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Can Firms Grow Without Credit? A Quantile Panel Analysis in the Euro Area

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Abstract

This paper explores the effects of changes in bank credit on firm growth before and after the recent global financial crisis, taking into account firm-specific and country-specific characteristics as well as structural characteristics of domestic banking sectors. Panel quantile analysis is used on a sample of 2075 euro area firms in 2005–2011, enabling thus the identification of potential differences in the dynamics between high-growth and low-growth firms. The post-2008 credit crunch is found to seriously affect mostly high-leveraged, low-growth firms operating in concentrated banking systems with weak foreign presence, and in riskier and less financially developed European economies. By contrast, high-growth firms are not affected and, thus, may be expected to facilitate and sustain the post-crisis credit-less recovery in the euro area. A policy implication of our findings is that creating the right conditions for the emergence of innovative high-growth firms may be a more effective growth strategy, especially in adverse times, as compared to a general policy covering all types of firms.

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Notes

  1. Hölzl (2010) argues that the most fruitful direction for entrepreneurship policy in developed economies is to focus on innovation and firm growth in the Schumpeterian context, being different from SME’s policy which is highly correlated with self-employment.

  2. Several studies (e.g., Dell’ Ariccia et al. 2008; Claessens et al. 2012; Puri et al. 2011; Jimenéz et al. 2012) recognize that in times of financial crises, apart from credit supply, credit demand may also be an important constraining factor of credit growth through the firm balance-sheet channel. In other words, a demand-side explanation of the fall in lending focuses on the generally weak state of borrowers’ balance sheets. This, in turn, leads firms to abandon investment projects and, ultimately, demand for bank credit. Thus, potential borrowers who are more leveraged or possess collateral of lower quality will express lower demand for bank credit and may delay investment.

  3. In this direction, the creation of a capital market union in Europe would be important in offering an alternative credit channel to the business sector and help reduce obstacles to finance (ECB 2015a).

  4. The problem was solved using the algorithms of “R” econometric software, and in particular the command package “rqpd” as more suitable for the purposes of the specific analysis.

  5. A cleaning process was performed in order to reach the final sample of 2075 quoted firms. The initial sample consisted of 7237 firms participating exclusively in the stock market of their country of origin. The cleaning process we undertook was based on the following two steps: first, we excluded firms operating in the financial sector; and second we selected those quoted firms for which data on firm-level variables, (i.e., for firm growth and financial variables such as liquidity and leverage), were available annually over the 7-year period.

  6. The density presented in Fig. 1 is estimated using the bandwidth of 0.5. The bandwidth parameter (i.e., the width of the neighborhood at each point) determines the degree of smoothing in the density under estimation (Silverman 1986). Estimation with different bandwidths does not yield qualitatively different results.

  7. New loans are considered as the typical measure for bank credit growth since they refer to net additions to the existing stock and reflect current credit policy decisions on behalf of the banks. In addition, as it is noted in the ECB’s Economic Bulletin 4/2015 (p.49) “ during the crisis period, “credit-constrained firms”—those firms which in the Survey on the Access to Finance of Enterprises in the Euro Area (ECB 2015b), reported that they had limited access to bank loans - tended to switch to non-bank financing (trade credit, leasing) more often than firms without credit constraints. It appears, though, that firms in the countries most affected by the crisis faced more difficulties in making this switch in financing.” As a result, new loans reflect bank credit conditions in an harmonized manner across the euro area, while credit lines, overdrafts and other forms of financing may be incorporating country-specific aspects of credit provision.

  8. We consider 2009 as the first year of crisis that hit the euro area since in that year all the member countries experienced a sharp recession while, in the previous years, almost all of them exhibited positive GDP growth rates. In particular, the percentage changes in GDP in 2009 by country were the following: Belgium: -2.8 %, Germany: −5.1 %, Estonia: −14.1 %, Ireland: −6.4 %, Greece: −3.1 %, Spain: −3.8 %, France: −3.1 %, Italy: −5.5 %, Cyprus: −1.9 %, Latvia: −17.7 %, Luxembourg: −5.6 %, Malta: −2.8 %, Netherlands: −3.7 %, Austria: −3.8 %, Portugal: −2.9 %, Slovenia: −7.9 %, Slovakia: −4.9 %, Finland: −8.5 % and euro area: −4.5 %. For more details see the European Economic Forecast report provided by European Commission (2013).

  9. Using total assets instead of sales as a proxy for firm size does not alter the results in any significant way. To perform a robustness check we run complementary regressions using total assets as a proxy for firm size. The relevant results are available upon request from the authors.

  10. Several empirical works in industrial economics and financial economics literature examining the effects of financial factors on firm performance (e.g., Fotopoulos and Louri 2004; Greenaway et al. 2007; Tsoukas 2011) use as proxy for firm leverage the ratio of total debt over total assets in order to capture the overall indebtedness of firms or in other words the degree of debt burden. Also, Greenaway et al. (2007) use current ratio as indicator of firm liquidity since it allows to measure a firm’s ability to meet short-term debt obligations. Thus, they stress that the higher the current ratio, the more liquid the firm is. Moreover, Musso and Schiavo (2008) explore the effects of liquidity constraints on firm growth and survival measuring liquidity by the ratio of current assets over current liabilities.

  11. Political risk is a variable that considers jointly factors such as: government stability, socioeconomic stability, investment profile, internal conflict, external conflict, corruption, military involvement in politics, religion involvement in politics, law and order, ethnic tensions, democratic accountability, and bureaucratic quality. Economic risk is composed of GDP per capita, real GDP growth, annual inflation rates, budget balance as a percentage of GDP, and current account balance as a percentage of GDP. Financial risk assesses the ability of a country to finance its official, commercial, and trade debt obligations. This index considers foreign debt as a percentage of the country’s GDP, foreign debt service as a percentage of exports of goods and services, current account as a percentage of exports of goods and services, net international liquidity as the months of import cover, and exchange rate stability.

  12. The Global Financial Development Database is an extensive dataset of financial system indicators for 203 countries, containing annual data until 2011. Čihák et al. (2012) provide an extensive description of the database.

  13. The lack of information covering the whole 2005–2011 period for this variable is the main reason for the classification of countries in the two groups. With the values of foreign bank presence ending in 2009, it could not be possible to estimate our model over the entire period (2005–2011). However, the separate regressions enabled the estimation of differential effects between these two groups.

  14. An alternative to private credit over GDP is total banking assets over GDP, which is also included in the Global Financial Development Database. Compared to private credit, this variable includes also credit to government and bank assets other than credit. However, this proxy is available for a smaller number of countries and has been used less extensively in the literature on financial development. Čihák et al. (2012) find a strong correlation coefficient of about 0.9 between these two proxies.

  15. The estimation results from the respective OLS model are more or less in the same direction regarding firm-specific and country-specific variables. These results are available upon request.

  16. It should be noted that enhanced regulation on liquidity and ring-fencing due to proceeding with the European Banking Union is expected to affect credit provision further and probably increase bank disintermediation (Constancio 2013; Draghi 2014).

  17. Separate estimations for different size groups of firms (micro, small, medium, large) yield interestingly differentiated results as presented in Dimelis et al. (2015).

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Acknowledgments

Thanks are due to Heather Gibson, Svetoslav Danchev, participants at the EARIE 2013, ASSET 2013, and World Finance 2014 conferences, the seminar participants of the University Torcuato Di Tella (UTDT) and the Central Bank of Argentina, as well as to two anonymous referees for many useful and insightful comments and suggestions.

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Authors and Affiliations

  1. Department of Informatics, Athens University of Economics and Business, 76 Patission Str., Athens, 10434, Greece

    Sophia Dimelis

  2. Department of Economics, School of Economy, Management & Informatics, University of Peloponnese, Tripoli Campus, Tripoli, 22100, Greece

    Ioannis Giotopoulos

  3. Bank of Greece, Athens, Greece

    Ioannis Giotopoulos & Helen Louri

  4. Department of Economics, Athens University of Economics and Business, 76 Patission Str., Athens, 10434, Greece

    Helen Louri

  5. European Institute, London School of Economics, London, UK

    Helen Louri

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  1. Sophia Dimelis
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  2. Ioannis Giotopoulos
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Correspondence to Helen Louri.

Appendix

Appendix

Table 8 Summary statistics of country-level variables per euro area country
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Table 9 Summary statistics of country-level variables per year for the euro area
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Table 10 Firm growth quantiles by country (2005–2011)
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Table 11 Evolution of firm growth quantiles by year in the euro area
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Table 12 Firm growth quantiles by sector of activity in the euro area for the 2005–2011 period
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Dimelis, S., Giotopoulos, I. & Louri, H. Can Firms Grow Without Credit? A Quantile Panel Analysis in the Euro Area. J Ind Compet Trade 17, 153–183 (2017). https://doi.org/10.1007/s10842-016-0216-1

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