metadata: authors: 0: author: Apostolou,Apostolos
1: author: Jorgensen,Ole Hagen
count: Brazil
docna: 0: docna: Brazil's bank spread in international
            context : from macro to micro drivers
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Fiscal & Monetary Policy,Industrial Economics,Financial Crisis Management & Restructuring,Economic Growth,Inflation,Finance and Development
teratopic: Finance and Financial Sector Development,Private Sector Development
abstracts: cdata!: In an international context, this paper
            analyzes the main drivers of Brazil's bank spreads
            measured by the net interest margin, by estimating
            internationally comparable measures for (i) institutional
            and regulatory (micro-) factors; (ii) macro-economic
            factors; and (iii) banking competition factors. The paper
            produces and applies a novel data set covering 197 areas and
            countries; ranging from 1995 to 2009, including 106 banks
            for Brazil and 16,434 banks worldwide. The analysis finds
            that micro-factors are the main drivers of spreads across
            the world. In the case of Brazil, the spread is found to be
            strongly accounted for by micro-factors -- also in
            international comparison. For example, micro-factors
            contributed 7.2 percentage points (79 percent) of the 11.5
            percent total spread in Brazil in 2009, while macro-factors
            and banking competition factors jointly accounted for only
            1.9 percentage points (21 percent). Conversely, Brazil does
            not rank high in international comparison in terms of
            macro-economic risk: Brazil and other countries from Latin
            America and the Caribbean are found to feature the highest
            micro-factors in the world while having the second-highest
            spreads and the second-lowest contribution of macro-factors.
            These unique findings suggest that countries striving toward
            reducing bank spreads should consider policies aimed at
            reducing microeconomic frictions in their banking sectors,
            in particular, (i) the economic costs of holding reserves,
            (ii) credit risk, and (iii) implicit interest payments. In
            terms of policy dialogue, this would be especially relevant
            for Brazil and for Latin American and Caribbean countries in general.
geo_reg_key: 119228,517191
repnme: repnme: Brazil's bank spread in international
            context : from macro to micro drivers
display_title: Brazil's bank spread in
            international context : from macro to micro drivers
listing_relative_url: /research/2013/09/18274331/brazils-bank-spread-international-context-macro-micro-drivers
topicv3: International Accounting Standards
            m1326097 315,Economic Growth m1326241 471,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Credit Risk m1505083 3885,Interest Rate Risk m1505086 3888
publishtoextweb_dt: 2013-09-01T00:00:00Z
historic_topic: Finance and Financial Sector Development,Private Sector Development
no_of_pages: 42
author_keys: 0: author_key: 1071762
1: author_key: 805008
model_extractions: WPS6611 Policy Research Working Paper 6611 Brazil’s Bank Spread in International Context From Macro to Micro Drivers _Ole Hagen Jorgensen Apostolos Apostolou_ The World Bank Latin America and the Caribbean Region Poverty Reduction and Economic Management Department September 2013
Policy Research Working Paper 6611

# **Abstract**

In an international context, this paper analyzes the main drivers of Brazil’s bank spreads measured by the _net interest margin,_ by estimating internationally comparable measures for (i) institutional and regulatory (micro-) factors; (ii) macro-economic factors; and (iii) banking competition factors. The paper produces and applies a novel data set covering 197 areas and countries; ranging from 1995 to 2009, including 106 banks for Brazil and _micro-_ 16,434 banks worldwide. The analysis finds that factors are the main drivers of spreads across the world. In the case of Brazil, the spread is found to be strongly accounted for by _micro-factors_ —also in international comparison. For example, micro-factors contributed 7.2 percentage points (79 percent) of the 11.5 percent total spread in Brazil in 2009, while macro-factors and banking competition factors jointly accounted for only 1.9 percentage points (21 percent). Conversely, Brazil does not rank high in international comparison in terms of _macro-_ economic risk: Brazil and other countries from Latin America and the Caribbean are found to feature the _highest_ micro-factors in the world while having the second- _highest_ spreads and the second- _lowest_ contribution of macro-factors. These unique findings suggest that countries striving toward reducing bank spreads should consider policies aimed at reducing microeconomic frictions in their banking sectors, in particular, (i) the economic costs of holding reserves, (ii) credit risk, and (iii) implicit interest payments. In terms of policy dialogue, this would be especially relevant for Brazil and for Latin American and Caribbean countries in general.

This paper is a product of the Poverty Reduction and Economic Management Department, Latin America and the Caribbean Region. 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:// econ.worldbank.org. The authors may be contacted at OleHagenJorgensen@gmail.com and Apostolos.Apostolou@ graduateinstitute.ch.

_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
# **BRAZIL’S BANK SPREAD IN INTERNATIONAL CONTEXT: FROM MACRO TO MICRO DRIVERS**<sup>**1**</sup>

Ole Hagen Jorgensen and Apostolos Apostolou<sup>2</sup> _JEL classification_ : D43, E43, E43, E44, E51, G15, G21, L51,O16.

_Keywords:_ Brazil, Bank Spreads, International Ranking, Institutional and Regulatory Determinants, Macroeconomic Determinants, Competition.

> 1 An earlier version of this paper was presented at the conference: _Money, Credit and Growth in Brazil_ , organized by the World Bank and Banco Central do Brasil, Brazil, June 16-17, 2010. The main parts of the paper were written when the authors worked for Economic Policy, PREM in the World Bank, and the finding of the paper is presented in chapter 4 of the World Bank study _The Real Paradox: Untangling Credit Market Outcomes in Brazil_ . For useful comments we thank Augusto de la Torre, Marcio Nakane, Claudio Raddatz, Tito Cordella, Pablo Fajnzylber, Yaye Seynabou Sakho, Barbara Cunha, Rodrigo Fuentes, and other conference participants. For excellent research assistance we are grateful to Andresa Lagerborg.

> 2 Ole Hagen Jorgensen: olehagenjorgensen@gmail.com; Apostolos Apostolou: apostolos.apostolou@graduateinstitute.ch.
# **1. Introduction**

In a financially liberalized environment, the difference between what banks charge to lend money and what they pay to borrow is not only an indicator of risk, but also an indicator of banking sector lack of competition, which may have detrimental implications for saving, investment, and growth. This is evidently the case in Brazil where there is no apparent lack of profitable investment opportunities, but rather a high opportunity cost of capital that prevents new investments from being profitable (Hausmann, Rodrik, and Velasco, 2005).

However, in terms of social welfare, it is not necessarily clear whether high spreads are detrimental or not. A narrow spread may indicate that the banking market is competitive, but may also render the banking system less stable and less insulated from macroeconomic shocks through low bank capital and low profitability. Nevertheless, as spreads widen, the cost of interacting with the financial system becomes prohibitive for some borrowers and, since spreads reflect the cost of intermediation, policymakers and central bankers care about its level and volatility, as well as its determinants.

Brazil’s high spreads could be reduced by dampening the institutional and regulatory forces that keep intermediation costs high at the bank level. Spreads would, furthermore, diminish if banking market competition was increased and the volatility of interest rates and economic growth were reduced. As a result, both micro-factors and macro-factors are relevant to consider when assessing the efficiency of a banking system and the associated levels of spreads.

By international comparison, the spread in Brazil is considered to be high, but such comparisons are based on questionable methodologies that do not address cross-country heterogeneity and representativeness issues regarding borrowing and lending costs. As a result, an analysis of countryspecific as well as cross-country determinants of spreads would serve to identify some common factors relating to the efficiency of the banking system and, thereby inform policy discussions for countries with high spreads.<sup>3</sup> Such an empirical strategy requires a systematic treatment of the determinants of spreads in a large number of countries, including Brazil. Following the methodology proposed by Saunders and Schumacher (2000), the analysis in this paper will first filter out the bank-specific institutional and regulatory determinants of spreads. For example, Saunders and Schumacher (2000) find that the institutional and regulatory factors accounted for about 60 percent of the net interest margin (NIM; the difference between a bank’s interest earnings and expenses as a percentage of interest earning assets) in the United States in 1995. The residual from this analysis (the 40 percent in the case of the United States) then encompasses the contribution to the size of the spread caused by macroeconomic risk factors and banking market structural factors, such as the degree of competition. Such an exercise therefore informs policy makers with information about the potential sources of high spreads and, in the case of Brazil, provides the basis for evaluating whether to address micro or macroeconomic constraints in reducing spreads.

> 3 In the 1990s, many Latin American countries eliminated interest rate ceilings, reduced reserve requirements, and halted direct credit controls. These market-oriented reforms have promoted financial deepening and produced economic benefits. The generally high spreads in Latin America must be evaluated in light of a transition from repressed financial systems to liberalized financial environments.

2
The purpose of this paper is to shed light on the dimensions of inefficiencies and the lack of competition in the banking system in Brazil with the ultimate goal of informing policy discussions. The paper focuses on estimating from in an international perspective, the micro and macro-drivers of spreads in Brazil. The paper presents a novel analysis of spreads in Brazil in an international context by comparing a large number of countries in terms of not only their net interest margin but, more importantly, in terms of the main drivers of spreads as a path towards a more nuanced measure of risk.

The paper is structured as follows: Section 2 discusses issues related to measurements and international comparisons of spreads focusing on Brazil and presents a review of the literature on the determinants of spreads in an international context. Section 3 outlines the methodological framework we used to estimate the main groups of determinants of spreads in Brazil and across 197 countries and areas in the world. Section 4 presents and analyzes the data. Section 5 discusses the baseline results from our estimations for Brazil from a country-specific perspective. Brazil is compared to other countries across different dimensions, such as the degree of economic and financial market development. We compare Brazil to the other BRIC countries, to the US and to its geographical region. Section 6 presents a battery of robustness analyses that reveals caveats but also the strength of our baseline results. Section 7 concludes and discusses policy implications.

# **2. Measurements and Determinants of Spreads in Brazil and Internationally**

## **_Measurement Issues and International Comparisons of Spreads_**

Empirical measures of bank spreads attempt to capture the cost of financial intermediation such as the difference between what banks charge borrowers and what they pay depositors. The theoretical concept of the cost of financial intermediation, however, has no unique empirical counterpart. The reason is that banks do not charge only one loan rate or pay a single deposit rate. Indeed, on any particular day, every bank charges and offers a multitude of rates depending on classes of customers and types of products the bank supplies. Moreover, it is not an uncommon practice for banks to increase their revenues from loans by charging fees and commissions. These fees and commissions, while not included as interest charged, effectively increase the cost faced by bank borrowers (Brock and Suarez, 2000).

An additional problem in measuring bank spread is that, by including all interest earning assets and liabilities, net interest margins may deviate significantly from the marginal spread that reflects a bank’s marginal costs and revenues (Brock and Suarez, 2000). This is particularly true in countries where banks hold non-interest bearing assets as reserves and a significant amount of low-yielding bonds (largely government bonds in Latin American countries). The concept is also subject to important misrepresentation when banks experiencing serious difficulties are allowed to re-capitalize themselves by issuing bonds to be bought by the government (or the central bank) at below market prices.

We use a homogenous measure using the same methodology for all countries in our dataset to ensure cross country comparisons. We use the net interest margin (NIM) because we believe is an appropriate measure of the bank spread since it is a homogeneous measure of banks' profitability measured using international accounting standards collected by BankScope. Other measures of the bank spread provided by the International Monetary Fund’s, International Financial Statistics (IMF, IFS) database may not be appropriate because reported interest rates are not homogeneous between countries. Using 3
the NIM provided by BankScope, from financial statements of banks worldwide, we derive a pure spread (or margin) that is comparable across banks in any country across time. This pure spread varies across countries (and over time) according to the degrees of bank competition and macroeconomic (interest-rate) volatility in each country. For example, if a significant proportion of bank margins in a given country are determined by macroeconomic volatility rather than the monopolistic behavior of banks, then policy should be focused on macro-economic policies as a tool for reducing intermediation costs. Alternatively, if a large proportion of bank margins are due to reserve requirements, then a policy of paying interest on bank reserve holdings may reduce intermediation costs.

A few studies have exclusively focused on comparing and identifying patterns related to bank spreads across countries. Most studies use a common measure of the spread such as the NIM, which is comparable across countries. This measure poses a number of problems in terms of its appropriateness for international comparison: (i) Countries with different banking sector specialization can have lower or higher net interest margins, (ii) NIMs do not take into account overhead costs—the costs of doing credit checks, monitoring of the borrower, and recovering the collateral (Beck et al., 1999), (iii) NIMs do not take into account the product mix offered in different countries—for example, in less developed markets most borrowing is done in shorter maturities while in more developed markets, where mortgages are prevalent, borrowing is done with longer maturities.

Many studies use data from the IMF's IFS database to calculate the interest rate spread (lending – deposit rate). The IFS data are not homogeneous across countries because the data for lending and deposit rates are different among countries. The IFS methodology is to report the most relevant interest rates for each country or use the most available interest rates. We cannot solely rely on this absolute spread when evaluating the relative cost of borrowing or the profitability of the banking sector in each country. During the course of this study we have attempted to find the same lending and deposit rates reported by Brazil to the IFS for other countries but it is impossible to find the exact rates. Therefore, we cannot directly compare the spreads in Brazil using the IFS database with other countries because the maturity, type of borrower and the type of interest (fixed or variable) are not the same.

**Figure 1a. The link between IFS and NIM across countries in 2009 is weak**

**Figure 1b. The link between IFS and NIM in Brazil from 1997 to 2009 is non-existent**

<!-- Start of picture text --> 40 IFS 60 IFS<br>35 Brazil<br>55<br>30<br>25 50<br>20<br>45 R 2 = 0.0282<br>15 R2 = 0.1438<br>10 40<br>5<br>35<br>0<br>0 10 20 30<br>-5 30<br>8 10 12 14 16<br>-10<br>NIM NIM<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data 4
Since the IFS spreads are not comparable across countries (due to differences in maturities, type of interest such as fixed or variable, the credit worthiness of the creditor, etc.) we use the NIM because it covers a wide range of borrowers and several types of interest rates. Moreover, we find that there is very little correlation between the interest rate spreads reported in the IFS database and the NIM across countries (Figure 1a). As expected, we find little correlation between Brazil's NIM and IFS spreads (Figure 1b). Brazil ranks at the top of the world in terms of the IFS spreads but not in terms of the NIM. Over time both the IFS and the NIM spreads have varied considerably in Brazil, from 33 to 58 percent for the IFS spread and from 8 to 15 percent for the NIM.

In order to make sure that our results are comparable across countries we need to control for institutional and regulatory features. A method to account for such heterogeneous institutional and regulatory features requires the adjustment of the net interest margin (NIM) for such factors. The resulting measure is the pure spread which is comparable across countries. The key reference for the method of deriving the pure spread is Saunders and Schumacher (2000). The same method is used in cross-country studies in Brock and Rojas Suarez (2000) and Maudos and de Guevara (2004).

# **_Determinants of Spreads in Brazil and Internationally: A Review of the Literature_**

The literature on the determinants of spreads, which often include cross-country studies, mainly focuses on the spreads’ determinants. Saunders and Schumacher (2000) for example find that the regulatory components in the form of interest-rate restrictions on deposits, reserve requirements and capital-to-asset ratios have a significant impact on banks NIMs. They also find that the more segmented or restricted the banking system is, both geographically and by activity, the stronger the monopoly of power existing banks hold appears to be, and the higher their spreads. They find that macro interest-rate volatility has a significant impact on bank NIMs, suggesting that macro policies consistent with reduced interest-rate volatility could have a positive effect in reducing bank margins.

In a paper that adopts a similar methodology, Brock and Suarez (2000) find that both high operating costs as well as high levels of non-performing loans increase spreads, although the size of these effects differ across the countries. In addition, reserve requirements in a number of countries still act as a tax on banks translating into a higher spread. They also note that beyond bank specific variables, uncertainty in the macroeconomic environment facing banks appears to increase interest spreads.

Demirguc-Kunt and Huizinga (1999) note that recent research, as surveyed by Levine (1997), shows that the efficacy of financial intermediation can affect economic growth. Crucially, financial intermediation affects the net return of savings and the gross return of investment. The spread between these two returns mirror bank interest margins, in addition to transaction costs and taxes borne directly by savers and investors. Thus bank interest spreads could be interpreted as an indicator of the efficiency of the banking system.

Demirguc-Kunt and Huizinga (1999) investigate how bank interest spreads are affected by taxation, the structure of the financial system, and financial regulations, such as deposit insurance. A comprehensive review of the determinants of interest spreads are offered by Hanson and Rocha (1986), who summarize the role that implicit and explicit taxes play in raising spreads and discuss some of the determinants of bank costs and profits, such as inflation, scale economies, and market structure.

5
Claessens, Demirguc-Kunt and Huizinga (1999) use 7,900 bank observations from 80 countries for the period 1988-1995 and examine the extent and effect of foreign presence in domestic banking markets. This data set includes all OECD countries, as well as many developing countries and economies in transition. They investigate how NIMs, overhead, taxes paid, and profitability differ between foreign and domestic banks. However, the authors do not derive the pure spread as we do in our study. They use information from the financial statements of domestic and foreign commercial banks from the BankScope database provided by IBCA. Coverage by IBCA is comprehensive, with banks included accounting for roughly 90 percent of the assets of banks in each country.<sup>4</sup> Saunders and Schumacher (2000) study the determinants of bank net interest margins in six selected European countries and the U.S. during the period 1988-1995 using a sample of 614 banks. They apply the model by Ho and Saunders (1981) to a multi-country setting and decompose bank margins into (i) a regulatory component<sup>5</sup> , (ii) a market structure component, and (iii) a risk premium component. The overall purpose of the paper by Saunders and Schumacher is to investigate the impact of the structure of bank competition and interest-rate volatility on interest margins in a sample of banks in seven major OECD countries over time. Data for these banks have been obtained in a standardized fashion from IBCA and the BankScope database.

The estimated spreads by Saunders and Schumacher (2000) vary widely across banks, both within and across countries. For example, in 1995 the mean NIM for the US (4.197 percent) is over twice that for Switzerland (1.732 percent). Moreover, the relative size of cross-country margins appears to change over time. For example, Spain had the highest NIMs over the period 1988–1992 before being superseded by the US during the period 1993–1995. They note the importance of the microdeterminants of spreads and compared them to macroeconomic determinants. The pure spreads are, importantly, comparable across countries according to Saunders and Schumacher (2000) since the same method is used to adjust the NIMs for institutional and regulatory factors.

Brock and Rojas Suarez (2000) explore the determinants of bank spreads in a systematic way for Argentina, Bolivia, Chile, Colombia, Mexico, Peru, and Uruguay during the mid-1990s. The methodology chosen by Brock and Rojas Suarez (2000) is similar to that of Saunders and Schumacher (2000) analyzing the behavior of bank spreads in Latin America based on bank-specific data. Since, in most cases, banks do not report the whole array of specific interest rates charged and paid, bank spreads are estimated from data in banks’ balance sheets and income statements in an effort to obtain the implicit loan and deposit rates offered by each individual bank.

> 4 The data are compiled by IBCA mostly from the balance sheet, income statement and applicable notes found in audited annual reports. Each country has its own data template which allows for differences in reporting and accounting conventions. These are converted to a “global format” which is a globally standardized template derived from the country-specific templates. The global format contains thirty six standard ratios which can be compared across banks and between countries. This is the most comprehensive data base that allows cross-country comparison. While the underlying data reflects international accounting standards as much as possible, and IBCA makes an effort to standardize individual bank data while converting to global format, some differences in accounting conventions regarding valuation of assets, loan loss provisioning, hidden reserves, etc., no doubt remain.

> 5 The regulatory components in the form of interest-rate restrictions on deposits, reserve requirements and capitalto-asset ratios have a significant impact on banks NIMs.

6
Highlighting the difficulty in finding the most appropriate measure of banking spreads, Brock and Rojas Suarez (2000) present six alternative proxies for bank spreads, ranging from a narrow concept— one that includes loans on the asset side and deposits on the liability side—to a broad concept where all interest earning assets and liabilities plus associates fees and commissions are included. Their calculations are based on data from the Bank Superintendents of the countries in the sample.

A single country study by Afanasieff, Lhacer, and Nakane (2002) aims to uncover the main determinants of the bank interest spreads in Brazil. The main question the paper addresses is whether macro or microeconomic factors are the most relevant in affecting the behavior of such rates. The twostep approach of Ho and Saunders (1981) is employed to measure the relative relevance of the micro and the macro elements. The first step involves estimating the pure spread along the lines of Saunders and Schumacher (2000). The role played by the inflation rate, risk premium, economic activity, required reserves (all macroeconomic factors) and CAMEL-type indicators (microeconomic factors) are highlighted. Their results suggest that macroeconomic variables are the most relevant factors to explain the behavior of bank interest spread in Brazil.<sup>6</sup> In a paper related to that of Afanasieff, Lhacer, and Nakane (2002), da Silva, Oreiro, and de Paula (2006) analyze the determinants of bank spreads in Brazil, especially the macroeconomic determinants of spreads. A VAR model is used to identify the macroeconomic variables that may directly or indirectly have been influencing spreads in Brazil over the period 1994-2005. They present evidence that interest rate levels and, to a lesser degree, the inflation rate are the main macroeconomic determinants of high bank spreads in Brazil, and note that the actual net interest margin comprises two elements: the “pure” bank spread and the “impure” net interest margin explained by institutional and regulatory factors.<sup>7</sup> Another relevant study of the determinants of bank spreads in Brazil was conducted by the Banco Central do Brasil (BCB) in connection with the project “ _Juros e spread bancário_ ” (“Interest rates and bank spread”). This study offers an accounting breakdown of spreads. The spreads in Brazil are broken down on the basis of the margins charged by a sample of banks that from 2004 onwards, encompasses all banks operating in Brazil for which information (on their fixed-rate, freely-allocated credit operations only) is available at each base date. The following components are considered: (a) a residual corresponding, by and large, to bank net margin; (b) tax wedge, including direct and indirect taxes; (c) _Fundo Garantidor de Crédito_ (FGC, credit guarantee fund); (d) overhead; and (e) default (provision expenses for non-performing loans). The authors show how each of these components affects bank spreads in Brazil, using a methodology revised in 2004.

The study by the BCB finds that the most important constituent factors of spreads are respectively, net interest margin (a 2000-2003 average of 26.9 percent) and overhead (26.0 percent), followed by tax wedge (21.6 percent) and provision expenses (19.9 percent). Their estimations conclude that the average spreads among Brazilian banks depend on the basic interest rate, bank overhead, risk and taxes. As the variables were expressed as natural logarithms, it follows that the coefficients of the equation

> 6 The pure rate has also been derived in single-country studies by Ho and Saunders (1981) and Angbazo (1997) for US banks, by McShane and Sharpe (1985) for Australian banks, and by Afanasieff, Lhacer, and Nakane (2002) for Brazilian banks.

> 7 The methodology assumes actual spreads comprise of “pure” spreads adjusted upwards or downwards by implicit interest expense (bank charges for certain classes of customer exempt), by the opportunity cost of holding reserves and by capital requirements resulting from regulatory standards and bank supervision rules.

7
estimated are simply the elasticity of spreads to each of these variables. The most striking about the Central Bank study is the high sensitivity of bank spreads to variations in bank overheads: from the equation estimated by the Central Bank, a 1.0 percent reduction in bank overheads would yield a 1.55 percent reduction in spreads charged by banks. Also, banks’ net interest margins contribute substantially to spread composition. The analysis by BCB (2007) complements our analysis, and comes to similar conclusions on the determinants of the spreads.

Furthermore, Demirguc-Kunt, Laeven and Levine (2003) examines the impact of bank regulations, market structure, and national institutions on bank net interest margins and overhead costs using data on over 1,400 banks across 72 countries ranging from 1995-1999 while controlling for bank-specific characteristics. Two dependent variables are examined in order to gauge the cost of financial intermediation: the NIM and overhead expenditures. Peria and Mody (2003) investigate the impact of foreign bank participation and concentration on bank spreads in a sample of developing Latin American countries during the late 1990s.<sup>8</sup> Maudos and de Guevara (2004) perform an analysis that extends the data coverage from Saunders and Schumacher (2000). Maudos and de Guevara still use the NIM as the dependent variable in their estimations of the determinants of spreads, but also derive the pure margin along the lines of Saunders and Schumacher (2000). Their model shows that the “pure” interest margin depends on the competitive conditions of the market, the interest rate risk, the credit risk, the average operating expenses and the risk aversion of banking firms, as well as on other variables not explicitly introduced into the model (opportunity cost of reserves, payment of implicit interest and quality of management).<sup>9</sup> Their starting point is the methodology developed in the original study by Ho and Saunders (1981) and Saunders and Schumacher (2000) but it is expanded to explicitly account for banks operating costs.

The Maudos and de Guevara (2004) study differs from Saunders and Schumacher (2000) in several aspects: (a) they introduce the influence of operating costs into the modeling of the interest margin; (b) use direct measurements of market power; (c) the determinants of the interest margin are analyzed in a single stage; (d) it extends the period of study until the year 2000, though it is centered on the principal European countries (Germany, France, United Kingdom, Italy and Spain); and (e) the sample consists of a panel data of 1,826 banks (in 2000), as opposed to the 614 of Saunders and Schumacher’s study. However, the study by Maudos and de Guevara (2004) does not perform the analyses on the pure spread but rather on the NIMs as Saunders and Schumacher (2000) do.

Moreover, Gelos (2006, 2009) finds that intermediation spreads in Latin America are high by international standards. The paper examines the determinants of bank interest margins in the region using bank and country-level data from 85 countries, including 14 Latin American economies, for the period 1999–2002. The focus is on ex-post net interest margins, as opposed to ex-ante spreads between deposit and loan rates, which allows for a broader examination of the costs of financial intermediation.

> 8 Using bank-level data for Argentina, Chile, Colombia, Mexico, and Peru, they examine a number of hypotheses. Their econometric analysis is on the impact of concentration and foreign bank presence on bank spreads; they particularly study the effect of market structure changes on bank spreads, while controlling for a host of bank characteristics and macroeconomic variables.

> 9 They study the NIM in the main European banking systems (Germany, France, the United Kingdom, Italy and Spain) during the period 1993-2000 using a panel of 15,888 observations and identify the fundamental elements affecting this margin.

8
# **3. Methodology**

In this section we discuss and present the methodological framework used to estimate the main drivers of bank spreads across various countries throughout the world. We adopt the method devised by Saunders and Schumacher (2000) who built on Ho and Saunders (1981) to construct a multi-country framework for decomposing bank margins into (i) an institutional and regulatory component, (ii) a market structure component conveying the degree of banking market competition, and (iii) a macroeconomic risk premium component.

Saunders and Schumacher (2000) do not directly disaggregate the share of the spreads, into three groups of factors, however it is possible and that is what we do in this paper. Initially, we derive the share of the spreads that are attributed to institutional and regulatory factors; secondly, the share of the spreads that are attributed to banking market competition; and thirdly, the share of the spreads that are attributed to macroeconomic factors. These three shares will be derived and compared across all the countries in our sample. Then we rank in terms of the different types of drivers of the spreads and we proceed to analyze the main drivers of the spreads in Brazil thoroughly.

The estimation procedure consists of three steps; the first two following Saunders and Schumacher (2000) and a third step to derive the share each factor contributes to the bank spread as follows:

1. In Step 1, we regress a cross section of NIMs on banks specific variables such as (i) the ratio of non-interest bearing assets to total assets, (ii) the ratio of non-performing loans to total loans, and (iii) the capital asset ratio. In the regression, the three explanatory variables convey the importance of institutional and regulatory factors and leave the constant term of the regression to convey the unexplained part of the spreads. In that sense the constant term is, in the literature, called a “pure” spread, which then contains information about macroeconomic factors and the degree of banking competition, as interpreted by Saunders and Schumacher (2000). From this exercise, the pure spreads should theoretically be equal across all banks in any country at any point in time. However, pure spreads can be shown to vary across countries (and over time) according to the degrees of bank competition and interest-rate volatility in each country.

2. In Step 2 of the estimation procedure, the constant term from the regression in Step 1 (the pure spread) is regressed against macro variables, such as the volatility of interest rates and economic growth rates. In Step 2, the constant term captures the effect of market structure on the determination of the pure spread. This market structure is, in the literature, generally interpreted as the degree of market competition; i.e. the higher the constant term is in Step 2 regressions, the lower the degree of banking competition.

3. We add a third step to the estimation procedure. While Saunders and Schumacher (2000) provide the methodology, we exploit the framework to derive, in a systematic way across countries, the level and the percentage share of the spreads that are explained by (i) institutional and regulatory factors, (ii) banking competition, and (iii) macroeconomic factors, respectively.

9
NIM = f{s ~~(—~~ .2.a0), ~~=u~~ } (1) s=s( ~~-~~ ,R,Q0)= ~~-~~ +) 5
# **_Step 2. Estimating the Impact of Competition and Macroeconomic Volatility on Spreads_**

Now we separate the effects on NIMs for which macro-economic policies are responsible (such as interest-rate volatility), and components of the margin for which market structure (monopoly power) is responsible. The variable of interest is γ which is the estimate of the pure spread, where γtc is a time series of pure spreads ( _t=1...15_ ) for 197countries ( _c=1....197_ ), which are also the intercepts of the regressions in Step 1 above; θo is a constant that reflects the average effect of market structure on the pure spread across 197 countries; θ1 is the sensitivity of the pure spread to intermediation risk changes (interest-rate volatility) over time; and ε _i_ is the residual. Saunders and Schumacher (2000) performed Step 2 jointly for all countries (panel approach) in order to identify common effects, but since we are interested in international comparisons, we use standard regressions.

At any moment in time, actual NIMs comprise of a _pure spread_ that is constant across banks in any country in any given year, reflecting bank market structure and interest-rate risk plus markups or adjustments for implicit interest expense, the opportunity cost of required reserves, and capital requirements for credit-risk exposure. All other institutional and regulatory effects and frictions are reflected in the residual variable _u_ .

# **_Step 3. Decomposing the Net Interest Margin into Its Determinants_**

Each of the three factors behind the spread for a given country can be derived using the approach described below, where Institutional and Regulatory drivers are denoted as IR; Banking Market Competition factors are as BC; and Macro-Economic factors are labeled as ME.

In levels, the factors are: In shares as a percentage of NIM, the factors are: 11
# **4. Data**

For Step1 of the regression we use data from the BankScope database for the years 1995-2009. We have collected individual bank data on net interest margin, non-interest expense, other operating income over average assets, total assets, non interest earning assets and total capital ratio. We use these data to calculate the four variables used in our estimation namely the net interest margin, the implicit interest payments/total average assets (non-interest expense – other operating income), the cost of reserves (non interest earning assets/total assets) and the extra capital held, total capital/total assets. We have data for 197 countries and areas, a total of 231,834 bank observations.

For Step2 we use data from Thomson Reuters’ DataStream regarding interest rates for 47 countries. We are using weekly data from 1995-2009 to calculate the volatility of interest rates measured as the yearly standard deviation of the weekly observations. For Step2 we use short term interest rates defined as three month or 90 days interest rates. We use yearly data for the gross domestic product growth, and inflation from the International Monetary Fund’s _World Economic Outlook_ database.

For many countries we have data that span fifteen years (1995-2009), but some of the countries do not provide data for all of the years assessed. We present findings for these countries by concentrating on the most recent years. For most countries we present results for 2009 in this paper, but for countries that we do not have data for 2009, we present results for the year immediately preceding 2009.

For Step1 we used countries with 5 or more bank observations per year to make sure we had enough observations to make reasonable conclusions about the micro and macro situation in the country. The dataset adds significant value to the literature since there is no previous attempt to calculate spreads in as many countries and offer comparisons among countries, regions, degrees of development and reforms. The dataset is very extensive with a wide coverage and comparable data. The data have been collected from BankScope.

In terms of representativeness of the data, it is important that a large share of the assets in the banking sector is accounted for. If that was not the case, the results for a given country would be less reliable as the sample would not be a good representation of the banking sector in that country. As a result, we evaluate the assets of the banks in our sample as a percent of total assets in each country’s banking system (Figure 15b).

We find that our data set shows an overwhelming degree of representativeness since 100 percent of assets are accounted for in almost all countries in the sample. For countries where this is not the case, the country is excluded from the analysis in order to ensure reliability of the cross-country (world-wide) analysis. In many countries especially developing countries the banking sector owns much of the financial assets in the country, which is a key ingredient in ensuring the representativeness of the data set. The total data set is displayed in Annex 3.

12
# **5. Baseline Results**

The high cost of financial intermediation in Brazil in absolute terms, and in comparison to other countries, is a key source of policy concern. The difference between bank lending and deposit interest rates measured by the IFS spreads illustrates that Brazil has one of the highest interest spreads in the world, well above 30 percent in 2009 (see Figure 2).

**Figure 2. Link between IFS and SELIC rate is weak but link between NIM and SELIC rate is strong**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>NIM Actual SELIC Rate IFS<br>Percentage points<br><!-- End of picture text --> _Source_ : Banco Central do Brasil, BankScope and IMF (IFS); Note: NIM vs. SELIC Rate The high cost of intermediation, as measured by the IFS spreads, has been reduced considerably in Brazil from 58.4 percent in 1998 to 35.4 percent in 2009 (Figure 2). However, the IFS spread remains very high compared to other countries as well as compared to the SELIC rate (the official BCB rate), which has declined to less than 10 percent in 2009 (Figure 2). Brazil experienced very high inflation incidents in the past but has successfully brought inflation under control. Nevertheless, there has not been a sufficient reduction in interest spreads to moderate levels even as the fiscal and monetary environment has improved in recent years.

Improving the performance of the financial system is a main concern for development policy in Brazil because it is inhibiting investment and growth despite the impressive performance of the Brazilian economy. In that vein, there has been a substantial amount of policy and academic research in recent years led by the BCB and others. There were two important studies done to address these issues, the first by Afanasieff, Lhacer and Nakane (2002) and the second by de la Torre, et al. (2006). Despite the insights provided by the studies there remains considerable disagreement as to which methods should be used to tackle the issue and to the nature and causes of the high spreads in Brazil. Our paper attempts to separate the spreads into the macro and micro components.

De la Torre, et al. (2006) first established a link between the SELIC rate and interest spread. They calculate the adjusted SELIC rate measured by multiplying the SELIC rate with one minus the probability of default. Then macroeconomic variables are used as explanatory variables to explain the 13
‘adjusted’ SELIC rate. Their study finds that macroeconomic variables are significant and explain a substantial part of the adjusted SELIC rate (R<sup>2</sup> =0.86). Then they investigate various microeconomic factors in an effort to explain the remainder of the spread not previously explained by macroeconomics factors. Their analysis finds that the macroeconomic factors are the most significant drivers of interest spreads in Brazil. We use a different methodology, a larger dataset and a longer time period to evaluate the results of de la Torre eta al. (2006) and gauge the effects of the significant growth in Brazil in the middle of the 2000s and the subsequent global crisis.

Research by Afanasieff, Lhacer and Nakane (2002) use the technique developed by Ho and Saunders (1981), to estimate the variables affecting spreads in Brazil measured as the difference between loan and deposit rates in Brazilian banks. The study uses several variables such as the number of bank branches, operating costs, bank leverage and the ratio of non interest-bearing deposits to total operational assets to calculate the pure spread or the macro part of the total spread. They use an unbalanced panel excluding misreported and outliers data, which possibly diminishes the power of their results. Furthermore, the definition of the spread, defined as the difference between the loan rate measured by the average rate charged on fixed-rate free-allocated operations and the deposit rate measured by the rate paid on 30-day certificates of deposits, is not wholly representative of the spreads faced by banks.

The loan interest rate is the average rate charged on fixed rate free-allocated operations, which excludes floating rates. Therefore, the spread calculated is not representative of the spreads in Brazil, in terms of maturity and fixed or floating rates. We evaluate their finding that spreads in Brazil are mainly caused by macroeconomic factors by using a dataset that extends to 2010, and includes the global crisis.

Our approach uses the same methodology used by Afanasieff, Lhacer and Nakane (2002)first introduced by Ho and Saunders (1981) and expanded by Saunders and Schumacher (2000). We use the Net Interest Margin (NIM) as a proxy to interest spreads in Brazil. Three institutional and regulatory (micro) variables are used to explain the variation in the NIM leaving the rest to be explained by macroeconomic and competition factors. These variables are consistently collected by BankScope and are comparable between banks, countries and regions. Hence, the methodology adds significant value making the analysis of spreads more consistent and comparable across banks and countries.

- The first variable is payments by banks of implicit interest on deposits through service charge remissions and other forms of savers subsidy because of regulatory restrictions on explicit interest payments. Implicit interest payments are measured by non-interest expense minus other operating income divided by total average assets.

- The second variable is the bank’s opportunity cost of depositing reserves with the monetary authorities. The economic cost of holding reserves is calculated by the ratio of non-interest earning assets to total assets variable.

- The third variable is capital reserves that banks decide to hold to protect themselves against anticipated and unanticipated credit risk. Consequently, banks that have high capital ratios for regulatory or credit reasons are likely to pass on the cost to borrowers. We proxy the extra capital held by banks using the equity over total assets variable.

14
# **_5.1 Micro and Macro Drivers of Bank Spreads in Brazil_**

Our results suggest that Institutional and Regulatory (micro) factors are the main drivers of the NIM spreads in Brazil from 1995-2009 (Figure 3a) and that a high correlation exists between Institutional and Regulatory (I&R) factors and the NIM (Figure 3b). This is contrary to what Afanasieff, Lhacer and Nakane (2002) and de la Torre, et al. (2006) find. Both studies highlight _macroeconomic_ variables as the most relevant factors in explaining interest spreads in Brazil. In particular, de la Torre, et al. find that macro-factors play an important role in the determination of spreads and a systematic relationship between spreads and the SELIC rate in the period ranging from 1994 to 2005. Our result therefore suggests a new perspective for interpreting bank spreads in Brazil—indicating a move from macro to micro drivers.

**Figure 3a. I&R factors a large part of NIM                  Figure 3b. Correlation between NIM and I&R**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009<br>Brazil NIM<br>Percentage points<br><!-- End of picture text --> <!-- Start of picture text --> 18 I&R<br>16<br>R2 = 0.4165<br>14<br>12<br>10<br>8<br>6<br>6 11 16 21<br>I&R Linear (I&R) NIM<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data Contrary to previous studies, we find that I&R factors are the most significant factors in determining NIM spreads, when examining the period 1995-2009. On the other hand, our analysis complements the study by de la Torre, et al. (2006) which, too, suggests that micro-factors play a significant role. We differ in the magnitude of I&R factors but we agree with de la Torre, et al. (2006) in the sense that there are high administrative costs in the Brazilian banking system. The high administrative costs reflect the lack of competition in Brazil, resulting in high I&R factors. Moreover, the argument for the importance of micro-factors is supported by the low position of Brazil in the Doing Business Report (2010) in the Enforcing Contracts category ranking 98th out of 183 countries (Table 1).

De la Torre, et al. (2006) point out that as the SELIC rate decreases micro-factors become _more_ significant. We have witnessed the SELIC rate declining from 19percent in 2005 to under 10 percent in 2009, which might imply that during this period micro-factors became much more prominent. As the SELIC rate decreases, I&R factors progressively become more binding and constrain further declines in intermediation spreads. In 2009, 7.21 percentage points of the NIM were accounted for by I&R and only 1.94 percentage points by macro-factors (Figure 4). This shows that I&R factors continue to be the most significant component of the NIM.

15
**Table 1. Doing Business Report 2010**

<!-- Start of picture text --> Figure 4. Brazil Spread Breakdown (2009)<br><!-- End of picture text --> <!-- Start of picture text --> 9.14<br>Ease of Doing Business Rank 124<br>7.21<br>Getting Credit Rank 87<br>Protecting Investors Rank 73 1.94<br>Enforcing Contracts Rank 98<br>NIM I&R Pure spread<br>(Macro)<br><!-- End of picture text --> _Source:_ Authors’ calculations based on BankScope data _Source:_ Authors’ calculations based on BankScope data Our analysis suggests a positive correlation between NIM and I&R factors in Brazil (Figure 3b). This positive correlation is an additional indication that there is a strong case for institutional and regulatory reforms in Brazil. Our results presented in Figure 3aand Figure 5a show that, despite the falling significance of I&R factors, they clearly remain the dominant components of the NIM.

**Figure 5a. Summary breakdown of NIM Spread**

# **Figure 5b. NIM and I&R Vs Actual SELIC rate**

|**year**|**Obs. per**<br>**Year**|**R2**|**NIM**|**I&R**|**Pure**<br>**spread**<br>**(macro)**|40<br>45||
|---|---|---|---|---|---|---|---|
|1995|99|0.831|15.8|16.91|-1.11|35||
|1996|111|0.374|13.12|9.5|3.62|30<br>ts||
|1997|119|0.588|11.06|12.14|-1.08|oin||
|1998|123|0.448|11.68|7.56|4.12|25<br>e p||
|1999|119|0.489|15.25|12.86|2.39|tag||
|2000|129|0.682|11.53|9.46|2.07|20<br>cen||
|2001|154|0.061|7.96|11.83|-3.87|15<br>er||
|2002|162|0.299|15.01|13.17|1.84|P||
|2003|143|0.677|13.09|12.24|0.85|10||
|2004|137|0.833|12.53|12.71|-0.18|||
|2005|140|0.794|13.58|12.79|0.8|5||
|2006|142|0.446|14.59|11.28|3.31|0||
|2007|153|0.576|13.96|11.83|2.13||1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009|
|2008|124|0.825|9.33|7.87|1.46||Actual SELIC Rate<br>NIM<br>I&R|
|2009|106|0.761|9.14|7.21|1.94||<br><br>|

_Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data, Banco Central do Brasil 16
We observe that the level of the NIM is consistently lower than the SELIC rate (Figure 5b). This means that the returns of banks are lower than by just lending to the government, which is theoretically risk free. This is counter-intuitive because theory suggests that by taking risk, lending to the individuals and corporations, banks should theoretically earn a higher return than by lending to the government. A possible explanation for this is that banks underestimate default risk and the realized default rates are higher than the anticipated default rates. However, the fact that the SELIC rate is consistently higher than NIM is of concern. A risk-averse bank would choose to lend risk free to the government and get higher returns. There is an arbitrage opportunity that is not being exploited by the banking sector.<sup>11</sup> Even when we take out the competition factors from the pure spread, in our Step 2 regression, we see that macro-factors are still a very small part of the NIM in 2009 (Figure 6a and 6b). This supports our argument that macro-factors only play a small part in the determination of spreads in Brazil and microfactors are the most significant (Figure 6b).

**Figure 6a.  Breakdown of NIM Spread in Brazil** **<u>(2009)</u>**

<!-- Start of picture text --> Figure 6b. Breakdown of NIM Spread as % in<br>Brazil (2009)<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>9<br>4%<br>8<br>17%<br>7<br>6<br>5<br>4<br>3 79%<br>2<br>1<br>0<br>NIM Pure Spread Macro Micro Competition Macro<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data

# **_5.2 Brazil's Bank Spreads in an International Context_**

Many studies have concluded that spreads in Brazil are the highest in the world (generally as measured by the data provided by IMF’s IFS). We find that Brazil’s IFS spreads are the highest among the countries we investigate in the paper (Table 2) for 2009. However, when we further investigate the measurement of these spreads we find they are not comparable across countries. The IFS spreads cannot reliably be used for cross-country comparison because of the disparate rates reported by countries. Some countries report the difference between lending and borrowing prime rates, while others report interest rates of different maturities.

> 11 Another possibility would be that the banks believe that the government would not repay its debts. Nevertheless, this rationale is improbable given that the Brazilian government’s debt ratings have improved significantly and the debt to GDP ratio has decreased.

17
Using the methodology developed by Saunders and Schumacher (2000) and data from BankScope we are able to consistently measure and compare spreads across countries. We present our findings for 2009 in Table 2, and for 2008 in Annex 2, Table 1. We find that despite the fact the Brazil ranks at the top of table with the highest IFS spreads, it ranks 15<sup>th</sup> in terms of the highest NIM out of 121 countries, 17<sup>th</sup> in terms of I&R factors and 56<sup>th</sup> in terms of macro-factors (Table 2) in 2009. These findings show that Brazil’s intermediation costs are overestimated when simply looking at IFS spreads and arguing that most of Brazil’s spreads are explained by I&R factors.

In 2009, 79 percent of Brazil’s spreads come from I&R factors. Our findings for Brazil are robust, with the R<sup>2</sup> of our regression measuring at 0.76 in 2009. Therefore, the I&R factors for Brazil are very significant in explaining the NIM in 2009. In addition, we find the macro-factors for Brazil, meaning the constant of our regression, to be significant in the 5 percent interval. This points to the robustness of our macro-factors findings and their statistical significance. In terms of I&R factors Brazil ranks 17<sup>th</sup> , much higher than Russia, ranked 49<sup>th</sup> , India, ranked 119<sup>th</sup> , China, ranked 85<sup>th</sup> and the U.S., ranked 107<sup>th</sup> out of 121 countries in 2009.

It is not surprising that in 2009, Brazil ranks 56<sup>th</sup> in terms of spreads attributed to macro-factors, which is lower than the U.S. which ranks 21<sup>st</sup> , Russia that ranks 8<sup>th</sup> , India that ranks 31<sup>st</sup> and only slightly higher than China that ranks 59<sup>th</sup> . These rankings indicate that Brazil’s I&R factors explain most of the total spread (NIM) but are very high compared to other BRICs and the U.S. Therefore, Brazilian authorities should focus on reducing the impact of I&R factors in order to reduce spreads but also to improve the country’s ranking in the global tables of spreads.

Brazil’s IFS spreads are high compared to the rest of the LAC region. Nevertheless, the region has 13 countries in the top 30 with the highest IFS spreads, with Brazil at the top with the highest IFS spreads in 2009. Moreover, the region has 12 countries in the top 30 with the highest NIM but this time Brazil does not rank at the top but it is replaced by Ecuador being ranked 3<sup>rd</sup> among 121 countries. In terms of I&R factors LAC has 11 countries in the top 30 with the highest spreads with Ecuador ranked 3<sup>rd</sup> , Jamaica 4<sup>th</sup> and Brazil ranked 17<sup>th</sup> among 121 countries in 2009. On the other hand, in terms of macro-factors LAC has 6 countries in the top 30 with the highest spreads with Costa Rica ranking4<sup>th</sup> , Paraguay7<sup>th</sup> , while Brazil is not ranked in the top 30 countries in 2009. It appears that the region and Brazil in particular, are doing well in terms of macro-factors but poorly in terms of I&R.

# **_5.3 Brazil and International Trends_**

Our findings suggest that I&R factors are the main drivers of NIM spreads across the world. We observe a high positive correlation between NIM and I&R factors (Figure 7a) in 2008 as well as in 2009. Brazil follows this global trend of where I&R are highly correlated with NIM, where the profitability of the banks is mainly determined by I&R factors (Figure 3b). In many countries around the world, banks seem to place more significance on I&R factors when determining their lending and borrowing spreads. However, the opposite should occur. Banks should have a tendency towards basing their spreads partly according to macro-factors because they lend to a broad spectrum of firms and individuals whose ability to repay is largely based on the health of the macro-economy and the markets they operate. Brazil’s banks place greater significance on I&R factors accounting for 79 percent of the NIM in 2009.

18
**Table 2. Ranking of Countries by Spreads in 2009**

|**Rank**<br>**1**|**IFS (2009)**<br>BRAZIL|**Nim (2009)**<br>ZAMBIA|**Institutional and Regulatory (2009)**<br>ZAMBIA|**R2**<br>0.66|**Macro & Competition (2009)**<br>MOZAMBIQUE**|
|---|---|---|---|---|---|
|**2**|PARAGUAY|SOUTH AFRICA|SOUTH AFRICA|0.38|SIERRA LEONE|
|**3**<br>**4**|MALAWI<br>PERU|ECUADOR<br>GUATEMALA|ECUADOR<br>JAMAICA|0.99<br>0.91|UKRAINE***<br>COSTA RICA***|
|**5**|GEORGIA REP. OF|MOZAMBIQUE|PAKISTAN|0.75|KENYA***|
|**6**|ZAMBIA|GHANA|PERU|0.82|ALGERIA|
|**7**|SIERRA LEONE|JAMAICA|GUATEMALA|0.99|PARAGUAY**|
|**8**|COSTA RICA|PERU|AZERBAIJAN|0.93|RUSSIA***|
|**9**|UGANDA|UGANDA|COLOMBIA|0.60|DOMINICAN REPUBLIC*|
|**10**|URUGUAY|MALAWI|ALBANIA|0.98|HONDURAS|
|**11**|MAURITIUS|AZERBAIJAN|UGANDA|051|CAMBODIA**|
|**12**|DOMINICAN REPUBLIC|ARGENTINA|VENEZUELA|.<br>0.45|MOLDOVA REP. OF**|
|**13**|ARMENIA|DOMINICAN REPUBLIC|UZBEKISTAN|0.83|MALAWI|
|**14**|JAMAICA|SIERRA LEONE|GHANA|0.63|SERBIA**|
|**15**<br>|BOLIVIA<br>|BRAZIL<br>|ARGENTINA<br>|0.80<br>|GHANA<br>|
|**16**<br>**17**|KENYA<br>TRINIDAD AND TOBAGO|ARMENIA<br>PARAGUAY|BELARUS<br>BRAZIL|0.86<br>0.76|NEPAL***<br>INDONESIA***|
|**18**|CROATIA|VENEZUELA|PHILIPPINES|0.90|NAMIBIA***|
|**19**<br>**20**|HONDURAS<br>GUATEMALA|URUGUAY<br>BELARUS|ARMENIA<br>MALAWI|0.76<br>0.62|ETHIOPIA<br>YEMEN**|
|**21**|LATVIA|GEORGIA REP. OF|SAUDI ARABIA|0.98|USA***|
|**22**|ANGOLA|HONDURAS|GEORGIA REP. OF|0.54|BANGLADESH***|
|**23**|MOROCCO|EL SALVADOR|EL SALVADOR|0.83|CAYMAN ISLANDS|
|**24**|AZERBAIJAN|SERBIA|ROMANIA|0.67|URUGUAY|
|**25**|ICELAND|RUSSIA|NIGERIA|0.58|TANZANIA**|
|**26**|UKRAINE|UKRAINE|URUGUAY|0.61|SUDAN|
|**27**|TANZANIA|KENYA|DOMINICAN REPUBLIC|0.33|BOLIVIA**|
|**28**|ECUADOR|TURKEY|ANGOLA|0.68|OMAN***|
|**29**|GERMANY|PAKISTAN|BOSNIA-HERZEGOVINA|0.91|QATAR***|
|**30**<br>**31**|COLOMBIA<br>RUSSIA|UZBEKISTAN<br>NIGERIA|BOTSWANA<br>TURKEY|0.73<br>0.38|KAZAKHSTAN**<br>INDIA***|
|**32**|BANGLADESH|TANZANIA|MEXICO|0.40|TUNISIA***|
|**33**|BAHRAIN|MEXICO|SWEDEN|0.99|GEORGIA REP. OF|
|**34**|NEW ZEALAND|BOTSWANA|UNITED KINGDOM|0.14|POLAND***|
|**35**|ALGERIA|COSTA RICA|ICELAND|0.51|EGYPT***|
|**36**|BOTSWANA|ALGERIA|PARAGUAY|0.39|TRINIDAD AND TOBAGO|
|**37**|MOZAMBIQUE|ALBANIA|TANZANIA|0.67|SRI LANKA*|
|**38**|PAKISTAN|NAMIBIA|MACEDONIA (FYROM)|0.76|TURKEY**|
|**39**|ALBANIA|SRI LANKA|SRI LANKA|0.42|THAILAND***|
|**40**|PHILIPPINES|SUDAN|SWITZERLAND|0.15|KUWAIT**|
|**41**|MOLDOVA REP. OF|CAMBODIA|NEW ZEALAND|0.90|IRAN**|
|**42**<br>**43**|NEPAL<br>EGYPT|ROMANIA<br>MOLDOVA REP. OF|BULGARIA<br>HONDURAS|0.44<br>0.53|MONTENEGRO<br>BERMUDA|
|**44**|MONTENEGRO|SAUDI ARABIA|SERBIA|0.15|ARMENIA**|
|**45**|ROMANIA|BOSNIA-HERZEGOVINA|MOROCCO|0.87|SLOVAKIA**|
|**46**|INDONESIA|TRINIDAD AND TOBAGO|SINGAPORE|0.23|GREECE***|
|**47**|CHILE|COLOMBIA|CZECH_REPUBLIC|0.81|EL SALVADOR|
|**48**|BULGARIA|BULGARIA|MONTENEGRO|0.90|MEXICO|
|**49**|MACAU|MONTENEGRO|RUSSIA|0.24|BULGARIA|
|**50**<br>|BELGIUM<br>|INDONESIA<br>|TRINIDAD AND TOBAGO<br>|0.42<br>|NIGERIA<br>|
|**51**|MEXICO|BOLIVIA|CYPRUS|0.65|ARGENTINA|
|**52**|SINGAPORE|MACEDONIA (FYROM)|SUDAN|0.07|BOTSWANA|
|**53**|SRI LANKA|<br>KAZAKHSTAN|NAMIBIA|0.98|MAURITIUS|
|**54**<br>|NIGERIA<br>|SWEDEN<br>|CROATIA<br>|0.64<br>|VIETNAM***<br>**|
|**55**<br>**56**|HONG_KONG<br>ITALY|ANGOLA<br>PHILIPPINES|JORDAN<br>GERMANY|0.79<br>0.04|UAE<br>BRAZIL**|
|**57**|THAILAND|ETHIOPIA|CHILE|0.53|HONG_KONG**|
|**58**<br>|NAMIBIA<br>|NEPAL<br>|PANAMA<br>|0.63<br>|PANAMA***<br>***|
|**59**<br>**60**|PANAMA<br>DENMARK|YEMEN<br>OMAN|VIETNAM<br>PORTUGAL|0.89<br>0.63|CHINA<br>MACEDONIA (FYROM)<br>_Table continues_|

19
|**61**|CZECH_REPUBLIC|VIETNAM|SYRIA|0.62|MALAYSIA***|
|---|---|---|---|---|---|
|**62**|EL SALVADOR|PANAMA|DENMARK|032|MACAU|
|**63**|<br>ESTONIA|TUNISIA|UAE|.<br>0.43|SPAIN***|
|**64**|UAE|CZECH_REPUBLIC|LEBANON|0.31|JORDAN***|
|**65**|FRANCE|USA|SIERRA LEONE|0.89|DENMARK***|
|**66**|GREECE|UAE|BOLIVIA|0.69|CHILE|
|**67**<br>|JORDAN|CROATIA|LATVIA|0.50|CROATIA*|
|**68**|BOSNIA-HERZEGOVINA|JORDAN|KAZAKHSTAN|0.40|FINLAND***|
|**69**|ARGENTINA|CHILE|MALAYSIA|0.26|BAHRAIN|
|**70**|SYRIA|DENMARK|NORWAY|0.56|ITALY***|
|**71**|LITHUANIA|BANGLADESH|INDONESIA|0.50|FRANCE***|
|**72**|VENEZUELA|POLAND|KENYA|0.15|LITHUANIA*|
|**73**|KUWAIT|UNITED KINGDOM|KOREA|0.24|MALTA*|
|**74**|AUSTRIA|MOROCCO|NETHERLANDS|1.00|CZECH_REPUBLIC|
|**75**|POLAND|MALAYSIA|MOLDOVA REP. OF|0.74|AUSTRIA***|
|**76**|OMAN|THAILAND|CAMBODIA|0.66|ISRAEL|
|**77**|CYPRUS|SINGAPORE|ESTONIA|0.85|CANADA***|
|**78**|AUSTRALIA|QATAR|ITALY|0.28|KOREA**|
|**79**|SOUTH AFRICA|HONG_KONG|BELGIUM|0.71|AUSTRALIA***|
|**80**|CHINA|CYPRUS|ISRAEL|0.91|GUATEMALA|
|**81**|VIETNAM|EGYPT|HONG_KONG|0.07|ROMANIA|
|**82**|MALAYSIA|SLOVAKIA|MONACO|0.31|JAPAN***|
|**83**|MACEDONIA (FYROM)|SYRIA|AUSTRIA|0.54|BOSNIA-HERZEGOVINA|
|**84**|QATAR|NEW ZEALAND|CANADA|0.22|BELARUS|
|**85**|PORTUGAL|CHINA|CHINA|0.39|SYRIA|
|**86**|SWITZERLAND|ITALY|ALGERIA|0.29|TAIWAN***|
|**87**|FINLAND|PORTUGAL|BAHRAIN|0.03|LIECHTENSTEIN***|
|**88**|UNITED KINGDOM|ICELAND|JAPAN|0.38|LEBANON|
|**89**|IRELAND|LEBANON|TUNISIA|0.23|BELGIUM***|
|**90**|ISRAEL|KOREA|ETHIOPIA|0.47|ESTONIA|
|**91**|MALTA|IRAN|AUSTRALIA|0.54|UGANDA|
|**92**|SWEDEN|ISRAEL|SPAIN|0.19|LUXEMBOURG***|
|**93**|CANADA|GERMANY|TAIWAN|0.25|MONACO|
|**94**|LEBANON|LATVIA|LUXEMBOURG|0.26|LATVIA|
|**95**|KOREA|SPAIN|FRANCE|0.26|PORTUGAL|
|**96**|NORWAY|BAHRAIN|LITHUANIA|0.62|IRELAND***|
|**97**|SPAIN|AUSTRIA|OMAN|0.95|NORWAY***|
|**98**|JAPAN|BERMUDA|COSTA RICA|0.57|SAUDI ARABIA|
|**99**|BELARUS|CANADA|THAILAND|0.10|CYPRUS|
|**100**|ETHIOPIA|SWITZERLAND|IRELAND|0.18|SWEDEN|
|**101**|NETHERLANDS|MAURITIUS|MALTA|0.55|MOROCCO|
|**102**|IRAN|ESTONIA|YEMEN|0.78|SINGAPORE|
|**103**|SERBIA|NORWAY|SLOVAKIA|0.07|GERMANY|
|**104**||GREECE|POLAND|0.12|NETHERLANDS|
|**105**||BELGIUM|NEPAL|0.04|ANGOLA|
|**106**||FRANCE|MAURITIUS|0.76|NEW ZEALAND|
|**107**||LITHUANIA|USA|0.02|VENEZUELA|
|**108**||AUSTRALIA|UKRAINE|0.08|UNITED KINGDOM|
|**109**||JAPAN|LIECHTENSTEIN|0.39|SWITZERLAND|
|**110**||KUWAIT|EGYPT|017|UZBEKISTAN|
|**111**||MALTA|FINLAND|.<br>0.31|ICELAND|
|**112**||MACAU|MACAU|0.83|PHILIPPINES***|
|**113**||TAIWAN|IRAN|091|AZERBAIJAN|
|**114**||NETHERLANDS|BANGLADESH|.<br>0.53|PERU|
|**115**||FINLAND|QATAR|0.63|ALBANIA|
|**116**||INDIA|BERMUDA|0.02|COLOMBIA**|
|**117**||LUXEMBOURG|GREECE|0.70|ECUADOR|
|**118**||MONACO|KUWAIT|007|JAMAICA|
|**119**||IRELAND|INDIA|.<br>0.78|SOUTH AFRICA|
|**120**||LIECHTENSTEIN|CAYMAN ISLANDS|0.97|PAKISTAN**|
|**121**||CAYMAN ISLANDS|MOZAMBIQUE|0.84|ZAMBIA|

_Source_ : Authors’ calculations based on BankScope data and IFS (IMF).

Note: We rank countries according to their spreads in descending order. In the first column we rank countries according to their IFS spreads (lending-deposit rates).In the second column we rank countries according to the NIM. In the third column we rank countries according to I&R (micro) factors and include the R<sup>2</sup> measure for our regression used to measure I&R factors. In the fifth column we rank countries according to their macro spreads (*=10% significance, **=5% significance, ***=1% significance).

20
# **_5.4 Brazil and Regional Trends_**

We find that in 2009 and 2008, Brazil had higher NIM spreads than the LAC average, while the LAC average was higher than all other regions except Sub-Saharan Africa (Figures 8a & 8b). This indicates a LAC region wide problem of high NIM spreads. Despite LAC’s robust growth in the last few years NIM spreads remain the second highest of all regions in the world. Moreover, LAC had the highest micro-factors in the world and the second lowest macro behind South Asia in 2009. On the other hand, Brazil in 2009 had lower I&R spreads than the average in LAC but higher than the average macro spreads. In 2008, on average, LAC had the third highest micro spreads behind East Asia and SubSaharan Africa and the 6<sup>th</sup> highest macro spreads in 2008. However, in 2008, Brazil had higher I&R spreads than the LAC average but lower macro spreads.

**Figure 8a. Average I&R and macro (2009)**

**Figure 8b. Average I&R and macro (2008)**

<!-- Start of picture text --> Brazil Brazil<br>Latin America and the... Latin America and the...<br>East Asia and Pacific East Asia and Pacific<br>South Asia South Asia<br>Europe and Central Asia Europe and Central Asia<br>Middle East and North... Middle East and North...<br>Sub-Saharan Africa Sub-Saharan Africa<br>Midde East (Developed) Midde East (Developed)<br>Asia (Developed) Asia (Developed)<br>Europe (Developed) Europe (Developed)<br>North America (Developed) North America (Developed)<br>-0.5 1.5 3.5 5.5 7.5 9.5 11.5 -0.5 1.5 3.5 5.5 7.5 9.5 11.5<br>Average of I&R Average of Macro Average of I&R Average of Macro<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data In 2009 Sub-Saharan Africa had the highest NIM, perhaps because of the fragility of states, the uncertainty regarding repayments, and a much smaller banking sector. In 2008, Eastern Europe had the highest macro spreads because of the crisis in Eastern Europe resulting from the global crisis. Developed Asia has the lowest NIM in both 2008 and 2009. The year 2008 is the year of the crisis with all regions, except East Asia & Pacific. and marginally the Middle East & North Africa, having higher NIM compared to 2009. In Brazil, where the government in the latter part of 2008, and through 2009, introduced measures to help the flow of credit as explained by Takedaa and Dawid (2010), helping smaller banks sell their credit portfolios and relaxing larger banks regulations it experienced a marginal decrease in the NIM from 2008 to 2009.

The high R<sup>2</sup> of our regression results, show that our findings regarding the importance of micro-factors are robust. R<sup>2</sup> s are generally high and when they are low this can be interpreted that micro-factors are not important in explaining the total NIM (Figure 9a & 9b). In this case, the residual constant term (macro-factors) becomes more significant. This is the case for some developed countries, because the main drivers behind the NIMs are macro-factors since they have more competitive banking sectors.

22
<!-- Start of picture text --> Figure 9a. Average R 2 is high in Brazil (2009) Figure 9b. Average R 2 is high in Brazil (2008)<br>0.8 0.9<br>0.7 0.8<br>0.7<br>0.6<br>0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1 0.1<br>0.0 0.0<br>Developed Developing BRAZIL Developed Developing BRAZIL<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data

# **_5.5 Brazil and the United States_**

We have already mentioned that Brazil’s micro spreads are the dominant determinants of NIM between 1995 and 2009 (Figure 10a) but the opposite is true for the U.S. NIMs. We observe that I&R factors started decreasing in the U.S. at the end of the 1990s, when the Clinton administration allowed the banking sector to consolidate by permitting commercial banks to acquire or develop investment banking arms. We see a steady decline in the U.S. I&R spreads, becoming almost non-existent in 2003-2004 and increasing again from 2005 to 2007 before they decrease dramatically during the crisis in 2008-2009 (Figure 10b). Our results show that macro-factors are driving the spread in the U.S. over the last decade. At the same time the NIM did not change dramatically, even during the two recessions of 2001 and 2007-2009, while micro-factors decrease and macro-factors increase at the same time.

Despite the greater volatility of NIM in Brazil, I&R remain the dominant factors in the determination of the spread and there is no clear trend during recessions. It appears that attempts to reform the banking sector in Brazil are not noticeable in terms of changing the amount of the spread determined by micro-factors. We observe that I&R determine the trend and the strength of the NIM (Figure 10a).

We find that Brazil’s macro spreads are surprisingly lower in absolute terms than in the U.S. in 2009 but also for several years between 1995 and 2009 (Figure 10a). Banks in Brazil place less emphasis on macro-factors when making investment decisions than their counterparts in the U.S. U.S. banks’ macro-factors account for a large percent of the total spread. When making lending and borrowing decisions, U.S. banks take into account mostly macro-factors that affect their borrowers’ and the banks’ ability to repay the loans. We observe that macro-factors account for an average of 75 percent of the total spread of U.S. banks, while for Brazilian banks macro-factors account for 9 percent between 1995 and 2009 (Figure 11b).

23
**Figure 10a. Brazil, NIM and I&R (1995-2009)**

<!-- Start of picture text --> Figure 10b. U.S., NIM and I&R (1995-2009)<br><!-- End of picture text --> <!-- Start of picture text --> 18 5<br>16 4.5<br>14 4<br>3.5<br>12<br>3<br>10<br>2.5<br>8<br>2<br>6<br>1.5<br>4<br>1<br>2<br>0.5<br>0 0<br>1995 1997 1999 2001 2003 2005 2007 2009 1995 1997 1999 2001 2003 2005 2007 2009<br>NIM I&R NIM I&R<br>Percentage points<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data Our results in 2009 are robust since for micro-factors we have an R<sup>2</sup> of .76 and macro-factors are significant at the 5 percent level. In 1995, 1997 and 2001 in Brazil we observe a negative spread for macro-factors. These are odd results but can be interpreted as an underestimation of macro-factors and overestimation of micro-factors when banks make lending and borrowing decisions. The decisions made by banks in those years were not in line with the fundamentals of macro and micro-factors. Interestingly in 1998-1999 Brazil faced a crisis and again in 2002, just before the Lula administration came to power. These crises happened one year after we see massive underestimation of macro-factors.

**Figure 11a. Brazil, NIM and macro**

**Figure 11b. U.S.A., macro as a percent of NIM**

<!-- Start of picture text --> 5 120%<br>4 100%<br>3 80%<br>2<br>60%<br>1<br>40%<br>0<br>20%<br>1995 1997 1999 2001 2003 2005 2007 2009<br>-1<br>0%<br>-2 1995 1997 1999 2001 2003 2005 2007 2009<br>-20%<br>-3<br>-40%<br>-4<br>-60%<br>-5<br>Macro Brazil Macro US Macro/NIM Brazil (%) Macro/NIM US (%)<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data Macro signals should be the most important factor in banks’ NIM, in a well-functioning competitive banking market. Banks should be mainly taking into consideration what is happening in the general economy and not simply what their competitors are doing. Additionally governments should refrain 24
from establishing barriers to an efficient banking system by reducing I&R factors. However, as we have seen in the recent crisis, the inter-connectedness of the banking system and its capital allocation role, have been misjudged by banks and policy makers alike.

Therefore, the lower spreads in the U.S. might have been the result of underestimating the macro risk as well as the micro, or counterparty risk in the banking sector. Perhaps there was a structural problem or a miscalculation of the inter-connectedness between the banking sector (micro) and the economy in general (macro).

# **_5.5 Brazil and the BRIC Countries_**

Brazil consistently had higher micro spreads than the NIM of each BRIC country. Figure 12 shows that Brazil’s micro-factors in all years, except in 1996, are higher than the total spread in the other BRICs. Spreads in Brazil are unlikely to come down significantly unless the micro-factors are addressed. In absolute terms, macro spreads in Brazil are just a little higher than China’s macro-factors in line with their high growth rates and growth potentials.

We observe from Figure 12 that India had similar NIM spreads as Brazil in 1995 but by 2007 it had almost half the spreads as in Brazil. However, we detect that during the crisis in 2008 and 2009 spreads in India and Brazil converge. Perhaps banks in both countries measured risk in similar ways since both countries are part of the BRICs bloc and their growth is perceived to move in tandem.

## **Figure 12. BRICs NIM and Brazil’s I&R**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>BRAZIL NIM CHINA_NIM INDIA_NIM RUSSIA_NIM BRAZIL I&R (Micro)<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data Furthermore, we observe that Brazil’s macro-factors play a smaller role in 2009 (Figure 13a) than in 2008 (Figure 13b), but not significantly. At the same time, China’s macro and micro-factors remained more or less constant in 2008 and 2009. The crisis did not seem to affect significantly spreads in China, perhaps because the Chinese government had tighter control over the financial sector and the overall 25
economy. Most of the NIM spread in Brazil is due to I&R factors, and changes in I&R are highly correlated with changes in NIM changes. China on the other hand, appears to be different and I&R factors are less significant in the determination of the NIM spreads.

<!-- Start of picture text --> Figure 13a. BRICs - I&R and macro (2009)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 13b. BRIC - I&R and macro (2008)<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>10<br>8 8<br>6 6<br>4 4<br>2<br>2<br>0<br>0<br>I&R Macro<br>I&R Macro -2<br>-2<br>-4<br>-4<br>-6<br>-6<br>India Russia Brazil China India Russia Brazil China<br>Percentage points<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data India is an interesting case, where both in 2008 and 2009 it experiences negative micro spreads. It seems that banks in India have underestimated micro-factors and have overestimated macro-factors. This is exactly the opposite of what happened in Brazil in 1995, 1997 and 2002. In India the macroeconomic situation in both 2008 and 2009 turned out to be better than the banks had anticipated. Banks in India were too pessimistic about the macroeconomic situation, while at the same time banks were too concerned with the microeconomic situation in their industry when making lending and borrowing decisions.

In the case of China and Russia we observe almost no movement in the spreads both in terms of micro and macro-factors in both 2008 and 2009. This might be the case because the banking sectors are tightly controlled and a large part of the banking sector is controlled by the state. A further reason for the stability of the spreads in China and Russia might be the presence of large state-sponsored corporations, which borrow a significant amount of money from the domestic banking sector.

The amount of borrowing by these large state-sponsored corporations is generally stable because they have a high degree of bargaining power with the banks. Hence, the interest rates these large corporations borrow are generally fairly stable and primarily based on inflation. Inflation in both China and Russia was generally stable or decreasing because of the collapse in commodity prices and the global economic activity.

Overall, BRIC countries seem to be doing better than other developing countries. They seem to have weathered the financial crisis without major volatility in both micro and macro-factors. Compared to all developing countries, BRIC countries saw less volatility in terms of NIM in 2008 and 2009 (Annex 1 Figures 1a & 1b). The fact that Russia’s spreads have remained constant is surprising given the sharp devaluation of the Ruble and the large drop in financial assets’ prices in 2009. Perhaps the considerable 26
reserves held by the government helped the banks retain their confidence in the macro and micro fundamentals in Russia.

# **Figure 14. BRICs and the Development of NIM and I&R (1995-2009)**

<!-- Start of picture text --> 18 Brazil 25 Russia<br>16<br>20<br>14<br>12<br>15<br>10<br>8<br>10<br>6<br>4 5<br>2<br>0 0<br>1995 1997 1999 2001 2003 2005 2007 2009 1995 1997 1999 2001 2003 2005 2007 2009<br>NIM I&R NIM I&R<br>India China<br>10 3.5<br>8 3<br>6 2.5<br>4 2<br>2 1.5<br>1<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009<br>0.5<br>-2<br>0<br>-4<br>1995 1997 1999 2001 2003 2005 2007 2009<br>-0.5<br>-6<br>NIM I&R NIM I&R<br>Percentage points Percentage points<br>Percentage points Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data We depict the behavior of the NIM and I&R factors in all four BRICs in Figure 14. The immediate observation is that in all four countries micro-factors play a significant role in the determination of the spreads. The banking sector in all four countries is dominated by a few large banks and competition is limited. Moreover, the significant growth rate of all BRIC countries has pushed banks to take economic growth for granted when making their lending and borrowing decisions.

It would be interesting to see if this trend continues after the crisis. Despite the significant capital outflows from all four countries, banks seem to not have significantly changed their behavior in terms of NIM, micro or macro-factors. This fact speaks to the confidence in BRICs' economies and their growth potential.

27
# **6. Robustness and Discussion**

We have performed several robustness checks to test the reliability of our results. First, we checked our data for the coverage they provide in terms of the total banking market in each country. Second, we checked how many countries have the representativeness that would enable cross country comparisons. We observe that we have a more complete dataset in the last few years, as almost all the countries had 100 percent of their banking sector, in terms of assets, represented in the sample (Figure 15b).

**Figure 15a. Assets of banks/total Assets**

**Figure 15b. Countries with 100% of total Assets**

<!-- Start of picture text --> 250 100%<br>100% 87% 90%<br>90%<br>90% 79% 82% 83%<br>200 75% 72% 73% 75% 80%<br>80%<br>70%<br>70% 61% 62% 63% 64% 62% 64%<br>150 60%<br>60%<br>50%<br>50%<br>100 40%<br>40%<br>30%<br>30%<br>20% 50 20%<br>10% 10%<br>0% 0 0%<br>1995 1997 1999 2001 2003 2005 2007 2009 1995 1997 1999 2001 2003 2005 2007 2009<br>No. of Countries with 100% of Assets (left axis)<br>Total No. of Countries (left axis)<br>Countries with 100% of Assets as % of total no. of<br>countries (right axis)<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data For Brazil, we had a representative sample of the banking system in terms of total banking assets, for all years. From 2004-09 we had a completely representative sample (Figure 15a), which makes us confident of the robustness of our results. Since financial assets in Brazil are mainly held by the banking sector, our sample is consequently strongly representative of the whole financial sector.

Furthermore, we use the R<sup>2</sup> test to check the robustness of our regressions. We find that a high R<sup>2</sup> for our regressions means that micro-factors play a significant role in the total spread (NIM), such as in Brazil. In general R<sup>2</sup> s are high, but when they are not, this means micro-factors are not important in explaining the NIM (Table 2); in this case we test the significance of the residual constant term, the macro-factors. This is the case for quite a few developed countries, since they have already dealt with many micro issues. In these countries the main drivers of NIM spreads are macro-factors, for example Denmark with an R<sup>2</sup> of 0.32 and macro-factors significance at the 1% interval in 2009. However, having a competitive banking sector, such as the one in the U.S.(R<sup>2</sup> of 0.02 in 2009), and significant macrofactors does not prevent the whole banking sector from entering into a crisis by underestimating the total risk it is taking.

28
# **7. Conclusion and Policy Implications**

Our main findings are: (i) I&R (micro) factors are the main drivers of spreads across the world; there is a high positive correlation between I&R and NIM; (ii) Brazil follows this global trend of I&R driving the NIM spreads but both the level of the I&R and the correlation between I&R and NIM are higher; (iii) Brazil follows this global macro trend and we find that the macro part of the spread is very low—even compared to developed countries; and (iv) macro-factors are not significantly linked to the NIM as a driver of spreads.

In developing countries I&R factors lead to higher NIM spreads and I&R and NIM are highly and positively correlated. NIM and micro-factors are more volatile in developing countries and at the same time we observe that in absolute terms spreads in developing countries are generally higher when compared to developed countries. For developed countries macro-factors do, in fact, lead to higher spreads and are usually the most significant part of the total spread.

When we compare Brazil against the other BRICs we find that Brazil has higher I&R spreads than the NIM of each BRIC. This means that the part of the spread that is only accounted for by I&R factors is bigger than the total spread in the other BRICs. Moreover, when we compare Brazil with the U.S. and we find that Brazil macro spreads are at even lower levels than the U.S. in 2009. This suggests banks perceived that there is lower macro-risk in Brazil than in the U.S. and reinforces our view that the main problem of high spreads in Brazil is a result of the lack of competitiveness in the banking sector.

We conclude that spreads in Brazil cannot be reduced significantly unless micro-factors are addressed. Brazil’s macro-factors are a small part of the total spread and therefore do not significantly affect the level of the total spreads. Macro signals should be the most important determinants in banks NIM, since banks should be taking into consideration what is happening in the general economy and not by the herding behavior of the banking system. Therefore, we suggest a closer look at the banking sector in Brazil and potential reforms that could assist in the reduction of spreads.

29