Bank for International Settlements (BIS)
What data do they report on? What data do we use? Why?
The Bank of International Settlements (BIS) is an international financial institution composed of central banks. BIS is located in Basel, Switzerland and facilitates the interaction of central banks in order to make international monetary policy more predictable and transparent. BIS does this by hosting meetings with central banks, conducting research and policy pertaining to monetary issues and financial stability, and acting as a counterparty for central banks with their transactions. They also serve as an agent with international financial operations and engage in dialogue with other financial actors. Their ultimate goal is to ensure monetary and financial stability among its 60 central bank members. "With regard to its banking activities, the customers of the BIS are central banks and international organisations. As a bank, the BIS does not accept deposits from, or provide financial services to, private individuals or corporate entities" (BIS.org).
BIS' goals are portrayed in its 3 pillars:
- Pillar 1 (Regulatory capital): Credit risk, F-IRB, A-IRB, PD, LGD, EAD, opeartional risk, market risk, value at risk
- The first pillar of regulation relates to capital adequacy, which applies to equity and capital assets. Since these two assets do not alway reflect the current market nor can they account for every risk present among every trading position, BIS is able to manage and change these when needed. BIS requires that members banks have capital/asset ratios to be above a prescribed minimum in order to keep banks strong against shocks.
- Pillar 2 (Supervisory review): economic capital, liquidity risk, legal risk
- Member banks are required to ensure liquidity and limit liability in order to reduce the risk of bank runs and to make borrowing safer for customers. BIS works to control asset inflation due to members rising fear of "bubbles". Furthermore, exporting countries are finding it difficult to manage a range of domestic monetary requirements while maintaing an export economy. Thus, BIS helps prescribe reserve levels for each countries style of exporting and domestic policy.
- Pillar 3 (Market disclosure)
- BIS makes its research and findings free to the public in order to help ensure financial and monetary stability. Their analysis covers monetary and financial policy along international banking statistics.
BIS data covers a wide range of international banking and financial data. BIS reports on:
- Locational Banking Statistics
- Consolidated Banking Stastics
- Debt Securities
- Exchange-traded derivatives
- Semiannual OTC derivatives
- Triennial OTC derivatives
Global Liquidity Indicators
Credit to the Non-Financial Sector
Debt Service Ratios for the Private Non-Financial Sector
Effective Exchange Rates
Data categorization: Pardee uses the year-on-year changes by per cent (ratio) shows more detailed changes in consumer prices compared to the 2010=100 index.
The BIS’s data set for consumer prices contains long monthly and annual time series for 60 countries. Pardee uses the annual series for the 60 countries because the International Futures model uses annual data. Each country's data is produced by the respective country and correlates with the most recent index of consumer price. Some of the annual series go back to the 19th century, and some go back into the 17th century. The BIS was able to construct these long consumer price index series by joining available series for consecutive periods by working with national statistical offices.
Credit to the private nonfinancial sector (needs data)
Data Documentation: The BIS publication is presented in quarterly datasets with subsets. The Pardee coder averaged the four quarters within a year and used the result as the data. that include:
- Non-financial sector corporations, or sectors or all sectors banks, households, NPISHs, non-financial corporations, corporations, market value, respective country currency, domestic currency in US Dollars, adjusted for breaks, not adjusted for breaks, and GDP
- Percentage of GDP adjusted for breaks or US Dollar adjusted for breaks or Domestic currency not adjusted for breaks
All series on credit to the non-financial sector cover 43 economies, both advanced and emerging. They capture the outstanding amount of credit at the end of the reference quarter. Credit is provided by domestic banks, all other sectors of the economy and non-residents. In terms of financial instruments, credit covers the core debt, defined as loans, debt securities and currency & deposits.
All series are published in local currency, in US dollars and as percentages of nominal GDP.[which did you pull? there is only one series in the excel you sent] The regional aggregates as percentages of GDP are calculated based on conversion to the US dollar at market and at purchasing power parity (PPP) exchange rates.
Long series on total credit and domestic bank credit to the private nonfinancial sector
The “private non-financial sector” includes non-financial corporations (both private-owned and public-owned), households and non-profit institutions serving households as defined in the System of National Accounts 2008[what is this? is that a report you are referencing? add a link to it or citation.]. In terms of financial instruments, credit covers loans and debt securities. The series have quarterly frequency and capture the outstanding amount of credit at the end of the reference quarter [copied from above]. Table 1[there is no table 1] below shows the list of financial instruments, borrowers and lenders covered by the series.
To encompass as long a period as possible, the construction of the long series required combining data from several sources, such as the financial accounts by institutional sector, the balance sheets of domestic banks, international banking statistics, and the balance sheets of non-bank financial institutions. In turn, some of these statistics were compiled in past periods according to earlier methodological frameworks (eg the System of National Accounts 1968, which was replaced by the System of National Accounts 1993). Where original data were published at annual frequency, the intra-annual observations were interpolated.
The combination of different sources and data from various methodological frameworks resulted in breaks in the series. The BIS is therefore, in addition, publishing a second set of series adjusted for breaks, which covers the same time span as the unadjusted series. The break-adjusted series are the result of the BIS’s own calculations, and were obtained by adjusting levels through standard statistical techniques described in the special feature on the long credit series of the March 2013 issue of the BIS Quarterly Review.
The data for each country include (i) credit to private non-financial sectors by domestic banks and (ii) total credit to private non-financial sectors. Moreover, for most countries, total credit is broken down into (iii) credit to non-financial corporations and (iv) credit to households and non-profit institutions serving households. [so which did you pull?] Tables 2 and 3 below [where?] contain more information about the methodology followed in the compilation of each time series, as well as links to websites where the most recent national data can be found. For more information, see the above-mentioned article about the long credit series.
Since March 2016, the BIS has added the regional aggregates in the data set. Four aggregates are available: G20, advanced economies, emerging market economies and all reporting economies.2 [same comments as above] The data in billions of US dollars are calculated using market exchange rates and the percentages of GDP are calculated based on conversion to US dollars at market and at purchasing power parity (PPP) exchange rates.
This data set on credit to private non-financial sectors combined with the one on general government debt can provide a useful picture of the aggregated indebtedness of all non-financial sectors.[sounds good, but we only pulled, it looks like, one series, and it's unclear what that series is]
Debt service ratios for the private non-financial sector
Data Documentation: BIS' publication captures different subsets for the 32 countries listed in the report. These subsets include Households and NPISHs, non-financial corporations, and private non-financial sector. Pardee has chosen to use the 17 private non-financial sector countries datasets because captures the sum of the two income measures from the household and NFC sectors comprises the income of the total PNFS
The debt service ratio (DSR) is defined as the ratio of interest payments plus amortisations to income.
The BIS publishes debt service ratios (DSR) for the household, the non-financial corporate and the total private non-financial sector (PNFS) for 17 countries. Total PNFS DSRs are also available for 15 additional countries, using different income and interest rates measures, due to data availability at the national level. As such, the DSR provides a flow-to-flow comparison – the flow of debt service payments divided by the flow of income. To derive the DSR on an internationally consistent basis, the BIS applies a unified methodological approach and uses, as much as possible, input data that are compiled on an internationally consistent basis.
The DSR reflects the share of income used to service debt and has been found to provide important information about financial-real interactions. For one, the DSR is a reliable early warning indicator for systemic banking crises. Furthermore, a high DSR has a strong negative impact on consumption and investment. The DSRs are constructed based primarily on data from the national accounts.
For a few countries, no lending rates on outstanding MFI loans are available. In these cases, the average lending rate on the stock of debt is proxied by the short-term money market rate plus 2.18 percentage points – the average markup between lending rates and the money market rates across countries. In addition, a smoothing factor calibrated on a cross-country data set is applied.7 Income: The income measure for the total PNFS is proxied by the nominal quarterly GDP series. Four-quarter moving averages are applied to the raw GDP data
Methodology The methodology follows the approach used by the Federal Reserve Board to construct DSRs for the household sector (Dynan et al (2003)).2 It starts with the basic assumption that, for a given lending rate, debt service costs – interest and amortisations – on the aggregate debt stock are repaid in equal portions over the maturity of the loan (instalment loans). The justification for this assumption is that the differences between the repayment structures of individual loans will tend to cancel each other out in theaggregate.3 Using a number of simulations, Drehmann et al (2015) show that this indeed seems to be the case. By using the standard formula for calculating the fixed debt service costs of an instalment loan and dividing it by income, the DSR for sector j at time t is calculated as denotes the total stock of debt, Yj,t denotes quarterly income, ij,t denotes the average interest rate on the existing stock of debt per quarter and sj,t denotes the average remaining maturity in quarters.
The non-linearities in the installment loan formula can generate an approximation error when aggregate data are used. However, this approximation error turns out to be relatively independent of average interest rates, debt-to-income ratios or maturities (Drehmann et al (2015). Thus, the methodology should correctly capture how the DSR in a particular country changes over time, even if it does not necessarily accurately measure its level relative to what one could obtain from the correct micro data.
For practical purposes, the difficulties in pinpointing the level imply that it is most meaningful to compare DSRs over time – by, for instance, removing country-specific means.
- Sectors DSRs are derived for the household sector, non-financial corporations (NFCs) and the total private non-financial sector (PNFS).
- Frequency DSRs are compiled at quarterly frequency.
- Data The ratio uses input data from the national accounts. If these data are not available, alternative sources are used.
- Stock of debt Debt is defined as credit (in terms of loans and debt securities) from all sources to the PNFS, as compiled by the BIS. This includes information for the household and NFC sectors.
- Average interest rate on the existing stock of debt: To accurately measure aggregate debt servicing costs, the interest rate has to reflect average interest rate conditions on the stock of debt, which contains a mix of new and old loans with different fixed and floating nominal interest rates attached to them.
- The average interest rate on the stock of debt is computed by dividing gross interest payments plus financial intermediation services indirectly measured (FISIM) by the stock of debt. FISIM is an estimate of the value of financial intermediation services provided by financial institutions. When national account compilers derive the sectoral accounts, parts of interest payments are reclassified as payments for services and allocated as output of the financial intermediation sector. In turn, this output is recorded as consumption by households and NFCs. As the aim is to identify the total burden of interest payments on borrowers regardless of their economic function, FISIM is added back to interest payments reported in the national accounts to derive effective interest payments.
- Income: Income in this context corresponds to the amount of money available to economic agents to pay debt service costs. Gross disposable income (GDI) is a close approximation. GDI measures the income available to households and NFCs after interest payments and, in the case of NFCs, dividends. Hence, to accurately reflect the amount of money available to service debt, GDI has to be augmented by interest payments (and dividends for the NFCs). 4 Excluding SDR allocations, deposits and other accounts receivable, which include trade credits.3 GDI complemented with these other items is called “augmented GDI”. Below is the definition of augmented GDI for each sector.
- NFC sector: Augmented GDI is equal to GDI plus interest payments including FISIM(item D41g) [and same comment as above] and distributed income (dividends). FISIM is an element of intermediate consumption, which is deducted from the various types of income NFCs earn and has to be added to GDI to reflect the income available to NFCs.
- Total PNFS: The sum of the two income measures from the household and NFC sectors comprises the income of the total PNFS.5
- Average remaining maturity: For the household and NFC sectors, 18 and 13 years are assumed, respectively, which is fixed across time and countries. The maturity of the total PNFS is the average of the remaining maturities of the two subsectors, weighted by the stock of debt of each sector. Data adjustments [looks like we're missing something here...]
- Smoothing of some components of income: Four-quarter moving averages are applied to GDI and dividends paid.
- Interpolation and extrapolation: Some data in a number of countries are originally compiled at an annual frequency. In these cases, quarterly series are derived by interpolating the annual data with the Chow-Lin method (Chow and Lin (1971)),6 using nominal GDP for GDI as well as dividends, and average bank lending rates for the average interest rate on the stock of debt.
- To derive the most recent quarters following the last available annual data point, series are extrapolated using the growth rate of nominal GDP for both GDI and dividends, and the change in average bank lending rates for the average interest rate on the stock of debt.
- Series used when national accounts sectoral data are not available For data from 15 countries, there is only information to construct the DSR for the total PNFS. For many of these countries, information on sectors from the national accounts is limited, and data are not available to derive average interest rates and income as detailed above. In these cases, the series are proxied as follows:
The BIS effective exchange rate (EER) indices cover 61 economies, including individual euro area countries and, separately, the euro area as an entity. The most recent weights are based on trade in the 2011-13 period, with 2010 as the indices' base year.
Nominal EERs are calculated as geometric weighted averages of bilateral exchange rates. Real EERs are the same weighted averages of bilateral exchange rates adjusted by relative consumer prices. The weighting pattern is time-varying (see broad and narrowweights). The EER indices are available as monthly averages. An increase in the index indicates an appreciation.
Measuring international price and cost competitiveness [is this just a straight copy of someone elses paper?]
BIS Economic Papers No 39
Few economic indicators attract as much controversy as those of international competitiveness. One reason for this is the imprecision of the concept: in common parlance "competitiveness" can be used to cover almost any aspect of market performance. Product quality, the ability to innovate, the capacity to adjust rapidly to customers' needs and the absence of restrictive practices in the labour market are frequently evoked in discussions of competitiveness. This paper, however, will focus on a much narrower meaning, that based on relative prices or costs. It might be stressed at the outset that the link between this narrow concept and economic performance more generally is not unambiguous. The ambiguity arises from the fact that its international relative price or cost position can be both cause and result of a country's economic performance. On the one hand, it is clear that if relative costs are too high, the ability to compete internationally can be compromised. On the other hand, successful economic performance can lead to an exchange rate appreciation, and thus to higher relative costs or prices. For instance, if enterprises in a country become more successful in the non-price dimensions of performance - if they are innovative, flexible, produce high-quality goods and so on - then the real exchange rate would be expected to strengthen. Price and wage competitiveness - the narrow concept - would thus appear to "worsen". But such "deterioration" would of course be a symptom of success, not of failure. A second reason for controversy is that even the narrow concept of competitiveness can be given many distinct statistical forms, using prices, wages and other costs. There is no one ideal measure, and the large number of different measures that are in common use often diverge appreciably. One purpose of the present study is to survey these measures and to examine key trends of the major currencies in the light of the various indicators, attempting where possible to account for the divergences observed.
Two general issues are raised in the construction of indices of real effective exchange rates. The first is the choice of currencies to be included in the calculation of relative indices. This is reviewed in the first part of Section I of this paper. One important aspect is the increased number of countries that "count" in international trade. In particular, the rise of the Asian NlEs and other dynamic economies in South-East Asia has increased the number of currencies that may need to be included in effective exchange rate calculations. The inclusion of such currencies implies significant modifications to the movement in real effective exchange rates. This is considered in more detail below.
The second general issue concerns the choice of price or cost measure used. As for industrial countries, there are basically three sorts of measures in common use: those based on unit labour costs in manufacturing industry; those based on consumer prices (or some other broadly-based price measure); and those based on export unit values. These classic measures are reviewed in the second part of Section I; recent revisions to the indices calculated by the BIS are also discussed. The final part of Section I uses these measures to review actual developments in some major currencies over the past twenty years, paying particular attention to how and why different indicators can "tell a different story". The broad developments in competitiveness between Europe, the United States. Japan and the dynamic Asian economies provide a central focus. The successive exchange rate crises in Europe from September 1992 have of course given measures of intra-European competitiveness added interest, and what the different measures tell about this is also considered. The issues raised in measuring the competitiveness of commodity exporters (mainly in the developing world) are rather special and these are the subject of Section Ill.
However, few observers any longer rely solely on the classic real effective exchange rate indices. An apparently relatively simple extension is to take the ratio of one measure to another to paint a wider picture of a country's competitive position. Most common among these are ratios of price to cost indices as a proxy for profitability; but other ratios have also been used. These are considered in Section II of this paper.
A more radical departure from the classic real effective exchange rate is the greater emphasis placed on levels of competitiveness. The standard measures of relative costs and prices are limited by dependence on a quite arbitrary choice of base year. They do not allow statements such as "unit labour costs were X% higher in country X than in country Y in 1990" to be made; only statements about relative changes are possible. Yet popular assertions about actual differences in labour costs are legion. Translating this perception into operational measures has, however, always faced formidable difficulties - notably as regards the valuation of output at a consistent set of prices. But the considerable research effort made in recent years to develop carefully constructed measures of relative productivity and to compute detailed estimates of purchasing power parities has begun to tip the scales in favour of developing level-based measures, at least for a number of industrial countries. Section IV reviews some recent work in this area.
A final element of recent efforts to broaden the scope of competitiveness measures is the greater attention paid to non-manufacturing: as the relative importance of manufacturing industry declines, the need for such a re-emphasis is likely to grow. Not only are non-manufacturing outputs increasingly traded, but service inputs are frequently key components of traded goods even if these inputs are themselves not directly traded. The advent of detailed sectoral national accounts in most industrial countries has greatly widened the range of measures that can be construcred. The penultimate section of this paper uses national account statistics to take an empirical look at the distinction between tradable and non-tradable goods production, examining in particular relative productivity and profitability. This analysis is, of course. highly preliminary; nevertheless, it does serve to warn against placing undue reliance on any one measure, and also uncovers other important aspects or symptoms of competitiveness.
Looking carefully at all the different measures of competitiveness cannot but instil a good deal of reticence about basing strong conclusions on any one measure. Indeed, this is the single most important point underlined in the concluding section. Nevertheless, there are a number of conclusions that would seem to be borne out by several measures. The first concerns the competitiveness of the main areas. South-East Asia remains highly competitive. The United States has become more competitive in recent years. The Japanese situation was always more ambiguous because of marked differences between sectors, with the country appearing extremely competitive in certain goods - notably in the electronics area - and over-priced in others. At any event, the recent sharp appreciation of the yen brought about a marked loss in the country's earlier competitiveness. Europe was much worse placed than the others in the early 1990s, with many indicators pointing to a serious competitiveness problem. Exchange rate changes in recent months have gone some way towards alleviating this. The second conclusion is that a number of European countries had become relatively uncompetitive even within Europe: here too the exchange rate adjustments since September 1992 have done much to correct divergences in intra-European competitiveness.