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Bank liquidity creation: A new global dataset for developing and emerging countries

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Abstract

The pre-Global Financial Crisis build-up, followed by the post-crisis collapse, in bank liquidity creation in developed countries is well-documented (Berger and Bowman, Berger and Bouwman, Review of Financial Studies 22:3779–3837, 2009). Comparable analyses on developing and emerging countries (DECs) have been severely hindered by the lack of detailed bank-by-bank balance sheet data. This paper proposes a new, high-frequency, Aggregate Bank Liquidity Creation (A-BLC) measure for 114 DECs on a comparable cross-country basis, which relies on macroeconomic, country-wide, banking systems’ balance sheet data. The A-BLC database allows us to assess the extent of bank fragility arising from illiquidity associated with intermediation at the banking system level for every DEC, at a monthly frequency over the period 2001–2016. Our measure captures more accurately than other measures proposed in the literature the evolution of bank liquidity creation in the DECs. Stylised facts and panel-regression analysis suggest a sharp pre-crisis build-up and post-crisis fall in liquidity creation in DECs, larger then that observed for developed countries. In addition, financial depth and stability appear as particularly important drivers of A-BLC in DECs.

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Notes

  1. The Liquidity Mismatch Index can be constructed using balance sheet data calculating the cash equivalent value of the bank at each point in time under some assumptions. The metric is computed by using different liquidity weights for both asset and liabilities items. See Bai et al. (2016) for an empirical application of this measure.

  2. Bank liquidity creation is also interconnected with other measures of financial liquidity, most notably funding liquidity as described by Nikolaou (2009) and Brunnermeier and Pedersen (2009). A higher degree of liquidity creation by a bank, associated with the holding of more illiquid assets and/or liquid liabilities, exposes the financial institution to higher funding-liquidity risk.

  3. See Fungáčová et al. (2015) and references therein.

  4. The exclusion of off-balance sheet items is due to data unavailability and is not a worrisome limitation of our measure given limited dependence on off-balance sheet commitments in emerging and developing countries.

  5. https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b

  6. This point is further discussed in Supplementary Information 1.

  7. Country listings can be found in Table A.3 of the BIS statistical releases: https://www.bis.org/statistics/bankstats.htm?m=6%7C31%7C69.

  8. World Development Indicators data base: https://databank.worldbank.org/source/world-development-indicators.

  9. GDP per capita calculations are based on the averages over the period 2001–2013.

  10. https://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database.

  11. Unless otherwise specified the variables are obtained from the World Bank database.

  12. The choice of these proxies depends on the availability of data over a larger set of countries in the sample.

  13. All series entering the regressions are I(0). Panel unit root tests (Levine et al., 2002) have been carried out on the series prior to estimation.

  14. The two-step estimator has been showed to be more asymptotically efficient than the one-step estimator, see Arellano and Bond (1991) for a discussion.

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Acknowledgements

The authors thank an anonymous referee, as well as the editor of the Journal, for remarks which helped us improve the paper. The second author acknowledges support the French National Research Agency Grant ANR-17-EURE-0020, and the Excellence Initiative of Aix-Marseille University—A*MIDEX.

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Authors and Affiliations

  1. ICN Business School (Nancy) and CEREFIGE, Université de Lorraine, Nancy, France

    Carmela D’Avino

  2. Aix-Marseille School of Economics, Aix Marseille University, CNRS & EHESS, Marseille, France

    Eric Girardin

  3. Royal Docks School of Business and Law, University of East London, London, England

    Mimoza Shabani

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  1. Carmela D’Avino
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  2. Eric Girardin
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  3. Mimoza Shabani
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Correspondence to Carmela D’Avino.

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Supplementary file1 (DOCX 39 KB)

Appendix 1

Appendix 1

See Figs.

Fig. 4
figure 4

Source: Authors’ computations based on data from the World Bank Financial Development Indicators and IFS

A-BLC-to-TA, banking sector development and income (2001–2013 averages). Notes Banking sector access is proxied by bank branches by 100,000 adults; depth is proxied by private credit to GDP (%); efficiency is proxied by interest rate spread and stability is proxied by bank nonperforming loans to gross loans (%).

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4 and

Fig. 5
figure 5

Source: Authors’ computations based on data from the World Bank Financial Development Indicators and IFS

A-BLS-to-TA by country groups, monthly averages. Notes GDP per capita is classified according to the following criteria: high income (y > $8000), medium–high income ($7999 < y < $3000), medium–low income ($2999 < y < $1000), low income (y < $1000). Financial markets access is proxied by bank branches by 100,000 adults, financial markets depth is proxied by private credit by deposit money banks to GDP (%); efficiency is proxied by bank return on assets (%, after tax) and financial markets stability is proxied by bank nonperforming loans to gross loans (%). High, medium and low thresholds for financial market developments are determined by splitting the sample countries in three groups of equal sizes.

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5.

See Tables

Table 5 Two-step difference dynamic GMM estimation: extended model II
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Table 6 The 114 countries included in the dataset
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Table 7 Descriptive statistics A-BLC-to-TA (2001–2016)
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Table 8 A-BLC-to-TA by country groups
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Table 9 Correlation between average A-BLC-to-TA and banking sector development proxies
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Table 10 Variables used in the construction of A-BLC
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Table 11 A-BLC construction for China and India
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Table 12 A-BLC-to-TA descriptive statistics by country groups and sub-periods
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Table 13 IFRS-complying countries
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D’Avino, C., Girardin, E. & Shabani, M. Bank liquidity creation: A new global dataset for developing and emerging countries. Rev World Econ 158, 529–570 (2022). https://doi.org/10.1007/s10290-021-00434-1

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