Abstract
This paper investigates the pass-through from observed and expected policy interest rates to the remarkably high lending rates in the Brazilian economy, accounting for financial-institution specific characteristics, borrower types, asymmetric adjustment and persistence in loan rates. We use a unique and non-public dataset with expected variables identified by professional forecasters and apply a fixed-effects approach to alternative specifications as robustness checks. Financial institutions correctly forecast the next target level of the policy rate and anticipate adjustments in their loan rates. There is evidence of over-proportional and positively asymmetric pass-through to loans with higher interest rate margins, implying a positive correlation between degrees of pass-through and spreads across persistent lending rates. These findings contribute to explain why loan interest rates are so high in the Brazilian economy.
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Notes
Banerjee et al. (2013) used aggregate data for the four major Euro area economies and argued that banks anticipate short-term market rates when setting interest rates on loans and deposits, and even more so when they will have to refinance the loans that they make in the future. We found a similar result by using a loan-specific dataset with expected policy interest rate identified by professional forecasters.
From the Latin American region, the panel included Colombia, Jamaica, Mexico and Venezuela, but not Brazil.
See Gregor et al. (2021) for a comprehensive review of the pass-through literature.
According to Schmeling et al. (2022), Cieslak (2018) and Divino and Haraguchi (2022), the financial institutions forecast ability of the policy rate depends on the knowledge of the interest rate rule followed by the Central Bank. We highlight that similar results for observed and expected interest rate pass-through indicate anticipated adjustments in the lending rates, but with room for some misalignment due to the asymmetric pass-through.
An alternative approach claims that the financial institutions forecasting strategy of the policy rate might rely on a pro-conservative monetary policy convention in Brazil. See Bresser-Pereira et al. (2020) for details.
The Over-Selic rate is the daily average of the overnight rates of interbank loans backed by federal securities, carried out in the Special System for Settlement and Custody (the Selic System).
It is available from the Open Data Portal https://opendata.bcb.gov.br/, from where we also extracted the observed and expected Over-Selic rates. Data on the Monetary Policy Committee meetings and financial institutions were obtained from Central Bank of Brazil website https://www.bcb.gov.br/en.
See Appendix A for a detailed description of the loan types.
Focus Survey monitors the market expectations for several economic indicators, including Selic target level and inflation rate.
The confidential financial institutions codes list was kindly provided by Department of Statistics (DSTAT) of the Central Bank of Brazil only for the purposes of this work.
Estimates using median Selic expectations were significantly different from those with Selic expectations identified by financial institutions, especially in models with disaggregated loan operations. The higher the disaggregation in the sub-samples, the bigger the difference in the estimated pass-through coefficients between the median Selic expectations and the identified expectations by financial institutions. These results are available from the authors upon request.
As a robustness check, we also used winsorized data by setting the top 3% to the 97th percentile. The results were similar and are available from the authors upon request.
National Monetary Council Resolution 4549 of 2017 (http://www.bcb.gov.br/pre/normativos/busca/downloadNormativo.asp?arquivo=/Lists/Normativos/Attachments/50330/Res_4549_v1_O.pdf) states that the outstanding balance in the credit card invoice, once not completely paid at the due date, may be financed by revolving credit only until the next invoice. This measure led consumers to settle down the debt in full, to pay it in instalments, or to seek more advantageous credit sources for financing the debt. The new rule has become effective in April 3, 2017.
Advances on exchange contracts is a credit type directed at foreign trade, mainly to advance funds to exporters before payment by importers. Financial institutions that offer this type of credit line obtain funds from abroad and charge interest rates indexed to credit costs in the international markets. As stated earlier, it is included as a placebo in the analysis by loan rate type because no pass-through should be observed from the domestic interest rates.
We do not control for credit risk because this information is confidential and not released by loan type and financial institution. The Central Bank of Brazil computes loan ratings and borrower ratings for every new loan in the credit registry system (SCR). However, the SCR is strictly confidential and subject to specific rules and special authorization to be accessed. We only had access to monthly default rates for some loan types that did not match our weekly-basis sample. While controlling for credit risk of loan operations is relevant to explain interest rate margins (or spread), this might also be the case in the estimation of the degree of pass-through. However, the correlation between the credit risk by loan type and the Over-Selic rate (observed and expected) might not be strong enough to bias the pass-through estimates, an issue that deserves further investigation depending on data availability.
Kopecky and Hoose (2012) developed a dynamic adjustment cost model with imperfect competition where bank retail deposit and loan rates depend on own lagged values and on lagged, current, and expected future values of the security rate, but without providing further empirical evidence. The problem with applying this framework is that the observed Over-Selic rate varies only over time and is highly correlated with the expected rate, which changes over time and by financial institutions. This prevented us from including both observed and expected Over-Selic rates in a unique panel-data pass-through regression. The results were meaningless and are available from the authors upon request.
In a robustness check, we applied the random effects specifications to all regressions and there was no significant change in the results, which are available from the authors upon request.
As explained earlier, funding for this type comes from abroad and is not related to the domestic interest rates.
The Central Bank of Brazil Banking Report 2018 brings a decomposition of the average cost of outstanding loans in which delinquency—losses arising from non-payment of debts or interest and discounts granted—represented 23% of the total cost and 37% of the spread in the last three years. The report is available at https://www.bcb.gov.br/content/publications/bankingreport/BAR_2018.pdf.
The Central Bank of Brazil established the S1 segmentation for proportional implementation of prudential regulation to prevent any “domino effect” in the financial system. It is composed of financial institutions with the largest market shares in addition to other features, as explained in Sect. 2. The S1 institutions (Banco do Brasil, Bradesco, BTG Pactual, Caixa Econômica Federal, Itau, and Santander) accounted for 80.45% market share in outstanding credit for households and 58.24% share in outstanding credit for non-financial corporations in a universe of 172 authorized institutions, according to the Central Bank of Brazil Banking Report from 2018 (available at https://www.bcb.gov.br/content/publications/bankingreport/BAR_2018.pdf).
We also applied the traditional Arellano and Bond (1991) estimator, but the coefficient of the lagged dependent variable did not lie within the bounds defined by the OLS and Within estimators, indicating that these estimates are not reliable according to Bond (2002) and Roodman (2009). Another practical issue is that a large number of time periods adds too many instrumental variables to the IV matrix and generates a dimensionality problem that requires some sort of arbitrary truncation. By using a fixed-effects estimator, we also avoid this issue.
This is especially evident for the aggregate samples in Table 10, Credit card financing, Other goods financing, Personal credit, and Guaranteed overdraft (fixed rate) in Table 11. For the HH types, the estimates of \(\rho \) ranged from 0.69 to 0.94, except for Vehicle leasing, where it was 0.43. NFC rates showed lower estimated values of \(\rho \), ranging from 0.27 to 0.91. \(R^2\) coefficients are available from the authors upon request.
Expected inflation ranged from 4.85% to 5.71% for 2012 and from 5.00% to 5.60% for 2013, while the inflation target was 4.5% for both years, according to Focus Survey (available at https://www3.bcb.gov.br/expectativas2/#/consultas) and the inflation targeting track record (available at https://www.bcb.gov.br/en/monetarypolicy/historicalpath).
See Ferreira (2022) for a recent empirical evidence for the US economy.
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This work was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Apoio a Pesquisa do Distrito Federal (FAP-DF). J. A. Divino has received financial support from CNPq and CAPES (Grant Numbers 302632-2019-0 and 760/2018, respectively) and Carlos Haraguchi from CAPES (Grant Number 88887.201766/2018-00).
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We are grateful to Central Bank of Brazil, and in particular Fernando Rocha, Luciana Roppa, and Monica Une from Department of Statistics (DSTAT), and Cassio Silva from Information Technology Department (Deinf), for providing crucial data used in this work. We would like also to thank comments from Osvaldo Candido, Thiago Silva, Andre Minella, Joao Mello, Jose Renato Ornelas, Anderson Okinokabu, Sergio Leao, Thiago Trafane and from participants in the 2022 Econometric Society European Meeting (EEA-ESEM 2022), 42nd Meeting of the Brazilian Econometric Society (SBE) and of the Central Bank of Brazil Research Network Workshop. C. Haraguchi thanks CAPES Foundation and J. A. Divino thanks CNPq for financial support. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)—Finance Code 001. The authors acknowledge the Fundação de Apoio a Pesquisa do Distrito Federal (FAP-DF) for the financial support. The views expressed in the paper are those of the authors and do not necessarily reflect those of the Central Bank of Brazil. All remaining errors are the authors’ sole responsibility.
Appendix A: Description of the loan types
Appendix A: Description of the loan types
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Divino, J.A., Haraguchi, C. Observed and expected interest rate pass-through under remarkably high market rates. Empir Econ 65, 203–246 (2023). https://doi.org/10.1007/s00181-022-02335-0
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DOI: https://doi.org/10.1007/s00181-022-02335-0