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Credit information sharing and loan default in developing countries: the moderating effect of banking market concentration and national governance quality

Review of Quantitative Finance and Accounting volume 55, pages 55–103 (2020)Cite this article

Abstract

Departing from the existing literature, which associates credit information sharing with improved access to credit in advanced economies, we examine whether credit information sharing can also reduce loan default rate for banks domiciled in developing countries. Using a large dataset covering 879 unique banks from 87 developing countries from every continent, over a 9-year period (i.e., over 6300 observations), we uncover three new findings. First, we find that credit information sharing reduces loan default rate. Second, we show that the relationship between credit information sharing and loan default rate is conditional on banking market concentration. Third, our findings suggest that governance quality at the country level does not have a strong moderating role on the effect of credit information sharing on loan default rate.

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Fig. 1

Notes

  1. These statistics are based on our own computations using the relevant World Bank Doing Business (2016) data for our sampled countries over the period of investigation.

  2. We are grateful to an anonymous reviewer for suggesting this argument.

  3. Our sample period starts from 2004 because the World Bank Doing Business (WBDB) database on credit information sharing starts from 2004. There was a methodological change in the construction of the credit information sharing index in 2012. The index, therefore, now ranged from 0 to 8 instead of 0 to 6. To avoid the potential confounding effects of this methodological change, as well as to be consistent with prior studies (e.g., Kalyvas and Mamatzakis 2017), we decided to employ 2012 as our cut-off period. We also note that our sample period covers the period of the recent global financial crisis (2007–2009) which affected the level of bank loans and default rates. In unreported robustness analysis, which excludes the crisis period, our main results reported in this study and the conclusions drawn from them are not affected. These results are available upon request.

  4. We define developing countries based on United Nation’s Country Classifications contained in the World Economic Situation and Prospects. See links for the UN reports containing list of countries in 2014 (p. 146) and 2018 (p. 142):

    http://www.un.org/en/development/desa/policy/wesp/wesp_current/2014wesp_country_classification.pdf

    https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/publication/WESP2018_Full_Web-1.pdf

    Although China and India are classed as developing countries, they are excluded from our sample because they do not have country-level credit information sharing indexes. Rather, these countries have city-level credit information sharing indexes. In untabulated analyses, which include the city-level indexes for Shanghai (China) and Mumbai (India), with our main conclusions remaining unchanged.

  5. A more detailed description of the information sharing measures can be found at World Bank, Doing Business project (http://www.doingbusiness.org/).

  6. The list of countries showing major improvements in information sharing include: Costa Rica, Croatia, Kazakhstan, Lithuania, Macedonia (Fyrom), Mauritius, Mongolia, Oman, Romania, Saudi Arabia, Serbia,Albania, Armenia, Azerbaijan, Belarus, Egypt, Georgia, Ghana, Latvia, Morocco, Rwanda, United Arab Emirates, Zambia. Please note that the countries in italics (bold) experienced gradual (sharp) improvements in information sharing during the sample period.

  7. Note that the most recent lagged differences are used in order to avoid redundant moment conditions (see Arellano and Bover 1995).

  8. The correlation among the governance indicators is at least 0.83 and this could cause multicollinearity problems when they are included in the model together. On the contrary, the correlations among the other country-level variables (Depth, Concentration, Governance, GDP Growth, and Inflation) are moderate (less than 0.21) and could thus be included together in the model without serious econometric concerns. The results remain qualitatively similar when the country-level variables (other than the governance indicators) are also included alternately in the model. These results are available upon request.

  9. The magnitude of the economic significance that we find is similar to the findings of Vazquez et al. (2012) who examine the impact of GDP on default rate in Brazil (see page 76 of their paper).

  10. A separate table for the marginal effect of concentration is not reported for brevity but is available upon request.

  11. When we utilise Principal Component Analysis to construct a single composite governance quality measure from the three governance variables (rule of law, regulatory quality, and the control of corruption) and re-run regressions with the composite measure of governance quality, the results remain mixed. These results are untabulated to save space. We are grateful to an anonymous reviewer for suggesting this robustness testing.

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Acknowledgements

We thank Cheng-Few Lee (the Editor) and two anonymous referees for their invaluable comments. Samuel Fosu acknowledges, without implication, funding by the UK Economic and Social Research Council (ESRC) and the UK Department for International Development (DFID) under a research Grant, ESRC Reference: ES/N013344/2, on ‘Delivering inclusive financial development and growth’.

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Authors and Affiliations

  1. Leicester Castle Business School, De Montfort University, Leicester, UK

    Samuel Fosu & Albert Danso

  2. University of York Management School, York, YO10 5GD, UK

    Henry Agyei-Boapeah

  3. University of Southampton Business School, Southampton, SO17 1TR, UK

    Collins G. Ntim

  4. Nottingham University Business School, Nottingham, NG8 1BB, UK

    Emmanuel Adegbite

  5. James Cook University, Singapore, Singapore

    Emmanuel Adegbite

Authors
  1. Samuel Fosu
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  2. Albert Danso
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  3. Henry Agyei-Boapeah
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Appendices

Appendix 1

See Fig. 2.

Fig. 2
figure 2

The key channels through which credit information sharing reduces loan default rates

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Appendix 2

See Table 12.

Table 12 List of countries and cross-sectional sample distribution
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Appendix 3

See Table 13.

Table 13 Cross-sectional loan default and depth of credit information (mean)
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Fosu, S., Danso, A., Agyei-Boapeah, H. et al. Credit information sharing and loan default in developing countries: the moderating effect of banking market concentration and national governance quality. Rev Quant Finan Acc 55, 55–103 (2020). https://doi.org/10.1007/s11156-019-00836-1

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  • DOI: https://doi.org/10.1007/s11156-019-00836-1

Keywords

  • Credit information sharing
  • Developing countries
  • Banking market concentration
  • Governance quality

JEL Classification

  • G14
  • G21
  • G38