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What Makes Firms Dissatisfied with Their Bank Loans: New Evidence from Survey Data

Abstract

We use loan-by-loan association between non-financial firms and their banks to disentangle the effects of financial weakness of borrowers and lenders on the satisfaction with the loan contracted. We construct indices measuring the financial weakness of borrowers and lenders. We find evidence of both demand and supply factors determining firm satisfaction with bank loan financing, especially regarding cost and collateral requirement. We also find that the impact of supply factors differs across regions within the EU: it is significant in periphery and cohesion countries but not in core countries where access to market is easier.

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Notes

  1. Information about the EIBIS is available on http://www.eib.org/eibis.

  2. In some parts of the paper, the EU economy is split into three regions. Periphery countries are the countries which have suffered a downgrade of at least two notches during the sovereign debt crisis (Cyprus, Greece, Ireland, Italy, Portugal, and Spain), Cohesion countries consist of the countries that joined the EU after 2003 (Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia). The remainder of the EU countries is called Core or others - (Austria, Belgium, Denmark, Finland, France, Germany, Luxembourg, Netherlands, Sweden, and the United Kingdom). In 2018, these regions respectively accounted for 23, 8, and 69% of EU GDP.

  3. Declining supply of loans raises the demand for bond financing that, in turn, increases market risk premiums in order to attract risk-averse investors to buy risky corporate bonds. Higher risk premiums intensify the effects of the credit shock.

  4. The UK was a member of the EU in each of the four waves in the period 2016-19.

  5. Surveyed firms provide only the name of their main lender, which prevents us from identifying firms with multiple-bank relationships. Previous studies provide mixed evidence regarding the impact of multiple-bank relationships on credit conditions (Ongena and Smith 2000a). Using survey data, Ongena and Smith (2000b) show that the share of firms with multiple banks varies significantly across European countries. Using Orbis data, Kalemli-Ozcan et al. (2018) conclude that having relationships with more than one bank is not very common for firms in several euro area countries with the exception of Greece.

  6. The question is formulated as follows: “Thinking about all of the external finance you obtained, how satisfied or dissatisfied are you with it in terms of [dimension]? We defined as satisfied firms answering “Very satisfied”, “Fairly satisfied”, “Neither Satisfied or dissatisfied”. Dissatisfied firms are those answering either “Very dissatisfied or “Fairly dissatisfied”. Dimensions: amount, cost, collateral requirements, maturity.

  7. The number of observations varies as a function of the dependent variable used in the different regressions presented in the next sections.

  8. This is one of the robustness exercises conducted in Section 5.

  9. Indices built using variance-equal weights are defined as averages of standardized variables (i.e. variables demeaned and scaled by their standard deviation).

  10. Both papers exploit time-series and use principal component analysis to measure bank health.

  11. Throughout the paper we do not distinguish between “neutral” and “satisfied” observations.

  12. As explained above, these indices are constructed so that higher values indicate weaker firms or banks.

  13. The five categories of the firm age variable are: less than two years, two years to less than five years, five years to less than ten years, ten years to less than twenty years, twenty years or more.

  14. The percentage changes in the coefficient of the firm index between the specification without the bank index and the one with the bank index amount to 0.05, 0.1, 0.06 and 0.08 for satisfaction with amount, cost, maturity and collateral respectively (see Table (3).

  15. All the pairwise differences of the coefficients associated with the firm index are statistically significant with a p-value below 10%. It means for instance that the impact on cost satisfaction of the firm index is statistically more important than the impact of the same index on collateral satisfaction.

  16. Degryse et al. (2019) introduces sector-region-risk-time FE, but have a much larger number of observations. In our case, sector-country-time clusters have 16 observations on average. Adding an additional dimension such as risk or replacing the country dimension by a regional dimension (e.g. NUTS1) would lower this already small number of observations and prevent us from providing any useful estimates.

  17. Ideally, one would also use the stricter identification methods presented in Table 4 to confirme the conclusions of this regional analysis. However, more waves of the EIBIS are necessary to obtain reliable results from these demanding regressions.

  18. Oztürk and Mrkaic (2014) use the Survey on the Access to Finance of SMEs in the euro area (SAFE) conducted jointly by the European Central Bank (ECB) and the European Commission (EC). This survey does not provide a bank-firm matching. Consequently, authors have to rely on country-level data to measure the financial health of the banking sector.

  19. The indices are standardised as in the baseline estimation.

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Correspondence to Laurent Maurin.

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The views expressed in the paper are those of the authors. They do not necessarily reflect the position of the EIB or its shareholders. Comments received by Koray Alper, Philipp Bastian-Brutscher, Marcin Wolski, by participants at an EIB internal workshop and by an anonymous referee are gratefully acknowledged.

Appendix A: Annex

Appendix A: Annex

Table 8 Correlation coefficients across satisfaction dimensions (%)
Table 9 Variable definitions
Table 10 Summary statistics of the variables entering the indices
Table 11 Dissatisfaction with external finance across regions (%)
Table 12 Bank-Time FE & Sector-Location-Time FE with alternative weakness indicators

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Kolev, A., Maurin, L. & Segol, M. What Makes Firms Dissatisfied with Their Bank Loans: New Evidence from Survey Data. J Financ Serv Res 61, 407–430 (2022). https://doi.org/10.1007/s10693-021-00362-z

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Keywords

  • Financial constraints
  • Bank lending
  • Survey data
  • Indices
  • Cross-section linear models
  • Bank-firm matching
  • Satisfaction with bank loans
  • Bank weakness
  • EU regions

JEL Classification

  • E44
  • G01
  • G32
  • L25