Peru Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
Publication Date:
22 Feb 2013
eISBN:
9781475582604
Language:
English
Keywords:
ISCR; CR; Peru; commodity; export; interest rate regression; bank grouping; gross income level; banks RoA; market share; Commercial banks; Foreign banks; Financial statements; Credit; Personal income; return on equity
Download PDF (869.5 KB)

The purpose of the study is to examine Peru’s effective interest spread through accounting decompositions, financial ratio analysis, and spread regressions. The government’s financial restructuring programs accelerated the banking sector consolidation process. Robustness of Peru’s credit system and interest rate decomposition has also been viewed. Three key financial ratios—return on equity (RoE), return on assets (RoA), and net interest margin (NIM)—focused by financial statements, have also been studied. Finally, the framework of Espino and Carrera used for the estimation of interest rate spreads has also been discussed.

Abstract

The purpose of the study is to examine Peru’s effective interest spread through accounting decompositions, financial ratio analysis, and spread regressions. The government’s financial restructuring programs accelerated the banking sector consolidation process. Robustness of Peru’s credit system and interest rate decomposition has also been viewed. Three key financial ratios—return on equity (RoE), return on assets (RoA), and net interest margin (NIM)—focused by financial statements, have also been studied. Finally, the framework of Espino and Carrera used for the estimation of interest rate spreads has also been discussed.

Banking Sector Spreads1

1. Over the last decade, effective interest rate spreads for the Peruvian banking system have remained relatively stable and elevated. During this period, the system’s profitability improved markedly—in line with the country’s strong economic performance—and as banks (both foreign and domestic) entered and exited the industry.

2. The purpose of this note is to examine Peru’s effective interest spreads through accounting decompositions, financial ratio analysis and spread regressions. Banks play an important intermediation role by transforming customer deposits into loans. While interest rate spreads reflect this risk taking role, their level and evolution also contains information regarding regulation and operating costs as well as management decisions—all of which can be evaluated by accounting decompositions. Viewed from a slightly different angle, banks make profits through the use of leverage and an efficient deployment of their assets and operations. In this context, an examination of financial ratios focused on profit creation provides a complementary view to interest rate decompositions, and allows a deeper understanding of spreads. Finally, interest rate regressions offer a direct way to estimate the influence of risk, costs, bank concentration and market power factors on spreads.

A. Background and Stylized Facts

3. In the late 1990s, Peru’s economy and banking system were affected by several large shocks. The Asian (1997) and Russian (1998) crises caused a sudden stop and a reversal of capital flows, which weakened the currency and private sector balance sheets—deteriorating credit portfolios. Lower export prices and El Niño effects also affected production and reduced income, weakening domestic demand, and further worsening the quality of credit portfolios.

4. These factors coupled with some political instability resulted in a banking crisis. With balance sheets severely destabilized, six of the 26 banks left the system in 1999. In 2000, 2 banks failed, and 2 more were taken over by banking supervision (SBS). In November 2000, the Peruvian government launched a financial system restructuring program (“Programa de Consolidación del Sistema Financiero”). The program extended a $200 million credit line to the deposit insurance system (FDS), financed by issuing Treasury bonds, to be used in the restructuring of financial institutions. The program centered on subsidizing the purchase of institutions estimated to have negative value by stronger institutions. In addition, the government launched two more programs to refinance agricultural and commercial loans at a cost of $500 million.

5. The government’s financial sector restructuring program successfully accelerated the banking sector consolidation process. Further exits and mergers occurred during 2001–06 such that there were only 11 banks in operation by end-2006. During 2007–08, foreign banks such as Santander, Deutsche Bank and Mexico’s Azteca bank entered the Peruvian market. Moreover, two smaller financial cooperatives were transformed into retail banks specializing in consumer credit linked to retail department stores (i.e., Banco Falabella and Banco Ripley). In 2009, Scotia acquired Banco del Trabajo (a micro finance specialist). This operation left the banking system with 15 institutions, a configuration that has been maintained until end-2011.

6. The restructuring process has resulted in a more concentrated system—which has eased somewhat in recent years (Figure 1). Concentration ratios based upon the top 3 (4) banks in terms of asset size increased from 66 (74) percent in 2000 to 78 (87) percent in 2007, before falling back down to 73 (83) percent today. Herfindahl indices are between 0.16 and 0.18 (moderate concentration) from 2000–03, and remain above 0.20 (signaling high concentration), thereafter. Foreign ownership or control of total banking system assets has remained relatively stable at around 50 percent since 2002.

Fig 1.
Fig 1.

Peru: Banking Sector Concentration and Ownership

Citation: IMF Staff Country Reports 2013, 046; 10.5089/9781475582604.002.A003

Sources: SBS; and Fund staff estimates.

7. The empirical evidence on the relationship between banking system concentration and competition in Peru is inconclusive. Rojas (2000) found that banking concentration, credit risk and country risk were the main factors behind Peru’s high banking spreads in the 1990s. Using data from 1995–2004, Espino and Carrera (2005) also indicated that banking interest rate spreads in Peru were positively impacted by a lack of bank competition. However, Morón, et.al (2010), found evidence of intense banking competition by product line (e.g., mortgages, leasing,) in Peruvian banks—suggesting that while overall system concentration may be high, it has not resulted in collusive anticompetitive practices.

8. Today, Peru’s banking system stacks up very favorably in international comparisons. Figure 2 presents a number of financial soundness indicators using 2011 data for a variety of South American countries. Like many of its peers, the Peruvian banking system is well funded through internal sources, with deposit to loan ratios near 100 percent. Thus, although some Peruvian banks do tap foreign financing sources, there is no over-reliance on external financing of basic loan operations. Raw capital ratios in Peru are 10 percent, resulting in a leverage ratio similar to region averages. At the same time, regulatory tier 1 capital risk weighted capital ratios in Peru (not shown here) are near 15 percent—which are at the regional average.

Figure 2.
Figure 2.

Banking Sector Indicators, 2011

Citation: IMF Staff Country Reports 2013, 046; 10.5089/9781475582604.002.A003

Sources: Financial Soundness Indicators; IFS; and WEO.

9. Profitability ratios are solid, with low levels of non-performing loans which are amply provisioned. Financial sector deepening indicators such as private banking credit to GDP and broad money stock to GDP are well below middle income averages. However, this also implies that the banking system has sufficient room to expand.2 At first blush, efficiency indicators across countries suggest personal expenses in Peruvian banks may take up an inordinately large portion of non-interest costs. However, these expenses are still rather small in comparison to banks’ gross income levels—suggesting that labor costs are broadly in line with peer groups. Finally, dollarization levels of both assets and liabilities within the Peruvian system remain elevated.

10. The overall quality of the credit framework in Peru is robust. Table 1 presents data from the World Bank’s Doing Business survey on the ease of getting credit in a number of South American countries. Peru’s credit system was ranked 23rd out of 185 countries, above most of its’ regional comparators. This index is based upon 2 sub-component indicators related to the protection of legal rights and depth of available credit information. The legal framework, whereby the rights of borrowers and lenders with respect to secured transactions are protected, was rated a 7 out of 10—again above average. While private bureau credit coverage in Peru is somewhat below norms, the overall depth of available information (coverage, scope and accessibility) is viewed very favorably.

Table 1.

Quality of Legal Framework and Credit Information, 2011

article image
Source: World Bank, Doing Business database.

Getting credit, overall ranking. Higher value reflects lower rank.

Higher scores measures degree to which collateral and bankruptcy laws protects rights of borrowers and lenders, and thus facilitate lending.

Higher scores indicate greater accessibility of credit information.

B. Interest Rate Decompositions

11. In general, banks require a combination of effective interest and non-interest rate margins to cover costs and to earn a profit. With non-interest rate margins determined by price setting behavior on bank services (fees and commissions), the various factors that determine effective interest rate spreads can be assessed through simple accounting decompositions.3 Using balance sheet and income statement data, the effective interest rate spread can be decomposed into the following components4

(il−id)=[rr+p+oc+prov+tax+d]−nnii+e(1)

where:

article image

The identity indicates that effective interest rate spreads (il - id) will increase as bank costs—from reserve requirements, operations, provisions, taxes and deposit insurance—and profits increase, and fall with higher amounts of non-interest income.

12. System wide accounting interest rate spread decompositions from 2000–11 are provided in Table 2, and lead to the following conclusions:

Table 2.

Interest Rate Spread Decompositions: Total Banking System

(in percent)

article image
Source: SBS and fund staff estimates.

  • Operational costs and profits are the two main factors behind effective interest rate margins.

  • Effective interest margins increased as the system consolidated and the economy expanded.

    After declining during the financial crisis, margins have returned to about 7½ percent.

  • After 2000, non-interest income has remained around 4½ percent, with a slight decline within the last two years. This suggests little pressure to reduce the charges on non-lending banking services.

  • The relative stability of total cost factors—at around 10–13 percent—hides the fact that operational costs have gradually declined from above 7 percent in 2000 to around 4½ in 2011.

  • Profitability, which has risen strongly from about ½ percent in 2000–01 to 4 percent in 2011, and tax costs have partially offset operational cost improvements.

  • Moreover, the provisions and reserve requirement costs—reflecting monetary policy decisions—also increased.

13. Interest rate decompositions across various bank sub-groupings reveal substantial differences. Figures 3 and 4 graphically presents the interest rate decompositions for the total banking system, as well as for 2 separate comparative groupings: (i) the largest 3 banks (by asset size) versus the other banks in the system, and (ii) foreign versus domestic banks. The results using a 4 largest versus smaller bank decomposition are very similar. The main outcome is that margins and costs of the smaller bank grouping (other) are markedly different from the 3 largest banks:

Figure 3.
Figure 3.

Peru: Interest Rate Spread Decompositions, 2000-11

Citation: IMF Staff Country Reports 2013, 046; 10.5089/9781475582604.002.A003

Sources: SBS data; and Fund staff estimates.
Figure 4.
Figure 4.

Peru: Interest Spread Decompositions, 2000-11

Citation: IMF Staff Country Reports 2013, 046; 10.5089/9781475582604.002.A003

Sources: SBS data; and Fund staff estimates.

  • Non-interest margins for the other grouping of smaller banks increased from under 10 percent in 2000 to around 16 percent at the peak of the financial cycle in 2008, before returning back to 10 percent. Non-interest margins have been more stable for the top 3 banks, and similar to the foreign / domestic breakdown at around 3–5 percent. Still, domestic banks tended to obtain non-interest margins about 1 percent higher than those found in foreign banks.

  • Regarding effective interest spreads, smaller banks generally tended to increase their margins, reaching a spread of about 12–13 percent. On the other hand, the larger top 3 banks spreads fell to 4.5 percent in 2008–09 before returning back to 6 percent by end-2011. Outside of the 2008–09 periods, both foreign and domestic banks achieved margins of about 7.5 percent.

  • Looking at costs, the smaller banks markedly higher expenses (of 30–35 percent) were driven by higher operational costs (10–14 percent), reserve requirements (3–4 percent, and provisions (1–3 percent), with the remainder taken up by sharp rise in profits (10–14 percent). It would appear that any operational cost savings these smaller banks achieved since 2007 have been taken up by higher provisions, and offset by lower non-interest margins. The larger 3 banks reported much lower operational costs and provisions than smaller banks, while the breakdown and evolution between foreign and domestic banks was very similar. Profits for larger, as well as foreign and domestic banks have risen and now stand around 4 percent.

C. Financial Statement Analysis

14. Financial statement analysis focuses on three key financial ratios.5 These ratios are the return on equity (RoE), return on assets (RoA), and net interest margin (NIM), each of which can be further decomposed into two separate ratios. To assess profitability of the system, it would be important to analyze these ratios and their subcomponents to provide insights into banking sector performance and management over time.

  • RoE is the ratio between after tax earnings (EAT) and book value of equity (BE). It presents the earnings per unit of invested capital, making it a universally comparable indicator for measuring the profitability of investment. RoE consists of three components: (i) tax policy (TP = EAT / EBT); (ii) financial leverage (LEV = TA / BE); and (iii) return on assets (RoA = EBT / TA). RoA changes are often the main cause of changes in bank’s performance, whereas tax policy and leverage effects should be relatively stable.

    RoE=TP×LEV×RoA,(2)

  • A bank’s RoA can be further disaggregated into three components. This would include: (i) burden (B = NNIR / TA); (ii) earning assets ratio (EAR = EA/TA); and (iii) net interest margin (NIM = (IR-IE) / EA). Burden measures the success in maintaining control over operating costs. It is normal for the bank’s burden to have a negative value, since non-interest revenues (NNIR, revenues from fees and commissions) are not able to cover all non-income related costs. Earning asset ratios usually have a minor role in determining changes in RoA, but are a good indicator for analyzing the strategic focus of individual banks. The net interest margin (NIM) reveals the net income from investing through borrowed funds.

    RoA=B+EAR×NIM,(3)

  • Finally, the net interest margin (NIM) can also be decomposed into three variables: (i) return on earning assets (REA = IR / EA); (ii) cost of liabilities (COL = IE / L); and (iii) liabilities to earning assets (LEA = L / EA). The return on earning assets directly connects earning assets and interest revenue, and is a measure of the average rate of lent funds. COL is an indicator of the average price of borrowed capital, while LEA measures the intensity of the bank’s investment activities.

    NIM=REA−COL×LEA.(4)

15. Table 3 presents the financial statement analysis for the whole banking system. RoE has steadily increased, peaking at 27 percent in 2008 before falling back down to 22 percent. The main driver of banking sector profitability has been a greater generation of return on assets. RoA’s have gone from about ½ percent to above 3 percent as the burden of administrative expenses fell dramatically and as the percentage of assets deployed increased. However, the peak in RoE was strongly influenced by a spike in leverage at the peak of the economic expansion in 2007–08. Tax policy effects were actually a drag on profits until 2005, with a notable increase in after tax earnings in 2011. Finally, it appears that net interest margins have been relatively stable—with offsetting movements in cost of funds and return on earning assets—with a minor improvement in investment intensity.

Table 3.

Peru: Financial Statement Analysis of the Banking System

(in percent)

article image
Source: SBS and staff estimates.

16. The evolution of financial ratios is markedly different between smaller and larger banks (Figures 5 and 6). The smaller bank grouping generated much larger net interest margins, but due to a worsening of their administrative burden, they experienced a smaller improvement in their RoAs.6 Moreover, due to a lower use of leverage and an inferior tax policy effect, the smaller banks reported a smaller increase in bank profitability or RoE. Foreign banks also tended to do a better job than domestic banks in generating returns on assets, but have had not reduce their administrative burden until recently. Thus until 2008 the RoA for foreign banks was better than domestic banks. Since 2008, domestic banks RoA have worsened in line with a worsening of their administrative burdens. Still, overall profitability or RoEs are much better for domestic banks due to their greater use of leverage.

Figure 5.
Figure 5.

Peru: Financial Statement Analysis, 2000-11

Citation: IMF Staff Country Reports 2013, 046; 10.5089/9781475582604.002.A003

Sources: SBS data; and Fund staff estimates.
Figure 6.
Figure 6.

Peru: Financial Statement Analysis, 2000-11

Citation: IMF Staff Country Reports 2013, 046; 10.5089/9781475582604.002.A003

Sources: SBS data; and Fund staff estimates.

D. Interest Rate Regressions

17. Estimation of the interest rate spread panel regressions broadly follows the framework of Espino and Carrera (2005). Using monthly data (December 2001 to September 2012) provided by the SBS we estimated the following equation:7

Spreadit=α+β1NPLit+β2Liqit+β3MRit+β4Costit+β5Shareit+β6IC3t+β7Libort+β8ERt+β9Inft+εt(5)

The dependent variable is the effective interest rate spread (Spread), as defined previously. The main explanatory variables reflect credit and market risks, as well as operating and liquidity costs and all should positively impact interest margins. For example, as non-performing loans (NPL) increase, banks would have an incentive to increase margins in order to better reflect actual credit risks. Similarly, an increase in the liquidity asset ratio (Liq), calculated as liquid over total assets, represents a lost opportunity cost of undertaking financial intermediation and would increase margins. Market risk (MR) is defined as each bank’s disposable investment over their total assets, while (Cost) are administrative costs over total assets. Two other variables, market share (Share) and the bank concentration ratio (IC3), are also employed in separate regressions to test if banks market power or concentration in the system has an impact on margins. The general assumption is that both variables should increase margins. Additional macroeconomic conditioning variables: (i) the 3-month Libor rate; (ii) domestic inflation (Inf); and (iii) the nuevo sol-U.S. dollar exchange rate—are also included.

18. Table 4 presents the two panel regression estimates.

Table 4.

Interest Spread Regressions 1

article image
Source: SBS, and Fund staff estimates.

t-statistics are below the estimated coefficients.

Robust standard errors were estimated using cross sectional SUR (PCSE) methods.

  • The main result is that the key explanatory variables—credit and market risks, liquidity and operational costs—are all positive (in line with theory) and highly significant. These are the foremost factors behind effective interest rate spreads.

  • The coefficient estimate for bank concentration variable (IC3) is also positive, and indicates that for every 1 percentage point increase in system wide bank concentration, the effective interest rate margin rises by 0.074 percentage points. This estimate is more than double the 0.033 estimated coefficient reported by Espino and Carrera (2005) using quarterly data from 1995 to 2004.

  • The coefficient for an individual bank’s market share, however, is negative and implies that for every 1 percentage point increase in a bank’s market share, the effective interest rate margin falls by 0.16 percentage points. This result may seem counter intuitive, but stems from the fact that the larger banks which have gained market share have been the institutions that have experienced lower effective interest rate margins, while the smaller banks which have lost market shares have increased margins.

  • Finally, the results of the macro conditioning variables are somewhat surprising. The exchange rate has no impact on margins—which is unexpected if uncovered interest rate parity holds. Also, the Libor coefficient, a proxy for the cost of external funding is negative (although significant).

E. Conclusions

19. The Peruvian system compares very well with other Latin American banking systems. Banks are well capitalized, have ample access to deposit funding sources, and follow prudent provisioning policies. Most importantly, the credit delivery system is very healthy. Nevertheless, the consolidation process has resulted in a more concentrated banking system with slightly lower levels of foreign participation. An open question is whether an absence of spread reduction for the system as a whole reflects a lack of competition in the sector. The panel regressions indicated that increases in system wide concentration levels did raise spreads, but also implied that banks which gained market share ended up lowering effective spreads.

20. While effective interest rate spreads for the system as a whole have been stable, they have varied widely among certain bank groupings. Surprisingly, both interest and non-interest spreads as well as the return on interest earning assets on a system wide basis have remained relatively stable throughout Peru’s banking consolidation process and as the economy expanded rapidly. For the most part banks profits rapidly increased due to reductions in personnel and administrative expenses. Moreover, a greater deployment of interest earning assets and lower cost of funds environment also helped to bolster profits. Most striking, the experience and behavior of the smaller banks was significantly different from the larger top 3 banks. These smaller banks achieved much higher spreads, and while they also raised profits substantially, they have not been able to control costs to the same degree as larger banks. Finally, when split between domestic and foreign banks, the differences in outcomes appears much smaller.

References

  • Afanasieff, Tarsila S., Priscilla M. Villa Lhacer and Marcio I. Nakane, 2002, “The Determinants of Bank Interest Spreads in Brazil,” Central Bank of Brazil Working Paper Series No. 46, Banco Central do Brasil (August).

    • Search Google Scholar
    • Export Citation

  • Brock, Philip and Helmut Franken, 2003, “Measuring the Determinants of Average and Marginal Bank Interest Rate Spreads in Chile, 1994–2001,” Economia Chilena, Vol. 6(3), pp. 45–65.

    • Search Google Scholar
    • Export Citation

  • Carbo-Valverde, Santiago and Francisco Rodriguez-Fernandez, 2007, “The Determinants of Bank Margins in European Banking,” Journal of Banking and Finance, Vol. 31(7), pp. 2043–2063.

    • Search Google Scholar
    • Export Citation

  • Claeys, Sophie and Rudi Vander Vennet, 2008, “Determinants of Bank Interest Margins in Central and Eastern Europe. Convergence to the West?”, Economic Systems, Vo. 32(2), pp. 197–216.

    • Search Google Scholar
    • Export Citation

  • Espino, Freddy and Cesar Carrera, 2005, “Concentración bancaria y margen de las tasas de interés en Perú,” Estudios Económicos del Banco Central del Reserva del Perú.

    • Search Google Scholar
    • Export Citation

  • Estrada, Dario Esteban Gomez and Ines Orozco, 2006, “Determinants of Interest Rate Margins in Colombia,” Borradores de Economia No. 393, Banco de la Republica de Colombia.

    • Search Google Scholar
    • Export Citation

  • Fitch Ratings, 2012, “Efficiency in Andean Banks,” (www.fitchratings.com).

  • Gelos, Gaston R, 2006, “Banking Spreads in Latin America,” IMF Working Paper, 06/44, International Monetary Fund.

  • International Monetary Fund, 2004, “Bank Profitability and Competition,” Technical Note, Financial Sector Assessment Program Update, Republic of Kazakhstan, IMF Country Report No. 04/336.

    • Search Google Scholar
    • Export Citation

  • Morón, Eduardo, Johanna Tejada and Alonso Villacorta, 2010, “Competencia y Concentración en el Sistema Financiero en el Perú,” Documentos de Discusión, DD/10/03, Universidad del Pacifico.

    • Search Google Scholar
    • Export Citation

  • Rojas, Jorge, 2000, “Trends in Peruvian Bank Spreads,” In Why So High? Understanding Interest Rate Spreads in Latin America, edited by Philip Brock and Liliana Rojas-Suarez, pp. 211–251. Washington, D.C.: Inter-American Development Bank.

    • Search Google Scholar
    • Export Citation

  • SBS Annual Reports, Various Issues, 2000–2011.

  • World Bank, Doing Business Database, 2012.

1

Prepared by Kevin Ross (WHD) and Juan Alonso Peschiera (WHD-Lima Office).

2

Fitch (2012) has noted that demographic (number of branches per 100,000 people) and geographic (number of branches per Km2) penetration is comparatively low in Peru when compared to other countries in the region. With a relatively high rural population, an efficient expansion of banking services into these regions will require selecting the right distribution mechanisms.

3

See IMF (2004) for a full derivation. In the analysis, we use annual income and balance sheet data from the SBS.

4

It can be difficult to discern the true level of interest bearing assets and liabilities to use in the calculation of the effective lending and deposit interest rates. Thus the error term can be large.

5

Variables not defined in the text include: (i) EBT, earnings before tax; (ii) TA, total assets; (iii) IR, interest revenue; (iv) IE, interest expense; and (v) EA, earning assets.

6

The profit margins from the interest rate decompositions reflect earnings before taxes, and have been scaled by deposits. As noted above, financial ratio analysis allows a more precise view on generation of earnings and profits.

7

All estimations are done in EVIEWS using unbalanced panel data regression techniques assuming fixed effects. Robust estimators were calculated using White cross sectional SUR corrections to ensure robust standard errors.

Same Series

Other IMF Content

Other Publishers

Cover IMF Staff Country Reports
Peru: Selected Issues Paper
Author: International Monetary Fund. Western Hemisphere Dept.
Volume/Issue:
Volume 2013: Issue 046
Publisher:
International Monetary Fund
ISBN:
9781475582604
ISSN:
1934-7685
Pages:
51
DOI:
https://doi.org/10.5089/9781475582604.002

Peru: Banking Sector Concentration and Ownership

Banking Sector Indicators, 2011

Peru: Interest Rate Spread Decompositions, 2000-11

Peru: Interest Spread Decompositions, 2000-11

Peru: Financial Statement Analysis, 2000-11

Peru: Financial Statement Analysis, 2000-11