Does employment protection legislation affect credit access? : evidence from Europe.
Date: 2017-05-19
Institutions: European Central Bank
Subjects: banking policy, credit policy, job preservation, labour law, loan, type of business

Andrea Moro, Daniela Maresch, 
Annalisa Ferrando, Gregory F. Udell 

Working Paper Series 

Does employment protection 
legislation affect credit access? 
Evidence from Europe 

No 2063 / May 2017 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Abstract 

We investigate the impact of employment protection on firms’ credit access by looking at both 

credit obtained from banks and firms’ decision to apply for a loan. We find that greater flexibility in 
structuring the employees’ working hours and in dismissing employees increases the probability that 
firms obtain credit and that greater flexibility in dismissing employees decreases the probability that firms 
are discouraged from applying for credit. However, our findings also reveal that firms perceive 
regulations providing flexibility with regard to the employees’ working hours differently from banks, 
leading to a situation in which firms are more likely to be discouraged from applying for a loan, even 
though the probability to obtain a loan increases. Our results are robust to confounding, endogeneity, 
selection bias as well as to alternative specifications. 

Keywords: Credit Access, Discouraged Borrower, Employment Protection Legislation, Labour Market 
JEL Classification: D22, G21, G32, J41 

ECB Working Paper 2063, May 20171Non-technical summary 

Researchers have devoted considerable efforts to investigate the factors that impact firms’ capital 

structure. In recent years, employment protection legislation has been considered to be one of these 

factors. In particular, employment protection legislation has been found to impact on firms’ preferred 

leverage by crowding out financial leverage. 

In this paper we aim to advance the understanding of the role of employment protection legislation 

in firms’ ability to increase financial leverage. We argue that a higher level of employment protection 

legislation may reduce firms’ flexibility to adapt to changing business conditions, as it decreases their 

ability to deploy staff at discretion and to lay off personnel in case of financial distress. The reduction in 

firms’ flexibility indirectly makes labour costs more rigid: as in the context under examination (the 

European Union) employees are senior to other claimants, the cash flow available to other creditors, such 

as banks, decreases. Thus, banks are expected to attribute a higher credit risk to firms located in countries 

with a higher level of employment protection legislation and to be less willing to provide credit. In 

addition, firms’ managers are expected to anticipate such behaviour from banks and thus be more likely to 

be discouraged from applying for a loan. 

We base our research on a sample euro area firms that participated at the ECB/European 

Commission Survey on the Access to Finance in the period 2009-2014. In the survey, firms are 

interviewed about their access to bank loans, their use of alternative sources of finance as well as finance 

and liquidity constraints. We integrate this dataset with information on employment protection legislation 

in the respective countries obtained from the World Bank. We focus on regulations that impact either the 

on-going employment (i.e., flexibility in structuring the employees’ working hours) or regulations that 

influence the termination of the employment (i.e., requirements with regard to employees’ dismissal). 

Using logistic regressions we find that regulations that allow for greater flexibility in structuring 

employees’ working hours or dismissing employees tend to increase access to credit and reduce the 

probability to be discouraged borrowers. However, our findings reveal differences in the determinants of 

ECB Working Paper 2063, May 20172the banks’ decision not to provide credit and the firms’ decision to be discouraged from applying for a 

loan due to fear of rejection. As a consequence, firms might apply for a loan because they believe that a 

specific employment protection provision fosters credit access, even though this provision reduces their 

probability to obtain a loan from the bank. These econometric results are corroborated by several 

robustness tests that control for the impact of the financial crisis, selection bias and endogeneity issues. 

Our paper contributes to the policy discussion on the importance of flexible employment 

protection regulations for a firm’s credit access, which, in turn, facilitates firms’ growth. The important 

policy implications of our findings are reinforced by the context under examination, i.e. the European 

Union – an area where fundamental freedoms, such as the free movement of goods and services, prevent 

protectionist policies. Firms located in countries with more flexible employment protection regulations 

may thus have a competitive advantage vis-à-vis firms located in countries with less flexible employment 

protection regulations. 

ECB Working Paper 2063, May 20173Introduction 

The capital structure of firms is not only the result of firms’ preferred leverage (demand side), but 

also of firms’ ability to increase their leverage (supply side) (Faulkender and Petersen, 2006). 

Employment protection legislation has been found to impact firms’ preferred leverage by crowding out 

financial leverage (Serfling, 2016; Simintzi et al., 2015). Our paper aims to advance the understanding of 

the role of employment protection legislation in firms’ ability to increase their leverage.  

We argue that employment protection legislation may affect firm access to credit by increasing a 

bank’s credit risk in lending to an affected firm. A higher level of employment protection legislation 

reduces firm flexibility in adapting to changing business conditions by decreasing a firm’s ability to use 

discretion in deploying staff and decreasing its ability to lay off personnel in times of financial distress. 

As a consequence, firms’ adjustment costs increase. In addition, the reduction in firms’ flexibility 

indirectly makes labour costs more rigid (Serfling, 2016).  In our context - the European Union - 

employees are senior to other claimants decreasing the cash flow in some states of nature that is available 

to other creditors, such as banks. Thus, banks are expected to attribute a higher credit risk to firms located 

in countries with a higher level of employment protection legislation and to be less willing to provide 

credit.  

We empirically test this hypothesis by analysing the determinants of credit access using cross-

country firm-level data from European Central Banks Survey on the Access to Finance (SAFE). In this 

survey, firms are interviewed at semi-annual intervals about their access to bank loans, their use of 

alternative sources of finance as well as finance and liquidity constraints. We rely on 87,825 complete 

observations of firms in ten European countries for the period from 2009 to 2014, of which 21,332 

observations are from firms that applied for a bank loan. We integrate this dataset with information on 

employment protection legislation in these respective countries obtained from the World Bank.  In 

additional Logit regressions we consider the possibility that the impact of employment protection 

legislation might differ across various industries.  Specifically, we re-estimate our regressions using the 

ECB Working Paper 2063, May 20174original variable and its interaction with a dummy that identifies the industry. 

We also perform several robustness checks. First, our research might suffer from endogeneity, as 

employment protection legislation might be affected by the power of the unions. Such an effect might be 

greater for larger firms where employees are typically better organized and where firms often have to 

follow more rigid employee protection regulations. In order to address this issue, we re-estimate our 

original regressions by instrumenting the independent variables using the level of unionisation of a 

country interacted with whether the firm is medium-sized or large. Second, although our sample 

comprises firms that apply for a loan and firms that do not apply for a loan (which we deal with 

separately), we cannot rule out that our results are affected by selection bias. Thus, we re-test our findings 

by applying Heckman selection (Heckman, 1979), relying on a selection process that is based on the 

change in turnover and the change in profit similar to Moro et al. (2016). We also re-estimate our model 

by including additional controls (e.g., industry) and evaluate whether the results might be affected by the 

financial crisis and the sovereign debt crisis. 

We obtain robust results that suggest that employment protection legislation plays an important 

role in facilitating credit access. The regulations that provide firms with greater flexibility in structuring 

the employees’ working hours (for instance by limiting restrictions on night work) increase the 

probability of obtaining credit. In addition, we find that the probability of obtaining credit decreases with 

the length of the “notice period” (the notification lead time required for employee termination) applied to 

employees with 1-5 years of tenure. This result does not hold, however, for the notice period applied to 

employees with five or more years of tenure, possibly because firms’ need to lay off unsuitable 

employees and the benefits they derive from it decrease over the duration of the employment. The 

probability of obtaining credit decreases if a third party is required to approve the dismissal of a 

redundant employee, whereas the requirement to notify a third party before the dismissal does not affect 

firm credit access. Regarding a firm’s decision not to apply for a loan, we find mixed results for the 

relationship between firm flexibility in structuring employee working hours and the probability of being a 

discouraged borrower. Whereas the flexibility to extend employees’ working hours for seasonal needs 

ECB Working Paper 2063, May 20175reduces the probability being a discouraged borrower, restrictions on weekly holidays increase the 

probability of being a discouraged borrower. Moreover, the probability of being discouraged increases 

with the length of the notice period applied to employees with 1 and 5 years of tenure. In line with our 

findings on obtaining credit, the third party approval requirement for worker dismissal increases the 

probability to be a discouraged borrower. 

To the best of our knowledge, this work is among few to empirically investigate the role of 

employment protection legislation in determining the supply of debt capital. Our results contribute to two 

streams of research. First, this paper adds to the growing body of literature on employment protection and 

financial policies (Agrawal and Matsa, 2013; Berk et al., 2010; Kim, 2015; Kuzmina, 2013; Schmalz, 

2015; Serfling, 2016; Simintzi et al., 2015). These studies highlight the relationship between the demand 

side of the firm’s capital structure decision (i.e., the demand for debt capital) and the human costs of 

bankruptcy, collective bargaining, the size of the labour market, unionization, and firing costs. In contrast, 

we investigate the relationship between firms’ access to credit, - the supply of debt capital - and 

restrictions on structuring the employees’ working hours as well as on the dismissal of employees. 

Second, this paper contributes to the literature on the determinants of access to finance (Berger and Udell, 

1995; Elsas, 2005; Kysucky and Norden, 2014; Liberti and Mian, 2009; Moro and Fink, 2013; Neuberger 

et al., 2008; Petersen and Rajan, 1994), by expanding the literature that looks at the impact of legal 

regulations on credit access (Bae and Goyal, 2009; Chava et al., 2009; Cho et al., 2014; Laeven and 

Majnoni, 2005; Moro et al., 2016; Qian and Strahan, 2007; Tang et al., 2016). The latter studies show that 

credit access can depend on the quality of the legal system, in particular on the quality of shareholder 

rights, creditor protection and judicial enforcement. Building on this research, our paper provides insights 

into how employment protection legislation affects credit access. 

In addition to our contribution to research, our paper also contributes to practice and policy. By 

investigating both the probability of obtaining credit and the probability of being discouraged from 

applying for a loan, we are able to highlight differences between the perceptions of banks and firms 

regarding, for example, the relevance of flexibility in structuring the employees’ working hours for credit 

ECB Working Paper 2063, May 20176access. Further, our findings inform policy makers about the importance of flexible employment 

protection regulations in determining firm access to credit, which, in turn, facilitates firms’ growth. The 

policy implications of our findings are particularly important in the context of the European Union (our 

setting) where there is fundamental freedom of circulation of goods: firms located in countries with more 

flexible employment protection regulations may have a competitive advantage vis-à-vis firms located in 

countries with less flexible employment protection regulations, as the latter cannot be protected via trade 

barriers. 

The paper is organized as follows: Section 2 develops the hypotheses. Section 3 discusses the 

dataset as well as the methodology and describes the variables used in the analysis. Descriptive statistics 

are provided in Section 4. In Section 5 we present the econometric findings regarding the impact of 

employment protection regulations on obtaining credit and the submission of loan applications, further 

analysis regarding the role of industry, and robustness checks. In section 6 we discuss our findings. 

Section 7 concludes. 

2. Development of Hypotheses

Researchers have devoted considerable effort to investigating the factors that impact a firm’s 

capital structure. In recent years, employment protection legislation has been found to be one of these 

factors. Simintzi et al. (2015) find a negative relationship between reforms that increase employment 

protection and the amount of leverage in firms’ capital structure, suggesting that employment protection 

crowds out financial leverage by increasing operating leverage. Further, Serfling (2016) explores how an 

increase in employee dismissal costs driven by the adoption of employment protection laws affects a 

firm’s capital structure decision. His results also suggest that higher dismissal costs increase the costs of 

financial distress and reduce debt ratios. While these empirical studies focus on employment protection 

legislation as a factor that impacts firms’ demand for debt, Faulkender and Petersen (2006) argue that 

firm’s capital structure is not only a function of its demand for debt, but also of its ability to access debt. 

In order to obtain a clearer picture of how employment protection legislation affects firm capital structure, 

ECB Working Paper 2063, May 20177we will examine its impact on credit access. 

Employment protection legislation may affect credit access by increasing the credit risk of loans 

extended by banks that are lending to firms in these countries. A higher level of employment protection 

makes it more difficult for firms to manage their work force (Blanchard and Portugal, 2001), thereby 

increasing their adjustment costs (Nickell, 1978). In turn, higher adjustment costs make firms less likely 

to respond to the business cycle by hiring or dismissing employees (Kugler and Pica, 2008; Messina and 

Vallanti, 2007), which indirectly makes labour costs more rigid (Serfling, 2016). Due to these two 

interrelated effects, firms located in countries with higher employment protection will find it more 

difficult to structure their employees’ working hours as needed and to lay off employees in order to 

compensate for lower cash flows in times of financial distress. The inability to lay off employees when 

cash flow is low effectively creates a fixed cost that will be incurred irrespective of the firm’s 

performance. This increases the firm’s risk of financial distress and the likelihood of problems in 

repaying its bank loans. Anticipating these effects, banks will attribute a higher default risk to firms 

located in countries with higher levels of employment protection. Thus, a higher level of employment 

protection should reduce firm access to credit due to the higher credit risk associated with increased 

adjustment costs and lower flexibility in reacting to economic downturns. In addition, Cingano et al. 

(2010) and Calcagnini et al. (2014) show that firms with insufficient access to credit in high employment 

protection legislation environments are unable to substitute labour – the relatively expensive factor – for 

capital. Consequently, the negative effect of employment protection legislation on productivity and 

investment is reinforced among firms that are financially constrained. 

However, employment protection legislation comprises a large number of different regulations 

that may impact both on-going employment and employment termination. In our analysis we will 

investigate on-going employment regulations related to flexibility in structuring the employees’ working 

hours and employment termination regulations related to employee dismissal. Specifically, we consider 

three specific types of restrictions, one related to on-going employment and two related to employee 

dismissal. We structure our hypotheses on two dimensions: how these restrictions affect the probability of 

ECB Working Paper 2063, May 20178obtaining credit, and how these restrictions might discourage a firm from applying for a loan. Turning to 

the first type we consider a number of different restrictions on the number of working hours. Restrictions 

on night work, for example, might make it difficult for manufacturing firms to react to a sudden increase 

in demand or for trading firms to enter markets in different time zones. Greater flexibility in structuring 

employees’ working hours will reduce adjustment costs associated with hiring or dismissing employees, 

and enhance a firm’s ability to adapt employee working patterns to a changing business environment.1

This leads to the following hypothesis regarding the probability of obtaining credit: 

H1a: The greater the firm’s flexibility in structuring the employees’ working hours, the higher 

the probability that firms obtain credit. 

The termination of employment requires that several procedural regulations, such as notice 

periods or the notification and/or the approval of third parties, are adhered to. While these regulations do 

not directly entail costs for the firms, they make the termination process longer and more complex and 

may therefore indirectly increase firms’ adjustment costs and reduce their flexibility. As far as the notice 

period is concerned, the shorter it is, the easier it is for firms to adjust their workforce in response to an 

economic shock, organisational changes, employee performance or disciplinary issues. Therefore, we 

argue that a shorter notice period reduces firms’ adjustment costs and improves flexibility, which, in turn, 

decreases the banks’ risk that firms default on the loan. As a consequence, the probability that banks 

provide credit to firms increases. Accordingly, we formulate the following hypothesis: 

H1b: The shorter the statutory notice period, the higher the probability that firms obtain credit. 

In addition, we investigate how the involvement of third parties in the termination process impacts 

the firm’s probability of obtaining credit. Employment protection legislation may stipulate that third 

1 Restrictions on night work, for example, might make it difficult for manufacturing firms to react to a sudden increase in 
demand or for trading firms to enter markets in different time zones. 

ECB Working Paper 2063, May 20179parties, such as government agencies or unions, have to be notified or have to approve an employee’s 

layoff. In this case, a party outside the firm, which typically has little or no knowledge of the firm, has the 

right to be actively involved in one of the firm’s core strategic decisions, namely its human resource 

management. While this provision aims to protect the employee from wrongful dismissal, it might not 

always allow the firm to employ the best person for the job, as it might be forced to continue an 

inadequate employment relationship if the third party denies its approval. Even if the involvement of a 

third party leads to constructive negotiations between the firm and the third party, where both the firm’s 

and the employee’s interests are taken into consideration, the time period between the firm’s intention to 

dismiss the employee and the third party’s decision to approve or disapprove of the dismissal might be 

long and unpredictable. This scenario decreases firms’ flexibility and increases their adjustment costs. 

Thus, we expect banks to be less likely to grant credit in an environment where third parties are 

necessarily involved in the dismissal process. On the basis of these arguments, we propose the following 

hypothesis: 

H1c:  When the dismissal of an employee requires the involvement of a third party, the 

probability that firms obtain credit is lower. 

However, employment protection legislation may not only impact the banks’ decision to grant 

credit, but also the firms’ decision to apply for a loan. This is because firms may anticipate their banks’ 

lending decision in order to maximize their chances of a successful application and minimize their 

chances (and the cost and effort) of an unsuccessful application. That is, assuming that firms can 

anticipate the impact of employment protection regulations on banks’ lending decision, we would expect 

that greater flexibility in employment protection regulations would be associated with both a greater 

probability of obtaining credit and a lower probability of being discouraged from applying for credit. In 

this regard, we propose the following hypotheses: 

ECB Working Paper 2063, May 201710H2a: The greater the firm’s flexibility in structuring the employees’ working hours, the lower 

the probability firms are discouraged from applying for a loan. 

H2b: The shorter the statutory notice period, the lower the probability firms are discouraged 

from applying for a loan. 

H2c:  When the dismissal of one worker requires the involvement of a third party, the 

probability that firms are discouraged from applying for a loan is higher. 

3. Data and Methodology

3.1 Data 

Our analysis relies primarily on the data obtained from the Survey on the Access to Finance of 

Enterprises (SAFE) conducted on behalf of the European Commission and the European Central Bank 

(ECB). SAFE gathers facts about firms’ access to finance within the European Union. It is an on-going 

survey that has collected data every 6 months since 2009 and systematically covers thirteen euro area 

countries (namely: Austria, Belgium, France, Finland, Germany, Greece, the Netherlands, Ireland, Italy, 

Luxemburg, Malta, Portugal and Spain). We exclude Luxemburg and Malta because of the much reduced 

number of observations available and Greece because of the lack of data on employment protection 

legislation. 

Firms in the sample are randomly selected from the Dun & Bradstreet database. The sample is 

stratified by firm-size class, economic activity and country. The sample size for each economic activity is 

chosen to guarantee satisfactory representation across the four largest industries: manufacturing, 

construction, trade and services. Agriculture, forestry, fishing, financial intermediation, public 

administration, activities of households, extra-territorial organizations, as well as bodies and holding 

companies are excluded. Also, the sample sizes are selected on the basis of representation at the country 

level. The specific individual that is surveyed in each firm is a top-level executive and the questionnaire is 

administered in the local language. Between 4,500 and 6,000 firms were interviewed in each wave. 70% 

of surveyed firms were present in only one wave; less than 20% were present in two consecutive waves. 

ECB Working Paper 2063, May 201711In this respect, we do not have a proper panel dimension that allows us to exploit firm-level effects in our 

analysis, as only a very small number of firms were present in consecutive survey rounds. We end up 

with a dataset that contains 87,825 observations: 66,493 of these observations are firms that did not apply 

for a loan, and 21,332 are firms that applied for a loan in the period between spring 2009 and autumn 

2014. 

We combine the SAFE dataset with information from the quarterly Bank Lending Survey (BLS) 

collected by central banks on behalf of the ECB. The BLS contains information about the banks’ lending 

in the past three months and the banks’ propensity to lend in the next six months. It offers detailed data on 

the banks’ propensity to lend to large and small/medium-sized firms as well as households. We include 

the observations on banks’ lending to firms and assessment of general economic and business risks in the 

previous three months in order to control for credit availability in the market. We use the Eurostat 

homogeneous data on GDP growth and unemployment rates to control for macroeconomic conditions, 

and the Herfindahl-Hirschman Index of bank concentration (HHI) provided in the ECB data warehouse to 

control for the bank market structure. Data on employment protection legislation comes from the World 

Bank’s Doing Business dataset on Labour Market Regulation. We note, however, that this dataset does 

not contain information for the entirety of our sample period in the case of the Notice period after 1 year 

and the Notice period after 10 years. In both cases the observations for 2009 are missing. Thus, the 

analyses on these variables rely on a marginally smaller dataset. 

3.2 Methodology 

Our analysis uses traditional logit regressions (Hosmer and Lemeshow, 2000), since the dependent 

variables (whether a firm obtains credit or not; whether a firm is a discouraged borrower or not) are 

binary. As described above, the panel dataset is unmatched at the firm level so we use pooled logit 

regressions. Employment protection legislation differs at the country and the industry levels. This fact can 

generate clustering effects on the errors of the regressions. Thus, we estimate standard errors that are 

robust to clustering of errors by considering 40 different clusters (ten countries and four industries in each 

country). Since the dataset provides weights that restore the proportions of the economic weight (in terms 

ECB Working Paper 2063, May 201712of the number of employees) of each firm-size class, economic activity and country, we estimate our 

regressions by including these weights. We estimate a set of different regressions, where we enter the 

different independent variables one by one. This approach avoids multicollinearity problems linked to the 

fact that our independent variables are correlated with each other. 

Employment regulation might interact with the industry, as different industrial sectors might be 

more or less sensitive to different aspects of employment protection legislation. Flexibility in hiring for 

seasonal needs might for instance be more relevant for firms that operate in industries characterised by 

high seasonality (e.g. tourism sector), while restrictions on night work might be more relevant for asset-

intensive industries (e.g. automobile sector). In order to obtain a more granular picture of the role played 

by employment protection legislation, we re-estimate the original specification by including the original 

variable and its interaction with a dummy that identifies the industry. This analysis allows us to examine 

two different aspects: 1) whether there are differences regarding the role of employment protection 

legislation in different industries; 2) whether employment protection legislation matters over and above 

industry specific aspects. 

In addition, we conduct a number of robustness checks in order to address selection bias, omitted 

controls and endogeneity. Even though our analysis of credit access considers only firms that apply for a 

loan and our analysis of discouraged borrowers considers only firms that do not apply for a loan, we 

cannot rule out the possibility that our results are affected by some kind of selection bias. Thus, we re-

estimate our model implementing Heckman (1979) selection approach. We use the dataset that contains 

both firms that applied and firms that did not apply for a loan (up to 87,825 observations) and employ the 

binary response model with sample selection, where the dependent variable indicates whether or not the 

firm applies for a loan. The approach we follow is similar to that used by Piga and Vivarelli (2004) and 

Piga and Atzeni (2007). The selection equation requires at least one variable that affects the demand for a 

loan, but is not relevant in the main model. Due to limitations in the dataset, we rely on (i) the change in 

the firm’s profit, and (ii) the change in the firm’s turnover, an approach that is similar to that used by 

Moro et al., (2016). According to Myers and Majluf (1984) firms finance their activity by first using 

ECB Working Paper 2063, May 201713retained profit, then reverting to debt and finally to equity. Thus, greater profit is expected to reduce the 

probability that firms apply for a loan. Further, larger firms, as measured by turnover, are expected to face 

greater financial needs and to be more likely to apply for a loan. Both variables impact on the firm’s 

decision to apply for a loan as proven by their high correlation with the dummy variable that measures 

whether the firm applies for a loan. At the same time, even if they might also play a role in the bank’s 

lending decision or in being a discouraged borrower, such a role is limited as shown by the low 

correlation between the two variables and the dependent variables. 

In addition, we cannot rule out that our regressions might suffer from endogeneity in the sense that 

by looking at employment protection legislation we in fact investigate the power of unions in larger firms, 

as unions try to influence employment protection regulations in order to protect the interest of employees, 

particularly in larger firms. In order to address this issue we re-estimate our model by instrumenting our 

independent variables with the level of unionisation of a country (measured as the percentage of 

workforce that is member of a union) and by interacting it with firm-size classes. The reasoning is that the 

greater the share of employees who are members of unions, the greater the power unions have and the 

greater their ability to influence regulations. Moreover, employees are typically better organized in larger 

firms, implying that unions’ role will be more relevant in this case. If the instrumented variables are 

significant, this will mean that our dependent variables in fact measure the role of unions and not of 

employment protection legislation. At the same time, if the instrumented variables turn out to be not 

significant, this will mean that our analysis does not suffer from endogeneity issues. 

3.3 Dependent Variables 

In order to examine the probability that firms obtain credit, we rely on one of the questions asked 

in the SAFE, namely whether the firms obtained the credit they applied for in the last six months. We use 

the answer to this question (the firm obtained the credit = 1; the firm did not obtain the credit = 0) as our 

first dependent variable. 

However, firms might not even get the opportunity to obtain credit, as they decide not to apply for 

credit for fear of being rejected by the bank (discouraged borrowers). Thus, firms can also suffer from a 

ECB Working Paper 2063, May 201714lack of credit access due to self-selection. The SAFE dataset also collects information about this aspect, 

as it asks the firms that did not apply for a loan if they did not apply because they anticipated a rejection 

from the bank. We use the answer to this question (the firm is a discouraged borrower = 1; the firm is not 

a discouraged borrower = 0) as our second dependent variable. 

3.4 Independent Variables 

We use different independent variables for testing each of our three hypotheses. H1a and H2a are 

tested with three variables. The first variable looks at the possibility to extend the workweek to 50 hours 

per week for 2 months each year to respond to seasonal increase in demand. This flexibility makes it 

easier for the firm to structure the employees’ workweek so that seasonal variations can be met, which 

decreases adjustment costs. As a consequence, we expect banks to assess credit risk as lower for, and to 

be more willing to lend to, firms operating with this flexibility. The variable is a dummy that equals 1 if 

the firm has the possibility to extend the workweek and 0 otherwise. The second variable reflects 

restrictions on night work. It is a dummy that equals 1 if there are restrictions on night work and 0 

otherwise. Limitations on night work lower the firm’s ability to adapt its employees’ workweek to the 

level of its business activity and therefore increase adjustment costs. Thus, we expect these restrictions to 

adversely affect access to credit. The third variable looks at restrictions on weekly holidays. This variable 

is also a dummy that takes a value of 1 if there are restrictions and 0 otherwise. If employees can be 

restricted in taking holidays during the week, the firm will be able to respond to changes more easily and 

will therefore have lower adjustment costs. This should increase access to credit. 

To test H1b and H2b we use continuous variables for the length of the notice period for an 

employee with one year, five years and ten years of tenure. All variables are expressed in weeks of salary. 

If a firm can easily lay off unsuitable employees due to a short notice period, it will be able to reduce its 

adjustment costs. We therefore expect that short notice periods facilitate access to credit. Because of a 

lack of observations for the year 2009 in the case of notice period after one year and notice period after 

ten years, the regressions are estimated using only 60,881 observations in the case of firms that did not 

apply for a loan and 19,746 in the case of firms that applied for a loan. 

ECB Working Paper 2063, May 201715H1c and H2c are tested using three variables for the obligation to involve third parties in 

dismissing one or more employees. We use (i) the obligation to notify a third party for one dismissal, (ii) 

the obligation to notify a third party for a dismissal of nine or more employees, and (iii) the obligation to 

get a third party’s approval for one dismissal. All variables are dummies, with 1 indicating that an 

obligation exists and 0 indicating that the obligation does not exist. We expect that obligations to involve 

third parties make the termination process more complex and time-consuming, which increases 

adjustment costs and decreases the probability of obtaining credit.  

3.5 Controls 

We include a set of controls for firm characteristics, the economic context and the moment in time 

when the data was collected. In terms of firm characteristics, the SAFE dataset provides some information 

about the size of the firms by clustering them into the following categories: micro, small, medium-sized 

and large. We use three dummy variables that identify micro, small and medium-sized firms. Our 

expectation is that micro and small firms are more likely to face a rejection, since they are supposedly less 

solid and successful. As firms anticipate banks’ lending decisions, micro and small firms are also more 

likely to be discouraged borrowers. 

Firms are clustered according to four age categories: younger than two years, between two and 

five years, between five and nine years, and older than nine years. We use Firms up to 2 years, Firms 

between 2 and 5 years, and Firms between 5 and 9 years as dummy variables to identify the age group. 

Older firms are more likely to be successful in applying for a loan (Berger and Udell, 1995; Petersen and 

Rajan, 1994) since they have an established reputation that banks rely on when taking lending decisions 

(Martinelli, 1997). We control for the change in labour costs. This is a categorical variable, which takes a 

value of -1, if the firm faces a reduction in labour costs, 0, if there is no change, and +1, if there is an 

increase. 

Regarding the financial strategy of the firms, we include dummies that identify different sources 

of finance used: trade credit (TRADE_CREDIT), leasing and factoring 

(LEASING_AND_FACTORING), retained earnings (RETAINED_EARNINGS) and additional equity 

ECB Working Paper 2063, May 201716(EQUITY). 

Since the independent variables are time-invariant at country level, we do not include any control 

for the country. However, we consider a set of macroeconomic variables that are country-specific and 

time-varying to capture the macroeconomic context in which the firms operate. In particular, we include 

the change in the GDP and the unemployment rate. Additionally, we include the market structure of the 

financial industry by using the Herfindahl-Hirschman Index (HHI) of bank concentration in each country, 

as previous research suggests an impact of bank competition on credit access (Carbó-Valverde et al., 

2009; Neuberger et al., 2008). Moreover, we account for the financial context by including the index that 

measures industry risk as perceived by banks and the European Central Bank’s BLS coefficients for 

small/medium-sized firms. The BLS index measures if banks apply more rigid rules for the provision of 

credit (positive values) or more lenient ones (negative values). This control allows for the credit made 

available to firms by the bank system.  

The dataset provides unmatched observations for twelve semesters (2009-2014). Thus, we use 

eleven dummies that identify the semester in which the data was collected. 

4. Descriptive Statistics

The dataset used contains 87,825 complete observations from ten countries for the period between 

the first semester of 2009 and the second semester of 2014. The dataset is reduced to 21,332 observations 

when only the firms that applied for a loan are considered. The difference can be attributed either to firms 

that did not need finance (and, thus, did not apply for a loan) or to firms that were discouraged from 

applying for a loan (i.e., firms that needed a loan, but did not submit an application, because they feared 

that it would be rejected). Additional detailed information about country (and time) variation of the 

independent variables is reported in the appendix.  

Figure 1 reports the loan applications, the success in applying for a loan as well as the percentage 

of discouraged borrowers in the ten countries considered in our research. 

[FIGURE 1A] 

ECB Working Paper 2063, May 201717The summary statistics on 87,825 observations are reported in Table 1A. 

[FIGURE 1B] 

[TABLE 1A] 

The majority of the firms are SMEs (68%) and more than 77% are older than 9 years. In terms of 

financing, firms appear to use leasing and trade credit quite intensively. The BLS coefficient is positive 

(3.6), implying that, on average, the bank system is tightening credit standards and hence is reducing the 

provision of credit. 

As far as the flexibility in structuring the employees’ working hours is concerned, almost 82% of 

the firms enjoy flexibility in extending the workweek for seasonal needs, while only 39% face restrictions 

on night work. Interestingly, only in 25% of the cases do limitations on employees’ weekly holidays exist. 

The notice periods vary and increase with the employee’s tenure, ranging from a minimum of 1.36 

months (after one year of continuous employment) to a maximum of 17.33 months (after more than ten 

years of continuous employment). The average length of notice period goes from 4.26 months in case of 

one year of tenure to almost 10.6 months in case of more than ten years of tenure. The involvement of 

third parties in the dismissal process presents a quite heterogeneous picture: while 69% of the firms have 

to notify a third party in case of a single dismissal, approximately 97% of the firms have to notify a third 

party for a collective dismissal. The approval for a dismissal is needed in 6% of the firms in the sample.  

Moving from the overall dataset to the sub-sample which considers only firms that actually 

applied for a loan, it can be seen that in this group there are relatively less micro firms and more large 

firms and, overall, these firms report more often to have used internal funds as well as the various sources 

of external finance (Table 1B).  

[TABLE 1B] 

As far as employment protection regulations are concerned the picture is slightly different. The share  of 

firms facing restrictions on night work goes up to 41%, whereas the share of firms that may extend the 

workweek for seasonal needs goes down to 79%. At the same time, the proportion of firms that may 

restrict weekly holidays increases to 27%. In terms of notice period there are no major differences. The 

ECB Working Paper 2063, May 201718same is true for the proportion of firms that have to notify to third parties in case of a single or collective 

dismissal. However, the share of firms that has to obtain the approval of a third party in order to dismiss 

an employee declines to 3.4%. 

Regarding firms that did not apply for a loan (Table 1C), there are proportionately slightly more 

micro firms than in the entire sample. There are no major changes in the employment protection 

regulations: the proportion of firms that face restrictions on night work or weekly holidays or that are 

flexible to extend the workweek for seasonal needs is very close to that of the entire dataset. Also the 

lengths of notice periods are very close to the ones in the entire dataset and the same applies to the share 

of firms that has to involve third parties in the termination process.  

[TABLE 1C] 

5. Results

The analysis is split into two stages: first, we focus on the impact of employment protection 

legislation on the probability of obtaining credit; second, we analyse whether employment protection 

legislation discourages firms from applying for a loan. Further analyses and robustness checks are 

presented in separate subsections. 

5.1 Employment Protection Legislation and Obtaining Credit 

The results regarding the impact of flexibility in structuring the employees’ working hours on 

obtaining credit from the bank are reported in table 2A.  

[TABLE 2A] 

The first regression (Model 1) reports the results linked to the flexibility of extending the 

workweek for seasonal needs. While flexibility is significant its sign is negative. That is, flexibility in 

extending the workweek for seasonal needs reduces the probability of obtaining credit inconsistent with 

H1a. One explanation is that greater flexibility causes operational problems or increases administrative 

burdens (Maxwell et al., 2007) which could, in turn, impact on the quality of management. Further, 

extending the workweek for seasonal needs might deteriorate employee working conditions, as longer 

ECB Working Paper 2063, May 201719working hours increase occupational injuries and illnesses (Dembe et al., 2005). As a consequence, the 

firm’s risk profile could increase, thereby making banks more reluctant to provide credit. The size of the 

firm affects its access to credit, as smaller firms are less likely to obtain credit. A similar picture can be 

found with respect to the age of the firm: firms younger than two years of age appear to be the ones that 

are least likely to obtain a loan. Regarding the source of finance used by firms, only the use of 

leasing/factoring and trade credit are significant and negatively related to obtaining credit.  This could be 

consistent with lower quality and/or especially opaque firms being relying on these more expensive 

sources of finance. Change in labour costs is significant and positively related to obtaining credit. 

Economic expansion (GDP) is positively related to obtaining credit, whereas the increase in 

unemployment is negatively related to obtaining credit. The index that measures industry risk from the 

banks’ point of view is not significant. As expected, the European Central Bank’s BLS coefficient is 

negatively associated with obtaining credit.   

Flexibility in Model 2 is significant and is consistent with H1a.  That is, in line with our 

expectations, limitations on night workforce deployment decrease its flexibility and increase adjustment 

costs. As a consequence, banks attribute a higher credit risk to the firm and are less willing to provide 

credit. The controls do not reflect major differences with Model 1 in terms of sign and significance. 

The last specification presented in table 2A (Model 3) focuses on the ability (i.e., the flexibility) of 

firms to constrain employees from taking a holiday during the week.  The independent variable of interest 

is significant and consistent with hypothesis H1a.  That is, the ability (i.e., flexibility) to limit holidays 

during the week improves access to finance and is therefore consistent with banks assessing lower credit 

risk, since firms are able to respond more easily to changes in demand. Again in Model 3 the significance 

and the sign of the control variables are not markedly different from the other two Models. On balance the 

results tend to provide support to H1a in that in two of the three models flexibility in structuring 

employees’ working hours increases credit access. 

The results regarding the relationship between the length of the notice period and the probability 

of obtaining credit are reported in table 2B.  

ECB Working Paper 2063, May 201720[TABLE 2B] 

In the first regression, Model 4, there are only 19,199 observations, since we do not have any 

information on the length of the notice period after one year of continuous employment for the year 2009.  

In this Model the key independent variable we focus on is the continuous variable that reflects the notice 

period for employees with one year of tenure. This variable is significant and negatively related to 

obtaining credit: the longer the notice period, the harder it is for firms to obtain credit. This is in line with 

our expectation, since longer notice periods increase the costs that firms incur before being able to end 

employment relationships with employees who do not match the firm’s requirements. Regarding the 

controls included in the specification, there are no major changes in significance or sign with respect to 

the models presented in table 2A.  Models 5 and 6 focus on the notice period for employees with five 

years and ten years of tenure respectively. Neither is significant suggesting that banks do not perceive 

longer notice periods as detrimental for employees with five/ten years of tenure, possibly because the 

layoff is less likely to be linked to the employee’s suitability for the job. There are no changes in the 

significance and sign of the controls in these models. In Model 6, as in Model 4, the regression is based 

on a reduced number of 19,199 observations due to the lack of information on the independent variable 

for the year 2009. All in all, our evidence suggests that the notice period affects access to credit but this 

effect is limited to employees with short tenure. 

The results on the relationship between the involvement of third parties and the firm’s probability 

of obtaining credit are reported in table 2C.  

[TABLE 2C] 

In Model 7 third party notification for the dismissal of one employee is not significant and in 

Model 8 third party notification for collective dismissal is not significant.  However, in Model 9 third 

party approval for a single worker dismissal is significant and negative. That is, the requirement that a 

third party must approve an employee dismissal reduces the probability that a firm can obtain credit, since 

it increases the firm’s credit risk due to higher costs and greater uncertainty about the final outcome. 

There are no changes in the significance and sign of the controls in Model 9. 

ECB Working Paper 2063, May 2017215.2 Employment Protection Legislation and Discouraged Borrowers 

The findings regarding the impact of employment protection legislation on the probability that 

firms are discouraged from applying for credit for fear of rejection are reported in tables 3A, 3B and 3C. 

[TABLE 3A] 

Model 10 examines the role of flexibility in extending the employees’ working hours for seasonal 

needs on being a discouraged borrower. The specification is based on 64,312 observations. Regarding our 

key independent variable, flexibility in extending the employee workweek for seasonal needs, the variable 

is significant and negatively related to being a discouraged borrower. The flexibility to adapt the 

workweek to the firm’s seasonal needs hence lowers the probability that the firm will be discouraged 

from applying for a loan. This finding is in line with our expectations. Regarding controls, firm size 

affects the probability of being a discouraged borrower, with small firms appearing to be the most 

discouraged ones. The firm’s age also affects the likelihood of being discouraged from applying for a 

loan. Firms between two and five years of age are the most discouraged ones, while firms up to two years 

of age are not discouraged, possibly because they have limited financial needs and therefore rely less on 

bank lending. Among the different sources of finance EQUITY as well as LEASING_FACTORING and 

TRADE_CREDIT are significantly and positively related to being a discouraged borrower. The cost of 

labour is negatively associated with being a discouraged borrower. Among the macroeconomic variables 

the change in GDP, the Herfindahl-Hirschman Index of bank concentration and the BLS index are not 

significant. At the same time, the unemployment rate is positively related to being a discouraged 

borrower.  

Model 11 examines the relationship between restrictions on night work and the probability of 

being a discouraged borrower. The variable of interest is positive, but not significant. Qualitatively, there 

are no changes in the controls compared to Model 10. In the last regression presented in table 3A (Model 

12) the variable of interest, restrictions on weekly holidays, is marginally significant and positive,

suggesting that firms are more likely to be discouraged borrowers when they are allowed to limit the 

possibility of holidays during the week. A possible reason for this unexpected finding might be that firms 

ECB Working Paper 2063, May 201722using this flexibility experience less motivated employees, which, as a consequence, might negatively 

affect their productivity and thus their probability to obtain credit. There are no changes in the sign or in 

the significance level of the controls in comparison with Models 10 and 11. 

Table 3B presents the regressions that examine the role of the notice period on the probability that 

the firm is a discouraged borrower.  

[TABLE 3B] 

Model 13 examines the notice period after one year of tenure. The regression includes only 60,881 

observations, since there is no information on the length of the notice period after one year of continuous 

employment for the year 2009. Turning our attention to the variable of interest, the length of the notice 

period for employees with one year of tenure, it is positively related to being a discouraged borrower. The 

longer the notice period, the higher is the probability that the firm will be discouraged from applying for a 

loan. There are no changes in the controls with respect to the previous regressions except for the BLS 

index, which used to be not significant and now turns to be (marginally) significant and negatively related 

to being discouraged from applying for a loan. Similar results are shown in Model 14. The notice period 

after five years is significant and positively related to being a discouraged borrower. Both findings are in 

line with our expectations. The longer the notice period for employees with one or five years of tenure, 

the more difficult it is for the firm to dismiss somebody who is not suited for the job, and the higher are 

the firm’s adjustment costs. As a consequence, the firm will anticipate that it poses a higher credit risk for 

the bank and thus be less likely to apply for a loan. Except for the BLS index, which turns to be not 

significant, the regression does not present any changes in the sign and the significance of the controls as 

compared to Model 13. However, the results for the notice period after ten years of continuous 

employment presented in Model 15 differ in that the notice period after ten years is not significant.  

Table 3C presents the last of our basic results. Here we examine the relationship between the 

involvement of third parties in the dismissal process and the probability of being a discouraged borrower. 

Model 16 focuses on whether the firm has to notify a third party of the planned dismissal of one 

[TABLE 3C] 

ECB Working Paper 2063, May 201723employee and Model 17 focuses on third party notification for collective dismissal. As in our findings for 

obtaining credit (Models 7 and 8), neither are significant suggesting that the additional red tape associated 

with third party notification is not particularly problematic in raising firm adjustment costs and inhibiting 

credit access. Model 18 presents a different picture. The requirement to obtain a third party’s approval in 

order to dismiss an employee negatively (and statistically) impacts the probability that a firm is a 

discouraged borrower (just as it negatively affects the probability of obtaining a loan when it applies 

(Model 9). Thus, firms perceive the stipulation that third parties, such as unions or governmental 

agencies, are allowed have a say in the dismissal process (i.e. in the selection or the number of people to 

be dismissed) as decrease in flexibility, which might adversely affect their application for a loan. 

Regarding controls, the only change is linked to the Herfindahl-Hirschman Index of bank concentration, 

which turns to be significant and negatively related to being a discouraged borrower. 

5.3 Further Analysis: The Role of Industry 

Our analysis relies on the joint observations of firms that belong to different industries. However, 

we cannot rule out the fact that the industry might impact the role that employment protection legislation 

plays in obtaining credit or in being discouraged from applying for a loan. For instance, both the service 

and the trade industry might be more sensitive to seasonal flexibility than the manufacturing industry, 

which might in turn be more sensitive to restrictions on night work. The service and the trade industry 

might also be more sensitive to the length of the notice period since they typically suffer from higher 

fluctuation. All in all, it is therefore necessary to examine the impact that employment protection 

legislation can have at industry level and, at the same time, verify whether our initial results hold. 

In order to explore the importance of the firm’s industry, we re-estimate the regressions by 

including the original independent variable, a dummy variable that identifies the industry (one at a time) 

as well as the interaction between the respective dummy variable and the employment protection 

legislation variables. We deploy this analysis only for those variables that are significant in the analyses 

presented in sections 5.1 and 5.2. The results on obtaining credit are reported in tables 4A, 4B and 4C. 

For reason of space the tables only report information about the variables of interest. There are no major 

ECB Working Paper 2063, May 201724changes in the controls in the different specifications. 

[TABLE 4A] 

[TABLE 4B] 

[TABLE 4C] 

Regarding flexibility in extending the employees’ workweek for seasonal reasons, the industry 

dummy is significant for the construction and the trade industries, whereas the interaction variable is 

significant for the construction (negative sign) and the manufacturing (positive sign) industries. This 

suggests that banks are less likely to provide credit to the construction industry if such flexibility exists, 

while they are more likely to provide credit to firms in the trade sector. The original non-interacted 

variable is always significant and negatively related to obtaining credit, suggesting that flexibility is 

relevant irrespective of the industry. Regarding restrictions on night work, its interactions with the 

industry dummies are always not significant, while the service industry dummy is significant and the 

trade industry dummy is marginally significant. At the same time, the original non-interacted variable is 

always significant and negatively related to obtaining credit, suggesting an important role for restrictions 

on night work over and above any industry effect. When we look at restrictions on weekly holidays, only 

the construction industry dummy is significant. The interaction variable is highly significant for the 

construction industry (positive sign) and marginally significant for the manufacturing industry (negative 

sign). The original non-interacted variable is always significant and positively related to obtaining credit 

except for the regression that includes the interaction with the construction industry, suggesting that in 

this specific case the provision of credit is driven mainly by factors specific to the industry. For the notice 

period, the industry dummies and the interaction variables are not significant. This result suggests that the 

role of the notice period is independent from any industry effect. Finally, we examine the role of third 

party approval. In this case, the interaction variables are not significant and the industry dummy is only 

significant in the case of the trade industry. As far as the original variable is concerned, it is always 

significant and retains the same sign, suggesting that third party approval plays a role over and above the 

industry. 

ECB Working Paper 2063, May 201725The results for the role of the firm’s industry and employment protection legislation on 

discouraged borrowers are reported in tables 5A, 5B and 5C. Also in this case, the tables report 

information only about the variables of interest for reasons of space. There are no major changes in the 

controls in the different specifications. 

[TABLE 5A] 

[TABLE 5B] 

[TABLE 5C] 

Regarding flexibility in extending the employees’ workweek for seasonal needs, the original non-

interacted variable is always significant, whereas its interaction with different industries it turns out to be 

only significant in the case of the construction industry. Among the industry dummies only the trade 

industry is significant. The results suggest that different industries do not play a real role. Restrictions on 

weekly holidays is marginally significant in the original specification (Model 12). This is confirmed when 

we interact the original variable with the industry dummies, as the interaction variable is only significant 

in case of the construction industry. This result suggests an “industry” effect of restrictions on weekly 

holidays. Turning our attention to the length of the notice period, the results indicate a clear role for both 

the notice period after one year and the notice period after five years over and above the industry. 

Regarding the notice period after one year, the original non-interacted variable is always significant, but 

neither the interaction variable nor the industry dummies are significant. These results also suggest that, 

as far as discouraged borrowers are concerned, the difference in industries does not play any role. 

Regarding the notice period after five years, only the construction industry seems to play a role, since 

both the construction industry dummy and the interaction with the construction industry dummy are 

significant. The final regressions look at the role of the obligation to obtain a third party’s approval for an 

employee’s dismissal. The original non-interacted variable is always highly significant, but only the 

interaction with the trade industry dummy and the manufacturing industry dummy are significant. This 

result suggests that even if industry plays a role, third party approval nevertheless is also relevant per se. 

5.4 Robustness Checks 

ECB Working Paper 2063, May 201726In this section we conduct robustness checks to rule out that our findings are affected by the 

financial crisis, selection bias or endogeneity issues. The financial crisis might impact both credit access 

and employment protection legislation. One might expect that countries like Italy or Spain, which were 

hit more strongly by the economic downturn, faced a greater reduction in credit access. At the same time, 

these countries might also be the ones, which experienced more reforms in employment protection 

legislation. Here we conduct additional analyses in order to explore whether this is the case. The Doing 

Business coefficients on employment protection legislation used are constructed on an annual basis, but 

do not reflect any variability during the period considered. This suggests that the countries in the sample 

did not implement any change in their respective employment protection regulations. Moreover, our data 

show a very low correlation between the firms’ reliance on bank credit in a particular country (proxied by 

the trade credit to total liabilities ratio) and the employment protection legislation variables. We can 

therefore rule out confounding effects linked to simultaneous changes in credit access and employment 

protection legislation. 

Although our analysis of the probability of obtaining credit considers only firms that apply for a 

loan and our analysis of the probability of being discouraged from applying for a loan considers only 

firms that do not apply for a loan, we cannot completely rule out the possibility that our results are 

affected by selection bias. Thus, we re-estimate the regressions where the employment protection 

legislation variables are significant by using the Heckman sample selection model (Heckman, 1979). 

Because of the dataset we use and the variables we employ, it is not easy to find a variable that affects the 

firm’s decision to apply for a loan, but that does not affect the probability of obtaining credit from a bank. 

In that regard, as we discussed in the methodology section, we exploit firm-specific characteristics as 

elements that affect the firm’s decision to apply for a loan (but not the probability of obtaining credit) by 

using variables that measure the change in turnover and the change in profit (results not reported here).  

However, the regressions are significant in all specifications used. In addition, there are no major 

changes in the sign and the significance level of the controls. More importantly, the independent variables 

have the same sign and significance level as in the original regressions. All in all, we can conclude that 

ECB Working Paper 2063, May 201727our original models appear not to suffer from selection bias. 

As discussed in the methodology section, it could be argued that our analysis suffers from 

endogeneity in the sense that we are not measuring the role of employment protection legislation, but in 

fact the role of the power of unions in large and small firms. In order to deal with this issue we re-

estimate our regressions by instrumenting the variables of interest by using a measure of union power 

(namely the level of unionisation in the country, measured as percentage of workers that are member of a 

union) and interact it with a dummy that identifies whether the firm is medium-sized or large. The 

interacted variable appears to be a good instrument, since it is more correlated with the independent 

variables than the dependent variables. If our analysis is affected by endogeneity, we should end up with 

instrumented variables that are significant (results not reported here). None of the variables of interest is 

significant when it is instrumented and this applies to both the regressions that focus on obtaining credit 

and those that focus on discouraged borrowers.  

We also re-estimate our models controlling for the industry. In the original models the industry 

dummies have not been included, as we pursued more detailed analyses with the interaction variables. 

However, when we include the industry dummies as controls there are no changes in the significance 

level and sign of both controls and variables of interest.  

All in all, our additional analyses and robustness checks suggest that our original findings are not 

affected by confounding. In addition, we provide evidence that the results are robust to sample selection, 

endogeneity and alternative controls variables. 

5.4 Magnitude of the effects 

The analyses presented above supports our argument that more rigid employment protection 

legislation has an impact on firms’ credit access by both reducing the probability of obtaining credit and 

increasing the probability of being discouraged from applying for credit. However, it is also important to 

examine the economic impact of such statistically significant relationships. To do so, we estimated the 

probability of obtaining credit and being discouraged from applying for credit for each independent 

variable at its highest and lowest value. The results are reported in table 6A (obtaining credit) and table 

ECB Working Paper 2063, May 2017286B (being discouraged from applying for credit). 

[TABLE 6A] 

[TABLE 6B] 

Our findings suggest a considerable economic effect of employment protection legislation on the 

probability of obtaining credit. The probability of obtaining credit increases from 69% to 74% when firms 

have the possibility to extend the workweek for seasonal needs. A lack of restrictions on night work 

increases the probability of obtaining credit from 63% to 76%. In addition, restrictions on weekly 

holidays increase the probability of obtaining credit from 69% to 74%, whereas the length of the notice 

period increases the probability of obtaining credit from 64% (in case of the longest notice period of 6.5 

months) to 73% (in case of the shortest notice period of 1 month). The obligation to get a third party’s 

approval for one dismissal has the biggest economic effect, as the probability of obtaining credit rises 

from 39% (third party’s approval is required) to 72% (third party’s approval is not required). 

When we look at the economic impact of employment protection legislation on being discouraged 

from applying for a loan, we find an overall reduction with less rigid employment protection. The 

probability of being discouraged from applying for credit decreases from 7% to 5% when firms have the 

possibility to extend the workweek for seasonal needs. While this is a proportionally big increase, it is 

nevertheless a marginal economic effect. The same applies to restrictions on weekly holidays (reduction 

from 6% when firms face restrictions to 5% when they do not) and for notice periods (reduction from 6% 

to 4% when firms have to observe shorter notice periods for employees with both 1 year and 5 years of 

tenure). Also with regard to being discouraged from applying for a loan, third party involvement has the 

biggest effect: the probability to be discouraged from applying for a loan decreases from 10% to 5% when 

third party approval is not required. 

Overall, our results suggest that employment protection legislation has a considerable economic 

effect on both the probability of obtaining credit and the probability of being discouraged from applying 

for a loan, even though these effects are stronger with regard to the banks’ decision to provide credit than 

the firms’ decision to apply for credit. In addition, the biggest effect on constraining credit access (both in 

ECB Working Paper 2063, May 201729terms of being denied credit and being discouraged from applying for credit) is the obligation to obtain a 

third party’s approval for the dismissal of an employee. This involvement of third parties is considered 

extremely detrimental, possibly because it makes the termination process more complex and time-

consuming. 

6. Discussion

The results of the initial econometric analysis are strongly supported by the robustness checks we 

pursue. However, the empirical evidence deserves further elaboration.  

Regulations that allow for greater flexibility in structuring the employees’ working hours affect 

the banks’ decision to lend. Interestingly, even though all three measures used are significant, flexibility 

in extending the employees’ workweek for seasonal needs reduces the probability of obtaining credit 

from banks, whereas a lack of restrictions on night work and on weekly holidays increases the probability 

of obtaining credit. One possible explanation for this finding could be that, whereas a temporary increase 

in employees’ weekly working hours enables firms to adapt to changes in demand without having to hire 

or dismiss employees, thereby reducing costs and increasing competitiveness (Corominas et al., 2004), it 

also complicates the firms’ planning of working hours and deteriorates the employees’ working 

conditions (Lusa et al., 2008). If banks perceive these challenges to outweigh the chances related to 

greater flexibility in structuring the employees’ working hours, they might be reluctant to grant credit. 

Our results also suggest that greater flexibility in structuring the employees’ working hours affects firms’ 

decision to apply for a loan. However, while flexibility in increasing working hours for seasonal needs 

reduces the probability to be discouraged from applying for a loan, the effect becomes positive when 

restrictions on weekly holidays increase. Restrictions on night work, instead, do not impact on firms’ 

decision to apply for credit. These results also show a clear mismatch between the firms’ and the banks’ 

interpretation of the role of employment protection legislation. First, firms are less likely to be 

discouraged borrowers when regulations provide the flexibility to extend the working hours for seasonal 

needs, while banks tend to be more rigid in providing credit in such a context. Second, firms are more 

likely to be discouraged borrowers when laws stipulate restrictions on weekly holidays, whereas banks 

ECB Working Paper 2063, May 201730are more willing to provide credit in this case. Lastly, the results regarding restrictions on night work are 

mixed: whereas banks’ lending decision is negatively affected by these restrictions, firms’ decision to 

apply for a loan is not affected. 

With respect to termination procedures the evidence suggests that greater flexibility in dismissing 

employees increases the probability to obtain credit and decreases the probability to be a discouraged 

borrower by indirectly reducing adjustment costs. In this case, firms and banks interpret the role of notice 

periods and the involvement of third parties in the dismissal process similarly. Interestingly, we find that 

the possibility to dismiss employees at short notice is only relevant in the case of employees with short 

tenure, but not in the case of employees with longer tenure. This finding supports earlier empirical 

evidence on the relationship between employment protection legislation and the quality of job matching 

(Marinescu, 2009). Shorter notice periods enable firms to adjust their workforce at lower cost and hence 

improve the sorting of employees into jobs they are best suited for. However, these benefits will be 

mitigated with employees’ increasing tenure, since bad matches will more likely already be weeded out. 

Thus, the length of the notice period plays a more pronounced role at the beginning of employment 

relationships, which is also reflected by our empirical results.  

Further, our findings indicate that both banks’ lending decision and firms’ decision to apply for a 

loan are affected by the firms’ obligation to obtain the approval of a third party in order to terminate an 

employment, but they are not affected by the firms’ obligation to notify a third party before the dismissal 

of an employee. While banks and firms regard the obligation to notify a third party merely as 

administrative hurdle, they see the obligation to obtain a third party’s approval as interference in the 

firms’ human resource management, which might prevent the dismissal of unsuitable employees and 

therefore affect the firms’ flexibility and increase their adjustment costs.  

All in all, our results suggest that both banks and firms value employment protection legislation 

that allows for greater flexibility in structuring working hours and dismissing employees. However, while 

we find consistent results for the impact of dismissal regulations on banks’ lending decision and firms’ 

decision to apply for credit, we find a clear mismatch with regard to regulations on working hours.  

ECB Working Paper 2063, May 2017317. Conclusion

This research examines the relationship between employment protection legislation and firms’ 

ability to increase their leverage. Specifically, we investigate how employment protection legislation 

affects firms’ access to credit by looking at both the credit obtained from the bank and the firms’ decision 

to apply for a loan in ten European countries. The results suggest that employment protection legislation 

plays an important role in facilitating credit access and thus in firms’ capital structure: regulations that 

allow for greater flexibility in structuring employees’ working hours or dismissing employees tend to 

decrease the probability of being credit constrained. We find that employment protection legislation 

affects both banks’ decision not to provide credit and firms being discouraged from applying for a loan 

due to fear of rejection.  

Our findings have considerable implications for policy and practice. Since the countries included 

in the research are characterised by the free movement of people, goods, services and capital, differences 

in employment protection legislation can have a substantial impact on a country’s GDP. Firms that 

operate in countries with more favourable employment protection legislation might have easier access to 

credit and, as a possible consequence, enjoy faster growth. This implies that firms in a favourable legal 

environment might have a significant competitive advantage vis-à-vis firms that are located in a more 

unfavourable legal environment since there are no barriers to protect them.  

This paper could be extended along several lines. For instance, differences with regard to specific 

employment protection regulations at the industry level could be an interesting area of research in order to 

investigate if particular industries are put at a disadvantage in accessing credit. Moreover, since the 

present research includes ten European Union countries, it could be interesting to expand the research to 

both other developed countries (i.e. United States, Canada, Australia) and developing countries. 

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ECB Working Paper 2063, May 201735Figure 1A Applications for Bank Loans and Loans Obtained (over the preceding six months; 
percentage of respondents) 

50%

40%

30%

20%

10%

0%

 '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14  '10 '12 '14

AT

BE

DE

ES

FI

FR

IE

IT

NL

PT

Loan requested

Loan obtained

Source: ECB/EC SAFE and authors’ calculation. 

Notes: The figure shows the evolution over time and across countries of firms that applied for a bank loan and firms that 
obtained a bank loan. All figures are presented as a percentage of total firms in each country in a given wave.  

ECB Working Paper 2063, May 201736Figure 1B No Application for Bank Loans and Discouraged Borrowers (over the preceding six 
months; percentage of respondents) 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

'10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14 '10 '12 '14

AT

BE
discouraged borrowers

DE

ES

FI

FR

IE

IT

NL

PT

not applied for a loan

DB/not applicants

Source: ECB/EC SAFE and authors’ calculation. 

Notes: The figure shows the evolution over time and across countries of firms that did not apply for a bank loan and firms that 
did not apply for a loan because of fear of rejection (discouraged borrowers). All figures are presented as a percentage of total 
firms in each country in a given wave.  

ECB Working Paper 2063, May 201737Table 1A Descriptive statistics – Entire Dataset 

Variable 

Obs 

Mean 

Std. Dev. 

Min 

Max 

Credit Obtained 
Discouraged Borrowers 
Micro firm 
Small firms 
Medium-sized firms 
Large firms 
Firms up to 2 years old 
Firms 2-5 years old 
Firms 5-9 years old 
Firms older than 9 years 
Equity 
Retained profits 
Trade credit 
Leasing and factoring 
Change in labour costs 
Change in GDP 
Unemployment rate 
Herfindahl-Hirschman Index of bank concentration 
Industry risk (Bank perception) 
BLS index 
Flexibility in extending the workweek for seasonal needs 
(1=yes; 0=no) 
Restrictions on night work (1=yes; 0=no) 
Restrictions on weekly holidays (1=yes; 0=no) 
Notice period after 1 year (2010_2014) 
Notice period after 5 years (2009_2014) 
Notice period after 10 years (2010_2014) 
Third party notification for one dismissal (1=yes; 0=no) 
Third party notification for collective dismissal (1=yes; 
0=no) 
Third party approval for one dismissal (1=yes; 0=no) 

87825 
87825 
87825 
87825 
87825 
87825 
87825 
87825 
87825 
87825 
86445 
86422 
86812 
87286 
87825 
87825 
87825 
87825 
87825 
87825 

87825 
87825 
87825 
82705 
87825 
82705 
87825 

87825 
87825 

0.1842 
0.0453 
0.3086 
0.2124 
0.1654 
0.3136 
0.0147 
0.0555 
0.1182 
0.7760 
0.0568 
0.2921 
0.2714 
0.3909 
0.4675 
0.5598 
10.4029 
0.0644 
0.0999 
0.0364 

0.8172 
0.3896 
0.2511 
4.2674 
7.0551 
10.6259 
0.6904 

0.9694 
0.0635 

0.3877 
0.2079 
0.4619 
0.4090 
0.3715 
0.4640 
0.1205 
0.2289 
0.3228 
0.4170 
0.2314 
0.4548 
0.4447 
0.4880 
0.6399 
1.9527 
5.9520 
0.0574 
0.1945 
0.1718 

0.3865 
0.4877 
0.4336 
1.3509 
2.3604 
5.3251 
0.4623 

0.1723 
0.2438 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
-1
-7.49
4.33
0.02
-0.375
-0.375

0 
0 
0 
1 
2 
2 
0 

0 
0 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
7.13 
26.2 
0.37 
1 
1 

1 
1 
1 
6.5 
8.7 
17.33 
1 

1 
1 

Notes: This table presents weighted summary statistics for the variables used in the empirical analysis for the entire dataset. 
The weights restore the proportions of the economic weight (in terms of number of employees) of each size class, economic 
activity and country and are applied to the variables derived from the survey. 

ECB Working Paper 2063, May 201738Table 1B Descriptive statistics – Firms Applying for Credit 

Variable 

Obs 

Mean 

Std. Dev. 

Min 

Max 

Credit Obtained 
Discouraged Borrowers 
Micro firm 
Small firms 
Medium-sized firms 
Large firms 
Firms up to 2 years old 
Firms 2-5 years old 
Firms 5-9 years old 
Firms older than 9 years 
Equity 
Retained profits 
Trade credit 
Leasing and factoring 
Change in labour costs 
Change in GDP 
Unemployment rate 
Herfindahl-Hirschman Index of bank concentration 
Industry risk (Bank perception) 
BLS index 
Flexibility in extending the workweek for seasonal needs 
(1=yes; 0=no) 
Restrictions on night work (1=yes; 0=no) 
Restrictions on weekly holidays (1=yes; 0=no) 
Notice period after 1 year (2010_2014) 
Notice period after 5 years (2009_2014) 
Notice period after 10 years (2010_2014) 
Third party notification for one dismissal (1=yes; 0=no) 
Third party notification for collective dismissal (1=yes; 
0=no) 
Third party approval for one dismissal (1=yes; 0=no) 

21332 

0.6880 

0.4633 

0 

1 

21332 
21332 
21332 
21332 
21332 
21332 
21332 
21332 
21052 
21091 
21162 
21260 
21332 
21332 
21332 
21332 
21332 
21332 

21332 
21332 
21332 
19746 
21332 
19746 
21332 

21332 
21332 

0.2165 
0.2035 
0.1819 
0.3981 
0.0148 
0.0473 
0.1054 
0.7997 
0.0818 
0.3729 
0.3767 
0.5039 
0.4853 
0.4031 
10.9031 
0.0582 
0.1075 
0.0386 

0.7909 
0.4114 
0.2663 
4.3273 
6.9163 
10.1502 
0.6509 

0.9706 
0.0340 

0.4119 
0.4026 
0.3858 
0.4895 
0.1208 
0.2124 
0.3071 
0.4002 
0.2740 
0.4836 
0.4846 
0.5000 
0.6537 
1.9751 
6.0830 
0.0476 
0.1904 
0.1680 

0.4067 
0.4921 
0.4421 
1.3939 
2.3900 
5.2729 
0.4767 

0.1689 
0.1811 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
-1
-7.49
4.33
0.02
-0.375
-0.375

0 
0 
0 
1 
2 
2 
0 

0 
0 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
7.13 
26.2 
0.37 
1 
1 

1 
1 
1 
6.5 
8.7 
17.33 
1 

1 
1 

Notes: This table presents weighted summary statistics for the variables used in the empirical analysis for the subsample of 
firms that applied for a bank loan. The weights restore the proportions of the economic weight (in terms of number of 
employees) of each size class, economic activity and country and are applied to the variables derived from the survey. 

ECB Working Paper 2063, May 201739Table 1C Descriptive statistics –Firms Not Applying for Credit 

Variable 

Obs 

Mean 

Std. Dev. 

Min 

Max 

Credit Obtained 
Discouraged Borrowers 
Micro firm 
Small firms 
Medium-sized firms 
Large firms 
Firms up to 2 years old 
Firms 2-5 years old 
Firms 5-9 years old 
Firms older than 9 years 
Equity 
Retained profits 
Trade credit 
Leasing and factoring 
Change in labour costs 
Change in GDP 
Unemployment rate 
Herfindahl-Hirschman Index of bank concentration 
Industry risk (Bank perception) 
BLS index 
Flexibility in extending the workweek for seasonal needs 
(1=yes; 0=no) 
Restrictions on night work 
Restrictions on weekly holidays (1=yes; 0=no) 
Notice period after 1 year (2010_2014) 
Notice period after 5 years (2009_2014) 
Notice period after 10 years (2010_2014) 
Third party notification for one dismissal (1=yes; 0=no) 
Third party notification for collective dismissal (1=yes; 
0=no) 
Third party approval for one dismissal (1=yes; 0=no) 

66493 
66493 
66493 
66493 
66493 
66493 
66493 
66493 
66493 
65393 
65331 
65650 
66026 
66493 
66493 
66493 
66493 
66493 
66493 

66493 
66493 
66493 
62959 
66493 
62959 
66493 

66493 
66493 

0.0618 
0.3423 
0.2157 
0.1593 
0.2827 
0.0147 
0.0584 
0.1228 
0.7673 
0.0476 
0.2624 
0.2326 
0.3494 
0.4610 
0.6171 
10.2200 
0.0666 
0.0971 
0.0357 

0.8268 
0.3817 
0.2455 
4.2458 
7.1059 
10.7977 
0.7048 

0.9689 
0.0742 

0.2409 
0.4745 
0.4113 
0.3660 
0.4503 
0.1204 
0.2346 
0.3282 
0.4226 
0.2129 
0.4400 
0.4225 
0.4768 
0.6347 
1.9413 
5.8929 
0.0604 
0.1959 
0.1731 

0.3785 
0.4858 
0.4304 
1.3344 
2.3475 
5.3335 
0.4561 

0.1735 
0.2622 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
-1
-7.49
4.33
0.02
-0.375
-0.375

0 
0 
0 
1 
2 
2 
0 

0 
0 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
7.13 
26.2 
0.37 
1 
1 

1 
1 
1 
6.5 
8.7 
17.33 
1 

1 
1 

Notes: This table presents weighted summary statistics for the variables used in the empirical analysis for the subsample of 
firms that have not applied for a bank loan for fear of rejection. The weights restore the proportions of the economic weight (in 
terms of number of employees) of each size class, economic activity and country and are applied to the variables derived from 
the survey. 

ECB Working Paper 2063, May 201740Table 2A Obtaining Credit: Flexibility in Employment Management (Logit Coefficients) 

VARIABLES 
Micro firm 

Small firms 

Medium-sized firms 

Firms up to 2 years old 

Firms 2-5 years old 

Firms 5-9 years old 

Equity 

Retained profits 

Trade credit 

Leasing and factoring 

Change in labour costs 

Change in GDP 

Unemployment rate 

Herfindahl-Hirschman Index of bank concentration 

Waves 
Industry risk (Bank perception) 

BLS index 

Flexibility in extending the workweek for seasonal needs 

Restrictions on night work 

Restrictions on weekly holidays 

Constant 

Observations 
chi2 
p 
Mc Fadden Pseudo R2 
N_clust 

Model 1 
-0.760***
(0.129) 
-0.422***
(0.120) 
-0.133
(0.110) 
-0.523***
(0.201) 
-0.508***
(0.110) 
-0.278***
(0.0990) 
-0.130
(0.0829) 
0.0534
(0.0644) 
-0.249***
(0.0770) 
-0.197***
(0.0337) 
0.210***
(0.0390) 
0.167***
(0.0365) 
-0.0372***
(0.00896)
-2.370
(1.506) 

-0. 158
(0.00316)
-0. 442**
(0.00224)
-0.242**
(0.0975) 

2.498*** 
(0.215) 
20,748 
3136 
0.0000 
0.0749 
40 

Model 2 
-0.720***
(0.125) 
-0.414***
(0.114) 
-0.139
(0.108) 
-0.527**
(0.207) 
-0.503***
(0.111) 
-0.264***
(0.102) 
-0.153* 
(0.0824) 
0.0386
(0.0646) 
-0.199**
(0.0792) 
-0.231***
(0.0363) 
0.214***
(0.0373) 
0.0991***
(0.0252) 
-0.0181**
(0.00848)
-2.468**
(0.988) 
Included
-0. 200
(0.00243)
-0. 304* 
(0.00171)

-0.604***
(0.0911) 

2.158*** 
(0.192) 
20,748 
9143 
0.0000 
0.0808 
40 

Model 3 
-0.761***
(0.128) 
-0.423***
(0.120) 
-0.134
(0.109) 
-0.515**
(0.202) 
-0.502***
(0.110) 
-0.277***
(0.0986) 
-0.127
(0.0832) 
0.0571
(0.0652) 
-0.255***
(0.0775) 
-0.195***
(0.0335) 
0.212***
(0.0386) 
0.165***
(0.0353) 
-0.0372***
(0.00891)
-2.644*
(1.549) 

-0. 294
(0.00304)
-0. 366
(0.00227)

0.218** 
(0.0997) 
2.290*** 
(0.236) 
20,748 
6184 
0.0000 
0.0748 
40 

Notes: Dependent Variable: Whether or not the bank provided the loan; Independent Variables: firm characteristics (dummy 
variables for micro, small and medium-sized firms); age of the firm (dummy variables for younger than 2 years, between 2 and 
5 years, between 5 and 9 years); firm’s sources of finance (dummy variables for use of retained earnings, trade credit, leasing 
and equity); firm performance (categorical variable for change in labour costs); macroeconomic controls (change in GDP, 
unemployment rate, Herfindahl-Hirschman Index of bank concentration, industry risk perceived by banks, and Bank Lending 
Survey index); flexibility in structuring the employees’ working hours (dummy variables for flexibility to extend the working 
hours for seasonal needs, restrictions on night work and restrictions on weekly holidays).  
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1 

ECB Working Paper 2063, May 201741Table 2B Obtaining credit: Notice Period (Logit Coefficients) 

VARIABLES 
Micro firm 

Small firms 

Medium-sized firms 

Firms up to 2 years old 

Firms 2-5 years old 

Firms 5-9 years old 

Equity 

Retained profits 

Trade credit 

Leasing and factoring 

Change in labour costs 

Change in GDP 

Unemployment rate 

Herfindahl-Hirschman Index of bank concentration 

Waves 
Industry risk (Bank perception) 

BLS index 

Notice period after 1 year (2010_2014) 

Notice period after 5 years (2009_2014) 

Notice period after 10 years (2010_14) 

Constant 

Observations 
chi2 
p 
Mc Fadden Pseudo R2 
N_clust 

Model 4 
-0.751***
(0.132) 
-0.422***
(0.118) 
-0.139
(0.109) 
-0.531**
(0.252) 
-0.428***
(0.123) 
-0.240**
(0.107) 
-0.123
(0.0838) 
0.0154
(0.0711) 
-0.237***
(0.0774) 
-0.222***
(0.0361) 
0.217***
(0.0411) 
0.0982***
(0.0265) 
-0.0600***
(0.00977)
-3.049* 
(1.562) 

0. 350
(0.00300) 
-0. 769***
(0.00218)
-0.143***
(0.0397) 

2.771*** 
(0.303) 
19,199 
2347 
0.0000 
0.0767 
40 

Model 5 
-0.762***
(0.125) 
-0.424***
(0.117) 
-0.135
(0.108) 
-0.501**
(0.203) 
-0.497***
(0.110) 
-0.274***
(0.0972) 
-0.127
(0.0839) 
0.0443
(0.0671) 
-0.266***
(0.0756) 
-0.197***
(0.0330) 
0.220***
(0.0389) 
0.179***
(0.0393) 
-0.0262**
(0.0107) 
-2.590* 
(1.534) 
Included 
-0. 0981
(0.00325)
-0. 501**
(0.00232)

0.0365 
(0.0252) 

1.968*** 
(0.277) 
20,748 
4008 
0.0000 
0.0741 
40 

Model 6 
-0.776***
(0.129) 
-0.427***
(0.118) 
-0.136
(0.111) 
-0.498**
(0.252) 
-0.417***
(0.124) 
-0.239**
(0.104) 
-0.124
(0.0848) 
0.0309
(0.0708) 
-0.272***
(0.0729) 
-0.206***
(0.0347) 
0.219***
(0.0427) 
0.170***
(0.0423) 
-0.0199
(0.0127) 
-2.711* 
(1.478) 

0. 0753
(0.00299)
-0. 548**
(0.00214)

0.0276 
(0.0179) 
1.284*** 
(0.362) 
19,199 
2914 
0.0000 
0.0744 
40 

Notes: Dependent Variable: Whether or not the bank provided the loan; Independent Variables: firm characteristics (dummy 
variables for micro, small and medium-sized firms); age of the firm (dummy variables for younger than 2 years, between 2 and 
5 years, between 5 and 9 years); firm’s sources of finance (dummy variables for use of retained earnings, trade credit, leasing 
and equity); firm performance (categorical variable for change in labour costs); macroeconomic controls (change in GDP, 
unemployment rate, Herfindahl-Hirschman Index of bank concentration, industry risk perceived by banks, and Bank Lending 
Survey index); length of notice period after one, five and ten years of tenure.  
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1 

ECB Working Paper 2063, May 201742Table 2C Obtaining credit: Third Party Involvement (Logit Coefficients) 

VARIABLES 

Micro firm 

Small firms 

Medium-sized firms 

Firms up to 2 years old 

Firms 2-5 years old 

Firms 5-9 years old 

Equity 

Retained profits 

Trade credit 

Leasing and factoring 

Change in labour costs 

Change in GDP 

Unemployment rate 

Herfindahl-Hirschman Index of bank concentration 

Waves 
Industry risk (Bank perception) 

BLS index 

Third party notification for one dismissal 

Third party notification for collective dismissal 

Third party approval for one dismissal 

Constant 

Observations 
chi2 
p 
Mc Fadden Pseudo R2 
N_clust 

Model 7 

-0.769***
(0.125) 
-0.427***
(0.117) 
-0.136
(0.108) 
-0.499**
(0.204) 
-0.493***
(0.110) 
-0.273***
(0.0964) 
-0.123
(0.0828) 
0.0387
(0.0630) 
-0.276***
(0.0737) 
-0.192***
(0.0313) 
0.220***
(0.0393) 
0.177***
(0.0400) 
-0.0373***
(0.00927)
-2.399
(1.526) 

-0. 0642
(0.00301)
-0. 565**

(0.00222) 
-0.0181
(0.0979) 

2.333*** 
(0.222) 
20,748 
5349 
0.0000 
0.0737 
40 

Model 8 

-0.769***
(0.125) 
-0.428***
(0.117) 
-0.136
(0.108) 
-0.497**
(0.205) 
-0.492***
(0.109) 
-0.272***
(0.0953) 
-0.123
(0.0830) 
0.0376
(0.0677) 
-0.277***
(0.0748) 
-0.192***
(0.0321) 
0.221***
(0.0395) 
0.176***
(0.0398) 
-0.0375***
(0.00869)
-2.441
(1.599) 
Included
-0. 0550
(0.00317)
-0. 560**

(0.00240) 

-0.0356

(0.183) 

2.358*** 
(0.288) 
20,748 
4570 
0.0000 
0.0737 
40 

Model 9 

-0.776***
(0.126) 
-0.436***
(0.117) 
-0.142
(0.109) 
-0.501**
(0.208) 
-0.498***
(0.112) 
-0.264***
(0.0953) 
-0.150* 
(0.0825) 
0.0407
(0.0674) 
-0.255***
(0.0756) 
-0.208***
(0.0341) 
0.210***
(0.0384) 
0.161***
(0.0379) 
-0.0487***
(0.00822)
1.115 
(0.849) 

-0. 530* 
(0.00291)
-0. 278

(0.00212) 

-1.388***
(0.148) 
2.367***
(0.222) 
20,748 
5955 
0.0000 
0.0802 
40 

Notes: Dependent Variable: Whether or not the bank provided the loan; Independent Variables: firm characteristics (dummy 
variables for micro, small and medium-sized firms); age of the firm (dummy variables for younger than 2 years, between 2 and 
5 years, between 5 and 9 years); firm’s sources of finance (dummy variables for use of retained earnings, trade credit, leasing 
and equity); firm performance (categorical variable for change in labour costs); macroeconomic controls (change in GDP, 
unemployment rate, Herfindahl-Hirschman Index of bank concentration, industry risk perceived by banks, and Bank Lending 
Survey index); third party involvement in the dismissal (dummy variable for the requirement to notify a third party  before a 
single or collective dismissal and for the requirement to obtain third party approval for a dismissal).  
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1 

ECB Working Paper 2063, May 201743Table 3A Discouraged Borrowers: Flexibility in Structuring Employees’ Working Hours (Logit 
Coefficients) 
VARIABLES 
Micro firm 

Small firms 

Medium-sized firms 

Firms up to 2 years old 

Firms 2-5 years old 

Firms 5-9 years old 

Equity 

Retained profits 

Trade credit 

Leasing and factoring 

Change in labour costs 

Change in GDP 

Unemployment rate 

Herfindahl-Hirschman Index of bank concentration 

Waves 
BLS index 

Flexibility in extending the workweek for seasonal needs 

Model 10  Model 11  Model 12 
1.056*** 
1.033*** 
1.066*** 
(0.0920) 
(0.0905) 
(0.0940) 
0.728*** 
0.710*** 
0.737*** 
(0.127) 
(0.126) 
(0.128) 
0.379*** 
0.366*** 
0.386*** 
(0.0989) 
(0.0984) 
(0.100) 
0.111 
0.127 
0.0951 
(0.124) 
(0.118) 
(0.126) 
0.412*** 
0.420*** 
0.403*** 
(0.0842) 
(0.0885) 
(0.0827) 
0.228*** 
0.231** 
0.224*** 
(0.0868) 
(0.0913) 
(0.0850) 
0.266*** 
0.280*** 
0.264*** 
(0.103) 
(0.101) 
(0.102) 
-0.0464
-0.0632
-0.0477
(0.0617) 
(0.0627) 
(0.0616) 
0.383***
0.355***
0.388***
(0.0782) 
(0.0823) 
(0.0764) 
0.222***
0.224***
0.219***
(0.0653) 
(0.0643) 
(0.0658) 
-0.114***
-0.104***
-0.121***
(0.0375) 
(0.0352) 
(0.0380) 
-0.0146
0.00535
-0.0179
(0.0318) 
(0.0299) 
(0.0332) 
0.0259***
0.0207**
0.0263***
(0.00898)
(0.0101) 
(0.00881)
0.175 
0.204 
0.395 
(0.976) 
(0.766) 
(0.925) 
Included 
-0. 312
(0.00198)

-0. 260
(0.00185)

-0. 228
(0.00192)
-0.254***
(0.0681) 

Restrictions on night work 

Restrictions on week holidays 

0.155 
(0.110) 

0.152* 
(0.0891) 
-3.657***
(0.192) 
64,312 
3546 
0.0000 
0.0385 
40 

Constant 

-3.541***
(0.196) 
64,312 
2903 
0.0000 
0.0384 
40 

-3.444***
(0.186) 
64,312 
Observations 
3153 
chi2 
0.0000 
p 
Mc Fadden Pseudo R2 
0.0391 
40 
N_clust 
Notes: Dependent Variable: Whether or not the firm is a discouraged borrower; Independent Variables: firm characteristics 
(dummy variables for micro, small and medium-sized firms); age of the firm (dummy variables for younger than 2 years, 
between 2 and 5 years, between 5 and 9 years); firm’s sources of finance (dummy variables for use of retained earnings, trade 
credit, leasing and equity); firm performance (categorical variable for change in labour costs); macroeconomic controls 
(change in GDP, unemployment rate, Herfindahl-Hirschman Index of bank concentration, industry risk perceived by banks, 
and Bank Lending Survey index); flexibility in structuring the employees’ working hours (dummy variables for flexibility to 
extend the working hours for seasonal needs, restrictions on night work and restrictions on weekly holidays). All regressions 
use sampling weights that adjust the sample to be representative of the population. Robust standard errors appear in 
parentheses. *** p<0.01, ** p<0.05, * p<0.1  

ECB Working Paper 2063, May 201744Table 3B Discouraged Borrowers: Notice Period (Logit Coefficients) 

VARIABLES 
Micro firm 

Small firms 

Medium-sized firms 

Firms up to 2 years old 

Firms 2-5 years old 

Firms 5-9 years old 

Equity 

Retained profits 

Trade credit 

Leasing and factoring 

Change in labour costs 

Change in GDP 

Unemployment rate 

Herfindahl-Hirschman Index of bank concentration 

Waves 
BLS index 

Notice period after 1 year (2010_2014) 

Notice period after 5 years (2009_2014) 

Notice period after 10 years (2010_14) 

Constant 

Observations 
chi2 
p 
Mc Fadden Pseudo R2 
N_clust 

Model 13 
1.059*** 
(0.101) 
0.733*** 
(0.139) 
0.404*** 
(0.103) 
0.0907 
(0.150) 
0.438*** 
(0.0930) 
0.249*** 
(0.0964) 
0.294*** 
(0.101) 
-0.0409
(0.0670) 
0.361***
(0.0870) 
0.280***
(0.0641) 
-0.124***
(0.0414) 
0.0263
(0.0359) 
0.0384***
(0.0116) 
0.134 
(0.848) 

-0. 401* 
(0.00213)
0.0852**
(0.0382) 

-4.817***
(0.297) 
60,881 
2474 
0.0000 
0.0402 
40 

Model 14 
1.058*** 
(0.0932) 
0.728*** 
(0.128) 
0.377*** 
(0.0983) 
0.115 
(0.124) 
0.411*** 
(0.0853) 
0.227*** 
(0.0875) 
0.258** 
(0.103) 
-0.0467
(0.0617) 
0.388***
(0.0810) 
0.214***
(0.0641) 
-0.111***
(0.0361) 
-0.00637
(0.0304) 
0.0476***
(0.0131) 
-0.00813
(0.943) 
Included
-0. 227
(0.00175)

0.0693** 
(0.0283) 

-4.297***
(0.367) 
64,312 
7069 
0.0000 
0.0391 
40 

Model 15 
1.078*** 
(0.101) 
0.737*** 
(0.139) 
0.405*** 
(0.103) 
0.0821 
(0.149) 
0.432*** 
(0.0919) 
0.249*** 
(0.0951) 
0.297*** 
(0.101) 
-0.0471
(0.0684) 
0.376***
(0.0839) 
0.268***
(0.0627) 
-0.120***
(0.0417) 
-0.0182
(0.0340) 
0.0161
(0.0150) 
-0.0465
(0.777) 

-0. 423**
(0.00203)

-0.0138
(0.0169) 
-3.992***
(0.342) 
60,881 
4935 
0.0000 
0.0395 
40 

Notes: Dependent Variable: Whether or not the firm is a discouraged borrower; Independent Variables: firm characteristics 
(dummy variables for micro, small and medium-sized firms); age of the firm (dummy variables for younger than 2 years, 
between 2 and 5 years, between 5 and 9 years); firm’s sources of finance (dummy variables for use of retained earnings, trade 
credit, leasing and equity); firm performance (categorical variable for change in labour costs); macroeconomic controls 
(change in GDP, unemployment rate, Herfindahl-Hirschman Index of bank concentration, industry risk perceived by banks, 
and Bank Lending Survey index); length of notice period after one, five and ten years of tenure.  
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1 

ECB Working Paper 2063, May 201745Table 3C Discouraged Borrowers: Third Party Involvement in the Dismissal Process (Logit 
Coefficients) 

VARIABLES 
Micro firm 

Small firms 

Medium-sized firms 

Firms up to 2 years old 

Firms 2-5 years old 

Firms 5-9 years old 

Equity 

Retained profits 

Trade credit 

Leasing and factoring 

Change in labour costs 

Change in GDP 

Unemployment rate 

Herfindahl-Hirschman Index of bank concentration 

Waves 
BLS index 

Third party notification for one dismissal 

Third party notification for collective dismissal 

Third party approval for one dismissal 

Constant 

Observations 
chi2 
p 
Mc Fadden Pseudo R2 
N_clust 

Model 16  Model 17  Model 18 
1.051*** 
1.046*** 
1.046*** 
(0.0915) 
(0.0917) 
(0.0907) 
0.718*** 
0.716*** 
0.722*** 
(0.126) 
(0.126) 
(0.126) 
0.366*** 
0.369*** 
0.375*** 
(0.0983) 
(0.0979) 
(0.0995) 
0.113 
0.123 
0.117 
(0.118) 
(0.120) 
(0.121) 
0.410*** 
0.418*** 
0.415*** 
(0.0891) 
(0.0874) 
(0.0845) 
0.225** 
0.231** 
0.230*** 
(0.0906) 
(0.0899) 
(0.0878) 
0.287*** 
0.268** 
0.270*** 
(0.103) 
(0.105) 
(0.104) 
-0.0549
-0.0622
-0.0561
(0.0633) 
(0.0625) 
(0.0610) 
0.367***
0.371***
0.369***
(0.0812) 
(0.0810) 
(0.0789) 
0.214***
0.217***
0.224***
(0.0649) 
(0.0646) 
(0.0661) 
-0.096***
-0.106***
-0.112***
(0.0355) 
(0.0355) 
(0.0398) 
-0.00568
-0.0123
-0.0101
(0.0311) 
(0.0300) 
(0.0313) 
0.0326*** 
0.0254*** 
0.0271*** 
(0.0106) 
(0.00966) 
(0.00944) 
-2.015**
0.342 
0.407 
(0.911) 
(0.836) 
(0.892) 
Included 
-0. 286
(0.00196)

-0. 244
(0.00206)

-0. 291
(0.00202)
-0.0960
(0.108) 

0.0471 
(0.154) 

-3.643***
(0.258) 
64,312 
3381 
0.0000 
0.0380 
40 

0.722*** 
(0.158) 
-3.558***
(0.183) 
64,312 
3206 
0.0000 
0.0401 
40 

-3.556***
(0.193) 
64,312 
2603 
0.0000 
0.0382 
40 

Notes: Dependent Variable: Whether or not the firm is a discouraged borrower; Independent Variables: firm characteristics 
(dummy variables for micro, small and medium-sized firms); age of the firm (dummy variables for younger than 2 years, 
between 2 and 5 years, between 5 and 9 years); firm’s sources of finance (dummy variables for use of retained earnings, trade 
credit, leasing and equity); firm performance (categorical variable for change in labour costs); macroeconomic controls 
(change in GDP, unemployment rate, Herfindahl-Hirschman Index of bank concentration, industry risk perceived by banks, 
and Bank Lending Survey index); third party involvement in the dismissal (dummy variable for the requirement to notify a 
third party  before a single or collective dismissal and for the requirement to obtain third party approval for a dismissal.  
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1 

ECB Working Paper 2063, May 2017467
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ECB Working Paper 2063, May 201747 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4B Obtain Credit: Interaction Variables (Logit Coefficients) 

VARIABLES 

Manufacturing 

Construction 

Trade 

Service 

Independent variables and controls as in Tables 2A, 2B and 2C 

Model 4 

0.190 

(0.262) 

-0.364

(0.373) 

0.191** 

(0.0935) 

-0.204

(0.182) 

0.259** 

(0.118) 

0.421 

(0.273) 

-0.262

(0.287) 

Notice period after 1 year (2010_2014) 

-0.143***

-0.144***

-0.144***

-0.129***

-0.155***

M * Notice period after 1 year 

C * Notice period after 1 year 

T * Notice period after 1 year 

S * Notice period after 1 year 

(0.0323) 

(0.0442) 

-0.00860

(0.0493) 

(0.0374) 

(0.0420) 

(0.0487) 

0.00983 

(0.0795) 

-0.0430

(0.0527) 

0.0289 

(0.0564) 

Constant 

2.671*** 

2.727*** 

2.799*** 

2.668*** 

2.876*** 

Observations 

p 
Mc Fadden Pseudo R2 
N_clust 

(0.255) 

19,199 

0.0000 

0.0800 

40 

(0.307) 

19,199 

0.0000 

0.0773 

40 

(0.286) 

19,199 

0.0000 

0.0783 

40 

(0.306) 

19,199 

0.0000 

0.0782 

40 

(0.354) 

19,199 

0.0000 

0.0774 

40 

Notes: Dependent Variable: Whether or not the bank provided the credit; Independent Variables: length of notice period after 
one year of tenure; industry (dummy variables for Construction, Manufacturing, Service); interaction of these variables 
(M=Manufacturing, C=Construction, T=Trade, S=Service).  
Independent and control variables not reported in the table: Firm characteristics (dummy variable for Micro firms); Age of the 
firm (dummy variable for younger than 2 years, between 2 and 5 years, between 5 and 9 years); Firm performance (categorical 
variable for change in labour costs); Firm sources of finance (dummy variable for Retained earnings, Trade credit, Leasing and 
Equity), Macroeconomic controls (change in GDP, unemployment rate, Herfindahl-Hirschman Index of bank concentration, 
industry risk perceived by banks, and Bank Lending Survey index). 
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses. 
*** p<0.01, ** p<0.05, * p<0.1 

ECB Working Paper 2063, May 201748Table 4C Obtain Credit: Interaction Variables (Logit Coefficients) 

VARIABLES 

Manufacturing 

Construction 

Trade 

Service 

Third party approval for one dismissal 

M * Third party approval 

C * Third party approval 

T * Third party approval 

S * Third party approval 

Constant 

Observations 
p 
Mc Fadden Pseudo R2 
N_clust 

Model 9 
 Independent variables and controls as in Tables 2A, 2B and 2C 

0.153 
(0.128) 
-0.185
(0.175) 
0.279**
(0.123) 

-1.395***
(0.135) 

0.109 
(0.133) 

-0.288
(0.176) 

0.261** 
(0.124) 

-1.365***
(0.140) 

-1.421***
(0.145) 

-1.352***
(0.165) 

-0.138
(0.164) 

-0.227
(0.184) 

2.266*** 
(0.165) 
20,748 
0.0000 
0.0833 
40 

2.330*** 
(0.204) 
20,748 
0.0000 
0.0805 
40 

2.375*** 
(0.212) 
20,748 
0.0000 
0.0816 
40 

0.0272 
(0.141) 

2.318*** 
(0.218) 
20,748 
0.0000 
0.0819 
40 

-0.134
(0.108) 

-1.428***
(0.191) 

0.104 
(0.170) 
2.425*** 
(0.223) 
20,748 
0.0000 
0.0808 
40 

Notes: Dependent Variable: Whether or not the bank provided the credit; Independent Variables: requirement to obtain third 
party approval for a dismissal; Industry (dummy variable for Construction, Manufacturing, Service); interaction of these 
variables (M=Manufacturing, C=Construction, T=Trade, S=Service). 
Independent and control variables not reported in the table: Firm characteristics (dummy variable for Micro firms); Age of the 
firm (dummy variable for younger than 2 years, between 2 and 5 years, between 5 and 9 years); Firm performance (categorical 
variable for change in labour costs); Firm sources of finance (dummy variable for Retained earnings, Trade credit, Leasing and 
Equity); Macroeconomic controls (change in GDP, unemployment rate, Herfindahl-Hirschman Index of bank concentration, 
industry risk perceived by banks, and Bank Lending Survey index). 
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1

ECB Working Paper 2063, May 2017491
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ECB Working Paper 2063, May 201751 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5C Discouraged Borrowers: Interaction Variables (Logit Coefficients) 

VARIABLES 

Manufacturing 

Construction 

Trade 

Service 

Independent variables and controls as in Tables 3A, 3B and 3C 

Model 18 

0.0477 

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(0.0986) 

(0.0958) 

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(0.143) 

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(0.103) 

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Third party approval for one dismissal 

0.740*** 

0.750*** 

0.746*** 

0.637*** 

0.775*** 

M * Third party approval 

C * Third party approval 

T * Third party approval 

S * Third party approval 

Constant 

Observations 

chi2 

p 
Mc Fadden Pseudo R2 
N_clust 

(0.164) 

(0.160) 

-0.217* 

(0.116) 

(0.164) 

(0.155) 

(0.157) 

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(0.158) 

0.337*** 

(0.106) 

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(0.191) 

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40 

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40 

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40 

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7732 

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40 

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6231 

0.0000 

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40 

Notes: Dependent variable: Whether the firm is a discouraged borrower; Independent Variables: requirement to obtain third 
party approval for a dismissal; Industry (dummy variables for Construction, Manufacturing, Services); interaction of these 
variables (M=Manufacturing, C=Construction, T=Trade, S=Service).  
Independent and control variables not reported in the table: Firm characteristics (dummy variable for Micro firms); Age of the 
firm (dummy variable for younger than 2 years, between 2 and 5 years, between 5 and 9 years); Firm performance (categorical 
variable for change in labour costs); Firm sources of finance (dummy variable for Retained earnings, Trade credit, Leasing and 
Equity), Macroeconomic controls (change in GDP, unemployment rate, Herfindahl-Hirschman Index of bank concentration, 
industry risk perceived by banks, and Bank Lending Survey index). 
All regressions use sampling weights that adjust the sample to be representative of the population. Robust standard errors 
appear in parentheses.  
*** p<0.01, ** p<0.05, * p<0.1. 

ECB Working Paper 2063, May 201752Table 6A Economic Effects – Obtaining Credit 

Flexibility in extending the workweek for seasonal needs 

Restrictions on night work 

Restrictions on weekly holidays 

Notice period after 1 year (2010_2014) 

Third party approval for one dismissal 

No (0) 
74.38% 

Yes (1) 
69.41% 

No (0) 
75.55% 

Yes (1) 
62.81% 

No (0) 
69.26% 

Yes (1) 
73.69% 

1 month 
79.78% 

6.5 months 
64.27% 

No (0) 
71.57% 

Yes (1) 
38.59% 

ECB Working Paper 2063, May 201753Table 6B Economic Effects – Discouraged Borrowers 

Flexibility in extending the workweek for seasonal needs 

Restrictions on weekly holidays 

Notice period after 1 year (2010_2014) 

Notice period after 5 years (2009_2014) 

Third party approval for one dismissal 

No (0) 
6.62% 

No (0) 
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Yes (1) 
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Yes (1) 
6.07% 

1 month 
4.08% 

6.5 months 
6.37% 

1 month 
3.62% 

8.7 months 
6.03% 

No (0) 
5.15% 

Yes (1) 
10.06% 

ECB Working Paper 2063, May 201754x
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ECB Working Paper 2063, May 201755 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements 

The opinions expressed herein are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. 

Andrea Moro 
Cranfield School of Management, Cranfield University, Bedford, United Kingdom; email: andrea.moro@cranfield.ac.uk 

Daniela Maresch 
Institute for Innovation Management, JKU Linz, Austria, Germany; daniela.maresch@jku.at 

Annalisa Ferrando 
European Investment Bank, Luxembourg, Luxembourg; email: a.ferrando@eib.org 

Gregory F. Udell 
Kelley School of Business, Indiana University; email: gudell@indiana.edu 

© European Central Bank, 2017 

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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

ISSN  
ISBN  

1725-2806 (pdf) 
978-92-899-2785-7 (pdf) 

DOI 
EU catalogue No 

10.2866/651417 (pdf) 
QB-AR-17-075-EN-N (pdf)