Financial Sector Assessment

Benchmarking and Decomposing Interest Rate Spreads and Margins

The analysis of interest spreads and margins can assist assessors in benchmarking a country’s banking system and in identifying and quantifying major deficiencies and impediments to depth, breadth, and efficiency of financial intermediation. As an illustra­tion of how spreads and margins may be analyzed, even in an environment with limited data, this appendix uses Kenya to describe how interest spreads may be decomposed into contributory factors and how interest margins may be benchmarked against international comparators.

Although Kenya has high interest rate spreads and margins that are similar to other countries in the region, it has substantially higher spreads and margins than OECD coun­tries (see table E.1). The term spread is used to mean the difference between lending and deposit rates, whereas net interest margin refers to the net interest actually received and expressed as a percentage of interest-bearing assets.

The most comprehensive international source for interest rates, and the one from which the data in table E.1 are drawn, is International Financial Statistics, which gener­ally publishes just one representative deposit rate and one loan rate. For any given bank, the spread conceals a wide variation in both deposits and lending rates charged by any given bank, depending on the marginal operating costs (and the provision for likely loan loss) and its market power vis-a-vis the customer. The marginal loan will be priced to ensure that the bank’s capital at risk is sufficiently remunerated, given the marginal cost of mobilized funds, including any taxes or reserve requirements that apply to the loan or to the mobilized funds. For a country’s banking system as a whole, the use of a single rep­resentative rate blurs much of the detail. Nevertheless, it helps throw some light on the relative magnitude of different contributors to the cost.

For Kenya, data on the average interest rate spread were calculated from individual bank returns and were averaged over different classes of banks as shown in table E.2.1 Then again for each bank, administrative costs and the additions to loan loss provisions were expressed as a percentage of loans. Finally, the opportunity cost of reserve require­ments was calculated.2 With before-tax profits as a residual (and a profits tax rate of 30 percent), the decomposition of table E.2.was arrived at. It points to overhead costs and the profit margin as the most important component of the interest rate spread in Kenya.

State-owned banks have the highest spread, followed by foreign-owned banks and pri­vately owned Kenyan banks. High operating costs may suggest inefficiency or may imply the use of more costly staff personnel and systems. Despite their higher operating costs, the foreign banks, benefiting from reputational advantages that allow them to mobilize deposits at lower interest rates, enjoy higher profit margins and, therefore, higher spreads. Overhead costs and loan loss provisions constitute two-thirds of government-owned banks’ spread, whereas overhead costs and the profit margin constitute two-thirds of the spread of privately owned banks. Although the profit margin seems relatively high, note that this is the profit on lending only, the most risky line of business for banks. The overall

profitability for banks is significantly lower, as indicated by the return on assets, which is of a level comparable to other banking markets.

An analysis of the overhead costs shows that they are driven by wage costs, which con­stitute 50 percent of total overhead costs. Other factors relating to the costs of financial service provision in the local market include fraud, security costs, the inefficient payment system, and a heavy regulatory burden, as illustrated by the high reporting requirements, the annual re-licensing process, and the licensing procedures for the opening and clos­ing of branches. Compared with banks in other sub-Saharan African countries and other emerging countries, Kenyan banks appear to be overstaffed, and their employees appear to be less productive (see table E.3). Kenyan banks have more than three times as many employees for a given amount of assets, loans, and deposits than other banks in emerg­ing countries, and the average Kenyan bank employee earns only half of the net interest revenue as the average employee in emerging markets.

However, there are large differences in productivity across different ownership groups of Kenyan banks (see table E.4). Employees in state-owned banks earn only half of the net interest revenue of employees in foreign-owned banks. State-owned banks have twice as many employees relative to their assets, loans, and deposits as foreign-owned banks. The higher productivity of foreign-owned banks compensates for the higher wage costs of those banks when compared with domestic banks. Private domestic banks are less productive and more overstaffed than foreign-owned banks but are more productive and less overstaffed than state-owned banks. This disparity across ownership groups indicates significant potential gains from increased competition and the resulting productivity improvements.

Instead of our looking at bank-level cost patterns, it is equally interesting to stand back and to examine what national structural features (and external characteristics of differ­ent banks, such as ownership) are associated with higher interest spreads and margins. A recent cross-country study of the determinants of net interest margins and overhead costs for banks in 72 countries (Demirgug-Kunt, Laeven, and Levine, 2004) provided the material for such an analysis. The authors provided a regression equation that explains a reasonable proportion of the variation in net interest margins in terms of national and bank-level characteristics. Inserting local values for the explanatory variables allows a predicted value for any given country and, indeed, any given bank.

The difference between average Kenyan interest margins and those in the rest of the world for the period studied by Demirgug-Kunt, Laeven, and Levine3 was 3.4 percent (7.0 percent compared with 3.6 percent). About two-thirds of the difference can be explained by differences in the values of the explanatory variables in Kenya compared with the rest of the world. In particular, as shown in table E.5, Kenya’s relatively weak protection of property rights and the small size of its banks are major contributors to the difference.4 Those two factors also provide the most important explanation for the higher overhead costs in Kenya—accounting for 0.8 percentage points of the costs. The relative smaller size—thus the lack of scale economies—of Kenyan banks explains 0.9 percentage points of the higher net interest margin and 0.7 percentage points of the higher overhead costs.

The lack of a sound legal and institutional environment and the small size of Kenyan banks thus seem to be two of the most important factors explaining why net interest margins and overhead costs are almost twice as high in Kenya as in the rest of the world. Overall, this analysis of national structural features confirms the conclusions that are based on cost and profit decomposition. In particular, the deficient legal and institutional framework contributes to the need for high loan loss provisions. The benchmarking exer­cise clearly suggests a desirable direction of policy.

Notes

1. The calculations and discussion follow Beck and Fuchs (2004).

2. For large loans to risk-free borrowers funded on the wholesale deposit market quasi­taxes, such as unremunerated reserve requirements, may contribute most of the spread. Calculating the break-even spread on such loans is a good way of inferring the mar­ginal contribution of reserve requirements to intermediation spreads.

3. Although Demirgug-Kunt, Laeven, and Levine (2004) use data over 1995-1999 and have a limited sample of banks for each country, the data for Kenya is based on 38 Kenyan banks representing 98 percent of the banking system and is for the year

2002.

4. These calculations were obtained by multiplying the coefficient estimates from two regressions in that paper (Table 8, column 3 and Table 11, column 3) with the differ­ence between values of the respective variables for Kenya and the mean value for all countries in the study.

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