metadata: authors: 0: author: Feyen,Erik H.B.
1: author: Alonso Gispert,Tatiana
2: author: Kliatskova,Tatsiana
3: author: Mare,Davide Salvatore
count: World
docna: 0: docna: Taking Stock of the Financial Sector Policy
            Response to COVID-19 around the World
docty: Policy Research Working Paper
subtopic: Financial Sector Policy,Health Care Services Industry,Public Health Promotion
teratopic: Industry,Health, Nutrition and Population
abstracts: cdata!: This paper introduces a new global
            database and a policy classification framework that records
            the financial sector policy response to the COVID-19
            pandemic across 154 jurisdictions. It documents that
            authorities around the world have taken a diverse array of
            measures to mitigate financial distress in markets and for
            borrowers, and to support the provision of critical
            financial services to the real economy. Measures that focus
            on the banking sector constitute the majority of policies
            taken and aim to take advantage of the flexibility embedded
            in the international standards. However, emerging markets
            and developing economies tend to rely more on prudential
            measures that go beyond this embedded flexibility compared
            with advanced economies, which may reduce bank balance sheet
            transparency and increase risks. Using Cox proportional
            hazards and Poisson regressions, the paper takes initial
            steps to analyze the determinants of policy makersâ€™
            responsiveness and activity in emerging markets and
            developing economies, respectively. The results indicate
            that policy makers have typically been significantly more
            responsive and have taken more policy measures in emerging
            markets and developing economies that are richer and more
            populous. Countries with higher private debt levels tend to
            respond earlier with banking sector and liquidity and
            funding measures. The spread of COVID-19, macro-financial
            fundamentals, and fiscal and containment policies appear to
            play a limited role. In a substantially smaller sample, the
            paper explores the role of banking characteristics and finds
            that emerging markets and developing economies with higher
            private credit levels and that have adopted Basel III
            features have taken fewer policy measures. Future work is
            necessary for better understanding the country determinants
            of the policy response as well as the effectiveness and
            potential unintended consequences of the measures.
repnme: repnme: Taking Stock of the Financial Sector Policy
            Response to COVID-19 around the World
display_title: Taking Stock of the Financial Sector
            Policy Response to COVID-19 around the World
listing_relative_url: /research/2020/12/32667001/taking-stock-financial-sector-policy-response-covid-19-around-world
topicv3: Health Emergencies m1326557 2165,Public Health m1327519 635,Financial Sector Development m1327681 1920,Health m1504661 3480,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2020-12-14T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population
origu: EFI-FCI-Finance-GD (EFNDR)
no_of_pages: 44
author_keys: 0: author_key: 783512
1: author_key: 1680518
2: author_key: 1816481
3: author_key: 1366794
model_extractions: Policy Research Working Paper 9497 Taking Stock of the Financial Sector Policy Response to COVID-19 around the World _Erik Feyen Tatiana Alonso Gispert Tatsiana Kliatskova Davide S. Mare_ Finance, Competitiveness and Innovation Global Practice December 2020
Policy Research Working Paper 9497

# **Abstract**

This paper introduces a new global database and a policy classification framework that records the financial sector policy response to the COVID-19 pandemic across 154 jurisdictions. It documents that authorities around the world have taken a diverse array of measures to mitigate financial distress in markets and for borrowers, and to support the provision of critical financial services to the real economy. Measures that focus on the banking sector constitute the majority of policies taken and aim to take advantage of the flexibility embedded in the international standards. However, emerging markets and developing economies tend to rely more on prudential measures that go beyond this embedded flexibility compared with advanced economies, which may reduce bank balance sheet transparency and increase risks. Using Cox proportional hazards and Poisson regressions, the paper takes initial steps to analyze the determinants of policy makers’ responsiveness and activity in emerging markets and developing economies, respectively. The results indicate that policy makers have typically been significantly more responsive and have taken more policy measures in emerging markets and developing economies that are richer and more populous. Countries with higher private debt levels tend to respond earlier with banking sector and liquidity and funding measures. The spread of COVID-19, macro-financial fundamentals, and fiscal and containment policies appear to play a limited role. In a substantially smaller sample, the paper explores the role of banking characteristics and finds that emerging markets and developing economies with higher private credit levels and that have adopted Basel III features have taken fewer policy measures. Future work is necessary for better unerstanding the country determinants of the policy response as well as the effectiveness and potential unintended consequences of the measures.

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 efeijen@worldbank.org, talonsogispert@worldbank.org, tkliatskova@worldbank.org, and dmare@worldbank.org.

_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
# **Taking Stock of the Financial Sector Policy Response to COVID-19 around the World**

**Erik Feyen, Tatiana Alonso Gispert, Tatsiana Kliatskova, and Davide S. Mare**<sup>1</sup>

_JEL Classifications:_ G18, G21, G23, G28, E58 _Keywords_ : Financial Regulation, COVID-19 Pandemic, Financial Stability, Government Policy 1 All authors are with the World Bank. We thank Ezio Caruso, Pierre-Laurent Chatain, Krishnamurti Damodaran, Matei Dohotaru, Valeria Salomao Garcia, Cedric Mousset, and Danilo Queiroz Palermo for screening and classifying the policy responses. We are grateful to Teymour Abdel Aziz, Philippe Marie Aguera, Karina Baba, Davit Babasyan, Ruben Barreto, Gracelin Christina Baskaran, Mariano Cortes, Fernando Dancausa, Alia Eldidi, Bryan Gurhy, Ejigayehu Teka Habte, Khuraman Hajiyeva, Syed Mehdi Hassan, Lucas Laurent Herzog, Paula Marcela Houser, Alena Kantarovich, Tanjit Sandhu Kaur, Katerina Levitanskaya, Antonia Preciosa Menezes, Harish Natarajan, Fahma B. Nur, Bujana Perolli, Guillermo Alfonso Galicia Rabadan, Deborah Sarpong, Margaux Louise Seeuws, Jiyoung Song, Venkat Bhargav Sreedhara, Radu Tatucu, Natalia Tsivadze, Tim De Vaan, Roya Vakil, Qianye Zhang for sharing policy measures taken in their respective countries, regions or thematic areas. We are thankful for comments, suggestions, and support from: Irina Astrakhan, Mario Guadamillas, Djibrilla Issa, Esperanza Lasagabaster, Yira J. Mascaro, Zafer Mustafaoglu, Cecile Thioro Niang, Douglas Pearce, Consolate K. Rusagara, Anderson Caputo Silva, Rashmi Shankar, Mahesh Uttamchandani, and Niraj Verma. We are also grateful to Katia D’Hulster, Jongrim Ha, Diego Mauricio Herrera Falla, Gene Kindberg-Hanlon, Ulf Lewrick, Fabiana Melo, Cedric Mousset, and Jean Pesme for detailed suggestions and comments. We appreciate technical support from Ramin Aliyev and research assistance from Matthias Poser. We thank Alfonso Garcia Mora for early guidance and support. 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: <u>efeijen@worldbank.org, talonsogispert@worldbank.org, tkliatskova@worldbank.org, and dmare@worldbank.org.</u>
# **1 Introduction**

The macro-financial shock caused by the COVID-19 pandemic precipitated a global economic recession and put severe pressure on financial markets and institutions around the world. Policy makers reacted by implementing an unprecedented array of public health, fiscal, monetary, macroprudential, and financial measures to contain the spread of the virus and support the real economy. For example, authorities introduced travel bans, mandated the closure of businesses, limited social gatherings, and scaled up unemployment and social protection programs. Financial sector authorities undertook a complementary, wide-ranging set of temporary measures to preserve the well-functioning of core markets and maintain the provision of critical financial services to the real economy, including credit and payments, while at the same time safeguarding prudent risk management standards, bank balance sheet transparency, financial resilience, and a globally coordinated response.

To date, financial sector policy measures have focused on providing liquidity to financial institutions and markets (e.g., lowering reserve requirements, purchasing financial assets); maintaining operational and business continuity (e.g. extensions of deadlines on supervisory reporting); facilitating the flow of credit and supporting borrowers that face short-term repayment difficulties either directly (e.g., lowering interest rates, introducing debt repayment moratoria, facilitating loan restructuring, offering government guarantees and loans to affected sectors) or by providing regulatory relief (e.g., encouraging banks to use available capital and liquidity buffers, allowing for the flexible treatment of non-performing loans and asset classification). Several standard-setting bodies for the financial sector have also issued guidance (e.g., encouraging and clarifying the use of the flexibility embedded in the global standards) to ensure a coordinated global response and some have deferred the implementation of components of certain global standards, notably Basel III.

This paper makes three contributions. First, to support policy formulation and evaluation by policy makers and researchers, the paper introduces a new global database and a simple policy classification framework. Containing over 3,100 individually classified measures, the database provides a comprehensive, though not exhaustive, overview of the financial sector policy response to the COVID-19 crisis, based on publicly available information. Second, the paper documents patterns of the measures taken around the world and introduces the _COVID-19 Financial Policy Response Activity Index_ , a simple country-level indicator that tracks the total number of measures taken. Third, as an initial step towards understanding the determinants of the policy response, the paper explores the association between relevant country characteristics (i.e., domestic exposure to COVID-19 and fiscal, economic, and financial fundamentals) and policy makers’ 2
reaction time and financial sector policy making activity, respectively. An in-depth analysis of temporary policy measures (for example, the effectiveness and potential unintended consequences of policy interventions) is left for future research.

# _1.1 Overview of policy response surveillance initiatives_

Various surveillance efforts exist which track the policy responses to COVID-19. Some take stock of a broad range of interventions, such as fiscal, monetary and prudential policies (e.g. IMF Policy Tracker, COVID-19 Financial Response Tracker (CFRT) by the Yale Program on Financial Stability, IDB Summary of Policy Measures, and OECD Policy Tracker); others focus exclusively on prudential regulatory measures (e.g. Institute of International Finance’s Regulatory Measures) or containment and economic support measures (e.g. Oxford COVID-19 Government Response Tracker (OxCGRT)).

Compared to these projects, our database offers three distinctive features. First, it tracks the financial sector policy response with a focus on emerging markets and developing economies (EMDEs),<sup>2</sup> although it also covers several advanced economies. Second, the database adopts a tiered classification system which provides a typology of policy measures to facilitate analysis. Third, each measure is dated and accompanied by the relevant authority which implemented the measure. Where available, a link to the primary source is also provided as well as a termination or extension date, if applicable. Taken together, these features enhance the ability of both policy makers and researchers to comprehensively analyze and benchmark policy responses and their impacts at different stages of the pandemic.

# _1.2 Related literature_

This paper contributes to a small, but burgeoning literature that explores the impact of COVID-19 on the financial sector, analyzes drivers of the policy response and evaluates policy effectiveness.

**Impact of COVID-19 on the financial sector.** The financial sector has been put under strain by the COVID-19 crisis. Core markets became dislocated, EMDEs experienced massive capital outflows, and some borrowers faced liquidity and repayment challenges (e.g., BIS (2020), IMF (2020), and Powell and Rojas-Suárez (2020)). Due to a concerted effort by policy makers, market functioning was largely restored,

> 2 In this paper, we define EMDEs as all countries that are not in the high-income group according the World Bank country classification.

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risk asset prices rebounded, capital flows to EMDEs resumed, and credit to the real economy was largely maintained. However, downside risks still loom large and legacy challenges of elevated debt and nonperforming loan levels may adversely interact with the pandemic’s impact in some countries. The pandemic represents the first major test of the G20 global regulatory reforms since the Global Financial Crisis. As a result of these reforms, the global financial system entered the pandemic on a strong footing and was able to mitigate rather than amplify the shock (Financial Stability Board, 2020). The sector also plays a central role in contributing to the foundations of economic recovery (Beck, 2020).

**Determinants of the policy response.** Few studies exist that provide a cross-country overview and rationale for the usage of different policy instruments to mitigate the impact of the COVID-19 crisis. Benmelech and Tzur-Ilan (2020) analyze the determinants of fiscal and monetary policies during the COVID-19 crisis. This study shows that high-income countries, and especially those with high credit ratings, announced larger fiscal policy packages. Further, high-income countries used predominantly nonconventional monetary policy as they entered the crisis with interest rates close to the zero-lower bound. Moreover, some central banks in EMDEs were able to cut interest rates even in the face of currency depreciation and capital outflows in part because cross-border spillovers of monetary policy intervention in advanced economies reduced the pressure to engage in procyclical domestic policies (Aguilar and Cantú, 2020).

**Effectiveness and impact of the policy response.** As the pandemic continues to unfold and the outlook remains uncertain, the literature on the effectiveness of the policy response remains sparse, but a few exceptions exist. First, quantitative easing proved to be effective at boosting prices and lowering spreads in the United States (Haddad et al., 2020) as well as reducing local government bond yields both in developed and developing economies (Hartley and Rebucci, 2020). Second, distinguishing between fiscal transfers and credit policies, Bigio et al. (2020) show that the former is preferable when debt limits are tight, whereas the latter is preferable when they are slack. Moreover, a credit policy has the advantage of targeting fiscal resources toward agents that matter most for stabilizing demand. In this regard, Li et al. (2020) analyze credit provision by banks in the United States and show a much larger increase in lending at banks near large COVID-19 outbreaks. Pre-crisis financial conditions did not limit US banks’ ability to supply liquidity, mostly due to inflows of funds from both the Federal Reserve and strong capital positions prior to the crisis. Third, with regards to the influence of stabilization policies on the banking sector, Aldasoro et al. (2020) suggest that policy measures have favored banks with higher profitability and healthier balance sheets, while less profitable banks saw their long-term rating outlooks revised to negative and their CDS spreads

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continued increasing. Using the database presented in this paper, Demirgüç-Kunt et al. (2020) show that measures of liquidity support, borrower assistance, and monetary easing moderated the adverse impact of the pandemic on banks’ stock prices. However, the effect is heterogeneous across banks and countries. Banks that were already undercapitalized and/or operated in countries with little fiscal space were adversely affected by borrower assistance and prudential measures. Finally, there is not much evidence on the effect of emergency prudential regulations and banks’ internal policies on banks per se. Bergant and Kockerols (2020) show that for the case of Ireland the most common measure taken by banks, that is an increase in drawdown limits of credit lines, was effective in the short run, but no other measure (e.g., suspension of installment payments, the extension of loan maturity, credit rollover, and decrease in interest rates) significantly reduced the probability of default in the long run. Moreover, forbearance and new lending are interlinked, as new lending is subdued because capital and other resources within the bank are allocated to forborne loans. On the contrary, for a sample of European banks, Altavilla et al. (2020) argue that in the absence of funding and capital relief, banks’ ability to supply credit to the real economy would have been severely affected.

The remainder of the paper is structured as follows. Section 2 introduces the COVID-19 Financial Policy Response Database and the policy classification framework. Section 3 describes the methodological approach to analyze the determinants of the policy response and Section 4 documents the empirical results. Section 5 concludes.

# **2 COVID-19 Financial Policy Response Database**

In this paper, we present a new global database that collects financial sector policy measures that have been publicly announced by the governments, central banks, and financial sector authorities in 154 jurisdictions since the outbreak of the COVID-19 pandemic (see Appendix C for the list of countries). The database offers a regularly updated repository of emergency measures, adopted by domestic authorities with a focus on EMDEs.<sup>3</sup> Policy measures and guidance provided by a selected group of large advanced economies and

> 3 The COVID-19 Financial Policy Response Database is updated regularly and is publicly available at the following link: https://datacatalog.worldbank.org/dataset/covid-19-finance-sector-related-policy-responses.

5
relevant supranational authorities (central banks in regional monetary unions) are also included in the database given their role as benchmarks to ensure a globally coordinated policy response.<sup>4</sup>

# _2.1 Classification of measures_

# _Measures that focus on liquidity and funding conditions_

In the first phase of the pandemic, in many countries market liquidity evaporated and funding markets were severely strained. Large capital outflows from many emerging markets amplified market moves and eclipsed the outflows seen during the 2007-2009 Global Financial Crisis. Many central banks reacted by easing monetary conditions and injecting domestic currency liquidity through banks. In some cases, exceptional measures were taken and liquidity was also provided through standing credit facilities to nonbank financial institutions and corporates in affected sectors, offering lending on favorable terms and longer tenors. The primary objectives of these measures were safeguarding liquidity conditions and ensuring the smooth flow of credit from banks to the real economy (Cavallino and De Fiore, 2020 and Lane, 2020). Central banks in advanced, and for the first time in several emerging markets, have relied on local government bond purchases to ease financial conditions and restore liquidity in local capital markets (Benigno et al., 2020). Moreover, confronted with outflows that caused currency depreciation and volatility and US dollar shortages, several monetary authorities in EMDEs intervened in foreign exchange markets and established temporary swap lines with other central banks, notably the US Federal Reserve.

# _Measures that focus on the banking sector_

Many authorities around the world implemented temporary relief measures in support of borrowers and to ensure the flow of credit to the real economy while safeguarding banks’ resiliency (Drehmann et al., 2020). These measures seek to avoid a rise of insolvencies of cash-strapped, but otherwise viable businesses,<sup>5</sup> by providing direct support to borrowers in the form of, inter alia, public guarantees for bank loans, state

> 4 In addition to EMDEs, the following jurisdictions are tracked in the database: United States, United Kingdom, Japan, European Union/European Monetary Union (European Central Bank, Single Supervisory Mechanism and other European Union agencies), Standard Setting Bodies (SSBs), Australia, Canada, Germany, France, Italy, and Spain.

> 5 Around 50 percent of firms do not have sufficient cash buffers to cover their debt-servicing and operating costs as a result of the COVID-19 shock (Banerjee et al., 2020). Moreover, a simple balance sheet stress test based on pre-COVID-19 data suggests that non-financial companies in EMDEs may be vulnerable to liquidity and earnings shocks (Feyen et al., 2020).

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subsidies, debt repayment moratoria, or encouraging loan restructuring.<sup>6</sup> This category also includes all prudential measures seeking to support and encourage the use of the flexibility embedded in global prudential standards (e.g. the use of capital and liquidity buffers,<sup>7</sup> the treatment of restructured loans, the treatment of non-performing exposures) while setting supervisory expectations about the use of such flexibility, for example by introducing payout restrictions (to ensure that released buffers are used to maintain the flow of credit) and transparency requirements. This category also includes measures aimed at bringing prudent flexibility to financial integrity requirements to help address COVID-19 related challenges (e.g., supporting digital onboarding, simplified due diligence). This category also includes crisis management measures, but few have been taken thus far (mainly introducing or modifying resolution tools and deposit guarantee funds so that they are fit for purpose in case of need).

# _Measures that focus on financial markets and non-bank financial institutions (NBFIs)_

Several countries banned short selling to curtail market volatility and some even decided to temporarily close their financial markets when circuit breakers were triggered. Market authorities also issued prudential and conduct measures to ensure the proper functioning of financial markets amid the crisis and to give guidance and support to market players other than banks such as asset managers and insurance companies (NBFIs). This category also includes public debt management actions, although few have been taken so far. However, strains in capital markets may prompt policy makers to adjust their debt management strategies including identifying funding from other sources to reduce pressure on traditional wholesale market borrowing.

# _Measures that focus on payments and financial market infrastructures_

Several countries took measures to ensure the smooth functioning of market infrastructures, notably the payment systems, including the relaxation of non-essential compliance requirements. Most importantly, financial authorities ensured the availability and acceptance of cash and digital payment methods. Among

> 6 See, for example, EBA (2020) for an overview of debt moratoria and public guarantee schemes in the European banking sector.

> 7 The estimates by Lewrick et al. (2020) suggest that the release of capital buffers (countercyclical capital buffers and other supervisory and management buffers) could unlock about US$5 trillion of additional loans, or 6% of total outstanding loans. In addition, restrictions on dividends can improve the effectiveness of the countercyclical capital buffer release as well as ensure provision of credit to firms and households (Muñoz, 2020; Beck et al., 2020).

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other reasons, this has been essential to disburse relief payments from governments to firms and individuals (e.g., through digital financial services) and to mitigate the shock to remittances flows, especially in lowincome countries.

# _2.2 Patterns of policy measures taken as of September 1, 2020_

As of September 1, 2020, the database contains more than 3,100 individual financial policy response measures.<sup>8</sup> Table 1 shows the measures by category (Level 1) and focus area within each Level 1 category (Level 2). Figure 1 presents the evolution of the cumulative number of measures taken since the World Health Organization (WHO) declared the COVID-19 a public health emergency of international concern on January 30<sup>th</sup> (Appendix D provides a country-level summary overview). Most financial sector measures pertain to the banking sector, followed by liquidity and funding measures. In absolute terms, EMDEs account for 58% of the total number of measures tracked. By region (ignoring that some regions comprise more countries than others), Latin America and the Caribbean (LAC), Sub-Saharan Africa (SSA), and Europe and Central Asia (ECA) each account for approximately 13%; the East Asia and Pacific (EAP) region and the South Asia Region (SAR) account for 8% and 9% of the global total respectively, and the Middle East and North Africa (MENA) region for 3%, the lowest overall tracked activity.

All of the 154 countries covered in the database issued at least one measure, 95% put in place at least two, and 71% at least three. Globally, most measures fall into the _Banking Sector_ category (54%), followed by _Liquidity and funding_ (25%). In both categories, almost all countries have taken at least one policy action. Conversely, less than 60% of countries have adopted at least one measure in the _Payment Systems_ or _Financial Markets and NBFIs_ categories.

Figure 1 shows that of all measures taken by September 1, 2020, 39% were put in place by April 1<sup>st</sup> and 80% by June 1<sup>st</sup> (see Appendix B for charts by region). A similar pattern is observed for the _Banking Sector_ category, where 37% and 80% of the measures in this group were undertaken by April 1<sup>st</sup> and by June 1<sup>st</sup> , respectively. For _Liquidity and funding_ measures, relatively more measures were put in place early in the observation period with 46% of measures recorded by April 1<sup>st</sup> and 78% by June 1<sup>st</sup> . Similarly, most of the

> 8  As of September 1, the data comprised 3,166 individual measures taken in 154 jurisdictions. In this paper, we do not consider data for Micronesia and measures issued at the G20 level. We also do not consider unclassified measures (i.e., category “Other”) or measures related to insolvency frameworks. Thus, the total number of policy measures summarized in this paper is 3,129 taken by 154 jurisdictions.

8
measures recorded in the _Payment Systems_ category have been taken by June 1<sup>st</sup> (92%), and around 43% were implemented by March 1<sup>st</sup> . Lastly, relatively fewer _Financial Markets and NBFI_ measures were put in place by April 1<sup>st</sup> (32%) and June 1<sup>st</sup> (75%).

The five most frequent focus areas (Level 2) represent 80% of all measures: _Prudential_ (28%), _Borrower Support_ (22%), _Liquidity Support_ (15%), _Policy Rates_ (7%), and _Digital Payments_ (6%). Within the set of _Prudential_ measures, over two-thirds aim to facilitate the banks’ role to maintain lending. These measures aim, inter alia, at introducing repayment moratoria, supporting the restructuring of loans, providing flexibility in the treatment of non-performing loans, releasing or deferring capital buffers, and offering guidance on supervisory expectations, for example regarding the distribution of payouts or the treatment of loans covered by debt repayment moratoria.

The database shows differences in the policy mix for high-income countries (HICs) and EMDEs:

- HICs have made greater use of measures targeting the banking sector (62% of measures versus 48% in EMDEs), prudential measures in particular. For the other sub-categories (direct support to borrowers, crisis management, and financial integrity) there are no substantial differences.

- HICs and EMDEs show a similar prevalence of measures aimed at ensuring the functioning of markets (14% of measures versus 12% in EMDEs), with a (slightly) higher prevalence in HICs of targeting non-bank financial institutions.

- EMDEs have made wider use of liquidity measures, including foreign currency liquidity (28% of measures versus 22% in HICs). HICs have a higher relative incidence of asset purchases measures, while EMDEs have adjusted policy rates more often.

- EMDEs have implemented relatively more payment measures (12% of measures versus 3% in HICs), mostly to promote and ensure the availability of digital payment mechanisms (e.g. suspension of fees and commissions at ATM and digital payment options; increased thresholds for allowed maximum transfers through mobile channels).

Although an in-depth analysis of the prudential regulatory response is beyond the paper’s scope, preliminary analysis of the measures in the _Banking Sector_ category shows several differences between HICs and EMEs:

- HICs and EMDEs have encouraged the release of capital buffers to a similar extent. However, many EMDEs, especially low-income economies (LICs), have mostly refrained from banning or discouraging discretionary capital distributions.

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- Some EMDEs, especially middle-income economies (MICs), have eased macroprudential tools other than countercyclical capital buffers (e.g. limits on loan-to-value or debt-to-income ratios) to support the flow of credit.

- Whereas both HICs and EMDEs have allowed some credit restructuring for loans with a public guarantee, a significant number of LICs and MICs have also encouraged credit restructuring without public guarantees.

- Compared with HICs, EMDEs show a lower prevalence of measures to underpin market discipline by preserving transparency through risk reporting.

- EMDEs have relied comparatively more than HICs on debt moratoria. Low-income and middleincome EMDEs show a high incidence of non-mandatory moratoria.

- EMDEs show a higher reliance on measures that run counter to the principles of global standards, including through the relaxation in the classification and provisioning of non-performing loans (NPL) or reduced risk weights even without a public guarantee. In many cases, this relaxation was not accompanied by measures aimed at preserving balance sheet transparency and proper underwriting standards, which could threaten the sector’s soundness in the medium term. International organizations have provided guiding recommendations for prudential regulators to navigate through the flexibility embedded in the global standards, pointing out that prudential policies that aim at stabilizing and stimulating short-term economic growth should not compromise medium-term stability and transparency of the financial sector (e.g., Borio and Restoy (2020); IMF, 2020; IMF and WB, 2020).

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# **Figure 1** **<mark>: Cumulative Number Financial Sector Policy Measures Around the World</mark> (up to September 1**<sup>**st**</sup> **, 2020)**

<!-- Start of picture text --> Banking Sector Financial Markets/NBFI<br>Liquidity/funding Payments systems<br>01feb2020 01mar2020 01apr2020 01may2020 01jun2020 01jul2020 01aug2020 01sep2020<br>3000<br>2000<br>1000<br># of measures taken<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database.

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# **Table 1: Classification of the Financial Sector Policy Response to COVID-19**

# _Panel A. Cumulative number of measures taken (up to September 1_<sup>_st_</sup> _, 2020)_

|**Category**<br>**Focus**|All<br>countries|High<br>Inc.|EAP|ECA|LAC|MENA|SAR|SSA|
|---|---|---|---|---|---|---|---|---|
|**Banking Sector**|**_1694_**|**_811_**|**_116_**|**_228_**|**_187_**|**_53_**|**_157_**|**_142_**|
|Prudential|861|486|42|121|70|17|59|66|
|Support borrowers|703|270|60|91|97|35|83|67|
|Integrity|63|28|5|7|12|0|7|4|
|Operational continuity|53|23|6|4|7|1|8|4|
|Crisis management|14|4|3|5|1|0|0|1|
|**Financial Markets and NBFIs**|**_399_**|**_177_**|**_38_**|**_43_**|**_57_**|**_2_**|**_45_**|**_37_**|
|Market functioning|215|93|17|24|33|2|32|14|
|Non-bank financial institutions|134|75|8|15|13|0|9|14|
|Public debt management|50|9|13|4|11|0|4|9|
|**Liquidity and funding**|**_788_**|**_281_**|**_71_**|**_88_**|**_138_**|**_17_**|**_66_**|**_127_**|
|Liquidity support|483|173|37|52|92|8|46|75|
|Easing of policy rates|232|49|33|35|42|9|17|47|
|Assetpurchases|73|59|1|1|4|0|3|5|
|**Payments Systems**|**_248_**|**_36_**|**_17_**|**_24_**|**_25_**|**_22_**|**_29_**|**_95_**|
|Digital payments|194|26|15|16|20|16|21|80|
|Relaxation of compliance requirements|19|3|0|3|2|0|2|9|
|Cash / Cheque usage restrictions|18|2|0|2|1|4|4|5|
|Cash acceptance|17|5|2|3|2|2|2|1|
|**TOTAL**|**_3129_**|**_1305_**|**_242_**|**_383_**|**_407_**|**_94_**|**_297_**|**_401_**|

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_Panel B. Number of countries that took at least one measure (up to September 1_<sup>_st_</sup> _, 2020)_

|**Category**<br>**Focus**|All<br>countries|High<br>Inc.|EAP|ECA|LAC|MEN<br>A|SAR|SSA|
|---|---|---|---|---|---|---|---|---|
|**_Total # of countries_**|**_154_**|**_37_**|**_14_**|**_19_**|**_25_**|**_11_**|**_8_**|**_40_**|
|**Banking Sector**|**150**|**37**|**14**|**19**|**25**|**11**|**8**|**36**|
|Prudential|117|32|11|18|20|4|7|25|
|Support borrowers|141|37|14|19|24|11|8|28|
|Integrity|48|24|3|7|7|0|4|3|
|Operational continuity|41|18|6|4|5|1|3|4|
|Crisis management|11|3|3|3|1|0|0|1|
|**Financial Markets and NBFIs**|**_85_**|**_28_**|**_7_**|**_12_**|**_9_**|**_1_**|**_4_**|**_24_**|
|Market functioning|65|23|7|10|8|1|4|12|
|Non-bank financial institutions|50|26|4|6|7|0|2|5|
|Public debt management|28|6|3|4|4|0|2|9|
|**Liquidity and funding**|**_143_**|**_36_**|**_13_**|**_18_**|**_22_**|**_7_**|**_8_**|**_39_**|
|Liquidity support|125|33|12|13|20|5|8|34|
|Easing of policy rates|106|25|12|15|18|5|5|26|
|Assetpurchases|30|18|1|1|3|0|2|5|
|**Payments Systems**|**_90_**|**_21_**|**_6_**|**_10_**|**_12_**|**_7_**|**_6_**|**_28_**|
|Digital payments|82|17|6|9|11|7|6|26|
|Relaxation of compliance requirements|17|3|0|2|1|0|2|9|
|Cash / Cheque usage restrictions|14|2|0|2|1|2|2|5|
|Cash acceptance|15|4|2|3|2|2|1|1|

Source: World Bank COVID-19 Financial Sector Policy Response Database.

Note: World Bank regional classification. High Inc. = High income; EAP = East Asia Pacific; ECA = Europe & Central Asia; LAC = Latin America & Caribbean; SAR = South Asia; MENA = Middle East & North Africa; and SSA = Sub-Saharan Africa. WBG regions exclude high income countries.

# _2.3 Financial Policy Response Activity Index_

This section introduces the _Financial Policy Response Activity Index_ (FPRAI), a simple, transparent proxy that can be used to compare financial sector policy activity across countries _._ FPRAI is computed by summing up all the policy measures implemented up to time ( _t_ ) in country ( _c_ ) across the four categories: _Banking Sector_ , _Financial Markets and NBFIs_ , _Liquidity and Funding_ , and _Payments Systems_ .<sup>9</sup> As such, it is important to keep in mind that the FPRAI is silent on the _scale_ and _effectiveness_ of the financial policy response. Figure 2 displays the FPRAI and shows that most financial sector policy makers around the world have been active, although to a lesser extent in Sub-Saharan Africa (see Table A3 in Appendix A for the descriptive statistics of FPRAI by country groupings).

> 9 Alternative, less transparent approaches, such as min-max transformations and averaging across categories produce similar results.

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<!-- Start of picture text --> Bn a A ’ aX, m<br>:<br>IBRD 45396 | NOVEMBER 2020<br><!-- End of picture text -->
**Figure 3. Financial Policy Response Activity Index Across Regions (September 1**<sup>**st**</sup> **, 2020)**

<!-- Start of picture text --> HInc EAP ECA LAC MENA SA SSA<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database. Note: Boxes show the median and the interquartile range. Dots show outliers.

Note: World Bank regional classification. High Inc. = High income; EAP = East Asia Pacific; ECA = Europe & Central Asia; LAC = Latin America & Caribbean; SAR = South Asia; MENA = Middle East & North Africa; and SSA = Sub-Saharan Africa. WBG regions exclude highincome countries.

**Figure 4. Financial Policy Response Activity Index and Selected Country Characteristics (September 1**<sup>**st**</sup> **, 2020)**

**Panel A. Log cumulative COVID-19 cases per Panel B. Log population size 100,000 people**

<!-- Start of picture text -->
-2 0 2 4 6 8 10 15 20<br>Log cumulative cases per 100,000 Log Population size<br> High Inc  EAP  ECA  LAC   High Inc  EAP  ECA  LAC<br> MENA   SA   SSA   MENA   SA   SSA<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>Financial Policy Response Activity Index Financial Policy Response Activity Index<br>0 0<br><!-- End of picture text -->

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# **Panel C. Log GDP per capita**

# **Panel D. COVID-19 Policy Stringency Index**

<!-- Start of picture text --> 0 20 40 60 80<br>7 8 9 10 11 Average over the period by country of Oxford Stringency Index<br>Natural logarithm GDP per capita High Inc EAP ECA LAC<br> EAP ECA LAC MENA SA SSA MENA SA SSA<br>100 100<br>80 80<br>60<br>60<br>40<br>40<br>20<br>20 Financial Policy Response Activity Index<br>Financial Policy Response Activity Index<br>0<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database, Hale et al. (2020); World Health Organization (WHO) and World Bank World Development Indicators; authors’ calculations. Note: For the Policy Stringency Index (Panel D), the chart shows the average between January 30<sup>th</sup> and September 1<sup>st</sup> , 2020.

# **3 Determinants of the Financial Sector Policy Response to COVID-19: Methodological Framework**

# _3.1 Modeling the time until the first policy measure is taken in each category_

The variable of interest is the time elapsed (in days) between the date when a country undertook its first policy measure in a specific category (e.g., _Banking Sector_ ) and January 30, 2020, the date when WHO declared COVID-19 to be a Public Health Emergency of International Concern (PHEIC).<sup>10</sup> An “event” is recorded the first time a country takes a policy measure in one of the four categories described in section 2.1. Thus, we are analyzing four separate events by following each country during the period between January 30, 2020, and September 1, 2020, the end of the study period. Countries that have not (yet) implemented a policy in a specific category as of September 1, 2020 are right-censored.<sup>11</sup> 10 The timing of the spread of the virus was uneven across different regions and, therefore, advanced economies that were affected by the pandemic first might have also been the first to respond. At the same time, many countries had very few or no cases at all, but their financial systems were exposed through cross-border financial and economic linkages with the rest of the world (i.e. spillovers from other economies under lockdowns). Therefore, we chose to use January 30, 2020 as a starting point, the date the WHO declared the COVID-19 outbreak to be a Public Health Emergency of International Concern, and many countries started responding (often preemptively) to the health and economic crisis.

11 In econometric models that model durations, right censoring arises when the specific event under analysis has not transpired during the study period (but it could occur at a later point in time). Other useful concepts for our analyses are “state”, that is whether a country has already taken an action or otherwise; “transition” or moving from no action to implementing a specific policy; and “duration” (also known as “spell length” or “lifetime”) or the time taken to implement a policy.

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We employ two widely used methodologies in the survival analysis literature: Kaplan-Meier (KM) survival curve estimates and Cox Proportional Hazards regression. The KM estimator provides the unconditional probability of the occurrence of an event – in this case, the first time a policy measure in a specific category is taken – during a certain interval (Kaplan and Meier, 1958). This non-parametric approach allows us to estimate and compare separate survival curves for country groups with different characteristics (e.g. high versus low income).

One limitation of KM estimates is that they do not allow for multivariate analysis. We, therefore, employ Cox proportional hazards regression (Cox, 1972; 1975), a semi-parametric approach. A key assumption underlying Cox regression is that the independent variables scale a baseline hazard function 0 that only depends on time. In our case, the unit of time _t_ is a day. λλ (tt) ) (1) cc cc 00 cc where the subscript λλ (tt|XX , ββ) = λλ (tt) × e _c_ denotes a country andee (ββXX is the conditional hazard rate. The Cox model does not impose any functional form on 0 . The multiplicative term cc ) (also known as scale factor) λλ comprises a vector of country characteristics ( ) that affects the hazard function cc . The relevant country λλ (tt) exp (ββXX characteristics are described in the next section. cc cc XX λλ

# _3.2 Country characteristics_

In this section, we describe four groups of country characteristics that may help explain the policy response to COVID-19 for which there are sufficient data across countries: domestic exposure to COVID-19; COVID-19 containment and economic support policies; economic development; macro-fiscal fundamentals; and banking sector characteristics.

**Domestic exposure to COVID-19.** Higher exposures to COVID-19 call for a stronger policy response. We compute the number of days elapsed since the WHO declaration, for the cumulative COVID-19 cases to exceed 100 people as a proxy for the sense of urgency to respond to the spread. We also test the sensitivity of our estimates using the cumulative number of confirmed COVID-19 cases per 100,000 people as a relative measure of exposure to COVID-19. The data are taken from the World Health Organization.<sup>12</sup>

> 12 A description of the data and the methodological underpinnings can be found here: https://covid19.who.int/.

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**COVID-19 containment and economic support policies.** Policies that aim to restrict community mobility to stem the spread of COVID-19 have also impeded economic activity and revenue mobilization which puts pressure on public, private, and financial balance sheets. Many countries have adopted fiscal stimulus measures to stimulate the economy. Some of these measures directly support the financial sector through guarantees and may therefore necessitate or complement a financial sector policy response. The data are taken from the Oxford Government Policy Response Tracker (see Appendix A for more detail).

**Economic development.** The level of economic development can be interpreted as a broad proxy of available economic resources and buffers to respond to COVID-19, as well as differences in economic structure, financial development, and institutional frameworks. We expect countries with higher economic development to be more active in their policy response as highlighted by Benmelech and Tzur-Ilan (2020). The notable exception could be the policy response on the _Payments Systems_ category. As more developed economies have more developed payment systems,<sup>13</sup> additional policy measures in this category could be less needed. Therefore, the observed correlation between economic development and policy response might be negative.  We use GDP per capita (expressed in current international dollars converted by purchasing power parity (PPP) as per the end of the year 2019 as a measure of economic development. The data are taken from the World Bank’s World Development Indicators.

**Macro-financial fundamentals.** On the one hand, the current account, fiscal deficits, and high levels of public and private debt may limit the capacity to mount an effective policy response. On the other hand, external and debt sustainability vulnerabilities as well as crowding out effects could amplify the economic shock of the COVID-19 prompting authorities to respond faster and at a larger scale. We use four indicators: the 2019 current account balance from the IMF World Economic Outlook (April 2020); the 2019 general government net lending (borrowing) taken from the IMF World Economic Outlook (April 2020);  and the 2018 total private-sector debt and total public sector debt as a percentage of GDP sourced through the World Bank and IMF Global Debt databases.

**Banking sector characteristics.** The initial state of the banking sector may also influence the reaction function of financial sector authorities. We explore the role of the following variables: regulatory Tier 1 capital to risk-weighted assets (IMF Financial Soundness Indicators, end-2019), ii) non-performing loans as a percent of total gross loans (IMF Financial Soundness Indicators, end-2019), iii)  domestic credit to the

> 13 https://www.bis.org/statistics/payment_stats/commentary2011.htm.

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private sector as a percent of GDP (World Bank FinStats, end-2019), and iv) adoption of the capital requirements of the Basel III framework (which include the creation of new buffers and the need for more and higher-quality capital) (World Bank Bank Regulation and Supervision Survey, 2016). However, the country sample size decreases significantly, so we report regression results in Appendix C as they need to be interpreted with more care.

# **4 Determinants of the Financial Sector Policy Response to COVID-19: Empirical Results**

# _4.1 Time until the first policy response is taken: Kaplan-Meier survival estimates_

Table 2 reports the descriptive statistics on the time taken (measured in days) to implement the first policy measure by category.<sup>14</sup> With a median of 50 days, _Banking Sector_ and _Liquidity and Funding_ measures were implemented faster compared to other categories. By the end of March 2020, around 21 percent of countries had taken their first measure in the _Banking Sector_ and _Liquidity and Funding_ categories. In contrast, by that time, around 10 percent of countries had implemented measures in the _Financial Markets and NBFIs_ and _Payments Systems_ categories.

**Table 2. Descriptive Statistics: Time Until First Policy Measure by Category (in days)**

|**Category**|**Countries**|**Mean**|**Std. Dev.**|**Min**|**Median**|**Max**|
|---|---|---|---|---|---|---|
|Banking Sector|154|56.5|31.8|2|50|216|
|Financial Markets and NBFIs|154|128.6|80.7|20|97|216|
|Liquidity and Funding|154|63.6|46.7|6|50|216|
|Payments Systems|154|128.7|77.2|21|101|216|

Note: Standard deviation, minimum, median, mean, and max of the distribution of the number of days taken to put in place a policy in the four categories described in section 2.1.

The panels in Figure 5 each display two Kaplan-Meier survival curve estimates corresponding to a group of lower- and higher-income countries, respectively.<sup>15</sup> In doing so, we split the country sample on median 14 The descriptive statistics in Table 2 reflect the fact that countries are right-censored, i.e. we assign to these countries the maximum value (216) to the time until the first policy measure was implemented. In ancillary regressions not shown in the paper we drop from the sample those countries that did not implement a measure in a specific category over the period in analysis. Results, available from the authors upon request, remain qualitatively the same.

15 For the sake of brevity, we just report the KM survival curve estimated for GDP per capita as this variable shows the strongest univariate relationship with the time of implementation of the first policy. Results for the other determinants are available from the authors upon request.

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GDP per capita. For the _Banking Sector_ , _Financial Markets and NBFIs_ , and _Liquidity and Funding_ categories, the curves appear to show that financial sector policy makers in more developed countries responded faster compared to less developed economies. For example, in the _Banking Sector_ category, almost all higher-income countries had taken at least one measure after 60 days since the WHO declaration - for lower-income countries, it took 120 days. In contrast, in the _Payments Systems_ category, less developed countries were faster in implementing payment systems measures. The log-rank test of the equality of the survival curves shows there is a significant difference (p-value < 0.01) in the policy reaction time between higher and lower-income countries, except for the _Payments Systems_ category (p-value = 0.25).<sup>16</sup>

# **Figure 5. Kaplan-Meier (KM) Survival Curve Estimates by Category: Countries Grouped by GDP per Capita**

**Panel A. Banking Sector**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 180 200 220<br>Days since declaration of Public Health Emergency of International Concern<br>Lower income Higher income<br>P-value: 0.000.<br>1.00<br>0.75<br>0.50<br>0.25<br>Countries implementing a measure (%)<br>0.00<br><!-- End of picture text -->

**Panel B. Financial Markets and NBFIs**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 180 200 220<br>Days since declaration of Public Health Emergency of International Concern<br>Lower income Higher income<br>P-value: 0.000.<br>1.00<br>0.75<br>0.50<br>0.25<br>Countries implementing a measure (%)<br>0.00<br><!-- End of picture text --> 16 For brevity, we just report results of the log-rank test of equality. As robustness, we also performed the Wilcoxon-BreslowGehan test (Breslow, 1970; Gehan, 1965), which gives more weight to events occurred at early time points; and the Peto–Peto– Prentice test (Peto and Peto, 1972; Prentice, 1978) to account for higher ratio of hazards earlier in the time period. These produce qualitatively similar results.

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# **Panel C. Liquidity and Funding**

# **Panel D. Payments Systems**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 180 200 220 0 20 40 60 80 100 120 140 160 180 200 220<br>Days since declaration of Public Health Emergency of International Concern Days since declaration of Public Health Emergency of International Concern<br>Lower income Higher income Lower income Higher income<br>P-value: 0.002. P-value: 0.246.<br>1.00 1.00<br>0.75 0.75<br>0.50 0.50<br>0.25 0.25<br>Countries implementing a measure (%) Countries implementing a measure (%)<br>0.00 0.00<br><!-- End of picture text --> Note: This figure reports the Kaplan-Meier survival curve estimates for two groups of countries: higher-income and lower-income groups based on a sample split on median GDP per capita. P-value refers to the statistic chi-squared of the log-rank test for equality of survivor functions.

While illustrative, these univariate results should be interpreted with caution. For example, some advanced countries experienced a COVID-19 outbreak before some developing economies did. As such, the level of economic development could be correlated with COVID-19 exposure. We, therefore, explore these results in more detail using Cox regression in the next section focusing on EMDEs countries only.

# _4.2 Time until the first policy response is taken in EMDEs: Cox proportional hazards regression_

Table 3 presents the hazard ratios of the Cox multivariate regressions. Broadly speaking, a hazard ratio greater than one indicates that the hazard of a policy being implemented for the first time is higher for countries with a higher value compared to countries with a lower value (e.g., a hazard ratio of 1.5 means that the proportional change of the baseline hazard is 1.5 for a 1-unit increase). In other words, these countries are more likely to take their first policy response sooner. For each category, Table 3 displays a baseline model which includes economic development ( _Log GDP per capita (USD)_ ), size of population ( _Log population size_ ), and domestic exposure to COVID-19 ( _Days until the 100_<sup>_th_</sup> _COVID-19 case_ ). We also include a set of macro-fiscal fundamentals to explore other relevant predictors of the speed of implementation of financial policies ( _Total private sector debt (% GDP); Total public sector debt (% GDP); Current account (% of GDP); Fiscal balance (% of GDP)_ ).

Economic development and population size are the most statistically significant predictors in the regressions across the different categories. EMDEs with higher GDP per capita were faster in putting in place financial policies in the _Banking Sector_ , _Financial Markets and NBFI_ , and _Liquidity and Funding_ categories (Table 21
3, columns 1 to 6), presumably because of, inter alia, larger policy space, more resources, and stronger institutional frameworks compared to developing countries. We also find some evidence that EMDEs with higher economic development were slower in implementing policies in the _Payments Systems_ category (Table 3, column 7).

Authorities in more populous countries have reacted faster than authorities in less populated countries, especially in the _Financial Markets and NBFI_ , _Liquidity and Funding_ , and _Payments Systems_ categories, perhaps because of scale economies related to regulation and supervision. Moreover, higher domestic exposure to COVID-19 is associated with faster implementation in the _Banking Sector_ and _Payment Systems_ categories, although the relationships become statistically insignificant after additional country characteristics are controlled for (Table 3, columns 2, 4, 6, and 8). In addition to the basic set of predictors, we also find statistically significant relationships between the level of private-sector debt and the time of implementation of _Banking Sector_ , _Liquidity and Funding_ , and _Payments Systems_ policies, possibly indicating that countries with deeper financial sectors and/or higher financial risks have responded differently. For the _Banking Sector_ and _Liquidity and Funding_ measures, they were more likely to respond earlier. The opposite appears to be the case for _Payment Systems_ . Similarly, economies with higher fiscal deficit (Fiscal balance (% of GDP)) where faster in implementing _Payments Systems_ measures (Table 3, column 8). Countries with lower public debt levels were more likely to respond earlier with _Liquidity and Funding_ measures, potentially because their public debt markets are shallower (Table 3, column 6). Finally, we do not find evidence of the current account playing a significant role in any policy category.

In terms of the magnitude of the estimated associations, a one-percent increase in GDP per capita increases the baseline hazard by at least 0.29 percent in the _Financial Markets and NBFIs_ category (Table 3, column 4), holding all the other predictors constant. Population size shows the largest effect for measures in the _Liquidity and Funding_ category: a one-percent increase in population size is associated with a 0.35 percent increase in the hazard (Table 3, column 6). In contrast, EMDEs countries where the domestic exposure to COVID-19 measure is lower have a lower hazard (0.4 percent as reported in Table 3, column 1). In terms of total private sector debt (as % of GDP), a 1-percentage point increase is associated with an increase in the hazard of 1.5 percent (Table 3, column 2).

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# **Table 3. Cox Regression: Time Until the First Policy Response by Category in EMDEs (days since January 30**<sup>**th**</sup> **, 2020)**

|VARIABLES|(1)<br>Banking|(2)<br>Sector|(3)<br>Financial M<br>NB|(4)<br>arkets and<br>FIs|(5)<br>Liquidity a|(6)<br>nd Funding|(7)<br>Payment|(8)<br>s Systems|
|---|---|---|---|---|---|---|---|---|
|Log GDP per capita (USD)|1.762***|1.495***|1.381***|1.334**|1.502***|1.353**|0.820**|0.936|
||(0.199)|(0.203)|(0.164)|(0.192)|(0.190)|(0.195)|(0.082)|(0.109)|
|Log population size|1.120*|1.099|1.256***|1.306***|1.384***|1.424***|1.134**|1.217***|
||(0.073)|(0.081)|(0.080)|(0.095)|(0.103)|(0.116)|(0.067)|(0.073)|
|Days until 100<sup>th</sup>COVID-19 case|0.996*|0.996|1.000|1.002|1.002|1.004|0.997**|0.998|
||(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|(0.003)|(0.002)|(0.002)|
|Government debt (% GDP)||0.997||0.997||0.992**||0.998|
|||(0.003)||(0.003)||(0.003)||(0.003)|
|Private debt (% GDP)||1.015***||1.005||1.009**||0.993**|
|||(0.004)||(0.004)||(0.004)||(0.003)|
|Current account (% of GDP)||0.993||0.997||0.996||0.988|
|||(0.010)||(0.008)||(0.011)||(0.008)|
|Fiscal balance (% of GDP)||1.001||1.005||0.994||0.947**|
|||(0.030)||(0.019)||(0.024)||(0.020)|
|Observations (countries)|113|105|113|105|113|105|113|105|
|Pseudo R<sup>2</sup>|0.035|0.054|0.016|0.019|0.025|0.038|0.017|0.027|

Note: The sample consists of EMDEs countries only (i.e., upper-middle, lower-middle, and low-income countries). The dependent variable is the time until 1<sup>st</sup> response for each policy category. Hazard ratios and robust standard errors are reported. For each specification, we test the proportional-hazards assumption separately for each covariate. We cannot reject the null hypothesis at the 10% level for all variables except the log of population size in column 3 (p-value 0.01), 4 (p-value 0.02), 5 (p-value 0.03), and 7 (p-value 0.07); and the log GDP per capita in column 3 (p-value = 0.04), and 5 (p-value = 0.06). *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively.

In ancillary regressions reported in Appendix C, Table C1, we explore the role of additional banking sector characteristics at the start of the pandemic. By including these variables, the sample size decreases substantially, so the results should be interpreted with care. First, the baseline results presented in Table 3 hold after including these additional explanatory variables. Second, none of these additional variables are consistently and significantly associated with the measures in all policy categories. However, regulatory Tier 1 capital is positively associated with a faster response in the _Banking Sector_ and _Liquidity and Funding_ categories. Private bank credit (as % of GDP) is also associated with a faster response in the _Banking Sector_ category, while countries which have adopted the capital requirements of the Basel III framework are slower in adopting _Financial Markets and NBFIs_ measures.

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# _4.3 Financial Policy Response Activity Index in EMDEs_

Table 4 shows Poisson regressions with the FPRAI as the dependent variable. Model 1 documents that EMDEs with higher GDP per capita implemented more financial policy measures. This perhaps suggests that financial supervisors in more developed countries may have more resources to do so. The statistical significance of population size suggests that scale economies may also matter for financial supervisors. These results appear economically meaningful. For example, using the estimated coefficients in Table 4, column 4, a one-percent increase in GDP per capita increases the mean value of the total policies by 0.56 percent. Similarly, a one-percent increase in population size increases the mean value of total policies by 0.35 percent. In contrast, the spread of COVID-19 does not appear to be a significant factor. Model 2 indicates that macro-financial factors do not play a significant role either. Further, Model 3 shows there is no significant association between financial policy activity and COVID-19 containment and fiscal support policies. Model 4 confirms these findings.

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# **Table 4. Poisson Regression: Financial Policy Response Activity Index in EMDEs (September 1**<sup>**st**</sup> **, 2020)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|Finan|cial PolicyRes|ponse Activity|Index|
|Log GDP per capita (USD)|0.450***<br>(0.062)|0.533***<br>(0.072)|0.467***<br>(0.064)|0.560***<br>(0.077)|
|Log population size|0.318***|0.353***|0.308***|0.347***|
||(0.044)|(0.046)|(0.049)|(0.052)|
|Days to record 100 Covid-19 cases|0.000|0.001|0.001|0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Fiscal balance (% of GDP)||-0.013||-0.013|
|||(0.020)||(0.020)|
|Current account (% of GDP)||-0.011||-0.015*|
|||(0.007)||(0.008)|
|Government debt (% GDP)||-0.002||-0.003|
|||(0.003)||(0.003)|
|Private debt (% GDP)||-0.002||-0.003|
|||(0.002)||(0.002)|
|Oxford fiscal support (% of GDP)|||-0.003|0.806|
||||(0.603)|(0.696)|
|Oxford government response stringency index|||0.246<br>(0.528)|0.169<br>(0.540)|
|Constant|-6.626***|-7.877***|-6.759***|-8.104***|
||(1.059)|(1.219)|(1.144)|(1.352)|
|Observations (countries)|113|105|102|94|
|Pseudo R<sup>2</sup>|0.446|0.474|0.435|0.465|

Note: The sample consists of EMDEs countries only (i.e., upper-middle, lower-middle and low-income countries). The dependent variable is the number of financial sector policy measures taken (FPRAI). Robust standard errors. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively.

In ancillary regressions, we explore the role of additional banking sector characteristics described in Section 3.2. As noted, the sample size decreases substantially, so the analysis should be interpreted with care. The results, reported in Appendix C, Table C2, show that economic development and population remain positively and strongly correlated with the total number of financial policies implemented. Of the banking characteristics, private bank credit (as % of GDP) and operating on Basel III capital requirements are associated with fewer financial policy measures.

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# **5 Conclusions and future work**

The database presented in this paper contains over 3,100 individual measures and captures in detail the unprecedented policy response by authorities in over 150 countries to mitigate the macro-financial impact of COVID-19 and the spillovers to the real sector in their jurisdictions. The objective of these policies is to safeguard financial stability, preserve core financial market functions, support vulnerable borrowers, and maintain the provision of critical financial services to the real economy, including credit and payments. The paper also introduces a simple _Financial Sector Policy Activity Index_ (FSPAI) — the sum of all financial policy measures taken in a country, which does not account for the scale or effectiveness of policies — and documents that the large majority of financial sector authorities has taken action, although the number of policies implemented is discernibly higher in most advanced economies and larger EMDEs compared to Sub-Saharan African countries.

This paper also offers initial analytical steps to help to understand the determinants of the policy response in terms of responsiveness and overall activity in EMDEs. Cox proportional hazards regressions suggest that EMDEs that are richer, more populous, and have higher private debt levels were significantly more likely to issue their _first_ policy response faster in the _Banking Sector_ and _Liquidity and Funding_ categories. However, richer countries and countries with higher private debt levels responded more slowly regarding _Payments Systems_ measures. The spread of COVID-19 and macro-financial fundamentals appear to have limited influence on policy makers’ responsiveness. We also explore the role of pre-pandemic banking sector characteristics, but these results need to be interpreted with care, since the sample size is substantially smaller. For _Banking Sector_ measures, we find that countries with higher bank capitalization and higher levels of private credit are significantly more responsive. After controlling for these factors, the adoption of capital requirement features of Basel III which cover certain buffers and higher-quality capital is associated with a slower response across all policy categories, but the results are not robustly statistically significant. Asset quality does not appear to play a significant role.

Consistent with the findings on policy responsiveness, Poisson regressions suggest that EMDEs that implemented more measures (i.e. have a higher FSPAI) tend to be significantly more economically developed and have larger populations. The spread of COVID-19, macro-financial fundamentals, the size of fiscal packages, and lockdown policies appear to play a limited role. We also find evidence that, after controlling for other bank characteristics, the adoption of Basel III features (which include new buffers and the need for higher-quality capital) and higher levels of private credit exhibit a significantly lower FSPAI.

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However, as noted, the sample size is substantially smaller, so these results also need to be interpreted with more care.

Taken together, these findings call for future work to better understand the country determinants of the policy response. Further, the global database can support policy makers and researchers in evaluating policies in terms of their effectiveness and potential unintended consequences. For certain measures, authorities should continue to balance the relevant trade-offs between keeping temporary measures in place to support the real sector and maintaining prudent credit risk and liquidity management standards. As discussed in Section 2, some countries, particularly low- and middle-income EMDEs, have resorted to policy measures (e.g., lowering certain risk weights without adequate public guarantees, relaxing the classification and treatment of non-performing loans) that are not consistent with the principles that underpin international financial standards and recent guidance by standard-setting bodies and the IMF and the World Bank (see for example IMF and World Bank (2020)). These countries have adopted such measures perhaps because they have fewer options at their disposal due to limited policy buffers, less diversified financial systems, weaker implementation capacity, and less sophisticated regulatory and supervisory frameworks (e.g., countercyclical capital buffer frameworks are typically not in place). This may have created some respite in the short term but may also generate new risks since such policies may weaken bank buffers, reduce bank balance sheet transparency, induce moral hazard, and contribute to market fragmentation. These risks could undermine the medium-term resilience and stability of the financial system and compound the economic impact of the pandemic. Furthermore, some of these measures may jeopardize hard-won gains in upgrading regulatory and supervisory frameworks to align them with global standards and longer-term policy credibility. Such risks should be weighed against the development of the global health crisis and the pace of the economic recovery, particularly if a timely economic rebound does not materialize and insolvency pressures surge.

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