The Economic Effects Of Mafia Firm Level Evidence

[Othon Sdcd]

Theproceeds generated by organised crime in illicit markets are estimated to amount to about 2% to 5% of global GDP (UNODC 2011). However, the presence and activities of criminal organisations are not limited to such markets but have progressively spread into legitimate businesses, where they benefit from a competitive advantage thanks to the availability of illegal capital and their coercive and corruptive power. The resulting market distortions can impose a loss on society as a whole in terms of long-run economic growth (Pinotti 2015, Acemoglu et al. 2019).

In a recent paper (Mirenda et al. 2019), we analyse the effects of mafia spread outside its areas of origin and into legitimate businesses. Specifically, we focus on the 'ndrangheta, a major criminal organisation originating from the south of Italy, and analyse its penetration in the centre and north of Italy, areas with no tradition of mafia settlements.

The resulting picture is one in which about 1% of the firms in the centre and north are classified as infiltrated, with yet significant heterogeneities both across geographical areas and across sectors of activity.

With respect to geographical areas, the highest concentration of infiltrated firms is found in the northwest of the country, areas in which, indeed, the presence of the 'ndrangheta has been most documented (Transcrime 2015).

Most of the infiltrated firms operate in the construction sector (19%), followed by real estate (15%), and wholesale and retail trade (11%). Looking at the odds ratios, the infiltrated firms appear to be overrepresented in the sector of utilities (e.g. electricity, gas or water provision, and waste disposal) and financial services (e.g. money transfers). All these are either activities that rely more heavily on public demand (e.g. construction and utilities) or that are more suitable for money laundering (e.g. retail or financial services). The sectoral distribution we trace closely mirrors that of the firms that have been seized by the Italian judiciary from criminal organisations from the 1980s.

According to our most restrictive specification, 'ndrangheta infiltrations increase the revenues of the treated firms by approximately 24%. Such increases are not permanent, but fade out gradually in the years after the infiltration (Figure 1). The positive impact of infiltration is the result of a marked increase in the revenues of the treated firms vis--vis the fairly stable patterns among control units.

Notes: Each point is the estimate of the treatment effect on the log of revenues for different years, before and after the infiltration (leads and lags); vertical bands are the corresponding 95% confidence intervals; t-1 is the reference category.

When considering the production inputs, we find that infiltrations generate an increase in the labour inputs but not in capital investments. This pattern is particularly enhanced among firms that operate in activities most closely related to the public sector, such as education and training, health, public utilities, and the whole construction business (mining and quarrying, construction itself, and real estate activities). Among the other activities, the increase in the production inputs is significantly less pronounced, suggesting that the higher revenues reported may be an accounting artefact to mask money laundering rather than real growth.

We then adopt a long-run perspective to shed light on the effect that such criminal infiltrations may generate broadly on the local economy. We build on the fact that, at the beginning of the 1970s, the 'ndrangheta was essentially absent in the centre and north of Italy, while it progressively infiltrated these areas in the following decades. Its current extent in the centre and north of Italy can thus be interpreted as the result of the progressive penetration that occurred over the past 40 years.

Using our proxy for 'ndrangheta infiltrations, we trace the patterns of penetration of the criminal organisation in the centre and north and match information at the municipal level with local economic activity indicators built from census data.

We find a strong negative effect of the 'ndrangheta on long-term employment growth: moving from a municipality at the bottom decile of the extent of 'ndrangheta penetration to one at the top decile would lead to a decrease in employment growth of about 28 percentage points over a forty-year period. Such an effect is mostly driven by those sectors in which organised crime is likely to operate with the objective of making business, i.e. activities most closely related to the public sector.

In research published in Management Science, Slutzky and co-author Stefan Zeume of the University of Illinois at Urbana-Champaign look at what happens to the economy in Italy when law enforcement cracks down on organized crime.

This is the first research to quantify the effect of the mafia at this micro level of how municipalities are affected, says Slutzky. The turnover of firms increases by about 12% relative to prior years, he says. This is mostly driven by an increase in the number of firms entering the market relative to the period before a crackdown.

In particular, after the seminal papers by Aigner et al. (1977) and Meeusen and van den Broeck (1977), frontier methodology has been widely used in empirical studies aiming to estimate the economic efficiency of various economic systems (firms, industries, regions, etc.) and, moreover, to analyse the factors that may affect the production process, such as production factors (shape of the frontier), technological factors (shift of the frontier) or efficiency factors (distance from the frontier) (Kumbhakar and Lovell 2000; Bădin et al. 2012). There are two different approaches, one parametric and the other nonparametric, to estimate frontier models. The parametric approach suffers from misspecification problems when the data-generating process is unknown, as usual in the applied studies, and nonparametric methods often give the most reliable results. The reduced number of assumptions needed to specify the data generating process (DGP) is quite an attractive feature of the nonparametric approach. Hence, we prefer using a nonparametric approach since it does not require restrictive assumptions on the production function form and distributions of efficiency and stochastic errors.

Our results highlight that firms awarded with the LR exhibit a higher efficiency, especially in the manufacturing, construction and retail sectors. This is against the common wisdom that firms which free-ride attain better rewards because they reduce the costs of compliance with legal standards (Becchetti et al. 2017). Conversely, our findings support the idea that a positive relation between legality and efficiency exists for reasons associated with a reduction in financial, legal and tax risks that lead, in turn, to less negative shocks for sound companies. At the same time, firms with the LR gain a better reputation in the economic environment that helps them to establish more trustworthy relationships with both their investors and customers (Branco and Rodrigues 2006; Alwi et al. 2017; Wanner and Janiesch 2019). Furthermore, companies that guarantee higher standards of corporate social responsibility (CSR) may offer better working environments that, in turn, attract more skilled and productive workers.

We also show that the release of certified information through the LR facilitates companies in terms of accessing to the bank credit. Thanks to the larger long-term funding by the credit system, firms undertake greater productive investments that translate into a higher level of efficiency. This is also true for healthy but less efficient businesses. Indeed, the LR allows these companies not only to improve their productivity, but also to converge towards the same levels as the most efficient companies. These results are also important at inter-regional level. In fact, considering that less efficient firms are generally located in less productive areas and that the LR produces a convergence in terms of productivity between firms that obtain it, the release of the LR also generates a convergence of productivity at inter-regional level. In addition, the reduction of information asymmetries, and therefore, of the corporate risk perceived by the credit system, allows banks to identify the companies on which to convey long-term credit better. This makes it possible to reduce the territorial variability in the granting of credit and, therefore, to converge credit transactions also at the inter-regional level.

The remainder of the paper proceeds as follows: Section 2 presents the Legality Rating introduced in Italy in 2012; Section 3 describes the methodology and Section 4 the data; Section 5 presents the empirical results; and Section 6 concludes.

Companies are granted the LR by the Autorit Garante della Concorrenza e del Mercato (AGCM), whose members are appointed by the presidents of Senate and Chamber of Deputies of the Italian Parliament and are independent from the government, if some minimum requirements are met, as follows:

Nonparametric estimators of the attainable sets can be obtained by plugging nonparametric estimators of the survivor functions into the definitions above. Only the variables (z) require smoothing and appropriate bandwidths, since we have

In applied studies, the application of these nonparametric techniques may be problematic due to the presence of outliers or extreme data points in real data samples, which fully determine the estimated frontier and the measurement of inefficiencies. Estimated frontier and the measurement of inefficiencies are totally unrealistic. This can be avoided by using partial order frontier with extreme orders. Approaches have been proposed in the frontier literature ( Cazals et al. (2002) and Daouia and Simar (2007)) not only to keep all the observations in the sample but also to replace the frontier of the empirical distribution by (conditional) quantiles or by the expectation of the minimum (or maximum) of a subsample of the data.

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