizations on the aggregate level. This is justified given our focus on crisis episodes with large numbers of such interventions. Moreover, during crises, recapitalizations and nationalizations send strong signals to the markets about future regulatory behavior which make banks anticipate future bailouts that distort ex ante incentives (Acharya and Yorulmazer (2007); Bayazitova and Shivdasani (2012); Keister (2012)).<sup>8</sup> _Recapitalizations._ A cornerstone during the resolution phase is the provision of capital support (e.g., Bayazitova and Shivdasani (2012); Duchin and Sosyura (2012a); Giannetti and Simonov (2013)). Recapitalizations come typically in the form of common or preferred stock.

Recent theories illustrate that recapitalizations cause distortions. Diamond (2001) states that government commitments to recapitalizations make banks anticipate bailouts. Similarly, Farhi and Tirole (2012) show that recapitalizations can sow the seeds of the next crisis as they impose deferred cost on society by incentivizing banks to operate with risky balance sheets. They highlight that “refusing to adopt a risky balance sheet then lowers banks’ rate of return. It is unwise to play safely while everyone else gambles” (Farhi and Tirole (2012, p. 62)).

_Nationalizations_ . Once a bank is identified as distressed, nationalizations are common and the government acquires a majority equity stake in the banks. In some crises, nationalizations take place at a large scale and all major banks are taken into state ownership.

Numerous studies document undesirable effects from government ownership.<sup>9</sup> Shleifer and Vishny (1994) show that government-owned banks have moral hazard incentives, suggesting that nationalizations result effectively in a bailout of all creditors and guaranteeing bank debt. Similarly, Acharya and Kulkarni (2010) argue that guarantees on state-owned banks yield an uneven playing field, and generate excessive risk-taking.

# **3. Effects of government interventions on banking competition**

We employ two alternative measures of competition: the Lerner index, and the net interest margin. Since our analyses are performed at the banking system level, we use the average Lerner index and the average net interest margin per country per year.

> 8 Appendix II offers evidence from the bank level to complement the main findings on the aggregate level. Using a hand-collected sample of 589 recapitalizations and 26 nationalizations during the recent crisis, we estimate how individual banks’ Lerner indices and net interest margins react to recapitalizations and nationalizations using matched samples. Our tests confirm the competition-increasing effect of recapitalizations and nationalizations on the competition measures, except for the effect of nationalizations on net interest margins. 9  LaPorta, Lopez-de-Silanes, and Shleifer (2002) show government ownership of banks reduces financial development, and Dinc (2005) shows that government-owned banks are prone to political interference.

7
The Lerner index captures market power by calculating the markup of prices above marginal costs. We use 181,830 bank-year observations for 21,988 banks in 124 countries to compute this index (Appendix III). The bank data are obtained from BankScope. BankScope is the limiting factor for the sample period. The Lerner index is a widely used measure of competition (e.g., Koetter, Kolari, and Spierdijk (2012)). By including non-interest (off-balance-sheet and fee) income and non-interest (operating) costs, the Lerner index is better suited to capture competition in broader banking activities. As an alternative, we use the net interest margin because competition in traditional activities that dominate less developed banking systems is best reflected by the spread between lending and deposit taking activities. Since our dataset contains numerous emerging markets, relying on the net interest margin provides a sensitivity check. Further, Gropp, Hakenes, and Schnabel (2011), in their analysis of the effect of public bailout guarantees, show that the channel making protected banks’ competitors more aggressive runs via interest margins. The two measures are not significantly correlated, the coefficient is -0.014.

The key explanatory variables are coded as binary variables that take the value of one in the year the intervention was announced and in subsequent years if a country was still affected by the intervention as detailed by Laeven and Valencia (2008, 2010, 2012). Our approach is identical to the coding suggested by Dell’Ariccia, Detragiache, and Rajan (2008). We register 11 blanket guarantees between 1996 and 2010. The dummy for liquidity support takes on the value one if liquidity support provided by the central bank is at least 5% of deposits and liabilities to nonresidents/GDP (34 instances). Our dummy for recapitalizations is restricted to significant recapitalizations, defined as recapitalizations whose costs exceed 3% of GDP. We register 32 recapitalizations. We code takeovers of systemically important banks and instances where the government takes a majority stake in banks’ equity capital as nationalizations. Our data show 26 cases of nationalizations.

# _3.1 Preliminary inspection_

In a preliminary data inspection, we demonstrate separately for each country that announced blanket guarantees, liquidity support, recapitalizations, and nationalizations the change in the average Lerner index in Figure 1, and in the net interest margin in Figure 2. We also show the corresponding change for all countries in the control group over the same period. Each panel illustrates the effect of the respective government action. Countries with interventions are represented by a triangle, and countries in the control group are depicted by a square. For example, Thailand issued a blanket guarantee in 1997 (shown at the bottom left-hand corner in Figure 1 in the panel with blanket guarantees). In this year, the Lerner index dropped by 0.14. At the same time, the control group (defined as countries not having 8
experienced a crisis and not being subject to blanket guarantees) experienced an increase in the Lerner index by 0.04.

[FIGURE 1: The effects of government interventions on Lerner indices]

[FIGURE 2: The effects of government interventions on net interest margins] The empirical patterns are striking. Most countries experience reductions in Lerner indices and in net interest margins following interventions. The increase in competition occurs primarily after recapitalizations and liquidity support, and nationalizations also reduce margins. However, the effects are not uniform. While Thailand and Ecuador display declines in Lerner indices and in net interest margins, other countries such as Germany do not post such declines, suggesting the effects of interventions are amplified or mitigated depending on other characteristics, e.g., the conditions in a banking system prior to these measures. We explore these issues in detail in Section 3.7 below.

# _3.2 Identification strategy_

We now turn to difference-in-difference estimations which compare treatment countries, i.e., countries whose banking systems experienced interventions with countries in a control group both before and after the treatment. The control group consists of countries that did not have interventions (i.e., non-crisis countries).

Our estimator considers the time difference of the group differences, i.e., it accounts for omitted variables that affect treated and untreated countries alike. For instance, Basel II may coincide with changes in competition, but as such changes affect all banks similarly, the estimator only attributes the additional changes in competition to the intervention. Our regression setup is as follows: _Cit = α + βIit +_ ρ _Xit + Ai + Bt +_ ε _it_ where the dependent variable _Cit_ denotes competition in country _i_ during year _t_ . The panel structure permits inclusion of dummy variables to eliminate time-varying omitted variables. We include country ( _A_ ) and year dummy variables ( _B_ ) to capture cross-country heterogeneity and year-fixed effects. The country-fixed effects net out any time-invariant unobserved country-specific factors. The year-fixed effects difference away trends that affect treatment and control group countries such as changes in contestability, consumer trends, and changes in technology that could reduce Lerner indices and interest margins over time. We also include dummies for World Bank income categories.<sup>10</sup> They account for time-invariant similarities between countries in terms of economic development. The vector _X_ captures time-varying country-level control variables explained below, and ε _it_ is the error term.

> 10 The income categories are high-income, lower-middle income, upper-middle income, and low-income.

9
Our coefficient of interest is _β_ , the dummy that equals one in the years affected by the intervention _I_ (blanket guarantee, liquidity support, recapitalization, nationalization), or zero otherwise. The slope of _β_ provides information about the effect of interventions. Our measures of competition are decreasing in competition. Hence, a positive slope coefficient suggests a decrease in competition, whereas a negative slope signals an increase in competition.

The vector of control variables _X_ contains determinants of competition. GDP growth, inflation, and real GDP per capita account for differences in the macroeconomy. Claessens and Laeven (2004) show that concentration affects competition. We include an asset-based Herfindahl-Hirschman index (HHI). Since we compare HHIs across markets, we also include banking system assets (ln) to account for the size of the industry (Bresnahan (1989)). To account for the government’s role in formulating regulation, we use a regulatory quality index which is increasing in regulatory quality, and is normalized between - 2.5 and +2.5 (Kaufmann, Kraay, and Mastruzzi (2009)). Provided that interventions are more pronounced when the banking system plays a bigger role in the economy, we also include a dummy that takes on the value of one if Beck, Clarke, Groff, Keefer, and Walsh (2001) classify a financial system as bank-based, and an index ranging from 1 to 3 that classifies the depth of a banking system by provision of domestic credit (scaled by GDP). We also control for the ratio of loan impairment charges to loans as reductions in interest income during crises can drive down our competition measures. Since many countries adopt multiple interventions, we also control for a dummy for multiple interventions that takes on the value of one if a country experienced more than one intervention. This dummy ensures that the coefficient for the individual intervention is not confounded by the other three interventions excluded from the regression.<sup>11</sup> We also use a dummy for assisted mergers. These mergers affect market structure and, indirectly, the way banks compete. Finally, we control for government expenditure consumption as a ratio of GDP to account for governments’ ability to bail out banks, and money market rates (ln) as a proxy for monetary conditions. Table 1 presents summary statistics.

[TABLE 1: Summary statistics] Difference-in-difference estimations require two assumptions. First, assignment to treatment is plausibly exogenous with respect to competition, suggesting competition is not driving the interventions. Second, in the absence of treatment, changes in competition would have been the same for countries in both treatment and control groups. This is the “parallel trends” assumption.

> 11  The interventions are highly collinear because they tend to be adopted at the same time (Appendix I). While only 13.5% of crises countries adopt one measure, 25% adopt at least two types of rescue measures; over 31% announce three measures. All four types of interventions are used by 30% of the countries. We cannot include them in the same regression and therefore estimate regressions separately for each type of intervention. 10
We first examine the exogeneity of the interventions. The correlation coefficients in Panel A and B of Table 2 between the average level of competition prior to the interventions and the announcement year of the interventions are inconsistent in terms of the direction, and they remain insignificant.<sup>12</sup> Next, we use Cox proportional hazard models to estimate the conditional probability of interventions. Our key explanatory variable captures competition prior to the policy response, and we also include the control variables discussed above. We focus on the time from the start of our sample period to the occurrence of interventions.<sup>13</sup> The hazard rate _h(t)_ represents the likelihood that an intervention is observed at time _t_ in country _i_ , given that there was no intervention until _t_ . In employing duration analysis, we can impose a structure on the hazard function. Since we have no reason to assume duration dependence in the data, we use a Cox model that does not impose a shape on the hazard function.<sup>14</sup> The model takes the form where _ho(t)_ denotes the baseline hazard, and _βx_ is the vector of parameters.<sup>15</sup> A positive coefficient for the competition measure increases the probability (hazard) of interventions.

Panel A of Table 2 reports the results with the Lerner index, and Panel B shows the coefficients for the net interest margin. The competition measures are insignificant. In combination with the correlations between the average level of competition prior to the interventions and the announcement year of the interventions at the top of Table 2, we conclude that interventions are not related to the prevailing competitive conditions. These tests also mitigate concerns related to reverse causality.

# [TABLE 2: Exogeneity of interventions and correlations]

We now examine the parallel trends assumption, and focus on competition in the three years prior to interventions. The assumption requires that we observe similar changes in competition between countries whose banking systems witnessed interventions and those in the control group. Importantly, this assumption does not require identical levels of competition between treatment and control groups; they are differenced out by the estimator. Figure 3 illustrates similar patterns that support the existence of parallel trends.

[FIGURE 3: Parallel trends: Behavior of competition measures]

> 12 All interventions are positively correlated (Panel C).

> 13  The number of observations in Table 2 changes depending on the regression specification because we drop countries from the sample following the occurrence of an intervention.

> 14 In unreported exercises we also use Weibull and exponential distributions but the results remain similar.

> 15  In this model, the explanatory variables shift the underlying hazard function up or down but it is not necessary to specify a functional form for the baseline hazard, reflecting proportionality.

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# _3.3 Main results_

Table 3 presents our main results for the Lerner index (Panel A) and the net interest margin (Panel B). Following Bertrand, Duflo, and Mullainathan (2004), we cluster heteroskedasticity-adjusted standard errors on the country level to allow for serial correlation in the error terms. All regressions are performed on annual data, and we drop countries with multiple crises (Russia and Ukraine), although the results are not affected when these countries are included.

# [TABLE 3: The effect of government interventions on banking competition]

All coefficients that capture policy responses exhibit a negative sign. In Panel A, liquidity support and recapitalizations assume significance at conventional levels, suggesting the provision of liquidity and capital injections increase competition. Panel B confirms the competition-increasing effects for liquidity support, capital injections, and nationalizations.<sup>16</sup> For the Lerner index, we also illustrate the effects in terms of their economic magnitude using Indonesia, a country where liquidity support was provided and banks were recapitalized. Indonesia is located at the 15<sup>th</sup> percentile of the distribution of the Lerner index in 1997 (0.138). Having had liquidity support, the banking systems experienced an increase in competition to the level of Haiti, located at the 8<sup>th</sup> percentile with a Lerner index of 0.103.

Blanket guarantees, mostly used in the South East Asian Crisis, remain insignificant. Guarantees that are not accompanied by other measures may not be credible, and foreign creditors tend to ignore them. Further, some countries introduced tax policies that undermined blanket guarantees at the time the guarantee was announced, e.g., Ecuador. The absence of an effect may also reflect the smaller number of blanket guarantees. Moreover, in some countries, such as Ireland, blanket guarantees exceed GDP, questioning the sovereigns’ ability to service such commitments.

# _3.4 Alternative explanations_

Next, we confront alternative explanations. A common shock, i.e., a banking crisis, rather than interventions can affect competition. However, some crisis countries did not revert to these government actions, and we show in Figure 3 that our data satisfy the key identification assumption of parallel trends between treatment and control groups. Further, not all coefficients display the same effect. If

> 16 Additional tests include a dummy that takes on the value of one if asset management companies or restructuring agencies assume nonperforming assets. These tests leave our inferences unchanged. We also replicate in unreported tests the regressions without the control variables, and we additionally use the PanzarRosse (1987) H-Statistic, an alternative competition measure at the system level, as dependent variable. Using WLS to account for the fact that the H-Statistic is obtained from a regression, we observe again competitionincreasing effects for the key variables of interest.

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interventions simply serve as a proxy for crises, they should display identical effects with similar economic magnitudes. This is not the case. A t-test for the null that the coefficients on the four interventions are equal across the regressions is rejected at the one percent significance level (χ<sup>2</sup> -value: 11.57, p-value: 0.00). Moreover, crisis durations are short (3.03 years) whereas blanket guarantees, recapitalizations and nationalizations remain in place for many years. Blanket guarantees lasted 78 months in Indonesia, and 89 months in Japan. On average, blanket guarantees remain in place for 5.2 years, and the public sector retained its equity participation for over 10 years in Japan. Thus, the effects of interventions are likely to go beyond the effect of the crisis.

Table 4 examines alternative explanations. Our regressions include all control variables but we suppress their coefficients. The first test replicates our main regressions on a sample that excludes countries with systemic crises. We only consider interventions in countries with borderline crises. This test reduces the number of policy responses and the power of our tests, but we still obtain competitionincreasing effects for liquidity support and recapitalizations for the Lerner index, suggesting we can rule out that the effects we attribute to the interventions simply capture systemic crises. Likewise, we confirm a negatively significant effect of nationalizations on net interest margins. There are no blanket guarantees in countries with borderline crises.

We offer another falsification test to rule out the concern that any type of crisis affects the evolution of competition between treatment and control groups differently. To this end, we find an event that increases competition but is unrelated to crises and then assign placebo interventions. Specifically, we eliminate the key confounding factor, i.e., crisis observations from our sample, and look for instances where we observe a decline in an index which provides information about restrictions on bank activities (Barth, Caprio, and Levine (2004)). The index increases in restrictiveness, ranging from 3 to 12. It provides information about banks’ ability to engage in non-traditional activities (securities, insurance, and real estate), and restrictions on conglomerates. The idea is that a drop in activity restrictions increases competition so that we can analyze if drops in activity restrictions create similar treatmentcontrol group patterns that we uncover in our main tests with the difference that the placebo interventions do not coincide with crises. If the interventions in our main tests simply act as a proxy for a crisis that triggers declines in the Lerner index and in the net interest margin, our placebo interventions will also enter significantly. If not, the placebo interventions will remain insignificant. The placebo interventions are assigned to the first year in which a country experiences a drop in activity restrictions. The durations of these placebo interventions are randomly generated based on the durations of the actual durations of the four interventions. To avoid confounding effects of multiple 13
reductions in activity restrictions per country, we omit countries with multiple reductions of the index, resulting in 37 placebo interventions. None of these placebo interventions displays a significant effect.<sup>17</sup> Further, we examine if our results reflect that the banks compete fiercely because they are _too-big-tofail_ , or because the entire sector is weak so that regulators always offer bailouts. The former effect posits that systemically important banks, engage in reckless competition because of the perceived responsibility of the government to rescue them. The latter effect suggests that banks have an incentive to herd, and invest in similar assets when many banks are weak. To reflect on the _too-big-to-fail_ effect, we exclude countries whose average HHI is above the 95<sup>th</sup> percentile of the distribution (HHI > 0.62). This analysis leaves the key variables unchanged. We address the _too-many-to-fail_ effect and omit countries whose average total capital ratio is below the 5<sup>th</sup> percentile of the distribution (Total capital ratio (average) < 0.05).<sup>18</sup> We again obtain similar coefficients. While these tests cannot rule out that _toobig-to-fail_ and _too-many-to-fail_ considerations play a role for the observed increase in competition, they mitigate concerns that our results are a simple reflection of these phenomena.<sup>19</sup>

# [TABLE 4: Robustness – Alternative explanations]

# _3.5 Further robustness tests_

We offer <mark>six</mark> additional tests. First, we are concerned that our results are driven by high-income economies. Approximately half of all crises and interventions occurred in these countries, and these countries have more sophisticated regulatory frameworks than low-income economies. We remove them from the sample. This test also addresses concerns that the recent crisis drives our results. Table 5 shows that blanket guarantees exhibit a significant competition-increasing effect, and we find a similar effect for recapitalizations in Panel A, while liquidity support is insignificant. The effects on net interest margins remain unchanged. Second, we exclude emerging markets. These tests strengthen our inferences. Third, we rule out that structural adjustment programs by the International Monetary Fund which can increase a country’s competitiveness drive our results. We omit countries that had such programs but our coefficients remain similar, with the exception of liquidity support in Panel A and liquidity support and recapitalizations in Panel B but blanket guarantees enter now significantly.

> 17 We replicate these tests with including crisis countries. None of the key coefficients assumes significance .

> 18 Argentina, Bulgaria, Dominican Republic, Ecuador, Indonesia, Korea, Philippines, Russian Federation, Thailand, Turkey, Uruguay, and Venezuela are International Monetary Fund program countries. The regressions that aim to reflect on the _too-big-to-fail_ effect are insensitive with respect to the cut off value for the HHI. Likewise, tests that address the _too-many-to-fail_ concern are insensitive towards alternative cutoffs.

> 19  As an additional check to investigate whether the competition-reducing effects are simply driven by a shift in levels of monetary policy rates, we examine the correlation of money market rates with the Lerner index and the net interest margin. We obtain conflicting findings. Downward shifts in market rates do not drive our results. While there is a negative correlation between money market rates and the Lerner index, there is a positive association of money market rates with the net interest margin.

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Fourth, we generate placebo treatments by pretending the interventions occurred two years prior to the actual intervention. If our identification strategy is correct, an insignificant placebo treatment effect suggests that the relationships in Table 3 are causally related to the government actions. This exercise fails to detect significant relationships.

# [TABLE 5: Robustness – Subsamples and placebo treatments]

Our fifth test addresses the fact that policy actions may not occur randomly (“selection problem’) through the use of propensity score matching methods based on a nearest-neighbor procedure. The lack of random assignment can bias our coefficients (Heckman, Ichimura, Todd (1997)). Subsequently, we also use instrumental variable techniques.

The propensity score method constructs control groups of countries that have a similar probability of experiencing interventions but no event takes place. The propensity score is defined as the probability of being subject to blanket guarantees, liquidity support, recapitalizations, or nationalizations, conditional on pre-intervention characteristics. Calculation of the propensity scores relies on probit models with blanket guarantees, liquidity support, recapitalizations, and nationalizations as dependent variables. The predicted probabilities from the probit models are then used to match each country-year observation to a set of observations from the control group of countries from the same World Bank income group category using the absolute value of the difference between the propensity scores as decision criterion. The nearest neighbor technique restricts the set of matches to those whose propensity scores fall in the common support of both groups. To evaluate the sensitivity of our findings, Table 6 presents treatment effects with one and two countries in the control group. These tests include the control variables used above.<sup>20</sup>

[TABLE 6: Robustness – Propensity scores] The treatment effects largely reinforce the previous inferences, and blanket guarantees also exhibit significant effects. Liquidity support retains its negative sign, although the effect is only significant in Panel A. In contrast, recapitalizations and nationalizations are insignificant (yet negative) in Panel A, but the effects on interest margins are both significant.

A limitation of propensity score matching estimators is that they do not control for the effect of unobservables on the selection of countries with interventions. The inferences from this method are therefore still to be taken with a grain of salt. Our sixth robustness test accounts for these unobservables through an instrumental variables approach using a two-stage estimator. We use a linear probability

> 20  To illustrate the similarities between the countries in the treatment and control group following the matching process, we plot the distribution of the control variables in Appendix IV. The plots demonstrate that our matching process results in very similar distributions for most control variables. 15
model in the first stage, and we rely on the same set of instruments for all types of interventions. The second stage uses the estimated probabilities.

Our instruments draw from different strands of literature. First, we exploit ideas according to which bailouts occur because of doubts about the accuracy with which investors can assess the value of bank assets. Second, we rely on arguments claiming that the electoral cycle and the political environment play a role for the decision to bail out banks. Third, we use a characteristic of the institutional environment.

Flannery, Kwan, and Nimalendran (2013) highlight interventions into distressed banking systems are founded on the belief that markets are unable to differentiate between sound and unsound institutions during crises. This spike in information asymmetries motivates bailout programs such as the TARP. To capture opacity, we focus on asset composition. Flannery et al. (2013) propose a bank‘s loan and security portfolio composition are informative about opacity. We therefore home in on different loan categories, scaled by net loans. Loans are separated into residential mortgage loans, other consumer and retail loans, corporate and commercial loans, and other loans. While residential mortgage loans and consumer and retail loans are standardized and should reduce opacity, corporate and commercial loans are difficult to value. We expect them to increase opacity and correlate positively with interventions. The remaining lending categories are grouped together as other loans. For security portfolio composition, we focus on available for sales and held to maturity securities, scaled by total securities. While the former are marked at fair value and should reduce opacity and the likelihood of interventions, the latter are reported at amortized cost, and make the balance sheet more opaque. Brown and Dinc (2005) show most bailouts occur 7-12 months after elections, whereas the period before an election reduces the likelihood of interventions. Two instruments capture the electoral cycle. The first one is a dummy that takes on the value of one in the year a parliamentary election takes place, and the second one provides information about the time (in years) since the last parliamentary election.<sup>21</sup> Both variables should correlate negatively with interventions.<sup>22</sup> A further instrument provides information about the orientation of the largest government party. We use a dummy that takes on the value of one if the largest government party has a right-wing orientation. Governments led by such parties focus on market-oriented policies to increase their chances of re-election (Bortolotti and Faccio (2009). Moreover, their partisan orientation impacts bailout propensities. While left-wing governments are keen to intervene into the economy to preserve jobs, right-wing governments tend to oppose such actions (Garrett and Lange (1991)). Our last instrument is an index about prompt

> 21 We collect this information from _Parties and Elections in Europe_ , the _Center on Democratic Performance (Election results archive)_ , and from _Electionresources.org_ The website sources are <u>http://www.parties-andelections.de/index.html, http://cdp.binghamton.edu/era/countries/, and http://electionresources.org/data/index_en.html.</u>

> 22 Duchin and Sosyura (2012b) document an important role of political connections for bank bailouts. They show that politically connected banks are more likely to be recapitalized under the TARP.

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corrective power. It captures legal requirements that establish levels of bank solvency deterioration that trigger regulatory enforcements (e.g., interventions, cease and desist orders) and the scope of supervisors in applying such powers. The index increases in corrective power, ranging from 0 to 6. We expect a more powerful regulator to press for and quickly implement bailout packages in times of crises as corrective powers would otherwise reduce the need for large scale bailouts during tranquil periods.

The tests in Panel A of Table 7 confirm the previous results. While the sample size shrinks due to the availability of political covariates, we obtain negatively significant coefficients for liquidity support and recapitalizations for the Lerner index, and we confirm significant effects for all interventions but blanket guarantees for net interest margins. All coefficients are larger than the ones reported in Table 3. Panel B shows the results for the first stage. All instruments are significant in at least one of the four first stage regressions, except for other consumer and retail loans, and they exhibit the anticipated sign. The Hansen J-Statistic for the null that the instruments are uncorrelated with the error cannot reject their exogeneity. All first stage F-Tests are above the rule of thumb of ten, and the Kleibergen-Paap tests reject the presence of weak instruments. For all specifications, we obtain statistics above the tabulated critical values for a size bias of ten % relative to OLS. At the bottom of the table, we show the partial R2 associated with the instruments. We run the 1<sup>st</sup> stage regressions without the instruments and compute the partial R2 as the difference between 1<sup>st</sup> stage regressions that include the instruments and those which exclude the instruments. The resulting partial R2 values range between 11 and 20 %, and these figures represent increases of between 67 and 307% relative to the 1<sup>st</sup> stage regressions without the inclusion of instruments, suggesting our instruments are strong.

# [TABLE 7: Robustness – Instrumental variables]

# _3.6 Do the competitive effects persist over time?_

We acknowledged above that the average duration of the policy measures goes beyond the duration of the banking crises. We now lend further support to the idea that the competitive effects are not reversed, and trace average values of Lerner indices and net interest margins in countries that were subject to the interventions over the five years following their announcement. The dark bars represent the level of competition in the announcement year of the intervention in Figure 4. The light bars track the competition indices in the five subsequent years. The Lerner indices remain below the initial level in the years following the interventions, and the effects are particularly strong in the first three years. While there is some reversal in the fourth and fifth year when we consider the Lerner index, interest margins experience a hefty drop in the first two years (with the exception of blanket guarantees), and remain compressed.

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[FIGURE 4: Long–run effects]

# _3.7 Initial conditions of banking systems prior to government interventions_

Figures 1 and 2 indicate the policy responses do not affect all countries equally. To better understand these heterogeneities, we now investigate whether the initial market conditions play a role for these cross-country differences. From a policy perspective, it is useful to understand if the competitive response to interventions varies in a predictive way to aid the actions taken by policymakers.

Demirguc-Kunt and Detragiache (2002), Claessens and Laeven (2004), Martinez Peria and Mody (2004), and Beck, de Jonghe, and Schepens (forthcoming) show that characteristics of banking markets such as structure, contestability, and moral hazard are related to competition. Thus, these characteristics are likely to either amplify or mitigate the effects of policy responses.

We illustrate our arguments about the effect of initial conditions as follows: If blanket guarantees boost competition by giving rise to moral hazard if a country had no deposit insurance before the announcement of blanket guarantees, then the effect of such guarantees should be greater in countries where no deposit insurance scheme was in place. Foreign banks may also play a role because their presence suggests greater contestability. If foreign banks are well represented prior to nationalizations, the competition-increasing effect may be relatively limited. Likewise, if a country does not have a contestable banking system prior to a crisis reflected in activity restrictions and high entry barriers, the competition-enhancing effect of liquidity support is likely to be muted.

We use the HHI to measure market structure (Claessens and Laeven (2004)). Foreign bank penetration, measured by the share of foreign-owned institutions, is our first indicator for contestability. Martinez Peria and Mody (2004) show that foreign banks’ conduct differs from the behavior of domestic banks. Contestability is also reflected in entry barriers and activity restrictions (Claessens and Laeven (2004)). The entry restrictions index summarizes the procedures required to obtain a bank license, the percentage of denied applications for licenses, and the minimum capital required. The index ranges between 0 and 8; it is increasing in restrictions. We capture activity restrictions with the activity restrictions index described in Section 3.4. To approximate moral hazard, we rely on a dummy that takes on the value one if a country has explicit deposit insurance. To calculate initial conditions for concentration, foreign ownership, activity restrictions, and entry restrictions, we take the mean value of these variables in the countries from the treatment group prior to the announcement year of the intervention. For the initial conditions of deposit insurance, we code the variable as one if a country had explicit deposit insurance in place or zero otherwise. Since we need to define the initial conditions also for the control group, we use a _1:n_ matching procedure that finds at 18
least one country from the set of countries that did not have a crisis. As a further criterion, we use World Bank income categories to ensure comparing countries with similar levels of economic development.

# [TABLE 8: The role of initial banking market conditions]

Each cell in Table 8 represents a single regression, and we only show the interaction term of the intervention variables with the corresponding initial condition. Since these regressions include countryfixed effects the initial condition itself is dropped from the regression.

We first discuss the results for the Lerner index. The increase in competition is significantly greater in magnitude in concentrated markets. Figure 1 reinforces this point. Concentration in Japan is low with a HHI of 0.097. In this market, we observe an increase in the Lerner index relative to the control group. Likewise, the concentration indices for Malaysia (0.101) and Ireland (0.289) are also below the mean, suggesting that the effect blanket guarantees is mitigated there. Regarding the presence of foreign banks, we find a positively significant coefficient, except for blanket guarantees. The negative relation between interventions and competition is mitigated in countries with higher foreign bank penetration because those institutions are not intervened. Figure 1 illustrates this phenomenon. Ireland and Latvia exhibit high foreign bank presence (0.63 and 0.66% of banks are foreign owned; sample mean 0.36%). Upon announcement of liquidity support, Lerner indices increase relative to the control groups. Activity restrictions have a weaker interaction with interventions – they are only significant for recapitalizations and nationalizations. Increases in competition from recapitalizations and nationalizations are reaped in less contestable markets. Reductions in the Lerner index induced by interventions tend to be larger in systems with more entry restrictions, except for liquidity support. Deposit insurance only plays a role for the effects of nationalizations. Explicit deposit insurance mitigates the improvement in competition.

The initial conditions play little role for net interest margins in Panel B, reflecting less cross-country variation. The exception is deposit insurance. The interaction term (except for blanket guarantees) implies that the deposit insurance offsets (at least partly) the former effect.

# **4. Extensions: Transparency, charter values, and the presence of zombie banks**

To further understand mitigating or amplifying factors for the effects of bailouts on competition, we offer several extensions that focus on other characteristics of the banking systems in this section.

# _4.1 Transparency_

The idea that transparency plays a role for the effectiveness of policy responses to crises has been advocated repeatedly (e.g., Hakenes and Schnabel (2010); Gropp, Hakenes, and Schnabel (2011)). The basic premise is that transparency mitigates the effects of bailouts which undermine market discipline. In a transparent system, depositors can easily observe risk. Therefore, they will discipline banks if they 19
compete too aggressively. In contrast, in opaque systems, it is difficult to infer information about the banks’ condition, so that the effect of market discipline remains muted. We create a Transparency index, ranging from 0 to 5 following Barth et al. (2004) to test this idea. Our index consists of two components. One is a dummy that takes on the value of one if a compulsory external audit is required and the second component is an accounting index that is increasing in the quality of bank accounts. This component considers information about whether the income statement includes accrued or unpaid interest or principal on nonperforming loans and whether banks are required to produce consolidated statements. The first subpanel in Table 9 tests the effect of transparency by presenting the effects of interventions on competition augmented by a term that interacts each of these policy responses with the transparency index. The number of observations is reduced in these tests due to data availability. The coefficients of interventions enter negatively, and the interactions with the transparency index display positive signs. All coefficients are significant except for those of the blanket guarantees for the Lerner index. Thus, the impact of interventions is reduced as transparency disclosure requirements become more stringent.

# [TABLE 9: Extensions – Transparency and bank charter values]

# _4.2 Charter values_

Others studies emphasize charter values. Keeley (1990) highlights that banks with valuable charters have little incentive to compete because future rents will be lost in case of failure. Cordella and Yeyati (2003) propose two offsetting effects. The first effect induces moral hazard and leads to risk-taking among banks, and the second one increases the charter value and creates incentives for prudent behavior. We refer to this as the charter value effect. According to Cordella and Yeyati (2003), the second effect can offset the first one, and become the dominant one. Since the majority of banks in our sample are not listed, we cannot use Tobin’s Q, typically used to measure charter value. Instead, we follow Hutchinson and Pennacchi (1996) who show core deposits reflect charter values. We approximate charter values with the ratio of current deposits to total deposits, and money market and short-term funding. Table 9 introduces in the second subpanel charter values, measured at the mean bank for each country per year, and an interaction term between the interventions and the charter value. The baseline effect of charter values displays a positive, yet insignificant effect, and the interaction terms also fail to assume significance. These findings do not support the idea that charter values are a disincentive to compete.

# _4.3 Zombie banks_

Interventions can delay the orderly exit of insolvent banks which enables them to continue as a going concern although they are economically unviable. By allowing these banks to remain in business, the 20
shakeout of these banks is suppressed. In turn, such zombie banks can originate loans with large upside potential. Limited liability in combination with the government support puts zombie banks in a no-lose situation and incentivizes them to compete aggressively (Kane (1990)).

Analyzing whether interventions correlate with the presence of zombie banks requires classifying them into viable and unviable ones, i.e., zombies that have an economic net worth below zero. We cannot use market values to measure net worth as most banks in our sample are not listed. Instead, we calculate tangible capital (Kroszner and Strahan (1996)). Specifically, we compute tangible capital as common equity minus intangible assets. Intangible assets consist of good will, other intangibles, and deferred tax assets. Next, we code a dummy that takes on the value one if tangible capital scaled by total assets is negative to identify zombies. This measure is more conservative than regulatory capital. In a final step, we calculate three indicators for zombie banks: the number of zombie banks/total number of banks, and we also calculate their market shares for loans and deposits for each country per year. Figure 5 illustrates the evolution of zombie banks for the five years following the interventions. While the number of zombies increases in the year following the interventions and then starts declining, zombies that are not dismantled are able to increase market shares. In the second year after the announcement of the interventions, zombie banks’ market shares increase from less than 2% to up to 9% in the case of blanket guarantees. Nearly on tenth of the banks in these countries is economically unviable two years after the government initiated the rescue measures. For liquidity support, recapitalizations, and nationalizations, zombie banks’ market shares increase to up to 5% in the second and third year following the announcement of the government measures.

[FIGURE 5: Evolution of zombie banks] Table 10 illustrates that liquidity support and recapitalizations are weakly significantly positively related to the number of zombie banks (Panel A). Thus, while recapitalizations increase net worth, they seem insufficient to eradicate zombie banks. This finding, however, does not manifest itself in larger market shares as shown in Panel B and C where the effects remain indistinguishable from zero.<sup>23</sup> . Panel D offers further support for the idea that zombie banks evolve simultaneously with interventions. We find a strictly monotonous relationship between the number of interventions and zombie banks. While this simultaneity could also reflect the intensity of a crisis, Panel E shows that the largest increase in competition coincides in most instances with the greatest presence of zombie banks. This could be interpreted as suggestive evidence that zombie banks are a driving force for the increase in competition.

[TABLE 10: Presence of zombie banks]

> 23 Note that the tests about the presence of zombie banks are run on a reduced sample because some of the countries do not report the components we require to compute the tangible capital ratio.

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# **5. Effects of government interventions on welfare**

So far, we documented the influence of interventions on producer welfare. This section now tests how consumer welfare is affected.

# _5.1 Who benefits? Competition in deposit and loan markets_

We next examine whether the effect is driven by competition in deposit or in loan markets. This analysis helps understand who benefits from the change in competition. Regressions are run with average deposit and average loan rates as dependent variables in Table 11. Panel A shows that liquidity support, recapitalizations, and nationalizations enhance deposit market power. Anecdotal evidence support this: Irish banks, supported in 2008, witnessed deposit inflows from abroad (Acharya and Mora (2012)). The analysis for loan rates in Panel B points towards rate-reducing effects for liquidity support and recapitalizations. Nationalizations also reduce loan rates, similar to the results reported for government ownership. Sapienza (2004) shows loans originated by state-owned banks carry lower rates, and Black and Hazelwood (forthcoming) show that TARP banks charge lower rates. These tests illustrate the disparate effects of interventions.<sup>24</sup> The competition-enhancing effect in loan markets offsets the effect in deposit markets, resulting in a competition-increasing effect overall.

To illustrate the implications for consumer welfare, we compare the largest and smallest coefficients in loan markets, using nationalizations in Indonesia in 1997 with an aggregate stock of loans of 58,611.95mn USD and aggregate deposits of 53,859.20mn USD as an example. At best, nationalizations reduce the average loan rate from 0.218 to 0.152 which increases borrower welfare by 3,868.39mn USD. However, depositors are worse off by 3,069.97mn USD due to the reduction in deposit rates from 0.200 to 0.143 resulting in a net increase in consumer welfare of 798.41mn USD (3,868.39 - 3,069.97mn USD) for nationalizations. This is the upper bound. On the other hand, recapitalizations reduce the average loan rate to 0.170 (increase in borrower welfare 2,813.37mn USD) while deposit rates drop to 0.159, giving rise to a loss in depositor welfare by 2,208.23mn USD. The lower bound for the welfare gain in Indonesia is therefore 605.15mn USD.<sup>25</sup>

[TABLE 11: Welfare effects] _5.2 Provision of credit and access to banking services_

> 24  Such disparate effects are not uncommon. Park and Pennacchi (2009) illustrate countervailing effects in loan and deposit markets following market-extension mergers by banks in the U.S. While large multi-market bank presence enhances competition in loan markets, it harms competition in deposit markets.

> 25 T he components of the Lerner index marginal cost (obtained by differentiating the Translog cost function, Appendix III) and the product price (total revenue scaled by total assets) are significantly negatively associated with interventions: Prices are reduced and marginal costs decline. Results are available upon request.

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Ultimately, banks’ _raison d’ être_ is to mobilize savings, evaluate projects, and allocate funds. During crises governments intervene, and lending typically contracts because banks become risk-averse and tighten underwriting standards (Ivashina and Scharfstein (2010); Puri, Rocholl, and Steffen (2011)). A question that arises is whether the competitive effects have implications for access to finance. This enquiry builds on the literature on the effects of competition on access to finance (e.g., Karceski, Ongena, and Smith (2005); Cetorelli and Strahan (2006)), and work on crises and credit (e.g., Ongena, Smith, and Michalsen (2003); Chava and Purnanandam (2011)). If interventions -through their effect on competition- reduce the number of banks, and curtail access, then firm output declines and growth rates decelerate. Theory predicts that banks’ propensity to lend and invest in information may be limited in competitive environments because competition reduces the possibility that banks recoup the costs involved in building borrower relationships (Petersen and Rajan (1995)). In this case, we anticipate competition to be inversely related to access to finance. On the other hand, the dominant view is that competition reduces loan rates which makes credit more affordable and increases access.

We now test how interventions correlate with lending, and then home in on access to finance. Access to finance is approximated with bank branch density. Although our regressions control for GDP growth to account for demand-side effects as in Black and Strahan (2002), a full analysis that disentangles demand and supply is beyond this paper.<sup>26</sup> Thus, in this section, we refrain from causal inferences. Panel C of Table 11 shows regressions with Domestic credit provided by the banking sector (% of GDP) as dependent variable. All measures (except for blanket guarantees) are positively associated with credit provision.<sup>27</sup> Panel D shows bank branch density correlates negatively with all interventions. By reducing access, interventions reallocate credit away from small and medium-sized borrowers that typically rely on direct access. Thus, interventions disproportionately affect smaller firms because geographic proximity is less important for large firms.

> 26  The key issue is that crises can trigger declines in demand, which would result in reductions in firm investment which shows up in less demand for credit.

> 27 Appendix V examines alternative explanations. Higher credit provision may reflect liberalization following a crisis (Demirguc-Kunt, Detragiache, and Gupta (2006)). We omit crisis countries whose Chinn-Ito index following a crisis is higher than prior to the crisis (Chinn and Ito (2006)). The index provides information about capital account openness. The coefficients remain similar. Further, crises can coincide with structural adjustment programs by the IMF. We remove countries subject to these programs, but the results remain similar. Another problem arises from lending targets imposed on banks as bailouts may coincide with instructions to lend. If this is the case, our coefficients may be significant but the reason may not be the intervention but rather an instruction to lend. We remove countries with recapitalizations or nationalizations where the proportion of government-owned banks increased relative to the period prior to these actions as government-owned banks are more prone to instructions to lend (Dinc (2005)). Only liquidity support remains significant, suggesting directed lending matters. A last concern relates to foreign currency loans. Crises often result in currency revaluations and the increase in credit provision could reflect this phenomenon. Omitting countries with currency crises leaves our results unchanged.

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# **6. Concluding remarks**

This paper analyzes how blanket guarantees, liquidity support, recapitalizations, and nationalizations during crises affect banking competition. We also study the effects of these interventions for consumer welfare in terms of loan and deposit pricing and access to finance. Our work builds on a growing literature that examines the intended and unintended consequences of government aid for the banking sector. Unlike these studies which focus on risk-taking on the micro level following bailouts using single-country datasets, we test aggregate effects using a sample of 124 countries.

The key result suggests that reservations by policymakers that interventions undermine banking competition can be dismissed: Liquidity support, recapitalizations, and nationalizations reduce Lerner indices and net interest margins. These effects are amplified in more concentrated and less contestable banking systems, and in the absence of deposit insurance. However, transparency mitigates the effects. While our regressions leave open the possibility that other policy responses that coincide with the interventions could drive these inferences, a battery of checks that confront alternative explanations and account for possible non-random assignment of interventions suggest the effects are causal in nature. Consistent with evidence from the bank level by Gropp, Hakenes, and Schnabel (2011), and Duchin and Sosyura (2012a), our findings underscore that banks’ conduct following government aid is conditional on the government measures. The evidence from the studies on the micro level in combination with our findings indicates that the mechanism by which government aid increases risktaking runs via competition.

Our analysis of consumer welfare effects points towards disparities. Reductions in interest margins are primarily driven by increased credit market competition, suggesting that borrowers benefit from liquidity support, recapitalizations, and nationalizations, whereas deposit rates are reduced. To illustrate the net impact for consumer welfare, we estimate the upper bound (based on nationalizations) for the net welfare gain for Indonesia, a country that experienced government interventions in 1997, to be 798.41mn USD. The lower bound (based on recapitalizations) is 605.15mn USD. These welfare effects are substantial in magnitude. With a GDP of 238.408bn USD in 1997, these effects are equivalent to 0.33 and 0.25% of the Indonesian GDP, respectively.

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