Paraguay: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
Publication Date:
21 Feb 2014
eISBN:
9781475513745
Language:
English
Keywords:
ISCR; CR; exchange rate; Paraguay; interest rate regression; Argentinean bank; bank law; financial system; banking sector; Personal income
Download PDF (4.8 MB)

This Selected Issues paper analyses Paraguay’s effective interest spreads using various methodologies. Operational costs, rising profits, and the need to cover credit and liquidity risks are the main factors behind Paraguay’s effective spreads. Improving data quality and mechanisms for sharing credit information could contribute to reduced spreads. The paper provides some background and stylized facts on the Paraguayan banking sector, and presents the methodology and results of interest rate decompositions, financial statement analysis, and interest rate regressions. It also analyzes the effects of financial dollarization and whether macro-prudential tools could deepen the de-dollarization process observed during the last decade.

Abstract

This Selected Issues paper analyses Paraguay’s effective interest spreads using various methodologies. Operational costs, rising profits, and the need to cover credit and liquidity risks are the main factors behind Paraguay’s effective spreads. Improving data quality and mechanisms for sharing credit information could contribute to reduced spreads. The paper provides some background and stylized facts on the Paraguayan banking sector, and presents the methodology and results of interest rate decompositions, financial statement analysis, and interest rate regressions. It also analyzes the effects of financial dollarization and whether macro-prudential tools could deepen the de-dollarization process observed during the last decade.

Effective Banking Sector Spreads in Paraguay1

This paper analyses Paraguay’s effective interest spreads using various methodologies. Operational costs, rising profits and the need to cover credit and liquidity risks are the main factors behind Paraguay’s effective spreads. Improving data quality and mechanisms for sharing credit information could contribute to reduced spreads. Although the empirical results suggested that banking concentration has not given rise to greater spreads, adding new banking entrants may lower margins by increasing competition within the sector.

A. Introduction

1. Interest rates spreads in Paraguay continue to be some of the highest in Latin America. A series of bank crises in the late 1990s and early 2000s weakened the financial system and pushed up spreads. In response, policymakers enacted a number of financial sector regulatory and supervisory reforms—assisted in part by the Fund’s FSAP consultation process. At the same time, the system’s profitability improved sharply as high commodity prices over last decade resulted in an expansion of bank deposits, which in turn led to an increase in credit growth. The prevailing view when these reforms were undertaken was that financial liberalization would improve the sector’s competitiveness and efficiency and result in lower interest rate margins. In this context, the authorities have been trying to understand the factors behind the persistence in interest rate spreads.

2. The purpose of this note is to examine Paraguay’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 contain 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.

3. The paper is organized as follows. Section B provides some background and stylized facts on the Paraguayan banking sector. Sections C through E present the methodology and results of interest rate decompositions, financial statement analysis, and interest rate regressions, respectively. Section E concludes.

B. Background and Stylized Facts

4. As in many Latin American countries, Paraguay suffered a period of banking crises in the late 1990s. Financial liberalization a decade earlier had resulted in a sharp increase in the number of bank institutions (thirty-four by end-1994), many of which were unable to take advantage of economies of scale. Overtime, poor bank practices, weak management and corporate governance, as well as lax supervision, resulted in a wave of bank failures in second half of the 1990s. To a large extent, the authorities’ use of extended central bank credits, forbearance, shifts of public deposits to weaker institutions, along with uncertain deposit insurance schemes served to extend and deepen the crisis. Throughout this period, depositors shifted funds from domestic banks under stress to foreign institutions, such that by end-2000 there were twenty-two banks in Paraguay, seventeen foreign and five domestic. Dollarization also increased sharply. The system suffered further disruptions in early part of the decade, as a deposit run linked to negative regional shocks caused the authorities to close the third largest bank (Banco Aleman, an Argentinean bank) in 2002. A second domestic bank was closed due to fraud, and six others left the system due to weak profitability—leaving just fourteen banks by end-2003.2 The system remained around that size over 2004–07 as the economy stabilized and strengthened as improvements in financial sector supervision were enacted (see Box 1). The number of banks expanded during 2007–12 as some local financing companies and cooperatives linked to agricultural production received bank charters, even as foreign players continued to leave the market—citing low profitability.3 Today the system has sixteen banks (seven foreign and nine domestic, including one state owned development bank).

5. Concentration ratios have increased over the last decade as banks entered and exited; however, the banking system still appears to be competitive (see below). Concentration ratios based upon the top three (four) banks in terms of asset size remained around 43 (52) percent in 2000–08, but have since risen to 50 (61) percent by end-2012. Herfindahl indices have risen only slightly from 0.09 to 0.11 (low concentration) and remain well below 0.20 (signaling high concentration). Foreign ownership or control of total banking system assets has steadily decline, from some 82 percent of the market in 2000-03 to 39 percent by end-2012.

A01ufig01

Bank Concentration and Ownership

Citation: IMF Staff Country Reports 2014, 061; 10.5089/9781475513745.002.A001

Sources: SIB and Fund staff estimates.

Financial Sector Regulatory Reforms Since 2003

The authorities took a number of decisive actions to strengthen the banking system after the 2003 crisis.

  • New bank intervention and resolution plans were enacted in 2003 which gave the Superintendent of Banks (SIB) more tools to deal with banks under distress. Intervention triggers and an allowable period to normalize operations (ninety days) were defined, while the resolution plan clearly pre-determined saleable and transferable assets, with remaining assets put under judicial liquidation. These plans also allowed the BCP to circumscribe and establish systemic risk and provided the authorities with much needed legal protections when they carried out their duties.

  • Various resolutions under these plans shifted the SIB’s supervisory framework toward a more risk based approach, with better definitions and categorization of credit risks, asset classes, provisions and accrual of interests. Provisions were increased and guidelines established on credit risk management, control and monitoring. In 2005 and 2010, the authorities also underwent FSAP assessments. In 2010 other BCP regulations were issued defining and categorizing financial and operational risks as well as a corporative governance framework for financial institutions.

  • The financial system was also bolstered by new legislation covering the BCP, the institution that supervises financial cooperatives (INCOOP), and public pension funds. A deposit guarantee fund was also established in 2003 and managed by the BCP, with funds from the government and deposit institutions. The fund sets coverage at seventy-five times the minimum monthly wage. Finally, in 2005 a second tier development bank was created (Agencia Financiera de Desarrollo).

6. It is interesting to note that the sharp decline in foreign owned institutions took place equally across both subsidiary and branch institutional structures (see below). Generally, it is assumed that higher levels of foreign ownership within banking systems provide an increase in transfers of technology and management know-how, leading to improvements in efficiency and productivity. Branch networks are particularly useful in this regard since they tend to centralize funding, asset allocation, and risk management functions. Subsidiary structures tend to rely more on local funding sources—which could help local market development and financial stability. Still, major foreign players (e.g., BBVA, Citibank) remain in the system, while some domestic banks have key foreign minority shareholders (e.g., Rabobank in Regional, IFC in Continental, and Oikocredit in Vision). In this context, an open question is how changes in Paraguay’s ownership structure over the last decade have affected productivity and efficiency within the system.

A01ufig02

Evolution of Bank Ownership Types

Citation: IMF Staff Country Reports 2014, 061; 10.5089/9781475513745.002.A001

Source: SIB Paraguay.

7. Current bank loan portfolios reflect Paraguay’s economic structure. Bank loan portfolios are concentrated in agriculture, livestock, wholesale-retail trade, and service sectors. Consumption, financial and industrial sector loans appear to be the focus of only a few particular banks. Surprisingly, some 43 percent of the loans of the state owned development bank, (seventh largest in the system) are consumer loans. For the system as a whole, 35 percent of the portfolio is in agriculture/livestock with a similar amount in trade/services; 15 percent in consumer loans, 10 percent in industry and 7 percent financial sector.

A01ufig03

Distribution of Bank Credits by Sector

Citation: IMF Staff Country Reports 2014, 061; 10.5089/9781475513745.002.A001

Source: SIB Paraguay.

8. Generally, Paraguayan banks appear to be well capitalized and funded. Figure 4 presents a number of financial soundness indicators using 2012 data for a variety of South American countries. Like many of its peers, the Paraguayan banking system is well funded through internal sources, with deposit to loan ratios near 100 percent. Thus, although some Paraguayan banks do tap foreign financing sources, there is no over-reliance on external financing of basic loan operations. Raw capital ratios in Paraguay are around 11 percent, resulting in a leverage ratio similar to region averages. At the same time, regulatory tier 1 capital risk weighted capital ratios in Paraguay (not shown here) are near 12.5 percent—which are at the regional average.

9. Profitability ratios are at regional highs. Non-performing loans are low and appear to be adequately 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. Given the size of the commodity based export market and past inflation history, Paraguay still retains a high level of dollarization. Paraguay’s efficiency indicators indicate that personal and other non-interest expenses relative to total gross income in Paraguayan banks are relatively low in comparison to other countries.

10. The overall quality of the credit framework in Paraguay is somewhat weak. 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. Paraguay’s credit system was ranked 80th out of 185 countries, below most South American comparators. Only Venezuela, Bolivia and Brazil were ranked lower. This index is based upon two 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 three out of ten—again below average. Both private and public bureau credit coverage in Paraguay are below norms, although the overall depth of available information (coverage, scope and accessibility) is viewed positively. Also, the preponderance of agricultural and livestock credits to relatively strong borrowers with solid real property collateral may reduce incentives toward improving credit scoring mechanisms. Finally, the authorities are in the process of modifying bank laws in order to strengthen risk based capital charges, prudential measures, and the definition of problematic loans—all of which should provide incentives to improve the credit framework.

Table 1.

Quality of Legal Framework and Credit Information, 2006 and 2012

article image
Sources: World Bank, Doing Business Database.

Getting credit, overall ranking. Higher value reflects lower rank. No overall ranking available for 2006.

Higher scores reflect better protection for both borrowers and lenders through collateral and bankruptcy laws.

Higher scores indicate greater access to credit information.

11. To date there has been little research on the Paraguay banking system. Previous work has focused on the role of foreign banks in the system and the factors behind relatively high interest margins. Using monthly panel data from January 2009 to May 2012, Biedermann, et.al (2012), found that high information costs in the financial intermediation process (proxied by non-performing loans) and elevated operating costs were the key factors behind expanded interest rate margins. They argue that the inability to acquire complete information on prospective clients increases adverse selection problems and credit risks, requiring a higher interest rate margin. They also noted the presence of substantial operating inefficiencies which have driven up costs and increase spreads. Espinola and Velazquez (2012) demonstrate that foreign banks have tended to focus on corporate sector loans in foreign currency, which inherently have smaller interest rate margins.

C. Interest Rate Decompositions

12. 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.1 Using balance sheet and income statement data, the effective interest rate spread can be decomposed into the following components2

(il-id)=[r⁢r+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.

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

Table 2.

Interest Rate Spread Decompositions: Total Banking System

(In percent)

article image
Sources: SIB and fund staff estimates.

  • Operational costs and profits tend to be the two main factors behind effective interest rate margins.

  • Effective interest margins were relatively high from 2000–03 during a period of banking instability, but stabilized at between 7 to 8 percent as weak banks left the system. The margin remained near these levels even as more domestic banks started to enter and as economic growth started to accelerate.

  • Non-interest income for the banking system was inflated during 2000–03 due to sizable valuation and foreign exchange trading volatility. Although non-interest income has remained around 3½ percent since 2004, in reality it most likely has risen slightly given the growing negative residual.3

  • The relative stability of total cost factors—at around 8–10 percent—hides the fact that operational costs have gradually declined from above 7 percent in 2000 to around 4½ in 2009, and then have risen to 5.6 percent in 2012.

  • Reserve requirement and deposit insurance costs have remained around 2–3 percentage points. The cost of taxes and provisions in Paraguay has remained very low.

  • Profitability averaged only around 2 percent through 2000–04, spiked above 4 percent in 2006-08, and has since declined to about 3½ percent over 2009–12. This more or less mimics changes in Paraguay’s real GDP growth.

14. Interest rate decompositions across various bank sub-groupings reveal substantial differences. Figure 5 graphically present the interest rate decompositions for the total banking system, as well as for our two separate comparative groupings: (i) the largest four banks (by asset size) versus other smaller banks in the system, and (ii) foreign versus domestic banks. While the graphs reflect sizable differences within the two groups, the gap between the behavior of domestic and foreign banks appears to be the most marked:

  • Non-interest margins for the larger top four and other smaller banks had been very similar, oscillating around 3½ percent from 2003–09. Since then, small banks non-interest margins have started to rise and are above 4 percent as of 2012. Margins were much higher for domestic banks relative to foreign banks during the crisis at the start of the decade, and tended to be about 1 percentage point higher until 2009. After 2009, the foreign and domestic banks had similar non-interest margins of about 3½ percent.

  • Effective interest spreads, for the most part have declined throughout the period. Spreads for the larger and smaller banks were of equal size and pattern until 2007, at which time spreads for the smaller banks increased substantially reaching around 10 percent by 2012. Larger bank effect spreads have continued to decline and now stand under 7 percent. The gap between foreign and domestic banks has remained sizable throughout the period. The effective interest rate margin of domestic banks has fallen from 12 percent in 2002 to around 8 percent today. Foreign bank effective margins have decline from around 9 percent to about 7 ½ percent by 2012.

  • Profits for larger banks expanded rapidly to about 6 percent in 2006–08 and have settled in at around 4 percent today. Smaller banks profits have increased more gradually and remain under 3 percent. Foreign bank profits have tended to be higher than domestic firms, and have ranged between 3 to 4 percent after recovering from Paraguay’s banking disruptions at the start of the decade.

  • Looking at costs, the smaller banks markedly higher expenses (of 10–12 percent) were driven by higher operational costs (7–8 percent), reserve requirements (2–3 percent), and provisions and deposit insurance (1–2 percent). Operational costs in particular have risen sharply by about 2 percentage points since 2007. The larger four banks reported much lower operational costs and reserve requirements than smaller banks (2 to 4½ percentage points), while the breakdown and evolution between foreign and domestic banks was very similar.

D. Financial Statement Analysis

15. Financial statement analysis focuses on three key financial ratios.4 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)

16. Broadly speaking, the main driver of banking sector profitability has been a greater generation of return on assets (Table 3). RoE has steadily increased from its 2003 low point, peaking at 28 percent in 2008 before falling back down to 21 percent in 2012.5 Negative tax policy effects were a drag on profitability in 2003 but since then reflect the sector’s stable and relatively low 10 percent rate. The systems gearing ratio increased during 2000-03, and but has more or less hovered around 9–9½ percent from 2004 onward. RoAs gradually increased from their 2003 nadir of only a ½ percent to 3½ percent in 2008, before settling down to around 2½ percent today. The stronger RoA numbers are the result of more intensive use of assets (as seen by the earning asset ratio), as well as by improved “burdens” given some reductions in administrative costs and healthy non-interest income revenues. From the lower end of the table we can see that net interest margins have remained relatively stable as lower returns on earning assets have been somewhat offset by lower liability costs and some reduction in the amount of liabilities relative to earning assets.

Table 3.

Paraguay: Financial Statement Analysis of the Banking System

(in percent)

article image
Source: SIB and fund staff estimates.

17. Small and domestic banks actually have better NIM results—but this advantage is eliminated by their lower levels of deployed assets and greater administrative burdens (Figure 6). Smaller and larger banks had similar net interest margins (NIMs) until 2008 when smaller banks return on earning assets increased sharply. However, smaller banks deploy a much lower amount of earning assets (around 65 percent versus larger banks 75 percent). They also have higher negative burdens (-3 to -4pps) which further detracts from their high NIMs. Thus their RoAs have tended to be lower than top 4 banks RoAs. Given somewhat similar leverage and tax policy impacts, this gap in RoAs has remained between small and large bank overall profitability (RoE). Despite higher liabilities to earning assets and higher liability costs, domestic banks tended to have higher NIMs given their greater return on earning assets. However, domestic banks tended to deploy less earning assets and have higher administrative burdens—which again results in lower RoAs. With slightly higher leverage ratios, domestic banks RoEs ratios end up being basically in line with foreign banks.

E. Interest Rate Regressions

18. Estimation of the interest rate spread panel regressions broadly follows the framework of Ross and Peschiera (2012). Using quarterly data (2000q1 to 2012q4) provided by the SIB we estimated the following equation: 6

Spreadit=α⁢  +β1NPLit+β2Liqit+β3MRit+β4costit+β5Shareit+β6IC4t+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 (IC4), 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 Guaraní-U.S. dollar exchange rate—are also included.

19. Table 4 presents the two panel regression estimates.

Table 4.

Interest Spread Regressions 1/

article image
Source: SIB, 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 risks, liquidity and operational costs—are all positive (in line with theory) and highly significant.7 In particular, a one percent increase in operational costs increases margins by between 0.36 and 0.44 percentage points. Changes in NPLs and liquidity factors have a much lower impact on margins.

  • The coefficient estimate for bank concentration variable (IC4) is negative, and indicates that for every 1 percentage point increase in system wide bank concentration, the effective interest rate margin declines by 0.03 percentage points. Thus concentration does not appear to be an impediment to bank competition. This result is not surprising in the Paraguay case given that margins have declined in recent years as concentration has slightly increase.

  • The coefficient for an individual bank’s market share is also negative and implies that for every 1 percentage point increase in a bank’s market share, the effective interest rate margin falls by 0.0326 percentage points. This result indicates that banks, however slightly, have been willing to reduce margins to gain a larger part of the market—providing some evidence of competition.

  • Finally, the results of the macro conditioning variables are somewhat surprising. The exchange rate and international interest rates have no impact on effective margins. This may be due to low currency mismatches in the banking system, and the limited linkages to international markets. Higher inflation, however, does appear to cause banks to raise margins.

F. Conclusions

20. The Paraguayan banking system compares relatively well with other Latin American banking systems. Banks are well capitalized, have ample access to deposit funding sources, and are profitable. Most importantly, the authorities have taken a number of decisive steps to strengthen supervision and ensure stability in the financial system within the last decade. Nevertheless, the credit delivery system still appears somewhat weak with little improvement in key metrics in recent years. The consolidation process has resulted in a slightly more concentrated banking system with substantially lower levels of foreign participation. Nevertheless, effective interest rate margins have declined by a small amount as concentration ratios increased. This suggests that greater concentration has not had a negative impact on competition in the sector. Indeed, the panel regressions indicated that increases in system wide concentration levels actually reduced effective margins. In addition, the regressions demonstrate that banks which gained market share also ended up lowering effective spreads. The impact of credit risks, in the form of NPLs, on margins has been relatively minor.

21. 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 Paraguay’s banking consolidation process and as the economy expanded. For the most part banks profitability increased—in line with strong growth in the overall economy—due to reductions in personnel and administrative expenses. Moreover, a greater deployment of interest earning assets and a lower cost of funds environment also helped to bolster profits. Most striking, the experience and behavior of the smaller and larger banks have been significantly different. While they had similar margins (especially until 2008), larger banks have been much better at controlling costs and more intensively using assets, and thus generating higher levels of profit. Despite having a larger cost of funds, domestic banks have higher net interest margins. However, they do not use assets as intensively and have higher operating costs—and thus lower profits than foreign banks.

22. Overall, the results provide some policy implications. The authorities acknowledge that the quality and sharing of credit information needs to be improved to foster financial deepening and social inclusion. This should help to reduce credit risk premiums and spreads within the banking system—allowing greater access to financial products. Taking steps to increase banking competition by attracting new entrants could also help to reduce spreads.

Figure 1.
Figure 1.

Banking Sector Indicators, 2012

Citation: IMF Staff Country Reports 2014, 061; 10.5089/9781475513745.002.A001

Sources: Financial Soundness Indicators, IFS, and WDI.
Figure 2.
Figure 2.
Figure 2.

Paraguay: Interest Rate Spread Decompositions, 2000-12

Citation: IMF Staff Country Reports 2014, 061; 10.5089/9781475513745.002.A001

Sources: SIB data and Fund staff estimates.
Figure 3.
Figure 3.
Figure 3.

Paraguay: Financial Statement Analysis, 2000-12

Citation: IMF Staff Country Reports 2014, 061; 10.5089/9781475513745.002.A001

Sources: SIB data and Fund staff estimates.

References

  • Afanasieff, T., P. Villa Lhacer and M. 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

  • Biderman, G., C. Velázquez, S. Giménez and Z. Espinola, 2012, “Costos de la Información en el Sistema Paraguayo,” Central Bank of Paraguay Working Paper, No. 36.

    • Search Google Scholar
    • Export Citation

  • Brock, P. and H. 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

  • Espino, F. and C. 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

  • Espinola, Z. and C. Velázquez, 2012, “Tras los Mitos de la Banca Extranjera en América Latina: Estudio del Caso Paraguayo,” Central Bank of Paraguay, mimeo.

    • Search Google Scholar
    • Export Citation

  • Estrada, D. and I. Orozco, 2006, “Determinants of Interest Rate Margins in Colombia,” Borradores de Economía No. 393, Banco de la Republica de Colombia.

    • Search Google Scholar
    • Export Citation

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

  • Gustale, J., 2011, “Hacia una Supervisión Basada en Riesgos: ¿Y Qué tal si Aprovechamos esta Coyuntura Económica para Fortalecer la Legislación Financiera?” Central Bank of Paraguay.

    • Search Google Scholar
    • Export Citation

  • 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

  • Mlachila, M., 2009, “Recurrent Financial Crises: Causes, Costs, and Consequences” in Paraguay: Addressing the Stagnation and Instability Trap.

    • Search Google Scholar
    • Export Citation

  • Morón, E., J. Tejada, and A. 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

  • Ross, K. and J. A. Peschiera, 2013, “Banking Sector Spreads,” Peru Selected Issues Paper, International Monetary Fund.

  • World Bank, Doing Business Database, 2012.

1

Prepared by Kevin Ross (WHD) and Viviana Garay (WHD-Asunción Office). The exact same analytical framework was used in Ross and Peschiera (2012) in their examination of Peruvian interest rate spreads—allowing for a comparison between these two Latin American banking systems.

2

The reduction reflected a generalized lack of confidence in the system rather than any particular “flight to quality” effect.

3

In 2010 Itau from Brazil merged with Interbanco, becoming the 2nd largest bank in the system. One small bank closed operations in 2011, while financieras (Vision, Itapúa, Familiar and Atlas) and one cooperative (Bancop) started operating as banks. CCB China (2005), Lloyds TBS (2007) and ABN Ambro (2009) left the system as HSBC (2007) began operations.

1

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

2

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. This can result in large residuals.

3

Paraguayan banks report large valuation inflows and outflows on foreign exchange trading and other non-core banking activities. Given that non-interest income is scaled by deposits, large valuation inflow effects that do not take into consideration non-interest valuation expenses can sharply increased this ratio, implying a large non-interest return that could be used to help to cover costs. This would be reflected by a large positive residual. To avoid this distortion, we report the net non-interest income ratio. However, this implies a somewhat smaller non-interest income margin—which can result in negative residuals.

4

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.

5

Looking from the bottom up in 2012, the return on earning asset was 11.7 percent. However, 4.4 pps (1.2 x 3.6) are subtracted given the cost of liabilities and their size relative to earning assets—leaving a 7.3 percent NIM. Since only 72 percent of all assets are utilized for earning activities, the return is further cut to 5.6 percent. Given the need to cover administrative burdens it is reduced by another 2.7pps to 2.6 RoA. This RoA, however, is levered up 8.7 times, increasing the return to 22.6 percent, whereby tax policies lower it to 20.7 percent RoE. Compared to 2011, lower leverage was a key factor behind the reduction in RoE.

6

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.

7

Banks in Paraguay hold few disposable investments (e.g., bonds) which are actively traded. Thus the market risk variable was dropped from the regression analysis.

Annex I. Description of Empirical Models

1. The empirical model follows Weber and Saborowski (2013) and implements an interaction panel VAR (IPVAR) framework introduced by Towbin and Weber (2013). The model includes the central bank policy rate and a weighted average of bank lending rates in a monthly frequency from 2000–12, for all countries for which data from the IMF’s International Financial Statistics is available (120 countries). The dynamic interaction between these endogenous variables is allowed to vary deterministically with the share of dollar denominated loans as a share of total loans. The relationship between the variables in this model is assumed to be governed by a system of “structural” equations. Ignoring the constant term, the system can be written as:

A0⁢yt=A1⁢yt-1+...+Ap⁢yt-p+εt

Where Aι=βι+μι*dollarj⁢ are⁢ (k×k) matrices of coefficients, εt⁢  is⁢  a⁡(k×1) vector of structural shocks that are assumed to be uncorrelated with one another; A0 is a (k×k) impact matrix that contains the contemporaneous relations among variables, and yt is a (k×1) vector of variables.

The reduced form of the structural model can be written as:

yt=B1⁢yt-1+...+Bp⁢yt-p+et

where Bp=A0-1⁢Ap⁢ and⁢ et=A0-1⁢εt⁢ for⁢  p=1,...,4.1

The model can be estimated by ordinary least squares, imposing restrictions on A0 to identify the coefficients in the structural form. Identification is achieved through a Choleski decomposition of the variance-covariance matrix Σe of reduced-form errorst. What is more, conditional impulse response functions are constructed and evaluated at different points of the sample distribution of the dollarization ratio.

2. Paraguay specific VARs are also estimated to quantify the effects of dollarization on interest rate transmission and the exchange-rate pass-through to inflation. The VAR includes the central bank policy rate, credit dollarization ratio, the weighted average of bank lending rates with monthly data from 2000–2013 (all in first differences). The pass-through of exchange rate to inflation is calculated using a VAR with the nominal effective exchange rate, deposit dollarization ratio, and CPI monthly inflation. In both estimations a recursive ordering is assumed, characterized by the idea that the more exogenous variables of the model precede the endogenous ones.2 To quantify the effects of dollarization on the interest rate transmission and in amplifying the exchange-rate pass-through, counterfactual scenarios are constructed by holding impulse responses of dollarization ratios fixed at zero at all forecast horizons.3 This hypothetical impulse response is then compared with the actual response to quantify the relative importance dollarization in hindering the interest transmission and amplifying the exchange-rate pass-through. To see this more clearly, consider the impulse response of the dollarization ratio (DOLLAR) to a shock to the policy rate (LRM):

IRFDOLLARLRM⁡(LRM,t)=Bt-1⁢A0-1⁡(DOLLARLRM,LRMLRM)⁢(1)

The counterfactual analysis looks at a sequence of shocks such that (1) is equal to zero. Therefore, in the first month after impact this entails:

A0-1(DOLLARLRM,LRMLRM)+A0-1⁢(DOLLARLRM,DOLLARLRM)⁢*εDOLLARLRM,t=1=0

The required innovation εDOLLARLRM,t=1 is:

εDOLLARLRM,t=H=-A0-1⁡(DOLLARLRM,DOLLARLRM)A0-1⁡(DOLLARLRM,LRMLRM)

The required values for subsequent innovations can be recursively estimated as:

εDOLLARLRM,t=H-IRFDOLLARLRM⁡(LRMLRM,H)+Σj=1H-1⁢BH-1⁢A0-1⁡(DOLLARLRM,LRMLRM)*εDOLLARLRM,jA0-1⁡(DOLLARLRM,DOLLARLRM)

where h= 1, …, H.

The impulse response to the a shock to the central bank policy rate holding dollarization equal to zero at all horizons h is computed as:

I⁢R˜⁢Fi⁡(LRM,t=H)=IRFDOLLAR⁢LRM⁢(LRMLRM,H)+ΣJ=1H-1⁢BH-j⁢A0-1⁡(DOLLARLRM,LRMLRM)*εDOLLARLRM,J

with i being an indicator for each variable in the system.

References

  • Bachmann, R. and E. Sims, 2011, “Confidence and the Transmission of Government Spending Shocks,” NBER Working Papers 17063, National Bureau of Economic Research, Inc.

    • Search Google Scholar
    • Export Citation

  • Bernanke, B., M. Gertler, and M. Watson, 1997, “Systematic monetary policy and the effects of oil price shocks,” Brookings Papers on Economic Activity, 28(1): 91–157.

    • Search Google Scholar
    • Export Citation

  • Daban Sanchez, T., 2011, “Bank Excess Reserves in Paraguay—Determinants and Implications,” Chapter II, IMF Country Report No. 11/239.

    • Search Google Scholar
    • Export Citation

  • García-Escribano, M., 2010, “Peru: Drivers of De-dollarization,” IMF Working Paper 10/169.

  • García-Escribano, M. and S. Sosa, 2011. “What is Driving Financial De-dollarization in Latin America?” IMF Working Paper No. 11/10

  • Ize, A. and E. Levy-Yeyati, 2005, “Financial De-dollarization: Is it for Real?,” IMF Working Paper 05/187.

  • Kokenyne, A., J. Ley, and R. Veyrune, 2010, “Dedollarization,” IMF Working Paper 10/188.

  • Leiderman, L., R. Maino, and E. Parrado, 2006, “Inflation Targeting in Dollarized Economies,” IMF Working Paper 06/157.

  • Reinhart, C., R. Rogoff, and M. Savastano, 2003, “Addicted to Dollars,” NBER Working Paper 10015 (Cambridge, MA: National Bureau of Economic Research, Inc.).

    • Search Google Scholar
    • Export Citation

  • Saborowski, C. and S. Weber, 2013, “Assessing the Determinants of Interest Rate Transmission Through Conditional Impulse Response Functions,” IMF Working Paper Series, WP/13/23.

    • Search Google Scholar
    • Export Citation

  • Saborowski, C., S. Sanya, H. Weisfeld and J. Yepez, 2014, “Effectiveness of Capital Outflow Restrictions,” IMF Working Paper Series, WP/14/8.

    • Search Google Scholar
    • Export Citation

  • Sims, C. and T. Zha, 2006, “Does monetary policy generate recessions?” Macroeconomic Dynamics, 10(02):231–272.

  • Towbin, P., and S. Weber, 2013. “Limits of floating exchange rates: The role of foreign currency debt and import structure,” Journal of Development Economics, Elsevier, vol. 101(C), pages 179–194.

    • Search Google Scholar
    • Export Citation
1

Prepared by Juan F. Yépez.

2

See Appendix I for description of the empirical models used in this section.

4

The empirical model follows the interaction panel VAR framework proposed by Towbin and Weber (2013).

1

The lag length was chosen according to the Akaike Information Criterion (AIC).

2

The model is qualitatively robust to different orderings.

3

This counterfactual analysis follows closely from the analysis of Sims and Zha (1998), Bernanke, Gertler, and Watson (2003), and Bachman and Sims (2012).

Same Series

Other IMF Content

Other Publishers

Cover IMF Staff Country Reports
Paraguay: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
Volume/Issue:
Volume 2014: Issue 061
Publisher:
International Monetary Fund
ISBN:
9781475513745
ISSN:
1934-7685
Pages:
33
DOI:
https://doi.org/10.5089/9781475513745.002

Bank Concentration and Ownership

Evolution of Bank Ownership Types

Distribution of Bank Credits by Sector

Banking Sector Indicators, 2012

Paraguay: Interest Rate Spread Decompositions, 2000-12

Paraguay: Interest Rate Spread Decompositions, 2000-12

Paraguay: Financial Statement Analysis, 2000-12

Paraguay: Financial Statement Analysis, 2000-12