Electronic copy available at: http://ssrn.com/abstract=2191449

Bankscope dataset: getting started∗

Duprey Thibaut†

Lé Mathias‡

Version 1 : December 19, 2012

Abstract

The Bankscope dataset is a popular source of bank balance sheetinformations among banking economists, which covers the last 20 yearsfor more than 30000 worldwide banks. This technical paper intendsto provide the critical issues one has to keep in mind as well as thebasic arrangements which have to be undertaken if one intends to usethis dataset. To that extent, we propose some straightforward waysto deal with data comparability, consolidation, duplication of assets ormergers, and provide Stata codes to deal with it.

Keywords : Bankscope dataset, joint work with Stata, duplicates, con-solidation, mergers, unbalanced, comparability.

JEL Classification : B40, C80, G10.

∗We accessed the dataset via the license granted to Bank of France agents. This isa preliminary version which should be enriched or modified further. Any comments arewarmly welcome. If you found this material useful, please consider including it in yourreference list.†Paris School of Economics and Bank of France. Campus Paris-Jourdan, 48 boulevard

Jourdan, 75014 Paris. E-mail : thibaut.duprey@gmail.com.‡Paris School of Economics. Campus Paris-Jourdan, 48 boulevard Jourdan, 75014

Paris. E-mail : mathias.le@gmail.com.

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Electronic copy available at: http://ssrn.com/abstract=2191449

Contents1 Introduction 3

2 What you may not know about Stata 32.1 How to load a dataset easily? . . . . . . . . . . . . . . . . . . 32.2 How to harmonize paths towards other do-files/datasets? . . 3

3 Working with an homogeneous dataset 63.1 How to handle duplicates? . . . . . . . . . . . . . . . . . . . . 63.2 How to obtain yearly observations? . . . . . . . . . . . . . . . 73.3 How to get comparable time series? . . . . . . . . . . . . . . . 103.4 How to get comparable entities? . . . . . . . . . . . . . . . . 123.5 How to get regional subsample? . . . . . . . . . . . . . . . . . 13

4 Tips 134.1 How to handle mergers and acquisitions? . . . . . . . . . . . . 134.2 How to handle this unbalanced dataset? . . . . . . . . . . . . 144.3 How to handle the over-representation of some regions? . . . 144.4 How to get better variable names? . . . . . . . . . . . . . . . 144.5 How to handle differences in programming languages with

LATEX? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

A Bankscope variables 16

B Countries and geographical areas 20

C Getting proper variable names 24

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1 IntroductionAfter facing several issues while handling the Bankscope dataset, we decidedto share our experience. Bankscope is a database reporting balance sheetstatements of more than 30 000 worldwide financial institutions which isprovided by Bureau van Dijk. We intend to provide the basic arrangementthat need to be completed before actually starting using your Bankscopedataset; failing to do so may result in wrong outcomes and raise unnecessarycriticism on behalf of your fellow students/researchers. The codes providedhere can be run directly using Stata. We hope that you will find this memouseful, else we apologize in advance for wasting your time.

The following lines of code are to be run in Stata and suit perfectly theBankscope dataset as obtained through the DVD provided by the Bureauvan Dijk. Else handling data downloaded from the website1 may require toadjust the lines of code provided here, but one has to keep those steps inmind even if some of them may be tackled directly via the on-line interface.

2 What you may not know about Stata

2.1 How to load a dataset easily?

If you ever tried to load large datasets, you may have reached several timesthe limit of your computer’s power... or you thought that was it, while youjust did not provide the maximum space possible to your Stata program2.The trick consists in increasing your memory requests in relatively smallincrements. So try something like this loop which looks for the maximumlevel of memory possible ranging from 1000m to 1500m :

1 set more off2 set maxvar 100003 set matsize 30004 forvalue i=1000(1)1500 {5 set mem `i'm, permanent6 }

2.2 How to harmonize paths towards other do-files/datasets?

When working on several computers or in collaboration, it is always painfulto readjust the different file paths to suit your own folders. You shouldrather write a do-file that easily adjusts and only requires the main pathsto be changed. We propose here three different solutions.

1https://bankscope2.bvdep.com/version-2012713/home.serv?product=scope20062The latest version of Stata (starting with Stata 12) does it automatically.

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First you can write the paths in the beginning of your do-file and con-catenate them so that only one part of the path, say the shared drive, hasto be adjusted.

Second you can choose to put all the paths you need in a separate do-filewhich you would invoke at the beginning of every other do-file. If you chooseto define a path in a global macro (which you then call with the $ symbol),the information will remain in all the subsequent do-files. For instance youcan write in the header of your main do-file :

7 //Define the path to the driver8 global MainPath = "C:\Users\Thibaut\Documents"9 //Use the paths

10 do "$MainPath\Paths"

It calls the do-file Paths.do which defines the paths. The path to thedriver, MainPath, has already been defined in a global macro in the maindo-file, while the specific paths which are created in Paths.do will still applyto the parent do-file since they are writen as global macros.

11 //Define the paths12 global PathDataset = "$MainPath\Data"13 global PathOutput = "$MainPath\LogFiles"14 global PathDoFiles = "$MainPath\DoFiles"

Third if you prefer to define local variables, for instance because yourmacros will not be constant throughout your work since you use many do-files, you can choose to put all the file paths you need in the first do-file andpass them as argument to other do-files if need be; this looks just more likemacros you can write with other languages/softwares.

For instance, if you want to launch a do-file called ConstructDataset.dowhich is located in a specific folder \DoFiles, and you need to pass to thisdo-file :

1. the path for the folder where to write the output \LogFiles

2. the path for the folder where you stored your data \Data

3. some numerical value, e.g. for a threshold to be used uniformly in youdataset.

Then write the following code in a "Master" Do-File:

15 //Create the paths16 global MainPath = "C:\Users\Thibaut\Documents"17 local PathDataset = "$MainPath\Data"

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18 local PathOutput = "$MainPath\LogFiles"19 local PathDoFiles = "$MainPath\DoFiles"20

21 //Launch the do-file22 local File = "ConstructDataset.do"23 local FullPath "`PathDoFiles'\ConstructDataset.do"24 display "`FullPath'"25 do "`FullPath'" "`PathDataset'" "`PathOutput'" 300, nostop

The last line calls the do-file ConstructDataset.do in the folder youspecified, assigns the three arguments to be passed to it, and executes itwithout stopping for errors (the nostop option).

In your do-file ConstructDataset.do, write now:

26 set more off27

28 //Name of the path passed to the .do file in macro `1'29 local PathDataset = "`1'"30 //Name of the path passed to the .do file in macro `2'31 local PathOutput = "`2'"32 //Numerical threshold passed to the .do file in macro `3'33 local Threshold = "`3'"34

35 //Load the dataset36 local File = "Dataset.dta"37 local Data `PathDataset'\`File'38 display "`Data'"39 use "`Data'", clear40

41 //Log on42 local File = "PutResultsHere.smcl"43 local Log `PathOutput'\`File'44 display "`Log'"45 log using "`Log'", replace46

47 //Threshold for some numerical value48 display "the threshold is : `Threshold'"49 drop if RankObservation > `Threshold'

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3 Working with an homogeneous dataset

3.1 How to handle duplicates?

First, Bankscope provides a broad list of financial institutions but finan-cial statements may not be available for all of them. The variable formatdisplays the type of data available for each bank:

• RD: statement available in the raw data format;

• RF: statement under processing by Fitchratings;

• BS: branch of another bank with financial statement available;

• BR: branch with no statement;

• DC: no longer existing bank, with statements for previous years;

• DD: no longer existing bank, without statements;

• NA: banks with no statement; only the name and address are available.

RF, BR, DD and NA should be dropped as it does not provide valuable bal-ance sheet observations. Nevertheless, depending on the research questionat hand, it may come handy to flag defunct banks for which past informationis still available, which are signaled by DC.

Then, if Bankscope provides company account statements for banks andfinancial institutions across the world, it collects financial statements withvarious consolidation status. There are 8 different consolidation status inBankscope that are detailed in the variable consol_code:

• C1: statement of a mother bank integrating the statements of its con-trolled subsidiaries or branches with no unconsolidated companion;

• C2: statement of a mother bank integrating the statements of its con-trolled subsidiaries or branches with an unconsolidated companion;

• C*: additional consolidated statement;

• U1: statement not integrating the statements of the possible controlledsubsidiaries or branches of the concerned bank with no consolidatedcompanion;

• U2: statement not integrating the statements of the possible controlledsubsidiaries or branches of the concerned bank with a consolidatedcompanion;

• U*: additional unconsolidated statement;

• A1: aggregated statement with no companion;

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• A2: aggregated statement with one companion;

• NA: bank with no statement; only the name and address are available.

The choice between using the consolidated or the unconsolidated financialstatements depends entirely on your research question. However, to be con-sistent you must imperatively drop either the statements C2 or the state-ments U2. Keeping both observations would lead to a double counting issuebecause you would consider two times the company account statement.

We suggest that you work with {C1/C2/U1} in order to get countryaggregates, in order to capture the actual size of the banking market forinstance or design concentration measures. While if you are interested inbank balance sheet sensitivity you may want to keep {U1/U2/C1} in orderto keep most banks at the disaggregated level to maximize sample size andavoid the variation you are looking at to be offset and reduced at the grouplevel. Unfortunately, note that the information available in Bankscope donot make possible to match parent banks to their subsidiaries.

Finally, note that BankScope provides two different bank identifiers :

• bs_id_number

• bvdidnum

There are several distinct banks identified with bs_id_number that re-port the same bvdidnum. As explained in the previous paragraph, Bankscopeprovides information for banks with various consolidation status. Actu-ally, bvdidnum identifies banks regardless the consolidation status of theirfinancial statement. A bank that reports differents balance sheet state-ments, U2 and C2 for instance, will have a unique bvdidnum. However,the unconsolidated (U2) and the consolidated (C2) financial statements willpresent different bs_id_number.Consequently, if you keep only the bankswith consol_code {C1/C2/U1} or {U1/U2/C1} (as suggested just above),you should have a one-to-one mapping between bs_id_number and bvdidnum.

3.2 How to obtain yearly observations?

Most of the financial companies publish their account statements at theend of the year, namely in December. Nonetheless, sometimes banks usenon-calendar fiscal years to report their balance sheet statement (in Marchfor almost all the Japanese and Indian banks, in January for most of theRussian banks...). On the top of that, eventhough Bankscope provides usonly with annual data, for a few hundred observations you have duplicatedobservations for balance sheet statements that closed at several dates withina single year. So one needs to handle both the allocation issue over year t ort+1 as well as the duplicated issue of yearly financial statements publishedseveral times a year.

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These differences raise an important issue. It is likely that you prefer tocompare data of financial statements reported in March of year t with dataof financial statements reported in December of year t-1 rather than withdata of financial statements reported in December of year t. The variableclosdate provides information concerning the company fiscal year end. Wecan easily create the variables year, month and day from the closdatevariable:

50 //Create time variable51 gen year=year(closdate)52 gen month=month(closdate)53 gen day=day(closdate)

Once you have created these variables, we propose to define a smallprogram in four steps that handles the situation in a compact way.

First, depending on the research question at hand, you may not wantto keep mid-year financial reports as if you are working at the yearly level,it is uncertain to which year t or t-1 you should attribute the observation.So we drop observations with a month number comprised in ]MonthEnd ;MonthStart[.

Then, you have to identify banks which have "natural" duplicates, i.e.banks with the same id (insured by id==id[_n+1]) having at least twoobservations within the same fiscal year. Essentially you can remove anobservation of the 30th November 2012 if you have an observation for the 31st

December 2012. You would always keep i) the month closest to MonthRefwhich would be 12 (for December) and ii) the day closest to the last day ofthe month.

Third, if you have banks which report their financial account in March2012, your best choice would be to consider it as end of 2011 data. So in theclosedate variable, it would still be recorded as a 2012 financial account,while the actual year variable which you would use would be 2011.

Last, for if you still have duplicates in your dataset, this comes neces-sarily from the step 3 due to the financial statements you approximated tobelong to the year t-1. So once again, the best strategy would be to keepthe data that have the least forward looking information, that is to say ob-servation which include a bit of t information while it is actually recordedas t-1. In essence, you would drop the observation reporting a closedatein 2012, with month March, but a variable year recorded as 2011, providedyou have already a closedate in 2011, with month September, and yearrecorded as 2011.

54 //Adjust dataset for financial statements reported at ///55 //other dates than 'MonthRef', if quarterly or ///56 //non-calendar fiscal year:

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58 program define HandleDuplicates59 args MonthEnd MonthStart MonthRef60

61 // 1- Drop if mid-year balance sheet data62 drop if month>`MonthEnd' & month <`MonthStart'63

64 // 2- If "natural duplicates", drop them first65 sort id year month day66 drop if year(closdate)==year(closdate[_n+1]) & ///67 month<`MonthRef' & month[_n+1]==`MonthRef' & id==id[_n+1]68 drop if year(closdate)==year(closdate[_n+1]) & ///69 month==`MonthRef' & month[_n+1]==`MonthRef' & ///70 day<day[_n+1] & id==id[_n+1]71

72 // 3- Change the year for banks reporting their account///73 //in the beginning of the year74 replace year = year-1 if month <=`MonthEnd'75

76 // 4- If several observations within a year due to step 3///77 //keep data where the year is the actual fiscal year78 sort id year month day79 drop if year==year[_n+1] & year(closdate)==80 year(closdate[_n+1])+1 & month<=`MonthEnd' & ///81 month[_n+1]>=`MonthStart' & id==id[_n+1]82

83 // 5- Check there are no more duplicates84 distinct id year85

86 end

The program HandleDuplicates can be launched in the following man-ner, without forgetting to pass the necessary arguments, which are respec-tively MonthEnd MonthStart MonthRef. For instance, just below we ask toStata to define the reference month as December and to drop all observationsreporting data between March and September :

87 //Treat duplicates88 HandleDuplicates 3 9 12

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3.3 How to get comparable time series?

3.3.1 Same unit?

The data collected by Bankscope are not homogeneous. The variable unitstates whether all other variables of a given observation are in thousands(3), millions (6), billions (9), or trillions (12). So for instance, to convert allyour numerical values in millions, you should do (except for the ratios !):

89 //Pick unit convention to apply to the whole dataset:90 gen NberOfZeros=691 //Convert unit excluding ratios:92 foreach var of varlist data2000-data2120 data2135-data6860 ///93 data7020-data7030 data7070-data7160 {94 replace `var'=`var'*10^(unit-NberOfZeros)95 }

You could also prefer to work with well-defined ratios. Indeed, Bankscopedoes not provide information for ratio in percentage term. If you want ratioswith value between 0 and 1, you have to divide them by 100.

96 ////Transform the ratios in percentage97 foreach var of varlist data4001-data4038 data2125 data2130 ///98 data7040 data7050 {99 replace `var'=`var'/100

100 }

3.3.2 Same currency unit?

The variable exrate_usd provides the exchange rate in USD of a given ob-servation with respect to the date and currency unit of the values expressedin currency unit. So you need to do the conversion before using variablesexpressed in currency (except for the ratios !):

101 //Get homogeneous variables in million USD102 foreach var of varlist data2000-data2120 data2135-data6860 ///103 data7020-data7030 data7070-data7160 {104 replace `var'=`var'*exrate_usd if currency!="USD"105 }

But handling the currencies may be a bit more complex than you think.Indeed, depending on the topic of interest, it may be a bad idea to convertall data from local currency to USD, as you are adding a valuation effectdue to the fluctuation in exchange rates which captures many more effects

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than what you want to focus on. First what you want is to make sure thedata you are interested in are comparable; so there should be no withinbank variation of the currency unit: if so, you have to convert everythingto USD. Second, there should be no within group (e.g. country) variationof the currency unit: if within, say, a country, you have banks reportingtheir financial statement in different currencies, and you want to control forcountry-wide aggregates, then you will have to convert those data into asingle currency. Naturally, the question is which one? In most cases, boththe national and the US currency are the two conflicting ones. For dollarisedeconomies, like many South-American countries, you may be closer to thetrue picture by using USD as the default currency. Else, for other cases, youmay need to ask yourself which part of the balance sheet matters most toyour study, and in which currency the largest part of the bank/the economyoperates. If the bank refinances itself mostly in USD, you may prefer thiscurrency; if the bank mostly extends loans in the local currency, you mayrather go for the latter, depending on the research question at hand.

106 //Handle currency missmatch/exchange rate valuation effect107

108 // 1- Check that each bank has only one currency109 bysort id currency : gen nvals1 = _n==1110 bysort id : egen SeveralCurncyPerBank = total(nvals1)111 tab SeveralCurncyPerBank112

113 // 2- Treat banks with more than one currency :114 // Convert everything in USD115 foreach var of varlist data2000-data2120 data2135-data6860 ///116 data7020-data7030 data7070-data7160 {117 replace `var'=`var'*exrate_usd ///118 if currency!="USD" & SeveralCurncyPerBank > 1119 replace currency="USD" ///120 if currency!="USD" & SeveralCurncyPerBank > 1121 }122

123 // 3- Tag countries with banks reporting in different curncies124 bysort country currency : gen nvals2 = _n==1125 bysort country : egen SeveralCurncyPerCntry = total(nvals2)126 tab SeveralCurncyPerCntry127

128 // 4- Handle conflicting currency within a country129 // Choice to be made : here only convert Assets into USD130 // to get consistent country-aggregates controls131 replace data2025 = data2025*exrate_usd ///

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132 if currency!="USD" & SeveralCurncyPerCntry > 1

3.4 How to get comparable entities?

You may need to consider the wide range of financial institutions reportedin the Bankscope database; all of them may not be relevant for your study.The variable special states into which broad categories the observationsfall :

• Bank Holding & Holding Companies

• Central Bank

• Clearing Institutions & Custody

• Commercial Banks

• Cooperative Bank

• Finance Companies (Credit Card, Factoring and Leasing)

• Group Finance Companies

• Investment & Trust Corporations

• Investment Banks

• Islamic Banks

• Micro-Financing Institutions

• Multi-Lateral Government Banks

• Other Non Banking Credit Institution

• Private Banking & Asset Mgt Companies

• Real Estate & Mortgage Bank

• Savings Bank

• Securities Firm

• Specialized Governmental Credit Institution

You may want to get rid of at least Central Banks, Clearing Institutionsand Supranational Institutions (such as the World Bank or the South Amer-ican Development Bank ...). The latter can as well be removed by exclud-ing country with cntrycde=="II" for Institutional Institutions. Moreover,

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some outliers may need to be dealt with specifically, like the US Federal Re-serve and its state components which are not recorded as Central Bank butas Specialized Governmental Credit Institution... which includes for instancethe Government National Mortgage Association-Ginnie Mae!

133 //Get rid of other non-relevant financial institutions134 tab name if special == ///135 "Specialized Governmental Credit Institution" & ///136 cntrycde=="US"137 drop if special=="Central Banks" | ///138 special=="Clearing Institutions & Custody" | ///139 special=="Multi-Lateral Government Banks"

3.5 How to get regional subsample?

We developed a dataset with country names and country codes as officiallystated by the UN so that one gets the possibility to merge any dataset withan ISO 3166 entry. In addition we provide the breakdown of countries be-tween continents, geographical areas and OECD membership which proxiesfor developed countries. See the dataset in Table 2 which you should inte-grate in a new CountryISO.dta by copy/pasting and using ";" as delimiter.Then merge it with you standard Bankscope database:

140 //Merge with code for geographical area141 merge m:1 cntrycde using "\$PathDataset\CountryISO.dta"142 drop if _merge!=3143 drop _merge

4 Tips

4.1 How to handle mergers and acquisitions?

In order to take into account the process of M&A’s between banks whichmay lead to strong discontinuities in the balance sheet variables, you couldtry to merge the Bankscope dataset with one dealing with M&A’s 3. Butthis is an extensive task. So it may be easier to control for M&A’s justby controlling for the growth of asset size, without necessarily censuringextreme variables and loosing observations. Alternatively, you may wantto drop the observations for which you observe an excessive growth rateof assets which cannot be driven by internal growth (more than 50% forinstance).

3Brei, M., Gambacorta, L. and von Peter, G. (2011). Rescue packages and banklending, BIS Working Paper, No 357, see part 3 and figure 4.

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4.2 How to handle this unbalanced dataset?

In the same way, the dataset may appear strongly unbalanced as some banksenter the market while others leave it, or rather some are reported for acouple of years and then are no longer reported as they have been mergedor went bankrupt. Or the database may be just poorly fed with some data,especially for developing countries. To take into account the evolution ofthe reporting of the market, you may want to control for the growth of therelative size of the bank compared to the pertinent market.

In the case where you prefer to work with a perfectly balanced dataset(at the cost of loosing a large number of observations), you could use theuser-written Stata command xtbalance (ssc install xtbalance).

4.3 How to handle the over-representation of some regions?

If you work in cross-country, you may be worried that your results could bedriven by the over-representation of some countries in your sample. To thatextent, you can do robustness checks using weights.

144 //Generate the total number of banks in the sample145 egen nb_bank=nvals(id)146

147 //Generate the total number of banks by country148 egen nb_bank_country=nvals(id), by(country)149

150 //Compute the weight151 gen weight=nb_bank/nb_bank_country152

153 //Use the pweight option154 reg y x [pweight=weight], robust

4.4 How to get better variable names?

You may also want to rename your variables to their true name insteadof the cryptic "data2000" convention, so that you can use the helpful *concatenation symbol, for instance in order to summarize all capital ratiovariables starting with capital_ratio*. See the appendix C. The list of allbalance sheet item is displayed in table 1.

4.5 How to handle differences in programming languageswith LATEX?

There exist several Stata packages which allow you to export your resultsdirectly in a suitable format for LATEX. First you need to be careful inthe way you define your variables or labels to avoid special characters, for

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instance underscores ("_") or ampersand ("&"). But if your dataset hasstring values –like bank names in the name variable– that contain some ofthose special characters and you want to export them in LATEXformat, thenyou should use the following line of code:

155 //Replace specific characters to be suitable for TEX156 replace name = subinstr(name, "&", "\&",.)

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A Bankscope variables

Table 1: List and label of variables

Variable Name; Label;

ACCSTAND ; Statement Accounting StandardsADDRESS ; AddressAUDITOR ; Name of AuditorBS_ID_NUMBER ; BankScope ID NumberBUILDING ; BuildingBVDIDNUM ; BvD ID NumberCITY ; CityCLOSDATE ; Company Fiscal Year End (SASDate Format)CNTRYCDE ; Country CodeCONSOL_CODE ; Consolidation CodeCOUNTRY ; CountryCPIRATE ; Consumer Price IndexCTRYNICK ; Country NickNameCTRYRANK ; Country RankCTRYROLL ; Country Rank, RollingCURRENCY ; Currency of the Annual FinancialDataDATA2000 ; LoansDATA2001 ; Gross LoansDATA2002 ; Less: Reserve for Impaired Loans/NPLsDATA2005 ; Other Earning AssetsDATA2007 ; DerivativesDATA2008 ; Other SecuritiesDATA2009 ; Remaining Earning AssetsDATA2010 ; Total Earning AssetsDATA2015 ; Fixed AssetsDATA2020 ; Non-Earning AssetsDATA2025 ; Total AssetsDATA2030 ; Deposits and Short term fundingDATA2031 ; Total CustomerDepositsDATA2033 ; OtherDeposits and Short-term BorrowingsDATA2035 ; Other Interest Bearing LiabilitiesDATA2036 ; DerivativesDATA2037 ; Trading LiabilitiesDATA2038 ; Long Term FundingDATA2040 ; Other (Non-Interest bearing)DATA2045 ; Loan Loss ReservesDATA2050 ; Other ReservesDATA2055 ; EquityDATA2060 ; Total Liabilities and EquityDATA2065 ; Off Balance Sheet ItemsDATA2070 ; Loan Loss Reserves (Memo)DATA2075 ; Liquid Assets (Memo)DATA2080 ; Net Interest RevenueDATA2085 ; Other Operating IncomeDATA2086 ; Net Gains (Losses) on Trading andDerivativesDATA2087 ; Net Gains (Losses) on Assets at FV through Income StatementDATA2088 ; Net Fees and CommissionsDATA2089 ; Remaining Operating IncomeDATA2090 ; OverheadsDATA2095 ; Loan Loss ProvisionsDATA2100 ; OtherDATA2105 ; Profit before TaxDATA2110 ; TaxDATA2115 ; Net IncomeDATA2120 ; Dividend PaidDATA2125 ; Total Capital RatioDATA2130 ; Tier 1 RatioDATA2135 ; Total CapitalDATA2140 ; Tier 1 CapitalDATA2150 ; Net-Charge OffsDATA2160 ; Hybrid Capital (Memo)DATA2165 ; SubordinatedDebts (Memo)DATA2170 ; Impaired Loans (Memo)DATA2180 ; Loans and Advances to BanksDATA2185 ; Deposits from BanksDATA2190 ; Operating Income (Memo)DATA2195 ; Intangibles (Memo)DATA4001 ; Loan Loss Res / Gross LoansDATA4002 ; Loan Loss Prov / Net Int RevDATA4003 ; Loan Loss Res / Impaired LoansDATA4004 ; Impaired Loans / Gross LoansDATA4005 ; NCO / Average Gross LoansDATA4006 ; NCO / Net Inc Bef Ln Lss ProvDATA4007 ; Tier 1 RatioDATA4008 ; Total Capital Ratio

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DATA4009 ; Equity / Tot AssetsDATA4010 ; Equity / Net LoansDATA4011 ; Equity /Dep and ST FundingDATA4012 ; Equity / LiabilitiesDATA4013 ; Cap Funds / Tot AssetsDATA4014 ; Cap Funds / Net LoansDATA4015 ; Cap Funds /Dep and ST FundingDATA4016 ; Cap Funds / LiabilitiesDATA4017 ; SubordDebt / Cap FundsDATA4018 ; Net Interest MarginDATA4019 ; Net Int Rev / Avg AssetsDATA4020 ; Oth Op Inc / Avg AssetsDATA4021 ; Non Int Exp / Avg AssetsDATA4022 ; Pre-Tax Op Inc / Avg AssetsDATA4023 ; Non Op Items and Taxes / Avg AstDATA4024 ; Return On Avg Assets (ROAA)DATA4025 ; Return On Avg Equity (ROAE)DATA4026 ; Dividend Pay-OutDATA4027 ; Inc Net OfDist / Avg EquityDATA4028 ; Non Op Items / Net IncomeDATA4029 ; Cost To Income RatioDATA4030 ; Recurring Earning PowerDATA4031 ; Interbank RatioDATA4032 ; Net Loans / Tot AssetsDATA4033 ; Net Loans /Dep and ST FundingDATA4034 ; Net Loans / TotDep and BorDATA4035 ; Liquid Assets /Dep and ST FundingDATA4036 ; Liquid Assets / TotDep and BorDATA4037 ; Impaired Loans / EquityDATA4038 ; Unreserved Impaired Loans / EquityDATA5020 ; Loans Sub 3 monthsDATA5030 ; Loans 3-6 monthsDATA5040 ; Loans 6 months-1yearDATA5060 ; Loans 1-5 yearsDATA5070 ; Loans 5 years +DATA5080 ; Loans (No split available)DATA5100 ; Loans to Municipalities / GovernmentDATA5110 ; MortgagesDATA5120 ; HP / LeaseDATA5130 ; Other LoansDATA5150 ; Loans to Group Companies / AssociatesDATA5160 ; Loans to Other CorporateDATA5170 ; Loans to BanksDATA5190 ; Total Customer LoansDATA5195 ; Problem LoansDATA5210 ; Overdue LoansDATA5220 ; Restructured LoansDATA5230 ; Other non-performing LoansDATA5240 ; Total Problem LoansDATA5260 ; General Loan Loss ReservesDATA5270 ; Specific Loan Loss ReservesDATA5280 ; Loan Loss ReservesDATA5285 ; Loan Loss Reserves (PreviouslyDeducted)DATA5300 ; Trust Account LendingDATA5310 ; Other LendingDATA5320 ; Total Other LendingDATA5330 ; Total Loans - NetDATA5350 ; Deposits with BanksDATA5360 ; Due from Central BanksDATA5370 ; Due from Other BanksDATA5380 ; Due from Other Credit InstitutionsDATA5410 ; Government SecuritiesDATA5420 ; Other Listed SecuritiesDATA5430 ; Non-Listed SecuritiesDATA5440 ; Other SecuritiesDATA5450 ; Investment SecuritiesDATA5460 ; Trading SecuritiesDATA5470 ; Total SecuritiesDATA5490 ; Treasury BillsDATA5500 ; Other BillsDATA5510 ; BondsDATA5520 ; CDsDATA5530 ; Equity InvestmentsDATA5540 ; Other InvestmentsDATA5560 ; Total Other Earning AssetsDATA5580 ; Cash andDue from BanksDATA5590 ; Deferred Tax ReceivableDATA5600 ; Intangible AssetsDATA5610 ; Other Non Earning AssetsDATA5620 ; Total Non Earning AssetsDATA5640 ; Land and BuildingsDATA5650 ; Other Tangible AssetsDATA5660 ; Total Fixed AssetsDATA5670 ; Total Assets

17

DATA5920 ; Deposits -DemandDATA5925 ; Deposits - SavingsDATA5930 ; Deposits - Sub 3 monthsDATA5940 ; Deposits - 3-6 monthsDATA5950 ; Deposits - 6 months-1yearDATA5970 ; Deposits - 1-5 yearsDATA5980 ; Deposits - 5 years +DATA5990 ; Deposits (No split available)DATA6000 ; CustomerDepositsDATA6010 ; Municipalities / GovernmentDepositsDATA6030 ; OtherDepositsDATA6050 ; CommercialDepositsDATA6060 ; BanksDepositsDATA6080 ; TotalDepositsDATA6100 ; Certificates ofDepositDATA6110 ; Commercial PaperDATA6120 ; Debt SecuritiesDATA6130 ; Securities LoanedDATA6140 ; Other SecuritiesDATA6150 ; Other Negotiable InstrumentsDATA6160 ; Total Money Market FundingDATA6180 ; Convertible BondsDATA6190 ; Mortgage BondsDATA6200 ; Other BondsDATA6210 ; SubordinatedDebtDATA6220 ; Hybrid CapitalDATA6230 ; Other FundingDATA6240 ; Total Other FundingDATA6260 ; General Loan Loss ReservesDATA6270 ; Other Non Equity ReservesDATA6280 ; Total Loan Loss and Other ReservesDATA6285 ; Other LiabilitiesDATA6290 ; Total LiabilitiesDATA6310 ; General Banking RiskDATA6320 ; Retained EarningsDATA6330 ; Other Equity ReservesDATA6340 ; Minority InterestsDATA6350 ; Total Equity ReservesDATA6370 ; Preference SharesDATA6380 ; Common SharesDATA6390 ; Total Share CapitalDATA6400 ; Total EquityDATA6410 ; Total Liabilities and EquityDATA6510 ; Interest IncomeDATA6520 ; Interest ExpenseDATA6530 ; Net Interest RevenueDATA6540 ; Commision IncomeDATA6550 ; Commision ExpenseDATA6560 ; Net Commission RevenueDATA6570 ; Fee IncomeDATA6580 ; Fee ExpenseDATA6590 ; Net Fee IncomeDATA6600 ; Trading IncomeDATA6610 ; Trading ExpenseDATA6620 ; Net Trading IncomeDATA6630 ; Other Operating IncomeDATA6640 ; Total Operating IncomeDATA6650 ; Personnel ExpensesDATA6660 ; Other Admin ExpensesDATA6670 ; Other Operating ExpensesDATA6680 ; Goodwill Write-offDATA6690 ; Loan Loss ProvisionsDATA6700 ; Other ProvisionsDATA6710 ; Total Operating ExpenseDATA6720 ; Non-Operating IncomeDATA6730 ; Non-Operating ExpenseDATA6740 ; Extraordinary IncomeDATA6750 ; Extraordinary ExpenseDATA6760 ; Exceptional IncomeDATA6770 ; Exceptional ExpenseDATA6780 ; Pre-Tax ProfitDATA6790 ; TaxesDATA6800 ; Post Tax ProfitDATA6810 ; Transfer to/(from) fund for general banking risksDATA6815 ; Published Net IncomeDATA6820 ; PreferenceDividendsDATA6830 ; OtherDividendsDATA6840 ; OtherDistributionsDATA6850 ; Retained IncomeDATA6860 ; Minority InterestsDATA7020 ; Tier 1 CapitalDATA7030 ; Total CapitalDATA7040 ; Tier 1 Capital RatioDATA7050 ; Total Capital Ratio

18

DATA7070 ; AcceptancesDATA7080 ; Documentary CreditsDATA7090 ; GuaranteesDATA7100 ; OtherDATA7110 ; Total Contingent LiabilitiesDATA7130 ; GrossDATA7140 ; Write OffsDATA7150 ; Write backsDATA7160 ; Net Charge offsEXRATE_USD ; Exchange Rate from Local Currency to USDFAX ; FaxFORMAT ; Format CodeINFLAT ; Inflation Adjusted IndicatorLASTYYMM ; Last Available Year and MonthLISTINS ; Listed InstitutionMONTHS ; Number of MonthsNAME ; Bank NameNCOMPAN ; Number of Companion Company RecordsNICKNAME ; NickNameNOBMO ; Number of Board Member RecordsNUMYEARS ; Number Years of FinancialDataPHONE ; PhonePOSTCODE ; Postal CodePREVNAME ; Previous NameRANKYEAR ; Ranking YearRELEASE ; ReleaseDateSOURCE ; SourceSOURCES ; Source StatusSPECIA2 ; Specialization (Country Specific)SPECIAL ; Specialization (General)STATE ; StateSTATQUAL ; Statement QualificationSWIFT ; Swift CodeUNIT ; Scaling Unit (0=units, 3=thousands, 6=millions)UNIT_TEXT ; Statement Unit (e.g. th, mil, bil, trl)WEBSITE ; Web SiteWORLDRK ; World RankWORLDROL ; World Rank, Rolling

19

B Countries and geographical areas

Table 2: Matching countries and geographical areas

countryname; cntrycde; continent; region; OECD;

ANDORRA ; AD ; Europe ; South West Europe ; 0UNITED ARAB EMIRATES ; AE ; Asia ; South West Asia ; 0AFGHANISTAN ; AF ; Asia ; South Asia ; 0ANTIGUA AND BARBUDA ; AG ; Americas ; West Indies ; 0ANGUILLA ; AI ; Americas ; West Indies ; 0ALBANIA ; AL ; Europe ; South East Europe ; 0ARMENIA ; AM ; Asia ; South West Asia ; 0ANGOLA ; AO ; Africa ; Southern Africa ; 0ANTARCTICA ; AQ ; ; ; 0ARGENTINA ; AR ; Americas ; South America ; 0AMERICAN SAMOA ; AS ; Oceania ; Pacific ; 0AUSTRIA ; AT ; Europe ; Central Europe ; 1AUSTRALIA ; AU ; Oceania ; Pacific ; 1ARUBA ; AW ; Americas ; West Indies ; 0ALAND ISLANDS ; AX ; ; ; 0AZERBAIJAN ; AZ ; Asia ; South West Asia ; 0BOSNIA AND HERZEGOVINA ; BA ; Europe ; South East Europe ; 0BARBADOS ; BB ; Americas ; West Indies ; 0BANGLADESH ; BD ; Asia ; South Asia ; 0BELGIUM ; BE ; Europe ; Western Europe ; 1BURKINA FASO ; BF ; Africa ; Western Africa ; 0BULGARIA ; BG ; Europe ; South East Europe ; 0BAHRAIN ; BH ; Asia ; South West Asia ; 0BURUNDI ; BI ; Africa ; Central Africa ; 0BENIN ; BJ ; Africa ; Western Africa ; 0SAINT BARTHELEMY ; BL ; ; ; 0BERMUDA ; BM ; Americas ; West Indies ; 0BRUNEI DARUSSALAM ; BN ; Asia ; South East Asia ; 0BOLIVIA, PLURINATIONAL STATE OF ; BO ; Americas ; South America ; 0BONAIRE, SINT EUSTATIUS AND SABA ; BQ ; ; ; 0BRAZIL ; BR ; Americas ; South America ; 0BAHAMAS ; BS ; Americas ; West Indies ; 0BHUTAN ; BT ; Asia ; South Asia ; 0BOUVET ISLAND ; BV ; ; ; 0BOTSWANA ; BW ; Africa ; Southern Africa ; 0BELARUS ; BY ; Europe ; Eastern Europe ; 0BELIZE ; BZ ; Americas ; Central America ; 0CANADA ; CA ; Americas ; North America ; 1COCOS (KEELING) ISLANDS ; CC ; Asia ; South East Asia ; 0CONGO, THE DEMOCRATIC REPUBLIC OF THE ; CD ; Africa ; Central Africa ; 0CENTRAL AFRICAN REPUBLIC ; CF ; Africa ; Central Africa ; 0CONGO ; CG ; Africa ; Central Africa ; 0SWITZERLAND ; CH ; Europe ; Central Europe ; 1COTE D’IVOIRE ; CI ; Africa ; Western Africa ; 0COOK ISLANDS ; CK ; Oceania ; Pacific ; 0CHILE ; CL ; Americas ; South America ; 1CAMEROON ; CM ; Africa ; Western Africa ; 0CHINA ; CN ; Asia ; East Asia ; 0COLOMBIA ; CO ; Americas ; South America ; 0COSTA RICA ; CR ; Americas ; Central America ; 0CUBA ; CU ; Americas ; West Indies ; 0CAPE VERDE ; CV ; Africa ; Western Africa ; 0CURACAO ; CW ; ; ; 0CHRISTMAS ISLAND ; CX ; Asia ; South East Asia ; 0CYPRUS ; CY ; Asia ; South West Asia ; 0CZECH REPUBLIC ; CZ ; Europe ; Central Europe ; 1GERMANY ; DE ; Europe ; Western Europe ; 1DJIBOUTI ; DJ ; Africa ; Eastern Africa ; 0DENMARK ; DK ; Europe ; Northern Europe ; 1DOMINICA ; DM ; Americas ; West Indies ; 0DOMINICAN REPUBLIC ; DO ; Americas ; West Indies ; 0ALGERIA ; DZ ; Africa ; Northern Africa ; 0ECUADOR ; EC ; Americas ; South America ; 0ESTONIA ; EE ; Europe ; Eastern Europe ; 1EGYPT ; EG ; Africa ; Northern Africa ; 0WESTERN SAHARA ; EH ; Africa ; Northern Africa ; 0ERITREA ; ER ; Africa ; Eastern Africa ; 0SPAIN ; ES ; Europe ; South West Europe ; 1ETHIOPIA ; ET ; Africa ; Eastern Africa ; 0FINLAND ; FI ; Europe ; Northern Europe ; 1FIJI ; FJ ; Oceania ; Pacific ; 0FALKLAND ISLANDS (MALVINAS) ; FK ; Americas ; South America ; 0MICRONESIA, FEDERATED STATES OF ; FM ; Oceania ; Pacific ; 0FAROE ISLANDS ; FO ; Europe ; Northern Europe ; 0FRANCE ; FR ; Europe ; Western Europe ; 1

20

GABON ; GA ; Africa ; Western Africa ; 0UNITED KINGDOM ; GB ; Europe ; Western Europe ; 1GRENADA ; GD ; Americas ; West Indies ; 0GEORGIA ; GE ; Asia ; South West Asia ; 0FRENCH GUIANA ; GF ; Americas ; South America ; 0GUERNSEY ; GG ; Europe ; Western Europe ; 0GHANA ; GH ; Africa ; Western Africa ; 0GIBRALTAR ; GI ; Europe ; South West Europe ; 0GREENLAND ; GL ; Americas ; North America ; 0GAMBIA ; GM ; Africa ; Western Africa ; 0GUINEA ; GN ; Africa ; Western Africa ; 0GUADELOUPE ; GP ; Americas ; West Indies ; 0EQUATORIAL GUINEA ; GQ ; Africa ; Western Africa ; 0GREECE ; GR ; Europe ; South East Europe ; 1SOUTH GEORGIA AND THE SOUTH SANDWICH ISLANDS ; GS ; ; ; 0GUATEMALA ; GT ; Americas ; Central America ; 0GUAM ; GU ; Oceania ; Pacific ; 0GUINEA-BISSAU ; GW ; Africa ; Western Africa ; 0GUYANA ; GY ; Americas ; South America ; 0HONG KONG ; HK ; ; ; 0HEARD ISLAND AND MCDONALD ISLANDS ; HM ; ; ; 0HONDURAS ; HN ; Americas ; Central America ; 0CROATIA ; HR ; Europe ; South East Europe ; 0HAITI ; HT ; Americas ; West Indies ; 0HUNGARY ; HU ; Europe ; Central Europe ; 1INDONESIA ; ID ; Asia ; South East Asia ; 0IRELAND ; IE ; Europe ; Western Europe ; 1ISRAEL ; IL ; Asia ; South West Asia ; 1ISLE OF MAN ; IM ; ; ; 0INDIA ; IN ; Asia ; South Asia ; 0BRITISH INDIAN OCEAN TERRITORY ; IO ; ; ; 0IRAQ ; IQ ; Asia ; South West Asia ; 0IRAN, ISLAMIC REPUBLIC OF ; IR ; Asia ; South West Asia ; 0ICELAND ; IS ; Europe ; Northern Europe ; 1ITALY ; IT ; Europe ; Southern Europe ; 1JERSEY ; JE ; Europe ; Western Europe ; 0JAMAICA ; JM ; Americas ; West Indies ; 0JORDAN ; JO ; Asia ; South West Asia ; 0JAPAN ; JP ; Asia ; East Asia ; 1KENYA ; KE ; Africa ; Eastern Africa ; 0KYRGYZSTAN ; KG ; Asia ; Central Asia ; 0CAMBODIA ; KH ; Asia ; South East Asia ; 0KIRIBATI ; KI ; Oceania ; Pacific ; 0COMOROS ; KM ; Africa ; Indian Ocean ; 0SAINT KITTS AND NEVIS ; KN ; Americas ; West Indies ; 0KOREA, DEMOCRATIC PEOPLE’S REPUBLIC OF ; KP ; Asia ; East Asia ; 0KOREA, REPUBLIC OF ; KR ; Asia ; East Asia ; 1KUWAIT ; KW ; Asia ; South West Asia ; 0CAYMAN ISLANDS ; KY ; Americas ; West Indies ; 0KAZAKHSTAN ; KZ ; Asia ; Central Asia ; 0LAO PEOPLE’S DEMOCRATIC REPUBLIC ; LA ; Asia ; South East Asia ; 0LEBANON ; LB ; Asia ; South West Asia ; 0SAINT LUCIA ; LC ; Americas ; West Indies ; 0LIECHTENSTEIN ; LI ; Europe ; Central Europe ; 0SRI LANKA ; LK ; Asia ; South Asia ; 0LIBERIA ; LR ; Africa ; Western Africa ; 0LESOTHO ; LS ; Africa ; Southern Africa ; 0LITHUANIA ; LT ; Europe ; Eastern Europe ; 0LUXEMBOURG ; LU ; Europe ; Western Europe ; 1LATVIA ; LV ; Europe ; Eastern Europe ; 0LIBYA ; LY ; Africa ; Northern Africa ; 0MOROCCO ; MA ; Africa ; Northern Africa ; 0MONACO ; MC ; Europe ; Western Europe ; 0MOLDOVA, REPUBLIC OF ; MD ; Europe ; Eastern Europe ; 0MONTENEGRO ; ME ; ; ; 0SAINT MARTIN (FRENCH PART) ; MF ; ; ; 0MADAGASCAR ; MG ; Africa ; Indian Ocean ; 0MARSHALL ISLANDS ; MH ; Oceania ; Pacific ; 0MACEDONIA, THE FORMER YUGOSLAV REPUBLIC OF ; MK ; Europe ; South East Europe ; 0MALI ; ML ; Africa ; Western Africa ; 0MYANMAR ; MM ; Asia ; South East Asia ; 0MONGOLIA ; MN ; Asia ; Northern Asia ; 0MACAO ; MO ; ; ; 0NORTHERN MARIANA ISLANDS ; MP ; Oceania ; Pacific ; 0MARTINIQUE ; MQ ; Americas ; West Indies ; 0MAURITANIA ; MR ; Africa ; Western Africa ; 0MONTSERRAT ; MS ; Americas ; West Indies ; 0MALTA ; MT ; Europe ; Southern Europe ; 0MAURITIUS ; MU ; Africa ; Indian Ocean ; 0MALDIVES ; MV ; Asia ; South Asia ; 0MALAWI ; MW ; Africa ; Southern Africa ; 0MEXICO ; MX ; Americas ; Central America ; 1MALAYSIA ; MY ; Asia ; South East Asia ; 0MOZAMBIQUE ; MZ ; Africa ; Southern Africa ; 0

21

NAMIBIA ; NA ; Africa ; Southern Africa ; 0NEW CALEDONIA ; NC ; Oceania ; Pacific ; 0NIGER ; NE ; Africa ; Western Africa ; 0NORFOLK ISLAND ; NF ; Oceania ; Pacific ; 0NIGERIA ; NG ; Africa ; Western Africa ; 0NICARAGUA ; NI ; Americas ; Central America ; 0NETHERLANDS ; NL ; Europe ; Western Europe ; 1NORWAY ; NO ; Europe ; Northern Europe ; 1NEPAL ; NP ; Asia ; South Asia ; 0NAURU ; NR ; Oceania ; Pacific ; 0NIUE ; NU ; Oceania ; Pacific ; 0NEW ZEALAND ; NZ ; Oceania ; Pacific ; 1OMAN ; OM ; Asia ; South West Asia ; 0PANAMA ; PA ; Americas ; Central America ; 0PERU ; PE ; Americas ; South America ; 0FRENCH POLYNESIA ; PF ; Oceania ; Pacific ; 0PAPUA NEW GUINEA ; PG ; Oceania ; Pacific ; 0PHILIPPINES ; PH ; Asia ; South East Asia ; 0PAKISTAN ; PK ; Asia ; South Asia ; 0POLAND ; PL ; Europe ; Eastern Europe ; 1SAINT PIERRE AND MIQUELON ; PM ; Americas ; North America ; 0PITCAIRN ; PN ; Oceania ; Pacific ; 0PUERTO RICO ; PR ; Americas ; West Indies ; 0PALESTINIAN TERRITORY, OCCUPIED ; PS ; Asia ; South West Asia ; 0PORTUGAL ; PT ; Europe ; South West Europe ; 1PALAU ; PW ; Oceania ; Pacific ; 0PARAGUAY ; PY ; Americas ; South America ; 0QATAR ; QA ; Asia ; South West Asia ; 0REUNION ; RE ; Africa ; Indian Ocean ; 0ROMANIA ; RO ; Europe ; South East Europe ; 0SERBIA ; RS ; Europe ; South East Europe ; 0RUSSIAN FEDERATION ; RU ; Asia ; Northern Asia ; 0RWANDA ; RW ; Africa ; Central Africa ; 0SAUDI ARABIA ; SA ; Asia ; South West Asia ; 0SOLOMON ISLANDS ; SB ; Oceania ; Pacific ; 0SEYCHELLES ; SC ; Africa ; Indian Ocean ; 0SUDAN ; SD ; Africa ; Northern Africa ; 0SWEDEN ; SE ; Europe ; Northern Europe ; 1SINGAPORE ; SG ; Asia ; South East Asia ; 0SAINT HELENA, ASCENSION AND TRISTAN DA CUNHA ; SH ; ; ; 0SLOVENIA ; SI ; Europe ; South East Europe ; 1SVALBARD AND JAN MAYEN ; SJ ; Europe ; Northern Europe ; 0SLOVAKIA ; SK ; Europe ; Central Europe ; 1SIERRA LEONE ; SL ; Africa ; Western Africa ; 0SAN MARINO ; SM ; Europe ; Southern Europe ; 0SENEGAL ; SN ; Africa ; Western Africa ; 0SOMALIA ; SO ; Africa ; Eastern Africa ; 0SURINAME ; SR ; Americas ; South America ; 0SOUTH SUDAN ; SS ; ; ; 0SAO TOME AND PRINCIPE ; ST ; Africa ; Western Africa ; 0EL SALVADOR ; SV ; Americas ; Central America ; 0SINT MAARTEN (DUTCH PART) ; SX ; ; ; 0SYRIAN ARAB REPUBLIC ; SY ; Asia ; South West Asia ; 0SWAZILAND ; SZ ; Africa ; Southern Africa ; 0TURKS AND CAICOS ISLANDS ; TC ; Americas ; West Indies ; 0CHAD ; TD ; Africa ; Central Africa ; 0FRENCH SOUTHERN TERRITORIES ; TF ; ; ; 0TOGO ; TG ; Africa ; Western Africa ; 0THAILAND ; TH ; Asia ; South East Asia ; 0TAJIKISTAN ; TJ ; Asia ; Central Asia ; 0TOKELAU ; TK ; Oceania ; Pacific ; 0TIMOR-LESTE ; TL ; ; ; 0TURKMENISTAN ; TM ; Asia ; Central Asia ; 0TUNISIA ; TN ; Africa ; Northern Africa ; 0TONGA ; TO ; Oceania ; Pacific ; 0TURKEY ; TR ; Asia ; South West Asia ; 1TRINIDAD AND TOBAGO ; TT ; Americas ; West Indies ; 0TUVALU ; TV ; Oceania ; Pacific ; 0TAIWAN, PROVINCE OF CHINA ; TW ; Asia ; East Asia ; 0TANZANIA, UNITED REPUBLIC OF ; TZ ; Africa ; Eastern Africa ; 0UKRAINE ; UA ; Europe ; Eastern Europe ; 0UGANDA ; UG ; Africa ; Eastern Africa ; 0UNITED STATES MINOR OUTLYING ISLANDS ; UM ; ; ; 0UNITED STATES ; US ; Americas ; North America ; 1URUGUAY ; UY ; Americas ; South America ; 0UZBEKISTAN ; UZ ; Asia ; Central Asia ; 0HOLY SEE (VATICAN CITY STATE) ; VA ; Europe ; Southern Europe ; 0SAINT VINCENT AND THE GRENADINES ; VC ; Americas ; West Indies ; 0VENEZUELA, BOLIVARIAN REPUBLIC OF ; VE ; Americas ; South America ; 0VIRGIN ISLANDS, BRITISH ; VG ; Americas ; West Indies ; 0VIRGIN ISLANDS, U.S. ; VI ; Americas ; West Indies ; 0VIET NAM ; VN ; Asia ; South East Asia ; 0VANUATU ; VU ; Oceania ; Pacific ; 0WALLIS AND FUTUNA ; WF ; Oceania ; Pacific ; 0

22

SAMOA ; WS ; Oceania ; Pacific ; 0YEMEN ; YE ; Asia ; South West Asia ; 0MAYOTTE ; YT ; Africa ; Indian Ocean ; 0SOUTH AFRICA ; ZA ; Africa ; Southern Africa ; 0ZAMBIA ; ZM ; Africa ; Southern Africa ; 0ZIMBABWE ; ZW ; Africa ; Southern Africa ; 0

23

C Getting proper variable names//non-Ratio

rename bs_id_number idrename data2000 loanrename data2001 Gross_Loansrename data2002 Less_Res_for_Imp_Loans_ov_NPLsrename data2005 Other_Earning_Assetsrename data2007 Derivativesrename data2008 Other_Securitiesrename data2009 Remaining_Earning_Assetsrename data2010 t_earning_assetrename data2015 fixed_assetrename data2020 non_earning_assetrename data2025 t_assetrename data2030 deposit_ST_fundingrename data2031 cust_depositrename data2033 other_dep_ST_borrorename data2035 other_int_bear_liabrename data2036 derivativesrename data2037 trading_liabrename data2038 LT_fundingrename data2040 non_int_bearing_liabrename data2045 loan_loss_reservesrename data2050 other_reservesrename data2055 equityrename data2060 t_liab_equityrename data2065 Off_Balance_Sheet_Itemsrename data2070 loan_loss_reserves_memorename data2075 liquid_assetrename data2080 Net_Interest_Revenuerename data2085 other_op_incomerename data2086 Net_Gain_Loss_on_Trad_and_Derivrename data2087 Net_Gain_Loss_on_Asset_at_FVrename data2088 Net_Fees_and_Commissionsrename data2089 Remaining_Operating_Incomerename data2090 overheadrename data2095 loan_loss_provrename data2100 Otherrename data2105 profit_bef_taxrename data2110 Taxrename data2115 Net_Incomerename data2120 Dividend_Paid

//Ratio

rename data2125 capital_ratiorename data2130 tier1_ratio

//non-Ratio

rename data2135 t_capitalrename data2140 tier1_capitalrename data2150 net_charge_offrename data2160 Hybrid_Capital_Memorename data2165 Subordinated_Debts_Memorename data2170 impaired_loanrename data2180 Loans_and_Advances_to_Banksrename data2185 dep_from_bankrename data2190 Operating_Income_Memorename data2195 Intangibles_Memorename data5020 Loans_Sub_3_monthsrename data5030 Loans_3_6_monthsrename data5040 Loans_6_months_1yearrename data5060 Loans_1_5_yearsrename data5070 Loans_5_yearsrename data5080 Loans_No_split_availablerename data5100 Loans_to_Municip_ov_Gvtrename data5110 Mortgagesrename data5120 HP_ov_Leaserename data5130 Other_Loansrename data5150 Loans_to_Group_Cies_ov_Assrename data5160 Loans_to_Other_Corporaterename data5170 Loans_to_Banksrename data5190 Total_Customer_Loansrename data5195 Problem_Loansrename data5210 overdue_loanrename data5220 restru_loanrename data5230 other_nonperf_loanrename data5240 t_problem_loanrename data5260 gen_llrrename data5270 specific_llr

24

rename data5280 loan_loss_resrename data5285 loan_loss_res2rename data5300 Trust_Account_Lendingrename data5310 Other_Lendingrename data5320 Total_Other_Lendingrename data5330 Total_Loans_Netrename data5350 Deposits_with_Banksrename data5360 Due_from_Central_Banksrename data5370 Due_from_Other_Banksrename data5380 Due_from_Other_Credit_Institrename data5410 Government_Securitiesrename data5420 Other_Listed_Securitiesrename data5430 Non_Listed_Securitiesrename data5440 Other_Securities2rename data5450 Investment_Securitiesrename data5460 Trading_Securitiesrename data5470 Total_Securitiesrename data5490 Treasury_Billsrename data5500 Other_Billsrename data5510 Bondsrename data5520 CDsrename data5530 Equity_Investmentsrename data5540 Other_Investmentsrename data5560 Total_Other_Earning_Assetsrename data5580 Cash_and_Due_from_Banksrename data5590 Deferred_Tax_Receivablerename data5600 Intangible_Assetsrename data5610 Other_Non_Earning_Assetsrename data5620 Total_Non_Earning_Assetsrename data5640 Land_and_Buildingsrename data5650 Other_Tangible_Assetsrename data5660 Total_Fixed_Assetsrename data5670 Total_Assetsrename data5920 demand_depositrename data5925 saving_depositrename data5930 Deposits_Sub_3_monthsrename data5940 Deposits_3_6_monthsrename data5950 Deposits_6_months_1yearrename data5970 Deposits_1_5_yearsrename data5980 Deposits_5_yearsrename data5990 Deposits_No_split_availablerename data6000 Customer_Depositsrename data6010 Municip_ov_Gvt_Depositsrename data6030 Other_Depositsrename data6050 Commercial_Depositsrename data6060 Banks_Depositsrename data6080 t_depositrename data6100 Certificates_of_Depositrename data6110 Commercial_Paperrename data6120 Debt_Securitiesrename data6130 Securities_Loanedrename data6140 Other_Securities3rename data6150 Other_Negotiable_Instrumentsrename data6160 Total_Money_Market_Fundingrename data6180 Convertible_Bondsrename data6190 Mortgage_Bondsrename data6200 Other_Bondsrename data6210 sub_debtrename data6220 Hybrid_Capitalrename data6230 Other_Fundingrename data6240 t_other_fundingrename data6260 gen_loan_loss_resrename data6270 Other_Non_Equity_Reservesrename data6280 t_loan_loss_and_other_resrename data6285 Other_Liabilitiesrename data6290 t_liabilitiesrename data6310 General_Banking_Riskrename data6320 Retained_Earningsrename data6330 Other_Equity_Reservesrename data6340 Minority_Interestsrename data6350 Total_Equity_Reservesrename data6370 Preference_Sharesrename data6380 Common_Sharesrename data6390 Total_Share_Capitalrename data6400 t_equityrename data6410 t_liabilities_equityrename data6510 int_incomerename data6520 int_expenserename data6530 net_int_revenuerename data6540 commission_increname data6550 Commision_Expenserename data6560 Net_Commission_Revenuerename data6570 fee_increname data6580 Fee_Expense

25

rename data6590 Net_Fee_Incomerename data6600 trading_increname data6610 Trading_Expenserename data6620 Net_Trading_Incomerename data6630 other_op_increname data6640 t_ope_incomerename data6650 Personnel_Expensesrename data6660 Other_Admin_Expensesrename data6670 Other_Operating_Expensesrename data6680 Goodwill_Write_offrename data6690 loan_loss_provisionrename data6700 Other_Provisionsrename data6710 t_ope_expenserename data6720 Non_Operating_Incomerename data6730 Non_Operating_Expenserename data6740 Extraordinary_Incomerename data6750 Extraordinary_Expenserename data6760 Exceptional_Incomerename data6770 Exceptional_Expenserename data6780 pretax_profitrename data6790 Taxesrename data6800 posttax_profitrename data6810 Transfer_fund_for_banking_risksrename data6815 Published_Net_Incomerename data6820 Preference_Dividendsrename data6830 Other_Dividendsrename data6840 Other_Distributionsrename data6850 Retained_Incomerename data6860 Minority_Interests2

//Ratio

rename data4001 LLRrename data4002 LLP_over_int_revrename data4003 LLR_over_NPLrename data4004 NPLrename data4005 NCOrename data4006 nco_over_income_before_llprename data4007 tier1_ratio2rename data4008 capital_ratio2rename data4009 equity_over_assetrename data4010 Equity_ov_Net_Loansrename data4011 Equity_ov_Dep_ST_Fundingrename data4012 equity_over_liabrename data4013 cap_fund_over_assetrename data4014 Cap_Funds_ov_Net_Loansrename data4015 Cap_Fund_ov_Dep_ST_Fundrename data4016 Cap_Funds_ov_Liabilitiesrename data4017 Subord_Debt_ov_Cap_Fundsrename data4018 net_int_marginrename data4019 net_int_rev_over_assetrename data4020 other_op_income_over_assetrename data4021 Non_Int_Exp_ov_Avg_Assetsrename data4022 pretax_inc_over_assetrename data4023 Non_Op_Items_Taxes_ov_Avg_Astrename data4024 ROAArename data4025 ROAErename data4026 Dividend_Pay_Outrename data4027 Inc_Net_Of_Dist_ov_Avg_Equityrename data4028 Non_Op_Items_ov_Net_Incomerename data4029 cost_income_ratiorename data4030 Recurring_Earning_Powerrename data4031 interbank_ratiorename data4032 Net_Loans_ov_Tot_Assetsrename data4033 Net_Loans_ov_Dep_ST_Fundingrename data4034 Net_Loans_ov_Tot_Dep_Borrename data4035 Liquid_Assets_ov_Dep_ST_Fundingrename data4036 Liquid_Assets_ov_Tot_Dep_Borrename data4037 impaired_loan_over_equityrename data4038 Unres_Imp_Loans_ov_Equity

//non-Ratio

rename data7020 tier1_capital3rename data7030 t_capital3

//Ratio

rename data7040 tier1_ratio3rename data7050 capital_ratio3

//non-Ratio

rename data7070 Acceptances

26

rename data7080 Documentary_Creditsrename data7090 Guaranteesrename data7100 Other2rename data7110 Total_Contingent_Liabilitiesrename data7130 Grossrename data7140 Write_Offsrename data7150 Write_backsrename data7160 net_charge_off2

27