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Credit allocation and the financial crisis: evidence from Spanish companies

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Abstract

The worldwide financial crisis of 2007–2008 raised serious concerns about the soundness of banks’ activities and about the extent to which banking regulation should supervise banks’ investment decisions. We contribute to this topic by examining the Spanish case, which has been emblematic of the bubble and burst dynamics in the credit market. In particular, we study the allocation of bank credit among Spanish companies from 1999 to 2014, showing that larger companies accumulated greater amounts of bank loans per unit of total assets, thus leading to a notable concentration. We also find that, during the Spanish boom period, bank loans shifted from the manufacturing to the construction industry, and in particular to the largest companies of the latter sector. This happened in spite of the high leverage of large construction firms, which was increasing also due to their growing debt. We argue that the higher operating benefits, reflecting the increase of the housing price during the boom period, overvalued construction firms as potential borrowers. The bankruptcy of several large construction companies during the Spanish crisis supports the need for monitoring and regulation, to avoid an excessive concentration of bank credit to a few large companies, especially if they belong to a specific sector.

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Notes

  1. See, for instance, the recent study “Financial systems in Europe and the United States: Structural differences where banks remain the main source of finance for companies” by the European Savings and Retail Banking Group (ESBG); http://www.wsbi-esbg.org/SiteCollectionDocuments/EU-US.study.ESBG%20May.2016.pdf.

  2. Available at: https://sabi.bvdinfo.com/version-2016119/home.serv?product=sabineo.

  3. According to The Consolidated Spanish Companies Law, all corporations, except for those authorized to present abridged financial statements, must have their financial statements audited. Moreover, according to the European Union legislation, companies are allowed to have abridged financial statements only if at least two of the following thresholds are fulfilled: (1) total assets of €2.85 million or less; (2) annual revenue of €5.7 million or less; and (3) average number of employees during the year of 50 or fewer.

  4. We also use other measures of dispersion such as the Herfindahl index, relative mean absolute deviation and relative median absolute deviation which confirm our findings, and they are available upon request.

  5. The long-term bank debt is the share that is not going to expire within the current accounting year. Therefore, this category includes only loans with a maturity longer than one year.

  6. We mainly checked information on financial leverage of US companies. Gitman et al. (2011) provides data on financial ratio benchmarks based on the RMA Annual Statement Studies from ValuSource (https://www.valusource.com/). For the construction sector, we also checked the CFMA’s Construction Financial Benchmarker Online questionnaires, see, e.g., the one of 2017 (https://secureii.com/cfma/reporting/insights.aspx). Other websites facilitate financial ratios, based on the Internal Revenue Service financial information, e.g., BizStats (http://www.bizstats.com/).

  7. See, for instance, the report on Spain of the European Construction Sector Observatory of the European Commission, published in March 2016; http://ec.europa.eu/growth/sectors/construction/observatory_en.

  8. We prefer ROA to ROE (Return On Equity) because ROA presents a variant of profitability available to both debt and equity investors, while ROE is more specific to equity investors. Operating margin is equivalent to return on sales (ROS).

  9. Due to the low share of bank credit in the Mining & Energy sector (see Fig. 4) we remove it from the following plots for the sake of readability and compactness.

  10. See Marzal-Martínez et al. (2014) and Ortega and Peñalosa (2012), or the Eurofound (http://www.eurofound.europa.eu/) case study on the collapse of Martinsa–Fadesa (the country’s leading real estate company): “Insolvency and restructuring in the Spanish real estate and construction sector: Martinsa-Fadesa”.

  11. We provide in appendix summary statistics (Table 10) as well as a pairwise correlation matrix (Table 11) of all variables used in the models in Sects. 4 and 5.

  12. The variable new loan is calculated as: \(NewLoan_{t} = max(\Delta TotalBankDebt_{t}, 0)\), where \(\Delta TotalBankDebt_{t} = TotalBankDebt_{t}-TotalBankDebt_{t-1}\). While positive amounts of new loan always indicate new loan inflow for a company, zero amounts of new loan can correspond to the following outcomes: (i) no new loans; (ii) new loans are opened up at time t, but in an amount smaller than the amount of expiring debt between \(t-1\) and t; (iii) new loans in t are opened in an amount exactly equal to the amount of debt expiring between \(t-1\) and t. Hence, the variable new loan might underestimate the actual amount of new loans in the analysis.

  13. See summary statistics in Table 10 in appendix.

  14. The dependent variable new loans can be either zero for those companies who do not have new loans at time t, or any positive amount of new loans. Since we estimate our model taking the logarithm of the dependent variable, we first replace all zero values with 0.001 and then take the logarithmic transformation, where all values lower than one become negative. Then, we apply the one-sided Tobit model, where all negative observations are censored to zero.

  15. \(AfterCrisis2008 = 1 \ if \ Year \ge 2008, \ and \ 0 \ otherwise.\)

  16. We define the default as an interruption of the economic activity of a company for any reason linked to financial issues. This is captured by the following values of the “status” variable: bankruptcy; dissolution; inactive; insolvency proceedings; official deregistration; provisional deregistration; provisional deregistration according to Art. 3781 RRMM*; provisional deregistration: financial statements not filed in; suspension of payments proceedings; and winding up.

  17. Note that in our case the set of explanatory variables \(\varvec{Z}_{igt} = \varvec{X}_{igt}\) and it is given in Eq. (4), while the estimation results are provided in Table 6.

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Funding

This work was supported by the EU STREP Project SYMPHONY (FP7-ICT-2013- 10, Grant Agreement 611875) and by the EU STREP project FinMaP (FP7-SSH-2013-2, Grant Agreement 612955).

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Correspondence to Andrea Teglio.

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Appendix

Appendix

1.1 Power law analysis and Hill tail index estimation

We estimate the Hill Tail index (a scaling parameter) presented in Fig. 2b using the Hill Tail estimator as suggested by Bottazzi et al. (2015), and following the procedure given by Clauset et al. (2009) we examine whether economic indicators follow a power low distribution. The index typically lies in the range of \(2< \alpha < 3\), and the lower the tail index, the thicker the tails (see Danielsson (2011) for details). If the index is smaller than 2, a variable has theoretically an infinite variance, which is the case of non-current bank debt, total assets, total bank debt, equity and total sales. The detailed estimations are given in Table 9 confirming that the distributions of all firm size proxies, such as total assets, annual sales, and number of employees, have heavy tails and also appear visually to follow a power law. Upon more careful analysis, a power law distribution is not ruled out, but log-normal distribution sometimes may offer better fit to the data, which is in line with findings of Bottazzi et al. (2015) (Tables 10, 11).

Table 9 Tests of power law behavior in the data sets. For each data set (each variable in each year), we estimate cutoff values \(X_{min}\) together with a scaling parameter (tail index) \(\alpha \) and perform the goodness-of-fit test providing p-values (\(p^{GoF}\)) for the fit to the power law model. The test quantifies the plausibility of the hypothesis, \(H_0\): data follow a power law; \(H_A\): data do not follow a power law. Following Clauset et al. (2009), we set the rejection rule to \(p<0.1\). In addition, we provide likelihood ratios for the alternatives—log-normal, Poisson and exponential distributions, \(LR^{LN}\), \(LR^{POIS}\), \(LR^{EXP}\), respectively. The likelihood-ratio test quantifies the plausibility of the hypothesis, \(H_0\): both distributions are equally far from the true distribution; \(H_A\): one of the test distributions is closer to the true distribution. We also present p-values \(p^{LR}\), \(p^{POIS}\), and \(p^{EXP}\) for each alternative. If we reject \(H_0\) meaning that \(p<0.1\) then the LR values indicates which distribution is favorable. Positive values of the log-likelihood ratios \(LR>0\) indicate that a power law distribution is favored over the alternative
Table 10 Descriptive statistics
Table 11 Pairwise correlations

1.2 Semi-parametric estimator and results

To implement the semi-parametric estimator, we first define our main model such as:

$$\begin{aligned} y_{igt} = \varvec{X}_{igt} \varvec{\beta }_{gt} + \varepsilon _{igt} , \end{aligned}$$
(7)

where y is new bank loans, and \(\varvec{X}\) is a vector of explanatory variables given in Eq. (4). We assume \(d_{igt} = 1\{x_{igt}\gamma _{gt} + \upsilon _{igt}>0\}\), \(y_{igt2}=y_{igt}\cdot d_{igt}\), and that \(f(\varepsilon _{igt},\upsilon _{igt})\) is independent of \(x_{igt}\). Since we do not want to assume a particular distribution f, in the first step we run a Probit model and estimate the probabilities that firms obtain new loans such as:

$$\begin{aligned} d_{igt} = \varvec{Z}_{igt} \varvec{\gamma }_{gt} + \upsilon _{igt} , \end{aligned}$$
(8)

where d is an indicator equal to 1 if a firm gets new bank loans at time t; \(\varvec{Z}\) is a vector of explanatory variables in the first-step selection equation.Footnote 17

In the second step, we choose firm i and j in industry g at time t such that from equations (8) we have the equality \(\varvec{Z}_{igt} \varvec{\hat{\gamma }}_{gt} = \varvec{Z}_{jgt} \varvec{\hat{\gamma }}_{gt}\), i.e., we match firm i and firm j within an industry and year with the same probability of receiving new bank loans and subtract one firm from another. In particular using Eq. (7), we do the following transformation:

$$\begin{aligned} y_{igt} - y_{jgt}= & {} (\varvec{X}_{igt} - \varvec{X}_{jgt}) \varvec{{\beta }}_{gt} + \hat{\lambda }_{gt}(\varvec{Z}_{igt}\varvec{\hat{\gamma }}_{gt}) - \hat{\lambda }_{gt}(\varvec{Z}_{jgt}\varvec{\hat{\gamma }}_{gt})\nonumber \\= & {} (\varvec{X}_{igt} - \varvec{X}_{jgt}) \varvec{{\beta }}_{gt}. \end{aligned}$$
(9)

In practice, we assume a second-order Gaussian kernel function. We calculate and assign the kernel weights to each possible pair of companies within the industry and year, i.e., we use an estimator such as:

$$\begin{aligned}&\bigg [ \sum K \bigg ( \frac{(\varvec{Z}_{igt}-\varvec{Z}_{jgt})\varvec{\hat{\gamma }}_{gt}}{n} \bigg ) (\varvec{X}_{igt} - \varvec{X}_{jgt})(\varvec{X}_{igt} - \varvec{X}_{jgt})' \bigg ]^{-1} \times \nonumber \\&\bigg [ \sum K \bigg ( \frac{(\varvec{Z}_{igt}-\varvec{Z}_{jgt})\varvec{\hat{\gamma }}_{gt}}{n} \bigg ) (\varvec{X}_{igt} - \varvec{X}_{jgt})(\varvec{Y}_{igt} - \varvec{Y}_{jgt})' \bigg ] \end{aligned}$$
(10)

The data-dependent bandwidths are chosen by generalized cross-validation over a crude grid of possible values. Using the same approach, we also recover the intercept terms from the main models that were lost due to the transformation (Tables 12, 13, 14).

Table 12 The dependent variable is the logarithm of new loans that is taken by firms in the current year. The coefficient estimates are from a semi-parametric selection model, for the construction industry and for each year separately
Table 13 The dependent variable is the logarithm of new loans that is taken by firms in the current year. The coefficient estimates are from a semi-parametric selection model, for the manufacturing industry and for each year separately
Table 14 The dependent variable is the logarithm of new loans that is taken by firms in the current year. The coefficient estimates are from a semi-parametric selection model, for the wholesale and retail trade industry and for each year separately

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Petrović, M., Teglio, A. & Alfarano, S. Credit allocation and the financial crisis: evidence from Spanish companies. J Econ Interact Coord 17, 1069–1114 (2022). https://doi.org/10.1007/s11403-022-00361-w

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