Peer Group Analysis and Descriptive Statistics
| Pages | 161-170 |
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15.1 Both users and compilers of FSIs have recognized the need for peer group analysis and dispersion analysis. This chapter sets out options and ideas in these areas for use by compilers and analysts.
15.2 Sector balance sheets and income and expense data can disguise important information. For example, the sector-wide capital-to-asset ratio for deposit takers is essentially the average capital-to-asset ratio for the system (derived by the summation of all institutions' capital and its division by all institutions' assets) and, if normally distributed, would convey information about the median capital asset ratio as well as the most frequently observed capital asset ratio (the mode). However, the ratio does not indicate whether the individual institution's capital ratios are clustered in a narrow range around the average value or are spread over a wide range. Moreover, if the data for one highly capitalized deposit taker offset the data for several undercapitalized deposit takers, the aggregate ratio may appear robust, masking significant vulnerabilities from weak deposit takers whose failure could lead to contagion throughout the system.
15.3 A wide variety of meaningful peer groups can be created for comparison purposes, and descriptive statistics can be compiled to examine the dispersion and concentration of the institutions within the peer group or sector. This chapter describes some types of peer groups and discusses measures of concentration and of dispersion. Issues in developing these data are set out, such as weighting the contribution of the individual institutions, and some guidance in analyzing the data is provided.
15.4 A peer group is a set of individual institutions that are grouped on the basis of analytically relevant criteria. Peer groups can be used to compare FSI ratios: (1) individual deposit takers with similar institutions, (2) peer groups with other domestic peer groups, and (3) peer groups across countries. Peer group analysis can be undertaken using either cross- border or domestic consolidated data.
15.5 Depending on analytical needs and data availability, different types of peer groups may be constructed. Some might be constructed on an ad hoc basis. For example, ad hoc peer groups might cover recent entrants into the market, deposit takers with low capital ratios or low return on equity, deposit takers with high levels of nonperforming loans, and deposit takers that concentrate on lending to particular types of borrowers. Other peer groups might be created to facilitate ongoing analysis, such as groups of similarly sized deposit takers (based on their total assets).
15.6 By way of example, peer group data could be constructed for groupings of deposit takers based on the following major characteristics:
- Size of assets or revenues. The size of institutions might affect market competitiveness or market power. Moreover, the condition of the peer group composed of the largest deposit takers-such as the three to five largest deposit takers, based on total assets-is often important for understanding overall stability, because these deposit takers are the most likely to be systemically important and may exercise the greatest market power. Such a group has a small enough number of institutions that it can be constructed for most economies and can facilitate international comparison.
- Line of business. For example, regular retail banks might be distinguished from mortgage banks.
- Type of ownership. For example, publicly controlled deposit takers might be distinguished from privately controlled deposit takers.
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- Offshore or onshore. Deposit takers that are offshore can have transactions with only nonresidents and thus might be an important group to identify.
- Region of the country.
15.7 From the above list, the Guide encourages, at a minimum, the compilation of core FSIs for peer groups based on the relative size of assets. The Guide discourages the dissemination of peer group data that might reveal information on specific institutions, unless the country normally requires deposit takers to publicly disclose such information.
15.8 A key consideration in constructing peer group data is determining how such data are to be compiled. Regardless of the approach taken, constructing peer group data depends critically on the cost of compiling these data and on the ease with which they can be reorganized to serve various analytical needs. To allow construction of peer group data, the Guide encourages compilers to maintain individual institution data in a database that allows quick, flexible, and low-cost data aggregation. Under such an approach, peer group data can potentially be compiled using the same principles as sector-level data. For example, intragroup income and expense items and, depending on data availability, intragroup equity holdings could be eliminated in constructing peer group data.
15.9 In constructing the data, a decision needs to be made on whether the peer group should be treated as a subgroup of the total population (that is, the data are the peer group's contribution to the total for the population) or as a stand-alone grouping (that is, the group is self-contained, with all institutions outside the group treated as external to the group). There are advantages in adopting either approach, but data compilation considerations may be decisive, particularly if ad hoc groups are created.
15.10 The stand-alone approach is likely to require less additional data than the subgroup approach. For instance, under either approach, intra-peer-group interest income and expense will be eliminated in the net interest income line. However, under the subgroup approach, the elimination of interest income and expense vis-à-vis institutions within the sector but outside the peer group requires the collection of additional data.
15.11 However, even the stand-alone approach will require additional data if the peer group data are to be compiled in line with the sector-level approach. Some of this information might be obtainable from the data reported in Tables 11.2 and 11.4, depending on the consolidation approach adopted. For instance, intra-peer-group holdings of equity could be eliminated to the extent that individual deposit takers identify their holdings of equity issued by other deposit takers. As a practical matter, peer group data might be compiled on an approximate best practice basis; this would still allow the identification of trends but- depending on the degree of approximation and the scope of analysis-could potentially mask relevant interrelationships. In such circumstances, it is encouraged that any relevant potential limitations of the data be identified for the user, such as capital and reserves' not being fully adjusted for intra-peer-group holdings.
15.12 In many ways, concentration and dispersion analysis uses specific techniques depending on the nature of the issue under review, the types of data available and the ease of using them, and any limitations on revealing information on specific institutions. Flexibility in selecting techniques should be maintained. This section provides a menu of techniques that are useful in a variety of situations. However, in disseminating information to the public, some types of descriptive statistics may prove particularly useful, because they can describe concentration and dispersion without revealing information on individual institutions.
15.13 The Herfindahl Index, H, is the sum of squares of the market shares of all firms in a sector, that is,
(Fórmula in Pdf File)
By using market shares, this index stresses the importance of the larger firms in the population. Higher values indicate greater concentration. In a situation with no concentration, where each of the 100 firms has an identical 1 percent share of the market, the value of H = 100. In contrast, with perfect concentration, where one firm has a 100 percent market share, H = 10,000; that is, the contribution of the monopoly firm is 100 x 100 = 10,000. A rule of thumb sometimes used is that H below 1,000 indicates relatively limited concentration, and H above 1,800 points to significant concentration. Table 15.1 illustrates how to compute H for a country consisting of 11 deposit takers.Page 163
Table 15.1. Example of Computing the Herfindahl Index
| Deposit Taker | Assets | Percentage Share | Share2 |
| 1 | 300 | 30 | 900.0 |
| 2 | 200 | 20 | 400.0 |
| 3 | 130 | 13 | 169.0 |
| 4 | 90 | 9 | 81.0 |
| 5 | 80 | 8 | 64.0 |
| 6 | 50 | 5 | 25.0 |
| 7 | 50 | 5 | 25.0 |
| 8 | 40 | 4 | 16.0 |
| 9 | 20 | 2 | 4.0 |
| 10 | 20 | 2 | 4.0 |
| 11 | 20 | 2 | 4.0 |
| Total | 1,000 | 100 | 1,692.0 |
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