metadata: authors: 0: author: Mare,Davide Salvatore
1: author: Melecky,Martin
2: author: Murina,Hanna
count: Central Asia,Kazakhstan
docna: 0: docna: Bank Bailouts and Fiscal Contingent Liabilities
docty: Policy Research Working Paper
theme: Inclusive Growth,Mitigation,Infrastructure Finance,Human Development and Gender,Financial Stability,ICT,Economic Policy,ICT Solutions,Fiscal Policy,Environment and Natural Resource Management,Disease Control,Finance for Development,Finance,Enterprise Development,Private Sector Development,Pandemic Response,MSME Development,Fiscal sustainability,Climate change,Adaptation,Disaster Risk Finance,Financial Sector Integrity,Financial Sector oversight and
            policy/banking regulation & restructuring,Mitigation,Subnational Fiscal Policies,Human Development and Gender,Data Development and Capacity Building,Economic Policy,Green Growth,Social Protection,Social Development and Protection,Public Finance Management,Economic Growth and Planning,Fiscal Policy,Environment and Natural Resource Management,Standards, Curriculum and Textbooks,Domestic Revenue Administration,Public Sector Management,Fiscal sustainability,Climate change,Data production, accessibility and use,Tax policy,Public Expenditure Policy,Education,Debt Management,Social Safety Nets,Public Expenditure Management
abstracts: cdata!: Government intervention to bail out
            troubled banks can produce a sizable fiscal contingent
            liability. Drawing on a rich history of various forms of
            staggered bailouts, this paper studies the link between bank
            bailouts and fiscal contingent liabilities using bank-level
            data for Kazakhstan&#x2014;an upper-middle-income
            country in Central Asia. The paper first estimates the
            probability that a bank in distress is bailed out,
            conditioning on bank characteristics and financial
            soundness. Second, it estimates the magnitude of bailout
            costs depending on the size of banks, their ownership type,
            financial soundness, and the type of bailout instrument used
            by the government. The latter aims to contrast the fiscal
            costs when the government uses bailout instruments without
            recourse on bank future profits&#x2014;such as
            government purchases of bad loans above market
            value&#x2014;versus instruments that require repayment
            of the public support with adequate compensation for the
            government&#x2019;s alternative costs&#x2014;such as
            properly governed equity injections. Third, the paper
            illustrates how the estimations could be used for projecting
            the expected contingent liabilities from bank bailouts.
display_title: Bank Bailouts and Fiscal Contingent Liabilities
listing_relative_url: /research/2023/01/33974218/bank-bailouts-fiscal-contingent-liabilities
topicv3: Public Finance m1326315 713,Contingent Liabilities m1327145 1145,Financial Vulnerability and Risks m1327455 1245
publishtoextweb_dt: 2023-01-09T00:00:00Z
dois: 10.1596/1813-9450-10273
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 41
author_keys: 0: author_key: 1366794
1: author_key: 915923
2: author_key: 1528041
model_extractions: Policy Research Working Paper 10273 Bank Bailouts and Fiscal Contingent Liabilities _Davide S. Mare Martin Melecky Hanna Murina_ Finance, Competitiveness and Innovation Global Practice January 2023
Policy Research Working Paper 10273

# **Abstract**

Government intervention to bail out troubled banks can produce a sizable fiscal contingent liability. Drawing on a rich history of various forms of staggered bailouts, this paper studies the link between bank bailouts and fiscal contingent liabilities using bank-level data for Kazakhstan—an upper-middle-income country in Central Asia. The paper first estimates the probability that a bank in distress is bailed out, conditioning on bank characteristics and financial soundness. Second, it estimates the magnitude of bailout costs depending on the size of banks, their ownership type, financial soundness, and the type of bailout instrument used by the government. The latter aims to contrast the fiscal costs when the government uses bailout instruments without recourse on bank future profits—such as government purchases of bad loans above market value—versus instruments that require repayment of the public support with adequate compensation for the government’s alternative costs—such as properly governed equity injections. Third, the paper illustrates how the estimations could be used for projecting the expected contingent liabilities from bank bailouts.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dmare@worldbank.org, mmelecky@worldbank.org, and hannamurina@hotmail.com.

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

Davide S. Mare<sup>a,b</sup> , Martin Melecky<sup>a</sup> , and Hanna Murina<sup>a</sup> Originally published in the Policy Research Working Paper Series on _January 2023._ This version is updated on _February 2024._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

**Keywords:** Bank bailouts; Contingent liabilities; Fiscal cost; Kazakhstan; Bank resolution; Systemic risk.

**JEL:** G01; G21; G28; H81

> * We thank Sergio Correia, Tatiana Didier, Mario di Filippo, Aneta Hryckiewicz-Gontarczyk, Maria Soledad Martinez Peria, Klaus Schaeck, and the participants of the World Bank author workshop for the Kazakhstan Public Finance Review and the YPFS Fighting a Financial Crisis Conference, the Agency of the Republic of Kazakhstan for Regulation and Development of Market, and the Halyk Bank for helpful comments on earlier drafts of the paper. This paper's findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank Group, their Executive Directors, or the countries they represent. The authors can be contacted at: dmare@worldbank.org, and mmelecky@worldbank.org. a World Bank.

> b University of Edinburgh
# **1 Introduction**

Governments deploy bank bailouts to mitigate risks of financial instability and reduce the likelihood or severity of recessions that systemic bank distresses and failures often trigger. Bailouts may mitigate financial disintermediation shocks, uphold credit supply, and maintain economic conditions by reducing corporate and personal bankruptcies, and thus unemployment. But they have social and fiscal costs. Financial support to distressed banks may impose large redistribution costs on society when taxpayers are not adequately compensated for the risks the government takes or for the funds that bank owners never return (Dell’Ariccia et al. 2022).<sup>b</sup> Government intervention can help bankers socialize losses and privatize profits. Past bailouts also raise the expectations of future bailouts and generate fiscal contingent liabilities. They can feed the moral hazard incentives of banks, reduce market discipline, and impact the competitive landscape in banking (Calderon and Schaeck, 2016; Gropp et al., 2011). Ultimately, rescuing distressed banks can distort the allocation of savings/capital in the economy because they could be partially allocated based on the political and regulatory connections of banks. Bailouts can also decrease confidence in banks and be politically unpopular, including because they are perceived by the citizens as unfair (Berger and Roman, 2017), and can sometimes lead to sovereign debt problems or even civil unrest (Berger and Roman, 2020).

This paper studies the link between bailouts and bank size, ownership, and financial soundness, using bank-level data from Kazakhstan from 2009 to 2021. Kazakhstan is a particularly interesting country for this type of analysis because of repeated bouts of financial instability and the staggered bank bailouts involving several types of public interventions. The country has experienced two waves of financial crises since 2008 because of commodity price shocks, economic concentration in extractive industries, and weak governance, among other challenges. The economic crisis triggered by COVID-19 also renewed pressure on banks and the banking system. From 2009 to 2021, the banking system comprised between 26 and 40 operating banks and witnessed 41 bank instabilities declared by the bank supervisor.<sup>c</sup> Out of these cases of financial distress, 23 were resolved through bank bailouts using public money. The bailouts were staggered over time and used different instruments (equity injection, debt or deposit funding, and purchases of non-performing loans in most cases at 100 percent of the nominal [gross balance sheet] value).<sup>d</sup> The cost of bailouts thus varied widely.

Kazakhstan is more than an interesting case study. As a country at a higher level of economic development compared with its regional peers, it can provide useful empirical evidence

> b Dell’Ariccia et al. 2022 show that divestiture takes a long time and recouping the full cost can be questioned including because of alternative costs such as forgone investment in education or health care.

> c During 2009-2021, there were 24 banks with instabilities and 41 actions to tackle the instabilities. Twelve banks were terminated with license withdrawal, including two banks that have been transformed into non-banks, and 10 banks liquidated or are in the process of liquidation. Data sources for bank instabilities resolved with bailout actions are described in section 4.1.

> d Just in one case, the price paid to acquire non-performing loans was lower than the full nominal value: the purchase from a non-bank entity (transaction value of KZT 2.6 tln in 2017). The purchase was at about a 50% discount to the - - gross value according to the Kazakh Ministry of Finance statement: (https://informburo.kz/novosti/fond <u>problemnyh kreditov-pereplatil-za-aktivy-bta-23-trln-tenge-v-minfine-razyasnili-situaciyu.html).</u>

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and policy lessons for other CIS countries with similar institutional setup and legal frameworks for bank resolution. It can also prompt earlier reforms in this area in peer countries to ensure systemic risk events can be resolved according to best practice and to the benefit of the economies.

To gain new insights into the link between bank bailouts and fiscal costs, and to develop a tool for projecting fiscal contingent liabilities from bank bailouts, this paper draws on a rich history of various forms of staggered bailouts in Kazakhstan. It studies the link between bank bailouts and fiscal contingent liabilities using bank-level data for Kazakhstan. The paper first estimates the probability that a bank in distress is bailed out, conditioning on bank characteristics such as financial soundness (CAMEL indicators), ownership type (state, politically exposed persons, local, foreign), and systemic importance (size).<sup>e</sup> Second, it estimates the extent of bailout costs depending on the size of banks, their ownership type, financial soundness, and the type of bailout instrument used by the government. The latter aims to contrast the fiscal contingent liabilities when the government uses bailout instruments without taxpayer recourse on bank future profits—such as government purchases of bad loans at 100 percent of the nominal value—versus instruments that require banks to repay state financial support with appropriate compensation for the alternative fiscal costs—such as properly governed and priced senior debt or equity injections. Third, the paper illustrates how the estimations could be used for projecting the expected contingent liabilities from bank bailouts.

We find that larger banks are more likely to experience a bailout despite their business diversification and economies of scale. We surmise that moral hazard could be also one underlying explanation given the history of bailouts in Kazakhstan. Foreign-owned banks get bailed out much less than other domestic banks (keeping size constant). Similarly, banks owned by local politically exposed persons (PEPs) get resolved through private solutions much less than other domestic banks (keeping size constant)—perhaps owing to their better management, on the one hand, and preferential political/ business connections, on the other hand. Interestingly, consistent with the hypothesis of soft budget constraints of state-owned businesses, ownership by the state significantly increases the likelihood that public money will be deployed to stabilize troubled banks.

Our estimations confirm that bigger banks in distress are more costly to stabilize. But we also find that if a troubled bank is state-owned, the bailout cost increases, on average, by 10 billion tenge. Moreover, if the applied bailout instrument is a debt financing of the distressed bank, the public expenditure increases relative to using an equity injection by, on average, 39 billion tenge. The costliest by far are bailouts using purchases of non-performing loans in which case the relative cost hikes, on average, by 324 billion tenge. The significance of the variables controlling for bank financial soundness—in particular, capitalization, provisioning, and profitability—confirms that sounder banks are much less costly to stabilize (rehabilitate), highlighting the importance of independent and credible supervision of banks.

> e CAMEL is an abbreviation for Capitalization, Asset quality, Management, Earnings, and Liquidity capturing a useful supervisory framework that has been used by many countries around the world to monitor bank financial soundness. See more discussion of this framework in the data section.

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The rest of the paper is organized as follows. Section 2 and Section 3 provide a literature review and an overview of bank bailouts in Kazakhstan. Section 4 describes the data and the summary statistics. Section 5 presents the estimation methodology. Section 6 discusses the estimation results and projected fiscal contingent liabilities from bank bailouts for 2022-23. Section 7 concludes and offers some policy recommendations.

# **2 Literature review and hypotheses**

Many studies investigate the outcomes of government intervention to rescue banks in distress. The aim is to identify whether bailout programs are effective in restoring banking sector stability while mitigating the negative effects of government intervention, especially on bank risk-taking behavior (Claessens et al., 2011). While the overarching goal of intervention is to restore financial soundness and market confidence, government support of distressed financial institutions has an indirect effect on real outcomes, ameliorating or preventing the negative effects following shocks to the supply of credit (Laeven and Valencia, 2013). Yet, banks may also react by increasing risktaking anticipating government intervention in case of distress (e.g., Farhi and Tirole, 2012; Dam and Koetter, 2012; Duchin and Sosyura, 2014). Distressed institutions can also perpetrate lending to unprofitable firms to avoid the recognition of losses (Giannetti and Simonov, 2013). Government support can also have a bearing on competition, favoring its recipients such as by increasing the net worth of distressed institutions without increasing borrowing costs (e.g., Calderon and Schaeck, 2016).

In general, government action can take different forms—such as direct capital injections in distressed institutions, liability guarantees, relief through the purchase of impaired assets, and funding liquidity support—which may have different implications for the long-term stability of the banking sector (Hryckiewicz, 2014). Each form of intervention entails a consideration of the benefits and the potential costs—be it in terms of fiscal costs or real outcomes (Veronesi and Zingales, 2010). Direct capital injections are needed because, during financial distress, capital buffers are depleted, and banks may find it difficult to raise new capital in public markets or through private investors. Nonetheless, recapitalization dilutes existing shareholders increasing their moral hazard incentives (Besanko and Kanatas, 1996; Bayazitova and Shivdasani, 2012) and the benefits of bank recapitalization should be weighed against the implicit transfer to banks’ debt holders (Philippon and Schnabl, 2013). Guarantees may be an alternative form of tackling financial distress, especially for governments that are fiscally constrained (Acharya et al., 2021). Guarantees on liabilities do not involve an immediate outlay of resources to cover losses while reducing uncertainty and helping distressed institutions with their funding. On the negative side, guarantees may spur moral hazard because they can incentivize banks to extend credit to the riskiest borrowers (Gropp et al., 2014) and exacerbate risk-shifting incentives that end up in “gambles for resurrection” (Kane and Klingebiel, 2004; Caprio et al., 2010). Asset management interventions such as the purchase of non-performing assets are deployed to restore the soundness and profitability of viable financial institutions. Yet, appraising the value of non-performing assets is challenging during crises and often a too-high purchasing price involves a transfer of money from taxpayers to distressed institutions (Brei et al., 2020; Landier and Ueda, 2009). Similarly, funding 4
liquidity support can be employed to bridge temporary funding shortages but often ends up being used to keep banks in distress alive.

Very few studies look at the cost of government intervention (e.g., Laeven and Valencia, 2020; Dell’Ariccia et al., 2022) and how political economy issues and links between the political establishment and the banking sector influence government interventions. Blau et al. (2013) suggest that politically connected banks were more likely to be a recipient of government support in the U.S. during the Global Financial Crisis (GFC). Likewise, these authors posit that a greater amount was granted to political-connected banks. Similarly, Grossman and Woll (2014) argue that the type and magnitude of bank bailouts are influenced by the close relationship between policy makers and bank management. Interventions during banking crises may be also related to the strategic negotiation between bank supervisors and creditors of distressed banks, with the outcome of the negotiation depending critically on the threat of liquidation of insolvent banks (Bernard et al., 2022). Government intervention may also depend on the character of domestic institutions (e.g., central bank independence) and international constraints (Rosas, 2006). Yet, there is scant evidence on whether these mechanisms are at play in emerging markets and what are the costs involved in rescuing distressed financial institutions. This issue is particularly relevant for these types of countries because political concerns play a great role in resolving bank failures and banking systems are dominated by systemically important institutions (Brown and Dinc, 2005). Therefore, we contribute to the literature by investigating the following research question: _H1: Is the ownership type an important determinant in explaining government bailouts versus the market resolution of bank distress in addition to size (systemic importance)?_ A gap in the literature that we try to fill concerns the assessment of the cost of government interventions depending on the type of financial tool (structuring) of the intervention—in addition to individual bank characteristics. The fiscal cost and contingent liabilities borne by the government over time can depend on the financial tool used to resolve bank distress and how much risk the government assumes with the deployment of that particular tool. We study this question in the context of a developing country that has witnessed repeated episodes of bank bailouts over the last 20 years. In doing this, we distinguish the varying forms of government interventions and quantify the costs associated with each type of financial tool (structure). Finally, we project the contingent liabilities associated with government intervention depending on the type of intervention. In sum, we investigate the following hypothesis: _H2: Do the costs of bank bailouts vary significantly depending on the bailout instrument(s) used by the government?_

# **3 Overview of bank bailouts in Kazakhstan**

Over time, Kazakhstan has experienced major bouts of financial instability triggered by developments in the world crude prices and the exchange rate volatility. A conjunction of the fall in the price of crude oil and currency pressures led to a transition to a free-floating exchange rate in 2015, triggering a banking crisis. This crisis came on top of previous turbulence experienced 5
during and in the aftermath of the Global Financial Crisis. The onset of the Covid-19 crisis globally did not spare Kazakhstan. Additional asset quality pressures because of pandemic-related lockdowns and disruptions, including in trade, put many companies and consumers under pressure. High legacy non-performing exposures (NPEs), weak governance and risk management at banks, as well as lax underwriting standards generated an opaque situation in the banking sector further clouded by numerous forbearance measures to cope with the Covid-19 crisis. Bank closures, mergers, and bailouts followed. Most recently, the regulator took proactive measures to decrease NPE levels in the banking sector, and largely succeeded. However, rapidly expanding consumer lending brings new risks for bank lending books.

One goal of our analysis is to identify the different tools through which bank distress has been resolved in Kazakhstan. We identify two main types of resolution: government intervention versus private/market resolutions (henceforth, the private/market solution). Within each type, there are different forms such as equity injection for government intervention and liquidation for the private/market solution (see section 2 for a detailed explanation).

A total of 42 unique banks operated in Kazakhstan from January 2009 to August 2021, ranging from the maximum of 40 banks operating in 2010 to a minimum of 26 banks in 2021 (Figure 1, panel A). Out of all banks observed over the period 2009-2021, 24 were subject either to government intervention, or to some form of private/market solution such as liquidation, merger, or other resolution events. These events represent an extraordinary course of action that, in Kazakhstan’s case, entailed the resolution of financial distress. Out of the 24 banks subject to resolution of distress, 16 experienced just 1 event, 3 banks were subject to 2 events, 2 banks to 3 events, 2 banks to 4 events, and one bank to 6 events. These 42 resolutions of financial distress could occur multiple times in a year for the same bank, where the most frequent action was government intervention in the form of bailouts (23 cases) followed by forced liquidation (9 cases), mergers (7 events), reorganization (2 events), and voluntary liquidation (1 event).

**Figure 1: Number of banks and events over the sample period (2009-2021)**

**A: Number of banks by year B: Number of events by year**

<!-- Start of picture text --> 40 38 39 38 38 38 38 8<br>37<br>35<br>33<br>32<br>30 28 27 6<br>26<br>4<br>20<br>2<br>10<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Government intervention Liquidation Merged Reorganisation Voluntary liquidation<br>Number of events<br>Number of banks<br><!-- End of picture text --> Source: Own elaboration using data from the National Bank of Kazakhstan, National Wealth Fund Samruk-Kazyna, the Kazakhstan Sustainability Fund, and public news reports.

Note: In Panel A, the total number of banks is taken over each year, i.e., if a bank exits the market in a year, it is still counted in the total number of banks. In Panel B, government intervention is the sum of the events where the provision of equity, purchase of non-performing exposures, and the extension of credit to ailing institutions were recorded.

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We classify as a government intervention any action undertaken by the Kazakh government that involves a public sector expenditure associated with helping troubled banks. The cost differs depending on the type of government intervention. We identify three main tools employed to rescue distressed banks: equity injections; purchase of non-performing exposures (NPEs); and provision of funding in the form of debt financing or bank deposits (Figure 2, panel B). NPE purchase and provision of funding, if fairly priced, are more NPE reduction and liquidity tools (that are preventing or reducing insolvency concerns). The deviations from fair pricing generated a direct impact on capital and are the reasons for bailout classification. Over time, the type of tools deployed to rescue financial institutions varied and the cost to the government associated with such bailouts did too. Specifically, early in the observation period, equity injections were used to restore bank financial stability (6 interventions between 2009 and 2010). Since 2013, the acquisition of non-performing exposures (7 interventions) and the provision of debt or deposit funding (10 interventions) were used to rescue ailing banks. The cost of government intervention was particularly large in 2017 when the public support of one bank (and one non-bank) entailed the acquisition of a large non-performing exposure to the tune of 2.6 trillion tenge.<sup>f,g</sup> In terms of frequency, 6 banks were bailed out once, 2 banks twice, 3 banks thrice, and 1 bank 5 times. Out of the 12 banks supported by public money, only 7 are still active—the rest were merged into other banks, closed, or liquidated.

Interestingly, the average interval between receiving the 1<sup>st</sup> bailout and the eventual bank exit is almost 4 years. The average interval between the 2<sup>nd</sup> bailout and the eventual closure is 2.5 years. The average interval between receiving the 1<sup>st</sup> and 2<sup>nd</sup> bailout is close to 5 years. This suggests that public bailouts of banks were often ineffective in rehabilitating bank financial stability in the medium to long term.

The (quasi)fiscal expenditures on government bailouts of troubled banks in our sample first spiked in 2009 to reach almost 15 percent of total tax revenues in the same year (Figure 3, Panel B). A few years later, 2014 and 2015 saw spikes in government support of over 9 percent of total tax revenues when combined. In 2017, the bailout cost peaked at around 48 percent of total government tax revenues. Years 2018 and 2019 continued with high-cost bailouts by historical standards of around 7 percent of government tax revenues each year. Even 2020, witnessed public bailouts through NPE purchases at 100 percent of the loans’ nominal value and financing of underpriced bank bonds, costing around 3 percent of government tax revenues.

> f In recognition of the bailout amount and troubled banks, we follow the provisions of IFRS Conceptual Framework: “Faithful representation. 2.12  Financial reports represent economic phenomena in words and numbers. To be useful, financial information must not only represent relevant phenomena, but it must also faithfully represent the substance of the phenomena that it purports to represent. In many circumstances, the substance of an economic phenomenon and its legal form are the same. If they are not the same, providing information only about the legal form would not faithfully represent the economic phenomenon”. Therefore, based on documented cash allocation, the total NPE bailout of 2.64 trillion tenge is assigned to troubled bank #38 in the amount of 2.4 trillion tenge and to a non-bank entity in the amount of 0.24 trillion tenge.

> g Throughout the paper we anonymize individual banks by randomly generated number. The bank number does not correspond to the bank age, size, or any other characteristic and is purely assigned by the random number generator.

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# **Figure 2: Frequency and cost of government intervention over time**

## **A: Number of government interventions by year B: Cost of government intervention (% of total tax revenue)**

<!-- Start of picture text --> 8 50<br>45<br>40<br>6<br>35<br>30<br>4 25<br>20<br>15<br>2<br>10<br>5<br>0 0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Equity NPE acquisitions Debt Equity NPE acquisitions Debt<br>Number of government interventions<br>Government expenditures (% of Government revenues)<br><!-- End of picture text --> Source: Own elaboration using data from the National Bank of Kazakhstan, National Wealth Fund Samruk-Kazyna, the Kazakhstan Sustainability Fund, public news reports, and the World Bank. Note: In Panel B, we report the cumulative government expenditures on bailouts in a given year.

Quasi-fiscal entities played a crucial role in the structuring of bank bailouts. The most recent purchases by the Problem Loan Fund (PLF) of bank non-performing loans much above their market price and often at 100 percent of their nominal value—while their market price can be often lower than 10-20 cents on the dollar<sup>h</sup> —have been the costliest bailout options for the general government without any claim on the future profits of banks received later in the form of dividends (Figure 4). The recoveries from the NPE purchases have been minuscule and the prospects for further recoveries deteriorating with time. The procedural steps in a bailout using the PLF purchases of bank NPEs are described in table A1.B in the annex.

The use of debt bailouts through NBK’s Kazakhstan Sustainability Fund has been the second most recent type of bailout used by the government. Injecting public money against senior debt issuance by the banks is a more disciplining bailout for bankers but still a very soft one— especially, if the debt is underpriced. For example, the bailout of bank #16 in 2020 was structured based on a debt with a coupon of 0.1 percent—with little return for the taxpayer money and a subsidized loan to bank #16. A similar bailout approach has been applied through Samruk Kazyna and PLF deposits which were placed in banks at well below the market rate and, for example, in the case of bank #38 ultimately at as low as a 0.1 percent interest rate.

The equity injections performed by Samruk Kazyna as a form of public bailouts could be considered a fairer and more equitable bailout option—because they reduced/diluted the ownership stakes of bankers that brought the bank into distress and required them to pay back dividends on

> h Fair value to purchase price ranged from the case of non-bank entity at 10%, to bank #31 transaction at 10%, to bank #31 transaction 2 at 20%, and to bank #16 at 20%: PLF’s audited consolidated financial statements: as of 2018, pp. 40-45, <u>https://www.fpl.kz/media/file/2018_con_fin.pdf,</u> as of 2019, pp. 39-41 <u>https://www.fpl.kz/media/file/2019_con_fin.pdf,</u> as of 2020, pp. 39-42 <u>https://www.fpl.kz/media/file/Консолидированная_FS_2020_RUS.pdf</u>

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<!-- Start of picture text --> 4000<br>3500<br>3000<br>b 2500<br>=<br>5 2000<br>1500<br>1000<br>500> os | |<br>zz 5 #© © F&F FF Bw © FF F HE Zs<br>Ss 8 2 f= ‘3 8 2 = ‘3 8 2 =<br>ag & a 8 & a of & a<br>a w a “ a “i<br>Samruk-Kazyna PLF NBK<br><!-- End of picture text -->
of public funds in bank resolution, introduce strong principles of fairness, and reduce the moral hazard incentives of bankers. In parallel, a new Financial Stability Committee (FSC) was established comprising representatives of NBK, AFR, MNE, MOF, and the Presidential Administration. The responsibility of the FSC concerning troubled bank resolution is to endorse the appropriate use of public funds in cases when systemically important banks in distress need to be resolved/recovered. The FSC with inputs from AFR and NBK first determines the systemic importance at the time of distress—which could be unconditional (through the cycle) or conditional (given the current state of the market). Second, it determines the best course of policy action if a systemically important bank in distress needs to be resolved. It determines the public support instruments to be used and their terms. The combination of the bank resolution law and the FSC serves as a public safeguard for prudent use of public money in resolving troubled (private) banks.

The beginning of 2020 was thus expected to mark the beginning of a new era of using public funds in bank resolution fairly and appropriately—following the global sound practice. However, the bailout of bank #16 through its merger with bank #14 and associated government bailout expenditures have not fully met these expectations. The government purchased 174 billion tenge of bank #16’s NPEs at 100 percent of their nominal value, 75 billion tenge of bank #16 bonds were purchased by NBK Sustainability Fund with a coupon of 0.1 percent, and 100 billion tenge face value of bank #16 bonds issued in 2017 were restructured with coupon rate revised from 5.5 percent to 0.1 percent and repayment shifted from 2032 to 2040.

Because of the prevailing bank bailout practices, the _net_ fiscal cost (in term of 2020 present value adjusted for recoveries) of supporting troubled banks reached 6.3 trillion tenge over 20092020 (Table A2, panel A, in the annex). The related _gross_ expenses in terms of present value as of 2020 were as high as 8.2 trillion tenge—mostly thanks to the PLF purchases of bank NPEs at 100 percent of their nominal value. And the estimated recoveries were rather low at 1.9 trillion which corresponds to a recovery rate of about 24 percent—including both realized and expected future recoveries. By 2020, the balance sheets of bank #2 and bank #14 can be associated with majority of the net public support—primarily because of their acquisition/assumption of troubled banks supported with public money. The resolution approach also contributed to increased concentration of the banking sector over 2009-2020 which may have implications for banking competition in the country.

# **4 Data and descriptive statistics**

In this section, we describe the data sources and the variables used in the analyses. We collect three main categories of data: data on bank financial indicators; data on bailout actions; and data on changes in bank ownership and major corporate events. The information covers the universe of banks operating in Kazakhstan from January 2008 to August 2021,<sup>i</sup> for a total number

> i The observation period for the dependent variable is 2009-2021. Since we use a one-year lag in the independent variables, information for these variables is collected from 2008 onwards.

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of 483 observations. Where possible, data is collected on a monthly frequency and then aggregated at the annual level.

# _4.1 Data sources_

Data on bank financial indicators are gathered from their public disclosures available on the website of the National Bank of Kazakhstan (NBK). This published bank information contains monthly data on the balance-sheet information,<sup>j</sup> key items of the financial position of banks,<sup>k</sup> prudential ratios, and prudential capital adequacy.<sup>l</sup> The financial items are based on unconsolidated reporting according to the International Financial Reporting Standards (IFRS), which were introduced in Kazakhstan in 2004.

The data on bailout actions contain hand-collected information primarily from the financial reporting of the public sector institutions, depending on their participation in the bailouts. Where possible, the bailout actions have been verified through disclosures in the audited financial statements of the banks. The information on equity bailouts comes primarily from annual reports of the National Wealth Fund Samruk-Kazyna<sup>m</sup> and official stock market announcements by KASE (the stock exchange) about the terms of equity divestments of bank #24, bank #41, and bank #2. The information on subordinated bond instruments is retrieved from the audited financial reports of the National Bank of Kazakhstan<sup>n</sup> and its subsidiary—the Kazakhstan Sustainability Fund.<sup>o</sup> The data on bailout actions in the form of acquisitions of non-performing assets is retrieved from the financial reporting of the Problem Loan Fund.<sup>p</sup> The bailout actions with long-term deposits at preferential terms are also described in the audited financial statements of Samruk-Kazyna and the Problem Loan Fund.

The data on bank ownership and major corporate events consists of hand-collected annual information primarily using the disclosed notes of the audited financial statements of banks and, only in some cases supplemented with news media reports and information from other IFIs for cross-checking. The ownership classification has been designed to reflect differences in bank operations that stem from shareholder profiles. There are 4 groups: foreign (state-owned and private) banks, local state-owned banks, local private banks, and PEP family-associated banks.<sup>q</sup> In general, for bank mergers, there are three important dates: (1) the announcement of possible transaction and application with the relevant authorities to receive their approval, (2) the closing of the transaction when ownership rights are transferred, and (3) the transfer of assets and liabilities. In our analysis, to identify a merger or acquisition, we consider the second date—that is, the closing of the transaction, while the date when the financials of the target institution are not compiled anymore is usually later. The bank financials during the transition period between the

> j <u>https://www.nationalbank.kz/file/download/72713.</u>

> k <u>https://www.nationalbank.kz/file/download/72716.</u>

> l <u>https://www.nationalbank.kz/file/download/72707.</u>

> m <u>https://sk.kz/investors/financial-performance/?lang=en.</u>

> n <u>https://www.nationalbank.kz/en/news/godovoy-otchet-nbk.</u> o <u>https://www.kfu.kz/index.php/ru/otchjotnost.</u>

> p <u>https://www.fpl.kz/en/audit</u> and https://www.fpl.kz/en/godovoi-otchet.

> q Our classification has been informally cross-checked against that of some IFIs operating in Kazakhstan.

11
announcement and the closing, a period which usually lasts several months, are included in the dataset. We use these dates because, although institutions are not merged, the new owner is in charge and managing the assets as it sees fit—which is apparent from the many changes in the assets of the acquired and not yet merged banks.

In cases of bank liquidations, there are two important dates: (1) announcement of a regulatory action that may suspend some of the licensed operations and/or impose certain restrictions on bank activity, (2) withdrawal of the banking license. The dataset of bank financials recognizes the second date and right after that date, the financials of the exiting banks are excluded. The bank financials during the transition period between the first and the second date, which may last up to several months, are included in the datasets.

- _4.2 Frequency and cost of financial distress_

The first variable of interest in our study is the occurrence of financial distress and the cost of resolving this distress. We define broadly an event of “acute” distress as the occurrence of either government support or private/market solutions that terminate the operations of a bank. Although the latter is a debated typology in the literature, in the case of Kazakhstan, an explicit recognition of an extraordinary operation to resolve financial instability is authorized and issued by the National Bank of Kazakhstan. The defining feature of an “acute” event of distress is an action taken for the main purpose of stabilizing the financial sector. Therefore, our scope does not include monetary or fiscal policy actions aimed at supporting the broader Kazakh economy. This definition presents some limitations as we are unable to include cases of vulnerabilities that are solved in a non-assisted way. Yet, resolution events are responses to events of distress and are observable. We, therefore, opt to classify banks in distress if there is a measurable objective ex-post outcome of this distress (i.e., a government expense) which can be related to cases of acute financial distress.

We further separate bank financial distresses into bailouts and private exits. The first one, “ _bail-out_ ”, captures support provided by the government in the form of equity injection, purchase of non-performing exposures, and provision of funding; the second one, “private exit”, accounts for instability resolved through private/market solutions such as mergers and acquisitions, voluntary liquidations, and forced supervisory closures.<sup>r</sup> In each year for each bank, the variable takes one of three values: 2 if a government expense is recorded, that is there is a transfer of some net expected value from taxpayers to bank shareholders (Metrick and Schmelzing, 2021); 1 if there is a private/market solution to bank distress; and 0 for all the years a bank is operating before undergoing a specific event. We also compute a variable “expenditure” that quantifies the direct monetary spending on government intervention. It takes a positive value for a bank in a year when the government payments are recorded and 0 otherwise.

Table 1 reports the summary of each dependent variable over time. We observe 41 events of distress over the period 2009-2021, 18 private/market resolution events, and 23 government bailouts. The annual event rate (percentage) for our sample is 9.2 percent, suggesting that

> r Indeed, private market solutions may be the result of strategic choices of acquirers and target institutions. Yet, in the case of Kazakhstan, we checked that these operations were carried out because of the distress of the credit institutions subject to such events.

12
approximately 1 in 11 banks experienced at least one episode of distress each year.<sup>s</sup> The cumulative expenditures are 5.6 trillion Kazakh tenge (simple sum), which corresponds to 8.4 percent of the cumulative share of tax revenues (Figure A1 panel B), and about 11.5 percent of the country’s gross domestic product over the period 2009-2020 (Figure A1, panel A, in the annex).

**Table 1: Overview of the dependent variables employed in the analysis**

|**Variable**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|**2021**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Distress (#)**|||||||||||||||
|No event|32|36|38|38|37|37|34|33|26|27|27|24|22|411|
|Event|5|2|1|0|1|1|4|2|7|5|1|3|4|36|
|Total|37|38|39|38|38|38|38|35|33|32|28|27|26|447|
|**Resolution (#)**|||||||||||||||
|Operating|18|20|21|21|20|20|17|16|9|10|10|7|5|194|
|Private/Market|1|1|1|0|0|0|3|2|1|3|0|1|4|17|
|Bail-out|4|1|0|0|1|1|1|0|6|2|1|2|0|19|
|Total|23|22|22|21|21|21|21|18|16|15|11|10|9|230|
|**Cost (KZT**<br>**billion)**|||||||||||||||
|Cost|332|24|0|0|0|220|250|0|3294|550|604|296|0||
|Cumulative|332|356|356|356|356|576|826|826|4120|4670|5274|5570|5570||
|Cost as % of<br>GDP|2%|0%|0%|0%|0%|1%|1%|0%|6%|1%|1%|0%|0%||
|Cost as % of<br>tax revenue|15%|1%|0%|0%|0%|4%|5%|0%|48%|7%|7%|4%|0%||

_Source_ : Own elaboration using data from the National Bank of Kazakhstan and audited financial statements of the banks supplemented with news reports. The total number of banks as of the start of the calendar year. _Note_ : The variable distress is computed considering all banks in the sample. We define an event as a bank-year observation where a bank is subject either to government intervention or private/market resolution of distress. The variable resolution is computed considering banks in the sample that have been subjected at least once to a resolution event over the period 2009-2021. The category operating refers to bank-year observations where a bank is not subject to an event in a specific year. Private resolution refers to bank-year observations where a bank is subject either to liquidation, merger, or reorganization in a specific year. The category bail-out captures bank-year observations where a bank is subject to government intervention in a specific year. The variable cost captures government expenditures related to bank bailouts.

# _4.3 Ownership type, systemic importance, and other control variables_

We are interested in analyzing how the ownership type and systemic importance affect the likelihood of distress, the use of specific resolution tools, and the associated fiscal expenditures. Because of the small sample size and the need to populate each category of ownership type over time, we select a parsimonious set of ownership types. Namely, if a bank is owned with a stake that is greater than 50 percent and ultimate operational control: (1) by private local shareholders or with the largest stake and operational control over the bank, the bank is classified as “ _private local_ ”; (2) by a restricted number of shareholders connected to PEP family, the bank is classified s Note that if a bank experienced multiple events of distress in one year, it is just counted once. Therefore, although 41 events of resolution happened during 2009-2021, just 36 are reported in Table 2.

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as “ _PEP_ ”; (3) by foreign shareholders—either foreign government or private shareholders—the bank is classified as _foreign-_ owned; and (4) by the Kazakhstan government, the bank is classified as “ _state local_ .” Table 2 reports the total number of observations in the sample and the breakdown over time of the number of banks by ownership type.

A sufficiently important bank can be rescued because of the large ramifications within the banking system (Freixas and Rochet, 2013). Systemic importance is a multidimensional concept (Financial Stability Board et al., 2009) that is often proxied using bank size (Drehmann and Tarashev, 2011), computed as the natural logarithm of bank total assets ( _size_ ). We follow this practice in the literature. We also include a measure of severity of banking system troubles computed as the total number of banks in distress in each year to control for the potential presence of a Too-Many-To-Fail effect that affects the regulatory response (Acharya and Yorulmazer, 2007; Brown and Dinç, 2011).

We also include additional controls that may explain the likelihood and severity (cost) of resolution. To that effect, we employ the traditional _CAMEL_ framework.<sup>t</sup> Through _C_ , we account for _capital_ adequacy using the variable total regulatory capital to risk-weighted assets. Through _A_ , we control for _asset_ quality measured by the ratio of loan loss provisions to total gross loans. Through _M,_ we control for _management_ quality using the ratio of overhead costs to total income. Through _E_ , we control for _earning_ s (profitability) using interest margin divided by total assets. Through _L_ , we control for _liquidity_ which is captured through the ratio of liquid assets to deposits. Due to the lag structure of the empirical model, data for CAMEL variables are drawn from 2008 through 2020. Furthermore, we compute annual averages for the indicators for each bank using the monthly information. Finally, we include macroeconomic variables to account for the overall economic conditions of the economy that may explain a rise in systemic risk. To keep a parsimonious specification, we source the data through the World Bank’s Development Indicators and consider real GDP growth and inflation rate.

**Table 2: Bank ownership in Kazakhstan between January 2009 and August 2021 (# of banks)**

|**Ownership**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|**2021**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Private|14|11|12|11|13|13|14|15|13|12|8|5|4|145|
|PEP|4|5|5|5|5|6|7|7|8|7|7|8|8|82|
|Foreign|15|17|17|17|15|14|13|12|11|12|12|13|13|181|
|State|4|5|5|5|5|5|4|1|1|1|1|1|1|39|
|Total|37|38|39|38|38|38|38|35|33|32|28|27|26|447|

Source: Own elaboration using data from the audited financial statements of the banks supplemented with news reports.

Note: The number of banks is taken over each year, i.e., if a bank exits the market in a year, it is still counted in the total number of banks per category in that year.

The sample of banks subject to at least one event of distress is smaller (253 observations) and the incidence of cases of resolution of distress is around 21 percent (see Table A3 in the

> t See Mare (2015) for a detailed explanation of the CAMEL framework.

14
Annex). The expenditures on resolving distress show high variability. The median public expenditure on bank bailout is 100 billion tenge while the maximum is more than 2.6 trillion tenge. Around 40 percent of the banks investigated in the study are foreign-owned, the largest share by numbers and years. Turning to bank controls, banks appear in general well-capitalized, though the high values of the ratio of regulatory capital to risk-weighted assets are due to relatively low levels of risk-weighted assets. This may reflect a lack of lending to small and medium-sized enterprises (SMEs), perhaps the riskiest lending category from the viewpoint of most banks. Moreover, there appears to be a negative and strong association between bank size and bank capitalization or liquidity. This may suggest moral hazard incentives because large banks appear to rely on an implicit government guarantee against losses thanks to their systemic importance or political connections. Overall, banks show weak performance over the sample period because the mean interest margin over total assets stands on average at minus 0.3 percent.

# **5 Estimation methodology**

To quantify the association between systemic importance, ownership, bank distress, and the cost of bank resolution, we employ different estimation techniques. We exploit the patterns of interventions across time to estimate the probability of government intervention and its determinants, focusing on the role of systemic importance and bank ownership type. We also estimate the cost associated with government intervention and how this cost may be related to similar variables.

We first investigate whether the typology of bank distress resolution, namely government intervention or private/market solution, can be associated with bank size or ownership type. We use a multinomial logit model to estimate the following relationships: where are three distinct values of the dependent variable “ _event only”_ . For each bank, the years preceding the event are set to 0, the base category. is a vector of regressands including ten jj{0,1,2} bank-level predictors all lagged by one year to ease endogeneity concerns, except for ownership. Specifically, bank size and three (contemporaneous) ownership variables (the omitted base XX category is _private local_ ) are our main variables of interest. The remaining bank-level covariates are five financial ratios controlling for banks’ financial soundness. We also control for macroeconomic determinants that may be relevant to control for the bunching of bailouts given by the rise of systemic banking risk and include the total number of banks in financial distress each year lagged by one year, real GDP growth and inflation. Standard errors are clustered at the bank level. This specification allows identifying differences in the relationship between bank size and 15
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distressed banks to estimate the probability of receiving government assistance (versus the probability of resolution of bank distress through a private/market solution) using a multinomial logit model that adjusts for a possible selection bias using the inverse Mills ratio constructed based on the first-stage probit regression estimates for bank distress. Third, we estimate the fiscal expense on bank bailouts using the Tobit model—again adjusting for possible selection bias using the inverse Mills ratio for bank distress. To project future contingent liabilities for 2022-23, the paper combines the estimation results from the three steps with the most up-to-date data from the financial statements of banks.

- _6.1 What factors can predict bank distress that could trigger resolution action?_

We begin by estimating the probability of bank distress using bank-level data. The results are summarized in Table 3. Column 1 presents the results when the CAMEL variables are lagged by one year while Column 2 reports the results when CAMEL variables are computed as averages over the previous two years. The CAMEL control estimates indicate that greater bank capitalization decreases the likelihood of distress. The total number of banks in distress is negatively related with individual bank trouble, mechanically suggesting that once the banking system problems are addressed, on average individual banks are less likely to experience distress. The results on the profitability ratio suggests that sustained profitability over the short (one year) and medium (average over two years) term decreases distress (Table 3). The estimation results highlight the role of timely and credible supervision to mitigate the likelihood of bank distress. Other macroeconomic variables appear to correlate with individual bank distress significantly (at the 5 percent level) only when we consider bank-level determinants lagged one year (Table 3, column 1) but there appears to be a weaker association when in the model specification we include the average value of CAMEL variables over the previous two-years (Table 3, column 2) —even though they may still be relevant for capturing the overall banking systemic risk.

**Table 3: Estimation of the probability of bank entering distress**

|VARIABLES|(1)<br>Distress|(2)<br>Distress|
|---|---|---|
|Regulatory Capital / Total Assets|-0.014**|-0.014**|
||(0.006)|(0.007)|
|Provisions / Gross Loans|0.004|0.005|
||(0.005)|(0.007)|
|Interest Margin / Total Assets|-0.101*|-0.195**|
||(0.052)|(0.082)|
|Liquidity Ratio|-0.000|0.000|
||(0.001)|(0.001)|
|Overhead Costs / Income|-0.004|-0.005|
||(0.003)|(0.004)|
|System distresst-1|-0.063***|-0.047*|
||(0.019)|(0.024)|
|Inflation rate|0.028***|0.009|
||(0.010)|(0.009)|
|Real GDP growth|0.057***|0.033*|
||(0.017)|(0.019)|

17
|VARIABLES|(1)<br>Distress|(2)<br>Distress|
|---|---|---|
|Constant|0.468|0.761**|
||(0.317)|(0.341)|
|Observations|442|442|
|McFadden's adjusted R-Squared|0.097|0.128|

Note: This table reports the estimation of equation 2. We consider all banks in the sample. Because data is not available for some of the independent variables, the total number of observations is 442 instead of 447. In column 1, CAMEL variables are lagged one year. In column 2, CAMEL variables are the average over the previous two years (e.g., the reference year 2021 for the outcome variable, the value for the CAMEL variable is computed as the average for years 2019 and 2020). Standard errors clustered at the bank level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

- _6.2 Which types of distressed banks get likely bailed out more often?_

Next, we distinguish two distinct approaches to resolving bank distress, namely between government bailout (financial) support versus private/market solutions. We attempt to estimate the candidate determinants of the past choices between the two approaches using the multinomial logit model. The estimation results of the multinomial logit applied to the bank-level data are summarized in Table 4. The coefficients express the relationship between each independent variable and the log odds of staying a normal course (being a healthy bank in a given period) versus private/market exit (Table 4, column 1) and versus government bailout using public money (Table 4, column 2).

**Table 4: Likelihood of market exit or M&A versus public bailout**

|Variables|(1)<br>Private exit|(2)<br>Bail-out|
|---|---|---|
|Sizet-1|-0.044|1.738***|
||(0.209)|(0.399)|
|Family Local|-1.695|-0.394|
||(1.269)|(0.577)|
|Foreign|-0.824|-16.152***|
||(0.539)|(0.950)|
|State Local|0.810|3.068*|
||(0.910)|(1.728)|
|Regulatory Capital / Total Assetst-1|0.074**|0.010|
||(0.032)|(0.078)|
|Provisions / Gross Loanst-1|-0.021*|-0.223**|
||(0.011)|(0.099)|
|Interest Margin / Total Assetst-1|0.082|0.783*|
||(0.142)|(0.471)|
|Liquidity Ratiot-1|0.001|0.044|
||(0.002)|(0.033)|
|Overhead Costs / Incomet-1|0.002|0.107**|
||(0.029)|(0.043)|

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|Variables|(1)<br>Private exit|(2)<br>Bail-out|
|---|---|---|
|Inverse Mills ratio|-9.614**|-42.580**|
||(4.019)|(18.435)|
|System distresst-1|0.743***|1.374**|
||(0.212)|(0.688)|
|Inflation rate|-0.148|-1.166***|
||(0.119)|(0.391)|
|Real GDP growth|-0.618***|-1.648***|
||(0.203)|(0.627)|
|Constant|3.431|-5.367|
||(3.546)|(8.167)|
|Observations|442|442|

Note: This table reports the estimation of equation 1. The normal course (i.e., no event) is the most frequent group and it is therefore chosen as the reference group. The inverse Mills ratio is obtained after estimating a probit model where the dependent variable is exiting or not exiting. We can reject the null hypothesis that the difference in coefficients is not systematic at the 1 percent level (Hausman specification test). Because data is not available for some of the independent variables, the total number of observations is 442 instead of 447. Standard errors clustered at the bank level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

The estimation results on private exit (column 1 of Table 4) suggests that the probability of observing this outcome relative to a healthy bank does not vary depending on most of the predictor variables except for capitalization (the higher the capitalization the more likely is a private market solution to distress) and provisioning (the lower the provisioning (and implicitly NPLs) the more likely is a private exit). This implies that the probability of a bank exiting the market through a private/market solution relative to the likelihood of the bank being financially stable is similar while conditioning on most of the determinants of bank distress. Contrary, if a bank were to increase the regulatory capital over total assets by one percentage point, the multinomial log-odds for market exit to healthy bank would be expected to increase by 0.074 unit while holding all other variables in the model constant. Higher provisioning, higher real GDP growth—the upturn of the business cycle— and system-wide banking distress all decrease the relative log-odds of market exit. No other control variable appears statistically significant in explaining the likelihood of private/market solutions relative to the normal course of a bank staying healthy. The inverse of the Mills ratio is estimated significantly negative, suggesting that unobservable factors that increase the probability of bank distress may also decrease the probability of private market exit.

Panel B (column 2 of Table 4) suggests that larger banks by assets are significantly more likely to be bailed out, conditioning on our other bank characteristics. This finding reflects the hypothesis that bank size is one major attribute of systemic importance and when a large bank is in trouble, public support is very likely—but the form of public support is the crucial aspect of market discipline and fairness.

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Controlling for size and financial soundness, foreign-owned banks (state or private) are significantly less likely to be bailed out using Kazakh taxpayer money than are domestic private banks—which may be still bailed out frequently as a counterfactual (the base category). PEPfamily controlled banks appear to be not different from local private banks because their coefficient is not statistically different from zero. As expected, domestic state banks are significantly more likely to be bailed out if needed and this finding illustrates the (implicit) contingent fiscal liability that state ownership of SOEs—both financial and non-financial ones—generates (Melecky, 2021).

Among the control variables, provisioning for expected credit losses decreases the relative log odds of being bailed out using public money while banks with higher interest margins (and risk-taking) are more likely bailed out (at the 10% significance level). Significant at 5% level, banks with greater overhead costs (less efficient ones) are more likely to be bailed out. Simply, healthier and more competitive banks may not require public resources to recover when in trouble—and active and timely supervision of financial soundness plays a crucial role in safeguarding taxpayer money.

Macroeconomic development, both real GDP growth and inflation, appears significant in explaining bailout probability. The negative estimated coefficients on both variables suggest that higher real GDP growth (positive demand shock) and inflation (positive price shock) decrease the probability that distressed banks will need to be bailed out by the state.

Overall estimates in Table 4 suggest that systemic importance is a relevant factor in explaining public bailouts, and that state ownership also significantly increases the likelihood that public money will be deployed to stabilize distressed banks. By contrast, foreign ownership reduces the probability of using local taxpayer money to stabilize distressed banks—and thus the occurrence of fiscal contingent liabilities from bank bailouts. We do not find significant evidence that bank ownership by PEP families predicts that the bank if distressed is less likely to exit the market through a private market solution or supervisory/resolution action, or be more likely bailed out relative to other local banks.

_6.3 How large are the fiscal expenditures on bank bailouts across different bailout instruments?_ This section analyzes the relationship between systemic importance, ownership, bailout structuring (financial tools), and public expenditure on bank bailouts (Table 5). It is not necessarily obvious that the same determinants for the probability of bank distress or government intervention, determine also the size of the fiscal cost of each intervention (Rosas, 2006). The estimation in Table 5 adjusts for possible selection bias using the inverse Mills ratio constructed based on the first-stage regression for bank bailout. The estimation results of the first-stage regression are reported in the Annex in Table A4.

Table 5 shows that, across estimation methods, we obtain consistent results in terms of the statistical significance of explanatory variables. The exception is the state ownership variable which becomes insignificant when running a linear panel regression for log-transformed (compressed scale) costs and including bank-level fixed effects. This may be expected as ownership does not vary greatly over time and bank fixed effects thus absorb most of the variation in this variable. The magnitude of coefficients varies between column 2 and column 3 in Table 5.

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This is also expected because total bail-out costs are a censored variable and panel regression models produce inconsistent estimates of the coefficients which may not approach the "true" population parameters while increasing the sample size.

**Table 5: Determinants of the total cost of government intervention**

||(1)|(2)|(3)|
|---|---|---|---|
||TOBIT MARGINS|TOBIT|PANEL REG|
|Variables|Bailout Expense|ln(Bailout Expense)|ln(Bailout Expense)|
|Sizet-1|14.135***|2.356***|0.164**|
||(3.575)|(0.336)|(0.065)|
|Family Local|-2.019|0.259|-0.137|
||(5.658)|(0.729)|(0.156)|
|State Local|9.618***|4.240***|0.051|
||(3.593)|(0.620)|(0.345)|
|NPE Bail Out|323.650*|8.389***|4.323***|
||(185.610)|(1.127)|(1.440)|
|Debt Bail Out|39.229***|6.409***|4.252***|
||(14.319)|(0.647)|(0.225)|
|Regulatory Capital / Total Assetst-1|5.641|0.806*|0.023***|
||(4.837)|(0.411)|(0.005)|
|Provisions / Gross Loanst-1|-3.570**|-0.537***|-0.006**|
||(1.712)|(0.129)|(0.002)|
|Interest Margin / Total Assetst-1|2.512|0.619|0.007|
||(2.988)|(0.443)|(0.005)|
|Liquidity Ratiot-1|-0.409**|-0.017|-0.001***|
||(0.204)|(0.024)|(0.000)|
|Overhead Costs / Incomet-1|0.676|0.112**|0.003**|
||(0.461)|(0.048)|(0.001)|
|Inverse Mills ratio|-334.299|-55.127**|-0.720***|
||(217.836)|(23.533)|(0.174)|
|System distresst-1|2.211|1.093**||
||(4.280)|(0.541)||
|Inflation rate|3.054|0.010||
||(2.123)|(0.127)||
|Real GDP growth|-8.265|-1.623**||
||(5.156)|(0.691)||
|Observations|442|442|442|
|Bank FE|No|No|Yes|
|Time FE|No|No|Yes|

Note: This table reports the estimation of equation 3. Column 1 reports the average partial effects obtained after a Tobit regression. Column 2 reports the coefficients after running a Tobit regression with the dependent variable computed as the natural logarithm of the total cost of government intervention. Column 3 reports the coefficients of a panel regression with the dependent variable computed as the natural logarithm of the bailout expense of government intervention, controlling for bank and time fixed effects. Because data is not available for some of the independent variables, the total number of observations is 442 instead of 447. Standard errors clustered at the bank level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

21
The estimated coefficient on the _size_ variable is positively and statistically significant, indicating that bigger banks are more costly to stabilize using public resources. The average marginal effect of size on the public expenditure for a bank bailout is 14.1 billion tenge (Table 5, Column 1). A one percent increase in the size of a troubled bank implies, on average, more than a 4-fold increase in bailout expenditures. This result is in line with evidence for the U.S. banks following the GFC where systemically important banks were more likely to receive larger government equity infusions (Bayazitova and Shivdasani, 2012). If the troubled bank is stateowned, the average marginal cost increases significantly by an additional 9.6 billion tenge.

If the bailout instrument is debt financing of the distressed bank, the additional cost relative to an equity injection reaches on average about 39 billion tenge. The costliest by far are bailouts using purchases of non-performing exposures (loans) in which cases the additional bailout expenditure compared with equity injections can reach, on average, 324 billion tenge.

The significance of the variables controlling for bank financial soundness—in particular, accumulated provisions and capitalization—suggests that banks with greater buffers against losses require much less public expenditure to stabilize (rehabilitate) and highlights the importance of independent and credible supervision of banks. The sign of the inverse of Mills ratio is estimated as negative, implying that unobservable factors that increase the probability of bank bailout may also decrease the cost of bailouts—however, the coefficient appears insignificant in the first Tobit regression (column 1).

# _6.4 Projecting expected contingent fiscal liabilities from bank bailouts for 2022-23_

We proceed with illustrative projections of fiscal contingent liabilities from bank bailouts by combining the estimates of the likelihood of bailing out a troubled bank with the average cost of a bank bailout depending on the applied bailout instrument. Specifically, we predict “annual” fiscal contingent liabilities from bank bailouts using the financial statement data for each bank from October 2022 published by the National Bank of Kazakhstan (NBK). We follow these steps:

- a) Obtain the October 2022 bank-level financial statement data,

- b) Compute the likelihood of a bailout for each bank by type of bailout instrument using estimated equation (2).

- c) Compute the average bailout expenditure for each bank using estimated equation (3).

- d) Use the bank financial data and the predicted bailout probabilities and expenditures obtained in the b), and c) steps. The expected annual contingent liability per bank is obtained by multiplying the predictions from (2) and (3) for each bank, and adjusting for the historical frequency with which each bailout instrument was used (equity 30%, debt 26% and NPE purchase 44% of the time on average during the studied sample). We then

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sum across banks to get the projected total annual government contingent liability from bank bailouts.

- e) Simulate the fiscal expense on bailout interventions using a)-d) under a more conservative scenario taking a more adverse value of coefficients (the value of the coefficient plus/minus one standard deviation, depending on the sign of the coefficient), and report a range of the projected fiscal contingent liabilities.

Multiplying the probability of bailout and public expenditure in case of the bailout (in less and more conservative systemic stress scenarios), the projected (forecasted based on in-sample estimates) total (banking system) fiscal contingent liabilities from bank bailouts can range between 1.1 trillion and 3.6 trillion tenge (US$2.5 billion and US$8.4 billion)<sup>v</sup> or 12 to 41 percent of planned tax revenues for 2022.<sup>w</sup> The projected average contingent liability from rescuing one troubled bank is 51 billion to 172 billion tenge. Between 6 to 8 banks may generate material fiscal contingent liabilities.<sup>x</sup> The anonymized calculation and projected fiscal contingent liabilities per bank are reported in Table 6.

> v We use the exchange rate of 431.8 tenge for one US dollar, which is the value observed by the end of 2021.

> w - - The planned tax revenue for 2022 is 8.9 trillion tenge <u>(https://kapital.kz/economic/104431/kakiye izmeneniya planiruyut-vnesti-v-byudzhet-na-2022-god.html).</u>

> x Future use of NPE bailout instrument was restricted in April 2023 by a regulatory amendment that imposed market valuation of NPE portfolios by professional appraisers and excluded the possibility of NPE purchase price being determined at book value. This follows from the Order of the Ministry of Finance #279 dated February 23, 2018, amended by the Order of the Deputy Prime Minister - Minister of Finance #430 dated April 27, 2023.

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**Table 6: Projected annual fiscal contingent liabilities from bank bailouts**

|**Bank**|**Probability**<br>**of bailout**|**Expected**<br>**loss Equity**<br>**(billion**<br>**KZT)**<br>**(lower**<br>**range)**|**Expected**<br>**loss Debt**<br>**(billion**<br>**KZT)**<br>**(lower**<br>**range)**|**Expected**<br>**loss NPE**<br>**(billion**<br>**KZT)**<br>**(lower**<br>**range)**|**Expected**<br>**loss Equity**<br>**(billion**<br>**KZT)**<br>**(upper**<br>**range)**|**Expected**<br>**loss Debt**<br>**(billion**<br>**KZT)**<br>**(upper**<br>**range)**|**Expected**<br>**loss NPE**<br>**(billion**<br>**KZT)**<br>**(upper**<br>**range)**|
|---|---|---|---|---|---|---|---|
|A|23%|513|657|1,015|898|1,119|1,713|
|B|11%|236|302|468|415|518|794|
|C|4%|98|126|195|170|212|326|
|D|4%|81|105|162|144|180|277|
|E|2%|42|54|85|76|95|146|
|F|2%|30|40|62|63|79|122|
|G|2%|33|43|66|61|77|118|
|H|1%|26|34|53|49|61|94|
|I|0%|3|4|6|7|9|14|
|J|0%|2|2|4|4|5|8|
|K|0%|1|2|3|3|3|5|
|L|0%|0|1|1|1|1|2|
|M|0%|0|0|0|0|0|0|
|N|0%|0|0|0|0|0|0|
|O|0%|0|0|0|0|0|0|
|P|0%|0|0|0|0|0|0|
|R|0%|0|0|0|0|0|0|
|S|0%|0|0|0|0|0|0|
|T|0%|0|0|0|0|0|0|
|U|0%|0|0|0|0|0|0|
|V|0%|0|0|0|0|0|0|

Note: The probability of bail-out is the conditional probability of bailout given distress multiplied by the probability of distress obtained from the probit model estimation (equation 1). It is computed using estimated regression equation (2) for banks that were subject to government intervention while requiring that all independent variables used in the calculation are statistically significant at least at the 10 percent level. The probability is then adjusted for the type of government intervention using historic frequencies. The lower-range estimated cost is obtained as the predicted value using estimated regression equation (3) while imposing that all independent variables used in the calculation are statistically significant at least at the 10 percent level. The upper-range estimated cost is obtained using all statistically significant predictors and considering the mean estimated coefficients plus one standard error. The lower-range expected loss is calculated as the product of bailout probability, and the estimated cost of bailout. The upper-range expected loss is calculated as the product of the bailout, and the upper-range estimated cost.

# **7 Conclusion**

This paper analyzed the link between bank bailouts (public support to the banking system) and fiscal costs in Kazakhstan. It showed that government interventions intensified during times of stress—namely during 2009-10, 2013-15, and 2017-20. The most fiscally costly was the period 24
2017-20 during which the fiscally most expensive approaches to resolving distressed banks were used. These approaches did not ensure adequate repayment of the fiscal expenses and alternative fiscal costs (forgone expenditure on healthcare and education for example) and may have created moral hazard incentives for repeated bailouts.

Our estimations suggest that, while systemic importance (proxied by bank size by assets) helps explain bank bailouts, the ownership by the state significantly increases the likelihood that public money will be deployed to stabilize such distressed banks. The likelihood of using public money to stabilize banks is inversely related to foreign ownership of banks. Our estimations confirm that bigger banks in distress are more costly to stabilize. We also find that if a troubled bank is state-owned, the bailout expenditure increases by approximately 10 billion tenge on average. Moreover, if the applied bailout instrument is a debt financing of the distressed bank (as opposed to equity injection), the public expenditure on bailouts increases by about 39 billion tenge —relative to using an equity injection. The costliest by far are bailouts using purchases of nonperforming loans in which case the relative additional public expenditure hikes, on average, by 324 billion tenge. The significance of the variables controlling for bank financial soundness—in particular, capitalization, provisioning, and profitability—suggest that sounder banks are much less costly to stabilize (rehabilitate) and highlights the importance of independent and credible supervision of banks. Using the estimated framework, the projected fiscal contingent liabilities from bank bailouts for 2022 could range between US$2.5 and US$8.4 billion or 12-41 percent of planned tax revenues for 2022.

The analytical results have important policy implications. Utilizing the bailout instruments allowed by the bank resolution law on non-market terms and in a non-transparent manner can increase moral hazard incentives on the side of banks. This approach also risks creating resentment among taxpayers and the broader public, who may question such allocation of state resources when there are pressing demands for development expenditure. Purchases of non-performing exposures (NPEs) of banks and stabilization of banks through debt instruments, if fairly priced (market to market or economic value), are permissible bank stabilization tools that reduce insolvency and systemic concerns as deemed socially desirable. However, it is the deviations from fair pricing that generate a direct impact on capital, unfair competitive advantage, and negative redistribution costs for the taxpayers. The bank resolution law and framework could be amended to limit the use of public funds to strictly systemically important banks on or close to market terms, with appropriate repercussions to the owners that mismanaged the bank and with an appropriate claim of the state on future profits of the bailed-out bank. The Financial Stability Committee may need to be further de-politicized by increasing the share of representatives from technocratic independent agencies (NBK and AFR). The FSC could possibly publish its decision on the use of public funds for the resolution of troubled systemically important banks with an appropriate time lag for greater transparency. The regulator could intensify fair and timely supervision including using forwardlooking stress tests based on regular asset quality reviews (AQRs) to ascertain a credible baseline for the stress test. Policy makers can consider using greater capital surcharges for _unconditionally_ systemically important banks and greater counter-cyclical capital buffers (capital surcharges) to more proactively and throughout the credit cycle limit the occurrence of unstable or failing _conditionally_ systemically important banks. The fiscal authority and public debt managers need to 25
regularly project fiscal contingent liabilities for the general government that can come from bank bailouts and consolidate them properly at the general government level to ensure bank bailouts do not endanger public debt sustainability and avoid crowding out of other essential developmental expenditures such as on education, health, and infrastructure.

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