Abstract
In this chapter we contribute to the recent literature that provides evidence that inequality is high and social mobility is low in the Italian regions and provinces where organized crime is widespread such as those of Southern Italy. We provide a theoretical justification for these pieces of evidence and, by using a novel panel dataset at the regional level for the period 1985–2014, we investigate the relationship between inequality and organized crime at the regional level, and assess the role of social mobility in organized crime. Our main finding is that higher inequality leads to higher organized crime development. The results are robust for different organized crime measures and inequality indices. For all measures the ratio of 90th quintile over the 10th in the distribution is a strong predictor of organized crime, even if we control for other covariates that capture economic development, education etc. We also find that consumption inequality performs better that income inequality as the relevant inequality measure. Finally, we conduct a provincial level analysis to study the effect of socio-economic mobility on organized crime, using three alternative measures of social mobility. We find that lower socio-economic mobility display a robust association with organized crime development.
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Notes
- 1.
- 2.
See also Gambetta (1993) for an analysis of the Sicilian Mafia as a provider of private protection and Lavezzi (2014) for a discussion of the concept of “demand for Mafia”.
- 3.
Work is in progress to analyze this issue in the city of Palermo.
- 4.
Other works investigate the effect of other types of crime on social mobility. For example, Savolainen et al. (2014), using birth cohort data from Finland, examine the relationship between intergenerational educational mobility and criminal activity noting that rates of criminal offending is strongly related to family background. Differently, Sharkey and Torrats-Espinosa (2017), using longitudinal data on US, find evidence that a decline on violent crime in a county increases the upward economic mobility in adults who had experienced this drop during the late adolescence. Moreover, they show that a decline in the violent crime rate reduces the high school dropouts at the county level.
- 5.
We are grateful to Magg. Domenico Martinelli and to Claudia Di Persio for invaluable help, and to the Department on “Analisi Criminale della Direzione Centrale della Polizia Criminale” at the Italian Ministry of Interiors for releasing the data.
- 6.
In our empirical analysis crime numbers are normalized by population.
- 7.
Crime data in Calderoni (2011) are integrated by data on other direct measures of Mafia activities: the number of city councils dissolved for Mafia infiltration, and a measure of assets confiscated to Mafia clans. See the same article for details on other methods to measure organized crime found in the literature.
- 8.
The errors were not allowed to be serially correlated, but this assumption can be relaxed later.
- 9.
The factor loadings (estimated as stationary, non time-varying, values) can be interpreted as weights telling how much the factor intensity depends (loads) on each crime over the period of interest.
- 10.
For definitions and details on the Gini and Atkinson index see e.g. Cowell (2011).
- 11.
We implement this estimation using the Stata package xtabond2 by Roodman (2009).
- 12.
Regressions are run on data from 19 out of 20 Italian regions. The region of Val d’Aosta is dropped for lack of data.
- 13.
In particular, results for income inequality return a significant coefficient only with the Calderoni Mean Index for the lagged values of the Atkinson index (at 5%) and of P90/P10 (at 1%).
- 14.
In particular, we chose P90/P10 as our preferred (consumption) inequality measure for the following reasons. P75/P50 in Tables 7 and 8 is significant 4 times out of 4, while P90/P10 is significant 3 times out of four. However, in Table 9, P75/P50 is scarcely significant. Moreover, in Table 9 we see that P90/P10 is significant in 2 cases out of 4 (considering both the coefficients for contemporaneous and lagged inequality indices), as other inequality measures, but it is the only one with a highly significant coefficient in at least one case. This result is likely to depend on the fact that the relationship between the extreme quintiles is more representative of the dynamics of inequality over time than measures based on quintiles that mostly capture the dynamics of the share of income of the middle class (see e.g. Garbinti et al. 2018)
- 15.
We only report results for consumption inequality. Regressions on income inequality returned positive and significant coefficients for the lagged value of P90/P10 in Models 1, 2, 4, 5 with the Calderoni Rank Index.
- 16.
We only report results for consumption inequality, as regressions on income inequality measures did not return significant results.
- 17.
We also estimated regressions using the Hierarchical dynamic factors, but the results were not significant. This is likely due to the fact that we used only five variables to estimate them.
- 18.
The set of covariates used for the regional and provincial analysis do not perfectly match because of data availability. Model 7 in Tables 15, 16 and 17 contains a dummy for the Northern regions as the latter proved to be highly significant in regressions on the mobility indices and macro-region dummies.
- 19.
The results for the other mobility indices are not significant.
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Battisti, M., Bernardo, G., Konstantinidi, A., Kourtellos, A., Lavezzi, A.M. (2020). Socio-Economic Inequalities and Organized Crime: An Empirical Analysis. In: Weisburd, D., Savona, E.U., Hasisi, B., Calderoni, F. (eds) Understanding Recruitment to Organized Crime and Terrorism. Springer, Cham. https://doi.org/10.1007/978-3-030-36639-1_9
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