Skip to main content

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


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. Information about the EIBIS is available on

  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.


  • Acharya VV, Eisert T, Eufinger C, Hirsch C (2018) Real effects of the sovereign debt crisis in europe: Evidence from syndicated loans. Review of Financ Stud 31(8):2855–2896

    Article  Google Scholar 

  • Albertazzi U, Marchetti DJ (2010) Credit supply, flight to quality and evergreening: an analysis of bank-firm relationships after Lehman Temi di discussione (Economic working papers) 756. Bank of Italy, Economic Research and International Relations Area

  • Albertazzi U, Ropele T, Sene G, Signoretti FM (2014) The impact of the sovereign debt crisis on the activity of italian banks. J Bank Financ 46:387–402

    Article  Google Scholar 

  • Altavilla C, Pagano M, Simonelli S (2017) Bank exposures and sovereign stress transmission. Eur. Finan. Rev. 21(6):2103–2139

    Article  Google Scholar 

  • Andrews D, Petroulakis F (2017) Breaking the shackles: Zombie firms, weak banks and depressed restructuring in Europe. OECD Economics Department Working Papers 1433. OECD

  • Bernanke B, Gertler M, Gilchrist S (1996) The financial accelerator in a quantitative business cycle framework. Rev Econ Stat 78(1):1–15

    Article  Google Scholar 

  • Bernanke B, Gertler M (1989) Agency costs, net worth, and business fluctuations. Am. Econ. Rev. 79(1):14–31

    Google Scholar 

  • Bottero M, Lenzu S, Mezzanotti F (2015) Sovereign debt exposure and the bank lending channel: impact on credit supply and the real economy Temi di discussione (Economic working papers) 1032. Bank of Italy, Economic Research and International Relations Area

  • Bremus F, Neugebauer K (2017) Don’t stop me now: the impact of credit market fragmentation on firms’ financing constraints. Discussion paper 1650, DIW Berlin

  • Brunnermeier MK, Pagano M, Langfield S, Van Nieuwerburgh S, Vayanos D. (2017) Esbies: Safety in the tranches, Economic Policy

  • Cerqueiro G, Ongena S, Roszbach K (2016) Collateralization, bank loan rates, and monitoring. J Financ 71(3):1295–1322

    Article  Google Scholar 

  • Cerqueiro G, Ongena S, Roszbach K (2020) Collateral Damage? Priority structure, credit supply, and firm performance. J Financ Intermed

  • Chen J, Song K (2013) Two-sided matching in the loan market. Int. J. Ind. Organ. 31(2):145–152

    Article  Google Scholar 

  • Chirinko RS, Schaller H (1995) Why does liquidity matter in investment equations?. J Money Credit Bank 27(2):527–548

    Article  Google Scholar 

  • Cook DO, Schellhorn CD, Spellman LJ (2003) Lender certification premiums. J Bank Financ 27(8):1561–1579

    Article  Google Scholar 

  • Degryse H, De Jonghe O, Jakovljevìc S, Mulier K, Schepens G (2019) Identifying credit supply shocks with bank-firm data: Methods and applications. J Financ Intermed

  • Ehrmann M, Fratzscher M (2015) Euro area government bonds: Integration and fragmentation during the sovereign debt crisis, Working paper 2015-13, Bank of Canada

  • EIB (2017) Investment Report 2017/2018: From Recovery to Sustainable Growth. European Investment Bank, chapter 6

  • Farhi E, Tirole J (2018) Deadly embrace: Sovereign and financial balance sheets doom loops. Rev. Econ. Stud. 85(3):1781–1823

    Article  Google Scholar 

  • Ferrando A, Mulier K (2015) Firms? financing constraints: Do perceptions match the actual situation?. Econ Soc Rev 46(1, Spring):87–117

    Google Scholar 

  • Gao H, Wang J, Yang X, Zhao L (2020) Borrower opacity and loan performance: evidence from China. J Financ Serv Research 57(2):181–206

    Article  Google Scholar 

  • Garcia-de-Andoain C, Hoffmann P, Manganelli S (2014) Fragmentation in the euro overnight unsecured money market. Econ. Lett. 125(2):298–302

    Article  Google Scholar 

  • Gertler M, Gilchrist S (1994) Monetary policy, business cycles, and the behavior of small manufacturing firms. Q J Econ 109(2):309–340

    Article  Google Scholar 

  • Gertler M, Kiyotaki N (2010) Chapter 11 - financial intermediation and credit policy in business cycle analysis, Vol. 3 of Handbook of Monetary Economics. Elsevier, Amsterdam, pp 547–599

    Google Scholar 

  • Gilchrist S, Zakrajšek E (2012) Credit spreads and business cycle fluctuations. Am Econ Rev 102(4):1692–1720

    Article  Google Scholar 

  • Haan de, Leo van den E., Willem J, Vermeulen P (2017) Lenders on the storm of wholesale funding shocks: saved by the central bank?. Appl. Econ. 49(46):4679–4703

    Article  Google Scholar 

  • Hadlock CJ, Pierce JR (2010) New evidence on measuring financial constraints: Moving beyond the kz index. Rev Financial Stud 23(5):1909–1940

    Article  Google Scholar 

  • Holmstrom B, Tirole J (1997) Financial intermediation, loanable funds, and the real sector. Q J Econ 112(3):663–691

    Article  Google Scholar 

  • Hubbard RG, Kuttner KN, Palia DN (2002) Are there bank effects in borrowers’ costs of funds? evidence from a matched sample of borrowers and banks. J Bus 75(4):559–581

    Article  Google Scholar 

  • Illing M, Liu Y (2006) Measuring financial stress in a developed country: An application to canada. J Financ Stab 2(3):243–265

    Article  Google Scholar 

  • Ippolito F, Peydró J-L, Polo A, Sette E (2016) Double bank runs and liquidity risk management. J. Financ. Econ. 122(1):135–154

    Article  Google Scholar 

  • Iyer R, Peydró J-L, da Rocha-Lopes S, Schoar A (2014) Interbank liquidity crunch and the firm credit crunch: Evidence from the 2007–2009 crisis. Rev Financ Stud 27(1):347–372

    Article  Google Scholar 

  • Jiménez G, Ongena S, Peydró J-L, Saurina Salas J (2017) Do demand or supply factors drive bank credit, in good and crisis times?, Discussion Paper 2012-003, European Banking Center.

  • Jiménez G, Ongena S, Peydró JL, Saurina J (2012) Credit supply and monetary policy: Identifying the bank balance-sheet channel with loan applications. Am Econ Rev 102(5):2301–2326

    Article  Google Scholar 

  • Kalemli-Ozcan S, Laeven L, Moreno D (2018) Debt overhang, rollover risk, and corporate investment: Evidence from the european crisis, Working Paper 24555, National Bureau of Economic Research

  • Kapan T, Minoiu C (2018) Balance sheet strength and bank lending: Evidence from the global financial crisis. J Bank Financ 92:35–50

    Article  Google Scholar 

  • Kaplan SN, Zingales L (1997) Do investment-cash flow sensitivities provide useful measures of financing constraints?. Q J Econ 112(1):169–215

    Article  Google Scholar 

  • Khwaja AI, Mian A (2008) Tracing the impact of bank liquidity shocks: Evidence from an emerging market. Ame Econ Rev 98(4):1413–42

    Article  Google Scholar 

  • Kiyotaki N, Moore J (1997) Credit cycles. J Polit Econ 105 (2):211–248

    Article  Google Scholar 

  • Lamont O, Polk C, Saa-Requejo J (2001) Financial constraints and stock returns. Rev. Financ. Stud. 14(2):529–554

    Article  Google Scholar 

  • Liberti JM, Sturgess J (2018) The anatomy of a credit supply shock: Evidence from an internal credit market. J. Financ. Quant. Anal. 53(2):547–579

    Article  Google Scholar 

  • Musso P, Schiavo S (2008) The impact of financial constraints on firm survival and growth. J. Evol. Econ. 18(2):135–149

    Article  Google Scholar 

  • Ongena S, Smith DC (2000a) Bank relationships: a review. Performance of financial institutions: Efficiency, innovation, regulation

  • Ongena S, Smith DC (2000b) What determines the number of bank relationships? cross-country evidence. J Financ Intermed 9(1):26–56

    Article  Google Scholar 

  • Oztürk B, Mrkaic M (2014) SMEs’ access to finance in the euro area; what helps or hampers?, IMF Working Papers 14/78, International Monetary Fund

  • Pancrazi R, Seoane HD, Vukotic M (2015) Sovereign risk, private credit, and stabilization policies, Economic Research Papers 270214, University of Warwick - Department of Economics

  • Popov A, Van Horen N (2015) Exporting sovereign stress: Evidence from syndicated bank lending during the euro area sovereign debt crisis. Rev Financ 19(5):1825–1866

    Article  Google Scholar 

  • Schwert M (2018) Bank capital and lending relationships. J. Financ. 73(2):787–830

    Article  Google Scholar 

  • Storz M, Koetter M, Setzer R, Westphal A (2017) Do we want these two to tango? On zombie firms and stressed banks in Europe. Working Paper Series 2104, European Central Bank

  • Turvey C, Xu X, Kong R, Cao Y (2014) Attitudinal asymmetries and the lender-borrower relationship: survey results on farm lending in Shandong, China. J. Financ. Serv. Res. 46(2):115–135

    Article  Google Scholar 

  • Whited TM, Wu G (2006) Financial constraints risk. Rev Financ Stud 19(2):531–559

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Laurent Maurin.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • 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