Legal environment and corporate finance: Evidence from the Italian manufacturing industry

Falavigna, Greta; Ippoliti, R. Article Legal environment and corporate finance: Evidence from the Italian manufacturing industry Journal of Economic Structures Provided in Cooperation with: Pan-Pacific Association of Input-Output Studies (PAPAIOS) Suggested Citation: Falavigna, Greta; Ippoliti, R. (2021) : Legal environment and corporate finance: Evidence from the Italian manufacturing industry, Journal of Economic Structures, ISSN 2193-2409, Springer, Heidelberg, Vol. 10, pp. 1-16, https://doi.org/10.1186/s40008-021-00252-6 This Version is available at: https://hdl.handle.net/10419/261621 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Legal environment andcorporate finance: evidence fromtheItalian manufacturing industry Greta Falavigna1 and Roberto Ippoliti2* 1 Introduction The financial market is fundamental to support companies and their business through credit access, which could be substituted by internal resources in case of financial constraints (Ughetto 2008; Farre-Mensa and Ljungqvist 2016). Nevertheless, companies can also be financed through trade credits, delaying payments and creating an alternative financial line with their suppliers (Garcia-Appendini and Montoriol-Garriga 2013; Carbo‐Valverde etal. 2016). This latter approach might be amplified by the inability of the current judicial system to efficiently enforce credit rights. Indeed, if courts are unable to settle insolvency cases in a reasonable amount of time (i.e., mortgage foreclosure and bankruptcy cases), we expect a higher level of uncertainty and risks, which could increase companies’ financial constraints, as well as their opportunities for moral hazard and strategic behaviors (Jappelli etal. 2005; Schiantarelli etal. 2020). In particular, Falavigna and Ippoliti (2020) suggest that inefficiency may lead debtors to postpone contractual deadlines, since they may remain unpunished, increasing trade credits and decreasing, in this way, the financial expenses. In other words, the longer the time needed to enforce payment, the greater the opportunities to delay such payment, due to the higher opportunity cost for a creditor of submitting an insolvency application to the competent court. Accordingly, we can expect judicial Abstract Considering the Italian manufacturing industry between 2014 and 2016 (more than 250,000 observations), this technical note analyzes the relation between the courts’ ability to enforce credit rights and the opportunity to finance business activities with trade credits instead of financial debts, delaying payments and decreasing the financial costs. According to our results, and considering mortgage foreclosure, if the time necessary to settle an insolvency case increases by 1000 days, we can expect an increase in operating debt between 3 and 11%, and a decrease of financial expenses between 3000 and 7000 Euro. Keywords: Manufacturing industry, Corporate finance, Institutional inefficiency JEL Classification: G32, G33 Open Access © The Author(s) 2021. 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TECHNICAL NOTES Falavignaand Ippoliti Economic Structures (2021) 10:21 https://doi.org/10.1186/s40008-021-00252-6 *Correspondence: [email protected] 2 Faculty of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany Full list of author information is available at the end of the article Page 2 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 inefficiency in enforcing credit rights to prompt debtors to substitute financial debts with trade credits. This technical note aims at shedding new empirical evidence to confirm the expected impact of judicial inefficiency on corporate finance, testing empirically whether judicial delay can affect the substitution of financial debt with trade credits on a specific case study: the Italian manufacturing industry. Current data on the Italian judicial system and the dynamics of the Italian manufacturing industry can help us establish whether this corporate strategy actually exists, testing the expected positive relation between the time needed to settle a case of insolvency (i.e., mortgage foreclosure and bankruptcy) and the access to operating debts. At the same time, as robust test, we have the opportunity to analyze the expected negative relation between the same proxy of judicial efficiency (i.e., time necessary to settle a case of insolvency) and financial expenses. If the proposed hypothesis is confirmed, the economic implications of this negative externality could be far-reaching for the whole economy, decreasing both the competitiveness and the financial stability of the national system, as well as triggering a cascade effect on the market. This may be even truer in the Italian market, with its characteristic structure based mostly on small– medium enterprises (SMEs). The remainder of this manuscript is organized as follows. The second section illustrates the case study and the methodology adopted to validate the above hypothesis and current evidence, as well as the results of the empirical analysis. The last section describes the conclusions of our research. 2 Methods andresults 2.1 Case study: theItalian judicial system andinsolvency procedures The Italian Ministry of Justice is in charge of administering civil and criminal justice, which is divided into two main tiers and one lowest level. At the lowest level are the so-called Justices of the Peace (i.e., Giudici di Pace), with specific civil and criminal competences. At a higher level, the first tier includes first instance courts (i.e., Tribunali Ordinari), while the second tier comprises second instance courts (i.e., Corte di Appello), which are responsible for appeals against first instance judgments. In the period considered (i.e., between 2014 and 2016), there were 140 first instance courts and 26s instance courts. The first and the second instance courts are grouped according to their judicial geography to form 26 judicial districts (i.e., Distretto di Corte di Appello). Finally, there is also a court of last resort (i.e. Corte Suprema di Cassazione), with seat in Rome and acting as the highest appellate court in all civil and criminal cases. According to the Italian law, there are two formal procedures to settle an insolvency case (Rodano etal. 2016; Falavigna and Ippoliti 2020). The creditors can either initiate a process of mortgage foreclosure (i.e., esecuzione forzata), which can target the debtors´ movables or real estate, or apply to certify the debtor’s insolvency (i.e., istanza di fallimento); after this preliminary step, the court will enforce their credit rights through a bankruptcy procedure (i.e., fallimento). Moreover, the debtor can apply for an arrangement with creditors through the mediation of the court (i.e., concordato Page 3 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 preventivo). Obviously, in the latter case, the creditors cannot expect to fully recover the amounts due, but the defaulting debtor may be expected to cooperate. Under the Italian law, all creditors can engage in a mortgage foreclosure procedure, that is to say, all secured and unsecured credits can be enforced by courts through mortgage foreclosure of debtors’ goods (i.e., movable and/or real estate). The main difference between secured and unsecured credits revolves around the goods involved in the insolvency procedure. In the former situation, there is a specific good that represents the collateral in case of insolvency with respect to that specific secured credit, which cannot be involved by other creditors in other insolvency procedures (i.e., there is an exclusive right to use that good as collateral for a single secured credit). In the latter situation, there are no specific guarantees for unsecured credits, i.e., all the remaining debtors’ goods that do not represent a collateral for a secured credit can be involved in the insolvency procedure to collect the due amount of money. In both cases, there is a specific judicial procedure aimed at certifying the insolvency status of the debtor (e.g., an unpaid invoice), and an order of payment is then issued by the same court (i.e., Decreto Ingiuntivo di Pagamento). After 40days, if there are no oppositions by the debtor and/or the due payment is not made, the aforementioned order is enforceable and the foreclosure procedure can start. At this point, the payment of secured credits is facilitated by the current procedure, since a foreclosure order is not necessary (i.e., Atto di Pignoramento), simplifying the bailiff´s work and reducing the time needed to collect the due amount of money, since the object of the foreclosure has already been identified (i.e., the collateral). The alternative approach to foreclosure is bankruptcy, which is largely a debtor-protective process and prevents creditors from seizing and selling the debtor’s assets piecemeal, giving time to liquidate the debtor in an orderly fashion or, more commonly, sell the business as a going concern or reorganize its capital structure. Note that only the biggest companies can be involved in bankruptcy procedures. Indeed, according to current regulations, creditors can initiate bankruptcy procedures if, and only if, the debtors have in the three previous year at least one of the following index: assets higher than 300,000.00 Euro, gross revenues higher than 200,000.00 Euro or total debts higher than 500,000.00 Euro. On the other hand, all insolvent debtors can be involved in mortgage foreclosure procedures. Lastly, the current procedures prescribe that judicial competence for insolvency cases depends on the location of either the registered office or the main production facility of the insolvent business. Therefore, the creditor will apply for a declaration of insolvency or mortgage foreclosure to the competent court of first instance, taking the location of the debtor’s registered office or production facility into account, and then, if necessary, present an appeal against this judgment to the competent court of second instance. Table4 in the Appendix presents an overview of the average time (expressed in days) necessary to settle insolvency procedures between 2014 and 2016, according to judicial districts and geographical macro-areas. 2.2 Empirical strategy The case study under investigation is the Italian manufacturing industry, which is extremely interesting. On the one hand, Italy’s judicial system is one of the worst Page 4 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 in the European Union (CEPEJ 2016); while, on the other hand, the manufacturing industry is characterized by one of the highest level of trade credits according to the European Committee of Central Balance Sheet Data Offices (ECCBSO). Considering judicial delay (see https:// webst at. giust izia. it), the average time needed to settle a bankruptcy case was equal to 3000days in 2016, which is absolutely unreasonable.1 This unsustainable opportunity cost may force creditors to wait for payments instead of submitting an insolvency application. In particular, authors analyze the Italian manufacturing industry (more than 250,000 observations) and a panel of 3years (from 2014 to 2016), with a strongly balanced sample. We test the proposed hypothesis by merging two main sources of information: data on judicial inefficiency at the first instance level (insolvency cases), extracted from the database of the Italian Ministry of Justice, and financial information on Italian manufacturing companies, extracted from AIDA (Bureau van Dijk´s database). Lastly, in order to collect more robust results, we look at the Median Absolute Deviation (MAD) to detect and then drop the outliers (Leys etal. 2013). Considering the ith company at time t, we study several OLS multivariate regression models (panel data with random effects and robust standard errors)2 with the following forms: where DEBT is the percentage of operating debts in the short term (i.e., less than 1year) over the total amount of debts in the short term (i.e., operating and financial debts),3 while FIN is the total amount of financial expenses in the short term (i.e., less than 1year). Both variables represent the dependent variable of the proposed models. The former model represents our main analysis, while the latter is a robust check to support our results. On the one hand, the operating debt ratio denotes how managerial strategies change according to the judicial environment, showing whether companies fund their activities with trade credits; while, on the other hand, financial expenses can confirm the previous model, highlighting whether the financial costs are coherently affected by the same judicial environment. In other words, the higher (1) DEBT i,t=𝛽0+𝛽1JUDi,t−1+ 3 ∑ z=1 𝛾zSIZEi,z,t+ 24 ∑ r=1 𝛼rINDUi,r,t+ 5 ∑ k=1 𝛿kAREAi,k, t + 3 ∑ z=1 𝛾zFORMi,z,t+𝛽2LIQi,t+𝛽3AGEi,t+𝛽4InnSMEi,t +𝛽 5 InnStUpi , t+ui , t+𝜀i , t, (2) FIN i,t=𝛽0+𝛽1JUDi,t−1+ 3 ∑ z=1 𝛾zSIZEi,z,t+ 24 ∑ r=1 𝛼rINDUi,r,t+ 5 ∑ k=1 𝛿kAREAi,k,t+ 3 ∑ z=1 𝛾zFORMi,z, t +𝛽 2 LIQi , t+𝛽 3 AGEi , t+𝛽 4 InnSMEi , t+𝛽 5 InnStUpi , t+ui , t+𝜀i , t, 1 According to the same statistics, the average time to settle a mortgage foreclosure case was equal to 250days (movable) and to 1,600days (real estate). 2 Authors adopt random effects since the key explanatory variables (i.e., mortgage foreclosure and bankruptcy) have a very slow year-to-year variation, and a fixed effects approach might bias the collected estimations. 3 Note that in this estimation we do not consider debts toward subsidiary, associate, and parent companies. Page 5 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 the judicial inefficiency, the higher the operating debt ratio (i.e., the higher the adoption of trade credits to support companies´ activities) and, consequently, the lower the financial expenses (i.e., the lower the access to external financial resources by companies). Afterwards, JUD is the time needed to settle an insolvency case (log transformation) in the jth district, in which the company is located, and represents our key explanatory variable. Both models look at judicial inefficiency at time t-1, i.e., on the lagged variable. In detail, three case matters are considered in order to observe the impact of judicial inefficiency: mortgage foreclosure (i.e., both movable and real estate) and bankruptcy. We also introduce some internal and external control variables, recalling the models proposed by Falavigna and Ippoliti (2020). Among the former controls, we include the following characteristics of companies: • LIQ, which is a continuous variable equal to financial and operating activities divided by debts payable before the end of the year, and it represents firms’ liquidity at short run; • AGE, which is a continuous variable indicating the seniority of our observations (log transformation); • SIZE, which is a matrix of dummy variables equal to 1 according to the European Union classification (3 total assets-based categories: large, medium, and small company); • FORM, which is a matrix of dummy variables equal to 1 according to the company’s legal classification (3 classes: public limited company, private limited company, other legal form); • InnSME, which is a dummy variable equal to 1 if the observation is classified as an innovative small and medium enterprise (SME), 0 otherwise; • InnStUp, which is a dummy variable equal to 1 if the observation is classified as an innovative start-up, 0 otherwise; • INDU, which is a matrix of dummy variables equal to 1 if the productivity sector (2 digit) belongs to the selected NACE sector, 0 otherwise; while, among the latter controls, we analyze the following environmental characteristics: • AREA, which is a matrix of dummy variables equal to 1 if the observation is located in that NUTS 1 geographical macro area (5 categories: North West, North East, Center, South, and Islands). Table1 shows some descriptive statistics of dependent and explanatory variables. Lastly, Tables5 and 6 in the Appendix present a more detailed overview of debt ratio and financial expenses between 2014 and 2016, according to judicial districts and geographical macro-areas. Page 6 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 2.3 Results Tables2 and 3 present the results of the multivariate regression models, using a panel sample with random effects and robust standard errors. In detail, Table2 illustrates the results of model A (i.e., with debt ratio as dependent variable), while Table3 shows the results of model B (i.e., with financial expenses as dependent variable). Note that the number of observations in the third column varies depending on the companies that may be subjected to a bankruptcy procedure (Italian Legislative Decree no. 169/2007). According to the Wald Chi-square statistics, the models are statistically significant (i.e., at least one of the regression coefficients is not equal to zero), while the R-squared is extremely interesting, ranging between 0.15 and 0.16 in model A, and between 0.31 and 0.32 in model B. Lastly, in order to verify whether the collected estimations are biased by this censoring phase, we propose as robust analysis the same models, but without dropping the outliers, i.e., considering the whole sample of observations. Tables7 and 8 present the collected results on the robust test. 2.4 Discussion Based on these results, we cannot reject the hypothesis that judicial inefficiency can affect corporate finance. The greater the judicial inefficiency in insolvency procedures, the higher the share of operating debts, as well as the lower the financial expenses. In other words, institutional inefficiency has an impact on corporate finance, prompting managers to adopt opportunistic strategies in the short run. These Table 1 Descriptive statistics: dependent and independent variables (Italy, 2014–2016) ψ logarithmic transformation Variable Explanation Obs Mean Std. Dev Min Max DEBTtDebt ratiot220,729 0.734 0.288 0.000 1.000 FINtFinancial expensest ψ215,509 2.579 1.595 0.000 6.907 Mortgage foreclosure (real estate)t-1 ψ160,273 7.044 0.337 6.041 8.482 JUDt-1 Mortgage foreclosure (movable)t-1 ψ160,273 5.252 0.401 3.804 6.541 Bankruptcyt-1 ψ160,273 7.699 0.419 4.875 8.889 LIQtLiquidityt258,469 43.553 140.597 − 534.609 1642.681 AGEtAget ψ258,469 2.556 1.025 0.000 4.963 Large companyt258,469 0.453 0.498 0.000 1.000 SIZEtMedium companyt258,469 0.096 0.295 0.000 1.000 Small companyt258,469 0.451 0.498 0.000 1.000 North Eastt258,469 0.290 0.454 0.000 1.000 North Westt258,469 0.362 0.481 0.000 1.000 AREAtCentert258,469 0.186 0.389 0.000 1.000 Southt258,469 0.126 0.332 0.000 1.000 Islandst258,469 0.036 0.185 0.000 1.000 Public limited companyt258,468 0.086 0.281 0.000 1.000 FORMtPrivate limited companyt258,468 0.888 0.315 0.000 1.000 Other legal formt258,468 0.026 0.158 0.000 1.000 InnStUptInnovative start-upt258,469 0.002 0.049 0.000 1.000 InnSMEtInnovative SMEt258,469 0.002 0.042 0.000 1.000 Page 7 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 results are rather robust, since we can observe the same positive relations across all case matters, which are statistically significant (p-value < 0.01). Focusing on the judicial procedures, ceteris paribus, we expect to detect the highest impact in relation to mortgage foreclosure (real estate). Indeed, taking model A into consideration, if the time necessary to settle a case using this procedure increases by 1000days, we expect operating debts to rise by 10.85%. Focusing on the other two case matters, assuming the same delay of 1000days, we expect an increase equal to 2.70% (movable goods) and to 3.20% (bankruptcy). Taking model B into account, if the time necessary to settle a case increases by the same delay of 1000days, we expect financial expenses to decrease by 7464 Euro (real estate), 3236 Euro (movable goods) and 2153 Euro (bankruptcy). Hence, we cannot reject previous evidence on Table 2 Model A: analysis of the Italian manufacturing industry with judicial insolvency procedures at time t-1 (Italy, 2014–2016) Robust standard errors in parentheses *** p < 0.01, **p < 0.05, *p < 0.1 ψ logarithmic transformation Variable Debt ratio tDebt ratio tDebt ratiot (1) (2) (3) Liquidity − 3.47e−05*** − 3.44e−05*** − 2.19e−05*** (4.64e−06) (4.64e−06) (4.83e−06) Age ψ− 0.0296*** − 0.0296*** − 0.0277*** (0.000893) (0.000893) (0.000979) Large company 0.0640*** 0.0640*** 0.0744*** (0.00178) (0.00178) (0.00190) Medium company − 0.0707*** − 0.0708*** − 0.0821*** (0.00391) (0.00391) (0.00415) Mortgage foreclosure (real estate)t-1 ψ0.0324*** (0.00234) Mortgage foreclosure (movable)t-1 ψ0.0214*** (0.00171) Bankruptcyt-1 ψ0.0163*** (0.00200) Private limited company − 0.0271*** − 0.0271*** − 0.00498 (0.00574) (0.00573) (0.00675) Public limited company − 0.217*** − 0.217*** − 0.181*** (0.00724) (0.00724) (0.00811) Innovative start-up − 0.00827 − 0.00877 − 0.0193 (0.0146) (0.0146) (0.0189) Innovative SME − 0.0788*** − 0.0779*** − 0.0747*** (0.0209) (0.0208) (0.0213) Constant 0.694*** 0.812*** 0.759*** (0.0187) (0.0120) (0.0183) Macro-area (FE) Yes Yes Yes NACE code (FE) Yes Yes Yes Wald Chi-square (p-value) 0.0000 0.0000 0.0000 R-squared (between) 0.16 0.16 0.16 R-squared (overall) 0.15 0.15 0.16 Observations 135,665 135,665 120,373 Number of companies 84,155 84,155 74,835 Page 8 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 the relation between the time needed by courts to enforce debtors’ obligations and the time needed by enterprises to repay their debts. Indeed, Falavigna and Ippoliti (2020) suggest that if the time needed to settle bankruptcy cases increases by 25%, we can expect the payment delay to increase by 1%; while, focusing on foreclosure cases, we can expect the payment index to increase by 2%. Figure1 highlights the same positive relation, seen from a different perspective. In this case, we plot the judicial districts, weighting the observations by the number of companies located in that district, and we look at the relation between the days needed to settle a bankruptcy case and our dependent variables, i.e., the debt ratio (on the left) and the financial expenses (on the right). According to the estimated coefficients (p-value < 0.01), on average, if judicial inefficiency increases by 1000days, Table 3 Model B: analysis of the Italian manufacturing industry with judicial insolvency procedures at time t-1 (Italy, 2015–2016) Robust standard errors in parentheses *** p < 0.01, **p < 0.05, *p < 0.1 ψ logarithmic transformation Variable Financial expenses t ψFinancial expenses t ψFinancial expensestψ (1) (2) (3) Liquidity 0.000212*** 0.000211*** 0.000171*** (1.74e−05) (1.74e−05) (1.81e−05) Age ψ0.228*** 0.224*** 0.208*** (0.00503) (0.00503) (0.00519) Large company − 0.778*** − 0.780*** − 0.796*** (0.0103) (0.0103) (0.0105) Medium company 0.610*** 0.611*** 0.633*** (0.0206) (0.0206) (0.0205) Mortgage foreclosure (real estate)t-1 ψ− 0.291*** (0.00957) Mortgage foreclosure (movable)t-1 ψ− 0.170*** (0.00717) Bankruptcyt-1 ψ− 0.111*** (0.00815) Private limited company 0.203*** 0.201*** 0.123*** (0.0330) (0.0329) (0.0350) Public limited company 1.291*** 1.292*** 1.154*** (0.0401) (0.0400) (0.0416) Innovative start-up − 0.0587 − 0.0624 0.0193 (0.0805) (0.0804) (0.0901) Innovative SME 0.643*** 0.640*** 0.629*** (0.0952) (0.0943) (0.0933) Constant 3.638*** 2.455*** 2.639*** (0.0820) (0.0580) (0.0793) Macro-area (FE) Yes Yes Yes NACE code (FE) Yes Yes Yes Wald Chi-square (p-value) 0.0000 0.0000 0.0000 R-squared (between) 0.32 0.32 0.32 R-squared (overall) 0.31 0.31 0.31 Observations 135,366 135,366 125,016 Number of companies 79,789 79,789 74,340 Page 15 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 Acknowledgements Authors acknowledge support by the National Research Council (CNR) for the Short Mobility Visiting in 2019 to the Research Institute on Sustainable Economic Growth (IRCrES), which was fundamental for data collection. Moreover, authors thank two anonymous referrees for their useful comments. Authors’ contributions The authors have made substantial contributions to the conception and design of the work, the acquisition, analysis, and interpretation of data. Moreover, the authors have approved the submitted version and agree to be personally accountable for the author’s own contributions and for ensuring that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and documented in the literature. Funding None. Availability of data and materials Judicial dataset are available on request. Table 8 Model B: analysis of the Italian manufacturing industry with judicial insolvency procedures at time t-1 (Italy, 2015–2016) Robust standard errors in parentheses *** p < 0.01, **p < 0.05, *p < 0.1 Ψ logarithmic transformation Variable Financial expenses t ψFinancial expenses t ψFinancial expenses t ψ (1) (2) (3) Liquidity 7.90e−05*** 7.62e−05*** 5.21e−05*** (1.15e−05) (1.16e−05) (1.21e−05) Age ψ0.227*** 0.224*** 0.207*** (0.00497) (0.00498) (0.00514) Large company − 0.783*** − 0.786*** − 0.798*** (0.0101) (0.0101) (0.0104) Medium company 0.589*** 0.590*** 0.613*** (0.0196) (0.0196) (0.0196) Mortgage foreclosure (real estate)t-1 ψ− 0.288*** (0.00944) Mortgage foreclosure (movable)t-1 ψ− 0.170*** (0.00704) Bankruptcyt-1 ψ− 0.104*** (0.00812) Private limited company 0.189*** 0.188*** 0.106*** (0.0327) (0.0327) (0.0347) Public limited company 1.274*** 1.276*** 1.133*** (0.0394) (0.0393) (0.0408) Innovative start-up − 0.0492 − 0.0526 0.0361 (0.0790) (0.0790) (0.0880) Innovative SME 0.616*** 0.612*** 0.597*** (0.0984) (0.0977) (0.0974) Constant 3.627*** 2.466*** 2.603*** (0.0809) (0.0571) (0.0790) Macro-area (FE) Yes Yes Yes NACE code (FE) Yes Yes Yes Wald Chi-square (p-value) 0.0000 0.0000 0.0000 R-squared (between) 0.32 0.32 0.32 R-squared (overall) 0.30 0.30 0.30 Observations 142,370 142,370 131,611 Number of companies 82,247 82,247 76,755 Page 16 of 16 Falavignaand Ippoliti Economic Structures (2021) 10:21 Declarations Competing interests The authors declare no conflict of interest. Author details 1 Research Institute On Sustainable Economic Growth (IRCrES), National Research Council of Italy (CNR), Via Real Collegio 30, 10024 Moncalieri, Italy. 2 Faculty of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany. Received: 16 March 2021 Revised: 19 October 2021 Accepted: 23 October 2021 References Carbo-Valverde S, Rodriguez-Fernandez F, Udell GF (2016) Trade credit, the financial crisis, and SME access to finance. J Money, Credit, Bank 48(1):113–143 CEPEJ (2016) “Evaluation of European Judicial Systems,” CEPEJ Report, European Commission for the Efficiency of Justice (CEPEJ). Falavigna G, Ippoliti R (2020) The impact of institutional performance on payment dynamics: evidence from the Italian manufacturing industry. J Bus Econ Manag 21(5):1285–1306 Falavigna G, Ippoliti R (2021) Reform policy to increase the judicial efficiency in Italy: the opportunity offered by EU postcovid funds. Journal of Policy Modeling 43:923–943 Farre-Mensa J, Ljungqvist A (2016) Do measures of financial constraints measure financial constraints? Rev Financ Stud 29(2):271–308 Fu K, Wennberg K, Falkenhall B (2018) Productive entrepreneurship and the effectiveness of insolvency legislation: a cross-country study. Small Bus Econ 1–22. Forthcoming. Garcia-Appendini E, Montoriol-Garriga J (2013) Firms as liquidity providers: evidence from the 2007–2008 financial crisis. J Financ Econ 109(1):272–291 Giacomelli S, Menon C (2017) Does weak contract enforcement affect firm size? Evidence from the neighbour’s court. J Econ Geogr 17(6):1251–1282 Haselmann R, Wachtel P (2010) Institutions and bank behavior: legal environment, legal perception, and the composition of bank lending. J Money Credit Bank 42(5):965–984 Hayo B, Voigt S (2013) The relevance of judicial procedure for economic growth. Cesifo Econ Stud 60(3):490–524 Jappelli T, Pagano M, Bianco M (2005) Courts and banks: effects of judicial enforcement on credit markets. J Money Credit Bank 37(2):223–244 Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol 49(4):764–766 Lichand G, Soares RR (2014) Access to justice and entrepreneurship: evidence from Brazil’s special civil tribunals. J Law Econ 57(2):459–499 Moro A, Maresch D, Ferrando A (2018) Creditor protection, judicial enforcement and credit access. Eur J Financ 24(3):250–281 Rodano G, Serrano-Velarde N, Tarantino E (2016) Bankruptcy law and bank financing. J Financ Econ 120(2):363–382 Schiantarelli F, Stacchini M, Strahan PE (2020) Bank quality, judicial efficiency, and loan repayment delays in italy. J Financ 75(4):2139–2178 Shah A, Shah HA, Smith JM, Labianca GJ (2017) Judicial efficiency and capital structure: an international study. J Corp Finan 44:255–274 Ughetto E (2008) Does internal finance matter for R&D? New evidence from a panel of Italian firms. Camb J Econ 32(6):907–925 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.