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SERIEs

, Volume 5, Issue 2–3, pp 287–332 | Cite as

Are there alternatives to bankruptcy? A study of small business distress in Spain

  • Miguel García-Posada
  • Juan S. Mora-Sanguinetti
Open Access
Original Article

Abstract

Small businesses, the majority of Spanish firms, rarely file for formal bankruptcy when dealing with financial distress. This is why business bankruptcy rates in Spain are among the lowest in the world, even during the current economic crisis. To explain this fact we present the following hypothesis. Filing for bankruptcy in Spain is very costly for both small firms and their creditors. Due to this, the capital structure of micro firms is biased towards mortgage loans, as it allows them to avoid bankruptcy by carrying out debt enforcement via mortgage foreclosures, which are cheaper procedures than bankruptcy, in case of financial distress. The empirical tests of our hypothesis consist of comparing the observed choices (choice of capital structure, choice between bankruptcy and mortgage) of Spanish firms with those of firms from countries (France and the UK) where their bankruptcy systems are more efficient and their laws do not incentivise them to bias their capital structure towards mortgage loans. Our findings corroborate the proposed hypothesis. As bankruptcy procedures and mortgage foreclosures are not perfect substitutes—i.e., they do not suit well the same type of firms- the underutilization of one of them—reflected in low bankruptcy rates- may lead to efficiency losses.

Keywords

Bankruptcy Mortgage Insolvency 

JEL Classification

G33 G21 K0 

1 Introduction

Business bankruptcy rates (ratio of the number of business bankruptcy filings to the number of business exits) in Spain are among the lowest in the world, which means that Spanish firms rarely enter a formal bankruptcy procedure. The goal of this paper is to explain this empirical observation, which may imply that economic agents regard the system as inefficient and try to deal with financial distress in alternative ways.1 For that purpose we employ a large sample of Spanish, French and UK firms, finding that small businesses in Spain, unlike their European counterparts, rely on mortgage foreclosures2 as the main alternative to bankruptcy proceedings.

According to Table 1 Spain had the second lowest bankruptcy rate out of 26 countries, including both high-income and emerging economies, in 2006. An even more striking observation is the difference in the orders of magnitude between Spain and other developed economies: for instance, while there were around 29 bankruptcies per 100 firm exits in France and 16 in the UK, there were 0.3 in Spain. Only the deep economic crisis that Spain is currently experiencing has modestly increased the number of bankruptcy filings, but the Spanish bankruptcy rate was still one of the lowest in the world in 2010 (see Table 1).

In contrast with the low incidence of business bankruptcies, business mortgage foreclosures have soared during the crisis. While around 8,000 firms filed for bankruptcy in 2012, there were nearly 26,000 business mortgage foreclosures3 in the same year. Moreover, the latter figure must be considered a lower bound, since small business owners may finance their firms with loans secured on their homes (Berkowitz and White 2004) but, if lenders repossess the collateral, they will be reflected as residential foreclosures in the official statistics.
Table 1

Business bankruptcy rates around the world

Country

Business bankruptcy rate (2006)

Business bankruptcy rate (2010)

Poland

0.3

0.3

Spain

0.4

1.7

South Korea

0.6

0.2

Greece

1.1

Czech Republic

1.2

2.0

Portugal

1.5

2.4

Singapore

1.5

1.0

Brazil

5.3

0.6

Ireland

3.2

4.9

Italy

4.0

3.7

Slovak Republic

4.5

2.8

USA

4.8

3.9

Canada

9.2

7.9

Denmark

9.5

24.1

Finland

11.7

10.8

Germany

12.2

13.6

Netherlands

12.4

13.7

UK

16.2

14.0

Hungary

16.8

29.3

Sweden

17.9

17.9

Norway

19.6

24.3

France

28.5

31.3

Austria

28.8

32.3

Belgium

30.0

51.6

Luxembourg

30.6

43. 5

Switzerland

43.6

Australia

4.9

Estonia

13.8

Latvia

14.6

Lithuania

2.7

Hong Kong

 

0.9

Business bankruptcy rates are computed as the ratio of the number of business bankruptcy filings to the number of business exits, in %. They include the figures for individual entrepreneurs, except in the UK, where they only represent companies. To enhance comparability across countries, we do not take into account exits from industries with high public sector presence (education, health, social and personal service activities). Source authors’ computations with data from Euler Hermes (2007, 2011), Eurostat, OECD and national sources

However, the use of bankruptcy procedures by Spanish businesses varies widely depending on the size of the distressed firms, as shown in Fig. 1. While the rates of micro firms (businesses with less than 10 employees) were around 0.15 % in 2006 and they have just reached 1.3 % during the economic crisis, those of non-micro firms were 10.4 % in 2006 and they have increased up to 90 % during the crisis, in line with the aggregate rates of developed countries. Since micro firms account for more than the 95 % of firms in Spain,4 they are the key drivers of the low bankruptcy rate of Spanish companies. They are also very important in terms of economic activity: they accounted for 51 % of total employment and 28 % of total value added before the economic crisis and they currently account for 39 and 25 %, respectively.5 Finally, although the available evidence is rather limited, Spanish micro firms seem to file for bankruptcy much less than some of their European counterparts: in 2006, the bankruptcy rates for self-employed and micro enterprises were 0.01 and 0.15 %, respectively, in Spain, while those in France were 11.1 and 23 %,6 and the bankruptcy rate for self-employed in the UK exceeded 16.2 %.7
Fig. 1

Bankruptcy rates by size in Spain. Data are quaterly except for the first period 04C3 (last 4 months of 2004). Rates are annualized. Source: authors’ calculations on data from the Spanish National Statistics Institute. Size is measured in terms of employees. Micro: [0,9], small: [10,49], medium and large: \(>\)50. Non-micro: \(>\)9

Spanish micro firms also have other distinct characteristics. They hold, by far, the largest proportion of mortgage loans over financial debt, as shown in Fig. 2. Filing for bankruptcy is especially unattractive for them because a significant proportion of the bankruptcy costs are fixed (Van Hemmen 2011).8 Personal bankruptcy may apply to many of those firms regardless of their legal form, because the distinction between limited and unlimited liability may be blurred for them, partly because lenders require personal guarantees or security in the form of a mortgage on the owner’s home (Berkowitz and White 2004).
Fig. 2

% Mortgage loans over bank debt by business size in Spain. Source: Authors’ elaboration with data from the Central Credit Register and the Central Balance Sheet Data Office, Banco de España

Consistent with those stylized facts, our hypothesis on the low business bankruptcy rates in Spain is the following. Filing for bankruptcy in Spain is very costly for both small firms and their creditors. Due to this, the capital structure of micro firms is biased towards mortgage loans (i.e., loans secured on land and buildings). Having this capital structure allows them to avoid bankruptcy by carrying out debt enforcement via mortgage foreclosures,9 which are cheaper procedures than bankruptcy, in case of financial distress.

In order to test this hypothesis the optimal identification strategy would be to analyse the impact of substantial changes in the Spanish bankruptcy law in both bankruptcy rates and firms’ capital structure. The current bankruptcy code entered into force in 2004 after a major legislative reform. But it seems that the de facto insolvency framework barely changed because the performance of bankruptcy proceedings did not seem to substantially improve (Gutiérrez 2005; Van Hemmen 2004), bankruptcy rates did not increase after the introduction of the new code and it seems that firms’ capital and asset structures have not changed either (Celentani et al. 2010).

By contrast, our identification strategy relies on cross-country comparisons. Specifically, we compare the observed choices (choice of capital structure, choice between bankruptcy and mortgage) of Spanish firms with those of firms from countries where their bankruptcy systems are more efficient and their laws do not incentivise them to bias their capital structure towards mortgage loans. France and the UK are chosen as the comparison group because their bankruptcy rates are much higher than the Spanish ones and because of the specific features of their insolvency frameworks.10

Our findings corroborate the proposed hypothesis. First, there is a positive and strong correlation between the ex-ante probability of default and the ratio of tangible fixed assets (the assets that can be pledged as mortgage collateral) to financial debt in the case of Spanish micro firms, suggesting that firms with risky business models bias their capital structure towards mortgage loans to avoid filing for bankruptcy in the event of default. Second, a higher proportion of tangible fixed assets over financial debt significantly decrease the probability of being in bankruptcy among Spanish micro firms in financial distress. By contrast, these two relations do not hold either for Spanish larger businesses or for firms from the other two countries.

Finally, we must stress the importance of the research question. The model of García-Posada (2013) predicts that, in the context of the Spanish insolvency framework, there is a positive relation between bankruptcy rates and welfare. The intuition is that low bankruptcy rates and low welfare are the outcome of an institutional design characterised by the low efficiency and low creditor protection of the bankruptcy system relative to those of an alternative insolvency institution, the mortgage system. In that context, firms and their creditors avoid filing for bankruptcy by heavily relying on mortgage collateral, which can be repossessed and liquidated in the event of default. The problem is that the mortgage system is not well suited for some firms, which need to bias their asset structure to have enough collateral, with the ensuing productive inefficiencies. Those firms would be better off if they had access to a bankruptcy system that worked relatively well. In other words, as the bankruptcy and mortgage systems are imperfect substitutes, the equilibrium in which only mortgage is widely used (reflected in low bankruptcy rates) is Pareto dominated by the equilibrium in which agents can choose between the two insolvency institutions (reflected in higher bankruptcy rates). His analysis also predicts that bankruptcy will be unfeasible for the smallest firms in the economy as long as some of the bankruptcy costs are fixed. As some of those firms will have to overinvest in capital assets to sign their contracts under mortgage, they will incur in productive inefficiencies. If the absence of a well-functioning bankruptcy system for those firms also reduces their growth opportunities—e.g., by hampering access to unsecured lending such as venture capital—then the current insolvency framework may help explain the firm size distribution and the low aggregate productivity of the Spanish economy. This is consistent with the evidence of Fabbri (2010) in Spain, who finds that lengthy bankruptcy procedures decrease firm size and raise funding costs and with that of Ponticelli (2012) in Brazil, who shows that congestion in bankruptcy courts substantially reduces firm-level investment and productivity.

The rest of the paper is structured as follows. Section 2 provides a brief literature overview and discusses the paper’s main contributions. Section 3 discusses some key features of the insolvency framework of Spain, France and the UK Sect. 4 focuses on data sources and sample selection criteria. Section 5 explains the empirical testing of the hypothesis. Section 6 concludes. Appendix A provides a description of the main legal concepts used in this paper and Appendix B contains some robustness analyses.

2 Contribution and related literature

This paper is mainly related to the works of Morrison (2008, 2009), and Celentani et al. (2010, 2012). Morrison (2008, 2009) studied why US small distressed firms—defined as those with 500 or fewer employees—rarely file for bankruptcy. He argued that there are cheaper procedures for these firms, such as assignments for the benefit of creditors,11 bulk sales,12 foreclosures and private workouts. Their implementation, however, require that neither the debtor firm nor the creditors’ file for bankruptcy. They also face, unlike the bankruptcy system, major coordination and asymmetric information problems that may hamper their use. Thus he identified the conditions under which these problems are not very important so those procedures can be implemented: small firms, with simple capital structures (i.e., low number of secured creditors) and with close and trustworthy relationships with their creditors are likely to avoid filing for bankruptcy. This paper applies a similar reasoning to the Spanish, British and French case: wherever there are cheaper alternatives to bankruptcy, the latter will only be used when parties don’t reach an agreement, becoming the residual option.

Celentani et al. (2010, 2012) were the first that studied the low bankruptcy rates in Spain. They proposed an explanation that was not immediately contradicted by a number of aggregate stylized facts. Specifically, they used the theoretical prediction of Ayotte and Yun (2009), according to which low creditor protection and low judicial ability imply low bankruptcy rates, to conjecture a wide set of activities (leverage reduction, lenders’ screening and monitoring, choice of projects that trade off return for lower risk and/or lower liquidation costs, use of mortgage collateral) in which firms and their creditors could engage to reduce the probability of bankruptcy. This paper focuses on one of their ideas, the use of mortgage foreclosures as an alternative to formal bankruptcy procedures. To the best of our knowledge, this is the first study that addresses the research question with firm-level data, which allows testing the hypothesis by means of econometric analyses.

3 Insolvency frameworks

In this section we will focus on the features of the insolvency frameworks of Spain, France and the UK related to our hypothesis, namely, the choice between bankruptcy procedures and mortgage foreclosures and the choice of firms’ capital and asset structures. We will examine the incentives to file for bankruptcy of both the debtor firm and its creditors, as alternative procedures such as mortgage foreclosures can only take place if both parties refrain from filing. For a more thorough analysis of the insolvency frameworks see Celentani et al. (2010, 2012) and Davydenko and Franks (2008).

3.1 Spain

The Spanish bankruptcy system (Ley Concursal) only had, until very recently, an insolvency procedure, the concurso de acreedores (bankruptcy13), both for firms and individual debtors.14 Both the debtor and the creditors may initiate the proceedings.

Bankruptcy procedures are costly and lengthy, rendering them unappealing for both distressed firms and their creditors. The direct costs of bankruptcy are high, as those procedures are complex, uncertain, involve many creditors and face high information asymmetries between the company and its lenders, requiring a great deal of intervention by the court, insolvency administrators, lawyers, etc. According to the Doing Business estimates, those costs would account for a 15 % of the firm’s total assets. As a substantial part of those costs are fixed (Van Hemmen 2008), bankruptcy procedures are especially costly in the case of small firms. The median duration of a bankruptcy process ranged between 20 and 23 months15 (Van Hemmen 2008) before the economic crisis. The modest increase in the number of bankruptcy filings due to the crisis has congested the courts and lead to a dramatic increase in the length of the procedures, which ranged between 28 and 42 months in 2011 (Van Hemmen 2012).16 Finally, as the law does not provide any debt discharge for individuals17 and homestead exemptions are very low, individual debtors—including self-employed people and owners of small limited-liability firms that pledge personal guarantees to obtain funding for their businesses—have no incentives to file for bankruptcy.

Mortgage foreclosures are much cheaper and quicker than bankruptcy procedures, as they are quite standardised processes with a low degree of uncertainty about its final outcome. According to European Mortgage Federation (2007), their total costs are between the 5 and 15 % of the price obtained in the auction of the collateral (the percentage decreases as the sale price increases), and their usual length is 7–9 months.18

Hence, mortgage foreclosures are an attractive alternative to bankruptcy, especially in the case of small firms. But, to make possible that a firm and their creditors use the mortgage system in case of financial distress, the firm’s capital structure must be biased towards mortgage loans and their asset structure must be biased towards assets—such as land and buildings—that can be pledged as mortgage collateral.

3.2 France

The redressement judiciaire (judicial reorganization) and the liquidation judiciaire (judicial liquidation) are the main insolvency procedures for corporations in France. As for personal bankruptcy, which may apply to both consumer and entrepreneurs, there are two different procedures: the plan de redressement (reorganization plan) and the procedure de rétablissement personnel (procedure of personal recovery). The debtor, creditors, the public prosecutor and the court itself may initiate the proceedings.

Bankruptcy procedures are relatively cost-effective. According to the Doing Business estimates, the direct costs would account for a 9 % of the firm’s total assets and the average duration in 2007 was 14.2 months (Ministère de la Justice 2010). Moreover, self-employed and small business owners may have incentives to file for personal bankruptcy as they may benefit from debt discharge in some circumstances.19

Another characteristic of the bankruptcy system is the high dilution that mortgage credit suffers inside bankruptcy (Davydenko and Franks 2008). First, there is an automatic stay for secured creditors until the end of the procedure. Second, bankruptcy courts tend to sell the assets below their potential market prices, as they are not obliged to sell the assets to the highest bidder, but they can sell the whole company to a lower bidder that commits to preserve employment, as creditors’ approval is not required for the sale of their collateral. Third, the state places its own claims and those of employees first in priority when the collateral is sold.

In that context, mortgage creditors would like to enforce their claims outside bankruptcy via mortgage foreclosures, but they are quite slow and expensive. According to European Mortgage Federation (2007), their total costs are between the 10 and 12 % of the price of allocation and their usual length is between 15 and 25 months. As a result, the response of creditors is to rely more on some types of collateral—such as personal guarantees and accounts receivable—that can be realised directly by secured creditors and are not diluted by preferential creditors. These collateral types are used more often than mortgage collateral (Davydenko and Franks 2008).

Hence, mortgage foreclosures are not an attractive alternative to bankruptcy and mortgage collateral is not a very appealing guarantee. As a consequence, we expect mortgage loans to have little weight in the firms’ capital structure and the assets that can be pledged as mortgage collateral—such as land and buildings—to account for a low proportion of their total assets.

3.3 UK

Although various corporate insolvency procedures coexist in the UK, administration is the most important one since the entry into force of the Enterprise Act 200220 and bankruptcy is the most common procedure used by individuals.21 Both the debtor and the creditors may initiate the proceedings.

Bankruptcy procedures are quite cheap and fast. According to the Doing Business estimates, the direct costs would account for a 6 % of the firm’s total assets and their average duration would be \(<\)1 year (Armour and Hsu 2012; Frisby 2006). In the case of personal bankruptcy, debt discharge is allowed one year after the end of the procedure, providing incentives to small firms and self-employed to file for bankruptcy.

Another characteristic of the UK insolvency framework is the existence of floating charges. A floating charge is a security interest over a fund of a firm’s changing assets that “floats” until it “crystallises” (converts) into a fixed charge,22 at which point the charge attaches to specific assets. The crystallisation can be triggered by a number of events, being one of them the borrower’s default. There are two main differences between a floating charge and other security interests such as a mortgage. First, because the security “floats”, the firm remains free to purchase and sell its assets. Second, the assets of the entire business can be pledged as collateral. Those characteristics grant high flexibility to the firm’s asset structure and permit it not to be biased through certain types of assets such as land and buildings.

Mortgage foreclosures are neither significantly faster nor cheaper than bankruptcy procedures. According to European Mortgage Federation (2007) their usual length is between 8 and 12 months and their total costs are around 5 %. These facts, together with the existence of floating charges, lead us to expect a low incidence of foreclosures and a relatively low weight of mortgage loans (land and buildings) in firms’ capital (asset) structures.

Table 2 summarises the main characteristics of the insolvency frameworks of Spain, France and the UK.
Table 2

Insolvency frameworks in Spain, France and the UK

 

Bankruptcy

Mortgage

Panel A: Spain

   Duration (months)

20–23

7–9

   Cost (% assets)

15 %

5–15 %

   Discharge for individual debtors?

No

No

Panel B: France

   Duration (months)

14.2

15–25

   Cost (% assets)

9 %

10–12 %

   Discharge for individual debtors?

Yes

No

   Other characteristics

High dilution of mortgage credit inside bankruptcy

 

Panel C: UK

   Duration (months)

\(<\)12

8–12

   Cost (% assets)

6 %

5 %

   Discharge for individual debtors?

Yes

No

   Other characteristics

Floating charge

 

Sources: European Mortgage Federation (2007), Van Hemmen (2008), Ministère de la Justice (2010), Kindly check Armour and Hsu (2012) given in Table footnote not present in reference list, Frisby (2006), Doing Business Database

4 Data

The firm-level data come from the OECD-Orbis database, which is the result of the treatment of the commercial database Orbis by the OECD (Ribeiro et al. 2010; Ragoussis and Gonnard 2011). Orbis contains financial information on both private and publicly held companies around the world although coverage, especially of small firms, greatly varies across countries. Orbis also provides other non-financial information, such as year of incorporation, industry, legal form and status. Status is a variable that tells the legal and economic condition of the firm: for instance, if the company is active or it has ceased its operations and if it is undergoing a bankruptcy procedure or not. The data have, however, some important limitations. First, if a business shut down without filing for bankruptcy, the records do not indicate which alternative procedure the firm used.23 Second, the status is only observed at the moment in which the data are extracted from the database, i.e., no historical records are kept. Since the data from Orbis were extracted in 2010 (December 31, 2010), we have the status of each company at that time. Finally, as Orbis is a commercial database, our sample may not be representative of the whole population. We address this potential criticism in Appendix B.

Regarding the sample selection, we use data on firms from three countries: Spain, France and the UK We only keep their financial data for 2008 because of two reasons. First, the main variable in all our analyses will be constructed using the information on status, which is only available for 2010. This makes panel data an unfeasible structure for the sample, since the variation in the main variable will happen across sections, but not across time. Second, because of the time lag in the submission of financial statements by firms, the Orbis database is characterised by a typical time lag of 2 years (Ribeiro et al. 2010), which implies that coverage (in number of companies with complete records) for 2009 and 2010 is very poor, leaving 2008 as the best choice. While this time gap could be problematic, it alleviates a simultaneity bias that may arise if the bankruptcy process or the alternative insolvency procedure affects the company’s financials (e.g. a foreclosure on some of the firm assets or a debt haircut), as our regressors will be lagged twice. We also apply some filters to clean the data. We exclude state-owned companies, non-profit organisations and membership organisations. To avoid double-counting of information we eliminate all consolidated accounts for which unconsolidated information exists. Finally, we remove inconsistent observations24 and extreme values. Our final sample has more than 560,000 firm-level observations.

For the empirical analyses of this paper it is crucial to distinguish between financially distressed firms and non-distressed ones. While it is probably safe to assume that all firms under bankruptcy proceedings are distressed (rarely will a healthy business file a bankruptcy petition), for the rest of observations we proxy distressed businesses as those whose interest coverage ratio (EBITDA25 over interest expenses) is lower than 1.

We then construct several variables. Bankruptcy is a dummy variable that equals 1 if the firm was bankrupt when the data were extracted (2010). To measure the probability of default we use the Altman’s Z-Score (Altman 2000).26 As the Orbis database does not contain specific information on mortgage loans, we need to construct a proxy for the proportion of those loans on total debt. The proposed proxy is “Tangibility”, which is computed as the ratio between tangible fixed assets (land, buildings, plant and machinery)27 to financial debt, in percentage terms. Since tangible fixed assets are the only assets that can be used as mortgage collateral in Spain, we relate those assets with the debts they may secure. For robustness, we have carried out all this paper’s analyses with an alternative proxy that includes trade credit, namely the ratio between tangible fixed assets to total debt, reaching very similar conclusions, which is not surprising given the high correlation between the two proxies.28

As controls, we use a dummy that equals 1 if the firm has limited liability, the firm’s age, the firm’s size—computed as the number of employees29—and industry dummies. According to Berger and Udell (1995) and Petersen and Rajan (1994), firm’s age captures the public reputation of the firm, since they find a negative relationship between firms’ age and interest rate premium charged by banks. Davydenko and Franks (2008) interpret age as a proxy for information asymmetries between a firm and its lenders, since they find negative impact of age on the probability of filing for bankruptcy (vis-à-vis using out-court procedures). Age may also capture coordination costs, as older firms are more likely to maintain multiple bank relationships (Hernández-Cánovas and Köeter-Kant 2008). With respect to firm’s size, small firms may file less for bankruptcy if a substantial proportion of the bankruptcy costs are fixed (Morrison 2008) or if personal insolvency laws are very severe, although the relationship between size and bankruptcy need not be linear because very large firms may prefer to avoid the adverse publicity of a bankruptcy filing. To correct for right skewness we will take logs of age and size in our statistical analyses.

Table 3 shows the descriptive statistics of the variables for the non-distressed firms differentiating by country and size class (micro and non-micro). Spanish firms are smaller and younger than their French and UK counterparts in both groups. More remarkably, their mean levels of Tangibility are substantially higher than those of UK and France in the case of micro firms (Panel A), while only slightly higher in the case of larger firms (Panel B). We obtain similar results in the case of distressed firms (Table 4). Tangibility is also higher in Spain than in the other countries when we disaggregate by industry (Table 5). This evidence supports our hypothesis that Spanish firms have their capital structure biased towards mortgage collateral as a response to the particular insolvency framework they face, as in countries where the bankruptcy system is more effective vis-à-vis mortgage and the law grants less protection to mortgage creditors relative to other secured creditors (France, UK) firms have less tangible fixed assets relative to their financial debt.
Table 3

Descriptive statistics (non-distressed firms)

 

Obs.

Mean

Std. dev.

Min

Max

Panel A: micro firms

   Spain

      Tangibility

143,491

148.2

148.0

0

797.0

      Limited liability

143,491

1.00

0.01

0

1

      Age

143,413

12.9

7.2

3

111

      Size

143,491

3.9

2.4

1

9

      Z-Score

143,491

2.1

3.1

\(-12\)

16.5

   France

      Tangibility

145,361

114.2

116.6

0

620.2

      Limited liability

145,361

1.00

0.06

0

1

      Age

145,342

12.1

9.7

3

177

      Size

145,361

3.3

2.3

1

9

      Z-Score

145,361

1.2

3.6

\(-13\)

16.8

   UK

      Tangibility

2,668

104.7

142.3

0

909.1

      Limited liability

2,668

1.00

0.03

0

1

      Age

2,668

16.6

14.8

3

149

      Size

2,668

4.3

2.5

1

9

      Z-Score

2,668

2.9

5.6

\(-23\)

26.9

Panel D: non-micro firms

   Spain

      Tangibility

64,750

157.0

149.8

0

797.0

      Limited liability

64,750

1.00

0.02

0

1

      Age

64,705

18.2

10.3

2

169

      Size

64,750

48.0

413.0

10

63.629

      Z-Score

64,750

2.5

2.5

\(-12\)

16.3

   France

      Tangibility

39,293

150.1

132.1

0

620.8

      Limited liability

39,293

0.99

0.09

0

1

      Age

39,293

23.5

16.4

3

211

      Size

39,293

61. 5

499.6

10

72,199

      Z-Score

39,293

2.6

2.4

\(-13\)

15.8

   UK

      Tangibility

16,923

147.3

169.4

0

931.3

      Limited liability

16,923

1.00

0.05

0

1

      Age

16,923

26.3

20.4

3

155

      Size

16,923

348.8

2116.5

10

105,664

      Z-Score

16,923

2.6

3.0

\(-22\)

21.6

Tangibility is the ratio between tangible fixed assets to financial debt, in %. Limited liability is a dummy that equals 1 if the firm is a limited-liability company and 0 otherwise. Age is number of years since registration. Size is the number of employees. Z-Score is the Altman’s Z-Score for non-listed firms that do not necessarily belong to the manufacturing sector (Altman 2000)

5 Empirical analyses

Our hypothesis on the low business bankruptcy rates in Spain leads to two testable hypotheses regarding firms’ behaviour ex-ante (i.e., prior default) and ex-post. From the ex-ante perspective, as filing for bankruptcy is very costly, small firms with risky business models will bias their capital structure towards mortgage loans to avoid filing for bankruptcy in the event of default. From the ex-post perspective, holding mortgage debt will reduce the probability of filing for bankruptcy by a small financially distressed firm. These two implications should not occur in the case of either Spanish larger businesses or firms from the other two countries.

5.1 Ex-ante perspective: capital structure and business risk

We run within-country regressions to assess the sign and size of the relationship between the proxy for the percentage of mortgage debt, Tangibility, and the ex-ante probability of default, as measured by the Altman’s Z-score.30 We only use, from our sample, non-distressed firms, i.e., those whose interest coverage ratio is equal or greater than 1, which are not under bankruptcy procedures and are active in the market. We split the data into two sub-samples, one for micro firms and another one for non-micro firms.
Table 4

Descriptive statistics (distressed firms)

 

Obs.

Mean

Std. dev.

Min

Max

Panel A: micro firms

   Spain

      Bankruptcy

31,009

0.03

0.2

0

1

      Tangibility

31,009

94.3

101.1

0

540.2

      Limited liability

31,009

1.00

0.01

0

1

      Age

30,987

12.2

7.7

3

111

      Size

31,009

3.5

2.3

1

9

      Z-Score

31,009

\(-1.4\)

5.5

\(-22.4\)

20.0

   France

      Bankruptcy

34,677

0.13

0.3

0

1

      Tangibility

34,677

66.3

74.1

0

394.7

      Limited liability

34,677

1.00

0.07

0

1

      Age

34,676

12.6

10.9

2

197

      Size

34,677

3.9

2.2

1

9

      Z-Score

34,677

\(-2.5\)

5.1

\(-22.0\)

17.9

   UK

      Bankruptcy

1,710

0.12

0.3

0

1

      Tangibility

1,710

70.8

89.8

0

504.4

      Limited liability

1,710

1.00

0.02

0

1

      Age

1,710

14.4

13.0

3

106

      Size

1,710

6.1

2.4

1

9

      Z-Score

1,710

\(-3.3\)

7.6

\(-33.9\)

19.0

Panel B: non-micro firms

   Spain

      Bankruptcy

8,583

0.14

0.3

0

1

      Tangibility

8,583

94.7

101.5

0

540.0

      Limited liability

8,583

1.00

0.03

0

1

      Age

8,577

17.2

11.2

3

169

      Size

8,583

38.7

110.6

10

3,538

      Z-Score

8,583

\(-0.9\)

4.6

\(-22.4\)

17.8

   France

      Bankruptcy

12,783

0.22

0.4

0

1

      Tangibility

12,783

80.8

65.7

0

394.1

      Limited liability

12,783

0.98

0.14

0

1

      Age

12,783

19.4

16.0

3

208

      Size

12,783

40.1

181.4

10

15.521

      Z-Score

12,783

\(-1.2\)

4.5

\(-22.0\)

18.1

   UK

      Bankruptcy

7,051

0.15

0.4

0

1

      Tangibility

7,051

78.6

91.6

0

518.5

      Limited liability

7,051

1.00

0.06

0

1

      Age

7,051

19.5

19.2

3

141

      Size

7,051

192.6

1022.8

10

40,855

      Z-Score

7,051

\(-2.3\)

6.5

\(-34.1\)

13.9

Bankruptcy is a dummy that equals 1 if the firm is bankrupt and 0 otherwise. Tangibility is the ratio between tangible fixed assets to financial debt, in %. Limited liability is a dummy that equals 1 if the firm is a limited-liability company and 0 otherwise. Age is number of years since registration. Size is the number of employees. Z-Score is the Altman’s Z-Score for non-listed firms that do not necessarily belong to the manufacturing sector (Altman 2000)

Table 5

Descriptive statistics of tangibility by industry (distressed and non-distressed firms)

 

Spain

France

UK

Panel A: distressed firms

   Primary sector

      Mean

129.6

106.7

114.5

      St. Dev.

(113.2)

(86.5)

(120.7)

      N

1,151

717

180

   Manufacturing and energy

      Mean

109.6

85.0

70.2

      St. Dev.

(102.0)

(85.4)

(84.1)

      N

5,083

5,299

1,110

   Construction

      Mean

90.9

76.4

78.7

      St. Dev.

(100.8)

(74.6)

(93.9)

      N

5,604

4,684

660

   Market services

      Mean

88.9

65.5

75.7

      St. Dev.

(99.7)

(76.0)

(89.4)

      N

25,854

33,415

5,927

   Non-market services

      Mean

117.2

77.0

86.2

      St. Dev.

(100.8)

(77.4)

(97.7)

      N

1,900

3,345

884

Panel B: non-distressed firms

   Primary sector

      Mean

179.4

156.4

171.7

      St. Dev.

(156.5)

(112.7)

(190.3)

      N

5,998

4,416

385

   Manufacturing and energy

      Mean

163.2

140.5

160.2

      St. Dev.

(149.2)

(126.8)

(175.1)

      N

35,928

23,208

4,636

   Construction

      Mean

137.5

143.9

153.8

      St. Dev.

(141.3)

(117.49)

(174.3)

      N

34,723

31,173

1,821

   Market services

      Mean

147.0

111.5

127.7

      St. Dev.

(148.8)

(120.0)

(158.0)

      N

118,979

110,894

11,020

   Non-market services

      Mean

175.8

113.2

159.1

      St. Dev.

(154.0)

(118.6)

(175.3)

      N

12,613

14,963

1,729

Tangibility is the ratio between tangible fixed assets to financial debt, in %. Non-market services are education, health, social work and personal service activities

Five different specifications, where Age, Size (both in logs), the limited liability dummy and 488 industry dummies31 are used as controls, are estimated through Tobit regressions. The results are displayed in Tables 6 (micro firms) and 7 (non-micro). The coefficient on the Z-score is negative for Spanish micro firms. As a lower Z-score represents a higher probability of default, the negative sign means that riskier firms rely more on mortgage collateral, suggesting that those firms bias their capital structure towards mortgage loans to avoid filing for bankruptcy if they experienced financial distress. The impact is also economically significant: a unit-decrease in the Z-score would increase Tangibility between two or three percentage points, depending on the specification. By contrast, the coefficient on the Z-score is positive in the rest of cases: firms with risky business models usually have little collateral to pledge, as their main assets are know-how, intellectual property -often unregistered- and firm-specific human capital and machinery.32
Table 6

Determinants of tangibility (micro firms)

 

(1)

(2)

(3)

(4)

(5)

Spain

   Z-Score

\(-1.89\)*** (0.15)

\(-2.77\)*** (0.16)

\(-2.78\)*** (0.16)

\(-1.93\)*** (0.16)

\(-1.93\)*** (0.16)

   Log (age)

 

31.92*** (0.72)

30.04*** (0.73)

28.51*** (0.74)

28.49*** (0.74)

   Log (size)

  

10.86*** (0.57)

9.34*** (0.59)

9.34*** (0.59)

   Limited liability

    

\(-60.01\) (61.27)

   Industry dummies (4 digits)

No

No

No

Yes

Yes

   N

143,491

143,413

143,413

143,413

143,413

   Pseudo-R2 (%)

0.01

0.12

0.14

0.38

0.38

France

   Z-Score

5.11*** (0.09)

3.67*** (0.09)

3.64*** (0.09)

3.63*** (0.10)

3.63*** (0.10)

   Log (age)

 

32.01*** (0.49)

30.70*** (0.51)

31.22*** (0.51)

31.25*** (0.51)

   Log (size)

  

4.84*** (0.43)

4.17*** (0.44)

4.16*** (0.44)

   Limited liability

    

20.27*** (4.71)

   Industry dummies (4 digits)

No

No

No

Yes

Yes

   N

145,361

145,342

145,342

145,342

145,342

   Pseudo-R2 (%)

0.22

0.47

0.48

0.94

0.94

UK

   Z-Score

2.98*** (0.40)

2.95*** (0.40)

2.95*** (0.40)

3.84*** (0.43)

3.83*** (0.43)

   Log (age)

 

33.86*** (3.96)

33.83*** (4.04)

30.25*** (4.28)

30.24*** (4.28)

   Log (size)

  

0.17 (4.18)

7.87* (4.19)

7.85* (4.19)

   Limited liability

    

22.68 (30.46)

   Industry dummies (4 digits)

No

No

No

Yes

Yes

   N

2,668

2,668

2,668

2,668

2,668

   Pseudo-R2 (%)

0.10

0.34

0.34

1.62

1.62

Dependent variable: tangibility. Estimator: Tobit. Industry is defined at 4 digits of disaggregation, leading to 488 different dummies. All regressions include a constant. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

Table 7

Determinants of tangibility (non-micro firms)

 

(1)

(2)

(3)

(4)

(5)

Spain

   Z-Score

6.88*** (0.29)

6.16*** (0.29)

6.03*** (0.29)

8.28*** (0.29)

8.28*** (0.29)

   Log (age)

 

15.89*** (1.05)

19.75*** (1.08)

17.95*** (1.12)

17.94*** (1.12)

   Log (size)

  

\(-11.44\)*** (0.77)

\(-11.80\)*** (0.78)

\(-11.81\)*** (0.78)

   Limited liability

    

\(-48.66\) (47.28)

   Industry dummies (4 digits)

No

No

No

Yes

Yes

   N

64,750

64,705

64,532

64,532

64,532

   Pseudo-R2 (%)

0.10

0.13

0.15

0.54

0.54

France

   Z-Score

8.66*** (0.28)

7.17*** (0.29)

7.27*** (0.29)

9.15*** (0.31)

9.15*** (0.31)

   Log (age)

 

25.10*** (0.99)

23.17*** (1.03)

22.84*** (1.05)

22.85*** (1.05)

   Log (Size)

  

6.29*** (0.87)

5.33*** (0.89)

5.32*** (0.89)

   Limited liability

    

\(-2.05\) (8.35)

   Industry dummies (4 digits)

No

No

No

Yes

Yes

   N

39,293

39,293

38,818

38,818

38,818

   Pttudo-R2 (%)

0.21

0.33

0.35

1.03

1.03

UK

   Z-Score

9.72*** (0.45)

8.59*** (0.44)

8.67*** (0.45)

11.04*** (0.48)

11.04*** (0.48)

   Log (age)

 

31.29*** (1.70)

30.92*** (1.71)

27.12*** (1.81)

27.16*** (1.81)

   Log (size)

  

1.80* (0.98)

0.22 (1.01)

0.27 (1.01)

   Limited liability

    

27.21 (30.97)

   Industry dummies (4 digits)

No

No

No

Yes

Yes

   N

16,923

16,923

16,923

16,923

16,923

   Pseudo-R2 (%)

0.23

0.37

0.37

1.02

1.02

Dependent variable: tangibility. Estimator: Tobit. Industry is defined at 4 digits of disaggregation, leading to 488 different dummies. All regressions include a constant. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

5.2 Ex-post perspective: capital structure and bankruptcy risk

A first descriptive check can be found in Table 8, where we split our sub-sample of distressed firms into bankrupt and non-bankrupt for each size class and each country. In the case of micro firms (panel A), distressed non-bankrupt firms have much higher levels of Tangibility in Spain, while the opposite occurs in France and the UK. Non-micro firms follow the same pattern, but the positive gap between bankrupt and non-bankrupt in Spain is now smaller. As expected, non-bankrupt Spanish firms are smaller and younger for both size classes, while those patterns are not so clear in France and the UK.
Table 8

Descriptive statistics by size and BANKRUPTCY status (unweighted sample)

 

Spain

France

UK

Panel A: micro firms

 

Firms with BANKRUPTCY = 0

Firms with BANKRUPTCY = 0

Firms with BANKRUPTCY = 0

 

Mean

St. Dev.

N

Mean

St. Dev.

N

Mean

St. Dev

N

   Tangibility

85.7

101.5

45,997

49.5

69.9

42,838

67.4

89.1

1,634

   Log (age)

2.3

0.6

45,967

2.2

0.7

42,835

2.4

0.7

1,634

   Log (size)

1.0

0.7

45,997

1.1

0.7

42,838

1.7

0.6

1,634

 

Firms with BANKRUPTCY = 1

Firms with BANKRUPTCY = 1

Firms with BANKRUPTCY = 1

 

Mean

St. Dev.

N

Mean

St. Dev.

N

Mean

St. Dev

N

   Tangibility

55.8

81.7

1,713

65.0

75.9

5,023

98.6

110.8

753

   Log (age)

2.3

0.6

1,713

2.4

0.6

5,023

2.3

0.6

753

   Log (size)

1.2

0.7

1,713

1.3

0.7

5,023

1.9

0.4

753

 

Differences in BANKRUPTCY = 0, 1

Differences in BANKRUPTCY = 0, 1

Differences in BANKRUPTCY = 0, 1

 

Diff. (means)

p value

Diff. (means)

p value

Diff. (mean)

p value

   Tangibility

29.9

0.00

 

\(-15.5\)

0.00

\(-31.2\)

0.00

   Log (age)

\(-0.1\)

0.00

 

\(-0.2\)

0.00

0.1

0.00

   Log (size)

\(-0.2\)

0.00

 

\(-0.2\)

0.00

\(-0.2\)

0.00

Panel B: non-micro firms

 

Firms with BANKRUPTCY = 0

Firms with BANKRUPTCY = 0

Firms with BANKRUPTCY = 0

 

Mean

St. Dev.

N

Mean

St. Dev.

N

Mean

St. Dev.

N

   Tangibility

89.5

103.6

10,008

62.5

82.3

13,374

74.2

90.8

6,228

   Log (age)

2.5

0.7

9,993

2.5

0.8

13,374

2.6

0.8

6,228

   Log (size)

3.1

0.8

10,008

3.2

0.9

13,374

3.8

1.3

6,228

 

Firms with BANKRUPTCY = 1

Firms with BANKRUPTCY = 1

Firms with BANKRUPTCY = 1

 

Mean

St.Dev.

N

Mean

St. Dev.

N

Mean

St. Dev.

N

   Tangibility

69.2

79.4

1,469

81.2

87.4

2,965

108.7

112.7

2,601

   Log (age)

2.7

0.6

1,469

2.7

0.7

2,965

2.3

0.7

2,601

   Log (size)

3.2

0.8

1.469

3.0

0.7

2,965

3.0

0.8

2,601

 

Differences in BANKRUPTCY = 0, 1

Differences in BANKRUPTCY = 0, 1

Differences in BANKRUPTCY = 0, 1

 

Diff. (means)

p value

Diff. (means)

p value

Diff. (means)

p value

   Tangibility

20.3

0.00

\(-18.7\)

0.00

\(-34.5\)

0.00

   Log (age)

\(-0.1\)

0.00

\(-0.2\)

0.00

0.3

0.00

   Log (size)

\(-0.1\)

0.00

0.2

0.00

0.8

0.00

The statistical significance of differences in means is evaluated through one-sided p values of two-sample t tests. These tests can be implemented with and without the assumption of equal population variances. In order to ascertain whether this assumption is plausible, two tests for the equality of variances have been implemented in each case. The selected tests are those of Brown and Forsythe (1974), since they are robust to non-normality and the variables of this study have been found to be non-normal

A more thorough test consists of running within-country regressions to assess the sign and size of the relationship between the proxy for the percentage of mortgage debt, Tangibility, and the probability of filing for bankruptcy by a financially distressed firm in each country, once other determinants are controlled for. We split the data into two sub-samples, one for micro firms and another one for non-micro firms. In analytical terms what we estimate is the following model:
$$\begin{aligned}&P({{ Bankruptcy}_{i}/{ FinancialDistress}})\\&\quad =f({{ Tangibility}_i ,{ Control}1_i ,\ldots ,{ ControlK}_i ,u_i}) \end{aligned}$$
Notice that we do not face a sample selection bias by only keeping the financially distressed firms. Denoting \( S_{i}\) as a selection indicator that equals 1 if the observation is included in the sample and 0 otherwise, and icr the interest coverage ratio: \(S_{i}=1\) if icr\(_{i}<\)1; \(S_{i}=0\) if icr\(_{i}\ge \)1. As long as icr is uncorrelated with \(u\), the unobserved that factors that influence the decision to file for bankruptcy conditional on being in financial distress, our sampling mechanism \(S\)(icr) will be exogenous. As our dependent variable, BANKRUPTCY, is measured in 2010, while the interest coverage ratio icr is measured in 2008, it seems safe to assume that BANKRUPTCY cannot have any influence on icr, implying \(E\)[\(u_{i}/S\)(icr\(_{i})\)] \(=\) 0.
In the case of micro firms, the first set of results is shown in Table 9, which displays OLS regressions33 for the probability of bankruptcy. Five specifications, where Age, Size (both in logs), the limited liability dummy and 14 dummies for industry34 are used as controls, are shown for robustness. The table reveals that Tangibility is negatively correlated with the probability of bankruptcy in Spain, while positivelycorrelated in France and the UK.
Table 9

Marginal effects (%) for the probability of bankruptcy in micro firms (OLS)

 

(1)

(2)

(3)

(4)

(5)

Spain

   Tangibility

\(-0.010\)*** (0.001)

\(-0.011\)*** (0.001)

\(-0.011\)*** (0.001)

\(-0.011\)*** (0.001)

\(-0.011\)*** (0.001)

   Log (age)

 

0.852*** (0.152)

0.700*** (0.152)

0.545*** (0.154)

0.546*** (0.154)

   Log (size)

  

1.334*** (0.124)

1.238*** (0.128)

1.238*** (0.128)

   Limited liability

    

3.529*** (0.536)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

47,710

47,679

47,679

47,679

47,679

   R-squared (%)

0.30

0.37

0.63

0.88

0.88

France

   Tangibility

0.029*** (0.002)

0.025*** (0.002)

0.023*** (0.002)

0.010*** (0.002)

0.010*** (0.002)

   Log (age)

 

3.529*** (0.183)

3.342*** (0.182)

2.669*** (0.184)

2.669*** (0.184)

   Log (size)

  

3.114*** (0.207)

1.872*** (0.209)

1.876*** (0.209)

   Limited liability

  

(0.207)

(0.209)

3.774*** (0.209)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

47,861

47.858

47,858

47,858

47,858

   R-squared (%)

0.45

1.14

1.60

5.54

5.56

UK

   Tangibility

0.071*** (0.010)

0.079*** (0.010)

0.070*** (0.010)

0.069*** (0.009)

0.069*** (0.009)

   Log (age)

 

\(-7.759\)*** (1.293)

\(-6.621\)*** (1.269)

\(-7.340\)*** (1.251)

\(-7.343\)*** (1.253)

   Log (size)

  

17.533*** (1.471)

15.046*** (1.444)

15.037*** (1.450)

   Limited liability

    

3.347 (2.912)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

2,387

2,387

2,387

2,387

2,387

   R-squared (%)

2.22

3.50

7.46

12.68

12.68

Dependent variable: Bankruptcy. Unconditional probabilities = 3.6 % (Spain), 10.5  % (France), 31.6  % (UK). All regressions include a constant. Estimator: OLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

However, we expect the estimates of Table 9 to be biased due to the endogeneity of capital structure, as explained in the previous section. In other words, as firms’ capital structure and the mechanism used to deal with insolvency are (ex-ante) jointly chosen by firms, we face a simultaneity bias. Moreover, we expect Tangibility to be measured with error because tangible fixed assets are valued at their acquisition (historical) cost, which may differ from their market/collateral values. To solve these problems we use as instrumental variable (IV) the average industry level of Tangibility—where industry is defined at 4 digits of disaggregation, leading to 473 different classes—for each size class (micro and non-micro).35 We expect this IV to be uncorrelated with any unobserved determinant of the probability of bankruptcy of a single firm because no firm chooses the asset and capital structure of its industry counterparts. Moreover, there is a positive and sizeable correlation between the IV and the endogenous regressor—as reflected by first-stage regressions36—since companies for the same industries tend to have similar levels of tangibility.

The selected IV estimator is two-stage least squares (2SLS). We prefer not to use IV probit as our main estimator because its consistency relies in some strong assumptions such as conditional normality of the endogenous regressor (Wooldridge 2002) that do not seem to hold in our case. Despite the well-known caveats of the linear probability model (heteroskedasticity, fitted probabilities out of [0, 1]), it requires weaker assumptions and it usually provides good approximations of the marginal effects (Angrist and Pischke 2009).37

The results for the estimation via 2SLS are displayed in Table 10. In the regressions for the Spanish subsample, the marginal effects of Tangibility are negative and highly significant, and they are substantially higher than those estimated without instrumenting the regressor. They are also economically significant. A 1 % increase in Tangibility—a small change, as its mean equals 85 % and its standard deviation 101 %—decreases the probability of filing for bankruptcy by a Spanish micro firm by around 0.03 %.38 As the unconditional probability39 of those firms is 3.6 %, the estimated semielasticity is 0.83 %, which is a sizeable effect. By contrast, the marginal effects of Tangibility are positive and significant both in France and the UK. This latter result is consistent with the results of Davydenko and Franks (2008) on their study of French, UK and German firms that defaulted on their bank debt. They find that higher levels of collateral imply a significantly higher incidence of bankruptcies and a somewhat higher probability of liquidation, suggesting that banks use formal bankruptcy procedures to force a sale of collateral in those countries. This is not the Spanish case, as there is an alternative insolvency procedure, a mortgage foreclosure, through which collateral can be more efficiently liquidated.

With respect to the control variables, size has a positive impact in the probability of bankruptcy in the three countries, suggesting that the fixed costs of bankruptcy procedures deter very small firms from using them, as argued by Morrison (2008). Age has a negative effect in the case of UK, suggesting that lower information asymmetries and higher reputation concerns incentivise older firms to avoid bankruptcy. By contrast, it has a positive impact in the Spanish and French subsamples, probably capturing the fact that, as older firms have more lenders, higher coordination costs reduce the chances of non-bankruptcy procedures. Limited liability has a positive effect in three countries—although not significant at 10 % in the UK40—consistent with the idea that, when the debtor may lose part of its personal wealth, she has fewer incentives to file for bankruptcy, even when some partial discharge—as in France and in the UK—may be granted.

The case of non-micro firms is analysed in Tables 11 and 12. As a benchmark, Table 11 shows the (biased) OLS estimates for the three countries, which reveals the same patterns as for micro firms: negative correlations in Spain and positive correlations in France and the UK.
Table 11

Marginal effects (%) for the probability of bankruptcy in non-micro firms (OLS)

 

(1)

(2)

(3)

(4)

(5)

Spain

   Tangibility

\(-\)0.022*** (0.003)

\(-\)0.025*** (0.003)

\(-\)0.024*** (0.003)

\(-\)0.026*** (0.003)

\(-\)0.026*** (0.003)

   Log (age)

 

3.735*** (0.462)

3.446*** (0.464)

1.550*** (0.487)

1.561*** (0.487)

   Log (size)

  

2.063*** (0.394)

2.521*** (0.400)

2.545*** (0.400)

   Limited liability

    

15.141*** (3.053)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

11,477

11,462

11,462

11,462

11,462

   R-squared (%)

0.45

0.98

1.21

4.53

4.55

France

   Tangibility

(0.040)***

0.035*** (0.004)

0.034*** (0.004)

0.008** (0.004)

0.008** (0.004)

   Log (age)

 

3.418*** (0.353)

4.081*** (0.355)

1.185*** (0.353)

1.093*** (0.354)

   Log (size)

  

\(-\)4.508*** (0.294)

\(-\)3.662*** (0.291)

\(-\)3.582*** (0.291)

   Limited liability

    

5.828*** (0.928)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

16,339

16,339

16,339

16,339

16,339

   R-squared (%)

0.74

1.23

2.18

14.45

14.54

UK

   Tangibility

0.073*** (0.005)

0.080*** (0.005)

0.073*** (0.005)

0.070*** (0.005)

0.070*** (0.005)

   Log(Age)

 

\(-7.747\)*** (0.558)

\(-\)5.354*** (0.569)

\(-\)6.746*** (0.583)

\(-\)6.722*** (0.584)

   Log (size)

  

\(-\)6.580*** (0.323)

\(-\)5.958*** (0.329)

\(-\)5.923*** (0.329)

   Limited liability

    

15.149*** (2.821)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

8,829

8,829

8,829

8,829

8,829

   R-squared (%)

2.52

4.30

7.49

12.76

12.79

Dependent variable: Bankruptcy. Unconditional probabilities = 12.8 % (Spain), 18.2 % (France), 29.5 % (UK). All regressions include a constant. Estimator: OLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

By contrast, the consistent IV estimates in Table 12 show that Tangibility has a positive impact on the probability of bankruptcy of Spanish non-micro firms in 3 out of the 4 specifications and it is not significant in specification (4), suggesting that it is not a robust determinant. The case of French and UK larger firms is similar to the one for micro: Tangibility has a robust positive impact in the probability of bankruptcy.

With respect to the control variables, size has a positive impact in the probability of bankruptcy in Spain—as it was the case in the subsample of micro firms—but a negative one in France and the UK. A possible interpretation is that the fixed costs of bankruptcy proceedings deter small and very small firms from using them but, in the case of quite large firms, other factors, such as the reputational loss of managers, make filing for bankruptcy less appealing. As in the case of micro firms, age has a positive effect in the Spanish subsample, but a negative one in the UK and no robust impact in France.
Table 12

Marginal effects (%) for the probability of bankruptcy in non-micro firms (2SLS)

 

(1)

(2)

(3)

(4)

(5)

Spain

   Tangibility

0.034*** (0.009)

0.025*** (0.009)

0.029*** (0.009)

0.001 (0.014)

0.001 (0.014)

   Log (age)

 

2.807*** (0.488)

2.414*** (0.495)

1.164** (0.526)

1.177** (0.526)

   Log (size)

  

2.510*** (0.410)

2.717*** (0.412)

2.740*** (0.413)

   Limited liability

    

15,144*** (3.062)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

11,477

11,462

11,462

11,462

11,462

France

   Tangibility

0.281*** (0.009)

0.284*** (0.010)

0.273*** (0.010)

0.142*** (0.013)

0.144*** (0.013)

   Log (age)

 

\(-\)0.677 (0.447)

0.088 (0.448)

\(-\)0.256 (0.397)

\(-\)0.370 (0.399)

   Log (size)

  

\(-\)3.991*** (0.344)

\(-\)3.642*** (0.305)

\(-\)3.565*** (0.306)

   Limited liability

    

5.650*** (1.048)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

16,339

16,339

16,339

16,339

16,339

UK

   Tangibility

0.194*** (0.022)

0.205*** (0.022)

0.192*** (0.022)

0.131*** (0.025)

0.130*** (0.025)

   Log (age)

 

\(-\)9.382*** (0.655)

\(-\)7.167*** (0.690)

\(-\)7.775*** (0.728)

\(-\)7.743*** (0.730)

   Log (size)

  

\(-\)5.820*** (0.375)

\(-\)5.536*** (0.376)

\(-\)5.514*** (0.375)

   Limited liability

    

11.957*** (3.279)

   Industry dummies (1 digit)

No

No

No

Yes

Yes

   N

8,829

8,829

8,829

8,829

8,829

Dependent variable: Bankruptcy. Unconditional probabilities = 12.8 % (Spain), 18.2 % (France), 29.5 % (UK). All regressions include a constant. Estimator: 2SLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

5.3 Robustness analysis of the ex-post perspective: private workouts and subsample of firm exits

In our sample of distressed firms we have two types: bankrupt and non-bankrupt. The former consists of firms under bankruptcy proceedings (i.e., still operating in the market) and firms that have been liquidated after a bankruptcy procedure (i.e., they have exited the market). The latter consists of companies that are still operating the market under financial distress and companies that exited the market while they were financially distressed. An alternative explanation for the negative impact of Tangibility on the probability of filing for bankruptcy by a Spanish micro firm is that firms with high levels of tangible fixed assets relative to their levels of financial debt still have assets they may pledge as mortgage collateral to get new loans or refinance their current ones. In that case, they would avoid bankruptcy by surviving and staying in the market thanks to a private workout, rather than exiting via a foreclosure.

We have two objections to this view: one is logical; the other one is based on empirical evidence. First, in France, due the high dilution that most secured creditors suffer inside bankruptcy (see Sect. 3.2), they may be willing to make debt concessions in a private workout to deter the debtor from filing for bankruptcy. By contrast, secured creditors are unlikely to be held up by a debtor in Spain because, while there is an automatic stay over the enforcement of some secured credit in bankruptcy, it is very limited in time and uncertain in scope.41 In fact, the LLSV42 index (La Porta et al. 1998, updated by Djankov et al. (2007)), which measures the protection of secured creditors in bankruptcy in a scale from 0 (lowest protection) to 4 (highest), assigns 3 to Spain while 0 to France. Hence it seems implausible to explain the large differences in the bankruptcy rates of small firms in Spain and France in terms of the relative incidence of private workouts. Since those rates are low in Spain and high in France, workouts should be abundant in Spain and rare in France, while our reasoning suggests the opposite.

Second, from the empirical point of view, we address this potential criticism by keeping in the sample only those firms that exited the market. We construct a new dependent variable, bankruptcy2, which takes the value 1 if the firm left the market after a bankruptcy procedure and 0 otherwise. Our main results are the same: Tangibility has a negative and significant impact on the probability of being bankrupt in the case of Spanish micro firms (see Table 14).43 This effect is not present in Spanish larger firms, since the correlation is not different from zero in our OLS estimates (see Table 15) and the causal impact is not robust to several specifications in our IV estimates (see Table 16). By contrast, Tangibility has a positive impact on the probability of leaving the market after bankruptcy in the case of French firms of both size classes, while there is no effect in the case of British firms.
Table 13

Marginal effects (%) for the probability of bankruptcy EXIT in micro firms (OLS)

 

(1)

(2)

(3)

(4)

Spain

   Tangibility

\(-\)0.072*** (0.013)

\(-\)0.069*** (0.013)

\(-\)0.070*** (0.013)

\(-\)0.067*** (0.013)

   Log (age)

 

\(-\)13.669*** (2.153)

\(-\)13.953*** (2.140)

\(-\)16.027*** (2.116)

   Log (size)

  

5.526*** (1.687)

2.976* (1.710)

   Industry dummies (1 digit)

No

No

No

Yes

   N

1,474

1,474

1,474

1,474

   Pseudo-R2 (%)

2.07

4.56

5.25

12.99

France

   Tangibility

0.040*** (0.012)

0.039*** (0.012)

0.036*** (0.012)

0.017 (0.012)

   Log (age)

 

\(-\)7.709*** (1.332)

\(-\)7.990*** (1.346)

\(-\)6.349*** (1.343)

   Log (size)

  

2.600* (1.393)

\(-\)0.756 (1.432)

   Industry dummies (l digit)

No

No

No

Yes

   N

2,220

2,220

2,220

2,220

   Pseudo-R2 (%)

0.52

2.10

2.26

10.62

UK

   Tangibility

0.038*** (0.009)

0.033*** (0.008)

0.032*** (0.008)

0.033*** (0.009)

   Log (age)

 

5.551*** (1.931)

5.562*** (1.927)

5.667*** (1.872)

   Log (size)

  

3.904 (3.704)

3.058 (3.876)

   Industry dummies (1 digit)

No

No

No

Yes

   N

636

636

636

636

   Peeudo-R2 (%)

1.99

3.17

3.39

9.15

Dependent variable: Bankruptcy. Unconditional probabilities = 61.8 % (Spain), 77.3 % (France), 89.8 % (UK). All regressions include a constant. Estimator: OLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

Table 14

Marginal effects (%) for the probability of bankruptcy EXIT in micro firms (2SLS)

 

(1)

(2)

(3)

(4)

Spain

   Tangibility

\(-\)0.119*** (0.034)

\(-\)0.112*** (0.035)

\(-\)0.125*** (0.036)

\(-\)0.146*** (0.036)

   Log (age)

 

\(-\)13.383*** (2.179)

\(-\)13.603*** (2.173)

\(-\)15.496*** (2.167)

   Log (size)

  

5.750*** (1.693)

2.855* (1.726)

   Industry dummies (1 digit)

No

No

No

Yes

   N

1,474

1,474

1,474

1,474

France

   Tangibility

0.283*** (0.049)

0.277*** (0.048)

0.280*** (0.051)

0.074 (0.047)

   Log (age)

 

\(-\)7.334*** (1.453)

\(-\)7.254*** (1.491)

\(-\)6.245*** (1.350)

   Log (size)

  

\(-\)0.707 (1.649)

\(-\)1.329 (1.507)

   Industry dummies (1 digit)

No

No

No

Yes

   N

2,220

2,220

2,220

2,220

UK

   Tangibility

0.080*** (0.028)

0.077*** (0.028)

0.073** (0.029)

0.068* (0.035)

   Log (age)

 

4.277** (2.100)

4.378** (2.092)

4.742** (2.016)

   Log (size)

  

2.520 (3.829)

2.098 (3.956)

   Industry dummies (1 digit)

No

No

No

Yes

   N

636

636

636

636

Dependent variable: Bankruptcy. Unconditional probabilities = 61.8 % (Spain), 77.3 % (France), 89.8 % (UK). All regressions include a constant. Estimator: 2SLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

Table 15

Marginal effects (%) for the probability of bankruptcy EXIT in non-micro firms (OLS)

 

(1)

(2)

(3)

(4)

Spain

   Tangibility

0.010 (0.025)

0.008 (0.024)

0.008 (0.024)

0.022 (0.020)

   Log (age)

 

8.497*** (2.793)

8.575*** (2.814)

0.511 (2.902)

   Log (size)

  

\(-\)1.288 (2.703)

\(-\)0.384 (2.441)

   Industry dummies (1 digit)

No

No

No

Yes

   N

544

544

544

544

   Pseudo R2 (%)

0.03

1.75

1.79

23.44

France

   Tangibility

0.033*** (0.012)

0.036*** (0.012)

0.038*** (0.012)

0.023* (0.012)

   Log (age)

 

\(-\)4.621*** (1.517)

\(-\)3.908** (1.525)

\(-\)4.157*** (1.509)

   Log (size)

  

\(-\)6.722*** (1.697)

\(-\)5.648*** (1.694)

   Industry dummies (1 digit)

No

No

No

Yes

   N

1,635

1,635

1,635

1,635

   Pseudo R2 (%)

0.42

1.05

2.16

11.88

UK

   Tangibility

0.032*** (0.005)

0.029*** (0.005)

0.030*** (0.005)

0.030*** (0.005)

   Log (age)

 

6.429*** (0.942)

6.337*** (0.944)

5.202*** (0.973)

   Log (size)

  

0.626 (0.743)

1.587** (0.768)

   Industry dummies (1 digit)

No

No

No

Yes

   N

1,862

1,862

1,862

1,862

   Pseudo R2 (%)

1.65

3.91

3.94

6.38

Dependent variable: Bankruptcy. Unconditional probabilities = 76.1 % (Spain), 76.3 % (France), 91.3 % (UK). All regressions include a constant. Estimator: OLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

Table 16

Marginal effects (%) for the probability of bankruptcy EXIT in non-micro firms (2SLS)

 

(1)

(2)

(3)

(4)

Spain

   Tangibility

\(-\)0.013 (0.058)

\(-\)0.021 (0.058)

\(-\)0.023 (0.058)

\(-\)0.090 (0.073)

   Log (age)

 

8.574*** (2.802)

8.671*** (2.823)

0.344 (2.957)

   Log (size)

  

\(-\)1.534 (2.728)

\(-\)1.064 (2.492)

   Industry dummies (l digit)

No

No

No

Yes

   N

544

544

544

544

France

   Tangibility

0.135*** (0.039)

0.143*** (0.040)

0.149*** (0.040)

0.055 (0.044)

   Log (age)

 

\(-\)5.409*** (1.573)

\(-\)4.644*** (1.580)

\(-\)4.302*** (1.516)

   Log (size)

  

\(-\)7.406*** (1.707)

\(-\)5.877*** (1.712)

   Industry dummies (1 digit)

No

No

No

Yes

   N

1,635

1,635

1,635

1,635

UK

   Tangibility

0.017 (0.018)

0.015 (0.018)

0.016 (0.018)

0.027 (0.018)

   Log (age)

 

6.625*** (1.000)

6.542*** (1.008)

5.250*** (1.017)

   Log (size)

  

0.535 (0.750)

1.561** (0.771)

   Industry dummies (1 digit)

No

No

No

Yes

   N

1,862

1,862

1,862

1,862

Dependent variable: Bankruptcy. Unconditional probabilities = 76.1 % (Spain), 76.3 % (France), 91.3 % (UK). All regressions include a constant. Estimator: 2SLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

6 Conclusions

Spain had, before the current economic crisis, one of the world’s lowest business bankruptcy rates, i.e., the number of business bankruptcy filings divided by the number of business exits. Only the crisis has modestly increased the number of bankruptcies, but the Spanish bankruptcy rate is still one of the lowest in the world. This fact is driven by the behaviour of micro firms—the majority of Spanish firms—which rarely file for bankruptcy when dealing with financial distress.

This paper presents and tests a hypothesis that attempts to explain this empirical finding. According to this hypothesis, filing for bankruptcy in Spain is very costly for both small firms and their creditors. Due to this, the capital structure of micro firms is biased towards mortgage loans (i.e., loans secured on land and buildings). Having this capital structure allows them to avoid bankruptcy by carrying out debt enforcement via mortgage foreclosures, which are cheaper procedures than bankruptcy, in case of financial distress.

To test this hypothesis our identification strategy relies on cross-country comparisons. Specifically, we compare the observed choices (choice of capital structure, choice between bankruptcy and mortgage) of Spanish firms with those of firms from countries where their bankruptcy systems are more efficient and their laws do not incentivise them to bias their capital structure towards mortgage loans. France and the UK are chosen as the comparison group because their bankruptcy rates are much higher than the Spanish ones and because of the specific features of their insolvency frameworks.

Our findings corroborate the proposed hypothesis. First, there is a positive and strong correlation between the ex-ante probability of default and the ratio of tangible fixed assets (the assets that can be pledged as mortgage collateral) to financial debt in the case of Spanish micro firms, suggesting that firms with risky business models bias their capital structure towards mortgage loans to avoid filing for bankruptcy in the event of default. Second, a higher proportion of tangible fixed assets over financial debt significantly decrease the probability of being in bankruptcy among Spanish micro firms in financial distress. By contrast, these two relations do not hold either for Spanish larger businesses or for firms from the other two countries.

We must stress the importance of the research question. Bankruptcy procedures and mortgage foreclosures are not perfect substitutes, and the underutilization of one of them—reflected in low bankruptcy rates—may lead to efficiency losses and lower welfare (García-Posada 2013). The reason is that the mortgage system is not well suited for some firms, which need to bias their asset structure to have enough collateral, with the ensuing productive inefficiencies. Those firms would be better off if they had access to a bankruptcy system that worked relatively well. If the absence of a well-functioning bankruptcy system for those firms also reduces their growth opportunities—e.g., by hampering access to unsecured lending such as venture capital—then the current insolvency framework may help explain the firm size distribution and the low aggregate productivity of the Spanish economy. This is consistent with the evidence of Fabbri (2010) in Spain, who finds that lengthy bankruptcy procedures decrease firm size and raise funding costs and with that of Ponticelli (2012) in Brazil, who shows that congestion in bankruptcy courts substantially reduces firm-level investment and productivity.

This paper is a first step towards understanding how agents respond to the Spanish insolvency framework and their implications for the real economy. Further work is required in two directions. First, better data without the limitations of our sample, especially regarding information on mortgage loans and the alternative procedures to formal bankruptcy, could be collected. Second, the impact of the low efficiency of the bankruptcy system vis-à-vis mortgage foreclosures on the performance of Spanish firms should be empirically analyzed.

Footnotes

  1. 1.

    Following Djankov et al. (2008), by “bankruptcy” we mean a legal procedure that imposes court supervision over the financial affairs of a firm or individual that has broken its promises to creditors or honours them with difficulty, and whose possible outcomes are reorganisation or liquidation. By “financial distress” we mean a situation in which a firm is close to default and it needs to take corrective action, such a selling major assets, merging with another firm or filing for bankruptcy (Ross et al. 2005). See Appendix A for a discussion on the legal terms used in this paper.

  2. 2.

    A foreclosure is “a debt enforcement procedure aimed at recovering the money owed to secured creditors” (Djankov et al. 2008). There are different types of foreclosures depending on which collateral can be repossessed using a single execution procedure. Since this paper concentrates on the analysis of small firms and entrepreneurs, and land and buildings are the main assets that can be pledged as collateral by them, we will focus on “mortgage over land and buildings” foreclosures (henceforth, mortgage foreclosures). In other words, by “mortgage” we will mean a loan secured by land and buildings, and not by other types of collateral. This is a necessary remark because there are other types of mortgages in some legal systems such as the British one. For more details see Appendix A.

  3. 3.

    Source: Consejo General del Poder Judicial (2012) and Registradores de España (2012).

  4. 4.

    Source: Central Business Register, National Statistics Institute of Spain.

  5. 5.

    Sources: Observatory of European SMEs (2003) and authors’ computations from Eurostat.

  6. 6.

    Sources: Instituto Nacional de Estadística, Altares (2011), Eurostat.

  7. 7.

    Figures on bankruptcy filings for self-employed are only available for England and Wales, so the computed bankruptcy rate (176) is a lower bound of that for the UK.

  8. 8.

    Compensation of the insolvency administrators, lawyers’ fees, etc.

  9. 9.

    Mortgage creditors can also enforce their claims inside a bankruptcy procedure, as any other creditors. Throughout this paper we will use the term “mortgage foreclosure” when we mean debt enforcement outside bankruptcy.

  10. 10.

    We must exclude other potentially interesting examples (e.g. Germany and the US) due data constraints. Our data come from the office of the Registrar of Companies of each country, but only large firms have the legal obligation to register their annual accounts in Germany. In the case of the US, the available data is at plant-level, while the decision to file for bankruptcy is made at firm-level.

  11. 11.

    In an assignment for the benefit of creditors, the business assigns its assets to a trustee, who auctions them off and distributes the proceeds to creditors.

  12. 12.

    In a bulk sale the debtor sells most or all of its business to a third party and distributes the proceeds to creditors.

  13. 13.

    Read Appendix A for further clarifications on the translation of the legal terms of this paper.

  14. 14.

    In September 2013 the Spanish Parliament has approved some legal reforms that will create some sort of special bankruptcy regime for self-employed individuals. See Appendix A, Sect. A.6, for details.

  15. 15.

    20 months for the so-called simplified procedure (concurso abreviado), 23 for the ordinary (concurso ordinario). See Appendix A for details.

  16. 16.

    Similar estimations are provided by the General Council of the Judicial Power (Consejo General del Poder Judicial 2011).

  17. 17.

    In other words, all the present and future income of the debtor must be used to pay back pre-bankruptcy debts.

  18. 18.

    Some of the legislative changes concerning the Spanish mortgage law (Ley Hipotecaria) introduced in 2013 may increase the length of mortgage foreclosures in the future (see Appendix A, Sect. 6 for details), but not in the period of study of this research.

  19. 19.

    There is immediate debt discharge in the procedure de retablissement personnel. In the plan de redressement, although it mainly consists of a reorganisation plan, the judge may enforce a debt-restructuring schedule and he can also partly reduce the debts. For more details see Blazy et al. (2011).

  20. 20.

    The main insolvency procedure before the Enterprise Act 2002, administrative receivership, is normally characterised as a foreclosure, since it was a procedure for the enforcement of a security interest (a floating charge) covering all or nearly all the assets of the debtor firm, while administration is normally classified as a bankruptcy procedure (Djankov et al. 2008).

  21. 21.

    In the UK the term “bankruptcy” only applies to individuals, while insolvency is the term that applies to companies.

  22. 22.

    See Appendix A for an explanation of floating and fixed charges.

  23. 23.

    But it does indicate mergers, so we can exclude them from the analysis.

  24. 24.

    For instance, those that violate basic accounting rules.

  25. 25.

    Earnings before interests, taxes, depreciation and amortization.

  26. 26.

    The Z-Score has several versions depending on the type of firms. The one used in this paper is for non-listed firms that do not necessarily belong to the manufacturing sector. The exact formula is: Z = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4 where X1 = (Current Assets\(-\)Current Liabilities)/Total Assets; X2 = Retained Earnings/Total Assets; X3 = Earnings Before Interest and Taxes/Total Assets; X4 = Book Value of Equity/Total Liabilities.

  27. 27.

    Plant and machinery can also be mortgage collateral as long as they are inside the buildings.

  28. 28.

    0.82 in the case of non-distressed firms, 0.89 for distressed ones. All estimations are available upon request.

  29. 29.

    Since the number of employees were missing for a non-negligible part of the sample, values have been imputed using Poisson regressions for each country, where the predictor variables were a proprietary variable of Orbis that has four size categories according to several size measures (revenue, total assets, employees and whether the firm is listed) and industry dummies. The paper’s results—available upon request—do not qualitatively change when total assets or turnover are used as alternative measures.

  30. 30.

    Although the Altman Z-Score was originally developed for bankruptcy prediction, it is now considered a good measure of other types of financial distress (Grice and Ingram 2001).

  31. 31.

    Industry is defined at 4 digits of disaggregation. NACE Rev. 1.1. classification.

  32. 32.

    An alternative explanation could be that firms with worse financials (lower Z-score) may not heavily invest in long-term costly assets such as land and buildings relative to their debt levels because they suffer from credit rationing. But, if that were the main reason, it would be very difficult to make sense of the case of Spanish micro firms, where the opposite would occur.

  33. 33.

    We use the linear probability model to avoid the separation problem we would face with non-linear models such as logit or probit, as no Spanish firm with unlimited liability is bankrupt in our sample, i.e, we have an empty cell for (Limited liability = 0, Bankruptcy = 1).

  34. 34.

    NACE Rev. 1.1. at the maximum aggregation level, e.g. D. Manufacturing.

  35. 35.

    The average level of tangibility is computed for each industry, regardlessof the country. Although we could have instead computed the average industry-country level of tangibility to increase the variability of the IV, that variable may not be exogenous, since it may be influenced by the country’s institutional framework.

  36. 36.

    Results available upon request.

  37. 37.

    Nevertheless, similar conclusions are reached when IV probit is used instead. Results available upon request.

  38. 38.
    One could argue that Tangibility is really a proxy for leverage and what we are capturing is a lower probability of using bankruptcy for firms with lower leverage. To rule out that alternative explanation, we have computed a leverage ratio, defined as financial debt over total assets. The correlation between the leverage ratio and Tangibility is very low, especially in the case of Spanish micro firms: \(-0.07\).
    Table 10

    Marginal effects (%) for the probability of bankruptcy in micro firms (2SLS)

     

    (1)

    (2)

    (3)

    (4)

    (5)

    Spain

       Tangibility

    \(-0.012\)*** (0.003)

    –0.014*** (0.003)

    \(-0.017\)*** (0.003)

    \(-0.027\)*** (0.004)

    \(-0.027\)*** (0.004)

       Log (age)

     

    0.909*** (0.160)

    0.809*** (0.159)

    0.807*** (0.170)

    0.807*** (0.170)

       Log (size)

      

    1.362*** (0.124)

    1.227*** (0.128)

    1.227*** (0.128)

       Limited liability

        

    3.609*** (0.850)

       Industry dummies (1 digit)

    No

    No

    No

    Yes

    Yes

       N

    47,710

    47,679

    47,679

    47,679

    47,679

    France

       Tangibility

    0.181*** (0.005)

    0.176*** (0.006)

    0.168*** (0.006)

    0.071*** (0.007)

    0.072*** (0.007)

       Log (age)

     

    1.867*** (0.205)

    1.816*** (0.203)

    2.070*** (0.196)

    2.064*** (0.196)

       Log (size)

      

    1.960*** (0.221)

    1.644*** (0.212)

    1.646*** (0.212)

       Limited liability

        

    3.707*** (0.797)

       Industry dummies (1 digit)

    No

    No

    No

    Yes

    Yes

       N

    47,861

    47,858

    47,858

    47,858

    47,858

    UK

       Tangibility

    0.233*** (0.068)

    0.222*** (0.064)

    0.142** (0.064)

    0.132* (0.074)

    0.132* (0.074)

       Log (age)

     

    \(-\)10.777*** (1.897)

    \(-\)8.184*** (1.866)

    \(-\)8.640*** (1.954)

    \(-8.641\)*** (1.953)

       Log (size)

      

    16.376*** (1.753)

    14.020*** (1.838)

    14.016*** (1.839)

       Limited liability

        

    1.389 (3.827)

       Industry dummies (1 digit)

    No

    No

    No

    Yes

    Yes

       N

    2,387

    2,387

    2,387

    2,387

    2,387

    Dependent variable: Bankruptcy. Unconditional probabilities = 3.6 % (Spain), 10.5 % (France), 31.6 % (UK). All regressions include a constant. Estimator: 2SLS. Robust standard errors in parentheses. *, **, and ***, significant at 10, 5, and 1 % level

  39. 39.

    By unconditional probability we mean the sample proportion of bankrupt firms.

  40. 40.

    Notice the large standard errors of the variable in the UK (Tables 8, 9), consequence of its little variability: only 0.03 % of the micro UK firms in the sample had (un)limited liability.

  41. 41.

    The stay only involves secured credit over assets that are integrated in the debtor’s production process—as considered by the court—and only for 1 year or until a restructuring plan that does not affect the rights of secured creditors is approved, whichever occurs first. The law also allows the insolvency administrator to pay secured creditors out of the company’s total assets during the stay.

  42. 42.

    La Porta, Lopez-de-Silanes, Shleifer, Vishny.

  43. 43.

    Notice that in the regressions of Tables 13, 14, 15 and 16 we do not include the variable Limited Liability, as in some country-size class combinations it had no variability at all, as all firms had limited liability.

  44. 44.

    In Spanish law, Article 1874 et seq of the Spanish Civil Code.

  45. 45.

    Articles 2393 et seq of the French Civil Code.

  46. 46.

    See Armour (2001) for a discussion on the different types of insolvency.

  47. 47.

    Law 2005-845 of July 26, 2005 “de sauvegarde des enterprises”.

  48. 48.

    A new procedure, the sauvegarde, was introduced in the latest reform of the bankruptcy code (Loi de sauvegarde des entreprises), which came became effective in 2006. In addition, the parties have the possibility to recur to a mandataire ad hoc, a process by which a court-appointed mediator assists in nonbinding negotiations between a debtor and its creditors and to a réglement amiable, a judicially supervised negotiation procedure in which the court may grant a stay against creditors.

  49. 49.

    Other (much less used) procedures are company voluntary arrangements—a reorganisation procedure—compulsory liquidation and creditors’ voluntary liquidations.

  50. 50.

    The current Act has been modified three times, in March 2009, in October 2011 and in March 2014, in order to solve various dysfunctional features in the initial design. For instance, formal workout negotiations on the brink of bankruptcy filing have been facilitated.

  51. 51.

    Bankruptcy in Scotland is referred to as “sequestration”. Other personal insolvency procedures in England, Wales and Northern Ireland are individual voluntary arrangements and debt relief orders, while another one in Scotland is a “protected trust deed”.

  52. 52.

    This last circumstance only occurs in some specific cases in French law (in which the judge appoints an administrateur judiciaire).

  53. 53.

    Article 1863 et seq of the Spanish Civil Code.

  54. 54.

    Ley de hipoteca mobiliaria y prenda sin desplazamiento of December 16, 1954.

  55. 55.

    Articles 2333 et seq of the French Civil Code in the first case and 2355 and subsequent articles in the second case.

  56. 56.

    In other Spanish-speaking or French-speaking legal systems and with evident influence of both continental and Anglo–Saxon Law, we could find legal figures such as the chargeflottante (nantissementflottante) of the Canadian Law and the prendaflotante that exists in some American legal systems.

  57. 57.

    For this reason, this legal figure may be “translated” in Spanish, not into Spanish law, as prenda rotativa.

  58. 58.

    The crystallization has the effect of designating the property referred to and make it enforceable against third parties.

  59. 59.

    This normally did not imply piecemeal liquidation of the assets, but the sale of the business to a new entrepreneur.

  60. 60.

    Regulated in the article 681 et seq of the Civil Procedural Law of Spain. Law 1/2000, of January 7, de Enjuiciamiento Civil.

  61. 61.

    Governed by article 1872 of the Spanish Civil Code in the case of movable property and article 222 et seq of the Reglamento para la Ejecución de la Ley Hipotecaria (Decree of February 14, 1947) if the property was secured by a hipoteca.

  62. 62.

    Articles 2347 and 2355 of the French Civil Code respectively.

  63. 63.

    The specific rules in these cases can be found in the Code des procédures civiles d’exécution (Title II, Articles L.311-1 to L.322-14 and Title III) and its regulations.

  64. 64.

    Ley 14/2013, de 27 de septiembre, de apoyo a los emprendedores y su internacionalización.

  65. 65.

    Preferential credit (créditos contra la masa) comprises salaries for the last month of activity, the costs of the procedure itself, including compensation for the insolvency administrators, plus the new debt incurred by the firm in its activities after the insolvency declaration. Privileged credit (créditos con privilegio general) mainly comprises other labour credits, tax debts and social security contributions.

  66. 66.

    Also articles 605 to 607 of the Civil Procedural Law.

  67. 67.

    Judgment in Case C-415/11of the Court of Justice of the European Union.

  68. 68.

    Article 3 of Law 1/2013, of May 14, de medidas para reforzar la protección a los deudores hipotecarios, reestructuración de deuda y alquiler social.

  69. 69.

    The size class breakdowns, according to the number of employees, are: 1–9, 10–19, 20–49, 50–249, 250 or more.

  70. 70.

    Unfortunately we did not have analogous information on French and British firms. However, the examination of the sample (see below) revealed that the main source of sampling bias was the case of Spanish bankrupt firms.

  71. 71.

    Specifically, the SDBS has no information on the following industries (ISIC Rev. 3): Agriculture, hunting and forestry; Fishing; Financial Intermediation; Education; Health and social work; Other community, social and personal service activities.

References

  1. Altman E (2000) Predicting financial distress of companies: revisiting the Z-score and ZETA models. (The paper is adapted and updated from Altman E (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ September 1968; and Altman E, Haldeman R, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1, 1977)Google Scholar
  2. Acharya VV, Sundaram RK, John K (2011) Cross-country variations in capital structures: the role of bankruptcy codes. J Financ Intermed 20(1):25–54CrossRefGoogle Scholar
  3. Altares (2011) Bilan 2010: défaillances et sauvegardes d’entreprises en France, AltaresGoogle Scholar
  4. Armour J (2001) The law and economics of corporate insolvency: a review, ESRC Centre for Business Research, University of Cambridge Working paper no 197Google Scholar
  5. Armour J, Hsu A (2012) The costs and benefits of secured creditor control in bankruptcy: evidence from the UK. Rev Law Econ 8:1Google Scholar
  6. Angrist JD, Pischke J-S (2009) Mostly harmless econometrics: an empiricist’s companion, Princenton University Press, PrincentonGoogle Scholar
  7. Ayotte K, Yun H (2009) Matching bankruptcy laws to legal environments. J Law Econ Org 25(1):2–30Google Scholar
  8. Berger A, Udell G (1995) Relationship lending and lines of credit in small firm finance. J Bus 68:351–381CrossRefGoogle Scholar
  9. Berkowitz J, White M (2004) Bankruptcy and small firms’ access to credit. Rand J Econ 35(1):69–84CrossRefGoogle Scholar
  10. Blazy R, Chopard B, Eric L, Ziane Y (2011) Personal bankruptcy law, fresh start and judicial practice. Available at SSRN: http://ssrn.com/abstract=1784703
  11. Bolton P, Scharfstein D (1996) Optimal debt structure and the number of creditors. J Polit Econ 104:1–25CrossRefGoogle Scholar
  12. Brown M, Forsythe A (1974) Robust tests for the equality of variances. J Am Stat Assoc 69:364–367CrossRefGoogle Scholar
  13. Celentani M, García-Posada M, Gómez F (2010) The Spanish business bankruptcy puzzle and the crisis, FEDEA working paper 2010–11Google Scholar
  14. Celentani M, García-Posada M, Gómez F (2012) The Spanish business bankruptcy puzzle, mimeoGoogle Scholar
  15. Consejo General del Poder Judicial (2012) Datos sobre el efecto de la crisis en los organos judiciales: Cuarto trimestre de 2012Google Scholar
  16. Consejo General del Poder Judicial (2011) La Justicia Dato a Dato. Año 2011. Estadística JudicialGoogle Scholar
  17. Davydenko S A, Franks JR (2008) Do bankruptcy codes matter? A study of defaults in France, Germany, and the UK. J Financ vol LXIII, no 2, April 2008Google Scholar
  18. Djankov S, McLiesh C (2007) Private credit in 129 countries. J Financ Econ 84:299–329CrossRefGoogle Scholar
  19. Djankov S, Hart O, McLiesh C, Shleifer A (2008) Debt enforcement around the world. J Polit Econ vol 116(6):1105–1149Google Scholar
  20. Hermes E (2007) Insolvency outlook 2007, no 2, Business insolvency worlwide. Euler Hermes, EvreuxGoogle Scholar
  21. Hermes Euler (2011) Economic outlook 2011, no 4, Business insolvency worlwide. Euler Hermes, EvreuxGoogle Scholar
  22. European Mortgage Federation (2007) Study on the efficiency of the mortgage collateral in the European union, EMF Publication, May 2007Google Scholar
  23. Fabbri D (2010) Law enforcement and firm financing: theory and evidence. J Eur Econ Assoc 8(4):776–816CrossRefGoogle Scholar
  24. Franks J, Sussman O (2005) Financial distress and bank restruc-turing of small and medium size UK companies. Rev Financ 9(March):65–96CrossRefGoogle Scholar
  25. Frisby S (2006) Report on insolvency outcomes. The insolvency service report, UKGoogle Scholar
  26. García-Posada M, Mora-Sanguinetti JS (2012) Why do Spanish firms rarely use the bankruptcy system? The role of the mortgage institution. Working paper 1234 Banco de España Working Papers, no 1234Google Scholar
  27. García-Posada, M (2013) Insolvency institutions and efficiency: the Spanish case, Banco de España Working papers, no 1302Google Scholar
  28. Gilson S, John K, Lang L (1990) Troubled debt restructurings: an empirical study of private reorganization of firms in default. J Financ Econ 27(October):315–353CrossRefGoogle Scholar
  29. Grice J, Ingram R (2001) Tests of the generalizability of Altman’s bankruptcy prediction model. J Bus Res 54:53–61CrossRefGoogle Scholar
  30. Gutiérrez M (2005) Los procedimientos concursales como instituciones de gobierno corporativo. Anuario de Derecho Concursal 6:307–328Google Scholar
  31. Hernández-Cánovas G, Köeter-Kant J (2008) The institutional environment and the number of bank relationships: an empirical analysis of European SMEs. Small Bus Econ 34:375–390CrossRefGoogle Scholar
  32. La Porta R, Lopez de Silanes F, Shleifer A, Vishny RW (1998) Law and finance. J Polit Econ 106:1113–1155CrossRefGoogle Scholar
  33. Ministère de la Justice (2010) Annuarie statistique de la Justice Édition 2009–2010Google Scholar
  34. Morrison E (2008) Bankruptcy’s rarity: an essay on small business bankruptcy in the United States. In: Proceedings from the second ECFR symposium on corporate insolvency (October 2007), pp 172–188Google Scholar
  35. Morrison E (2009) Bargaining around Bankruptcy: small business distress and state law. J Legal Stud vol 38(2):255–307Google Scholar
  36. Murray ZF, Vidhan KG (2008) Trade-off and pecking order theories of debt. In: Espen Eckbo B (ed) Handbook of corporate finance: Empirical Corporate Finance, vol 2, Handbooks in finance series, Elsevier, North-Holland, Ch 12Google Scholar
  37. Observatory of European SMEs (2003) SMEs in Europe 2003Google Scholar
  38. Petersen M, Rajan R (1994) The benefits of lending relationships: evidence from small business data. J Financ XLIX(1):3–37Google Scholar
  39. Ponticelli J (2012) Court enforcement and firm productivity: evidence from a bankruptcy reform in Brazil. Available at SSRN: http://ssrn.com/abstract=2179022
  40. Ragoussis A, Gonnard E (2011) The OECD-ORBIS database treatment and benchmarking procedures, mimeo. OECD Publishing, ParisGoogle Scholar
  41. Registradores de España (2012) Panorama Registral: Impagos hipotecarios de viviendaGoogle Scholar
  42. Ribeiro, Menghinello, De Backer (2010) The OECD ORBIS database: responding to the need for firm-level micro-data in the OECD. OECD working paper no 30-2010/1Google Scholar
  43. Ross, Westerfield, Jaffe (2005) Corporate finance, 7th edn, McGraw-Hill, IrwinGoogle Scholar
  44. Van Hemmen E (2004) Análisis institucional y económico de la nueva Ley Concursal, Estabilidad Financiera, no 6:189–210Google Scholar
  45. Van Hemmen E (2008) Estadística concursal. Anuario 2007. Colegio de Registradores de la Propiedad y Mercantiles de España, MadridGoogle Scholar
  46. Van Hemmen E (2011) Estadística concursal. Anuario 2010. Colegio de Registradores de la Propiedad y Mercantiles de España, MadridGoogle Scholar
  47. Van Hemmen E (2012) Estadística concursal. Anuario 2011. Colegio de Registradores de la Propiedad y Mercantiles de España, MadridGoogle Scholar
  48. Wooldridge J (2002) Econometric analysis of cross section and panel data, MIT PressGoogle Scholar

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This article is published under license to BioMed Central Ltd. Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Miguel García-Posada
    • 1
  • Juan S. Mora-Sanguinetti
    • 1
  1. 1.Banco de España, EurosystemMadridSpain

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