doing business below the line: screening, ma as and

Doing Business Below the Line:

Screening, Mafias and European Funds

Gianmarco Daniele 1 Gemma Dipoppa 2

Bocconi University 1 University of Pennsylvania 2

May 1, 2018

Daniele, Dipoppa May 1, 2018 SLIDE 1/ 23

The NGO ”Fraternita di Misericordia”was in charge of managing the refugeecamp Sant’Anna in Calabria

The NGO helped several ad hoccompanies created by the ’ndranghetafamily Arena to win contracts tomanage the refugee camp

According to the investigators, at least30 million in European Funds (out of100) went to the ’ndrangheta family


Motivation

Public funds misuse is a common problem in several countries and it appears indifferent forms

Bribes to state officials to obtain contracts and permits

Hidden violation of procurement rules (ad hoc written tenders)

Misuse of public funds for electoral purposes

Public fund misuse is often linked to the infiltration of criminal organizations(OLAF Report, 2016)

In 2016, the EU Anti-Fraud Office recommended the return of projects for 631million Euros, which had been used for illegal activities


Motivation

In Italy, criminal organizations invest more and more in legal economicactivities, which include appropriation of public funds (Barone and Narciso,2015): 2884 firms are currently seized to mafias

Negative effects of public funds misuse:

Missed opportunities for developmentReinforcement of criminal organization groups


Overview of the paper

Natural experiment: The Antimafia CertificateItalian law requires checks on firms’ connections with organized crime only ifthey request +150.000 Euros in funds

Other countries require controls against corruption. In Italy, the law specificallytargets organized crime.The Law has existed since 1990 but it was considerably strengthened in 2013(time-varying test)Data is available on all European subsidies received by Italian firms in the period2008-2015


Overview of the paper

Preview of the findings:

The Antimafia Certificate leads to a strategic response (self-sorting) only afterthe 2013 law strengthening. Projects below the threshold are more likely todisplay worse performances, such as delaying the conclusion of a project andco-financingWe provide suggestive evidence that, in areas with high mafia presence, firmsare able to circumvent screening using figureheads

Policy implications1 Strengthening was effective and it might be extended below the threshold2 Screening + monitoring to increase effectiveness in areas with high mafia

presence


Contribution

State policies to fight corruption (Avis et al., 2017; Bobonis et al., 2015; DiTella and Schargrodsky, 2003; Olken, 2007)

Literature on policies to fight organized crime (Galletta, 2017; Daniele andGeys, 2015)

Few studies actually testing the effects of anti-corruption policy: first onetesting the effectiveness of fighting criminal infiltration in public funds

Literature trying to explain the (in)effectiveness of EU structural funds atproducing development (Becker et al, 2010, 2012, 2013; Ciani and De Blasio,2015)

Our contribution: highlight the role of criminal organizations in divertingEuropean funds for development


Outline of the presentation

1 The Antimafia Certificate

2 Data & Estimation

3 Results & Robustness

4 Heterogeneity

5 Conclusions


Natural Experiment

The Italian law requires firms to have a certificate stating the absence of tieswith organized crime in order to receive subsidies (below the thresholds,self-certification).

Before 2013:

Not eligible: business owners charged for mafia crimesIt can be denied by the police independently on judicial evidenceA suspicious investigation can lead to a certificate denialCompulsory above 154.937 Euros

After 2013:

It is compulsory also for State-controlled firms and NgosNew crimes included: firms not reporting requests of extortion/corruption, illegalsubcontracting, waste trafficking, manipulation of public procurementsThe controls are extended to the family membersUnified legislation & unified firms’ datasetA suspicious investigation has to lead to a certificate denialCompulsory above 150.000 Euros


Natural Experiment

The tender process:1 A local public institution calls for a tender offering (un)conditional subsidies to

firms in a certain area2 Firms participate to the tender offer3 The public institution requires the Antimafia Certificate to the police4 The police provide or deny the Antimafia-Certificate within 45 days (if more

time is needed, the Certificate is considered granted and can be subsequentlywithdrawn).

5 The Antimafia Certificate is valid for 12 months


Hypothesis

Mafia-linked firms have three choices to access subsidies:1 Ask for any amount of money and risk to receive a denial - implies permanent

exclusion of that company from any future tender offer: very high risk and cost.2 Ask for subsidies below the threshold: relatively costly.3 Circumvent the law using figureheads or corruption: the cost varies depending

on criminals’ strength.

We can test for 2 estimating the difference in the density of projects at thethreshold.

We can (partially) test for 3 estimating the difference in the density ofprojects in areas with different mafias’ strength

Intuition: asking less than 150,000 will be avoided if there are cheaperalternatives (e.g. figureheads), which might be the case in areas with high mafiapresence


Data

Subsidies (co)financed by the European Funds to Italian firms from 2007 to2015. Our Sample = 21.201; 756 tenders; within tender max variation:123,000 Euros

Subsidies not funded by the EU are not entirely available (subsample asrobustness test)

Mafia presence: estimated using a host of alternative measures

Goods and companies seized to mafiasCity-councils dissolved because of mafia infiltrationsMafia-related homicidesNumber of mafia arrests (for 416bis, Court-level)


Mc Crary Test for the density of requests

Mc Crary Before 2013 Mc Crary After 2013


Estimation Strategy (New Law)

For company i participating to tender offer c in municipality m, we estimate theeffect of the new 2013 law on the probability of being just below/above thethreshold:

JustBelowThreshold150kicm = ζc + µm + βPostLawi + εicm (1)

JustBelowThreshold is a dummy = 1 when funding i is in the interval145k-150k Euros (different intervals are used for JBT ).

µ and ζ are municipality and public call fixed effects

PostLaw is a dummy = 1 after 2013

β is the coefficient of interest, capturing the probability that firms sort justbelow the threshold after the law is passed

Drop public competitions up to 150.000 Euros


Test New Law

Model 1 Model 2 Model 3

(1) (2) (3) (4) (5) (6) (7) (8) (9)130k-170k -1k -5k -10k -1k -5k -10k -1k -5k -10k

Post Law(2013) 0.106** 0.0941** 0.0747** 0.125** 0.114** 0.0936** 0.135** 0.140** 0.134**(0.0136) (0.0167) (0.0186) (0.0151) (0.0184) (0.0204) (0.0215) (0.0279) (0.0316)

City FE NO NO NO NO NO NO YES YES YESType FE NO NO NO YES YES YES YES YES YESObservations 3,187 3,187 3,187 3,187 3,187 3,187 3,187 3,187 3,187Mean Dep. Var. 0.110 0.220 0.34 0.110 0.220 0.34 0.110 0.220 0.34R-squared 0.024 0.011 0.005 0.053 0.035 0.026 0.463 0.419 0.390

Model 1 Model 2 Model 3

(1) (2) (3) (4) (5) (6) (7) (8) (9)50k-250k -1k -5k -10k -1k -5k -10k -1k -5k -10k

Post Law(2013) 0.0209** 0.0229** 0.0241** 0.0217** 0.0220** 0.0212** 0.0228** 0.0247** 0.0246**(0.00248) (0.00317) (0.00377) (0.00257) (0.00328) (0.00389) (0.00316) (0.00398) (0.00475)

City FE NO NO NO NO NO NO YES YES YESType FE NO NO NO YES YES YES YES YES YESObservations 21,401 21,401 21,401 21,401 21,401 21,401 21,401 21,401 21,401Mean Dep. Var. 0.016 0.032 0.052 0.016 0.032 0.052 0.016 0.032 0.052R-squared 0.005 0.003 0.002 0.009 0.008 0.009 0.185 0.180 0.168

Robust standard errors in parentheses** p<0.01, * p<0.05


Pre-Trends

Sample: 130k-170k Sample: 50k-250k


New Law After 2014

Restricted sample Full sample

(1) (2) (3) (4) (5) (6)-1k -5k -10k -1k -5k -10k

Post Law(2014) 0.164** 0.158** 0.151** 0.0295** 0.0308** 0.0322**(0.0182) (0.0212) (0.0230) (0.00330) (0.00403) (0.00470)

Type FE YES YES YES YES YES YESSample 130k-170k 130k-170k 130k-170k 50k-250k 50k-250k 50k-250kObservations 3,187 3,187 3,187 21,370 21,370 21,370Mean Dep. Var. 0.110 0.220 0.34 0.016 0.032 0.052R-squared 0.064 0.043 0.034 0.011 0.010 0.011



Additional Tests

1 No sorting with the old Antimafia-Certificate Estimation Table

2 Robust to different subsidies’ bandwidths

3 Placebo on other round numbers (100k, 200k, 250k) Figure

4 Placebo test of mafia-dissolved councils Figure

5 Placebo on agriculture subsidies (frequent subsidies) Table

6 Sector Heterogeneity Table


Alternative Explanations

1 Firms are avoiding bureaucratic costs, not mafia screening

But firms do not bear any bureaucratic cost: the full process is undertaken bythe police and the public institutionIf firms were just maximizing, firms bunching would look very similar or betterthan firms not bunching. Instead, they perform worse Table Figure Figure

2 Business owners are afraid screenings could expand beyond mafia crimesand find out, for example, that they evade taxes.

This effect would be noisierAnd it would be time invariant

3 After the new law is approved, public institutions make more calls setting amaximum request of 150,000 Euros of funding

Tender fixed effects Table

No empirical evidence


Mafia Presence

Circumvent the law using figureheads or corruption: the cost variesdepending on criminals’ strength

Asking less than 150,000 will be avoided if there are cheaper alternatives (e.g.figureheads), which might be the case in areas with high mafias presence. Inturn, they will obtain higher subsidies.

1 Self-sorting is not visible in high mafia areas:

test on seized firms Table Map

test on Power Syndicate Index Table Map

2 Figureheads are more common in high mafia areas Figure

3 Growth in high mafia areas after 2013 of typical types of figureheads Figure Table

4 Higher subsidies after 2013 in high mafia areas Table


Conclusions

1 The Antimafia Certificate leads to a strategic response (self-sorting) only afterthe 2013 law strengthening

2 Projects which sort below the threshold are more likely to display worseperformances, such as delaying the conclusion of a project and co-financing

3 We provide suggestive evidence that, in areas with high mafia presence, firmsare able to circumvent screening using figureheads

→ Strengthening was effective and it might be extended below the threshold→ Screening + monitoring to increase the effectiveness in high mafia areas


Next Steps

1 Bunching Estimation

2 Model to predict criminal organizations strategies

3 Complete qualitative analysis (questionnaires to all Italian police departmentsin charge of the Antimafia Certificate)


Mafias Presence: Seized Firms

(1) (2) (3) (4) (5) (6)Base cat. no mafia-seized firms -1k -5k -10k -1k -5k -10k

Post Law(2013) -0.0231 -0.0480 -0.0407 -0.00801 -0.0213** -0.0240*(0.0674) (0.0991) (0.119) (0.00600) (0.00936) (0.0124)

Post Law(2013)#Dmafia firm 0.169* 0.207* 0.193 0.0350** 0.0526** 0.0555**(0.0710) (0.103) (0.123) (0.00694) (0.0103) (0.0134)

City FE YES YES YES YES YES YESType Fe YES YES YES YES YES YESSample 130k-170k 130k-170k 130k-170k 50k-250k 50k-250k 50k-250kObservations 3,187 3,187 3,187 21,344 21,344 21,344Mean Dep. Var. 0.110 0.220 0.34 0.016 0.032 0.052R-squared 0.465 0.426 0.396 0.189 0.184 0.171


(1) (2) (3) (4) (5) (6)Base cat. no mafia-seized firms -1k -5k -10k -1k -5k -10k

Post Law(2013) -0.0238 -0.0459 -0.0382 -0.00824 -0.0216* -0.0242(0.0677) (0.100) (0.120) (0.00602) (0.00939) (0.0125)

Post Law(2013)#Low Seized Firms 0.280** 0.315** 0.290* 0.0581** 0.0786** 0.0818**(0.0762) (0.108) (0.128) (0.00843) (0.0116) (0.0147)

Post Law(2013)#High Seized Firms 0.0361 0.0792 0.0664 0.00809 0.0227* 0.0229(0.0731) (0.108) (0.130) (0.00677) (0.0105) (0.0140)



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Seized Firms

Figure: (a) Intensity of Sorting; (b) Number of seized firms

(0.173,0.415](0.144,0.173](0.095,0.144][0.000,0.095]

(20,365](4,20](1,4][0,1]

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Power Syndicate

Figure: (a) Intensity of Sorting; (b) Power Syndicate Index

(0.173,0.415](0.144,0.173](0.095,0.144][0.000,0.095]

(2,4](1,2][1,1]No data

Sciarrone (2014)Index of PowerSyndicate based on:

Mafia-relatedcrimes, includingmafia homicides andracketing

Goods and firmsseized to organizedcrime

Cities dissolvedbecause of mafiainfiltration

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Sorting: Placebo Round Numbers

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Power/Enterprise Syndicate Results

(1) (2) (3) (4) (5) (6)Base cat.: very-low power syndicate -1k -5k -10k -1k -5k -10k

Post Law(2013) 0.293** 0.310** 0.303** 0.0631** 0.0705** 0.0747**(0.0398) (0.0455) (0.0489) (0.00760) (0.00872) (0.00968)

Post Law(2013)#Low power synd -0.257** -0.231** -0.242** -0.0630** -0.0694** -0.0757**(0.0601) (0.0766) (0.0859) (0.00898) (0.0109) (0.0126)

Post Law(2013)#Medium power synd -0.270** -0.235* -0.160 -0.0589** -0.0628** -0.0634**(0.0669) (0.0985) (0.116) (0.00922) (0.0122) (0.0153)

Post Law(2013)#High power synd -0.277** -0.325** -0.353** -0.0633** -0.0734** -0.0855**(0.0509) (0.0678) (0.0787) (0.00841) (0.0105) (0.0125)


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Test on Figureheads and Over 75

(1) (2) (3) (4) (5) (6)Base cat: below median figureheads -1k -5k -10k -1k -5k -10k

Post Law(2013) 0.230** 0.228** 0.199** 0.0382** 0.0389** 0.0364**(0.0311) (0.0394) (0.0439) (0.00498) (0.00610) (0.00710)

Post Law(2013)#Figureheads -0.185** -0.152** -0.104 -0.0307** -0.0267** -0.0219*(0.0462) (0.0590) (0.0666) (0.00655) (0.00820) (0.00968)



(1) (2) (3) (4) (5) (6)Base cat: below median Over 75 (change) -1k -5k -10k -1k -5k -10k

Post Law(2013) 0.288** 0.304** 0.277** 0.0541** 0.0592** 0.0604**(0.0384) (0.0450) (0.0488) (0.00634) (0.00739) (0.00821)

Post Law(2013)#Over75 Above -0.264** -0.269** -0.238** -0.0533** -0.0579** -0.0610**(0.0466) (0.0583) (0.0658) (0.00713) (0.00870) (0.0101)



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An attempt of pricing the sorting

(1) (2)Av. Subsidy Av. Subsidy

Post Law(2013) -4,111 -3,679(3,194) (3,203)

Post Law(2013)#Low power synd -1,378(6,266)

Post Law(2013)#Medium power synd 9,541(5,511)

Post Law(2013)#High power synd 20,381**(6,093)

Post Law(2013)#No Seized Firms 3,614(5,404)

Post Law(2013)#High Seized Firms 13,868*(6,133)

Province FE YES YESObservations 213 215Sample 50k-250k 50k-250kMean Dep. Var. 115,566 115,255R-squared 0.107 0.047Number of provx 109 110


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Test on Agriculture Subsidies

(1) (2) (3) (4) (5) (6)-1k -5k -10k -1k -5k -10k

Post Law(2013) 0.0103 0.0104 0.00568 0.000794 0.000576 -0.000501(0.00562) (0.0106) (0.0138) (0.000437) (0.000830) (0.00117)

City FE YES YES YES YES YES YESObservations 6,809 6,809 6,809 79,217 79,217 79,217Mean Dep. Var. 0.030 0.122 0.254 0.002 0.010 0.021R-squared 0.342 0.299 0.310 0.061 0.060 0.070


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Sectors

(1) (2) (3) (4) (5) (6)Base cat.: social inclusion -1k -5k -10k -1k -5k -10k

Post Law(2013) 0.00352 0.0721 0.0250 -0.000222 0.00621 -0.00916(0.0415) (0.0661) (0.0788) (0.00473) (0.00833) (0.0109)

Innovation (Environment/Tech) 0.00127 0.0621 -0.0374 -0.0118 -0.00506 -0.0283*(0.0639) (0.0875) (0.0988) (0.00779) (0.0114) (0.0139)

Firms Investments -0.0376 -0.0106 -0.0637 -0.00491 -0.00314 -0.0223**(0.0281) (0.0404) (0.0513) (0.00285) (0.00512) (0.00712)

Post Law(2013)#Innovation (Environment/Tech) 0.0488 -0.120 -0.0354 0.0177 -0.00108 0.0224(0.0834) (0.116) (0.134) (0.0112) (0.0159) (0.0200)

Post Law(2013)#Firms Investments 0.170** 0.107 0.153 0.0287** 0.0247** 0.0417**(0.0481) (0.0726) (0.0861) (0.00598) (0.00939) (0.0120)



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Placebo on Dissolved City Councils

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Projects below the threshold: do they differ?

Dep. var: project delay in months (1) (2) (3) (4) (5) (6)

Post Law(2013) -1.457* -2.227** -2.247** -0.337* -0.376** -0.360*(0.597) (0.644) (0.674) (0.142) (0.143) (0.144)

Post Law(2013)#149k-150k 3.739* 2.631**(1.548) (1.019)

Post Law(2013)#145k-150k 4.234** 2.011**(1.153) (0.664)

Post Law(2013)#140k-150k 2.976** 1.244*(1.009) (0.545)

City FE YES YES YES YES YES YESType Fe YES YES YES YES YES YESSample 130k-170k 130k-170k 130k-170k 50k-250k 50k-250k 50k-250kObservations 2,740 2,740 2,740 19,168 19,168 19,168Mean Dep. Var. -1.04 -1.04 -1.04 -1.08 -1.08 -1.08R-squared 0.465 0.466 0.465 0.318 0.318 0.318

Dep. var: probability of co-founding (1) (2) (3) (4) (5) (6)

Post Law(2013) 0.101** 0.107** 0.1000** 0.0612** 0.0620** 0.0613**(0.0217) (0.0222) (0.0230) (0.00649) (0.00651) (0.00654)

Post Law(2013)#149k-150k -0.140** -0.100**(0.0504) (0.0351)

Post Law(2013)#145k-150k -0.116** -0.0902**(0.0367) (0.0269)

Post Law(2013)#140k-150k -0.0592* -0.0485*(0.0304) (0.0212)


Robust standard errors in parentheses; ** p<0.01, * p<0.05

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Tender FE

(1) (2) (3) (4) (5) (6)-1k -5k -10k -1k -5k -10k

After 2014 0.0559* 0.0692* 0.0866* 0.0123** 0.0162** 0.0220**(0.0231) (0.0326) (0.0381) (0.00369) (0.00529) (0.00669)

City FE YES YES YES YES YES YESTender FE YES YES YES YES YES YESType Fe YES YES YES YES YES YESSample 130k-170k 130k-170k 130k-170k 50k-250k 50k-250k 50k-250kObservations 3,187 3,187 3,187 21,370 21,370 21,370Mean Dep. Var. 0.110 0.220 0.34 0.016 0.032 0.052R-squared 0.403 0.305 0.244 0.160 0.133 0.118


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Excluding tenders up to 150k

(1) (2) (3) (4) (5) (6)-1k -5k -10k -1k -5k -10k

new law 0.112** 0.0976** 0.0788** 0.0187** 0.0179** 0.0170**(0.0150) (0.0186) (0.0208) (0.00247) (0.00318) (0.00382)

City FE YES YES YES YES YES YESType Fe YES YES YES YES YES YESSample 130k-170k 130k-170k 130k-170k 50k-250k 50k-250k 50k-250kObservations 3,124 3,124 3,124 21,232 21,232 21,232R-squared 0.413 0.315 0.242 0.140 0.136 0.128


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Figure: Share of mafia sentences with figureheads by mafia strength

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Figure: Change in over 75 female entrepreneurs by mafia strength

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Estimation Strategy (Old Law)

For company i participating to tender offer c in municipality m, we estimate theeffect of the old law (before 2013) on the probability of being just below/abovethe threshold:

JustBelowThreshold154kicm = ζc + µm + βBeforeLawi + εicm (2)

JustBelowThreshold is a dummy = 1 when funding i is in the interval150k-154.937 Euros (different intervals are used for JBT ).

µ and ζ are municipality and public call fixed effects

BeforeLaw is a dummy = 1 in all periods before 2013

β is the coefficient of interest, capturing the probability that firms sort justbelow the threshold with the old law

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Test Old Law

Restricted sample Full sample

(1) (2) (3) (4) (5) (6)-1k -2k -3k -1k -2k -3k

Before Law(2013) 0.00803 0.00239 0.0159 0.00207 0.00190 0.00733(0.00658) (0.0122) (0.0145) (0.00281) (0.00501) (0.00634)

City FE YES YES YES YES YES YESType FE YES YES YES YES YES YESSample 130k-170k 130k-170k 130k-170k 50k-250k 50k-250k 50k-250kObservations 3,196 3,196 3,196 21,401 21,401 21,401Mean Dep. Var. 0.017 0.044 0.067 0.002 0.006 0.010R-squared 0.001 0.000 0.001 0.000 0.000 0.000


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Estimation Strategy

Bunching with a kink, without missing mass

Figure: Example of kink fromKleven (2016)

Kleven and Waseem (2013) define bunchingwith a kink as a discontinuity in the choicesets of individuals or firms, introducing anincentive for companies to move from theregion just above the cutoff to a region justbelow, creating a missing mass.

In our case, however, the missing mass is likelyto be evenly distributed, rather thanconcentrated after the threshold, as in thetaxation literature (Figure). This is becausemafia-companies move from both just abovethe cutoff and from much above the cutoff toavoid mafia-screening.

Our estimation strategy is therefore similar toestimating bunching with a kink but without amissing mass.


Estimation Strategy: Bunching with a kink

Step 1: Estimating the counterfactual distribution

The absence of a missing mass after the threshold means that we cannotestimate the affected range as the are in which missing mass = excess mass.

Following Kleven (2016), we instead estimate the counterfactual distribution asthe probability density function of our distribution excluding the area where weobserve the kink and its symmetric correspondent on the other side of thethreshold.

This corresponds to estimating the following polynomial on a binned databasewith each bin i corresponds to a 1,000 euros range:

Ni =p

∑j=0

β0j Amounti

j + εi (3)

Ni number of requests per amount of funding requested (Amounti )

p order of polynomial

βj counterfactual distribution (when estimated without the second term)

γi effect of the threshold on number of requests in the affected range [z0−, z0

+]


Estimation Strategy: Bunching with a kink

Step 2: Estimating the amount of bunching

The amount of bunching corresponds to the difference between the probabilitydensity function of the counterfactual distribution estimated above and theobserved distribution.

In particular, this corresponds to estimating the following polynomial:

Ni =p

∑j=0

β0j Amounti

j + ∑i∈[z0

−,z0+ ]

γ0i 1{Amount = i}+ εi (4)

βj counterfactual distribution (when estimated without the second term)

γi effect of the threshold on number of requests in the affected range [z0−, z0

+]


doing business below the line: screening, ma as and

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