Abstract
While the contributions on the organized crime and Mafia environments are many, there is a lack of empirical evidence on the firm’s decision to resist to extortion. Our case study is based on Addiopizzo, an NGO that, from 2004, invites firms to refuse requests from the local Mafia and to join a public list of “non-payers”. The research is based on a dataset obtained linking the current administrative archives maintained by the chambers of commerce and the list updated by the NGO. The objective of this paper is twofold: first, to gather sound data on the characteristics of the Addiopizzo joiners; second to model the probability to join Addiopizzo by a two-level logistic regression model. We find that the resilience behavior is likely to be the result of both individual (firm) and environmental factors. In particular, we find that firm’s total assets, firm’s age and being in the construction sector are negatively correlated with the probability of joining AP, while a higher level of human capital embodied in the firm and a higher number of employees are positively correlated. Among the district-level variables, we find that the share of district’s population is negatively correlated with the probability to join, while a higher level of socio-economic development, including education levels, are positively correlated.
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Notes
Gunnarson (2014) provides an econometric analysis of firms’ decision to join. The sample and the methodology are, however, very different from ours: the sample comes from a survey and does not include a control group of non-joiners and second-level variables. This limits the analysis to a study of the different timings of the decision to join without, however, considering censored observations.
These numbers include different branches of the same firm. The list can be consulted in the AP website (http://www.addiopizzo.org/).
AP also provides legal support in trials against the racketeers, mainly in collaboration with the business association Libero Futuro, or psychological support to entrepreneurs wishing to stop paying the Mafia. For more details on AP activity, see Forno and Gunnarson (2010, pp. 109–111) and Gunnarson (2014, pp. 42–44).
Since we received the list of AP-firms as of May 2012, we cannot observe the relative weight of these causes on the fluctuations in the number of joiners in the period of observation.
This was confirmed by personal communication from AP staff. We consider joining AP as signaling the non compliance with paying the Mafia. However, while joining AP is observable, not paying the Mafia is not. To join AP, firms’ owners must sign a declaration of non compliance with extortionary requests but we are not able to control for the firms’ truthful disclosure of information. As remarked, however, existing evidence suggests that cases of “double game” players are rare. On the other hand, whether firms that did not join AP are payers or not-payers is not observable. The available evidence (see Confesercenti 2010 and Asmundo and Lisciandra 2008) suggests that the large majority of firms in Palermo pay the pizzo. Therefore, in this paper we posit that joining AP implies refusing to pay the pizzo, while not joining implies paying.
This is another slogan diffused by AP.
For simplicity we assume that the utility function is linear and additive in its arguments, and omit the representation of the flow of utilities as an integral.
Clearly, firms owned by entrepreneurs expressing sympathy and approval for the Mafia, could be characterized by \(v_i<0\). See Lavezzi (2014, pp. 177–78) for a discussion and references on anti-Mafia values in a society.
In principle, anti-mafia values can also be unobservable. In the specification of Eq. (1) we keep them separated from the error term as they can be proxied by observable firms’ characteristics (see below).
Factors \(p_i\) and \(Z_i\) correspond to those introduced by Becker (1968, p. 177) in his characterization of the supply of offenses by a criminal.
The details on the dataset are provided in Sect. 4.
In this paper we analyze the observable decision to join or not to join AP, and can therefore only speculate on firms’ expectations on the consequences of such decision. In a companion paper, Battisti et al. (2017a), we estimate the causal effect of joining AP on firms’ economic performance by adopting a propensity score matching technique.
Partridge (2012, p. 16) considers the fraction of “critical consumers” that, as of 2010, registered in the Addiopizzo website, and finds a correlation with the share of AP firms by district of 0.8. However, the measure considered is biased by self-selection. The current version of the Addiopizzo website contains an updated list of consumers classified by zip code (and not by district). The lack of the date in which consumers registered, however, hides a crucial piece of information: i.e. whether they registered before or after firms in the district joined AP.
Pedrini and Ferri (2014), for example, show that more educated and older individuals are more likely to become “responsible consumers”.
We became aware of this possibility by personal communication from AP staff. A similar reasoning to the one based on profits or revenues can be done with respect to other measures of firms’ economic “health”, such as the loans/revenues ratio.
The sample analyzed by Balletta and Lavezzi (2016) includes 120 firms that paid pizzo between 1991 and 2006.
We thank an anonymous referee for suggesting us this possibility. Recent literature (e.g. Mete 2011) highlights that large firms may establish economic deals with criminal organizations taking advantage of their bargaining power, for example in the adjudication of public works. In this case large firms are not victims of extortion and, therefore, might have lower incentives in cutting the ties with the Mafia and join an NGO such as Addiopizzo.
There exists recent evidence of intimidation to AP firms, reported in the local news (see, e.g., Fiasconaro 2007, Ziniti 2015 and MNews (2015)). There is, however, also evidence contrary to this assumption, according to which mafiosi are unwilling to retaliate AP-firms, because this would attract attention by the police (see, e.g, the declaration of M. Pasta in the news section of http://www.addiopizzo.org/).
Firms’ age will proxy for the unobservable owners’ age. Unfortunately, we have information on the age of AP firms owners (i.e. of the person who signed the form to join AP) for a subset of 94 observations, while this data is not available for the control group. The correlation with firm’s age equals 0.33 when all observations are included, but increases to 0.58 when 8 outliers are removed. The average age of the AP-firm owners of this sample (in 2005) is 41. In addition, firm’s age is a measure of the length of time in which the firm operated in a Mafia-infested environment. It is reasonable to assume that the longer the period, the stronger the relationship it may have developed with the racketeers, the harder is breaking these relationships. Also for this reason, therefore, we expect a negative effect of firm’s age on the propensity to join AP.
Census data are collected by ISTAT, the Italian National Institute of Statistics. Originally, the Census dataset contained data on 3021 census cells, from which we computed values for 25 districts, after matching each cell to a district.
Overall, we are considering a sample of 633 firms that have been operative in the period 2002–2011. The average number of joint-stock firms operating in the same period is around 9400, including both “active” and “inactive” firms (i.e. firms that have not yet started their activity or failed to communicate to the CCIAA the beginning of activities). Our sample, therefore, covers approximately a range of 7–8% of the population.
Nominal data were converted into real terms by the consumption price index (CPI) of Palermo, from the Istat System of Territorial Indicators (SITIS). All data are expressed at constant 2000 prices.
This category includes people with no schooling, but able to read and write.
The population considered to compute these shares includes individuals aged more than 6 years.
The value of the loans/revenues is obtained after cancelling few extremes observations. This variable is very sensitive to this problem, that we will take into account in the econometric analysis.
The average number of employees of Italian firms in 2005 was 12.6 and 3.7 for, respectively, joint stock companies and partnerships (Istat 2007). From this perspective, therefore, it can be noted that the AP (control) firms have a higher-than-average (lower-than-average) figure. Since the current analysis focuses on joint-stock companies, it is possible that we are observing a sample of somewhat larger firms (the number of employees in the official statistics is the criterium utilized to measure firms’s size). However, in the population of joint-stock companies in Italy and in Sicily, there exists a relevant share of firms with a number of employees belonging to the class 0–9. In particular, in 2015 this share was respectively for Italy and for the province of Palermo, 83 and 86% (source: http://dati.istat.it/. These figures are not available for previous years).
Quantitative variables having different magnitude (Total assets, personnel costs, loans, revenues, gross and net profits, loans/revenues ratio) were preventively standardized in order to make the effects more easily comparable. To measure the number of employees, we did not use the absolute values of employees declared by the firms as it is often not precise due to the diffusion of illegal labour. In addition, this number is not part of the yearly balance sheets, but it is recorded in a given year and not regularly updated. Therefore, the variable “Number of employees” was transformed into categorical with three levels: “No employees”, indicating firms that utilize self-employed workers; “1–9 employees” and “more than 9 employees”. This classification reflects the one utilized in the Italian official statistics. Data from Movimprese (https://www.infocamere.it/movimprese) show that in 2015 the share of firms in Italy and in Palermo with less than 10 workers is around 85% of the total number of active firms, so our categories allow focusing on two bins that represent respectively 85 and 15% of population, instead of looking to several class of firms with insignificant shares. Finally, the classification of economic sectors was considered at the most aggregate level, by distinguishing firms into three levels: “Manufacturing/Energy”, “construction” and “services”.
The reported p value is based on the likelihood-ratio (LR) test but it should be noted that the null hypothesis for this test is on the boundary of the parameter space because it refers to a variance component. As a consequence, the LR test does not have the usual central chi-square distribution with one degree of freedom but it is better approximated as a 50:50 mixture of central chi-squares with zero and one degree of freedom (Snijders and Bosker 2011). In Table 2 we report the significance level of the district variance for all models.
In particular, in some cases the estimation did not reach convergence, in all the others the estimated coefficient of the second variable were not significant. For this reason we did not try specifications with three second-level variables.
We found that, when introduced individually, the effects of the share of large families and of the dependency ratio are not significant.
All the other labour market variables have non-significant effects.
Results are available upon request. The two measures of primary education are strongly correlated (see Table 8). The consideration of other education measures returns non-significant results.
The exploration of the negative effect of firms’ size on the probability to join AP is an interesting topic for further research. In particular, this would require data on local indices of firms’ concentration in Palermo (currently not available), and information on whether existing large firms not belonging to AP owe the increase in their size to the protection from competition guaranteed by the Mafia or, as suggested by Mete (2011), they formed partnerships with the Mafia exploiting the bargaining power given by their size.
The overall amount of personnel costs can proxy for the amount of human capital embodied in a firm since we control for the number of workers. A more suitable measure of firm’s human capital would be the average personnel cost. However, given the low reliability of data on the numbers of employees (see Footnote 32), we did not utilize such measure.
An alternative useful measure would be the number of owners, assuming that they could be considered as responsible for the choice of joining AP, but this number is not observable. The largest majority of firms with 0 employees, i.e. firms utilizing self-employed workers, use a very low number of workers. According to Istat (2007), in 2005 the percentages of such firms employing respectively 1, 2, or 3 workers was 84, 13 and 3%. Joining the categories of firms with 0 and 1–9 workers does not affect our results. Results are available upon request.
In a companion paper, Battisti et al. (2017b), we will explore the actual profitability of the choice by comparing indicators of economic performance, such as profits, before and after joining AP.
Second-level variables can also explain the location choice of the firms. An analysis of this issue goes beyond the scope of this paper and will be carried out in Battisti et al. (2017b).
Since the research design is based on a retrospective unmatched case-control study, sampling of firms is performed conditional on the outcome variable with the consequence that the probabilities of joining AP are determined by the sample design. Accordingly, the baseline probability in the population is different from the corresponding proportion in the sample and interpreting the effect of independent variables in terms of the effects on the probability of being a case versus being a control has no substantive meaning. In such situations, odds ratios may provide the best alternative for interpretation since their values are invariant under study design (Agresti 2002; Hosmer et al. 2013; Keogh and Cox 2014). Odds ratio are obtained by taking the exponent of the regression coefficient, OR = exp(\(\beta\)), and represents the factor of expected change in the odds of joining AP, holding all other variables constant. The relevant null hypothesis for odds ratios usually is \(H_0\): OR = 1, and this corresponds directly to the null hypothesis that the corresponding regression coefficient is zero, \(H_0\): \(\beta =0\).
We also checked for the the possibility of spatial spillover in the decision of joining AP. We computed the univariate Moran index, based on rook contiguity, and found a value of spatial correlation of 0.31 by considering the absolute numbers of joiners. This value, however, drops to − 0.04 for the number of joiners is weighted by the number of firms in the district. So the decision to join may be driven by some spatial spillovers but it is unclear how strong this pattern is. This aspect represents an interesting topic for future research.
We refer to the average number of limited-liability firms in a district in the period 2004–2012.
The districts with the highest numbers of AP-firms are, starting from the East, Partanna-Mondello (9), Resuttana-San Lorenzo (17), Politeama (43), Liberta’ (25), and Malaspina-Palagonia (11). The western district of Brancaccio appears in the map similar to the former districts, but this is due to the very low number of AP-firms (2), combined with a very low number of registered firms in the district.
We refer in particular to the contiguous disticts of: Cruillas CEP, Borgo Nuovo, Boccadifalco, Mezzomonreale, Villagrazia-Falsomiele, Oreto and the district of Arenella - Vergine Maria.
These averages are computed considering the groups of districts listed in Footnotes 48 and 49. The dependency ratios, i.e. the ratio of the share of population over 64 on the population under 14, are generally largely lower than 1 in the former and higher than 1 in the latter districts.
References
Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley.
Alexander, B. (1997). The rational racketeer: Pasta protection in depression era Chicago. The Journal of Law and Economics, 40, 175–202.
Asmundo, A., & Lisciandra, M. (2008). The cost of protection racket in sicily. Global Crime, 9, 221–240.
Balletta, L., & Lavezzi, A. M. (2016). Extortion, firm’s size and the sectoral allocation of capital (in press).
Battisti, M., Fioroni, T., Lavezzi, A. M., Masserini, L., & Pratesi, M. (2017a). The costs and benefits of resisting the extortion racket (in press).
Battisti, M., Kourtellos, A., Durlauf, S., & Lavezzi, A. M. (2017b). Social interactions and crime prevention (in press).
Becker, G. (1957). The economics of discrimination. Chicago: University of Chicago Press.
Becker, G. (1968). Crime and punishment: An economic approach. The Journal of Political Economy, 76, 169–217.
Becker, G., Murphy, K. M., & Tamura, R. (1990). Human capital, fertility and economic growth. Journal of Political Economy, 98, S12–S37.
Browne, W. J., & Draper, D. (2000). Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models. Computational Statistics, 15, 391–420.
Bueno de Mesquita, E., & Hafer, C. (2007). Public protection or private extortion? Economics and Politics, 20, 1–32.
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge: Cambridge University Press.
Carlin, B. P., & Louis, T. A. (2000a). Bayes and empirical bayes methods for data analysis (2nd ed.). Boca Raton: Chapman and Hall-CRC.
Carlin, B. P., & Louis, T. A. (2000b). Empirical bayes: Past, present and future. Journal of America Statistical Association, 95, 1286–1289.
Clayton, D. G. (1996). Generalized linear mixed models. In W. R. Gilks, S. Richardson, & D. J. Spiegelhalter (Eds.), Markov chain Monte Carlo in practice. London: Chapman and Hall.
Confesercenti. (2010). XI Rapporto di Sos Impresa Le mani della criminalita’ sulle imprese. Confesercenti.
Congdon, P. (2006). Bayesian models for categorical data. New York: Wiley.
Demidenko, E. (2004). Mixed models. Theory and applications. Hoboken, NJ: Wiley.
Dixit, A. (2016). Anti-corruption institutions: Some history and theory, mimeo. In International economic association institutions, governance and corruption, conference montevideo (URY).
Draper, D. (2008). Bayesian multilevel analysis and MCMC. In J. de Leeuw (Ed.), Handbook of quantitative multilevel analysis. New York: Springer.
Efron, B., & Morris, C. (1973). Stein’s estimation rule and its competitors—An empirical Bayes approach. Journal of the American Statistical Association, 68, 117–130.
Efron, B., & Morris, C. (1975). Data analysis using Stein’s estimator and its generalizations. Journal of the American Statistical Association, 70, 311–319.
Fiasconaro, A. (2007). La citta’ avvolta dalla nube, La Sicilia. Retrieved from http://www.addiopizzo.org/.
Fioroni, T., Lavezzi, A. M., & Trovato, G. (2017). Organized crime, corruption and poverty traps (in press).
Forno, F., & Gunnarson, C. (2010). Everyday shopping to fight the Mafia in Italy. In M. Micheletti & A. McFarland (Eds.), Creative participation: Responsibility-taking in the political world. London: Paradigm Publisher.
Gambetta, D. (1993). The sicilian Mafia: The business of private protection. Cambridge: Harvard University Press.
Gambetta, D. (2009). Codes of the underworld. Princeton: Princeton University Press.
Gambetta, D., & Reuter, P. (1995). Conspiracy among the many: The Mafia in legitimate industries. In G. Fiorentini & E. S. Peltzman (Eds.), The economics of organised crime. Cambridge: Cambridge University Press.
Gelfand, A., & Smith, A. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398–409.
Goldstein, H. (2011). Multilevel statistical models. New York: Wiley.
Gunnarson, C. (2014). Changing the game: Addiopizzo’s mobilisation against racketeering in Palermo. European Review of Organised Crime, 1(1), 39–77.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). New York: Wiley.
Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York: Routledge.
Keogh, R., & Cox, R. J. (2014). Case-control studies. Cambridge: Cambridge University Press.
Konrad, K. A., & Skaperdas, S. (1998). Extortion. Economica, 65, 461–477.
La Spina, A. (2008). Recent anti-Mafia strategies: The Italian experience. In D. Siegel & H. Nelen (Eds.), Organized crime: Culture, markets and policies. New York: Springer.
Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.
Lavezzi, A. M. (2008). Economic structure and vulnerability to organised crime: Evidence from Sicily. Global Crime, 9, 198–220.
Lavezzi, A. M. (2014). Organised crime and the economy: A framework for policy prescriptions. Global Crime, 15(1–2), 164–190.
Maritz, J. S., & Lwin, T. (1989). Empirical bayes methods. London: Chapman and Hall.
Meridionews (Redazione) (2015). Nuova intimidazione alla pasticceria Marsicano. ’Il rione mi boicotta e ora mi hanno levato le telecamere’. Meridionews. Retrieved from http://meridionews.it/.
Mete, V. (2011). I lavori di ammodernamento dell’autostrada Salerno-Reggio Calabria. Il ruolo delle grandi imprese nazionali. In R. Sciarrone (Ed.), Alleanze nell’ombra. Rome: Donzelli.
Morris, C. (1983). Parametric empirical bayes inference, theory and applications. Journal of the American Statistical Association, 78, 47–65.
Paoli, L. (2003). Mafia brotherhoods. Organized crime, Italian style. Oxford: Oxford University Press.
Partridge, H. (2012). The determinants of and barriers to critical consumption: A study of Addiopizzo. Modern Italy, 17(3), 343–363.
Pedrini, M., & Ferri, L. M. (2014). Socio-demographical antecedents of responsible consumerism propensity. International Journal of Consumer Studies, 38(2), 127–138.
Pinheiro, I. C., & Bates, D. M. (1995). Approximations to the log-likelihood function in nonlinear mixed-effects models. Journal of Computational and Graphical Statistics, 4, 12–35.
Pinotti, P. (2015). The economic costs of organized crime: Evidence from southern Italy. Economic Journal, 125, F203–F232.
Rabe-Hesketh, S., & Skrondal, A. (2006). Multilevel modelling of complex survey data. Journal of the Royal Statistical Society, Series A, 169, 805–827.
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modelling. Psychometrika, 69, 167–190.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models (2nd ed.). Thousand Oaks: Sage Publications.
Schneider, J. C., & Schneider, P. T. (2003). Reversible destiny. Mafia, antimafia and the struggle for Palermo. Oakland, CA: University of California Press.
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.
Skrondal, A., & Rabe-Hesketh, S. (2009). Prediction in multilevel generalised linear models. Journal of the Royal Statistical Society, Series A, 172(3), 659–687.
Snijders, T. A., & Bosker, R. (2011). Multilevel analysis. An introduction to basic and advanced multilevel modelling (2nd ed.). Thousand Oaks, CA: Sage.
Struttura e dimensione delle imprese Archivio Statistico delle Imprese Attive (ASIA). Anno 2005, Rome.
Tuerlinckx, F., Rijmen, F., Verbeke, G., & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: A review. British Journal of Mathematical and Statistical Psychology, 59, 225–255.
Vaccaro, A., & Palazzo, G. (2015). Values against violence: Institutional change in societies dominated by organized crime. Academy of Management Journal, 58(4), 1075–1101.
Vaccaro, A. (2012). To pay or not to pay? Dynamic transparency and the fight against the mafia’s extortionists. Journal of Business Ethics, 106(1), 23–35.
Van Dijk, J. (2007). Mafia markers: Assessing organized crime and its impact upon societies. Trends in Organized Crime, 10, 39–56.
Varese, F. (2014). Protection and extortion. In L. Paoli (Ed.), Oxford handbook of organized crime (pp. 343–58). Oxford: Oxford University Press.
Varese, F. (2009). The Camorra closely observed. Global Crime, 10, 262–266.
Vroom, V. H., & Pahl, B. (1971). Relationship between age and risk taking among managers. Journal of Applied Psychology, 55(5), 399.
Ziniti, A. (2015). Fuga dalla Vucciria. Chiude il ristorante Santandrea. La Repubblica. Retrieved from http://palermo.repubblica.it/.
Acknowledgements
We are grateful to Addiopizzo for providing data and for support to this research. We wish to thank in particular Caterina Alfano, Daniele Marannano, Giuseppe Pecoraro and Pico di Trapani. We also thank the Chamber of Commerce of Palermo, the Istat office of Palermo (in particular Dott. Li Vecchi), the Ufficio Toponomastica of Palermo City Council (in particular Dott. Salamone); Matteo Zucca and Marcella Milillo for help with the data; Luigi Balletta, seminar participants in Livorno (Structural Change, Dynamics and Economic Growth), Pisa, Petralia (VII Applied Economics Workshop), and Naples (Old and New Forms of Organised and Serious Crime.) and four anonymous referees for comments. Gabriele Mellia, Alice Rizzuti and Giorgio Tortorici provided excellent research assistance. Financial support from MIUR (PRIN 2009, “Structural Change and Growth”), and University of Palermo (FFR 2012), is gratefully acknowledged. Usual caveat applies.
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Appendices
Appendix A: Firm-level variables
Table 3 contains the values of the correlation coefficients among the firm-level variables (standardized values) used in the econometric analysis of Sect. 5.2 (p values of tests for significance in parenthesis).
Appendix B: On Palermo districts’ characteristics
In this appendix we discuss the distribution of the second-level variables in the Palermo districts. Figure 1 and Table 4 show the map of the districts.
Palermo districts: map
Figure 2 highlights the distribution of AP-firms across the districts. To take into account the spread of economic activity in the districts, we report the shares of AP-firms over the number of limited-liability firms in each district.Footnote 47 Figure 2 shows that the spatial distribution of AP-firms is not homogeneous. The districts with the highest shares of AP firms are located in the central-eastern part of the city,Footnote 48 but there is a vast area including many peripheral districts in which no AP-firms are present.Footnote 49
Shares of AP firms in the 25 Palermo districts
Figures 3, 4 and 5 refer to measures of demographic structure, human capital and labour market conditions in the districts, while Tables 5, 6 and 7 present the descriptive statistics of all the second-level variables considered in this paper, while Table 8 shows their correlations. Figure 3, in particular, displays the population shares across the districts, that could represent a proxy for the potential market for a firm. Here no evidence of a clear spatial pattern appears: populous districts are present in both the central and peripheral areas of Palermo.
Shares of population in the 25 Palermo districts
Interestingly, a strongly divergent pattern is found when we observe human capital. Figure 4 shows that the spatial pattern of human capital, measured by the share of population with tertiary education, is similar to the one characterizing the presence of AP firms. This share can vary from approximately 20% in the some of the latter districts (e.g., Resuttana-San Lorenzo), to 2% in districts where no AP-firms are present (e.g. Arenella-Vergine Maria). In the latter districts a large majority of citizens has primary education only, approximately 60%, while in districts with higher presence of AP-firms this percentage drops to approximately 30% (similar proportions exist for elementary education levels).Footnote 50 These measures of human capital are strongly correlated with demographic indicators such as the shares of small and large families: high (low) human capital is correlated with low (high) family size, in line with the predictions of the child quality/quantity trade-off of the model of Becker et al. (1990).
Shares of population with tertiary education in the 25 Palermo districts
Unemployment rates in the 25 Palermo districts
A similar pattern is found in Fig. 5. Unemployment rates are, respectively in the “high-AP” and “low-AP” districts, around 10 and 17%, a pattern reflected by the employment rates, which amount to, approximately, 85 and 70%.
Tables 5, 6 and 7 report the statistics on the demographic characteristics of the districts, of the levels of human capital and on labour market indicators, while Tables 8 and 9 contain the correlations among these variables (p values of tests for significance in parenthesis).
Appendix C: On the stability of the coefficients
Table 10 presents the estimation of models aiming at proving the stability of the coefficients of the models of Table 2. In particular, we consider a model with only the two most significant firm-level variables from balance sheets (Model 1), then we add the two significant second-level variables (Model 2); firm’s age (Model 4), the balance-sheet variables that proved non-significant (Model 4) and finally the dummy variables (Model 5), corresponding to Model 5 of Table 2.
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Battisti, M., Lavezzi, A.M., Masserini, L. et al. Resisting the extortion racket: an empirical analysis. Eur J Law Econ 46, 1–37 (2018). https://doi.org/10.1007/s10657-018-9589-4
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DOI: https://doi.org/10.1007/s10657-018-9589-4