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Anti-Mafia Law Enforcement and Lending in Mafia Lands. Evidence from Judicial Administration in Italy

Francesca M. Calamunci ORCID logo, Marco Alberto De Benedetto and Damiano Bruno Silipo

Abstract

The paper analyses the impact of a preventive measure aimed at fighting the criminal organizations’ activities on the bank-firm relationship in the four Italian regions with the highest density of mafia over the period 2004–2016. Taking advantage of the staggered firm-level anti-mafia enforcement actions, we implement a difference-in-differences approach and find that after entering judicial administration mafia-infiltrated firms experience a 19 per cent contraction of bank credit and have a higher probability of being credit rationed than a matched sample of legal companies. We also find that firms confiscated from the mafia experience a negative change in some demand-driven (value of production) and supply-driven (profitability) determinants of loans. Finally, we study whether confiscation of infiltrated firms produces externalities on non-infiltrated companies, and show that banks do not reassess the overall credit risk in local markets.

JEL Classification: D22; E51; G21; K42; L25

Corresponding author: Francesca M. Calamunci, University of Catania, Piazza Università, Catania95124, Italy, E-mail:

Acknowledgments

We thank the editor (Prof. Giovanni Mastrobuoni) and two anonymous referees for comments and suggestions that improved the paper.

Appendix A: Further Descriptive Tables and Figures

Figure A.1: Number of OC firms subject to JA by year.The figure reports the number of firms subject to judicial administration by year.

Figure A.1:

Number of OC firms subject to JA by year.

The figure reports the number of firms subject to judicial administration by year.

Figure A.2: OC firms under JA by sector.The figure reports the number of OC firms subject to judicial administration by sector.

Figure A.2:

OC firms under JA by sector.

The figure reports the number of OC firms subject to judicial administration by sector.

Table A.1:

Variables description.

VariablesDescription
Bank debt/TATotal bank debt/Total assets
CollateralTangible assets/Total assets
SizeLn(Revenues)
Z-score0.717(net working capital/total assets) + 0.847(net income/total assets) + 3.107(operative net income) +
0.420(equity/total debt) + 0.998(revenues/total assets)
ROANet income/total assets
Ebitda/TA(Net income + interest expenses + taxes + depreciation + amortization)/total assets
Equity-to-AssetNet worth/total assets
RODInterest expenses/total bank debt
Ln(Value Added)[(Production value − production costs) + personnel costs] in logarithm
Trade payables/TATotal debt toward non financial suppliers/total assets
Fixed assets/TAInvestment/total assets
Net working capital/TA(Current assets − current liabilities)/total assets
Liquidity/TACash/total assets

  1. Source: Own processing of data provided by AIDA over the period 2004–2016.

Table A.2:

Robustness to the matching strategy.

Panel A: Bank debt
(1)(2)(3)(4)
Bank debt/TABank debt/TABank debt/TABank debt/TA
AfterJA−0.0247*−0.0219*−0.0217**−0.0305**
(0.0138)(0.01224)(0.0104)(0.0121)
Year FEYesYesYesYes
Firm FEYesYesYesYes
Sector-by-yearYesYesYesYes
Firm id-by-trendYesYesYesYes
Province-by-yearYesYesYes
Sll-by-yearYes
Observations3593329662902885
No. of firms6756131195540
R-squared0.79490.84610.76550.7524
Matching approachPSM with more controlsPSM with sll controlsPSM 5-nearest neighbors matchingPSM Mahalanobis matching
Panel B: Credit rationing
(1)(2)(3)(4)
Pr (credit rationing)Pr (credit rationing)Pr (credit rationing)Pr (credit rationing)
AfterJA0.0751***0.1042***0.0876***0.0820***
(0.0249)(0.0221)(0.0181)(0.0217)
Year FEYesYesYesYes
Firm FEYesYesYesYes
Sector-by-yearYesYesYesYes
Firm id-by-trendYesYesYesYes
Table A.2:

(continued)

Panel B: Credit rationing
(1)(2)(3)(4)
Pr (credit rationing)Pr (credit rationing)Pr (credit rationing)Pr (credit rationing)
Province-by-yearYesYesYes
Sll-by-yearYes
Observations9034795515,5217006
No. of firms6756131,195540
R-squared0.31710.38020.30700.3080
Matching approachPSM with more controlsPSM with sll controlsPSM 5-nearest neighbors matchingPSM Mahalanobis matching

  1. The dependent variable are the bank debt over total asset and probability of credit rationing conditional on the amount of loans observed. Standard errors (corrected for heteroskedasticity and clustered at firm level) are reported in parentheses. The symbols ***, **, * indicate that coefficients are statistically significant, respectively, at 1, 5, and 10 per cent level.

Appendix B: Loans Market Disequilibrium Model and Credit Rationing

A disequilibrium model in the loans market can be expressed as follows (Maddala and Nelson 1974):

(B.1)BankLoansitd=β0+β1RODit+β2ln(ValueAdded)it+β3(TradePayables/TA)it+β4(FixedAssets/TA)it+β5(NetWorkingCapital/TA)it+λt+μ1it
(B.2)BankLoansits=α0+α1Collateralit+α2Sizeit+α3ZScoreit+α4(Ebitda/TA)it+α5Leverageit+λt+u2it
(B.3)BankLoansit=minBankLoansitd,BankLoansits

The model consists of a demand equation (B.1), a supply equation (B.2) and a transaction equation (B.3),[20] while u1it and u2it are the error terms. BankLoansitd and BankLoansits are latent variables, whereas the only variable observed is the amount of loans (Bank Loansit) borrowed by firm i at time t.

In other words, we observe Eq. (B.1) only if the supply of loans is in excess of the demand of loans (D < S). Conversely, we observe Eq. (B.2) only if the supply of loans is lower or equal to the demand of loans (D > = S). Moreover, we do not know whether ex-ante firm i belongs to the first or the second regime (in the latter case a firm is credit rationed), and in turn, the separation point is unknown (Maddala 1986; Perez 1998).

For convenience of notation, we rewrite Eq. (B.2) as BankLoansitd=β1X1+u1it, and Eq. (B.2) as BankLoansits=α2X2+u2it. We compute the probability that a certain observation belongs to the excess demand regime (credit rationing), conditional on the amount of loans observed, in the following way (Gersovitz 1980):[21]

PrBankLoansitd>BankLoansitdBankLoansit
=fsBankLoansi,t1FdBankLoansi,tfdBankLoansi,t1FsBankLoansi,t+fsBankLoansi,t1FdBankLoansi,t

where:

  1. fdBankLoansi,t=1σd2πexp12σd2BankLoansitβ1X12 is the density function of bank loans if Eq. (B.1) is observed;

  2. fsBankLoansi,t=1σs2πexp12σs2BankLoansitα2X22 is the density function of bank loans if Eq. (B.2) is observed;

  3. FdBankLoansit=ϕBankLoansitβ1X1σdσsρBankLoansitα2X2σd21ρ2 is the normal cumulative function, allowing for a potential correlation with Eq. (B.2);

  4. FsBankLoansit=ϕBankLoansitα2X2σsσdρBankLoansitβ1X1σs21ρ2 is the normal cumulative function, allowing for a potential correlation with Eq. (B.1).

As previously explained, the aim of our paper is not to investigate which factors generate a disequilibrium in the corporate loans market. We only relied on the main literature in this field only to understand what the most important factors affecting either the demand or the supply of loans are.

Appendix C: Flexible Functional Form for the Determinants of Loans Demand and Supply

Figure C.1: Loans demand.The figures show the results from a difference-in-difference regression analysis. Each outcome variable is on the vertical axis. The key explanatory variables are the interactions between the treatment and a set of year dummies up to 5 years before/after OC companies enter into JA, and the year in which OC firms experience JA is taken as reference category. Coefficient estimates are provided together with the 90% (black), 95% (gray) and 99% (light gray) confidence intervals.

Figure C.1:

Loans demand.

The figures show the results from a difference-in-difference regression analysis. Each outcome variable is on the vertical axis. The key explanatory variables are the interactions between the treatment and a set of year dummies up to 5 years before/after OC companies enter into JA, and the year in which OC firms experience JA is taken as reference category. Coefficient estimates are provided together with the 90% (black), 95% (gray) and 99% (light gray) confidence intervals.

Figure C.2: Loans supply.The figures show the results from a difference-in-difference regression analysis. Each outcome variable is on the vertical axis. The key explanatory variables are the interactions between the treatment and a set of year dummies up to 5 years before/after OC companies enter into JA, and the year in which OC firms experience JA is taken as reference category. Coefficient estimates are provided together with the 90% (black), 95% (gray) and 99% (light gray) confidence intervals.

Figure C.2:

Loans supply.

The figures show the results from a difference-in-difference regression analysis. Each outcome variable is on the vertical axis. The key explanatory variables are the interactions between the treatment and a set of year dummies up to 5 years before/after OC companies enter into JA, and the year in which OC firms experience JA is taken as reference category. Coefficient estimates are provided together with the 90% (black), 95% (gray) and 99% (light gray) confidence intervals.

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Received: 2020-10-17
Revised: 2021-05-19
Accepted: 2021-06-11
Published Online: 2021-06-23

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