Mathias Lé
email: mathias.le@acpr.banque-france.fr

Abel Brodeur

Thomas Breda

Claire Célerier

Clément De Chaisemartin

Riccardo De

Hans Degryse

Thibaut Duprey

Henri Fraisse

Johan Hombert

Jean-Charles Rochet

Marc Sangnier

Gabriel Zucman

Yanos Zylberberg

Heterogeneous Adjustments in Bank Leverage after Deposit Insurance Adoption

Keywords: G18, G21, G28, G32 Deposit Insurance, Bank Risk-Taking, Leverage, Systemic Bank, Capital Buffer, Market Discipline, Too Big to Fail Deposit Insurance, Bank Risk-Taking, Leverage, Systemic Bank, Capital Buffer, Market Discipline, Too Big to Fail JEL Classifications: G18, G21, G28, G32

This paper empirically investigates the bank leverage adjustments after deposit insurance adoption. Banks are found to increase significantly their leverage after the introduction of deposit insurance. However, the banks' responses appear to be heterogenous. The magnitude of the change in bank leverage decreases with (i) the size, (ii) the systemicity and (iii) the initial capitalisation of banks so that the most systemic and the most highly leveraged banks are unresponsive to deposit insurance. As a result, implementing a deposit insurance scheme could have important competitive effects.

Introduction

Understanding the determinants of excessive bank risk-taking is a crucial issue, all the more since the outbreak of the financial crisis. This research agenda has received a lot of attention and many suspects have been identified. Among them, excessive leverage is now rightly considered as one of the key fragility that has triggered the Great Recession (Adrian and [START_REF] Shin | Liquidity and leverage[END_REF], [START_REF] Brunnermeier | Deciphering the liquidity and credit crunch 2007-2008[END_REF] and [START_REF] Acharya | How Banks Played the Leverage Game[END_REF]). Highly leveraged financial institutions increase significantly the risk of contagion as well as they have the potential to disrupt durably the functioning of some financial markets when facing large unexpected shocks. In case of rapid and simultaneous deleveraging process, the credit supply is likely to shrink drastically, with adverse consequences on the real economy [START_REF] Ivashina | Bank lending during the financial crisis of 2008[END_REF]). In a sense, excessive leverage can be seen as an unintended consequence of incomplete or inefficient regulations which failed to provide correct incentives to banks. In this respect, an intense debate concerning the adverse impact of state guarantees and the Too Big to Fail issue has been revived by the massive bailouts and almost full guarantees provided to the financial industry after the bankruptcy of Lehman Brothers [START_REF] Gropp | Competition, Risk-shifting, and Public Bail-out Policies[END_REF], [START_REF] Oliveira | Depositors' perception of "too-big-to-fail[END_REF]). Interestingly, similar concerns arose when a large number of countries started to implement an explicit deposit insurance scheme: these insurance schemes suffer from the same moral hazard issue as state guarantees by giving banks strong incentives to adopt risky behaviors. Against this background, the present paper examines the relations between deposit insurance and excessive leverage: does deposit insurance adoption makes banks more prone to increase their leverage ratio and, if so, is this unintended consequence uniformly distributed across banks ? Introducing a deposit insurance scheme aims to protect small and presumably uninformed depositors against bank failures.1 Accordingly, deposit insurance should rule out bank runs and inefficient liquidation of profitable projects [START_REF] Diamond | Bank runs, deposit insurance, and liquidity[END_REF]).

However, deposit insurance adoption is likely to mitigate, if not eliminate, market discipline by depositors as shown by [START_REF] Martínez-Peria | Do depositors punish banks for bad behavior? market discipline, deposit insurance, and banking crises[END_REF], [START_REF] Demirguc-Kunt | Market discipline and deposit insurance[END_REF] and [START_REF] Karas | Deposit insurance, banking crises, and market discipline: Evidence from a natural experiment on deposit flows and rates[END_REF]. In absence of actuarially fair premia, deposit insurance poses a crucial moral hazard issue: it provides to banks strong incentives 1 INTRODUCTION 3 to increase their risk-taking to exploit the put option value of deposit insurance [START_REF] Merton | An analytic derivation of the cost of deposit insurance and loan guarantees an application of modern option pricing theory[END_REF], [START_REF] Marcus | The valuation of fdic deposit insurance using option-pricing estimates[END_REF], [START_REF] Keeley | Deposit insurance, risk, and market power in banking[END_REF] and [START_REF] Pennacchi | Deposit insurance, bank regulation, and financial system risks[END_REF]). In particular, deposit insurance schemes with flat-premia should result in reducing significantly bank capital buffer [START_REF] Bond | Bank capitalization, deposit insurance, and risk categorization[END_REF]). Ultimately, if the deposit insurance fund is unable to efficiently manage this build-up of excessive risk-taking, the effect of deposit insurance might be to make depositors more exposed to bank failure. This paper empirically investigates the effects of deposit insurance adoption on bank risk-taking, paying particular attention to the leverage of banks. More importantly, it explores extensively the likely heterogeneity among banks' responses, and by doing so, it incidentally offers an analysis of the Too Big to Fail issue. For this purpose, I use a newly updated database on deposit insurance schemes around the world together with panel data set covering banks in 123 countries over the period 1986-2011. Deposit insurance adoption is found to increase bank risk-taking by significantly reducing bank capital buffer: the Capital-to-Assets ratio of banks decreases by around 15% after the implementation of deposit insurance scheme. However, this adjustment is not uniformly distributed across banks. I find strong evidence that the positive shift in bank leverage decreases with the (i) systemic importance and (ii) the relative size of banks. Consistently with the Too Big to Fail hypothesis, the largest and the most systemic banks appear to be unresponsive to deposit insurance adoption.

Previous research has investigated the adverse impact of deposit insurance adoption on bank risk-taking [START_REF] Kane | The S & L Insurance Mess: How Did it Happen[END_REF], [START_REF] Wheelock | Deposit insurance and bank failures: New evidence from the 1920s[END_REF], [START_REF] Wheelock | Explaining bank failures: Deposit insurance, regulation, and efficiency[END_REF], [START_REF] Demirguc-Kunt | Does deposit insurance increase banking system stability? an empirical investigation[END_REF], and [START_REF] Laeven | International evidence on the value of deposit insurance[END_REF]). All these studies conclude that explicit deposit insurance can be related to an increase in the probability of bank distress or a decrease in aggregate banking stability. Recently, DeLong and Saunders (2011) confirm these results by studying the deposit insurance adoption that occurred in the USA in 1933.

Using internal loan ratings in Bolivia, [START_REF] Ioannidou | Deposit insurance and bank risktaking: Evidence from internal loan ratings[END_REF] show that banks are more likely to originate riskier loans after deposit insurance implementation. All these papers usually focus on a specific country (mainly the USA) or a limited set of countries 2 and, when covering a large number of countries, they generally work at an aggregate 2 DeLong and [START_REF] Delong | Did the introduction of fixed-rate federal deposit insurance increase long-term bank risk-taking[END_REF], [START_REF] Ioannidou | Deposit insurance and bank risktaking: Evidence from internal loan ratings[END_REF] or [START_REF] Martínez-Peria | Do depositors punish banks for bad behavior? market discipline, deposit insurance, and banking crises[END_REF] level. 3 In this regard, the present paper does not consider aggregate indicators of financial stability but bank-level measurements of risk-taking: it is among the first to work at the bank-level with a large cross-country data set. It is important because aggregate data may mask valuable micro level patterns as the rest of the analysis will show. Above all, most of the previous work focus on the impact of deposit insurance on asset risk or volatility risk. In contrast the present paper investigates the consequences of deposit insurance on bank leverage. This paper hence contributes to the literature by quantifying the impact of deposit insurance adoption on individual bank risk of insolvency and by underlining the prominent role of leverage in this process.

The adverse effects of deposit insurance are not expected to be uniform across banks.

As underlined by [START_REF] Calomiris | The origins of federal deposit insurance. In The Regulated Economy: A Historical Approach to Political Economy[END_REF], Demirguc-Kunt and [START_REF] Demirguc-Kunt | Deposit insurance around the globe: Where does it work?[END_REF] and [START_REF] Demirgüç-Kunt | Deposit Insurance around the World: Issues of Design and Implementation[END_REF], deposit insurance adoption should mainly benefit to small banks. Similarly, systemic banks that benefit from implicit state guarantees (the Too Big to Fail hypothesis) should not react to deposit insurance adoption because they escape market discipline even before adoption. Finally, in absence of actuarially fair-premia, wellcapitalised banks implicitly subsidise highly leveraged banks [START_REF] Marcus | The valuation of fdic deposit insurance using option-pricing estimates[END_REF]).

These well-capitalised banks have also much more room for substituting deposits to equity.

It is thus expected that well-capitalised banks react more intensively than highly leveraged banks to deposit insurance adoption.

In this respect, the most significant contribution of this paper is to bring evidence that banks' responses to deposit insurance adoption are heterogeneous. Firstly, I find that the responsiveness of banks to the introduction of deposit insurance is positively and significantly related to the relative size and the systemicity of banks. For the most systemic banks -i.e. those belonging to the top 10%/top 5% of the distribution within a country -deposit insurance adoption has no significant impact on leverage. This finding is consistent with the view that systemically important banks already benefit from implicit state guarantees (as suggested by the Too Big to Fail hypothesis) so that they are unaffected by the introduction of an explicit system of deposit insurance. Accordingly, deposit insurance adoption could have important competitive effects by removing part of the comparative advantage of large and systemic banks and by improving competition on the banking market. Second, I provide evidence that banks' response to deposit insurance adoption is an increasing function of their initial leverage so that only the least leveraged banks react to implementation of explicit deposit guarantees. The results indicates that the 10% most highly leveraged banks before adoption do not change their capital buffer after implementation of deposit insurance. We thus observe a kind of convergence process across banks in terms of capital buffer: the whole banking system is less well-capitalised and thus less resilient to large and widespread unexpected shocks.

From a methodological point of view, this paper examines the effect of deposit insurance adoption by using essentially a differences-in-differences methodology. Identification relies on comparisons of the changes in risk-taking over time between banks in countries that adopted a deposit insurance scheme at a given date and banks in countries that did not.

There are potential estimation concerns that are carefully addressed. First, a correct identification of the effect relies on the common trends assumption. If the trends of the treatment and control groups differ in a systematic way, the estimated treatment effect is unidentified. I address this issue by adding linear and quadratic country-specific trends and by replicating the results on a sample using a different control group. Second, the decision to adopt a deposit insurance is likely to be endogenous: an increase in bank risk-taking can significantly raises the demand for insurance by depositors and puts governments under pressure to adopt a deposit insurance scheme. I carefully consider this reverse causality issue by running falsification tests. Third and most importantly, I investigate the possibility that deposit insurance adoption comes with simultaneous changes in financial regulation or with some country-specific aggregate shocks like banking crises. I check that the results are not affected by taking into account banking crisis episodes and by controlling explicitly for changes in banking regulation. In particular, I show that the heterogeneity in banks' responses to deposit insurance adoption identified in this paper cannot be confounded with alternative sources of heterogeneity in banks' behavior.

The remainder of this paper is organised as follows. The second section presents the data. I provide a short graphical and statistical analysis in the third section. In the fourth section, I explain in details the identification strategy. I present the results in the next section. The sixth section consists in robustness checks and the last one concludes.

Data

Sources and Construction This paper uses two distinct databases. The first one is the Fitch IBCA's BankScope database, widely recognised as the most important banking database in the world. Second, I construct a new data set by reviewing and updating existing databases about deposit insurance schemes. Especially, I collect rigorously the year of adoption of the deposit insurance scheme for each country. The data, the sources and the exact procedures implemented are described extensively in the appendix B.

The restrictions imposed on the data set are also detailed in the appendix B. In particular, I choose to keep the largest set of countries as possible by including those having already adopted a deposit insurance scheme before banking data started to be collected (like USA or Germany for instance). The estimates of the effect of deposit insurance adoption should not be affected directly by observations from these countries. However, these observations enlarge the control group used in the estimation process helping to smooth its size and its composition over time. To strengthen the validity of the results, I also perform the regressions on a sample restricted to countries adopting a deposit insurance during the period under study. 4 I will come back to this issue when discussing the estimation strategy.

Ultimately, the main sample consists in a database with bank-level balance sheet information over the years 1986-2011 for 123 countries. Among these 123 countries, 88 have an explicit deposit insurance scheme. For 57 of them, a deposit insurance scheme is implemented during a period for which we have bank balance sheet information. The database contains 222 099 bank-year observations and 20 703 unique banks. 5 On average a bank has 12 years of observations, with a minimum of 5 years and a maximum of 16 years. For each country, the exact year of adoption and the number of banks are presented in table 1.

I face two important issues with this sample. The main difficulty arises from the increasing coverage over time: the number of banks within a country and the number of countries reported in BankScope increases sensibly over time, especially during the first 4 1986-2011 5 First, note that 9 957 (almost 48% of the sample) are US banks (112 398 obs.) and 2 237 (almost 11% of the sample) are German banks (27 744 obs.). Second, among these 222 099 bank-year observation, 1 456 have missing log-transformed risk proxy because of negative value. I keep them because I run robustness check using risk-proxy without log-transformation. years i.e. between 1986 and 1999. It is an important weakness that can affect the results and it is an important motivation for using bank fixed-effects. Second, there are some heterogeneity in the deposit insurance characteristics across countries (upper bound, coinsurance mechanism, nature of the premia...). Unfortunately, I cannot control explicitly for these time-varying features by lack of information as explained in the appendices A and B.

I can only control for the time-invariant or slow-moving dimension of these characteristics with bank fixed-effects. Therefore, the main objective of this study is to evaluate the risk-shifting effect of deposit insurance which is independent of the characteristics of the deposit insurance schemes. I argue that adoption itself is likely to have the largest effect on bank risk-taking while the various features may only impact marginally this initial adverse effect.

Bank Risk Measurements

This paper aims to provide a new look on the effects of a specific regulatory change -the introduction of a deposit insurance scheme-on bank risktaking, with a particular focus on the leverage. Before presenting the indicators used, I discuss briefly data limitations. First, the database used consists in balance-sheet of banks, and only few of these banks are listed.6 Hence, market-based measurements are not used in this paper and only balance sheet measurements of risk are taken into consideration.

Second, Tier 1 and Total risk-weighted capital ratios are very often missing for many banks from countries other than USA. 7 The leverage ratio used in this paper is thus the Capital-to-Assets ratio, i.e. the ratio of equity over total unweighted assets. But there are also more fundamental reasons to focus on the Capital-to-Assets ratio.

While a preference toward a regulation based on risk-weighted leverage measures has been observed these last twenty years, the recent financial crisis has also stressed the importance to monitor raw leverage ratios. For instance, Basel III agreements will introduce "a simple, transparent, non-risk based leverage ratio that is calibrated to act as a credible supplementary measure to the risk based capital requirements". 8 This ratio will help to "constrain the build-up of leverage in the banking sector, helping avoid destabilising deleveraging processes which can damage the broader financial system and the economy" by introducing "additional safeguards against model risk and measurement error". The recent crisis has shed light on the limits of the regulation based on risk-weights [START_REF] Acharya | Robust capital regulation[END_REF]) and regulators now recognise the importance to also monitor raw leverage ratio of banks.

I also investigate the effect of deposit insurance on individual probability of default by using the (log of) z-score. This increasingly popular measure of bank risk-taking 9 is computed as follow:

Zt = µ(ROAAt) + CARt σ(ROAAt)
The z-score combines the leverage risk with two additional dimensions of risk: the profitability and the volatility of returns. Formally, the z-score measures the individual probability of insolvency [START_REF] Boyd | Size and performance of banking firms : Testing the predictions of theory[END_REF]). 10 It reflects the distance-to-default i.e. the number of standard deviations that a bank's ROAA has to fall for the bank to become insolvent for a given leverage ratio: the higher the z-score, the lower the risk of default. Additional information about the z-score can be found in appendix. 11

Graphical and Statistical Analysis

Before discussing the identification strategy and the econometric results, I present some graphical evidence that deposit insurance adoption can be suspected to increase both In figures 1 and 2 , I plot the evolution of the average and the median values of both 9 Beck (2008), [START_REF] Laeven | Bank governance, regulation and risk taking[END_REF], Demirgüç-Kunt and Huizinga (2010), [START_REF] Beck | Bank competition and stability: Crosscountry heterogeneity[END_REF] for instance 10 Defining insolvency as the state in which capital is fully depletes by negative returns on asset, i.e. CARt + ROAAt < 0, the probability of insolvency is defined as P [ROAAt < -CARt]. Then a simple application of the Bienaymé-Chebyshev inequality provides an upper bound of this probability (with strict equality if ROAAt is normally distributed):

P [ROAAt < -CARt] ≤ Z -2 t 11
To make both risk measurements homogeneous, I multiply the log of z-score by -1 such that lower values of log of z-score are now associated with higher insolvency risk.

risk measurements. The sample is restricted to banks facing deposit insurance adoption to be more in line with the econometric analysis: it excludes banks that are not used to identify the effect of adoption. We observe that both the Capital-to-Assets ratio and the log of z-score tend to increase over time (or at least to be flat). For instance, the average (median) value of the Capital-to-Assets ratio raises from 11% (7%) in 1990 to 15% (11%) in 2002 before stabilizing around 13% (11%). The only notable exception concerns the average Capital-to-Assets ratio during the years 2002-2005 and 2009-2011 during which it is slightly decreasing. From these figures, we conclude that the effect of deposit insurance adoption that we capture in this paper -a downward shift of both the capital buffer and the distance-to-default-is at odds with the trend observed these last 20 years in the sample. 12By comparing the distribution of both the log of z-score and the Capital-to-Assets ratio before and after deposit insurance adoption, we can now figure out more precisely the possible effect of deposit insurance adoption. We want to know whether we observe a systematic shift in the distribution after deposit insurance implementation that could indicate an increase in bank risk-taking. Note that we continue to restrict the sample to banks having observations before and after the introduction of deposit insurance adoption.

The figures 3 and 4 represent these two distributions and in both cases, we observe clearly a left shift in the distribution of risk after the implementation of deposit insurance. This indicates a decrease in both the Capital-to-Assets ratio and the distance-to-default, that is to say an increase in bank risk-taking. For instance in figure 3, we have much less very highly capitalised banks (i.e. those with a Capital-to-Assets ratio above 20%) and much more highly under-capitalised banks (i.e. those between 10% and 20%) after deposit insurance adoption. Figures 5 and6 in appendix show the kernel density estimates of these distributions. Kernel density estimates have the advantages of being smooth and of being independent of the choice of origin. The previous conclusions are entirely confirmed by these figures.

However, when there are much more observations per banks after adoption than before, these distributions are biased. To overcome this issue, I compute the average value before and after adoption for both risk-taking indicators. Then, I keep only one observation per bank and per period -i.e. one observation before and one observation after adoptionand I plot the distributions of these average values in the figures 7 and 8 in appendix.

The previous conclusions are strengthened by this additional restriction. We continue to observe a shift toward the left of the distribution after deposit insurance adoption denoting an increase in risk-taking. In the figures 9 and 10 in appendix, I present the distribution of the difference in the average values of risk before and after adoption for each banks. Distributions with a large mass of negative values would be evidence in favor of the risk-shifting effect of deposit insurance adoption. It would indicate that a large fraction of banks have lower average Capital-to-Assets ratio or lower average distance-todefault after adoption. This is exactly what is found: both distributions are highly skewed toward negative values.

Finally, I report descriptive statistics for the leverage and the distance-to-default before and after deposit insurance adoption in table 3. For both measurements, we have additional evidence that deposit insurance is likely to have a negative effect on risk-taking. The average (resp. median) Capital-to-Assets ratio decreases by 10.2% (5.3%) and the average (resp. median) log of z-score decreases by 4.9% (4.2%). Thresholds corresponding to the 25th and 75th percentiles also indicates an increase in risk-taking.

In conclusion, this graphical and statistical analysis offers preliminary evidence that deposit insurance adoption is likely to induce an increase in both the leverage ratio and the probability of default. To confirm this intuition, I conduct an econometric analysis.

The identification strategy and the results are presented in the next section.

Identification Strategy

The primary goal of this paper is to assess over a large panel of banks the impact of deposit guarantees on bank leverage. The identification uses essentially a differences-in-differences methodology. Define tj as the year in which a deposit insurance has been implemented in country j and:

DIj, t =      1 if t ≧ tj 0 if t < tj
as a dummy taking the value of one after a deposit insurance was introduced in a given country and zero before. This is the main independent variable of interest. The baseline regression performed is then:

Riski,j,t = α + β • DIj,t + γ • Xi,j,t + θt + ui + ǫi,j,t (4.1)
where i denotes the bank, t the year and j the country. Risk i,j,t stands for the different risk-taking proxies considered. X i,t,j is the vector of control variables, θ t are year fixedeffects and u i are bank fixed-effects. Standard errors are clustered at the country-level.

The main coefficient of interest is β, the effect of introducing a deposit insurance scheme.

The identification of β relies on the comparison of the changes in risk-taking over time between banks in countries that adopted a deposit insurance scheme at a given date and banks in countries that did not. The staggered passage of the deposit insurance means that the control group is not restricted to countries that never adopt a deposit insurance scheme. In fact, the identification implicitly takes as the control group all banks operating in countries that do not adopt a deposit insurance scheme at time t, even if they have already adopted a deposit insurance or will adopt one later on.

Compared with previous work, this paper focuses on the within-bank effect of deposit insurance adoption by using bank fixed-effects u i . It is a way to control for time-invariant unobserved heterogeneity at the bank level. 13 Recent work have also stressed that fixedeffects explain most of the variation in leverage, for both firms and banks [START_REF] Lemmon | Back to the beginning: Persistence and the cross-section of corporate capital structure[END_REF] and [START_REF] Gropp | The determinants of bank capital structure[END_REF]). But, an important benefit from using bank fixedeffects is more directly related to the issue investigated. Deposit insurance adoption can have two distinct effects on bank risk-taking and it is important to consider them separately. First, it can increase risk-taking for existing banks: this is the intensive margin effect. Second, it can promote the entry of riskier banks: this is the extensive margin effect. Using bank fixed-effects permits to focus on the intensive margin effect. Naturally, distinguishing these two effects is important. But this approach is even essential given the increasing coverage of BankScope. From 1985 to 2000, the number of banks reported in a given country and the number of countries covered tends to increase continuously in the sample. It is thus very difficult to assess the extensive margin effect because the results could be largely driven by these artificial changes in coverage. To my knowledge, this paper is one of the first to consider seriously this issue. 14 Other time-varying factors could also affect the choice of banks leverage and if these factors vary precisely at the time of deposit insurance adoption, it could produce spurious correlations. To overcome this issue, it is possible to include time-varying control variables.

However, any covariates included as control variable must be unaffected by the treatment [START_REF] Roberts | Endogeneity in empirical corporate finance[END_REF]). This condition severely limits the possible covariates to include. For instance, size is generally considered as an important determinant of leverage structure but it is also very likely to be affected by deposit insurance adoption. Hence, I only consider a restricted set of control variables and I report all the results both with and without these covariates. 15 The first control is the real GDP annual growth rate as the business cycles are one of the major source of fluctuation in the riskiness of bank's balance sheet. Then the inflation rate is included in the set of controls as a traditional determinant of bank risk-taking. In order to control for the degree of financial development, the logarithm of GDP per capita is included in the vector of controls. They are all obtained from the World Bank statistics over the period 1985-2011. Moreover, an important debate exists about the impact of concentration on bank risk-taking. 16 In any case, market structures like concentration are largely considered as an important determinant of bank risk-taking. Using market shares on the deposits market, an HHI index is constructed for each country measuring the concentration on the deposit market. 17 Finally, the inclusion of year fixed-effects in the regressions allow to control for aggregate fluctuations. In the next section, I will present and discuss the results. 14 The recent paper by [START_REF] Kalemli-Ozcan | Leverage across firms, banks, and countries[END_REF] also shed light on this important issue.

15 Regressions using a larger set of covariates, notably bank level controls, can be found in the appendix. 16 See [START_REF] Boyd | The theory of bank risk taking and competition revisited[END_REF] for an overview 17 Recall that

HHIt = n i=0 (M arketSharei, t) 2
where n is the total number of banks on a specific market. A higher HHI index denotes a more concentrated deposits market. Correlation between HHI on the loans market and HHI on the deposits market is 0.95. It doesn't make any differences to use the one or the other.

Results

Baseline specification

In this section, I present and discuss the results from the baseline specification (4.1). A negative and significant value for β means that on average banks tend to be more leveraged after the introduction of a deposit insurance scheme than before. Results are reported in table 4. The first two columns report regressions with only year fixed-effects and bank-fixed effects. Each regression is estimated with robust standard errors clustered at the countrylevel to correct for within-country serial correlation [START_REF] Petersen | Estimating standard errors in finance panel data sets: Comparing approaches[END_REF]). Nonetheless, correct estimation of standard errors is challenging in a difference-in-difference framework. This is why I also implement the method proposed in [START_REF] Bertrand | How much should we trust differences-in-differences estimates?[END_REF] to address serial correlation issues in the robustness checks section.

The first two columns of table 4 show the basic impact of deposit insurance adoption on bank risk-taking without any control variables. We observe an important and a very significant negative effect, meaning that banks tend to increase their leverage after the implementation of deposit insurance scheme. I then add the set of controls variables. The coefficients on deposit insurance adoption keep the same sign and magnitude, as well as they remain highly significant for both risk proxies.

The estimated coefficient indicates that the leverage of banks tends to decrease by 2.3 percentage points (resp. 1.8 ppts) or 15.2% (resp 12%) after deposit insurance adoption. 18 In the case of the log of z-score, adopting a deposit insurance scheme tends to reduce the z-score by 14.79%. 19 It is also possible to interpret these results in a different way.

Irrespective of the percentage changes in the level of risk-taking reflected by these two proxies, it is worth to know how these changes in risk-taking following deposit insurance adoption compare to "natural" fluctuations of risk-taking. Precisely, I want to relate the magnitude of these effects to the sample within standard deviations of the two risk-taking measurements. In table 3, I report the overall, between and within standard deviation of both the log of z-score and the Capital-to-Assets ratio computed on the sample of banks on 18 It is the magnitude of the effect evaluated at the mean of the sample on which the deposit insurance dummy is estimated: -0.0228/0.15. The magnitude of the effect becomes 20.73% when evaluated at the mean of the full sample: -0.0228/0.11.

19 This value is computed as follow 100

• [exp(c * -1 2 • v * (c * )) -1]
, where c * is the estimated coefficient and v * (c * ) is the estimated variance of c * as suggested by [START_REF] Kennedy | Estimation with correctly interpreted dummy variables in semilogarithmic equations [the interpretation of dummy variables in semilogarithmic equations[END_REF].

which the effect of deposit insurance adoption is estimated. It appears that implementation of a deposit insurance scheme produces an increase in risk-taking corresponding to 32.85% of one sample within standard deviation for the Capital-to-Assets ratio and 39.87% of one sample within standard deviation for the log of z-score. 20These first results suggest that there exists a negative and significant correlation between the implementation of a deposit insurance scheme and the bank financial soundness.

This relation appears to be mainly driven by a rise in the leverage ratio: we capture an increase of 15% in the leverage ratio of banks after deposit insurance adoption.

This finding is consistent with the literature [START_REF] Keeley | Deposit insurance, risk, and market power in banking[END_REF], [START_REF] Berger | The role of capital in financial institutions[END_REF], [START_REF] Saunders | The impact of consolidation and safety-net support on canadian, us and uk banks: 1893-1992[END_REF], or [START_REF] Acharya | Robust capital regulation[END_REF]). Bank creditors are particularly concerned by monitoring the amount of equity held by the bank. First it is the capital cushion intended to absorb unexpected losses: the lower the Capital-to-Assets ratio, the more fragile the bank in case of unexpected shocks. Second, a higher level of Capitalto-Assets ratio signals that banks has more skin in the game and thus less incentives to make risky investments. In presence of deposit insurance, depositors become much less concerned by the bank Capital-to-Assets ratio because of the guarantees offered (as shown by [START_REF] Demirguc-Kunt | Market discipline and deposit insurance[END_REF] or [START_REF] Nier | Market discipline, disclosure and moral hazard in banking[END_REF]). After deposit insurance adoption, banks being no longer charged for excessive risk-taking, shareholders and management have strong incentives to reduce their Capital-to-Assets ratio to boost the return on equity for a given return on assets. The adverse impact of deposit insurance on bank leverage is actually largely recognised: "Indeed, the entire system of capital regulation is the result of the recognition that incentives to take excessive risk arise as a result of demand deposit and other elements of the safety net of banks." [START_REF] Admati | Fallacies, irrelevant facts, and myths in the discussion of capital regulation: Why bank equity is not expensive[END_REF]).

Compared with the previous work of [START_REF] Gropp | Deposit insurance, moral hazard, and market monitoring[END_REF], this paper isolates the intensive margin effect, that is to say the change in leverage for existing banks. It is crucial because the identification is otherwise contaminated by the artificial changes in BankScope coverage. Moreover, their identification of the effect of deposit insurance relies on time-series variation in only 4 European countries while I am using variation in deposit insurance scheme in almost 54 countries. This finding also challenges the results established by [START_REF] Gropp | The determinants of bank capital structure[END_REF]. The authors find that bank leverage is insensitive to deposit insurance coverage. Two reasons may explain these contrasting results. First, they focus on large banks of developed countries while I am working mainly with banks in developing countries irrespective of their size. Second, their identification strategy is based on variations in deposit insurance coverage across countries. It is possible that the introduction of an explicit deposit insurance scheme sends an important signal to depositors and banks whereas variability in deposit insurance coverage is likely to be less noticeable explaining why these variations generate less differences in banks behavior.

Overall these results exhibit some evidence that adopting a deposit insurance scheme fosters risk-taking by reducing the capital buffer of banks by 15%. Before turning to the most important part of this paper investigating the heterogeneous effects of deposit insurance adoption, I need to address three potential identification issues.

Tests of Identification Strategy

The results established in the previous section seem to indicate that deposit insurance adoption makes banks much more leveraged. Nonetheless, the identification strategy summarised in equation (4.1) may suffer from three problems. The first one concerns the common trends assumption: to conclude that the changes in leverage observed are caused by deposit insurance adoption, we have to assume that in the absence of deposit insurance adoption, the leverage ratio and the distance-to-default would have evolved similarly between treatment and control groups.

Second, the identification strategy may face a reverse causality issue. Indeed, we could suspect that bank leverage starts to increase before deposit insurance adoption. This increase in bank leverage can then raise the demand for insurance by depositors and force the government to adopt a guarantee scheme. In this case, the coefficient estimated by equation (4.1) captures the effect of an increase in bank risk-taking on the probability to adopt a deposit insurance rather than vice versa.

Third and most importantly, identification may suffer from a simultaneity bias. It is possible to capture the effect of another change in banking regulation occurring at the same time as the deposit insurance implementation. Similarly, it is hard to separate out the effects of country-specific shocks contemporaneous with the deposit insurance adoption from the effects of the deposit insurance adoption itself. In particular, countries may adopt a deposit insurance scheme precisely at the time where they suffer from a severe financial crisis.

Common Trends Assumption It is quite difficult to test the common trends assumption, especially in a context in which the implementations of the law are staggered over time. One immediate solution relies on the inclusion of country-year interactions terms.

While completely nonrestrictive, such a specification is not possible in the current framework. 21 However, the inclusion of country-specific trends in the baseline regressions is an alternative solution allowing the outcome of treatment and control groups to follow different trends in a limited but potentially revealing way [START_REF] Angrist | Mostly Harmless Econometrics: An Empiricist's Companion[END_REF]).

Since I do not have any prior about the shape of these potential country-specific trends, quadratic trends are also included in the regressions allowing for a more flexible specification. In this case, the effect of deposit insurance is identified from a break in the pattern of bank's risk-taking that is distinguishable from a smooth quadratic. The regressions estimated are the following:

Riski,j,t = αi + β • DIj,t + γ • Xi,j,t + θt + ui + j τ lin. • T rendj,t + ǫi,j,t (5.1) Riski,j,t = αi + β • DIj,t + γ • Xi,j,t + θt + ui + j τ lin. • T rendj,t + j τ quad. • T rend 2 j,t + ǫi,j,t (5.2)
Results are reported in table 6. All the previous conclusions remain largely unchanged even after taking into account country-specific trends. The deposit insurance variable keeps the expected signs in both regressions and the significance remains very high. Controlling for country-specific trends tends to lower the economic significance of the effet, which nonetheless remains substantial : adopting a deposit insurance reduces the bank 21 Because it is no longer possible to estimate the deposit insurance dummy which is country-year specific.

capital buffer by 12.6% on average. An alternative way to check the sensitivity of the results to the common trends assumption consists in using a different control group. Finding different results should be a source of concerns. I run such a sensitivity test in the appendix C and the conclusions are unaltered. It is thus unlikely that treatment and control groups experience very different evolutions of leverage ratio in such a way that it could contaminate the identification of the effect of deposit insurance adoption.

Reverse Causality Another potential issue is that we cannot exclude a priori that a deposit insurance scheme is implemented in a country after bank risk-taking starts to increase significantly. A change in bank leverage observed by depositors can increase pressures on government to adopt a system of deposit guarantees. Alternatively, growing international competition may force banks to take more risk and then to lobby for implementing a deposit insurance scheme preserving them from paying excessive deposit rates. In both case, the identification strategy may suffer from a reverse causality issue. To rule out this possibility, I implement a falsification test. 22For this purpose, the main deposit insurance dummy variable is replaced by a set of dummy variables taking the value of one exactly τ years after or τ years before the true adoption (leads and lags in a sense):

   DI Bef ore j, tj -τ = 1 if t = tj -τ DI Af ter j, tj +τ = 1 if t = tj + τ
where tj denotes the year of adoption in country j. Then I run the following regression:

Riski,j,t = αi + β • DIj,t + 6 τ =1 λτ • DI Bef ore j, tj -τ + 7 τ =1 λτ • DI Af ter j, tj +τ + γ • Xi,j,t + θt + ui + ǫi,j,t (5.3)
With this specification, it is possible to assess whether an increase in risk-taking is observed in the years preceding the deposit insurance adoption. In this case, some dummy variables for the years before the true adoption should have a negative and statistically significant coefficient. Finding such an effect would be symptomatic of potential reverse causality.

The results can be found in table 7. The first two columns shows the results from a specification in which the reference year is set to be the year preceding adoption. 23 We observe that the dummy variables for the years preceding adoption are (i) almost never significantly different from zero -at least in the case of Capital-to-Assets ratio-and (ii)

systematically positive indicating a lower risk-taking before adoption. In contrast, all the dummy variables associated with the year following adoption present a negative and highly significant effect. These results indicates that both the leverage ratio and the distanceto-default have regular patterns before adoption: we cannot find any significant break in bank risk-taking before adoption, i.e. the leverage ratio or the distance-to-default start to decrease significantly only after the adoption. 24 The next two columns investigates a slightly different specification. Now, I include a dummy for all the periods around the adoption year. 25 This specification is more in line with those from [START_REF] Autor | Outsourcing at will: The contribution of unjust dismissal doctrine to the growth of employment outsourcing[END_REF] or [START_REF] Bertrand | Enjoying the quiet life? corporate governance and managerial preferences[END_REF]. Results turn out to be even more convincing and the conclusions remain the same: there is no evidence that risk-taking starts to increase significantly prior to adoption. The graphical representation of this specification can be found in figures 11 and 12 in appendix. 24 A source of concerns could be the downward trend that we can observed by looking at figures 7 and 8. In both cases, the risk-taking tends to slightly increase with time. However, we clearly identify a significant break in this downward trend exactly at the time of adoption: bank risk-taking increases much more than what we could have otherwise expected according to this long term trend. 25 Note that the reference year is no longer explicitly defined.

Simultaneity

values between 0 and 3 in which higher values denote more regulated banking sectors.26

Based on these two indicators, I run the following regressions aiming to control for simultaneous changes in regulation and banking crises:

Riski,j,t = α + β • DIj,t + ω • Crisisj, t + γ • Xi,j,t + θt + ui + ǫi,j,t (5.4) Riski,j,t = α + β • DIj,t + ω • Ref ormj, t + γ • Xi,j,t + θt + ui + ǫi,j,t (5.5)
We expect a positive coefficient for the financial reform indicators and a negative coefficient for the banking crisis dummy. In the case in which the effect of deposit insurance captures mainly the effect of simultaneous changes in regulation and banking crises, we should observe a large reduction in the magnitude (and possibly in the significance) of the coefficient for the deposit insurance dummy. Results of theses regressions are presented in table 8. The first two columns replicates the baseline results. The third and the fourth columns present results including the banking crisis dummy while the fifth and the sixth columns show the results after adding the banking supervision index. In both case, the coefficient for each indicator is insignificant. Above all, their economic significance is very small: the magnitude of the coefficients associated to banking crisis and banking supervision are five to ten times smaller than the coefficient of deposit insurance adoption.

In columns 7 and 8, the banking supervision index is replaced by the overall financial reforms index. 27 Results indicate that financial liberalization seems to be positively related to bank risk-taking but the inclusion of this indicator in the set of regressors continue to be without any effect on the estimated impact of deposit insurance adoption. Finally, in columns 9 and 10 I present results of regressions using both indicators simultaneously.

The most important message from these regressions is that the economic and statistical significance of deposit insurance adoption is only marginally affected by inclusion of these indicators: the maximum diminution corresponds to 3% of the magnitude of the effect.

Finally, note that bias related to simultaneous changes in banking regulation, if present, is likely to be a downward bias. These last twenty years, the regulatory framework of banking activities has been more directed toward higher capital ratio than the opposite as evidenced by the successive implementation of Basel I and Basel II. 28 It is even possible to think that such simultaneous regulatory changes could be designed to mitigate the perverse effect of deposit insurance adoption. Hence, regulatory changes implemented at the same time as deposit insurance are likely to induce an increase in the Capital-to-Assets ratio going against the expected effect of deposit insurance adoption: we cannot exclude that we are under-estimating the true effect.

To summarise, the identification strategy does not seem to suffer from simultaneity:

even after taking into account banking crisis and changes in banking regulations, the effect of deposit insurance remains mostly unchanged. After having considered carefully the potential biases that could affect the baseline results, we can turn to the most important contribution of this paper.

An Analysis of the Heterogeneity of Banks' Response: the Competitive Effects of Deposits Insurance Adoption

The results presented in the previous sections demonstrate that deposit insurance adoption adversely impacts banks' capital buffer. The theory predicts that deposit insurance should relax the market discipline from depositors. Accordingly, banks have strong incentives to adopt more risky behaviors because creditors no longer price efficiently these risky strategies. In particular, we expect that banks would operate with much lower Capitalto-Assets ratio in presence of deposit insurance. Consistent with these predictions, I find that deposit insurance adoption reduces the capital buffer of banks by 15%.

However the effect identified previously is an average effect over the whole sample of banks and it is quite plausible that adopting a deposit insurance should have heterogeneous effects on banks along various dimensions. For instance, number of authors suggest that deposit insurance should benefit mainly to small banks [START_REF] Calomiris | The origins of federal deposit insurance. In The Regulated Economy: A Historical Approach to Political Economy[END_REF], Demirguc-Kunt and [START_REF] Demirguc-Kunt | Deposit insurance around the globe: Where does it work?[END_REF] and [START_REF] Demirgüç-Kunt | Deposit Insurance around the World: Issues of Design and Implementation[END_REF] for instance). From the regulators perspective, it is very important to improve the knowledge about the heterogeneity in banks' response and to identify which banks react the most to deposit insurance 28 At least in the countries on which the effect of deposit insurance adoption is estimated, that is to say mainly developing countries. This is also what we observed in the figures 1 and 2 adoption.

The present section investigates these likely heterogeneous responses of banks. The main conclusion is that deposit insurance has important competitive effects on the banking industry: adoption of deposit insurance scheme seems to benefit mostly to small, non systemic and well-capitalised banks. First, I find that banks' response to the introduction of deposit guarantees is stronger for banks with small initial market shares. Similarly, the response of banks to deposit insurance adoption is negatively related to the initial systemic importance of banks. I thus do not find any effect for the largest and the most systemic banks. In both cases, these findings can be explained by the fact that large and systemic banks benefited from implicit state guarantees before deposit insurance adoption, i.e. the Too Big to Fail hypothesis. As a consequence, they already escaped market discipline and they do not take advantage from adoption of an explicit deposit insurance scheme. Second, I show that bank responsiveness is negatively related to their initial leverage leading to a convergence process among banks: those which are initially highly leveraged appear to be insensitive to deposit insurance adoption. This second features of deposit insurance adoption could be related to capital requirement: banks with a high initial leverage have much less room to increase it before capital constraint binds.

These results point out that deposit insurance adoption does not generate a build-up of fragility among a small set of banks, be they initially highly leveraged, relatively large or too-systemic-to-fail. However, they also indicate that the whole domestic banking industry tends to be less adequately capitalised after the implementation of deposit insurance.

The Mitigating Effects of Relative Size and Systemic Importance

There is no reason to consider that banks should react uniformly to the introduction of a deposit insurance scheme. The responses of banks depends entirely on the implicit subsidy they receive from the deposit insurance which is largely determined by the intensity of the market discipline existing before adoption: the stronger the market discipline ex ante, the larger the subsidy ex post. For instance, a bank that would not be subject to market discipline ex ante should not react at all to the deposit insurance adoption. If we thus assume that banks that are perceived as systemically important already benefit from implicit state guarantees, we should observe a negative relation between the responses of banks and their systemic importance. In this paragraph, I present evidence supporting this hypothesis.

For this purpose, I consider two indicators of systemic importance: the bank market share in terms of assets, i.e. the domestic relative size, and the ratio of bank assets to GDP.

However, these indicators of systemic importance are likely to be impacted by deposit insurance adoption. To address this endogeneity issue [START_REF] Roberts | Endogeneity in empirical corporate finance[END_REF]), I use the pre-treatment value of these two measures. Say differently, I utilise indicators computed over the period preceding adoption. Formally, I define the two indicators of ex ante systemic importance as an average value over the period before adoption (excluding the year of adoption) 29 :

M arketShare i, j = t< tj -1 M arketShare i, j, t ( tj -1 -t 0 ) AssetT oGDP i, j = t< tj -1 AssetT oGDP i, j, t ( tj -1 -t 0 )
Note that the implementation of this methodology drop a large number of observations. 30 I then extend the baseline specification by including interaction terms between these indicators and the deposit insurance adoption dummy:

Riski,j,t = α + β • DIj,t + ω • DIj,t • M arketSharei, j + µ • M arketSharei, j (5.6) +θt • M arketSharei, j + γ • Xi,j,t + θt + ui + ǫi,j,t Riski,j,t = α + β • DIj,t + ω • DIj,t • AssetT oGDPi, j + µ • AssetT oGDPi, j (5.7) +θt • AssetT oGDPi, j + γ • Xi,j,t + θt + ui + ǫi,j,t
where u i and θ t are bank and year fixed-effects. The identification of ω relies of on the comparison within the same country of the response of banks with different systemic 29 The results presented below are robust to alternative definitions of the ex ante indicators used. In unreported regressions, I confirm the results using indicators of systemic importance computed over the period that precedes adoption including the year of adoption, or excluding the year of adoption and the year immediately before. I have also used indicators computed as the last value one or two periods before adoption.

30 It is so because all the countries having adopted a deposit insurance scheme before the 90's have no observations for these years. But most of these observations are not used to identify the effect of deposit insurance adoption in the baseline specification importance to deposit insurance adoption. We expect to find a negative coefficient for β and a positive coefficient for ω: as banks become more and more systemically important, the intensity of their response should diminish.

The set of interaction terms between time fixed-effects and ex ante indicators (θt • M arketSharei, j and θt • AssetT oGDPi, j ) is crucial for the identification. It aims to control for the fact that banks with different ex ante systemic importance may also have different evolutions of risk-taking over time within the same country (independently of the deposit insurance adoption). I do not want to confound the heterogeneity in the risk-shifting effect of deposit insurance depending on the ex ante systemic importance with "natural" differences in the evolution of risk-taking over time for banks having different ex ante systemic importance. The results are presented in tables 9 and 10. In the first two columns, I replicate the baseline specification to confirm that the previous findings remain valid after the loss of observations caused by the construction of systemic indicators. The next two columns present the results of specification (5.6) and (5.7) without any covariates while the last two columns include them.

The first two columns confirm that introducing a deposit insurance scheme increases the leverage ratio of banks and then translates into higher risk of insolvency. Note that the loss of observations discussed just above induces only a marginal change in the magnitude of β in both regressions confirming that most of these lost observations are without any effects on the estimation of β. When considering the results of specification (5.6) and (5.7), it appears that deposit insurance adoption continues to have a negative and significant effect on the banks' capital buffer, i.e. β < 0 . However, the coefficient associated with the interaction terms DI j,t • M arketShare i, j and DI j,t • AssetT oGDP i, j is positive and significantly different from zero: the response of banks to the implementation of deposit insurance is strongly mitigated by systemic importance of banks.

For instance, in the case of the Capital-to-Assets ratio, the effect of deposit insurance diminishes with systemic importance and becomes indistinguishable from zero for banks having an ex ante domestic market share larger than 20%, which corresponds to banks within the last decile of the distribution. Similarly, the effect of deposit insurance becomes insignificantly different from zero when banks have an ex ante ratio of assets over GDP larger than 31%: those banks belong to the top 5% of the distribution. All these results remain valid if I use alternative indicators like the market share in terms of deposits or liabilities and the ratio of liabilities over GDP.

There is an important conclusion that can be drawn from these findings. First, the limited set of banks that can be considered as systemically important, those who are commonly referred to as Too Big to Fail, seems to be insensitive to adoption of deposit insurance. One plausible explanation is that these very large banks were perceived by depositors as benefiting from implicit state guarantees before deposit insurance adoption.

Alternatively, we could think about systemic importance as a source of market power that makes the banks less sensitive to market discipline by depositors. In both cases, it strongly reduces the implicit subsidy they get from deposit insurance. Conversely, small banks are intensively monitored by depositors in absence of safety net and they are immediately punished for any diminution of their capital buffer. As a result, they take the greatest advantage from the relaxation of market discipline induced by deposit insurance adoption.

These results are consistent with previous findings of [START_REF] Ioannidou | Deposit insurance and bank risktaking: Evidence from internal loan ratings[END_REF] showing that differences between large and small banks in terms of risky loans origination are reduced by deposit insurance. They are also in line with the paper of [START_REF] Gropp | Deposit insurance, moral hazard, and market monitoring[END_REF], but the strategy implemented in this paper has two advantages compared with their study. First, it controls for the possible endogenous reaction of systemic indicators to deposit insurance adoption as well as for the bias introduced by changes in coverage of BankScope. Second it does not use ad hoc threshold to define systemic banks. However, the present results could be perceived as inconsistent with those of Demirguc-Kunt and Huizinga (2012) who establish that systemically large banks are subject to greater market discipline because they appear to be Too Big to Save. 31 But, both findings can be reconciled if we consider that deposit insurance is not a credible protection for those banks that are Too Big to Save: they are thus not impacted by deposit insurance adoption as shown in this paper.

Finally, the evidence presented in this section suggest that, by allowing small and non systemic banks to reduce their capital buffer, deposit insurance adoption is likely to promote competition on the banking market by reducing the comparative advantage of large and systemic financial institutions. 32 Interestingly, this effect has often been stated as an important motivation for adopting a system of deposit insurance [START_REF] Garcia | Deposit insurance: Actual and good practices[END_REF] and [START_REF] Demirgüç-Kunt | Deposit Insurance around the World: Issues of Design and Implementation[END_REF]).

Deposit Insurance Adoption and Leverage: a Convergence Process Across

Banks It is equally important to examine how the risk-shifting effect of deposit insurance is distributed across banks with heterogeneous initial capital buffer. From an aggregate perspective, understanding which banks react the most to deposit insurance adoption according to their initial leverage is essential. Indeed, financial stability is impacted differently depending on whether a small group of highly leveraged banks tends to become even more under-capitalised or whether safer banks start to catch up more risky ones. In the first case, we face a build-up of fragility in a small segment of the banking market. In the second case, the risk-shifting effect of deposit insurance adoption is spread across the entire banking system.

To investigate this question, I use the same methodology as before. I start by computing indicators of ex ante leverage by taking the average value before adoption of two proxies for leverage: the Capital-to-Assets ratio and the Liabilities-to-Equity ratio. Then, I interact these indicators with the deposit insurance dummy. Formally, I run the following regressions:

Riski,j,t = α + β • DIj,t + ω • DIj,t • CARi, j + µ • CARi, j (5.8) +θt • CARi, j + γ • Xi,j,t + θt + ui + ǫi,j,t Riski,j,t = α + β • DIj,t + ω • DIj,t • LiabT oEquityi, j + µ • LiabT oEquityi, j (5.9) +θt • LiabT oEquityi, j + γ • Xi,j,t + θt + ui + ǫi,j,t
The results can be found in the table 11 and 12 and they are unambiguous. We observe a negative impact of ex ante leverage ratio on the banks' response to deposit insurance adoption: the least leveraged banks before the reform are those reacting the most 32 Cordella and Yeyati (2002) or [START_REF] Matutes | Competition for deposits, fragility, and insurance[END_REF] examine theoretically the relationships between deposit insurance and competition intensively to deposit insurance adoption. For instance, the coefficient in the third column of table 12 indicates that deposit insurance adoption has no effect on the Capital-to-Assets ratio when banks have an initial Capital-to-Assets ratio (resp. Liabilities-to-Equity ratio) below 5% (resp. above 18): the 10% most leveraged banks are thus insensitive to introduction of deposit guarantees.

Following the implementation of deposit insurance, deposits financing becomes relatively cheaper compare to capital. Indeed, deposit insurance schemes induce important deviations from the Modigliani-Miller world [START_REF] Admati | Fallacies, irrelevant facts, and myths in the discussion of capital regulation: Why bank equity is not expensive[END_REF]). Hence, in absence of actuarially fair premia (in particular risk-based premia), highly capitalised banks should substitute deposits to equity. Otherwise, they would implicitly subsidise most leveraged banks, because they would pay the same premium without taking full advantage from the cheaper source of funding provided by insured deposits. This explains why we observe a larger response from the most capitalised banks. In contrast, the absence of reaction from the least capitalised banks could also be explained by regulatory capital constraints: they have less room to reduce their Capital-to-Assets ratio by substituting deposits to equity financing. 33 Accordingly, deposit insurance adoption tends to make the distribution of leverage ratios across banks much more concentrated around its mean. The between standard deviation of the Capital-to-Assets ratio decreases from 0.12 before adoption to 0.10 after adoption. This pattern can also be observed by looking at figure 3.

All the findings established in this section are robust to the inclusion of countryspecific trends, both linear and quadratic. What is more, the results are also robust to the inclusion of banking crisis index and banking supervision index as well as interaction terms of these two indicators with the various dimensions used to detect the heterogeneity. In other words, the heterogeneous responses of banks to deposit insurance adoption cannot be confused with the heterogeneous responses of banks to banking crisis and changes in banking supervision. All these robustness checks are presented in tables 15, 16, 17 and 18 in appendix.

In this section, I bring evidence that small, non systemic and well-capitalised banks react the most to deposit insurance adoption. There are two important lessons from this analysis. First, we observe a convergence across banks in terms of leverage ratio after the implementation of deposit insurance: initially well-capitalised banks increase much more their leverage after deposit insurance adoption than initially highly leveraged banks.

Second, deposit insurance adoption has some important competitive effects by removing the comparative advantage of large and systemic banks and by improving competition on the banking market. But, if not supplemented with additional regulatory constraints, adopting a deposit insurance also makes the whole banking system less well-capitalised and thus less resilient to large and widespread shocks.

Robustness checks

In this section, I present additional robustness checks. First, I use the first-difference estimators to confirm the validity of the results under weaker assumptions. Then, I deal with a crucial issue in a quasi difference-in-difference framework: the so-called serial correlation issue. For the sake of brevity, the other robustness checks are presented in the appendix C.

First-Difference estimation

The baseline specification (4.1) estimates the impact of deposit insurance adoption in a fixed-effects framework. However, the consistency of these estimates relies on strong assumptions. In this paragraph, I present the results of regressions using the first-difference estimators. Under the assumption of homoskedasticity and no serial correlation in the error term, the fixed-effects estimators is more efficient than the first-difference estimators. In contrast, consistency of the first-difference estimator is obtained under a weaker assumption: the first-difference of the idiosyncratic error must be serially uncorrelated, i.e error terms must follow a random walk [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]). Above all, the first-difference estimation helps to know whether the deposit insurance adoption has an immediate impact on leverage. Precisely, I regress the following specification:

∆Risk i,j,t = β • ∆DI j,t + γ • ∆X i,j,t + ∆θt + ∆ǫ i,j,t
where ∆ is the difference operator. The results are presented in table 13. Additionally, I also perform an estimation allowing for bank-specific trends.34 This model writes:

Risk i,j,t = α + β • DI j,t + γ • X i,j,t + θt + u i + φ • T rendt • u i + ǫ i,j,t
If we first-difference this model, we get:

∆Risk i,j,t = β • ∆DI j,t + γ • ∆X i,j,t + ∆θt + u i + ∆ǫ i,j,t
Observe that we have now bank fixed-effects directly in the first-differenced equation.

Hence, we can estimate this equation by using the fixed-effect estimator or by differencing again. The results being roughly the same I only present those from the fixed-effects regressions. These results are also reported in table 13.

The coefficient in table 13 largely confirms the previous results. First, the two risk indicators give additional evidence that adoption of deposit insurance favors high leverage ratios even under weaker statistical assumptions. Second, while the fixed-effects estimator assesses the long term effect of deposit insurance, the first-difference estimator captures the immediate jump in risk-taking. Here, we see that providing guarantees on deposits has an immediate effect on the bank capital buffer. This short term reaction of banks to deposit insurance adoption is lower in magnitude than their long term response. Third, allowing for bank-specific trends in leverage and risk of insolvency gives almost the same results as before. We continue to observe an effect that is statistically and economically significant.

Serial correlation

In their influential paper, [START_REF] Bertrand | How much should we trust differences-in-differences estimates?[END_REF] argue that estimations based on the difference-in-difference method are subject to a possibly severe serial correlation problems. To overcome this issue, they propose a range of solutions. The present paper implements the solution that proposes to ignore time-series information when computing standard errors. First, the risk-taking measurements are regressed on bank and year fixed-effects and possibly, on all the covariates previously used except the deposit insurance adoption dummy. The residuals of the treated banks only35 are then divided into two groups: residuals from years before adoption, and those from years after adoption. Finally, the effect of adoption is estimated by OLS: the residuals are regressed on the deposit insurance dummy in a two periods model. The results are shown in table 14.

We are mostly interested by the statistical significance of the coefficients and it appears that all the coefficients presented in this table are highly significant. In conclusion, the potential serial correlation issue threatening the difference-in-difference estimates of policy change does not appear to be a crucial problem in the present paper.

Further robustness checks

Conclusion

This paper empirically investigates the causal relation existing between adoption of deposit insurance and bank risk-taking and it underlines the prominent role of changes in leverage in this process. This is clearly a topical issue as shown by the recent events in Cyprus in 2013. This moral hazard issue related to state guarantees offered to banks has been largely discussed when governments and central banks have decided to bailout the banking industry in the midst of the recent financial crisis. Focusing on the effect of deposit insurance adoption on bank capital buffer, this paper aims to provide a contribution to this very challenging issue.

This study shows that we observe a significant increase in bank risk of insolvency after introduction of deposit insurance. The magnitude of this effect is roughly 30% to 45% of one sample standard deviation of the various risk indicators used. Above all, this paper argues that the downward shift in bank distance-to-default is mainly caused by an increase in bank leverage: banks tend to reduce their capital buffer by almost 15% after implementation of deposit insurance. These results are consistent with both the theoretical and the empirical literature.

In order to rule out the possibility of spurious correlations due to reverse causation or simultaneity, I run various sensitivity checks. In particular, I run a falsification test showing that bank capital buffer starts to decrease significantly only after deposit insurance adoption and not before. Second, I also discuss the possibility that the adverse effect captured in this paper could be related to simultaneous changes in banking regulation or by contemporaneous banking crises. Tests provided in this paper show that it is quite unlikely.

Most importantly, I bring robust evidence that relatively large and systemic banks as well as the most highly leveraged banks tend to be unresponsive to the deposit insurance adoption. I cannot capture any significant change in the leverage ratio for the top 10% most systemic banks or most leveraged banks. The first result is consistent with the view that systemic banks are not subject to market discipline because they benefit from implicit state guarantees. Hence, they do not react to the introduction of explicit deposit insurance. As such, deposit insurance could have important competitive effects by removing the comparative advantage of large and systemic banks. The second result is interesting because it sheds light on the convergence process induced by deposit insurance adoption. To avoid to subsidise highly leveraged banks, well-capitalised banks reduce significantly more their capital buffer that banks with a lower initial capital buffer. Overall, these results offer contrasting views on deposit insurance: only the less fragile banks seem to increase their leverage after deposit insurance adoption but the whole domestic banking industry is less adequately capitalised after implementation of deposit guarantees. Similarly, deposit insurance adoption seems to promote banking competition by relaxcing the market discipline faced by small and non-systemic banks rather than by removing the implicit subsidy offered to large and systemic banks.

All the results presented in this paper tend to confirm that deposit insurance adoption has unintended consequences: it induces a strategic response from banks which increase their leverage to exploit the put option value of deposit insurance. These findings are in line with previous and recent research concerning the relaxation of market discipline caused by deposit insurance adoption [START_REF] Karas | Deposit insurance, banking crises, and market discipline: Evidence from a natural experiment on deposit flows and rates[END_REF] for instance). Recently, in reaction to the financial crisis of 2008 many countries have decided to increase the amount of deposits covered by guarantee funds (USA and EU for instance). Other countries (Australia, New-Zealand) have adopted an explicit deposit insurance scheme for the first time in their history. The European Union plan to implement a unified deposit insurance system in the very next years as part of the Banking Union. The results established in this paper reaffirm the necessity to control adequately the perverse incentives that deposit insurance provides to banks with a particular focus on the capital buffer of banks. The decision to include a raw leverage ratio in the Basel III regulatory standards to complement the traditional riskweighted capital ratios can be viewed as an important step in this direction. But results presented in this paper suggest that introduction of risk-based premia, in particular premia based on the capital buffer of banks (as proposed by [START_REF] Bond | Bank capitalization, deposit insurance, and risk categorization[END_REF] and more recently by [START_REF] Acharya | Systemic risk and deposit insurance premiums[END_REF]) would help to mitigate this unintended consequence of deposit insurance.

Tables and Figures

Figure 1 & In this table, the variables Year "i" or Year "-i" are dummy variables taking the value of one exactly "i" years before or after deposit insurance adoption. The variables Years ≥ "i" or Years ≤ "-i" are dummy variable taking the value of one "i" years, "i+1" years, "i+2" years... after deposit insurance adoption or "-i" years, "-i-1" years, "-i-2" years... before deposit insurance adoption. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance. -to-Equity is an interaction term between the deposit insurance adoption dummy and indicator reflecting the leverage of each bank before adoption. It is computed as the average value of the the Liabilities-to-Equity ratio over the periods preceding adoption. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance. [START_REF] Bertrand | How much should we trust differences-in-differences estimates?[END_REF] to deal with serial correlation. First, I regress the various risk proxies on banks FE, year FE and possibly covariates, excluding the deposit insurance dummy . Then I divide the residuals of banks treated, i.e. those facing an adoption of deposit insurance in the period covered, into two groups: residuals from years before the adoptions and residuals from years after the adoptions. Finally, I regress these residuals on the deposit insurance dummy in a two-peridos panel frameworks. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

advantage of ruling out bank runs and panics in case of financial stress or lack of confidence in the banking system. When depositors are uncertain about the liquidity position of their bank, the best individual strategy is to run withdrawing their funds from bank. However, this strategy is collectively inefficient because it forces banks to stop profitable projects and to sell assets at fire-sale prices, which may destabilise the entire banking system because of contagion [START_REF] Allen | Financial contagion[END_REF] and [START_REF] Diamond | Liquidity shortages and banking crises[END_REF]). 40 These contagion phenomena can lead to a drastic reduction in the amount of loans offered to the economy for an extended period of time. Deposit insurance is a powerful tool to remove this uncertainty so that there is no longer room for panics and inefficient bank runs [START_REF] Diamond | Bank runs, deposit insurance, and liquidity[END_REF]). This is definitely the main benefit from introducing an explicit deposit insurance scheme as shown by the recent financial crisis. Just after the Lehman fall, there were large doubts about the health of many banks. But no banks really faced a bank run by non-institutional depositors, excepting Northern Rock. It is also very likely that some bank liquidations have been facilitated because depositors didn't run even though failure was almost certain.

However, deposit insurance schemes may have significant adverse effects. It is often argued that deposit insurance reinforces moral hazard in banking: existence of deposit insurances makes depositors less interested in monitoring bank risk-taking. In other words, deposit insurance sensibly erodes market discipline as evidenced by Demirguc- [START_REF] Demirguc-Kunt | Market discipline and deposit insurance[END_REF]. Then, shareholders and bank management can keep any excess profits without having to support the cost of excessive risk-taking on deposit rates [START_REF] Merton | An analytic derivation of the cost of deposit insurance and loan guarantees an application of modern option pricing theory[END_REF]). This relaxation of market discipline is very likely concerning the bank leverage, i.e. the bank capital buffer. In presence of guarantees, creditors are much less concerned by the capital cushion of banks. Banks can thus improve the return on equity by increasing their leverage. Overall, there are strong presumptions that introduction of a deposit insurance scheme may foster bank risk-taking if deposit insurance premia are not adequately priced. 41

40 Nonetheless, some work show that bank runs can be seen as a way to introduce some contingency in demand deposit contract. Accordingly bank runs can be efficient. See [START_REF] Allen | Optimal financial crises[END_REF]. 41 That is exactly what [START_REF] Keeley | Deposit insurance, risk, and market power in banking[END_REF] explained in the introduction of his famous paper: "It has long been recognised that a fixed rate deposit insurance system, such as the Federal Deposit Insurance Corporation's (FDIC's), or the Federal Savings and Loan Insurance Corporation's ( FSLIC's) can pose a moral hazard for excessive risk taking. The reason is that banks or thrifts can borrow at or below the risk-free rate by issuing insured deposits and then investing the proceeds in risky assets with higher expected yields."

Accordingly, most of deposit insurance schemes are designed to limit these perverse incentives. First, there often exists upper bounds on the amount covered (100 000 € in the euro zone for instance). These limits make possible to discriminate between small, fragile, and uninformed depositors from large depositors who are supposed to have higher ability to monitor banks as shown by [START_REF] Ioannidou | Deposit insurance and bank risktaking: Evidence from internal loan ratings[END_REF]. Also, many deposit insurance schemes incorporate a coinsurance mechanism. In this case, depositors will have to support a small share of the losses in case of bank failure. Another way to curb the moral hazard related to deposit insurance can be to implement risk-based premium rather than flat premium: the more risky the bank strategy, the higher the premium the bank have to pay. But it requires to very accurately assess the ex ante risk of banks, which can be difficult [START_REF] Acharya | Systemic risk and deposit insurance premiums[END_REF]). More generally, deposit insurances schemes have various features that may induce some heterogeneity in the effects of these guarantee funds on bank risk-taking. 42To summarise, the main benefit associated with deposit insurance is to rule out inefficient and very destructive bank runs. This is why deposit insurance scheme has been largely promoted across the world by various institutions like the IMF or the World Bank.

However, in providing guarantees on the liabilities of banks, it can fuel bank risk-taking and make more likely to experience bank failures. adoption has also been inspected using the deposit insurance websites. 49The main task consist in checking the exact year of introduction of deposit insurance scheme. I also collect additional information about some deposit insurance features like the existence of coinsurance mechanism (yes/no), the nature of the premia collected (flat or risk-based), or the timing of the funding (ex ante, ex post, or both). However, there are important difficulties to get consistent, reliable and time-varying information about these features. In particular the various sources used do not indicate the year of implementation of these features. For instance, imagine a country that adopted a deposit insurance scheme in 1995. In the 2008 IADI survey, it is not possible to know whether this country have coinsurance mechanism since 1995 or whether such a feature have been implemented latter.

Hence, these information can only be exploited cross-sectionally. Using these information in a time-varying framework would be at cost of strong assumptions.

In general, the previous sources provide a year of adoption corresponding to the date at which the parliament votes the law establishing the deposit insurance fund. It is very likely to observe some delay before the deposit insurance scheme becomes effective. When available, I take advantage from the month of adoption: when the date provides a month after July, I change the year of adoption by the year immediately following. At the end, I obtain a database describing the year of adoption and some features of deposit insurance schemes in 197 countries as shown in table 3.

Compared to the database of [START_REF] Demirguc-Kunt | Deposit insurance around the world : a comprehensive database[END_REF], I collect the year of adoption for 24 additional countries. For 34 countries, the date of adoption differs from the one of [START_REF] Demirguc-Kunt | Deposit insurance around the world : a comprehensive database[END_REF]. For 19 of these 34 countries, the difference is related to the delay between enactment by the parliament and effective implementation as explained before. In these cases, the date of implementation is just one year after the one previously established. It remains 13 countries for which dates of adoption differ by more than one year (Argentina, Austria, Belarus, Bosnia-herzegovina, Bulgaria, Guatemala, Honduras, Malaysia, Mexico, Montenegro, Portugal, Spain, and Ukraine). However, in most of the cases, these differences are related to longer delay between enactment and implementation.

Treatment of date

Most of the financial companies publish their account statements at the end of the year, namely in December. Nonetheless, sometimes banks use non-calendar fiscal years to report their balance sheet statement (in March for several big Japanese banks, in October for several big Canadian banks...). On the top of that, even though BankScope provides us only with annual data, for a few hundred observations you have duplicated observations for balance sheet statements that closed at several dates within a single year. So one needs to handle both the allocation issue over year t or t -1 as well as the duplicated issue of yearly financial statements published several times a year. These differences raise an important issue. It is likely that one prefer to compare data of financial statements reported in March of year t with data of financial statements reported in December of year t -1 rather than with data of financial statements reported in December of year t. The help file from [START_REF] Duprey | Bankscope dataset: getting started[END_REF] proposes a small program that handles the situation in a compact way. Here I summarize their method.

• First, I identify banks which have "natural" duplicates, i.e. banks with the same id having at least two observations within the same fiscal year. Essentially I remove an observation of the 30 th November 2012 if I have an observation for the 31 th December 2012. Precisely, I always keep the observation with:

the month closest to December and if necessary, -the day closest to the last day of the month.

• Then, if I have banks which report their financial account in March 2012, it makes more sense to consider it as end of 2011 data. So for each observations with a reporting month before June, I replace the actual year, saying t, by the previous year, saying t -1.

• Last, note that the previous step is likely to create new duplicates. So once again, the best strategy would be to keep the observation that have the least forward looking information. Consequently, between two observations reporting the same year after the previous change, I keep the one with the variable year unchanged. For instance assume that I have two observations reported in 2011 after the previous step. Then I drop the observation reporting 2011 as year, with March as month (and so 2012 as "true" year), provided I have already an observation reporting 2011 as year, with September as month (and so 2011 as "true" year).

For more details, see [START_REF] Duprey | Bankscope dataset: getting started[END_REF]. Furthermore, I exclude from the sample: banks that report less than five observations, and countries with less than fifty observations. Last, I also deal with the presence of several observations for a specific bank during a given fiscal year and the fact that some observations are reported during the fiscal year and not at the end of the fiscal year.

Restrictions imposed on the balance sheet data

The exact procedure implemented is described in the previous paragraph and additional information about BankScope can be found in [START_REF] Duprey | Bankscope dataset: getting started[END_REF]. contemporaneous value of ROAAt and a standard deviation of ROAAt computed over the full sample.

To confirm that the results are not affected by the way I choose to construct the zscore, I provide results using alternative z-score. In table 20, I present results for the log of z-score in which the Capital-to-Assets Ratio and the Return on Average Asset are computed using a moving average with two lags and two leads. I also report results for regression using a log of z-score in which the standard deviation of the Return on Average Asset is computed as the absolute deviation from the average returns [START_REF] Nicolò | Bank risk-taking and competition revisited: New theory and new evidence[END_REF] and [START_REF] Lepetit | Bank insolvency risk and time-varying z-score measures[END_REF]):

σ(ROAAi,t) =| ROAAi,t - t ROAAi,t T |
In addition, to confirm that the main conclusions are not related to the log transformation, I re-run various regressions implemented in this paper using the z-score itself. The results can be found in table 21.

The z-score replication confirms entirely the previously established results. However note that the magnitude of the coefficient is largely reduced when including countryspecific trends. Concerning the coefficients from the regressions using alternative log of z-score, they are all highly significant and their magnitude is virtually similar. The main conclusions supported by this paper are thus independent from the way the z-score is computed.

Different samples

To make sure that the results established previously are not driven by some unobserved features of the main sample, I also run regressions using three distinct sub-samples. 52

First, a sub-sample restricted to the publicly listed banks is derived from the original sample. Generally, listed banks provide more reliable balance-sheet data. They also form a relatively more homogeneous group of banks across countries what should improve quality of estimations. Finally, we could also conjecture that these banks are more easily monitored and hence the market discipline is likely to be more effective on these banks. But focusing on these banks sensibly reduces the number of observations.

52 For all these distinct samples the same restrictions as before are applied.

The second sub-sample addresses the issue of failed banks. A lot of papers has emphasized that BankScope may be subject to a survivorship bias, namely the fact that the Bureau van Dijk deletes historical information on banks that no longer exist in the latest release of this database [START_REF] Gropp | The determinants of bank capital structure[END_REF]). However, the BankScope version used in this paper seems to be free from this survivorship bias. 53 While many researchers desire to be sure that their results are not affected by this survivorship bias, I face here the opposite issue: I want to make sure that the increase in risk-taking that I capture is not driven by some very risky banks that eventually went bankrupt. The results are shown in tables 22 and 23. The adverse effect of introducing a deposit insurance system is largely confirmed when using these three samples. The increase in risktaking after adopting a depositors protection fund remains statistically and economically significant in all the sub-samples. The magnitude of the effect of deposit insurance is roughly unchanged.

Finally, note that I also replicate the baseline regression after having dropped each countries from the main sample one after one. This aims to confirm that the results are not driven by a single country. In these 123 regressions, the main finding is always confirmed. 55

Mergers and Acquisitions

Studies of banking industry must deal with an important issue: the mergers and acquisitions. Mergers and acquisitions may induce large artificial changes in balance sheets provided by BankScope. Especially, large variations in the Capital-to-Assets ratio may be 53 For instance, AmTrade International Bank of Georgia failed in 2002 and the FDIC was unable to arrange a transfer of its deposits to another financial institution (http://www.fdic.gov/bank/individual/failed/amtrade.html).

However, the balance sheets (up to 2002) of this bank are reported in BankScope and appears in the main sample. issue by removing from the sample banks having a growth of assets higher than 50%.

These results are shown in table 24. It appears that controlling explicitly for a potential bias due to M&A leaves the main findings totally unchanged.

Additional control variables

Finally, I replicate the baseline regressions (with and without linear or quadratic countryspecific trends) and I include additional control variables at the bank-level. As explained before, including these variables may induce strong endogeneity issue, notably because these variables are likely to be affected by deposit insurance adoption. Even after including the bank-specific covariates, the risk-shifting effect of deposit insurance adoption continues to be captured, as it can be seen in table 25. variables for each year before and after deposit insurance adoption. The two very last dummy variables take the value of 1 for all the periods more than 6 years before adoption and for all the periods more than 7 year after the adoption. The reference year is the year preceding adoption (year -1). -to-Equity is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the leverage of each bank before adoption. It is computed as the average value of the the Liabilities-to-Equity ratio over the periods preceding adoption. The variable BC*ex ante Liab.-to-Equity is an interaction term between the Banking Crisis dummy and the indicator reflecting the leverage of each bank before adoption. The variable BS*ex ante Liab.-to-Equity is an interaction term between the Banking Supervision index and the indicator reflecting the leverage of each bank before adoption. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Tables and Figures

  leverage and probability of default. I start by presenting the evolution of both indicators across time. It is important to figure out what is the global trend of these outcome variables because identification of the effect of deposit insurance adoption relies on timeseries comparisons.

  The most important concerns about the identification strategy relates to the possibility that deposit insurance adoption comes with other changes in financial regulation or with some country-specific aggregate shocks like banking crises (Demirgüç-Kunt et al. (2008) and Demirguc-Kunt and Detragiache (2002)). To tackle this issue, I use the data collected by IMF researchers (Abiad et al. (2010) and Valencia and Laeven (2008)) concerning banking crises and financial reforms across the world. The banking crisis database provides the starting date and the ending date of 42 crisis episodes in 37 countries. I thus construct a dummy variable Crisisj, t taking the value of one for each crisis episode. The financial reforms database covers 91 countries over 1973-2005. It provides various index of financial reforms including an index relative to prudential regulations and supervision of the banking sector which are the kind of reforms the most likely to affect the leverage ratio of banks. This indicator sums up four distinct dimensions and takes23

  In the appendix C, I run several additional robustness checks: I run the baseline specification on a sample restricted to treated banks i.e. banks having at least one observation before and after a deposit insurance adoption, I consider the potential problem posed by Mergers and Acquisitions, I replicates the baseline regression on various sub-samples, and I add bank-level control variables. Replications of the baseline specification using the z-score in level and various versions of the log of z-score can also be found in this appendix C.

2

 2 Figure 1 & 2

  log of z-score across time Sample of Banks Facing Deposit Insurance Adoption Note: These figures show the evolutions of the average and the median Capital-to-Assets ratio (top) and log of z-score (bottom) across time computed over the sample of banks that face a deposit insurance adoption.

Figure 3

 3 Figure 3 & 4

First

  , I only work with Commercial Banks, Savings Banks, Cooperative Banks, Real Estate & Mortgage Bank, Islamic Banks and Other Non Banking Credit Institution: these are the financial institutions which are concerned by such an insurance scheme. 50 Second, BankScope indicates whether the data come from consolidated (coded as C1 and C2 ) or unconsolidated accounts (coded as U1 and U2 ). When a bank reports both consolidated and unconsolidated accounts in the sample, I keep only the unconsolidated entries to avoid double counting. The rationale for this choice is based on the observation that deposit insurance is generally provided by the host country to the subsidiaries operating in this country.

  To address this issue, I restrict the sample to active banks in 2007, i.e. banks reporting information in 2007. 54 Consequently, all the banks that went bankrupt before this date are not include in the sample. The third sub-sample just considers the possibility that the results could be strongly driven by the end of the sample including the 2007-2009 financial crisis. Hence, I drop the years after 2007.

  54 I choose the year 2007 to avoid the 2007-2009 financial crisis 55 Results available upon request observed after M&A. As regards the question studied in this paper, M&A may bias the previous results if these changes in the Capital-to-Assets ratio due to M&A are correlated in some way with deposit insurance adoption. For instance, deposit insurance adoption maight induce a restructuring process in the banking sector favoring M&A. I tackle this

Figure

  Figure 5 & 6

Figure

  Figure 7 & 8

Figure

  Figure 9 & 10

Figure 11

 11 Figure 11 & 12

  05. The columns 1 to 4 present the results from regressions on the sample of banks that have an observation in 2007. It thus excludes all the banks that went bankrupt before 2007. The columns 5 to 8 present the results from regressions on the sample excluding years after 2007. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 1 :

 1 Deposit Insurance Scheme. Year of Adoption

	Country Name	Year of Adoption Nb of banks Country Name	Year of Adoption	Nb of banks
	USA	1934	9957	BRAZIL	1996	166
	NORWAY	1961	156	KOREA REP. OF	1996	25
	INDIA	1961	97	LITHUANIA	1996	13
	DOMINICAN REP.	1962	49	THAILAND	1997	29
	PHILIPPINES	1963	61	SLOVAKIA	1997	24
	GERMANY	1966	2237	MACEDONIA	1997	15
	CANADA	1967	80	CROATIA	1998	45
	FINLAND	1969	18	LATVIA	1998	33
	JAPAN	1971	699	ALGERIA	1998	15
	BELGIUM	1975	92	JAMAICA	1998	9
	NETHERLANDS	1979	66	ESTONIA	1998	7
	FRANCE	1980	492	INDONESIA	1998	105
	SPAIN	1980	298	BULGARIA	1999	33
	UNITED KINGDOM	1982	284	ECUADOR	1999	23
	TURKEY	1983	63	BELARUS	2000	18
	SWITZERLAND	1984	407	KAZAKHSTAN	2000	23
	BANGLADESH	1984	34	EL SALVADOR	2000	18
	ICELAND	1985	13	BAHAMAS	2000	24
	COLOMBIA	1985	41	VIETNAM	2000	37
	KENYA	1986	43	HONDURAS	2001	24
	TRINIDAD & TOB.	1986	10	NICARAGUA	2001	6
	DENMARK	1987	132	SLOVENIA	2001	25
	ITALY	1987	1253	CYPRUS	2001	18
	SRI LANKA	1987	12	JORDAN	2001	15
	AUSTRIA	1988	298	BOSNIA-HERZ.	2002	15
	NIGERIA	1988	53	BOLIVIA	2002	14
	IRELAND	1989	44	UKRAINE	2002	57
	LUXEMBOURG	1989	145	GUATEMALA	2002	40
	SERBIA	1989	39	ALBANIA	2002	11
	MEXICO	1990	54	MALTA	2003	8
	PERU	1991	25	URUGUAY	2003	33
	MOROCCO	1993	17	RUSSIAN FED.	2004	900
	HUNGARY	1993	39	PARAGUAY	2004	23
	BAHRAIN	1994	23	MOLDOVA REP.	2004	20
	TANZANIA	1994	28	ARMENIA	2005	14
	UGANDA	1994	11	SINGAPORE	2006	28
	POLAND	1995	72	MALAYSIA	2006	56
	OMAN	1995	9	HONG KONG	2007	64
	GREECE	1995	41	AZERBAIJAN	2007	20
	PORTUGAL	1995	55	AUSTRALIA	2008	64
	ARGENTINA	1995	109	YEMEN	2008	13
	CZECH REPUBLIC	1995	36	NEW ZEALAND	2009	22
	SWEDEN	1996	110	CAMEROON	2011	7
	SUDAN	1996	17	GABON	2011	5
	The following countries don't have explicit deposit insurance scheme: Angola, Benin, Botswana, Burkina Faso,
	Burundi, Cambodia, China People's Rep., Costa Rica, Egypt, Georgia Rep. Of, Ghana, Iran, Israel, Ivory Coast,
	Kuwait, Macau, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Pakistan, Panama, Qatar, Saudi
	Arabia, Senegal, South Africa, Syria, Tunisia and Zambia

Table 2 :

 2 Summary statistics

	Variable	N	Mean	SD	P25	Median	P75
				Main Sample
	ln(z-score)	220643	3.12	1.03	2.49	3.23	3.83
	z-score	221654	35.61	39.86	11.86	25.24	46.02
	Capital-to-Asset ratio	222099	0.11	0.09	0.07	0.09	0.12
	Return on Average Asset	221656	0.01	0.03	0.00	0.01	0.01
	Log of total asset	222099	6.39	2.69	4.55	5.69	7.48
	Net Int Rev / Avg Assets	221153	0.03	0.03	0.02	0.03	0.04
	Deposit market share	222099	0.01	0.05	0.00	0.00	0.00
	Liquid asset/asset	222099	0.17	0.16	0.06	0.11	0.21
	Cost To Income Ratio	219996	0.71	0.38	0.57	0.67	0.77
	HHI index on deposits	2473	0.2633	0.2235	0.1242	0.1964	0.2994
	GDP growth (annual %)	2473	0.0396	0.0424	0.0199	0.0401	0.0607
	Inflation (annual %)	2473	20.9726	176.7996	2.2889	4.7332	9.6657
	Log of GDP per capita	2473	10.6145	2.1880	9.3571	10.3344	11.9793
			Sample Limited to Banks Facing Deposit Insurance Adoption
	ln(z-score)	29162	2.56	1.05	1.95	2.66	3.27
	z-score	29431	20.53	23.31	6.84	14.12	26.13
	Capital-to-Asset ratio	29560	0.15	0.13	0.07	0.11	0.17
	Return on Average Asset	29432	0.01	0.04	0.00	0.01	0.02
	Log of total asset	29560	8.18	2.91	6.22	7.65	9.56
	Net Int Rev / Avg Assets	29327	0.04	0.05	0.02	0.04	0.06
	Deposit market share	29560	0.03	0.09	0.00	0.00	0.02
	Liquid asset/asset	29560	0.30	0.20	0.15	0.25	0.40
	Cost To Income Ratio	28893	0.69	0.40	0.50	0.65	0.81
	HHI index on deposits	1125	0.2579	0.2091	0.1317	0.1887	0.2945
	GDP growth (annual %)	1125	0.0377	0.0463	0.0197	0.0415	0.0620
	Inflation (annual %)	1125	31.6439	230.0894	2.3881	4.6909	9.6394
	Log of GDP per capita	1125	10.3893	2.2747	9.0096	9.9409	11.4700

Table 3 :

 3 Sample Standard Deviation of Risk-Taking This table provides additional descriptive statistics for banks facing an adoption. N is the number of observation. Mean is the mean value. Within Std. Dev. is the standard deviation of within banks, i.e. the deviation from each individual's average. Between Std. Dev. is the standard deviation across banks, i.e the standard deviation of individual's average. p25, Median and p75 are the 25th, the 50th and the 75th percentile threshold.

	Mean	Std. Dev.	Observations

Table 4 :

 4 Descriptive Statistics Before and After Adoption

	Variable	N	Mean	SD	P25	Median	P75
				Before Deposit Insurance Adoption
	ln(z-score)	3313	2.3469	1.0242	1.7718	2.4359	3.0115
	Capital-to-Asset ratio	3404	0.1462	0.1187	0.0733	0.1090	0.1806
	Leverage	3399	10.9554	18.1110	4.5370	8.1581	12.6336
				After Deposit Insurance Adoption
	ln(z-score)	6673	2.2310	1.0195	1.6173	2.3326	2.9276
	Capital-to-Asset ratio	6786	0.1313	0.1062	0.0727	0.1032	0.1500
	Leverage	6783	11.2720	17.9915	5.6530	8.6882	12.7413
				Differences
	ln(z-score)		-0.116**
	Capital-to-Asset ratio		-0.015**
	Leverage		0.317

** p<0.01, * p<0.05. This table provides descriptive statistics before and after deposit insurance adoption for banks facing an adoption. N is the number of observation. Mean is the mean value. SD is the standard deviation of banks. p25, Median and p75 are the 25th, the 50th and the 75th percentile threshold.

Table 5 :

 5 Baseline specification

		CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0228**	-0.1592**	-0.0180**	-0.1687**
		(0.0029)	(0.0003)	(0.0056)	(0.0001)
	HHI index on deposit			0.0125	-0.0971
				(0.3769)	(0.3257)
	GDP growth (annual %)			-0.0184	0.5505
				(0.7388)	(0.1877)
	Inflation (annual %)			-0.0000	0.0001
				(0.8089)	(0.4513)
	Log of GDP per capita			-0.0387*	0.0666
				(0.0129)	(0.5764)
	Observations	222,099	220,643	222,099	220,643
	Number of id	20,703	20,697	20,703	20,697
	Adjusted R-squared	0.0056	0.0169	0.0072	0.0180
	Regression Type	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country

** p<0.01, * p<0.05. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not include the contribution of bank fixed effects to the explained variance.

Table 6 :

 6 Specification with linear and quadratic country-specific trends

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0188*	-0.1266**	-0.0186**	-0.1234**	-0.0169*	-0.1154**	-0.0160**	-0.1138**
		(0.0213)	(0.0006)	(0.0057)	(0.0008)	(0.0210)	(0.0000)	(0.0076)	(0.0000)
	HHI index on deposit			0.0245	0.0587			0.0187	0.0543
				(0.1752)	(0.5608)			(0.3055)	(0.5641)
	GDP growth (annual %)			-0.0639	-0.0827			-0.0392	-0.0740
				(0.1676)	(0.7842)			(0.3827)	(0.7877)
	Inflation (annual %)			-0.0000	0.0000			0.0000	0.0001
				(0.7117)	(0.7105)			(0.7873)	(0.1528)
	Log of GDP per capita			-0.0431	0.2835			-0.0920**	-0.0124
				(0.1607)	(0.0889)			(0.0099)	(0.9371)
	Observations	222,099	220,643	222,099	220,643	222,099	220,643	222,099	220,643
	Number of id	20,703	20,697	20,703	20,697	20,703	20,697	20,703	20,697
	Adjusted R-squared	0.0218	0.0495	0.0231	0.0500	0.0272	0.0596	0.0290	0.0598
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Ctry Specific Trends	Linear	Linear	Linear	Linear	Lin. & Quad. Lin. & Quad. Lin. & Quad. Lin. & Quad.

** p<0.01, * p<0.05. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not include the contribution of bank fixed effects to the explained variance.

Table 7 :

 7 Falsification test

		CAR	ln(z-score)	CAR	ln(z-score)
	Years ≤ -6	0.0112	0.1006
		(0.2165)	(0.0915)
	Years -6			0.0073	0.0021
				(0.2094)	(0.9565)
	Years -5	0.0179	0.1157*	0.0089	0.0157
		(0.0645)	(0.0443)	(0.1824)	(0.7036)
	Year -4	0.0123	0.1102*	0.0033	0.0102
		(0.0914)	(0.0167)	(0.6388)	(0.7946)
	Year -3	0.0109	0.0899*	0.0021	-0.0100
		(0.1793)	(0.0147)	(0.8199)	(0.8418)
	Year -2	0.0033	0.0458	-0.0054	-0.0541
		(0.5621)	(0.0746)	(0.5050)	(0.3003)
	Year -1			-0.0087	-0.0999
				(0.3505)	(0.1127)
	Year of adoption	-0.0013	-0.0385	-0.0101	-0.1384*
		(0.7279)	(0.1642)	(0.2622)	(0.0218)
	Year 1	-0.0163**	-0.1095**	-0.0250*	-0.2094**
		(0.0003)	(0.0000)	(0.0331)	(0.0050)
	Year 2	-0.0248**	-0.1457**	-0.0335*	-0.2456**
		(0.0006)	(0.0011)	(0.0157)	(0.0066)
	Year 3	-0.0288**	-0.1511**	-0.0375*	-0.2510**
		(0.0023)	(0.0022)	(0.0164)	(0.0075)
	Year 4	-0.0211**	-0.0995*	-0.0298*	-0.1994*
		(0.0009)	(0.0107)	(0.0158)	(0.0140)
	Year 5	-0.0088	-0.0681	-0.0175	-0.1680*
		(0.2910)	(0.1827)	(0.1115)	(0.0419)
	Year 6	-0.0144*	-0.1008*	-0.0231	-0.2007*
		(0.0370)	(0.0438)	(0.0505)	(0.0248)
	Years ≥ 7	-0.0200*	-0.1293*	-0.0287*	-0.2292*
		(0.0102)	(0.0387)	(0.0231)	(0.0229)
	Observations	222,099	220,643	222,099	220,643
	Number of id	20,703	20,697	20,703	20,697
	Adjusted R-squared	0.0077	0.0181	0.0077	0.0181
	Regression Type	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country
	** p<0.01, * p<0.05.

Table 8 :

 8 Controlling for Banking Crises and Simultaneous Changes in Banking Regulation In this table, the variable Banking Crisis is a country-year-specific dummy taking the value of one during the years a given country experiences a banking crisis. The variable Banking Supervision is a country-year-specific index taking values between 0 and 3 in which higher values indicate more banking regulation. The variable Financial Reform Index is a country-year-specific index taking values between 0 and 21 in which higher values indicate higher financial liberalization. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0178**	-0.1681**	-0.0183*	-0.1650**	-0.0178*	-0.1643**	-0.0181*	-0.1642**
		(0.0064)	(0.0001)	(0.0154)	(0.0007)	(0.0162)	(0.0005)	(0.0168)	(0.0007)
	HHI index on deposit	0.0126	-0.0969	0.0043	-0.1366	0.0026	-0.1546	0.0047	-0.1353
		(0.3755)	(0.3295)	(0.7949)	(0.2135)	(0.8768)	(0.1554)	(0.7792)	(0.2240)
	GDP growth (annual %)	-0.0098	0.5724	-0.0267	0.6787	-0.0267	0.6664	-0.0192	0.7041
		(0.8623)	(0.2267)	(0.6822)	(0.1847)	(0.6741)	(0.1785)	(0.7769)	(0.2200)
	Inflation (annual %)	-0.0000	0.0001	-0.0000	0.0001	-0.0000	0.0001	-0.0000	0.0001
		(0.7898)	(0.4570)	(0.8649)	(0.4207)	(0.7312)	(0.5233)	(0.8517)	(0.4255)
	Log of GDP per capita	-0.0365*	0.0721	-0.0391*	0.0625	-0.0360*	0.0925	-0.0375*	0.0676
		(0.0174)	(0.5363)	(0.0238)	(0.6237)	(0.0401)	(0.4994)	(0.0289)	(0.5975)
	Banking Crisis	0.0022	0.0058					0.0017	0.0058
		(0.2856)	(0.8343)					(0.4324)	(0.8411)
	Banking Supervision			-0.0026	-0.0402			-0.0026	-0.0400
				(0.4055)	(0.1314)			(0.4125)	(0.1269)
	Financial Reform Index, 0 to 21					-0.0020*	-0.0213*
						(0.0425)	(0.0175)
	Observations	222,099	220,643	214,242	212,989	214,242	212,989	214,242	212,989
	Number of id	20,703	20,697	19,929	19,923	19,929	19,923	19,929	19,923
	Adjusted R-squared	0.0073	0.0180	0.0074	0.0199	0.0076	0.0203	0.0074	0.0199
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	** p<0.01, * p<0.05.

Table 9 :

 9 The Effect of Systemic Importance: ex ante Market Share in terms of Assets

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0208**	-0.1563**	-0.0248**	-0.1841**	-0.0211**	-0.1644**
		(0.0004)	(0.0000)	(0.0001)	(0.0000)	(0.0002)	(0.0000)
	DI*ex ante Market Share			0.0769**	0.5984**	0.0782**	0.5943**
				(0.0033)	(0.0031)	(0.0058)	(0.0052)
	HHI index on deposit					0.0348	0.0900
						(0.0780)	(0.5515)
	GDP growth (annual %)					0.0599*	0.5341**
						(0.0441)	(0.0010)
	Inflation (annual %)					-0.0000	0.0001
						(0.8123)	(0.4189)
	Log of GDP per capita					-0.1220**	-0.4926**
						(0.0000)	(0.0055)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187
	Adjusted R-squared	0.0200	0.0138	0.0253	0.0249	0.0526	0.0357
	Regression Type	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full
	Year*ex ante Market Share			YES	YES	YES	YES
	Ctry Specific Trend			NO	NO	NO	NO

** p<0.01, * p<0.05. In this table, the variable DI*ex ante Market Share is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the systemic importance of each bank before adoption. It is computed as the average value of the market share on assets over the periods preceding adoption. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 10 :

 10 The Effect of Systemic Importance: ex ante Total Assets over GDP

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0208**	-0.1563**	-0.0227**	-0.1668**	-0.0188**	-0.1471**
		(0.0004)	(0.0000)	(0.0001)	(0.0000)	(0.0003)	(0.0000)
	DI*ex ante Assets/GDP			0.0396**	0.1849	0.0329**	0.1520
				(0.0024)	(0.0784)	(0.0012)	(0.1127)
	HHI index on deposit					0.0254	0.0131
						(0.2310)	(0.9362)
	GDP growth (annual %)					0.0657*	0.5663**
						(0.0306)	(0.0004)
	Inflation (annual %)					-0.0000	0.0001
						(0.8589)	(0.3701)
	Log of GDP per capita					-0.1215**	-0.4813**
						(0.0000)	(0.0078)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187
	Adjusted R-squared	0.0200	0.0138	0.0225	0.0203	0.0476	0.0298
	Regression Type	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full
	Year*ex ante Assets/GDP			YES	YES	YES	YES
	Ctry Specific Trends			NO	NO	NO	NO

** p<0.01, * p<0.05. In this table, the variable DI*ex ante Assets over GDP is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the systemic importance of each bank before adoption. It is computed as the average value of the ratio of Assets to GDP over the periods preceding adoption. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 11 :

 11 The Effect of the Initial Capital-to-Assets Ratio

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0208**	-0.1563**	0.0227**	-0.0067	0.0212**	-0.0072
		(0.0004)	(0.0000)	(0.0010)	(0.8622)	(0.0045)	(0.8545)
	DI*ex ante CAR			-0.2759**	-0.9342**	-0.2546**	-0.8731**
				(0.0001)	(0.0003)	(0.0006)	(0.0014)
	HHI index on deposit					0.0063	-0.0771
						(0.7159)	(0.5936)
	GDP growth (annual %)					0.0394	0.4557**
						(0.1574)	(0.0032)
	Inflation (annual %)					-0.0000	0.0000
						(0.3846)	(0.5052)
	Log of GDP per capita					-0.0695**	-0.2259
						(0.0044)	(0.1515)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187
	Adjusted R-squared	0.0200	0.0138	0.1661	0.0991	0.1736	0.1013
	Regression Type	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full
	Year*ex ante CAR			YES	YES	YES	YES
	Ctry Specific Trends			NO	NO	NO	NO

** p<0.01, * p<0.05. In this table, the variable DI*ex ante CAR is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the leverage of each bank before adoption. It is computed as the average value of the Capital-to-Assets ratio over the periods preceding adoption. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 12 :

 12 The Effect of the Initial Liabilities-to-Equity ratio

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0208**	-0.1563**	-0.0320**	-0.2685**	-0.0276**	-0.2511**
		(0.0004)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
	DI*ex ante Liab.-to-Equity			0.0013**	0.0123**	0.0011**	0.0119**
				(0.0001)	(0.0000)	(0.0002)	(0.0000)
	HHI index on deposit					0.0234	-0.0086
						(0.1735)	(0.9446)
	GDP growth (annual %)					0.0604*	0.4992**
						(0.0279)	(0.0003)
	Inflation (annual %)					-0.0000	0.0000
						(0.6799)	(0.5570)
	Log of GDP per capita					-0.1127**	-0.3952*
						(0.0000)	(0.0105)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187
	Adjusted R-squared	0.0200	0.0138	0.0486	0.0836	0.0704	0.0897
	Regression Type	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full
	Year*ex ante Liab.-to-Equity			YES	YES	YES	YES
	Ctry Specific Trends			NO	NO	NO	NO

** p<0.01, * p<0.05. In this table, the variable DI*ex ante Liab.

Table 13 :

 13 First-Difference and Bank-specific Trends The first four columns presents the First-Difference estimator while the next four columns present the random correlated trend model allowing for unit-specific (here bank) trends. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

			First Difference			Bank Specific Trend
		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	∆ Deposit Insurance	-0.0075**	-0.0784**	-0.0080**	-0.0829**	-0.0049	-0.0669*	-0.0059*	-0.0691**
		(0.0048)	(0.0012)	(0.0045)	(0.0004)	(0.0617)	(0.0120)	(0.0301)	(0.0055)
	∆HHI_d			0.0175	0.0287			0.0155	-0.0106
				(0.1123)	(0.6757)			(0.1213)	(0.8602)
	∆GDP			-0.0510	-0.0947			-0.0542	-0.1842
				(0.2915)	(0.7140)			(0.2756)	(0.4925)
	∆inflation			-0.0000	0.0000			0.0000	0.0001**
				(0.9046)	(0.0988)			(0.9253)	(0.0018)
	∆ln_GDPPC			-0.0159	0.2544**			-0.0146	0.3675**
				(0.4188)	(0.0097)			(0.5750)	(0.0031)
	Observations	199,844	197,979	199,844	197,979	199,844	197,979	199,844	197,979
	Adjusted R-squared	0.0025	0.0088	0.0032	0.0090	0.0042	0.0079	0.0050	0.0082
	Regression Type	Bank FD	Bank FD	Bank FD	Bank FD	Bank FD	Bank FD	Bank FD	Bank FD
	FE	NO	NO	NO	NO	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Number of id					20,703	20,695	20,703	20,695
	** p<0.01, * p<0.05.

Table 14 :

 14 Aggregating across Country and Ignoring Time Series Information

		CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0176**	-0.1221**	-0.0128*	-0.1192**
		(0.0052)	(0.0002)	(0.0130)	(0.0004)
	Packagvations	10,226	10,021	10,226	10,021
	Adjusted R-squared	0.0145	0.0172	0.0079	0.0164
	Regression Type	OLS	OLS	OLS	OLS
	FE	NO	NO	NO	NO
	Year FE	NO	NO	NO	NO
	Cluster	Country	Country	Country	Country
	First-Stage Controls	NO	NO	YES	YES
	** p<0.01, * p<0.05. Here I implement the method suggested by

Table 15 :

 15 The Effect of Systemic Importance: ex ante Market Share in terms of Assets. Robustness Checks In this table, the variable DI*ex ante Market Share is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the systemic importance of each bank before adoption. It is computed as the average value of the market share on assets over the periods preceding adoption. The variable BC*ex ante Market Share is an interaction term between the Banking Crisis dummy and the indicator reflecting the systemic importance of each bank before adoption. The variable BS*ex ante Market Share is an interaction term between the Banking Supervision index and the indicator reflecting the systemic importance of each bank before adoption. Standard errors clustered at the country level.

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0151**	-0.1048**	-0.0143**	-0.1021**	-0.0144**	-0.1014**	-0.0145**	-0.1006**
		(0.0027)	(0.0000)	(0.0021)	(0.0000)	(0.0017)	(0.0000)	(0.0061)	(0.0000)
	DI*ex ante Market Share	0.0466	0.3470	0.0454	0.3395	0.0455	0.3341	0.0314	0.2064
		(0.0647)	(0.0892)	(0.0731)	(0.0935)	(0.0743)	(0.1036)	(0.1805)	(0.2246)
	HHI index on deposit			0.0108	0.0607	0.0102	0.0669	0.0057	-0.0062
				(0.5987)	(0.5632)	(0.6062)	(0.5143)	(0.8075)	(0.9609)
	GDP growth (annual %)			0.0663**	0.3162*	0.0695*	0.2700	0.0663**	0.2548
				(0.0067)	(0.0213)	(0.0179)	(0.0888)	(0.0084)	(0.0930)
	Inflation (annual %)			0.0000	0.0001	0.0000	0.0001	0.0000	0.0001
				(0.7997)	(0.2265)	(0.8009)	(0.2294)	(0.7179)	(0.1375)
	Log of GDP per capita			-0.1339**	-0.3984	-0.1316**	-0.4360*	-0.1567**	-0.5236*
				(0.0004)	(0.0727)	(0.0002)	(0.0448)	(0.0000)	(0.0248)
	BC*ex ante Market Share					-0.0036	-0.1940
						(0.9192)	(0.4854)
	Banking Crisis (BC)					0.0020	-0.0203
						(0.7290)	(0.5129)
	BS*ex ante Market Share							0.0482	0.3337
								(0.0660)	(0.0818)
	Banking Supervision (BS)							-0.0100*	-0.0584
								(0.0431)	(0.0511)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064	10,285	10,088
	R-squared	0.1213	0.1263	0.1280	0.1283	0.1280	0.1287	0.1128	0.1151
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187	1,014	1,013
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Year*ex ante Market Share	YES	YES	YES	YES	YES	YES	YES	YES
	Ctry Specific Trend	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full	Full	Full
	** p<0.01, * p<0.05.

p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 16 :

 16 The Effect of Systemic Importance: ex ante Assets over GDP. Robustness Checks In this table, the variable DI*ex ante Assets over GDP is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the systemic importance of each bank before adoption. It is computed as the average value of the ratio of Assets to GDP over the periods preceding adoption. The variable BC*ex ante Assets over GDP is an interaction term between the Banking Crisis dummy and the indicator reflecting the systemic importance of each bank before adoption. The variable BS*ex ante Assets over GDP is an interaction term between the Banking Supervision index and the indicator reflecting the systemic importance of each bank before adoption. Standard errors clustered at the country level.

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0147**	-0.1020**	-0.0138**	-0.0988**	-0.0139**	-0.0973**	-0.0146**	-0.1077**
		(0.0013)	(0.0000)	(0.0011)	(0.0000)	(0.0009)	(0.0000)	(0.0026)	(0.0000)
	DI*ex ante Assets/GDP	0.0301**	0.1871	0.0275**	0.1771	0.0273**	0.1687	0.0271*	0.2368
		(0.0019)	(0.0956)	(0.0032)	(0.1159)	(0.0030)	(0.1421)	(0.0110)	(0.0591)
	HHI index on deposit			0.0044	0.0183	0.0035	0.0204	-0.0002	-0.0445
				(0.8312)	(0.8680)	(0.8677)	(0.8501)	(0.9925)	(0.7286)
	GDP growth (annual %)			0.0689**	0.3234*	0.0720*	0.2757	0.0698**	0.2467
				(0.0063)	(0.0166)	(0.0136)	(0.0638)	(0.0073)	(0.1000)
	Inflation (annual %)			0.0000	0.0001	0.0000	0.0001	0.0000	0.0001
				(0.7977)	(0.2305)	(0.8078)	(0.2471)	(0.7687)	(0.1466)
	Log of GDP per capita			-0.1364**	-0.4074	-0.1345**	-0.4508*	-0.1581**	-0.5217*
				(0.0003)	(0.0654)	(0.0002)	(0.0402)	(0.0000)	(0.0222)
	BC*ex ante Assets/GDP					0.0180	0.3017
						(0.5754)	(0.3813)
	Banking Crisis (BC)					0.0011	-0.0400
						(0.8222)	(0.1868)
	BS*ex ante Assets/GDP							0.0549**	0.4321*
								(0.0004)	(0.0145)
	Banking Supervision (BS)							-0.0087*	-0.0540
								(0.0471)	(0.0533)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064	10,285	10,088
	R-squared	0.1165	0.1200	0.1233	0.1219	0.1234	0.1224	0.1104	0.1120
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187	1,014	1,013
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Year*ex ante Assets/GDP	YES	YES	YES	YES	YES	YES	YES	YES
	Ctry Specific Trend	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full	Full	Full
	** p<0.01, * p<0.05.

p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 17 :

 17 The Effect of the Initial Capital-to-Assets Ratio. Robustness Checks In this table, the variable DI*ex ante CAR is an interaction term between the deposit insurance adoption dummy and an indicator reflecting the leverage of each bank before adoption. It is computed as the average value of the Capital-to-Assets ratio over the periods preceding adoption. The variable BC*ex ante CAR is an interaction term between the Banking Crisis dummy and the indicator reflecting the leverage of each bank before adoption. The variable BS*ex ante CAR is an interaction term between the Banking Supervision index and the indicator reflecting the leverage of each bank before adoption. Standard errors clustered at the country level.

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	0.0272**	0.0390	0.0260**	0.0370	0.0261**	0.0403	0.0277**	0.0406
		(0.0021)	(0.2961)	(0.0070)	(0.3382)	(0.0075)	(0.2962)	(0.0035)	(0.3087)
	DI*ex ante CAR	-0.2667**	-0.8822**	-0.2536**	-0.8490**	-0.2544**	-0.8685**	-0.2874**	-0.9765**
		(0.0006)	(0.0003)	(0.0014)	(0.0006)	(0.0015)	(0.0005)	(0.0004)	(0.0001)
	HHI index on deposit			-0.0029	-0.0161	-0.0035	-0.0097	-0.0101	-0.0856
				(0.8835)	(0.8891)	(0.8574)	(0.9293)	(0.6885)	(0.5377)
	GDP growth (annual %)			0.0585*	0.3032*	0.0606*	0.2466	0.0619*	0.2489
				(0.0260)	(0.0326)	(0.0440)	(0.1285)	(0.0445)	(0.1286)
	Inflation (annual %)			0.0000	0.0001	0.0000	0.0001	0.0000	0.0001
				(0.7066)	(0.1510)	(0.7078)	(0.1564)	(0.6576)	(0.0908)
	Log of GDP per capita			-0.0958**	-0.2327	-0.0937**	-0.2627	-0.1038**	-0.3074
				(0.0089)	(0.2833)	(0.0065)	(0.2235)	(0.0058)	(0.1813)
	BC*ex ante CAR					0.0052	0.1820
						(0.9263)	(0.4173)
	Banking Crisis (BC)					0.0006	-0.0592
						(0.9323)	(0.2585)
	BS*ex ante CAR							-0.1595	-0.5923*
								(0.0590)	(0.0214)
	Banking Supervision (BS)							0.0154	0.0455
								(0.1265)	(0.2954)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064	10,285	10,088
	R-squared	0.2283	0.1724	0.2316	0.1735	0.2316	0.1739	0.2281	0.1623
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187	1,014	1,013
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Year*ex ante CAR	YES	YES	YES	YES	YES	YES	YES	YES
	Ctry Specific Trend	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full	Full	Full
	** p<0.01, * p<0.05.

p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

Table 18 :

 18 The Effect of the Initial Liabilities-to-Equity ratio. Robustness Checks

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0249**	-0.2139**	-0.0235**	-0.2099**	-0.0235**	-0.2081**	-0.0248**	-0.2175**
		(0.0001)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0002)	(0.0000)
	DI*ex ante Liab.-to-Equity	0.0012**	0.0126**	0.0012**	0.0125**	0.0012**	0.0124**	0.0012**	0.0125**
		(0.0003)	(0.0000)	(0.0003)	(0.0000)	(0.0005)	(0.0000)	(0.0021)	(0.0000)
	HHI index on deposit			-0.0030	-0.0660	-0.0034	-0.0529	-0.0105	-0.1359
				(0.8812)	(0.5427)	(0.8635)	(0.6154)	(0.6524)	(0.3011)
	GDP growth (annual %)			0.0667**	0.2972*	0.0689*	0.2392	0.0683*	0.2239
				(0.0087)	(0.0341)	(0.0196)	(0.1316)	(0.0122)	(0.1640)
	Inflation (annual %)			0.0000	0.0001	0.0000	0.0001	0.0000	0.0001
				(0.8637)	(0.2167)	(0.8641)	(0.2238)	(0.8055)	(0.1393)
	Log of GDP per capita			-0.1221**	-0.2742	-0.1203**	-0.3161	-0.1440**	-0.3857
				(0.0008)	(0.1788)	(0.0005)	(0.1145)	(0.0001)	(0.0737)
	BC*ex ante Liab.-to-Equity					-0.0000	-0.0005
						(0.9447)	(0.6523)
	Banking Crisis (BC)					0.0014	-0.0299
						(0.7819)	(0.3555)
	BS*ex ante Liab.-to-Equity							0.0001	0.0019
								(0.5112)	(0.4026)
	Banking Supervision (BS)							-0.0083	-0.0551
								(0.0580)	(0.1586)
	Observations	12,299	12,064	12,299	12,064	12,299	12,064	10,285	10,088
	R-squared	0.1352	0.1610	0.1406	0.1621	0.1406	0.1625	0.1262	0.1454
	Number of id	1,188	1,187	1,188	1,187	1,188	1,187	1,014	1,013
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Year*ex ante leverage Terms	YES	YES	YES	YES	YES	YES	YES	YES
	Ctry Specific Trend	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Sample	Full	Full	Full	Full	Full	Full	Full	Full
	** p<0.01, * p<0.05. In this table, the variable DI*ex ante Liab.

Table 19 :

 19 Sample of Treated Countries * p<0.01, * p<0.05. In this table, the sample excludes all countries with no change in the Deposit Insurance dummy, i.e. those having already adopted a Deposit Insurance scheme before the first year of the sample and those without Deposit Insurance Scheme at the end of the sample. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

		CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0199**	-0.1443**	-0.0142**	-0.1204**
		(0.0065)	(0.0002)	(0.0081)	(0.0004)
	HHI index on deposit			0.0220	0.0413
				(0.3319)	(0.8022)
	GDP growth (annual %)			-0.0082	0.4746*
				(0.7984)	(0.0451)
	Inflation (annual %)			-0.0000	0.0001
				(0.8599)	(0.3494)
	Log of GDP per capita			-0.1144**	-0.3916**
				(0.0000)	(0.0026)
	Observations	29,560	29,162	29,560	29,162
	Number of id	3,197	3,195	3,197	3,195
	Adjusted R-squared	0.0111	0.0096	0.0248	0.0147
	Regression Type	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country

*

Table 20 :

 20 Various log of z-score In all the column the dependent variable is the logarithm of z-score. The lines CAR, ROAA and Stand. dev. ROAA indicates how each of the z-score component is computed. Mov Av. (2 1 2) means Moving Average with a window centered around the contemporaneous value and including two lags and two leads. Sample Av. means average computed for each bank over the entire sample. Instantaneous ROAA reefers to the difference between contemporaneous value of ROAA and the bank sample average of ROAA. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

	CAR	Contemporaneous Contemporaneous	Mov. Av. (2 1 2) Contemporaneous Contemporaneous
	ROOA	Contemporaneous	Mov. Av. (2 1 2)	Mov. Av. (2 1 2)	Sample Av.	Contemporaneous
	Stand. dev. ROAA	Sample Av.	Sample Av.	Sample Av.	Sample Av.	Instantaneous
	Deposit Insurance	-0.1687***	-0.1578***	-0.1325***	-0.1490***	-0.0745**
		(0.0186)	(0.0185)	(0.0168)	(0.0168)	(0.0351)
	HHI index on deposit	-0.0971*	-0.0619	-0.0499	-0.0908*	-0.4822***
		(0.0569)	(0.0545)	(0.0468)	(0.0499)	(0.1081)
	GDP growth (annual %)	0.5505***	0.4517***	0.6945***	0.1808**	1.4350***
		(0.0840)	(0.0792)	(0.0684)	(0.0706)	(0.2015)
	Inflation (annual %)	0.0001	0.0001	0.0000	-0.0000	0.0001
		(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0001)
	Log of GDP per capita	0.0666	0.0495	0.0435	-0.0679*	0.6258***
		(0.0467)	(0.0456)	(0.0425)	(0.0398)	(0.0839)
	Observations	220,643	221,491	221,803	221,839	220,645
	Number of id	20,697	20,697	20,697	20,696	20,699
	Adjusted R-squared	0.0180	0.0169	0.0242	0.0146	0.0091
	Bank FE	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES
	** p<0.01, * p<0.05.

Table 21 :

 21 z-score regressions * p<0.01, * p<0.05. In all the regression the dependent variable is the z-score. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

	z-score

*

Table 22 :

 22 Sample of Listed Banks * p<0.01, * p<0.05. This table presents results from regressions on the sample of banks publicly listed. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0168	-0.1249*	-0.0178*	-0.1557*	-0.0190**	-0.0797	-0.0183**	-0.0876
		(0.0550)	(0.0439)	(0.0186)	(0.0105)	(0.0043)	(0.0776)	(0.0022)	(0.0823)
	HHI index on deposit			0.0041	-0.3123			0.0199	-0.1192
				(0.9063)	(0.1968)			(0.4947)	(0.5528)
	GDP growth (annual %)			0.0479	1.0656**			-0.0353	0.3765
				(0.1053)	(0.0001)			(0.2792)	(0.2390)
	Inflation (annual %)			-0.0001**	-0.0003**			-0.0001**	-0.0005**
				(0.0000)	(0.0021)			(0.0000)	(0.0000)
	Log of GDP per capita			0.0065	0.1491			0.0121	0.6787**
				(0.8432)	(0.4595)			(0.7213)	(0.0022)
	Observations	13,240	13,096	13,240	13,096	13,240	13,096	13,240	13,096
	Number of id	1,253	1,253	1,253	1,253	1,253	1,253	1,253	1,253
	Adjusted R-squared	0.0172	0.0193	0.0204	0.0305	0.1279	0.1121	0.1319	0.1210
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	Ctry Specific Trends					Linear	Linear	Linear	Linear

*

Table 23 :

 23 Sample of Banks Active in 2007 and Sample Excluding Years Post-2007

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0246*	-0.0192*	-0.1378**	-0.1685**	-0.0249**	-0.0173**	-0.1752**	-0.1478**
		(0.0134)	(0.0207)	(0.0094)	(0.0007)	(0.0003)	(0.0007)	(0.0000)	(0.0000)
	HHI index on deposit		0.0294		-0.0143		0.0134		-0.0948
			(0.2857)		(0.9364)		(0.2867)		(0.2449)
	GDP growth (annual %)		-0.0470		0.4798		0.0455		0.6113*
			(0.5129)		(0.3584)		(0.0644)		(0.0136)
	Inflation (annual %)		-0.0000**		-0.0000		-0.0000		0.0001
			(0.0008)		(0.9425)		(0.8410)		(0.3903)
	Log of GDP per capita		-0.0267		0.1664		-0.1024**		-0.3994**
			(0.0966)		(0.1672)		(0.0000)		(0.0058)
	Observations	178,949	178,949	178,046	178,046	165,687	165,687	164,760	164,760
	Number of id	15,515	15,515	15,514	15,514	20,703	20,703	20,692	20,692
	Adjusted R-squared	0.0058	0.0074	0.0200	0.0215	0.0048	0.0113	0.0129	0.0155
	Regression Type	FE	FE	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country	Country	Country
	** p<0.01, * p<0.

Table 24 :

 24 Controlling for M&A * p<0.01, * p<0.05. This table presents the results from regressions on the sample excluding bank observations having a growth of assets higher than 50% from one year to another. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

		CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0209**	-0.1503**	-0.0168*	-0.1621**
		(0.0065)	(0.0006)	(0.0111)	(0.0002)
	HHI index on deposit			0.0093	-0.1388
				(0.5128)	(0.1717)
	GDP growth (annual %)			-0.0220	0.5666
				(0.6883)	(0.1801)
	Inflation (annual %)			-0.0000	0.0001
				(0.7629)	(0.3730)
	Log of GDP per capita			-0.0367*	0.0784
				(0.0121)	(0.4985)
	Observations	219,217	217,803	219,217	217,803
	Number of id	20,402	20,396	20,402	20,396
	Adjusted R-squared	0.0053	0.0172	0.0070	0.0186
	Regression Type	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country

*

Table 25 :

 25 Specification with bank-specific covariates * p<0.01, * p<0.05. These regressions include bank-specific covariates taken from BankScope. Standard errors clustered at the country level. p-value in parentheses. Two-way Fixed-effects model. All regressions includes a constant term. The Adjusted R-squared does not take into account the contribution of bank fixed effects to the explained variance.

		CAR	ln(z-score)	CAR	ln(z-score)	CAR	ln(z-score)
	Deposit Insurance	-0.0198**	-0.1782**	-0.0188**	-0.1292**	-0.0164**	-0.1266**
		(0.0018)	(0.0000)	(0.0021)	(0.0004)	(0.0060)	(0.0000)
	Cost To Income Ratio	0.0359**	0.0271	0.0364**	0.0324	0.0365**	0.0334
		(0.0001)	(0.6415)	(0.0000)	(0.5594)	(0.0000)	(0.5432)
	Net Interest Margin	0.0000**	0.0001**	0.0000**	0.0001**	0.0000**	0.0001**
		(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
	Total deposits/liabilities	-0.0278	0.0051	-0.0358*	-0.0684	-0.0342*	-0.0478
		(0.0685)	(0.9245)	(0.0143)	(0.1573)	(0.0204)	(0.3262)
	Liquid asset/asset	0.0441	0.1144	0.0414	0.0756	0.0419	0.0731
		(0.0837)	(0.2521)	(0.1379)	(0.5082)	(0.1380)	(0.5305)
	HHI index on deposit	0.0139	-0.0725	0.0254	0.0947	0.0208	0.0622
		(0.4120)	(0.4971)	(0.1708)	(0.3741)	(0.2671)	(0.5338)
	GDP growth (annual %)	0.0040	0.5947	-0.0523	-0.0479	-0.0341	-0.0224
		(0.9344)	(0.1575)	(0.2403)	(0.8777)	(0.4415)	(0.9391)
	Inflation (annual %)	-0.0000	0.0001	-0.0000	0.0000	0.0000	0.0001
		(0.9628)	(0.5109)	(0.8121)	(0.7955)	(0.7126)	(0.1936)
	Log of GDP per capita	-0.0295	0.0859	-0.0230	0.2903	-0.0689	-0.0514
		(0.1048)	(0.5144)	(0.4911)	(0.1362)	(0.0670)	(0.7761)
	Observations	219,986	219,115	219,986	219,115	219,986	219,115
	Number of id	20,679	20,675	20,679	20,675	20,679	20,675
	Adjusted R-squared	0.0848	0.0239	0.1014	0.0558	0.1074	0.0652
	Regression Type	FE	FE	FE	FE	FE	FE
	FE	Bank	Bank	Bank	Bank	Bank	Bank
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster	Country	Country	Country	Country	Country	Country
	Ctry Specific Trends			Linear	Linear	Lin. & Quad.	Lin. & Quad.

*

A brief presentation of the deposit insurance schemes as well as the associated costs and benefits can be found in appendix A.

[START_REF] Demirguc-Kunt | Does deposit insurance increase banking system stability? an empirical investigation[END_REF],[START_REF] Demirguc-Kunt | Market discipline and deposit insurance[END_REF] or[START_REF] Beck | Resolution of failed banks by deposit insurers: Cross-country evidence[END_REF] for instance

Only 904 banks corresponding to 10 823 obs. are listed.

Tier 1 ratio is missing for 77 292 observations over 94 774 (81%)

http://www.bis.org/publ/bcbs189_dec2010.pdf

Recall that BankScope suffers from an artificial trend in coverage, especially in the years before 2000 that could explain the rapid increase observed between 1985 and 1991.

[START_REF] Kalemli-Ozcan | Leverage across firms, banks, and countries[END_REF] detail various bank-level or country-level time-invariant differences that are accounted for by using fixed-effects. Among them are accounting practices, balance sheet representation and domestic regulatory adjustment. As explained previously, in the present situation it also controls for any time-invariant differences in the feature of the deposit insurance schemes.

These are the in-sample magnitudes of the risk-shifting effect of deposit insurance. When comparing with the within standard deviation computed over the entire sample (and not just on the sample of banks on which the effect of deposit insurance adoption is estimated), the out-sample magnitudes become much larger: 49.43% (0.0228/0.0461227) for the Capital-to-Assets ratio and 54.26% (0.1592/0.2935417) for the log of z-score.

[START_REF] Angrist | Mostly Harmless Econometrics: An Empiricist's Companion[END_REF] and[START_REF] Roberts | Endogeneity in empirical corporate finance[END_REF].

Note that the limited time coverage of this indicator reduces the sample size. To keep sample size similar across regressions, I assign the value of the year 2005 to the financial reform indicator for the years 2006-2011.

This index aggregates seven dimensions to obtain a single liberalization index for each economy and for each year.

Note that the authors show the opposite pattern when using the market share which is now consistent with the results established in this paper.

However regulatory constraints cannot fully explain the relation exhibited because even in restricting the sample to banks having an initial Capital-to-Assets ratio higher than 15% or 20%, we continue to capture a negative relation between initial leverage and banks' reaction. Results available upon request.

These class of models are called correlated random trend models. See[START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF] p. 315 and also http://www.cemmap.ac.uk/resources/imbens_wooldridge/slides_11.pdf

i.e. those facing an adoption during the period covered.

But collecting time-varying data about these features is a hard task. See Appendix A for further discussion on this issue

http://www.cdic.ca/CDIC/Cooperation/IntlLinks/Pages/default.aspx or http://www.iadi.org/ aboutIADI.aspx?id=48

The literature generally uses only the three first types of banks, but after looking in detail at the list of banks participating to the deposit insurance scheme in some countries, I note that the three last types of bank are very often members of the deposit insurance funds.

I gratefully acknowledge financial support from "Région Île-de-France" for this research.

Online Appendix [For online publication only]

Appendix A

Deposit Insurance Scheme: a Brief Presentation

The first deposit insurance scheme in the world was the Federal Deposit Insurance Corporation (FDIC) in the United States. 36 The decision was taken just after the wave of bank failures experienced during the Great Depression. On June 16, 1933, President Roosevelt signs the Banking Act (also known as the Glass-Steagal Act) "creating a Federal Deposit Insurance Corporation and providing for the insurance of deposits in member banks of the Federal Reserve System and also in nonmember banks under certain conditions.". 37 The temporary scheme was fully implemented on January 1, 1934 and the Banking Act of 1935 established the FDIC as a permanent agency of the government. The explicit goal was to raise the confidence of the Americans in the banking system by alleviating the disruptions caused by bank failures and bank runs. 38 Since then, a large number of countries have adopted an explicit deposit insurance scheme as part of their regulatory framework. 39 Establishment of deposit insurance schemes has been largely promoted by IMF and World Bank in the 90's. Similarly, a deposit insurance scheme is now required to become member of the European Union.

These last years, an international harmonization of these deposit insurance schemes has been initiated by the International Association of Deposit Insurers and the European Forum of Deposit Insurers, both founded in 2002. In 2010, 109 countries have an explicit deposit insurance system.

Protecting small and unsophisticated depositors with deposit insurance has the main 36 In Norway there was a guarantee fund for savings banks with voluntary membership as early as 1921 which then became mandatory in 1924, whereas a guarantee fund for commercial banks was first introduced in 1938. However, Norway's guarantee fund is not considered a pure deposit insurance scheme so it had no official explicit deposit insurance until 1961. (? and EFDI 2006 report) 37 http://archive.org/details/FullTextTheGlass-steagallActA.k.a.TheBankingActOf1933 38 See http://www.fdic.gov/bank/historical/brief/brhist.pdf for further details on the history of the FDIC 39 Countries having an explicit deposit insurance scheme and years of adoption are presented in table 1.

Appendix B Deposit Insurance Scheme Database

There already exists two important databases about deposit insurance schemes. The and the "Deposit Guarantee Systems: EFDI's First Report" (2006). 45 I also look at some reports of the Financial Sector Assessment Program 46 from the World Bank and the International Monetary Fund: in many cases, they provide information about the existence and the year of adoption of deposit insurance scheme. For European countries, I also confront the sources with a report from the European Commission. 47 Regarding countries from the MENA region, I used a document summarizing the main information about deposit insurance systems in this region. 48 Finally, the consistency of the year of 43 However the part concerning deposit insurance scheme doesn't seem to have been updated.

Appendix C

In this appendix, I present the additional robustness checks that have not been include in the paper for the sake of brevity.

Treated sample

As explain in the section concerning the data, I choose to perform the estimations on the largest sample, mainly to have a smooth and stable control group over time. This sample includes countries for which we do not observe implementation of deposit insurance scheme during the period covered. 51 It can be countries that adopted a deposit insurance scheme before the first year of the period studied (1986), or countries that do not have a deposit insurance system yet. When using this extended sample, the control group on which the identification relies includes these countries. A classical robustness check consists in replicating the regressions using a different control group [START_REF] Roberts | Endogeneity in empirical corporate finance[END_REF]).

In particular, finding different results would cast doubt on the fundamental common trend assumption. I thereby restrict the sample by excluding countries with no policy change.

The results are shown in table 19.

For both the leverage ratio and the distance-to-default, the coefficient associated with the deposit insurance adoption dummy remains highly significant. The magnitude of the coefficients are only slightly lower than those from the baseline regressions. Hence, the main result established previously appears robust to the use of alternative control group.

Various log of z-score's

When using the z-score in a time-varying framework, there is an issue to consider carefully: the way to compute the mean and the standard deviation of ROAAt. There is no clear consensus about this issue. [START_REF] Lepetit | Bank insolvency risk and time-varying z-score measures[END_REF] compares the various time-varying z-score used in the literature. They conclude that while appealing, the use of time-varying standard deviation of ROAAt is not the best way to compute the z-score. They also suggest to use the mean of ROAAt computed over the full period of analysis. However, they remark that contemporaneous value of ROAAt provides almost the same results. Here I use the