Abstract
Using cross-country cross-industry data, this paper explores how industry’s growth in number of firms in Central-East Europe (CEE) region is influenced by bank concentration in both the pre-crisis and crisis periods. The CEE region shows highly concentrated banking markets and less-developed financial markets; thus, the level of bank concentration and the resulting credit supply are crucial for firm creation and survival. Despite this, there is little evidence on these countries in the literature. Our empirical results suggest an inverted-U relationship: industry growth is fostered by bank concentration, but there is a turning point from which higher concentration begins producing the opposite effect. Moreover, the positive impact has a greater intensity during the crisis period compared to the pre-crisis period. Between sectors’ analysis shows that high-tech sectors are less reactive to changes in the concentration level.
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Source Authors’ elaboration on European Central Bank data
Source Authors’ elaboration on World Development Indicator (WDI), World Bank
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Notes
See Northcott (2004) for an extensive survey.
Banks incur a cost when they engage in the screening activity to discriminate between high- and low-quality entrepreneurs. Competitors can extract information about the screened entrepreneurs by simply observing whether the loan has been granted or denied. Therefore, in competitive banking markets, the free-riding problem weakens banks’ incentives of screening [see Fischer (2000) for the empirical evidence].
However, Schnitzer (1999b) reveals a downside. Restructuring generates positive externalities by increasing the profitability of the entire banking sector, of which also non-restructuring firms benefit. If firms can benefit from restructuring without incurring the costs, they are induced to leave the restructuring to the other firms.
Cetorelli (2001) shows that concentration in the banking sector stimulates concentration in other industrial sectors, especially in sectors highly dependent on external finance.
The effect is heterogeneous across industries as concentration discourages the growth of more informationally transparent industries. Ratti et al. (2008) show that when bank concentration increases, the credit constraints decrease for firms in less opaque industries.
Rajan and Zingales (1998) design a model to explore the effect of financial markets’ development on the growth rate of value added at industry level, thereafter revisited to test the impact of banking market structure (Cetorelli and Gambera 2001 and Claessens and Laeven 2005). The cross-sectional model also has been used to investigate different but related topics. See, among the others, Bonaccorsi di Patti and dell’Ariccia (2004), Ayyagari et al. (2008), Beck et al. (2008, 2011), Chong et al. (2013), Fernandez et al. (2013), Scott and Dunkelberg (2010).
We use data on industries at 3-digit level. However, if observations are missing at this level, we employ data at the 2-digit level.
Given the level of data disaggregation available in Eurostat, it is not possible exactly match observations from one classification to the other.
Data are available until 2014; however, we focus on the crisis period.
The sample of high-tech industries is defined following the classification of industry provided by Eurostat. For Nace rev. 1.1, high-tech industries are those identified by codes: 24, 29, 30, 31, 32, 33, 34, 35 (excluding 35.1) 64, 72, and 73; for Nace rev. 2, high-tech industries are those identified by codes: 20, 21, 26, 27, 28, 29, 30, 59, 60, 61, 62, 63, and 72.
Both measures are collected from the European Central Bank and are available for European transition countries from the 2001 onwards.
As well established in the growth literature (Barro and Sala-I-Martin 1995).
Hayashi (2000, p. 220).
2SLS estimations are available from the authors.
The critical value with 10% maximal IV size is equal to 7.03, with 15% maximal IV size is equal to 4.58 (Stock and Yogo 2005). It has also to be noted that the critical value is computed under the assumption of i.i.d. errors.
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The authors would like to thank the anonymous reviewers for the constructive comments that greatly contributed to improve the quality of the paper. Special thanks to Marco Alderighi, Michele Bernasconi, Paolo Coccorese and Ulrich Woitek for very useful comments and suggestions. The authors would also like to thank Sandro Montresor and participants at the 55th SIE annual conference, Christian Fons-Rosen and participants at the 40th EARIE Conference as well as participants at the 53rd ERSA Congress, at the 21st MBF Conference, Ramazan Gencay and participants at the 4th Quantitative Finance Workshop in Rimini and participants at the 33rd AISRe Conference for very helpful discussion and comments on earlier version. All remaining errors are ours.
A. Appendix
A. Appendix
As shown in Table 5, for the pre-crisis period, the U-inverted relationship is not captured by the variable HHI, which also appears to be exogenous since this variable considers all the banks within a country and not only the larger five. We still report GMM results for consistency, given that they are very close to OLS. For the crisis period, instead, the U-inverted relationship is also captured by the variable HHI and the variable is endogenous, possibly because the crisis has intensified the mutual relationships between firms and banks.
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Bergantino, A.S., Capozza, C. Banking market structure and industry growth in the Central-East Europe region. Empir Econ 54, 1319–1333 (2018). https://doi.org/10.1007/s00181-017-1262-1
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DOI: https://doi.org/10.1007/s00181-017-1262-1