Global Financial Development Report 2017 / 2018:
Bankers without Borders

Key Terms Explained

  • Banking crisis

    Banks are susceptible to a range of risks. These include credit risk (loans and others assets turn bad and ceasing to perform), liquidity risk (withdrawals exceed the available funds),  and interest rate risk (rising interest rates reduce the value of bonds held by the bank, and force the bank to pay relatively more on its deposits than it receives on its loans).

    Banking problems can often be traced to a decrease the value of banks’ assets. An deterioration in asset values can occur, for example, due to a collapse in real estate prices or from an increased number of bankruptcies in the nonfinancial sector. Or, if a government stops paying its obligations, this can trigger a sharp decline in value of bonds held by banks in their portfolios. When asset values decrease substantially, a bank can end up with liabilities that are bigger than its assets (meaning that the bank has negative capital, or is “insolvent”). Or, the bank can still have some capital, but less than a minimum required by regulations (this is sometimes called “technical insolvency”)

    Bank problems can also be triggered or deepened if a bank faces too many liabilities coming due and does not have enough cash (or other assets that can be easily turned into cash) to satisfy those liabilities. This can happen, for example, if many depositors want to withdraw deposits at the same time (depositor run on the bank). It can also happen also if the bank’s borrowers want their money bank and the bank does not have enough cash on hand. The bank can become illiquid. It is important to note that illiquidity and insolvency are two different things. For example, a bank can be solvent but illiquid (that is, it can have enough capital but not enough liquidity on its hands). However, many times, insolvency and illiquidity come hand in hand. When there is a major decline in asset values, depositors and other banks borrowers often start feeling uneasy and demand their money bank, deepening the bank’s troubles.

    A (systemic) banking crisis occurs when many banks in a country are in serious solvency or liquidity problems at the same time—either because there are all hit by the same outside shock or because failure in one bank or a group of banks spreads to other banks in the system.  More specifically, a systemic banking crisis is a situation when a country’s corporate and financial sectors experience a large number of defaults and financial institutions and corporations face great difficulties repaying contracts on time. As a result, non-performing loans increase sharply and all or most of the aggregate banking system capital is exhausted. This situation may be accompanied by depressed asset prices (such as equity and real estate prices) on the heels of run-ups before the crisis, sharp increases in real interest rates, and a slowdown or reversal in capital flows. In some cases, the crisis is triggered by depositor runs on banks, though in most cases it is a general realization that systemically important financial institutions are in distress.

    Systemic banking crises can be very damaging. They tend to lead affected economies into deep recessions and sharp current account reversals. Some crises turned out to be contagious, rapidly spreading to other countries with no apparent vulnerabilities. Among the many causes of banking crises have been unsustainable macroeconomic policies (including large current account deficits and unsustainable public debt), excessive credit booms, large capital inflows, and balance sheet fragilities, combined with policy paralysis due to a variety of political and economic constraints. In many banking crises, currency and maturity mismatches were a salient feature, while in others off-balance sheet operations of the banking sector were prominent.
    A global database of banking crises was first compiled by Caprio and Klingebiel (1996). The latest version of the database, updated to reflect the recent global financial crisis, is available as Laeven and Valencia (2012). It identifies 147 systemic banking crises (of which 13 are borderline events) from 1970 to 2011. It also reports on 218 currency crises (defined as nominal depreciation of the currency vis-à-vis the U.S. dollar of at least 30 percent that is also at least 10 percentage points higher than the rate of depreciation in the year before) and 66 sovereign debt crises (defined by  government defaulting on its debt to private creditors) over the same period. The database has detailed information about the policy responses to resolve crises in different countries. Analyses based on the dataset, such as Cihák and Schaeck (2010) suggest that consistently predicting banking crises is very difficult, but there are some variables (such as those capturing high leverage and rapid credit growth) that indicate increased likelihood of a crisis.

    Chapter 2 of the Global Financial Development Report uses the Laeven and Valencia (2012) version of the database of banking crises to analyze what works (and what does not) in banking supervision and regulation. The chapter and the underlying paper (Čihák, Demirgüç-Kunt, Martínez Pería, and Mohseni-Cheraghlou 2012) use the responses from the World Bank’s Banking Regulation and Supervision Survey (accompanying the Global Financial Development Report ) and performs an econometric analysis comparing countries that ended up in banking crises and those that managed to avoid them. The report and the paper find that find that crisis-hit countries had less stringent and more complex definitions of minimum capital, lower actual capital ratios, were less strict in the regulatory treatment of bad loans and loan losses, and faced fewer restrictions on non-bank activities. They had greater disclosure requirements but weaker incentives for the private sector to monitor banks’ risks. Overall, changes in regulation and supervision during the global financial crisis have been only gradual at best. Some changes, such as increasing capital ratios and strengthening resolution regimes, have gone in the right direction (making regulation in crisis countries closer to that in non-crisis countries), but at the same time, private sector incentives to monitor banks’ risks have been weakened by some of the policy interventions during the crisis. The analysis shows scope for strengthening regulation and supervision as well as private sector’s incentives to monitor risk-taking.

    Suggested reading

    Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2006. “Bank Concentration, Competition and Crises: First Results.”Journal of Banking & Finance 30: 1581–1603.

    Caprio, Gerard, and Daniela Klingebiel, 1996, “Bank Insolvencies: Cross-Country Experience.” Policy Research Working Paper No.1620. World Bank, Washington, DC.

    Čihák, Martin and Schaeck, Klaus, 2010. "How well do aggregate prudential ratios identify banking system problems?"Journal of Financial Stability, 6(3), 130–144.

    Čihák, Martin, Asli Demirgüç-Kunt, María Soledad Martínez Pería, and Amin Mohseni-Cheraghlou. 2012. “Bank Regulation and Supervision around the World: A Crisis Update”, Policy Research Working Paper 6286, World Bank, Washington, DC.

    Demirgüç-Kunt, Asli, and Enrica Detragiache. 1997. “The Determinants of Banking Crises in Developing and Developed Countries.” IMF Staff Papers 45: 81–109.

    Kaminsky, Graciela, and Carmen Reinhart. 1999. “The Twin Crises: The Causes of Banking and Balance of Payments Problems.” American Economic Review 89 (3): 473–500.

    Laeven, Luc, and Fabian Valencia. 2012. “Systemic Banking Crisis Database: An Update.” Working Paper 08/224, International Monetary Fund, Washington, DC.

    Reinhart, Carmen, and Kenneth Rogoff. 2009. This Time Is Different: Eight Centuries of Financial Folly. Princeton University Press, Princeton.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC.

  • Banking competition

    The global financial crisis reignited the interest of policy makers and academics in bank competition and the role of the state in competition policies (that is, policies and laws that affect the extent to which banks compete).  Some believe that increases in competition and financial innovation in markets such as subprime lending contributed to the financial turmoil. Others worry that the crisis and government support of the largest banks increased banking concentration, reducing competition and access to finance, and potentially contributing to future instability as a result of moral hazard problems associated with too-big-to-fail institutions.

    As in other industries, competition in the banking system is desirable for efficiency and maximization of social welfare. However, due to its roles and functions, there are some properties that distinguish it from other industries. It is important to not only make sure that banking sector is competitive and efficient, but also stable.

    Measuring and assessing competition

    There are several approaches to measuring bank competition. These include decomposition of interest spreads, measures of bank concentration under the so-called “structure-conduct-performance” paradigm, regulatory indicators that measure the contestability of the banking sector, and direct measures of bank pricing behavior or market power based on the “new empirical industrial organization” literature.

    An approach used by some studies to analyze bank competition is based on interest spread decomposition. But spreads are outcome measures of efficiency, and in addition to the competition environment, cross-country differences in spreads can reflect macroeconomic performance, the extent of taxation of financial intermediation, the quality of the contractual and judicial environment, and bank-specific factors such as scale and risk preferences. So these effects need to be controlled for before analysis competition.

    The so-called structure-conduct-performance paradigm assumes that there is a stable, causal relationship between the structure of the banking industry, firm conduct, and performance. It suggests that fewer and larger firms are more likely to engage in anticompetitive behavior. In this framework, competition is negatively related to measures of concentration, such as the share of assets held by the top three or five largest banks and the Herfindahl index.

    According to this approach, banking concentration can be approximated by the concentration ratio—the share of assets held by the k largest banks (typically three or five) in a given economy—or the Herfindahl-Hirschman index (HHI), the sum of the squared market share of each bank in the system. The HHI accounts for the market share of all banks in the system and assigns a larger weight to the biggest banks. Instead, concentration ratios completely ignore the smaller banks in the system. The concentration ratio varies between nearly 0 and 100. The HHI has values up to 10,000. If there is only a single bank that has 100 percent of the market share, the HHI would be 10,000. If there were a large number of market participants with each bank having a market share of almost 0 percent, the HHI would be close to zero.

    However, concentration measures are generally not good predictors of competition.  The predictive accuracy of concentration measures on banking competition is challenged by the concept of market contestability. The behavior of banks in contestable markets is determined by threat of entry and exit. Banks are pressured to behave competitively in an industry with low entry restrictions on new banks and easy exit conditions for unprofitable institutions—even if the market is concentrated.

    Therefore, instead of using concentration, much of the recent research on the subject focused on direct measures of bank pricing behavior or market power based on the “new empirical industrial organization” literature.  These include the Panzar-Rosse H-statistic, the Lerner index, and the so-called Boone indicator.

    The H-statistic captures the elasticity of bank interest revenues to input prices. The H-statistic is calculated in two steps. First, running a regression of the log of gross total revenues (or the log of interest revenues) on log measures of banks’ input prices. Second, adding the estimated coefficients for each input price. Input prices include the price of deposits (commonly measured as the ratio of interest expenses to total deposits), the price of personnel (as captured by the ratio of personnel expenses to assets), and the price of equipment and fixed capital (approximated by the ratio of other operating and administrative expenses to total assets).

    Higher values of the H-statistic are associated with more competitive banking systems. Under a monopoly, an increase in input prices results in a rise in marginal costs, a fall in output, and a decline in revenues (because the demand curve is downward sloping), leading to an H-statistic less than or equal to 0. Under perfect competition, an increase in input prices raises both marginal costs and total revenues by the same amount (since the demand curve is perfectly elastic); hence, the H-statistic will equal 1.
    Another frequently used measure is based on markups in banking. The indicator, so-called Lerner index, is defined as the difference between output prices and marginal costs (relative to prices). Prices are calculated as total bank revenue over assets, whereas marginal costs are obtained from an estimated translog cost function with respect to output. Higher values of the Lerner index signal less bank competition.

    Finally, the Boone indicator is a recent addition to this family of indices. It measures the effect of efficiency on performance in terms of profits. It is calculated as the elasticity of profits to marginal costs. To calculate this elasticity, the log of a measure of profits (such as return on assets) is regressed against a log measure of marginal costs. The elasticity is captured by the coefficient on log marginal costs, which are typically calculated from the first derivative of a translog cost function. The main idea of the Boone indicator is that more-efficient banks achieve higher profits. The more negative the Boone indicator is, the higher the level of competition is in the market, because the effect of reallocation is stronger.

    Did competition cause the big financial crisis?

    The basic observation that competition increased before the crisis does not necessarily suggest that greater competition in itself spurred the crisis. Recent studies suggest the problem was in other things, in particular in missing incentives for adequate risk management missing, and in lax supervision.  In fact, the run-up to the crisis was characterized by an increase in market power (Anginer, Demirgüç-Kunt, and Zhu, 2012).

    The financial crisis—and the subsequent policy responses by governments—may have affected the competitive conduct of financial intermediaries in industrial countries.  Bank competition in developed countries deteriorated during this period, especially in countries that had large credit and housing booms (such as the United States and Spain). This is confirmed by measures such as the Lerner index and the Boone indicator.

    Chapter 3 of the Global Financial Development Report 2013 examines bank competition and the role of the state in competition policy in more detail.

    Suggested reading:

    Anginer, Deniz, Asli Demirgüç-Kunt, and Min Zhu. 2012. “How Does Bank Competition Affect Systemic Stability?” Policy Research Working Paper 5981, World Bank, Washington, DC.

    Beck, Thorsten. 2008. “Bank Competition and Financial Stability: Friends or Foes?” Policy Research Working Paper 4656, World Bank, Washington, DC.

    Boone, Jan. 2001. “Intensity of Competition and the Incentive to Innovate. International Journal of Industrial Organization19: 705–26.

    Caprio, Gerard, Asli Demirgüç-Kunt, and Edward J. Kane. 2010. “The 2007 Meltdown in Structured Securitization: Searching for Lessons, not Scapegoats.” World Bank Research Observer 25 (1): 125–55.

    Panzar, John, and James Rosse. 1982. “Structure, Conduct and Comparative Statistics.” Bell Laboratories Economic Discussion Paper No. 248. Bell Labs Statistics Research Department, Murray Hill, NJ.

    Panzar, John, and James Rosse. 1987. “Testing for ‘Monopoly’ Equilibrium.” Journal of Industrial Economics 35: 443–56.

    Schaeck, Klaus, Martin Čihák, and Simon Wolfe. 2009. “Are Competitive Banking Systems More Stable?” Journal of Money, Credit, and Banking 41 (4): 711–34.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment), Chapter 3.

  • Credit bureau

    Definition and comparison to credit registries

    A credit bureau is one of the two main types of credit reporting institutions. It collects information from a wide variety of financial and nonfinancial entities, including microfinance institutions and credit card companies, and provides comprehensive consumer credit information with value-added services such as credit scores to private lenders.

    Credit bureaus are privately owned and privately operated companies. In contrast, credit registries—the other main type of credit reporting institution—tend to be public entities, managed by bank supervisors or central banks.  

    As privately owned commercial enterprises, credit bureaus tend to cater to the information requirements of commercial lenders. Though there is variation in the type and extent of information they collect, credit bureaus generally strive to collect very detailed data on individual clients. They therefore tend to cover smaller loans than credit registries and often collect information from a wide variety of financial and nonfinancial entities, including retailers, credit card companies, and microfinance institutions. As a result, data collected by credit bureaus are often more comprehensive and better geared to assess and monitor the creditworthiness of individual clients. In contrast, credit registries are often geared towards collecting system-wide information for macroprudential and other policy purposes.

    Compared to credit registries, credit bureaus are a relatively recent institution. Although credit bureaus have existed in Germany, Sweden, and the United States for nearly a century, they emerged in many other high-income countries, including France, Italy, and Spain, as recently as the 1990s. Various countries use somewhat different names for credit bureaus. For example, credit bureaus are also called “consumer reporting agencies” in the United States and “credit reference agencies”in the United Kingdom.

    Chapter 5 of the 2013 Global Financial Development Report provides an overview of the state of public and private credit reporting. It presents data on the ownership structure and extent of information collected by credit reporting institutions around the world.

    The World Bank Group has supported the development of credit reporting systems around the world for more than a decade. The International Finance Corporation’s Credit Bureau Knowledge Guide (IFC 2006) provides an overview of experiences in developing the capabilities of private credit reporting institutions through public private partnerships and institutional innovation. The World Bank’s General Principles for Credit Reporting (2011) reviews best practices and makes policy recommendations for  developing credit reporting systems.

    Why credit bureaus matter?

    Transparent credit information is a prerequisite for sound risk management and financial stability. Credit reporting institutions, such as credit bureaus, support financial stability and credit market efficiency and stability in two important ways. First, banks and nonbank financial institutions (NBFIs) draw on credit reporting systems to screen borrowers and monitor the risk profile of existing loan portfolios. Second, regulators rely on credit information to understand the interconnected credit risks faced by systemically important borrowers and financial institutions and to conduct essential oversight functions. Such efforts reduce default risk and improve the efficiency of financial intermediation. In a competitive credit market, these efforts ultimately benefit consumers through lower interest rates.

    Effective credit reporting systems can mitigate a number of market failures that are common in financial markets around the world, and most severely apparent in less developed economies. The availability of high-quality credit information, for example, reduces problems of adverse selection and asymmetric information between borrowers and lenders. This reduces default risk and improves the allocation of new credit. Information sharing can also promote a responsible “credit culture” by discouraging excessive debt and rewarding responsible borrowing and repayment.

    Perhaps most important, credit reporting allows borrowers to build a credit history and to use this “reputational collateral” to access formal credit outside established lending relationships. This is especially beneficial for small enterprises and new borrowers with limited access to physical collateral. Stylized evidence from the recent financial crisis also suggests that positive credit information helped to safeguard the financial access of creditworthy borrowers that would have otherwise been cut off from institutional credit.

    Suggested reading:

    Avery, Robert; Paul Calem, and Glenn Canner. 2004. Credit Report Accuracy and Access to Credit. Federal Reserve Bulletin, Summer 2004. Federal Reserve, Washington, DC.

    International Finance Corporation (IFC). 2006. Credit Bureau Knowledge Guide, Washington, DC.

    Miller, Margaret. 2003. Credit Reporting Systems and the International Economy. MIT Press, Cambridge, Massachusetts.

    World Bank. 2011. General Principles for Credit Reporting. World Bank, Washington, DC.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment)

  • Credit registry

    Definition and comparison to credit bureaus

    A credit registry is one of the two main types of credit reporting institutions. Credit registries generally developed to support the state’s role as a supervisor of financial institutions. Where credit registries exist, loans above a certain amount must, by law, be registered in the national credit registry. In some cases, credit registries have relatively high thresholds for loans that are included in their databases. Credit registries tend to monitor loans made by regulated financial institutions.

    One of the main differences in comparison with credit bureaus—the other main type of credit reporting institution—is that credit registries tend to be public entities. They are usually managed by central banks or bank supervision agencies.  In contrast, credit bureaus tend to be privately owned and privately operated companies

    More substantively, credit registry data are geared towards use by policymakers, regulators, and other officials. Against the backdrop of the financial crisis, many countries have made efforts to optimize the credit registry data so that they can be better used in macroprudential regulation and oversight. In comparison to credit registries, credit bureaus, as privately owned commercial enterprises, tend to cater to the information requirements of commercial lenders. Thus, they typically provide additional value-added services, such as credit scores and collection services.

    Chapter 5 of the 2013 Global Financial Development Report provides an update on the state of public and private credit reporting. It presents data on the ownership structure and extent of information collected by credit reporting institutions around the world. For a detailed discussion of the historical development of credit reporting institutions, see Miller (2003).

    The World Bank Group has supported the development of credit reporting systems around the world for more than a decade. The World Bank’s General Principles for Credit Reporting (2011) reviews best practices and makes policy recommendations for  developing credit reporting systems.

    Why credit reporting?

    Transparent credit information is a prerequisite for sound risk management and financial stability. Credit reporting institutions, such as credit bureaus, support financial stability and credit market efficiency and stability in two important ways. First, banks and nonbank financial institutions (NBFIs) draw on credit reporting systems to screen borrowers and monitor the risk profile of existing loan portfolios. Second, regulators rely on credit information to understand the interconnected credit risks faced by systemically important borrowers and financial institutions and to conduct essential oversight functions. Such efforts reduce default risk and improve the efficiency of financial intermediation. In a competitive credit market, these efforts ultimately benefit consumers through lower interest rates.

    Effective credit reporting systems can mitigate a number of market failures that are common in financial markets around the world, and most severely apparent in less developed economies. The availability of high-quality credit information, for example, reduces problems of adverse selection and asymmetric information between borrowers and lenders. This reduces default risk and improves the allocation of new credit. Information sharing can also promote a responsible “credit culture” by discouraging excessive debt and rewarding responsible borrowing and repayment.

    Perhaps most important, credit reporting allows borrowers to build a credit history and to use this “reputational collateral” to access formal credit outside established lending relationships. This is especially beneficial for small enterprises and new borrowers with limited access to physical collateral. Stylized evidence from the recent financial crisis also suggests that positive credit information helped to safeguard the financial access of creditworthy borrowers that would have otherwise been cut off from institutional credit.

    Suggested reading:

    Avery, Robert; Paul Calem, and Glenn Canner. 2004. Credit Report Accuracy and Access to Credit. Federal Reserve Bulletin, Summer 2004. Federal Reserve, Washington, DC.

    Djankov, Simeon, Caralee McLiesh, Andrei Shleifer. 2007. “Private Credit in 129 Countries.” Journal of Financial Economics 84 (2): 299–329.

    Girault, Matias Gutierrez, and Jane Hwang. 2010. “Public Credit Registries as a Tool for Bank Regulation and Supervision.” Policy Research Working Paper 5489, World Bank, Washington, DC.

    Miller, Margaret. 2003. Credit Reporting Systems and the International Economy. MIT Press, Cambridge, Massachusetts.

    World Bank. 2011. General Principles for Credit Reporting. World Bank, Washington, DC.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment), chapter 5

  • Financial access

    The topic of access to finance and financial inclusion has been of growing interest throughout the world, particularly in emerging and developing economies. Policymakers are increasingly concerned that the benefits produced by financial intermediation and markets are not being spread widely enough throughout the population and across economic sectors, with potential negative impacts on growth, income distribution and poverty levels, among others. Furthermore, they may also be concerned with the potential negative consequences for macro stability when financial system assets are concentrated in relatively few individuals, firms, or sectors.

    Financial inclusion is the use of financial services by individuals and firms. Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirgüç-Kunt, Beck, and Honohan 2008).

    It is important to distinguish between the use of and access to financial services.  Actual use is easier to observe empirically. Some individuals and firms may have access to but choose not to use some financial products. Some may have indirect access, such as using somebody else’s bank account, or are already using a close substitute. Others may not use financial services because they do not need them or because of cultural or religious reasons.  The nonusers include individuals who prefer to deal in cash and firms without promising investment projects. From a policy makers’ viewpoint, nonusers do not constitute an issue since their non-use is driven by lack of demand.  However, financial literacy can still improve awareness and generate demand;  and non-use for example due to religious reasons can be overcome by allowing entry of financial institutions that offer Sharia-compliant financial products.

    Some customers may be involuntarily excluded from the use of financial services.  Several groups belong in this category.  One notable group consists of individuals and firms that are unbankable from the perspective of commercial financial institutions and markets, because they do not have enough income or present too high a lending risk. These customers have effectively no demand since their lack of use is not due to any market failure., Other groups in this category may not have access due to discrimination, lack of information, shortcomings in contract enforcement, information environment, shortcomings in product features that may make a product inappropriate for some customer groups, or price barriers due to market imperfections. If high prices exclude large parts of the population, this may be a symptom of underdeveloped physical or institutional infrastructures, regulatory barriers or lack of competition. Financial exclusion deserves policy action when it is driven by barriers that restrict access for individuals for whom the marginal benefit of using a given financial service would otherwise be greater than the marginal cost of providing that service.

    Moral hazard and adverse selection

    Financial markets differ from markets for other products and services. For example, one often hears about an access problem in credit markets but not about an access problem, say, for cars.  One of the basic rules of economics is that prices adjust so that at market equilibrium, supply equals demand. Hence, if demand for cars exceeds the supply, the price of cars will rise until demand and supply are equated at the new equilibrium price.  If this price is too high for some, they will not be able to own a car. But all those who are willing and able to pay the price will be able to own a car.  So if prices do their job, there should be no access problem.

    In a famous paper, Stiglitz and Weiss (1981) provide a compelling explanation of why financial markets –particularly credit and insurance - are different. They show that information problems can lead to credit rationing and exclusion from financial markets even in equilibrium. Credit and insurance markets are characterized by serious principal agent problems, which include adverse selection (the fact that borrowers with less intention of repaying a loan are more willing to seek out external finance) and moral hazard (once the loan is received borrowers may use funds in ways that are inconsistent with the lenders interest). Therefore, when considering involuntarily excluded users, it is very important to distinguish between those facing price barriers and financial exclusion due to high idiosyncratic risk or poor project quality, and those facing such barriers due to market imperfections such as asymmetric information.

    The reason why rationing can arise even in a competitive credit market, is because interest rates and bank charges affect not only demand but also the risk profile of bank’s customers: higher interest rates tend to attract riskier borrowers (adverse selection) and change repayment incentives (moral hazard). As a result, the expected rate of return of a loan will increase less rapidly than the interest rate and, beyond a point, may actually decrease. Because banks do not have perfect information about the creditworthiness of prospective borrowers, the supply of loans will be backward bending at rates above the bank’s optimal rate. This means that financial exclusion will persist even in market equilibrium. Since it is not profitable to supply more loans when the bank faces excess demand for credit, the bank will deny loans to borrowers who are observationally indistinguishable from those who receive loans. The rejected applicants would not receive a loan even if they offered to pay a higher rate. Hence they are denied access (they may be bankable but are involuntarily excluded).

    Determining whether individuals or firms have access to credit but chose not to use it or were simply excluded is complex, and the effects of adverse selection and moral hazard are difficult to separate. Hence, attempts to broaden access beyond the equilibrium level come with challenges, as they require the bank to lower screening standards, and may translate into higher risks for banks and borrowers. The global financial crisis has highlighted that extending access at the expense of reduced screening and monitoring standards can have severely negative implications both for consumers and for financial stability. Therefore, in the case of credit, it is generally preferable to promote financial inclusion through interventions that increase supply by removing market imperfections. Examples are new lending technologies that reduce transaction costs, or improved borrower identification that can mitigate (even if not fully eradicate) problems of asymmetric information.

    Other financial services, such as deposits and payments, do not suffer from moral hazard and adverse selection that affect credit and insurance markets, but they still present policy challenges for financial inclusion. Non-price barriers are often very important. For example, potential customers may be discriminated against by the design features of a product, or they may face barriers to access due to red tape. Some individuals will have no access to financial services because there are no financial institutions in their area, as is the case in many remote rural areas. Yet others may be excluded because of poorly designed regulations, for example documentation requirements of opening an account, such as having a formal address or formal sector employment.   For those individuals, the supply curve is vertical at the origin, and the supply and demand for services do not intersect, again leading to financial exclusion.

    When it comes to access to simple deposit or payments services, policymakers also care if high prices and fixed costs make it impossible for large segments of the population to use these basic services. This is not an access issue in the strict sense, but it still represents a policy challenge because the high price often reflects lack of competition or underdeveloped physical or institutional infrastructures, where government can play an important role. 

    Measuring the use of and access to financial access

    Until recently, measurement of financial inclusion around the world has focused on density indicators, such as the number of bank branches or ATMs per capita. Much of this “provider-side” information on financial inclusion is now collected as part of the Financial Access Survey (fas.imf.org), which has annual data for 187 jurisdictions from 2004 to 2011. While these indicators made it possible to obtain basic provider-side information on the use financial services, relatively little has been known until recently about the global reach of the financial sector—the extent of financial inclusion and the degree to which groups such as the poor and women are excluded from formal financial systems.

    This gap in data has now been addressed with the release of the Global Financial Inclusion (“Global Findex”) database, built by the World Bank, in cooperation with the Bill and Melinda Gates Foundation and Gallup, Inc. These “user-side” indicators, compiled using the Gallup World Poll Survey, measure how adults in 148 economies around the world manage their day-to-day finances and plan for the future. The indicators are constructed with survey data from interviews with more than 150,000 nationally representative and randomly selected adults over the 2011 calendar year. The database is publicly available online and includes over 40 indicators related to account ownership, payments, saving, borrowing, and risk management. The complete country- and micro-level database is available through the Global Findex website, www.worldbank.org/globalfindex.

    In its current form, the value of the Global Findex data lies in benchmarking, diagnostics, and cross-sectional analysis. However, with the complete updates in 2014 and 2017, the Global Findex will allow users to compare indicators within countries over time. This will allow for a better understanding the impact of financial inclusion policies over time. The database can serve as an important tool for benchmarking and to motivate policy makers to embrace the financial inclusion agenda. The questionnaire, translated into 142 languages to ensure national representation in 148 economies, can be used by local policy makers to collect additional data. Adding its questions to country-owned efforts to collect data on financial inclusion can help build local statistical capacity and increase the comparability of financial inclusion indicators across economies and over time.

    The World Bank’s Enterprise Surveys (www.enterprisesurveys.org) is currently the leading dataset to measure financial inclusion by firms of all sizes across countries. The World Bank also compiles so-called Informal Surveys, similar in format to the Enterprise Surveys but focused on firms in the informal sector.

    As for access to financial markets,  the Global Financial Development Database, available at www.worldbank.org/financialdevelopment, contains cross-country indicators capturing firms’ access to securities markets. One of the proxy variables for access to stock and bond markets is market concentration. The idea behind this measurement is that a higher degree of concentration reflects greater difficulties for access for newer or smaller issuers. The variables in this category include the percentage of market capitalization outside of top 10 largest companies, the percentage of value traded outside of top 10 traded companies, government bond yields (3 month and 10 years), ratio of domestic to total debt securities, ratio of private to total debt securities (domestic), and ratio of new corporate bond issues to GDP.

    Chapter 1 of the 2013 Global Financial Development Report provides an introductory discussion into the measurement of financial access, as part of a broader discussion on financial development and key characteristics of financial systems. The forthcoming 2014  Global Financial Development Report will focus on financial inclusion, and chapter 1 will provide a more in-depth look at the relevant data sources and a discussion of what is known about financial access around the world.

    Suggested reading:

    Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2007. “Finance, Inequality and the Poor.” Journal of Economic Growth 12 (1): 27–49.

    Beck, Thorsten, Ross Levine, and Alexey Levkov. 2010. “Big Bad Banks? The Winners and Losers from Bank Deregulation in the United States.” Journal of Finance 65 (5): 1637–67.

    Čihák, Martin, Asli Demirgüç-Kunt, Erik Feyen, and Ross Levine. 2012. “Benchmarking Financial Development Around the World.” Policy Research Working Paper, World Bank, Washington, DC.

    Demirgüç-Kunt, Asli, Thorsten Beck, and Patrick Honohan. 2008. Finance for All? Policies and Pitfalls in Expanding Access. Washington, DC: World Bank.

    Demirgüç-Kunt, Aslı and Leora Klapper. 2012. “Measuring Financial Inclusion: The Global Findex,” Policy Research Working Paper 6025, World Bank, Washington, DC.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment).

    World Bank. 2013. Global Financial Development Report 2014: Rethinking the Role of the State in Finance. World Bank, Washington, DC (forthcoming).

  • Financial depth

    Financial depth captures the financial sector relative to the economy. It is the size of banks, other financial institutions, and financial markets in a country, taken together and compared to a measure of economic output.
    A proxy variable that has received much attention in the empirical literature in this regard is private credit relative to gross domestic product (GDP). More specifically, the variable is defined as domestic private credit to the real sector by deposit money banks as percentage of local currency GDP.  The private credit, therefore, excludes credit issued to governments, government agencies, and public enterprises. It also excludes credit issued by central banks.

    Private credit to GDP differs widely across countries, and it correlates strongly with income level. For example, private credit to GDP in high-income countries is 103 percent in high-income countries, more than 4 times the average ratio in low-income countries. Based on this measure, economies with deep financial systems include many of those in Europe; Canada, Australia, and South Africa are also among those in the highest quartile in terms of private credit to GDP. China’s financial system is also in the highest quartile in terms of this measure, higher than other major emerging markets such as Russia, Brazil, and India. The United States’ financial system, while above average, is not as deep as China’s.  This reflects in part the more market-based nature of the U.S. financial system. 

    Financial depth, approximated by private credit to GDP, has a strong statistical link to long-term economic growth; it is also closely linked to poverty reduction. For example, the annual average value of private credit across countries was 39 percent with a standard deviation of 36 percent. Averaging over 1980–2010, private credit of financial institutions was less than 10 percent of GDP in Angola, Cambodia, and Yemen, while exceeding 85 percent of GDP in Austria, China, and the United Kingdom. For financial markets, research has shown that the trading of ownership claims on firms in an economy is closely tied to the rate of economic development. For instance, the mean value of stock value traded is about 29 percent of GDP. In Armenia, Tanzania, and Uruguay, stock value traded annually averaged less than 0.23 percent over the 1980‐2008 sample (10th percentile). In contrast, stock value traded averaged over 75 percent in China (both Mainland and Hong Kong SAR), Saudi Arabia, Switzerland, and the Unites States (90th percentile).

    A very high ratio of private sector credit to GDP is not necessarily a good thing. Indeed, all the 8 countries with the highest ratios of private sector credit to GDP as of 2010 (Cyprus, Ireland, Spain, Netherlands, Portugal, United Kingdom, Luxembourg, and Switzerland, going from the highest to the lowest) had a major crisis episode since 2008. For more on the cross-country relationships between financial depth, economic development and poverty reduction, see, for example King and Levine 1993, Demirgüç-Kunt and Levine 2008, and World Bank 2012.

    An alternative to private credit to GDP is total banking assets to GDP, a variable that is also included in the Global Financial Development Database. It is arguably a more comprehensive measure of size, because it includes not only credit to private sector, but also credit to government as well as bank assets other than credit. However, it is available for a smaller number of economies and has been used less extensively in the literature on financial development. In any case, the two variables are rather closely correlated (with a correlation coefficient of about 0.9 over the whole sample).

    Despite the empirical literature’s focus on banks (due to data availability), measures of financial depth should ideally go beyond just banks. Indeed, the recent crisis has highlighted issues in non-bank financial institutions (NBFIs). The coverage of NBFIs by data is much less comprehensive than that of banks. Nonetheless, to acknowledge this point, the Global Financial Development Databaseincludes total assets of NBFIs to GDP, which includes pension fund assets to GDP, mutual fund assets to GDP, insurance company assets to GDP, insurance premiums (life) to GDP, and insurance premiums (non-life) to GDP.

    For financial markets, earlier work by Levine and Zervos (1998) indicates that the trading of ownership claims on firms in an economy is closely tied to the rate of economic development. To approximate the size of stock markets, a common choice in the literature is stock market capitalization to GDP. For bond markets, a commonly used proxy for size is the outstanding volume of private debt securities to GDP. The sum of these two provides a rough indication of the relative size of the financial markets in various countries. There is substantial variation in this indicator among countries, by size and by income level. For example, over the 2008-2010 period, the world-wide average value of this ratio was 131 percent, but individual country observations ranged from less than 1 percent to 533 percent. The average for developed economies was 151 percent, while the average for developing economies was about a half, at 76 percent. Also, in bigger countries, financial markets tend to play a relatively larger role relative to the size of the economy.  Countries in the highest quartile of the world-wide distribution include not only the United States, Canada, Japan, and other major developed economies, but for example also China and Malaysia.

    The size of financial institutions relative to the size of financial markets is often called the financial structures. A large literature is devoted to the topic of whether and under which conditions the mixture of financial institutions and financial markets in an economy exerts an influence on economic development (for an overview, see World Bank 2012). Financial structure differs markedly across economies. Over the full sample period, the annual average value of the financial structure ratio is 279. Countries such as Australia, India, Singapore, and Sweden have this ratio at or below 2.35 (10th percentile), while Bolivia, Bulgaria, Serbia, and Uganda are examples of countries where this ratio is over 356 (90th percentile).

    Suggested reading:

    Čihák, Martin, Asli Demirgüç-Kunt, Erik Feyen, and Ross Levine. 2012. “Benchmarking Financial Development Around the World.” Policy  Research Working Paper 6175, World Bank, Washington, DC.

    Demirgüç-Kunt, Asli, and Ross Levine. 2008. “Finance, Financial Sector Policies, and Long-Run Growth.” M. Spence Growth Commission Background Paper 11, World Bank, Washington, DC.

    Demirgüç-Kunt, Asli, Erik Feyen, and Ross Levine. 2011. “The Evolving Importance of Banks and Securities Markets.” Policy Research Working Paper 5805, World Bank, Washington, DC.

    King, Robert, and Ross Levine, 1993, "Finance, Entrepreneurship, and Growth: Theory and Evidence," Journal of Monetary Economics 32(3), December, pp. 513-542.

    Levine, Ross, and Sara Zervos. 1998. “Stock Markets, Banks, and Economic Growth.” American Economic Review 88: 537–58.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment).

  • Financial development

    Financial sector is the set of institutions, instruments, markets, as well as the legal and regulatory framework that permit transactions to be made by extending credit. Fundamentally, financial sector development is about overcoming “costs” incurred in the financial system. This process of reducing the costs of acquiring information, enforcing contracts, and making transactions resulted in the emergence of financial contracts, markets, and intermediaries. Different types and combinations of information, enforcement, and transaction costs in conjunction with different legal, regulatory, and tax systems have motivated distinct financial contracts, markets, and intermediaries across countries and throughout history.

    The five key functions of a financial system are: (i) producing information ex ante about possible investments and allocate capital; (ii) monitoring investments and exerting corporate governance after providing finance; (iii) facilitating the trading, diversification, and management of risk; (iv) mobilizing and pooling savings; and (v) easing the exchange of goods and services.

    Financial sector development thus occurs when financial instruments, markets, and intermediaries ease the effects of information, enforcement, and transactions costs and therefore do a correspondingly better job at providing the key functions of the financial sector in the economy.

    Importance of financial development

    A large body of evidence suggests that financial sector development plays a huge role in economic development. It promotes economic growth through capital accumulation and technological progress by increasing the savings rate, mobilizing and pooling savings, producing information about investment, facilitating and encouraging the inflows of foreign capital, as well as optimizing the allocation of capital.

    Countries with better-developed financial systems tend to grow faster over long periods of time, and a large body of evidence suggests that this effect is causal: financial development is not simply an outcome of economic growth; it contributes to this growth.

    Additionally, it reduces poverty and inequality by broadening access to finance to the poor and vulnerable groups, facilitating risk management by reducing their vulnerability to shocks, and increasing investment and productivity that result in higher income generation.

    Financial sector development can help with the growth of small and medium sized enterprises (SMEs) by providing them with access to finance. SMEs are typically labor intensive and create more jobs than do large firms. They play a major role in economic development particularly in emerging economies.
    Financial sector development goes beyond just having financial intermediaries and infrastructures in place. It entails having robust policies for regulation and supervision of all the important entities. The global financial crisis underscored the disastrous consequences of weak financial sector policies. The financial crisis has illustrated the potentially disastrous consequences of weak financial sector policies for financial development and their impact on the economic outcomes. Finance matters for development‐‐both when it functions well and when it malfunctions.

    The crisis has challenged conventional thinking in financial sector policies and has led to much debate on how best to achieve sustainable development. Reassessing financial sector policies after the crisis in an important step in informing this process. To help achieve this, publications such as the World Bank’s Global Financial Development Report can play a role. Chapter 1 and the Statistical Appendix of the reportpresent data and knowledge on financial development around the world.

    Measurement of financial development

    A good measurement of financial development is crucial to assess the development of the financial sector and understand the impact of financial development on economic growth and poverty reduction.
    In practice, however, it is difficult to measure financial development as it is a vast concept and has several dimensions. Empirical work done so far is usually based on standard quantitative indicators available for a long time series for a broad range of countries. For instance, ratio of financial institutions’ assets to GDP, ratio of liquid liabilities to GDP, and ratio of deposits to GDP.

    Nevertheless, as the financial sector of a country comprises a variety of financial institutions, markets, and products, these measures are rough estimation and do not capture all aspects of financial development.
    The World Bank’s Global Financial Development Database developed a comprehensive yet relatively simple conceptual 4x2 framework to measure financial development around the world. This framework identifies four sets of proxy variables characterizing a well-functioning financial system: financial depth, access, efficiency, and stability. These four dimensions are then measured for the two major components in the financial sector, namely the financial institutions and financial markets: 

     Financial InstitutionsFinancial Markets
    Depth
    • Private Sector Credit to GDP
    • Financial Institutions’ asset to GDP
    • M2 to GDP
    • Deposits to GDP
    • Gross value added of the financial sector to GDP 
    • Stock market capitalization and outstanding domestic private debt securities to GDP
    • Private Debt securities to GDP
    • Public Debt Securities to GDP
    • International Debt Securities to GDP
    • Stock Market Capitalization to GDP
    • Stocks traded to GDP 
    Access
    • Accounts per thousand adults(commercial banks)
    • Branches per 100,000 adults (commercial banks)
    • % of people with a bank account (from user survey)
    • % of firms with line of credit (all firms)
    • % of firms with line of credit (small firms) 
    • Percent of market capitalization outside of top 10 largest companies
    • Percent of value traded outside of top 10 traded companies
    • Government bond yields (3 month and 10 years)
    • Ratio of domestic to total debt securities
    • Ratio of private to total debt securities (domestic)
    • Ratio of new corporate bond issues to GDP 
    Efficiency
    • Net interest margin
    • Lending-deposits spread
    • Non-interest income to total income
    • Overhead costs (% of total assets)
    • Profitability (return on assets, return on equity)
    • Boone indicator (or Herfindahl or H-statistics) 
    • Turnover ratio for stock market
    • Price synchronicity (co-movement)
    • Private information trading
    • Price impact
    • Liquidity/transaction costs
    • Quoted bid-ask spread for government bonds
    • Turnover of bonds (private, public) on securities exchange
    • Settlement efficiency 
    Stability
    • Z-score
    • Capital adequacy ratios
    • Asset quality ratios
    • Liquidity ratios
    • Others (net foreign exchange position to capital etc) 
    • Volatility (standard deviation / average) of stock price index, sovereign bond index
    • Skewness of the index (stock price, sovereign bond)
    • Vulnerability to earnings manipulation
    • Price/earnings ratio
    • Duration
    • Ratio of short-term to total bonds (domestic, int’l)
    • Correlation with major bond returns (German, US) 

    Suggested reading:

    Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2000. “A New Database on the Structure and Development of the Financial Sector.” World Bank Economic Review 14 (3): 597–605.

    Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2010. “Financial Institutions and Markets across Countries and over Time.” World Bank Economic Review 24 (1): 77–92.

    Čihák, Martin, Asli Demirgüç-Kunt, Erik Feyen, and Ross Levine. 2012. “Benchmarking Financial Development Around the World.” Policy  Research Working Paper 6175, World Bank, Washington, DC.

    Demirgüç-Kunt, Asli, and Ross Levine. 2008. “Finance, Financial Sector Policies, and Long- Run Growth.” M. Spence Growth Commission Background Paper 11, World Bank, Washington, DC.

    Levine, Ross. 2005. “Finance and Growth: Theory and Evidence.” In Philippe Aghion and Steven Durlauf(eds. ) Handbook of Economic Growth, 865–934.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment).

  • Financial stability

    There are numerous definitions of financial stability. Most of them have in common that financial stability is about the absence of system-wide episodes in which the financial system fails to function (crises). It is also about resilience of financial systems to stress.

    A stable financial system is capable of efficiently allocating resources, assessing and managing financial risks, maintaining employment levels close to the economy’s natural rate, and eliminating relative price movements of real or financial assets that will affect monetary stability or employment levels. A financial system is in a range of stability when it dissipates financial imbalances that arise endogenously or as a result of significant adverse and unforeseen events. In stability, the system will absorb the shocks primarily via self-corrective mechanisms, preventing adverse events from having a disruptive effect on the real economy or on other financial systems. Financial stability is paramount for economic growth, as most transactions in the real economy are made through the financial system.

    The true value of financial stability is best illustrated in its absence, in periods of financial instability. During these periods, banks are reluctant to finance profitable projects, asset prices deviate excessively from their intrinsic values, and payments may not arrive on time. Major instability can lead to bank runs, hyperinflation, or a stock market crash. It can severely shake confidence in the financial and economic system.

    Firm-level stability measures

    A common measure of stability at the level of individual institutions is the z-score. It explicitly compares buffers (capitalization and returns) with risk (volatility of returns) to measure a bank’s solvency risk. The z-score is defined as z ≡ (k+µ)/σ, where k is equity capital as percent of assets, µ is return as percent of assets, and σ is standard deviation of return on assets as a proxy for return volatility. The popularity of the z-score stems from the fact that it has a clear (negative) relationship to the probability of a financial institution’s insolvency, that is, the probability that the value of its assets becomes lower than the value of its debt. A higher z-score therefore implies a lower probability of insolvency. Papers that used the z-score for analysis bank stability include Boyd and Runkle (1993); Beck, Demirgüç-Kunt, Levine (2007); Demirgüç-Kunt, Detragiache, and Tressel (2008); Laeven and Levine (2009); Čihák and Hesse (2010).

    The z-score has several limitations as a measure of financial stability. Perhaps the most important limitation is that the z-scores are based purely on accounting data. They are thus only as good as the underlying accounting and auditing framework. If financial institutions are able to smooth out the reported data, the z-score may provide an overly positive assessment of the financial institutions’ stability. Also, the z-score looks at each financial institution separately, potentially overlooking the risk that a default in one financial institution may cause loss to other financial institutions in the system. An advantage of the z-score is that it can be also used for institutions for which more sophisticated, market based data are not available. Also, the z-scores allow comparing the risk of default in different groups of institutions, which may differ in their ownership or objectives, but face the risk of insolvency.

    Other approaches to measuring institution-level stability are based on the Merton model. It is routinely used to ascertain a firm’s ability to meet its financial obligations and gauge the overall possibility of default. The Merton model (also called the asset value model) treats an institution’s equity as a call option on its held assets, taking into account the volatility of those assets. Put-call parity is used to price the value of the “put,” which is represented by the firm's credit risk. So, the model measures the value of the firm’s assets (weighting for volatility) at the time that the debtholders will “exercise their put option” by expecting repayment. The model defines default as when the value of a firm’s liabilities exceeds that of its assets (in different iterations of the model, the asset/liability level required to reach default is set at a different threshold). The Merton model can calculate the probability of credit default for the firm.

    Merton’s model has been modified in subsequent research to capture a wider array of financial activity using credit default swap data. For example, it is part of the KMV model that Moody’s uses to both calculate the probability of credit default and as part of their credit risk management system. The Distance to Default (DD) is another market-based measure of corporate default risk based on Merton’s model. It measures both solvency risk and liquidity risk at the firm level.

    Systemic stability measures

    To measure systemic stability, a number of studies attempt to aggregate firm-level stability measures (z-score and distance to default) into a system-wide evaluation of stability, by averaging or by weighting each measure by the institution’s relative size. The shortcoming of these aggregate measures is that they do not take into account the interconnectedness of financial institutions; that is, that one institution’s failure can be contagious.
    The First-to-Default probability, or the probability of observing one default among a number of institutions, has been proposed as a measure of systemic risk for large financial institutions. It uses risk-neutral default probabilities from credit default swap spreads. The probability, unlike distance-to-default measures, recognizes that defaults among a number of institutions can be connected. However, studies focusing on probabilities of default tend to overlook the fact that a large institution failing causes bigger ripples than a small one.
    Another assessment of financial system stability is Systemic Expected Shortfall (SES), which measures each institution’s individual contribution to systemic risk. SES takes the individual taking leverage and risk-taking into account and measures the externalities from the banking sector to the real economy when these institutions fail. The model is especially good at identifying which institutions are systemically relevant and would have the largest effects, if they fail, on the wider economy. One drawback of the SES method is that it is difficult to determine when the systemically-important institutions are likely to fail.

    In further research, the retrospective SES measure was extended to be somewhat predictive. The predictive measure is SRISK. SRISK evaluates the expected capital shortfall for a firm if there is another crisis. To calculate this predictive systemic risk measure, one must first find the Long-Run Marginal Expected Shortfal (LRMES), which measures the relation between a firm’s equity returns and the returns of the broader market (estimated using asymmetric volatility, correlation, and copula). The model estimates the drop in equity value of the firm if the aggregate market falls more than 40 percent in a six-month window to determine how much capital is needed during the simulated crisis in order to achieve an 8 percent capital to asset value ratio. SRISK% measures the firm’s percentage of total financial sector capital shortfall. A high SRISK% simultaneously indicates the biggest losers and contributors to the hypothetical crisis. One of the assumptions of the SES indicator is that a firm is “systemically risky” if it is especially likely to face a capital shortage when the financial sector is weak overall.

    Another gauge of financial stability is the distribution of systemic loss, which attempts to fill some of the gaps of the previously-discussed measures. This combines three key elements: each individual institution’s probability of default, the size of loss given default, and the “contagious” nature of defaults across the institutions due to their interconnectedness.

    There is also a range of indicators of financial soundness. These include the ratio of regulatory capital to risk-weighted assets and the ratio nonperforming loans to total gross loans. These are reported as part of the “financial soundness indicators” (fsi.imf.org). Variables such as the nonperforming loan ratios may be better known than the z-score, but they are also known to be lagging indicators of soundness (Čihák and Schaeck (2010).

    Another alternative indicator of financial instability is “excessive” credit growth, with the emphasis on excessive. A well-developing financial sector is likely to grow. But very rapid growth in credit is one of the most robust common factors associated with banking crises (Demirgüc-Kunt and Detragiache 1997, Kaminsky and Reinhart 1999). Indeed, about 75 percent of credit booms in emerging markets end in banking crises. The credit growth measure also has pros and cons: Although it is easy to measure credit growth, it is difficult to assess ex-ante whether the growth is excessive.

    For financial markets, the most commonly used proxy variable for stability is market volatility. Another proxy is the skewness of stock returns, because a market with a more negative skewed distribution of stock returns is likely to deliver large negative returns, and likely to be prone to less stability. Another variable is vulnerability to earnings manipulation, which is derived from certain characteristics of information reported in the financial statements of companies that can be indicative of manipulation. It is defined as the percentage of firms listed on the stock exchange that are susceptible to such manipulation. In the United States, France, and most other high-income economies, less than 10 percent of firms have issues concerning earnings manipulation; in Zimbabwe, in contrast, almost all firms may experience manipulation of their accounting statements. In Turkey, the number is close to 40 percent. Other variables approximating volatility in the stock market are the price-to-earnings ratio and duration, which is a refined version of the price-to-earnings ratio that takes into account factors such as long-term growth and interest rates.  

    Suggested reading:

    Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2007. "Finance, Inequality and the Poor," Journal of Economic Growth 12(1): 27–49.

    Boyd, John, and David Runkle. 1993. “Size and Performance of Banking Firms: Testing the Predictions of Theory,” Journal of Monetary Economics 31: 47–67.

    Čihák, Martin. 2007. “Systemic Loss: A Measure of Financial Stability” Czech Journal of Economics and Finance, 57 (1-2): 5-26.

    Čihák, Martin, and Heiko Hesse. 2010. "Islamic Banks and Financial Stability: An Empirical Analysis", Journal of Financial Services Research, 38 (2-3): 95–113.

    Čihák, Martin, Asli Demirgüç-Kunt, Erik Feyen, and Ross Levine. 2012. “Benchmarking Financial Development Around the World.” Policy  Research Working Paper 6175, World Bank, Washington, DC.

    Cihák, Martin and Schaeck, Klaus, 2010. "How well do aggregate prudential ratios identify banking system problems?" Journal of Financial Stability, 6(3): 130-144.

    Demirgüç-Kunt, Asli and Enrica Detragiache, 1997, "The Determinants of Banking Crises in Developing and Developed Countries," IMF Staff Papers, 45: 81–109.

    Demirgüç-Kunt, Asli, Enrica Detragiache, and Thierry Tressel. 2008. "Banking on the Principles: Compliance with Basel Core Principles and Bank Soundness," Journal of Financial Intermediation 17(4): 511–42.

    Kaminsky, Graciela, and Carmen Reinhart, 1999, “The Twin Crises: The Causes of Banking and Balance of Payments Problems,” The American Economic Review 89 (3): 473–500.

    Laeven, Luc and Ross Levine, 2009, “Bank Governance, Regulation, and Risk Taking” Journal of Financial Economics 93(2): 259–275.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment).

  • Long Term Finance

    Definition

    Long-term finance can be defined as any financial instrument with maturity exceeding one year (such as bank loans, bonds, leasing and other forms of debt finance), and public and private equity instruments. Maturity refers to the length of time between origination of a financial claim (loan, bond, or other financial instrument) and the final payment date, at which point the remaining principal and interest are due to be paid. Equity, which has no final repayment date of a principal, can be seen as an instrument with nonfinite maturity. The one year cut-off maturity corresponds to the definition of fixed investment in national accounts. The Group of 20, by comparison, uses a maturity of five years more adapted to investment horizons in financial markets (G-20 2013). Depending on data availability and the focus, the report uses one of these two definitions to characterize the extent of long-term finance. Moreover, because there is no consensus on the precise definition of long-term finance, wherever possible, rather than use a specific definition of long-term finance, the report provides granular data showing as many maturity buckets and comparisons as possible.

    Importance of long-term finance

    Extending the maturity structure of finance is often considered to be at the core of sustainable financial development. Long-term finance contributes to faster growth, greater welfare, shared prosperity, and enduring stability in two important ways: by reducing rollover risks for borrowers, thereby lengthening the horizon of investments and improving performance, and by increasing the availability of long-term financial instruments, thereby allowing households and firms to address their life-cycle challenges (Demirgüç-Kunt and Maksimovic 1998, 1999; Caprio and Demirgüç-Kunt 1998; de la Torre, Ize, and Schmukler, 2012).

    The term of the financing reflects the risk-sharing contract between providers and users of finance. Long-term finance shifts risk to the providers because they have to bear the fluctuations in the probability of default and other changing conditions in financial markets, such as interest rate risk. Often providers require a premium as part of the compensation for the higher risk this type of financing implies.  On the other hand, short-term finance shifts risk to users as it forces them to roll over financing constantly.

    The amount of long-term finance that is optimal for the economy as a whole is not clear.  In well-functioning markets, borrowers and lenders will enter short- or long-term contracts depending on their financing needs and how they agree to share the risk involved at different maturities. What matters for the economic efficiency of the financing arrangements is that borrowers have access to financial instruments that allow them to match the time horizons of their investment opportunities with the time horizons of their financing, conditional on economic risks and volatility in the economy (for which long-term financing may provide a partial insurance mechanism). At the same time, savers would need to be compensated for the extra risk they might take.

    Where it exists, the bulk of long-term finance is provided by banks; use of equity, including private equity, is limited for firms of all sizes. As financial systems develop, the maturity of external finance also lengthens. Banks’ share of lending that is long term increases with a country’s income and the development of banking, capital markets, and institutional investors. Long-term finance for firms through issuances of equity, bonds, and syndicated loans has also grown significantly over the past decades, but only very few large firms access long-term finance through equity or bond markets. The promotion of nonbank intermediaries (pension funds and mutual funds) in developing countries such as Chile has not always guaranteed an increased demand for long-term assets (Opazo, Raddatz and Schmukler, 2015; Stewart, 2014).

    Policy challenge

    Attempts to actively promote long-term finance have proved both challenging and controversialThe prevalent view is that financial markets in developing economies are imperfect, resulting in a considerable scarcity of long-term finance, which impedes investment and growth. Indeed, a significant part of lending by multilateral development banks (including World Bank Group lending and guarantees) has aimed at compensating for the perceived lack of long-term credit. At the same time, research shows that weak institutions, poor contract enforcement, and macroeconomic instability naturally lead to shorter maturities on financial instruments. Indeed, these shorter maturities are an optimal response to poorly functioning institutions and property rights systems as well as to instability.

    From this perspective, the policy focus should be on fixing these fundamentals, not on directly boosting the term-structure of credit. Indeed, some argue that attempts to promote long-term credit in developing economies without addressing the fundamental institutional and policy problems have often turned out to be costly for development. For example, efforts to jump-start long-term credit through development financial institutions in the 1970s and 1980s led to substantial costs for taxpayers and in extreme cases to failures (Siraj 1983; World Bank 1989). In response, the World Bank reduced this type of long-term lending in the 1990s and the 2000s. On the other hand, well-designed private-public risk-sharing arrangements – such as Public Private Partnerships for large infrastructure projects, or credit guarantee schemes –  may hold promise for mobilizing financing for long-term projects, and allow governments to mitigate political and regulatory risks and mobilize funding for private investment.

    Suggested reading:

    G-20 (Group of 20). 2013. “Long-Term Investment Financing for Growth and Development: Umbrella Paper.” Found at:https://g20.org/wpcontent/uploads/2014/12/Long_Term_Financing_for_Growth_and_Development_February_2013_FINAL.pdf

    Caprio, Gerard, and Asli Demirgüç-Kunt. 1998. “The Role of Long-Term Finance: Theory and Evidence.” World Bank Research Observer 13 (2): 171–89.

     

    Demirgüç-Kunt, Asli, and Vojislav Maksimovic. 1998. “Law, Finance, and Firm Growth.”  Journal of Finance 53 (6): 2107–37.

    Demirgüç-Kunt, Asli, and Vojislav Maksimovic. 1999. “Institutions, Financial Markets and Firm Debt Maturity.” Journal of Financial Economics 54 (3): 295–336.

    de la Torre, Augusto, Alain Ize, and Sergio L. Schmukler. 2012. “Financial Development in Latin America and the Caribbean: The Road Ahead.” Policy Research Working Paper 2380, World Bank, Washington, DC.

    Opazo, Luis, Claudio Raddatz, and Sergio Schmukler. 2015. “Institutional Investors and Long-Term Investment: Evidence from Chile.” Word Bank Economic Review 29 (2).

    Siraj, Khalid. 1983. “Report of the Task Force on Portfolio Problems on Development Finance Companies." World Bank, Washington, D.C.

    Stewart, Fiona. 2014. “The Use of Outcome-Based Benchmarks: Proving Incentives for Long-Term Investment by Pension Funds.” Policy Research Working Paper 6885, World Bank, Washington, DC.

    World Bank. 1989. Report of the Task Force on Financial Sector Operations. Financial Sector Development Department. World Bank, Washington, DC.

  • Nonbanking financial institution

    Anonbank financial institution (NBFI) is a financial institution that does not have a full banking license and cannot accept deposits from the public. However, NBFIs do facilitate alternative financial services, such as investment (both collective and individual), risk pooling, financial consulting, brokering, money transmission, and check cashing. NBFIs are a source of consumer credit (along with licensed banks). Examples of nonbank financial institutions include insurance firms, venture capitalists, currency exchanges, some microloan organizations, and pawn shops. These non-bank financial institutions provide services that are not necessarily suited to banks, serve as competition to banks, and specialize in sectors or groups.

    Risk pooling institutions

    Insurance companies underwrite economic risks associated with death, illness, damage to or loss of property, and other risk of loss. They provide a contingent promise of economic protection in the case of loss. There are two main types of insurance companies: life insurance and general insurance. General insurance tends to be short-term, while life insurance is a longer contract, ending at the death of the insured. Both types of insurance, life and property, are available to all sectors of the community. Because of the nature of the insurance industry (companies must access a plethora of information to assess the risk in each individual case), insurance companies enjoy a high level of information efficiency.

    Life insurance companies insure against economic loss of the insured’s premature death. The insured will pay a fixed sum as an insurance premium every term. Because the probability of death increases with age while premiums remain constant, the insured overpays in the earlier stages and underpays in the later years. The overpayment in the early years of the agreement is the cash value of the insurance policy.

    General insurance is further divided into two categories: market and social insurance. Social insurance is against the risk of loss of income due to sudden unemployment, disability, illness, and natural disasters. Because of the unpredictability of these risks, the ease at which the insured can hide pertinent information from the insurer, and the presence of moral hazard, private insurance companies frequently do not provide social insurance, a gap in the insurance industry which government usually fills. Social insurance is more prevalent in industrialized Western societies where family networks and other organic social support groups are not as prevalent.

    Market insurance is privatized insurance for damage or loss of property. General insurance companies take a single premium payment. In return, the companies will make a specified payment contingent on the event that it is being insured against. Examples include theft, fire, damage, natural disaster, etc.

    Contractual savings institutions

    Contractual savings institutions (also called institutional investors) provide the opportunity for individuals to invest in collective investment vehicles in a fiduciary rather than a principle role. Collective investment vehicles invest the pooled resources of the individuals and firms into numerous equity, debt, and derivatives promises. The individual, however, holds equity in the CIV itself rather what the CIV invests in specifically. The two most popular examples of contractual savings institutions are mutual funds and private pension plans.

    The two two main types of mutual funds are open-end and closed-end funds. Open-end funds generate new investments by allowing the public buy new shares at any time. Shareholders can liquidate their shares by selling them back to the open-end fund at the net asset value. Closed-end funds issue a fixed number of shares in an IPO. The shareholders capitalize on the value of their assets by selling their shares in a stock exchange.

    Mutual funds can be delineated along the nature of their investments. For example, some funds make high-risk, high return investments, while others focus on tax-exempt securities. Still others specialize in speculative trading (i.e. hedge funds), a specific sector, or cross-border investments.

    Pension funds are mutual funds that limit the investor’s ability to access their investment until after a certain date. In return, pension funds are granted large tax breaks in order to incentivize the working public to set aside a percentage of their current income for a later date when they are no longer amongst the labor force (retirement income).

    Other nonbank financial institutions

    Market makers are broker-dealer institutions that quote both a buy and sell price for an asset held in inventory. Such assets include equities, government and corporate debt, derivatives, and foreign currencies. Once an order is received, the market maker immediately sells from its inventory or makes a purchase to offset the loss in inventory. The difference in the buying and selling quotes, or the bid-offer spread, is how the market-maker makes profit. Market makers improve the liquidity of any asset in their inventory.

    Specialized sectoral financiers provide a limited range of financial services to a targeted sector. For example, leasing companies provide financing for equipment, while real estate financiers channel capital to prospective homeowners. Leasing companies generally have two unique advantages over other specialized sectoral financiers. They are somewhat insulated against the risk of default because they own the leased equipment as part of their collateral agreement. Additionally, leasing companies enjoy the preferential tax treatment on equipment investment.

    Other financial service providers include brokers (both securities and mortgage), management consultants, and financial advisors. They operate on a fee-for-service basis. For the most part, financial service providers improve informational efficiency for the investor. However, in the case of brokers, they do offer a transactions service by which an investor can liquidate existing assets.

    Role in financial system

    NBFIs supplement banks in providing financial services to individuals and firms. They can provide competition for banks in the provision of these services. While banks may offer a set of financial services as a package deal, NBFIs unbundle these services, tailoring their services to particular groups. Additionally, individual NBFIs may specialize in a particular sector, gaining an informational advantage. By this unbundling, targeting, and specializing, NBFIs promote competition within the financial services industry.

    Having a multi-faceted financial system, which includes non-bank financial institutions, can protect economies from financial shocks and recover from those shocks. NBFIs provide multiple alternatives to transform an economy's savings into capital investment, which act as backup facilities should the primary form of intermediation fail.

    However, in countries that lack effective regulations, non-bank financial institutions can exacerbate the fragility of the financial system. While not all NBFIs are lightly regulated, the NBFIs that comprise the shadow banking system are. In the runup to the recent global financial crisis, institutions such as hedge funds and structured investment vehicles, were largely overlooked by regulators, who focused NBFI supervision on pension funds and insurance companies. If a large share of the financial system is in NBFIs that operate largely unsupervised by government regulators and anybody else, it can put the stability of the entire system at risk. Weaknesses in NBFI regulation can fuel a credit bubble and asset overpricing, followed by asset price collapse and loan defaults.

    Bank/non-bank integration and supervisory integration

    The banking, securities, and insurance markets have become increasingly integrated, with linkages across the markets rapidly increasing. In response, one of the most notable developments in financial sector regulation in the past 20 years has been a shift from the traditional sector-by-sector approach to supervision  (with separate supervisors for banks, securities markets, and insurance companies) toward a greater cross-sector integration of financial supervision (Čihák and Podpiera 2008). This had an important impact on the practice of supervision and regulation around the globe.

    Three broad models are being used around the world: a three-pillar or “sectoral” model (banking, insurance, and securities); a two-pillar or “twin peak” model (prudential and business conduct); and an integrated model (all types of supervision under one roof). One of the arguably most remarkable developments of the past 10 years, confirmed by the World Bank’s Bank Regulation and Supervision Survey, has been a trend from the three-pillar model toward either the two-pillar model or the integrated model (with the twin peak model gaining traction in the early 2000s). In a recent study, Melecky and Podpiera (2012) examined the drivers of supervisory structures for prudential and business conduct supervision over the past decade in 98 countries, finding among other things that countries advancing to a higher stage of economic development tend to integrate their supervisory structures, small open economies tend to opt for more integrated supervisory structures, financial deepening makes countries integrate supervision progressively more, and the lobbying power of the concentrated and highly profitable banking sector acts as a negative force against business conduct integration. (The related data on the structure of supervision are available on this website, http://www.worldbank.org/financialdevelopment.)

    How do these various institutional structures compare in terms of crisis frequency and the limiting of the crisis impact? Cross-country regressions using data for a wide set of developing and developed economies provide some evidence in favor of the twin peak model and against the sectoral model (ˇCihák and Podpiera 2008). Indeed, during the global financial crisis, some of the twin peak jurisdictions (particularly Australia and Canada) have been relatively unaffected, while the United States, a jurisdiction with a fractionalized sectoral approach to supervision, has been at the crisis epicenter. However, the crisis experience is far from black and white, with the Netherlands, one of the examples of the twin peaks model, being involved in the Fortis failure, one of the major European bank failures. It is still early to make a firm overall conclusion, and isolating the effects of supervisory architecture from other effects is notoriously hard.

    Suggested reading:

    Carmichael, Jeffrey, and Michael Pomerleano. 2002. The Development and Regulation of Non-bank Financial Institutions. World Bank, Washington, DC.

    Čihák, Martin, and Richard Podpiera. 2008. “Integrated Financial Supervision: Which Model?” North American Journal of Economics and Finance 19: 135–52.

    Melecky, Martin, and Anca Podpiera. 2012. “Institutional Structures of Financial Sector Supervision, Their Drivers, and Emerging Benchmark Models.” MPRA Paper 37059, University of Munich, Germany.

    World Bank. 2012. Global Financial Development Report 2013: Rethinking the Role of the State in Finance. World Bank, Washington, DC (http://www.worldbank.org/financialdevelopment)