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Bank market power and firm finance: evidence from bank and loan-level data

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Abstract

We investigate the impact of bank market power on the interest rates charged for loans to nonfinancial firms within the context of a developing country. Employing a distinctive amalgamation of data encompassing banks, firms, and loan specifics, alongside panel data fixed-effect models, we elucidate that banks wielding greater market power tend to impose higher interest rates on their loan products. This effect becomes more pronounced for banks positioned at the upper echelons of the market power spectrum (relative market power) and in instances of lengthier credit relationships. However, its severity can be mitigated for firms managing multiple credit connections (subjective market power). Our findings shed light on the presence of practices aimed at extracting economic rents and accentuate the substantial costs associated with changing lending partners in the corporate credit landscape. Various papers have delved into the empirical examination of how competition impacts the accessibility and expenses tied to bank credit for nonfinancial firms, yielding a mosaic of outcomes. Our contribution to this body of the literature manifests as a more incisive empirical analysis, enabling us to disentangle the opposing dynamics at play. This analytical depth is achievable solely due to the exceptional dataset we have curated. Significantly, our study stands out as one of the initial endeavors to interlink dynamic, bank-level gauges of market power with directly observed interest rates at the firm level, all while controlling for bank and loan-specific characteristics.

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Notes

  1. The greatest difficulty for finding a causal relation in this paper comes from the fact that there may be unobserved determinants of commercial loan interest rates that covary with banks’ measured market power. If present, these determinants are most likely to be related to unobserved demand effects, something we control for in Sect. 4 with the inclusion of firm-fixed effects.

  2. We compute the dependent variable this way following the literature on relationship lending. Alternatively, the real interest rate on each loan was used, obtaining qualitatively identical results.

  3. The length of a credit relationship is computed as the difference between the date of loan l, and the period in which the firm-bank pair appeared for the first time in the loan-level dataset. This may imply left-censoring for the length of some relationships which may have been established before the initial date of our dataset. However, left-censoring itself does not constitute a source of estimation bias. It would be troublesome only in the case in which the longest relationships, those which are left-censored, would be biased toward banks with a particularly high (or low) level of market power. But there is no reason to believe this is the case in our dataset.

  4. Additional descriptive statistics such as means by market power quartiles are presented.

  5. All figures here and in what follows are expressed in USD at 2020 current exchange rates.

  6. In order to check for the potential effect of left-censoring of the relationship-length variable we include estimation results using a shorter period (2012–2019) in Appendix D. Main results do not vary with the trimming of the data which confirms our hypothesis that left-censoring is not an issue in our data.

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Authors and Affiliations

  1. Department of Finance, Information Systems and Economics, City University of New York - Lehman College, Bronx, NY, USA

    Jose E. Gomez-Gonzalez

  2. Financial Sector, Banco de la Republica, Bogotá, Colombia

    Sebastian Sanin-Restrepo

  3. School of Finance, Economics, and Government, Universidad Eafit, Medellín, Colombia

    Cesar E. Tamayo

  4. Fiscal Division, Inter-American Development Bank, Washington, DC, USA

    Oscar M. Valencia

  5. Visiting Professor of the Summer School, Escuela Internacional de Ciencias Económicas y Adinistrativas, Universidad de La Sabana, Chía, Colombia

    Jose E. Gomez-Gonzalez

Authors
  1. Jose E. Gomez-Gonzalez
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  2. Sebastian Sanin-Restrepo
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  3. Cesar E. Tamayo
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  4. Oscar M. Valencia
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Corresponding author

Correspondence to Jose E. Gomez-Gonzalez.

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For comments and suggestions we thank Thorsten Beck, Julian Caballero, Hans Degryse, Andrew Powell, Banjamin Tabak, Lars Norden, and seminar participants at Lehman College, EAFIT, Universidad de La Sabana, Banco de Mexico, Banco de la Republica and ICESI, the IDB “Workshop on Bank Competition in Latin America” (Washington, D.C., October 3rd, 2017), IFABS Porto Conference (2017), the 1st Colombian Economic Conference (2018), and the IDB-Banco Central do Brasil “International Workshop on the Cost of Credit” (Brasilia, November 26–27th, 2018). Laura C. Diaz, Juan A. Paez and Juan G. Salazar provided superb research assistance. Financial support by the IDB ESW-RG-K1342 is gratefully acknowledged.

Appendices

Appendix A: Mergers and acquisitions in the Colombian banking system

See Table 7.

Table 7 M &A in the Colombian Banking Industry 2004–2019
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Appendix B: Data sources, variable definitions and TOC estimation

1.1 Bank-level data

All of our bank-specific measures come from the financial supervisor in Colombia, Superintendencia Financiera. In particular, we access the excel workbooks provided by SuperFinanciera under the link https://www.superfinanciera.gov.co/publicacion/60776 (“Estados Financieros - Moneda total - COLGAAP”). These spreadsheets contain both balance sheet and income statement accounts. Our variable definitions are as follows:

  • Total bank assets: is taken as account number 100000 (“Activo”).

  • Fixed assets: is taken as account number 180000 (“Propiedades y equipos”).

  • Total bank investments: is taken as account number 130000 (“Inversiones”).

  • Equity: is taken as account number 300000 (“Patrimonio”).

  • Total bank net loans: is taken as account number 140000 (“Cartera de creditos y operaciones de leasing financiero“) which records net commercial, consumer, housing, and microcredit loans; and we exclude net financial leasing loans by subtracting account numbers for gross commercial, consumer, housing, and microcredit leasing loans (141183 to 141198; 141983 to 141998; 143283 to 143298; 143383 to 143398; 143683 to 143698; 144183 to 144198; 144283 to 144298; 144283 to 144498; 144583 to 144598; 145083 to 145098; 145983 to 145998; 146083 to 146098; 146283 to 146298; 146383 to 146398; 146583 to 146598; 146683-146698; 146783 to 146798; 146883 to 146898; 146983 to 146998; 147083 to 147098) and adding accounts for commercial, consumer, housing, and microcredit leasing provisions (149109, 149114, 149119, 149124, 149149, 149309, 149314, 149319, 149324, 149329, 149508, 149509, 149513, 149514, 149518, 149519, 149523, 149524, 149528, 149529, 149810).

  • Net Commercial loans: is the sum of account numbers 145900, 146000, 146200,146300 and 146500 to 147000 which record commercial loans under different risk categories (A to E) and using different collateral (“garantia idonea” and “otra garantia”); and exclude net commercial leasing loans by subtracting account numbers for gross commercial leasing loans (145983 to 145998; 146083 to 146098; 146283 to 146298; 146383 to 146398; 146583 to 146598; 146683-146698; 146783 to 146798; 146883 to 146898; 146983 to 146998; 147083 to 147098) and adding commercial leasing provisions (149508,149509,149513,149514,149518,149519,149523,149524,149528,149529).

  • Financial Income: is the sum of the account numbers for interest income (4102000), commissions (4115000), price level restatement (411015), return on investments (410403 + 410404 + 410405 + 410409 + 410421 + 410423 + 410424 + 4123000), dividends (414000), net profit in investment sales (4116000 + 4125000  –  5116000  –  5125000) investment valuation (410700 + 410800 + 410900 + 411100 + 411200 + 411300  –  510600  –  510800  –  510900  –  511100  –  511200  –  511400), other net financial income (410400 + 411005 + 412800 + 412900  –  410403  –  410404  –  410405  –  410409  –  410421  –  410423  –  410424  –  512800  –  512900), and net changes (413500  –  513500).

1.2 TOC estimation results

See Table 8.

Table 8 TOC Translog Function Estimates
Full size table

Appendix C: Statistical tests

Figure 6 presents the correlation matrix plot between the variables included. Most of the variables do not present high significant correlations, ruling out concerns about multicollinearity. The unique variables that present high correlations are not included together into the regressions (i.e. Lerner index and Adjusted Lerner index).

Fig. 6
figure 6

Included variables correlation plot

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Appendix D: Robustness checks

We trim our dataset to the period 2012–2019 in order to investigate whether our results are affected by the potential left-censoring of the relationship-lending variable. The results are presented in Table 9 and Fig. 7 below. Results are quantitatively similar to the ones observed in the main results of the document. This result help us to confirm our prior about the absence of left-censoring in our exercise.

Table 9 Bank Market Power and Firm Finance: Lerner Index -Sample (2012-2019)
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Fig. 7
figure 7

Heterogeneous Effects of Bank Market Power (2012–2019). The figures plot marginal effects obtained using the coefficients from column 3 to 8 in Table 1

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Gomez-Gonzalez, J.E., Sanin-Restrepo, S., Tamayo, C.E. et al. Bank market power and firm finance: evidence from bank and loan-level data. Econ Change Restruct 56, 4629–4660 (2023). https://doi.org/10.1007/s10644-023-09570-0

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