Next Article in Journal
Knowledge of Infection Prevention and Control and Practice Behaviors among Career and Volunteer Firefighters in Rural Communities
Previous Article in Journal / Special Issue
Training to Achieve Sustainable Employment for Youth and Young Adults
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Merits
Volume 4
Issue 2
10.3390/merits4020010
merits-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exploring the Influence of Crime on NEET Rates: A Regional Analysis of Italy

by
Iacopo Odoardi
*,
Dario D’Ingiullo
,
Ada Di Nucci
and
Davide Quaglione
DISFIPEQ-Economic and Quantitative Methods Section, University of Chieti-Pescara, 65127 Pescara, Italy
*
Author to whom correspondence should be addressed.
Merits 2024, 4(2), 132-145; https://doi.org/10.3390/merits4020010
Submission received: 5 March 2024 / Revised: 2 April 2024 / Accepted: 8 April 2024 / Published: 10 April 2024
(This article belongs to the Special Issue Young People and the Labor Market: Challenges and Opportunities)
Versions Notes

Abstract

:
The occurrence of criminal activities has the potential to hinder socioeconomic advancement, preventing individuals from investing in human capital and pursuing employment opportunities. Our investigation focuses on the hypothesis that the NEET (not in education, employment, or training) rate is related to crime levels. Through an econometric analysis based on regional data, we examine the impact of crimes against property and against persons on NEET rates within central-northern and southern Italy, while controlling for prevalent determinants of the NEET phenomenon. Our findings reveal that, compared with prevailing discouragement factors such as youth unemployment and lack of interest in tertiary education, crime exerts a more pronounced influence on elevating NEET rates. This effect is particularly evident in the relatively less developed southern regions, where violent crimes, although relatively uncommon, may disproportionately contribute to feelings of apprehension and uncertainty regarding future prospects.

1. Introduction

The economic and social costs of crime are extensively documented and scrutinized within the scholarly literature [1], with numerous criminal activities noted to yield significant adverse impacts on the economy (e.g., [2]). Specifically, crime has the potential to influence entrepreneurial and investment decisions [3], thereby undermining future prospects for societies most at risk [4]. Moreover, the media often accentuates the repercussions of crime, thereby exacerbating overall distress and diminishing quality of life for both victims and society as a whole, owing to heightened community apprehension [5,6].
In this study, we examine the macroeconomic impact of various forms of criminal activity on not in education, employment, or training (NEET) rates across Italian regions during the period of 2005–2019. Our investigation is motivated by the recognition that crime influences economic performance, thereby affecting employment levels [7]. At the same time, crime can alter the job preferences and opportunities of young individuals and notably hinder the accumulation of human capital [8]. Essentially, the presence of diverse criminal activities and the associated apprehension they instil tend to heighten the risk of unemployment while diminishing incentives for educational investment. These adverse effects of criminal activities are intricately linked to the primary determinants of the NEET phenomenon.
Italy constitutes a notable case study for two reasons. First, this nation faces significant challenges in the cultivation of human capital [9], resulting in a level of educational advancement—as somewhat mirrored in the associated NEET status—that is disheartening compared to other developed economies [10]. Second, numerous regions within Italy grapple with elevated economic and social ramifications (e.g., diminished investment, heightened unemployment) attributable to the prevalence of assorted criminal activities [11].
Our investigation adds to the substantial body of literature concerning the impact of socioeconomic context on the conditions of “fragility”, precariousness, and insecurity among younger cohorts, often leading to their disengagement from active participation in society, culminating in their NEET status [12]. Our study contributes to exploring the influence of criminal activity on human capital or its associated indicators. Our results furnish macroeconomic insights that can inform future inquiries focusing on the significance of individual attributes among at-risk youth, thereby fostering a deeper understanding of the issue [13].
Indeed, the impact of various forms of illegal activities on the NEET phenomenon remains poorly explored. Specifically, aside from the study conducted by Karyda [14], there is a lack of additional insights concerning the correlation between residing in socioeconomically disadvantaged areas characterized by elevated crime rates and the propensity for young individuals to transition to NEET status. Our research also builds on the well-known repercussions of crime on education and employment, such as the need for specific interventions to help individuals involved return to study, training, and work after criminal experiences or imprisonment [15,16].
Furthermore, the well-known Italian socioeconomic dualism between the “wealthy” Center-North and the relatively “poor” South (e.g., [17]) suggests segmentation into two macro-areas for comparative analysis, each of which is expected to exhibit distinct responses based on local characteristics. The hypothesized the mechanism linking heightened criminal activity to the NEET phenomenon suggests a greater prevalence of evidence within the southern area. Within this geographical area, NEET rates have soared above 30% in certain regions in recent years, as indicated by data from the Italian National Institute of Statistics (ISTAT). Moreover, the entrenched presence of organized criminal activities historically rooted in the South, alongside the deliberate propagation of illicit practices, is widely acknowledged, as are their profound repercussions on local communities (e.g., [18,19]). For example, Coniglio et al. [20] observed a significant adverse impact stemming from the existence of organized crime on the acquisition of human capital in this area. However, this observation does not suggest exclusivity to the southern regions in terms of this supposed relationship. Indeed, it is recognized that the incidence of certain crimes (such as theft as opposed to homicide) correlates with the degree of economic development [21]. For example, according to ISTAT data, in 2019, 11.8 families per 1000 experienced home burglaries in the Center-North, compared to 6.9 in the South. Consequently, we utilize two proxies for criminal activity: crime against property and crime against persons.
With the aim of addressing the research question of how crime can influence the NEET rate in Italian regions, we conduct a panel analysis spanning from 2005 to 2019 that tests the role of the two types of crime (both being proxies for crime; they are tested separately) and the main causes of the NEET phenomenon recognized in regional studies. Additionally, in light of the well-known “North–South” dualism in Italy, we present the results with a focus on two distinct regional areas: the central-northern and southern regions. This analysis considers the potential for reverse causality between the NEET rate and crime proxies through the implementation of a two-stage least squares (2SLS) estimation method.
The structure of this article is as follows: Section 1.1 and Section 1.2 delineate some of the causes of the NEET phenomenon and provide a brief overview of the Italian context; Section 2 details our methodology and data sources; Section 3 presents our findings concerning Central-North and South Italy; and finally, Section 4 and Section 5 offer discussion and concluding remarks, respectively.

1.1. Some Causes of the NEET Phenomenon

Numerous studies have contributed to the identification of the risk factors that influence individuals’ risk of becoming NEET. Some analyses have focused on individual characteristics, such as low skill levels [22,23], low self-esteem [24], cognitive and socioemotional competencies, and aspirations and expectations [25,26]. Conversely, other studies have explored factors related to schooling and education, such as school dropout rates [27,28], learning difficulties [29], educational and training levels [22,28], and school grades [30]. In addition to these aspects, there is a complexity of life after studies, namely, the school-to-work transition and distrust and fear in contexts with high unemployment [31].
Further research has examined the relationships between personal health factors, such as anxiety, bipolar disorder, social phobia, psychosis, and eating disorders, and the increased likelihood of unemployment and consequently increased NEET status [24,32]. Additionally, factors associated with various addictions, including smoking, alcohol, food, and drugs, have been identified as contributors to adverse outcomes, thus potentially facilitating the transition of young individuals to an NEET status [32,33,34,35].
Furthermore, evidence is found in the literature about the role of family background, whose socioeconomic status affects study and work opportunities, both within the original family and potentially within the one formed by young NEETs, with the responsibilities and constraints that this entails [36,37].
On the other hand, there is limited research examining the relationship between youth and the impact of their living environment. Indeed, a disorganized district with a high crime rate can emerge as a risk factor predisposing young individuals to become NEETs. Involvement in criminal activities can result in significant social stigmatization, affecting employment and training opportunities, thereby causing young people to disengage from such activities and become NEETs [14,38]. Similarly, the association between youth, criminal behavior, and legal charges is concerning, as it may steer young individuals towards NEET status. Legal charges and convictions may impede young people’s access to the job market and discourage them from actively seeking employment, increasing their risk of becoming NEETs [38].

1.2. The NEET Phenomenon in Italy

The scientific literature on the NEET phenomenon in Italy, much like the international one, is extensive and encompasses studies from various disciplines, including sociology, economics, education, and the labor market [39]. According to data provided by Eurofound (as of [40]), in the post-pandemic era, the working conditions, living conditions, and psychological well-being of Italian youth have all deteriorated.
Some studies offer a broader perspective on the NEET situation, which is heavily influenced by the regional context [41]. In northern regions of Italy, the NEET status among young people is less frequent than that in southern regions, where job searches tend to be prolonged, depleting both social and human capital. Consequently, NEET status creates a sort of ‘scar effect’, justifying the prolonged absence from employment [42]. Continued NEET status tends to push young people into a downwards spiral, leading to reduced civic participation [43] and instilling a sense of disillusionment with the world around them [44]. Hence, a socioeconomic context marked by poverty and limited opportunities influences the likelihood of young people becoming NEETs [45].
Young individuals from disadvantaged backgrounds can be particularly susceptible, especially in the absence of effective policy support to education and employment. Public policies, such as rehabilitation programs, social reintegration, and vocational training opportunities, have the potential to shape the trajectories of young people by decreasing their likelihood of becoming NEETs [46].
The association between engagement in criminal activity and the likelihood of entering the NEET status is intricate. Therefore, our study explores uncharted territory, specifically examining the impact of crime on proxies of human capital. We believe that participation in criminal activities may perpetuate a cycle of criminal behavior, heightening the risk of individuals becoming NEETs.

2. Materials and Methods

2.1. Method

To empirically analyze the determinants of the NEET phenomenon and on the basis of the background presented in Section 1, we assume that the regional NEET rate can be modeled as follows:
NEETi,t = α0 + β1CRIMEi,t + ∑ γn Xni,t + τt + μi + εi,t,
where NEETi,t is the dependent variable and captures the NEET rate, while CRIMEi,t indicates the two dimensions of crime (i.e., the rate of homicides and the rate of home burglaries). We consider data for the 21 Italian regions and autonomous provinces (i) for the period 2005–2019 (t). The regression also includes a set of control variables (Xni,t), while τt, μi, and εi,t represent the time dummies, the time-invariant characteristics, and the idiosyncratic error term, respectively. We analyze both Italy as a whole (all regions) and the two macro-areas separately: the central-northern area (composed of 11 regions and 2 autonomous provinces) and the southern area (composed of 8 regions).
We are aware of a possible reverse causality problem between an education-related variable such as our dependent variable (NEET rate) and the proxies of crime. In fact, education notably contributes to influencing numerous criminal activities [47]. For instance, Groot and van den Brink [48] found that an increase in years of schooling tends to decrease many types of crime (e.g., shoplifting and threat) and increase others (tax fraud), while Lochner [49] observes that investment and time spent in education affect the opportunities and returns from criminal activities, shifting one’s lifestyle toward education and work and away from crime.
Thus, we take into account this endogeneity issue by adopting an instrumental variable estimation approach based on a two-stage least squares procedure. The purpose is to obtain exogenous variation in criminality, and the economic literature provides several solutions for these exclusion restrictions. In particular, our instrumental variables represent two commonly acknowledged drivers of criminal activity. The selection of these variables is not an easy task and is strongly conditioned by the availability of macro data at the local level. Specifically, we focus on two indicators that reflect the level of satisfaction with aspects of daily life, namely, the percentage of individuals reporting complete dissatisfaction with family relationships and with the economic situation (sourced from the ISTAT dataset: aspects of daily life). Satisfaction with living conditions is an element that has been proven to have a close relationship with criminal activities [50]. In this sense, lower social controls induce subjects to expeditiously satisfy their own needs through delinquency, since low commitments and attachment increase frustration and anger [51], and for example, high life satisfaction has proven to reduce acts of violence [52].
To verify the relevance and orthogonality of the external instruments, at the bottom of each table, we report the underidentification test, which helps evaluate if the correlation between the excluded instruments and endogenous variables is high enough, and Hansen’s [53] J-test is used for overidentifying restrictions when the standard errors are robust to heteroskedasticity (orthogonality). The results of both tests confirm the validity of the selected instrumental variables.

2.2. Data

Our dependent variable is the percentage of individuals aged 15–24 classified as NEETs within the respective population. This measure is commonly utilized for examining Italian NEETs (e.g., [54]) and is employed in regional (NUTS-2 level) analyses (e.g., [55]). To our knowledge, the NEET rate cannot be divided into the categories of inactive and unemployed NEETs due to the unavailability of proper data at the local level for the period considered.
In our dataset, we incorporate two proxies of criminality utilized in regional analysis [56] as regressors. These proxies refer to the rates of crimes against property and against individuals. The first variable denotes the rate of home burglaries (referred to as THEFTS in Table 1), a type of crime more prevalent on average in wealthier areas, predominantly in the Center-North. The second variable represents the homicide rate (labeled VIOLENT), which is higher in the South (refer to Table 2). Our selection of these two proxies aligns with the metrics examined by Carboni and Detotto [57] in their estimation of the economic cost of crime in Italy, given that robbery and murder are considered the most reliable indicators within crime data.
We included a set of control variables that can influence the NEET phenomenon.
We examine two primary factors contributing to discouragement among young people. One is the DROPOUT rate from education and training paths, as youths who do not receive adequate training or education face economic and social disadvantages compared to those who pursue studies [46]. We also consider the youth unemployment rate (YUR) because the discouraging impact of high unemployment rates among young people can be significant in contexts such as Italy, where there are evident challenges in providing employment opportunities for young people [58].
Another independent variable is the proxy for local social capital (SC), which is measured by the number of individuals engaged in volunteering activities. Both the quantity of individuals participating in volunteer work and the various motivations driving youth involvement in charitable endeavors contribute to the understanding of the social dynamics among young people, influencing their future trajectories [59]. We also incorporated the POVERTY rate, as it is one of the most significant risk factors for youth transitioning to NEET status [60]. Furthermore, we consider a proxy for human capital (HC), denoted by the average educational attainment level of the population and its impact on younger generations’ educational paths, as well as the significance each region attributes to human capital resources. Similarly, per capita gross domestic product (GDP) can serve as a proxy for income, providing insight into the average welfare level, which is a recognized determinant of youth NEET status [61].
The descriptions and sources of the variables used in the econometric model are presented in Table 1.
In Table 2, we present the summary statistics for Italy and the two macro-areas separately, with the aim of illustrating the socioeconomic differences characterizing the Italian “North–South problem”.

3. Results

The results for Italy and for the Center-North and South areas are presented in Table 3, Table 4, and Table 5, respectively.
In Table 3, we observe that the two proxies of criminal activity significantly influence the NEET rate across all regions. The effect of VIOLENT has a coefficient approximately eight times greater than that of THEFTS, suggesting that the former is the primary indicator of the impact of widespread crime on society. This effect may be mediated by varying perceptions of crime against individuals and property. For instance, Cornaglia et al. ([62] on the effects of crime on mental well-being) described the impact of violent crime on both victims and nonvictims, as well as on social functioning. This would not be the case for property crime.
Both models for Italy indicate that the two phenomena influencing the study and employment prospects of young people—namely, school DROPOUT and YUR—reveal the fragility of the juvenile condition. Surprisingly, the average HC concentration in the adult population also directly impacts the NEET rate. While school DROPOUT and YUR are evident structural weaknesses in Italy, the role of HC could be observed in fostering discouragement, a typical feature of the NEET issue. This implies that young individuals might perceive lower returns from education, as evidenced by those who have invested in advanced education, or even experience widespread frustration with career expectations despite securing better job positions. This phenomenon is well documented in the Italian context [63] and may indicate that advanced education does not necessarily lead to significant wage differentiation in the country [64]. In some cases, individuals with upper secondary education may encounter greater employment opportunities than those with higher qualifications [65].
With the aim of detailing the mechanism hypothesized, we present the results for the central-northern regions in Table 4.
Neither of the crime proxy variables exerts any influence on the NEET rate in the most developed macro-area of the country. The statistically significant control variables remain consistent with those observed in the Italian context (Table 3). Notably, we observe higher coefficients for the YUR (compared to those observed in Italy), likely attributable to the pronounced growth of this indicator following the 2007–2008 crisis in the area, contrasting with trends in the rest of the country. This reaffirms how inadequate employment protections for young people have exacerbated their circumstances during periods of economic downturn [66].
In Table 5, we present the results for southern regions.
First, we observe tangible evidence of the North–South divide in Italy. The southern macro-area is renowned for hosting many mafia organizations that have profoundly influenced the past and present state of local development [11], rendering it particularly vulnerable to the ramifications of general crime-related fear [67]. The exacerbating effect on NEET arises from VIOLENT. Historically, the prevalence of homicides in southern regions has surpassed that in the northern regions due to the proliferation of criminal syndicates and a more permissive cultural attitude towards violence [68]. Homicides are more prevalent in the South than are suicides, for instance, and are motivated differently, exhibiting an inverse correlation with civic-mindedness [69], thereby supporting our hypothesis of deeper societal connections.
Moreover, the control variables highlight the complex relationship that the South has with its human capital. Despite high unemployment being a persistent issue in the region, the true challenge for young people stems from human capital. This challenge may be attributed to the relatively low-tech economic specialization of the macro-area, which has historically lagged behind the rest of the country and faces local obstacles and conflicts [17]. This economic context fails to generate sufficient demand for skilled workers, leading to employment uncertainties for young people (e.g., [70]). The coefficients related to the percentage of young people leaving school after compulsory education provide insight into the South’s significant educational challenges, showing percentages of people aged 18–24 years leaving education and training constantly above 20% in the first decade of the twenty-first century and peaking above 25%, compared to the EU average of 14–17% in the same period (in the Eurostat data). This issue aligns with the findings of other control variables, as it is connected with economic and cultural factors [71].
Furthermore, Model (5) illustrates the significance of GDP per capita: an enhancement in economic conditions would entail, for instance, a greater average household income, which is a recognized factor in reducing the risk of NEET, as it diminishes the prevalence of disadvantaged families [72].
Finally, the proxy of social capital shows no statistical significance in any of the analyses, suggesting that the expected support beyond the immediate family network is absent. A comparable finding was also noted regarding the impact of social capital on the risk of dropout in Italy by Odoardi et al. [73].

4. Discussion

Our regional analysis revealed the adverse impact of crime on young Italians, at a time when a majority of them face the risk of becoming NEET, regardless of sociodemographic background, with a significant probability of remaining in that state [74]. Our findings indicate that both violent crimes and property crimes, each with distinct social significance and consequences, contribute to exacerbating the incidence of NEET among the population aged 15–24 years. However, when examining regions based on their geographical macro-areas (Center-North and South), our results suggest that crime has a more pronounced impact in the less resilient area, the South. This highlights further disparities and new dimensions in the unequal educational opportunities for young Italians, particularly disadvantaging those in the southern regions. Moreover, the two indicators of crime exhibit an even greater effect on regional NEET rates compared to persistent issues, almost evolving into potential social habits, such as youth unemployment or the tendency to leave school immediately after completing compulsory education.
Moreover, the fact that the least affluent area of the country is also the only one where crimes against property, which are more common in wealthier regions, have statistical significance suggests a deeper underlying issue. Another concerning aspect is the substantially higher coefficient indicating the impact of homicides, despite being less frequent. Therefore, the analysis of macrolevel phenomena suggests that the fear stemming from crime awareness could affect NEETs, particularly in socioeconomic contexts that are more fragile due to limited economic support from families, inadequate investments in training infrastructure, and less efficient local labor markets.
The deterrence effect on young individuals may arise from exposure to both crime and crime-related news, heightening the fear of becoming a victim of violence [75]. This could elucidate the disparities in the results across regions, despite nine out of ten provinces with the highest crime rates (complaints per capita) being located in the Center-North. The impact of widespread fear could affect NEETs due to their tendency to hold more pessimistic views regarding the future or significant social milestones (such as seeking their first job) compared to those engaged in studying or employment [76].
In addition to these presumed “collateral effects”, crime can directly impact the NEET rate. While the literature on the effects of criminal activities on proxies of human capital is scarce (and as far as we know, almost absent for NEETs), the evidence gathered suggests economic deterioration, particularly for those who are more exposed to high local crime rates. This could lead to premature discontinuation of studies [8]. Similarly, actively engaging in criminal activities leads to long-term consequences (e.g., after years of incarceration), such as a lower likelihood of resuming studies, as well as a higher risk of behavioral disorders [15]. Specifically, for NEETs, it appears that living in high-risk neighborhoods disengages them from mainstream societal roles [77].
Conversely, the effects of attaining higher education on reducing many types of crime (e.g., shoplifting and assault) are widely observed, although there is the possibility of an increase in education-related crimes (e.g., tax fraud) [48]. Generally, the inverse relationship between higher education and crime rates is particularly evident for young people [78], which the NEET population is part of. The mutual relationship between crime and NEET can largely derive from personal human capital, as education itself appears to be a determinant of NEET status because it affects the potential aspect of job searches (e.g., [79]).
More specifically, we must consider that not all criminal activities play the same role in the aforementioned relationships. For example, exposure to violence and antisocial behaviors are relevant for young people at risk of becoming NEETs [77].
Finally, in studies on the causes of NEET status, it is necessary to consider the various categories of NEET, although this is a limitation to our research due to data unavailability. Mostly, the effects on “inactive” and “unemployed” NEETs should be considered (additional categorizations are present depending on the definition). This depends on the various causes that could affect, for instance, the attitude towards studies and especially work by choice or discouragement. For example, women may be classified as NEET (inactive) due to temporary choices after childbirth, or a young person may choose to do so to be free to travel. In economies with high unemployment and high discouragement, we might observe unemployed NEETs who, after their studies, struggle to find work (see the literature review by Zudina [79]). These differences appear significant when considering the damage that crime could cause to the economy and thus to employment opportunities, especially in less resilient contexts.

5. Conclusions

In this article, the relationship between crime and the exclusion of young people from active participation in society was investigated. The significance of the NEET phenomenon has drawn the attention of researchers during various recent crises, spanning from the 2007 financial crisis through the Great Recession to post-COVID-19 implications (e.g., [80]). Moreover, the connection between criminal activities and the NEET phenomenon appears to manifest along at least two paths: crime exacerbates the already precarious economic conditions in the most vulnerable areas (such as southern Italy), and it instils fear in vulnerable individuals (who are already apprehensive about their uncertain future). Why is this problematic for NEETs and deserving of socioeconomic analysis? Once individuals enter the NEET status, it becomes challenging for them to exit, and the consequences, whether economic, social, or health-related, are enduring (e.g., [81,82]).
Of course, the impact of crime on the NEET rate is likely more intricate than observed in this preliminary investigation, potentially stemming from the fear induced by violent crimes, independent of the actual likelihood of victimization (numerous specific cases have been examined [83,84]).
Our findings thus underscore an additional vulnerability affecting individuals with poorer well-being and concerns about their future [85]. Given that crime often correlates more strongly with particular backgrounds (e.g., neighborhood) and social classes, there is a need for more targeted policies that consider the barriers they face in accessing education and employment opportunities [86]. In this context, an emerging trend is the provision of individualized support in the transition to adulthood, which extends beyond traditional school-to-work transition assistance. Examples of such social interventions include the GOL program (Garanzia di occupabilità dei lavoratori—Employability guarantee for workers) established by the Italian Ministry of Labor, which offers tailored pathways into the labor market and training for vulnerable groups, including young people.
Furthermore, the issue of the Italian North–South divide in the NEET problem would remain apparent despite the existence of these support programs. It necessitates a commitment to restructuring training courses to align with labor market demands, along with assistance to reinforce family and social networks, which hold greater significance in the poorest regions. These networks serve as primary safety nets for young people at risk (e.g., [28,87]).
This article represents an initial attempt to identify how macroeconomic background variables associated with crime levels influence the discouragement of young Italians. This study does so within the context of the NEET literature, and our analysis is not without limitations, such as the omission of inactive and unemployed NEETs. Nonetheless, it offers a macroeconomic knowledge framework for future research utilizing microdata. Future research will also need to incorporate the intrinsic motivations of at-risk individuals and the actual extent of the spread of uncertainty due to fear of experiencing criminal activities.

Author Contributions

Conceptualization, I.O.; methodology, D.Q. and D.D.; formal analysis, D.D.; investigation, D.D. and I.O.; data curation, D.D. and A.D.N.; writing—original draft preparation, I.O., D.D., A.D.N. and D.Q.; writing—review and editing, I.O., D.D., A.D.N. and D.Q.; visualization, I.O., D.D. and A.D.N.; supervision, D.Q.; project administration, I.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data used in this manuscript are freely accessible online.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wickramasekera, N.; Wright, J.; Elsey, H.; Murray, J.; Tubeuf, S. Cost of crime: A systematic review. J. Crim. Justice 2015, 43, 218–228. [Google Scholar] [CrossRef]
  2. Estrada, M.A.R.; Ndoma, I. How crime affects economic performance: The case of Guatemala. J. Policy Model. 2014, 36, 867–882. [Google Scholar] [CrossRef]
  3. Matti, J.; Ross, A. Does crime affect entrepreneurship? A discussion of the current literature. J. Entrep. Public. Policy 2016, 5, 254–272. [Google Scholar] [CrossRef]
  4. Detotto, C.; Otranto, E. Does crime affect economic growth? Kyklos 2010, 63, 330–345. [Google Scholar] [CrossRef]
  5. Cohen, M.A. The effect of crime on life satisfaction. J. Legal Stud. 2008, 37, S325–S353. [Google Scholar] [CrossRef]
  6. Sulemana, I. The effect of fear of crime and crime victimization on subjective well–being in Africa. Soc. Indic. Res. 2015, 121, 849–872. [Google Scholar] [CrossRef]
  7. Detotto, C.; Pulina, M. Does more crime mean fewer jobs and less economic growth? Eur. J. Law Econ. 2013, 36, 183–207. [Google Scholar] [CrossRef]
  8. Brown, R.; Velásquez, A. The effect of violent crime on the human capital accumulation of young adults. J. Dev. Econ. 2017, 127, 1–12. [Google Scholar] [CrossRef]
  9. Odoardi, I.; D’Ingiullo, D.; Furia, D. Human capital and the reduction of inequalities: An intra-national analysis in Italy. Appl. Econ. 2020, 52, 6215–6228. [Google Scholar] [CrossRef]
  10. OECD. Education at a Glance 2020: OECD Indicators; OECD Publishing: Paris, France, 2020. [Google Scholar]
  11. Daniele, V. Organized crime and regional development. A review of the Italian case. Trends Organ. Crime 2009, 12, 211–234. [Google Scholar] [CrossRef]
  12. Bell, D.N.F.; Blanchflower, D.G. Young people and the Great Recession. Oxf. Rev. Econ. Policy 2011, 27, 241–267. [Google Scholar] [CrossRef]
  13. Rahman, K.M. NEETs’ challenge to Japan: Causes and remedies. Japanstudien 2007, 18, 221–244. [Google Scholar] [CrossRef]
  14. Karyda, M. The influence of neighbourhood crime on young people becoming not in education, employment or training. Br. J. Sociol. Educ. 2020, 41, 393–409. [Google Scholar] [CrossRef]
  15. Aizer, A.; Doyle, J.J., Jr. Juvenile incarceration, human capital, and future crime: Evidence from randomly assigned judges. Q. J. Econ. 2015, 130, 759–803. [Google Scholar] [CrossRef]
  16. Isherwood, L. NEET: Young People Not in Education, Employment, or Training and Violent Crime. 2023. Available online: https://www.westyorks-ca.gov.uk/media/10866/neet-young-people-not-in-education-employment-or-training-and-violent-crime.pdf (accessed on 21 March 2024).
  17. Capello, R. What makes Southern Italy still lagging behind? A diachronic perspective of theories and approaches. Eur. Plan. Stud. 2016, 24, 668–686. [Google Scholar] [CrossRef]
  18. Paoli, L. Italian organised crime: Mafia associations and criminal enterprises. Global Crim. 2004, 6, 19–31. [Google Scholar] [CrossRef]
  19. Pinotti, P. The economic costs of organised crime: Evidence from southern Italy. Econ. J. 2015, 125, F203–F232. [Google Scholar] [CrossRef]
  20. Coniglio, N.D.; Celi, G.; Scagliusi, C. Organized Crime, Migration and Human Capital Formation: Evidence from the South of Italy; Southern Europe Research in Economic Studies WP No. 28; University of Bari: Bari, Italy, 2010. [Google Scholar]
  21. Bennett, R.R. Development and crime: A cross-national, time-series analysis of competing models. Sociol. Q. 1991, 32, 343–363. [Google Scholar] [CrossRef]
  22. Apunyo, R.; White, H.; Otike, C.; Katairo, T.; Puerto, S.; Gardiner, D.; Kinengyere, A.A.; Eyers, J.; Saran, A.; Obuku, E.A. Interventions to increase youth employment: An evidence and gap map. Campbell Syst. Rev. 2022, 18, 1–28. [Google Scholar] [CrossRef]
  23. Kevelson, M.J.C.; Marconi, G.; Millett, C.M.; Zhelyazkova, N. College Educated Yet Disconnected: Exploring Disconnection From Education and Employment in OECD Countries, With a Comparative Focus on the US. ETS Res. Rep. Ser. 2020, 1, 1–29. [Google Scholar] [CrossRef]
  24. Liew, K.; Uchida, Y.; Cruz, C.D.; Lee, L.N. Examining the cultural marginalisation theory of NEET/HIKIKOMORI risk tendencies in Singaporean youth. Psychologia 2021, 63, 4–19. [Google Scholar] [CrossRef]
  25. Alvarado, A.; Conde, B.; Novella, R.; Repetto, A. NEETs in Latin America and the Caribbean: Skills, aspirations, and information. J. Int. Dev. 2020, 32, 1273–1307. [Google Scholar] [CrossRef]
  26. de Almeida, A.N.; Simões, F. Professional development perspectives across gender and age groups of under–qualified rural NEETs. J. Community Psychol. 2020, 48, 1620–1636. [Google Scholar] [CrossRef]
  27. Alcazar, L.; Balarin, M.; Glave, C.; Rodriguez, M.F. Fractured lives: Understanding urban youth vulnerability in Peru. J. Youth Stud. 2020, 23, 140–159. [Google Scholar] [CrossRef]
  28. Avagianou, A.; Kapitsinis, N.; Papageorgiou, I.; Strand, A.H.; Gialis, S. Being NEET in youthspaces of the EU South: A post–recession regional perspective. Young 2022, 30, 425–454. [Google Scholar] [CrossRef]
  29. Ringbom, I.; Suvisaari, J.; Kääriälä, A.; Sourander, A.; Gissler, M.; Ristikari, T.; Gyllenberg, D. Psychiatric disorders diagnosed in adolescence and subsequent long–term exclusion from education, employment or training: Longitudinal national birth cohort study. Br. J. Psychiatry 2022, 220, 148–153. [Google Scholar] [CrossRef]
  30. Tayfur, S.N.; Prior, S.; Roy, A.S.; Maciver, D.; Forsyth, K.; Fitzpatrick, L.I. Associations between Adolescent Psychosocial Factors and Disengagement from Education and Employment in Young Adulthood among Individuals with Common Mental Health Problems. J. Youth Adolesc. 2022, 6, 1–12. [Google Scholar] [CrossRef]
  31. Caroleo, F.E.; Rocca, A.; Mazzocchi, P.; Quintano, C. Being NEET in Europe before and after the economic crisis: An analysis of the micro and macro determinants. Soc. Indic. Res. 2020, 149, 991–1024. [Google Scholar] [CrossRef]
  32. Gariépy, G.; Danna, S.M.; Hawke, L.; Henderson, J.; Iyer, S.N. The mental health of young people who are not in education, employment, or training: A systematic review and meta–analysis. Soc. Psychiatry Psychiatr. Epidemiol. 2021, 57, 1107–1121. [Google Scholar] [CrossRef]
  33. Zhang, G.; Zhou, S.; Xia, X.; Yuksel, S.; Bas, H.; Dincer, H. Strategic Mapping of Youth Unemployment with Interval–Valued Intuitionistic Hesitant Fuzzy DEMATEL Based on 2–Tuple Linguistic Values. IEEE Access 2020, 8, 25706–25721. [Google Scholar] [CrossRef]
  34. Su, X.; Wong, V.; To, S.M. Characteristics of longer–term versus transitional NEETs in Hong Kong: Implications for career support services. Br. J. Guid. Counc. 2022, 50, 950–965. [Google Scholar] [CrossRef]
  35. Andrade, S.B.; Jarvinen, M. More risky for some than others: Negative life events among young risk-takers. Health Risk Soc. 2017, 19, 387–410. [Google Scholar] [CrossRef]
  36. Rahmani, H.; Groot, W. Risk factors of being a Youth not in Education, Employment or Training (NEET): A scoping review. Int. J. Ed. R. 2023, 120, 102198. [Google Scholar] [CrossRef]
  37. Şahin, L.; Ersöz, H.Y.; Demir, İ.; Kocakaya, M.E.; Akgül, O.; Bükey, A.M. The Relationship between Cause and Effect Dimensions of Young People’s Being “Not in Education, Employment, or Training (NEET)” in Turkey. Sustainability 2023, 15, 15274. [Google Scholar] [CrossRef]
  38. Karyda, M.; Jenkins, A. Disadvantaged neighbourhoods and young people not in education, employment or training at the ages of 18 to 19 in England. J. Educ. Work 2018, 31, 307–319. [Google Scholar] [CrossRef]
  39. Bello, B.G.; Cuzzocrea, V. Introducing the need study young people in contemporary Italy. J. Mod. Ital. Stud. 2018, 23, 413–433. [Google Scholar] [CrossRef]
  40. Mascherini, M.; Ledermaier, S. Eurofound Exploring the Diversity of NEETs; Publications of the European Union: Luxembourg, 2021. [Google Scholar]
  41. Ciampi, S.; Lion, C.; Santomieri, K.; Sciatta, V. Per un bilancio della Garanzia Giovani. Evidenze dalle valutazioni e sfide di policy. Sinappsi 2023, 13, 101–114. [Google Scholar]
  42. D’Isanto, F. Scarring effect of unemployment e disoccupazione giovanile. In I Circoli Viziosi Della Disoccupazione e le Politiche Giovanili del Lavoro: Il Ruolo del Terzo Settore; Garofalo, A., Musella, M., Eds.; De Frede Editore: Napoli, Italy, 2012; pp. 2–20. [Google Scholar]
  43. ActionAid—CGIL. NEET Tra Disuguaglianze e Divari. Alla Ricerca di Nuove Politiche Pubbliche per i Giovani; Futura Editrice: Roma, Italy, 2022. [Google Scholar]
  44. Istituto Toniolo. Rapporto Giovani 2021; Il Mulino: Bologna, Italy, 2021. [Google Scholar]
  45. Ciccarelli, A.; Fabrizii, E. Family background and persistence in NEET status. Riv. Ital. Econ. Demogr. Stat. 2017, 71, 29–40. [Google Scholar]
  46. De Luca, G.; Mazzocchi, P.; Quintano, C.; Rocca, A. Going behind the high rates of NEETs in Italy and Spain: The role of early school leavers. Soc. Indic. Res. 2020, 151, 345–363. [Google Scholar] [CrossRef]
  47. Buonanno, P.; Leonida, L. Non–market effects of education on crime: Evidence from Italian regions. Econ. Educ. Rev. 2009, 28, 11–17. [Google Scholar] [CrossRef]
  48. Groot, W.; van den Brink, H.M. The effects of education on crime. Appl. Econ. 2010, 42, 279–289. [Google Scholar] [CrossRef]
  49. Lochner, L. Education, work, and crime: A human capital approach. Int. Econ. Rev. 2004, 45, 811–843. [Google Scholar] [CrossRef]
  50. Manning, M.; Fleming, C.M.; Ambrey, C.L. Life satisfaction and individual willingness to pay for crime reduction. Reg. Stud. 2016, 50, 2024–2039. [Google Scholar] [CrossRef]
  51. Agnew, R. Why do they do it? An examination of the intervening mechanisms between “social control” variables and delinquency. J. Res. Crime. Delinq. 1993, 30, 245–266. [Google Scholar] [CrossRef]
  52. MacDonald, J.M.; Piquero, A.R.; Valois, R.F.; Zullig, K.J. The relationship between life satisfaction, risk–taking behaviors, and youth violence. J. Interpers. Violence 2005, 20, 1495–1518. [Google Scholar] [CrossRef]
  53. Hansen, L.P. Large sample properties of generalized method of moments estimators. Econometrica 1982, 50, 1029–1054. [Google Scholar] [CrossRef]
  54. Amendola, S. Trends in rates of NEET (not in education, employment, or training) subgroups among youth aged 15 to 24 in Italy, 2004–2019. J. Public Health 2022, 30, 2221–2229. [Google Scholar] [CrossRef]
  55. Bradley, S.; Migali, G.; Navarro Paniagua, M. Spatial variations and clustering in the rates of youth unemployment and NEET: A comparative analysis of Italy, Spain, and the UK. J. Reg. Sci. 2020, 60, 1074–1107. [Google Scholar] [CrossRef]
  56. Kposowa, A.J.; Breault, K.D.; Harrison, B.M. Reassessing the structural covariates of violent and property crimes in the USA: A county level analysis. Br. J. Sociol. Educ. 1995, 46, 79–105. [Google Scholar] [CrossRef]
  57. Carboni, O.A.; Detotto, C. The economic consequences of crime in Italy. J. Econ. Stud. 2016, 43, 122–140. [Google Scholar] [CrossRef]
  58. Odoardi, I.; Liberatore, A. Youth unemployment: A serious problem for young Italians Neet and a comparison with the social exclusion condition. J. Urban Reg. Anal. 2021, 13, 177–193. [Google Scholar] [CrossRef]
  59. Degli Antoni, G. Intrisic vs extrinsic motivations to volunteer and social capital formation. Kyklos 2009, 62, 359–370. [Google Scholar] [CrossRef]
  60. Hyejin, N.; Bong Joo, L. Risk factors of NEET (Not in Employment, Education or Training) in South Korea: An empirical study using panel data. Asia Pac. J. Soc. Work Dev. 2017, 27, 28–38. [Google Scholar]
  61. Bynner, J.; Parsons, S. Social exclusion and the transition from school to work: The case of young people not in education, employment, or training (NEET). J. Vocat. Behav. 2002, 60, 289–309. [Google Scholar] [CrossRef]
  62. Cornaglia, F.; Feldman, N.E.; Leigh, A. Crime and mental well–being. J. Hum. Resour. 2014, 1, 110–140. [Google Scholar]
  63. Strauss, H.; de la Maisonneuve, C. The Wage Premium on Tertiary Education: New Estimates for 21 OECD Countries; OECD Economics Department Working Papers No. 589; OECD Publishing: Paris, France, 2007. [Google Scholar]
  64. Franzini, M.; Raitano, M. Earnings inequality and workers’ skills in Italy. Struct. Chang. Econ. Dyn. 2019, 51, 215–224. [Google Scholar] [CrossRef]
  65. Scarpetta, S.; Sonnet, A.; Manfredi, T. Rising Youth Unemployment during the Crisis: How to Prevent Negative Long-Term Consequences on a Generation? OECD Social Employment and Migration Working Papers No. 106; OECD Publishing: Paris, France, 2010. [Google Scholar]
  66. Liotti, G. Labour market flexibility, economic crisis and youth unemployment in Italy. Struct. Chang. Econ. Dyn. 2020, 54, 150–162. [Google Scholar] [CrossRef]
  67. Miceli, R.; Roccato, M.; Rosato, R. Fear of crime in Italy: Spread and determinants. Environ. Behav. 2004, 36, 776–789. [Google Scholar] [CrossRef]
  68. Preti, A.; Miotto, P. Death by homicide in Italy, 1980–1994: Age and gender differences among victims. Med. Sci. Law 2000, 40, 233–240. [Google Scholar] [CrossRef]
  69. Gatti, U.; Tremblay, R.E.; Schadee, H.M.A. Community characteristics and death by homicide, suicide and drug overdose in Italy: The role of civic engagement. Eur. J. Crim. Pol. Res. 2007, 13, 255–275. [Google Scholar] [CrossRef]
  70. Moscati, R.; Rostan, M. Higher education and graduate employment in Italy. Eur. J. Educ. 2000, 35, 201–209. [Google Scholar] [CrossRef]
  71. O’Higgins, N.; D’Amato, M.; Caroleo, F.; Barone, A. Gone for good? Determinants of school dropout in Southern Italy. G. Econ. Ann. Econ. 2007, 66, 207–245. [Google Scholar] [CrossRef]
  72. Drakaki, M.; Papadakis, N.; Kyridis, A.; Papargyris, A. Who’s the Greek Neet? Neets’ profile in Greece: Parameters, trends and common characteristics of a heterogeneous group. Int. J. Humanit. Soc. Sci. 2014, 4, 240–254. [Google Scholar]
  73. Odoardi, I.; Furia, D.; Cascioli, P. Can social support compensate for missing family support? An examination of dropout rates in Italy. Reg. Sci. Policy Prac. 2021, 13, 121–139. [Google Scholar] [CrossRef]
  74. Contini, D.; Filandri, M.; Pacelli, L. Persistency in the NEET state: A longitudinal analysis. J. Youth Stud. 2019, 22, 959–980. [Google Scholar] [CrossRef]
  75. Smolej, M.; Kivivuori, J. The relation between crime news and fear of violence. J. Scand. Stud. Criminol. Crime Prev. 2006, 7, 211–227. [Google Scholar] [CrossRef]
  76. Parola, A.; Marcionetti, J. Youth unemployment and health outcomes: The moderation role of the future time perspective. Int. J. Educ. Vocat. Guid. 2022, 22, 327–345. [Google Scholar] [CrossRef]
  77. Karyda, M. The Effect of Crime in the Community on Becoming Not in Education, Employment or Training (NEET) at 18–19 Years in England. Doctoral Dissertation, UCL Institute of Education, London, UK, 2015. [Google Scholar]
  78. Machin, S.; Vujić, S.; Marie, O. Youth crime and education expansion. Ger. Econ. Rev. 2012, 13, 366–384. [Google Scholar] [CrossRef]
  79. Zudina, A. What Makes Youth Become NEET? The Evidence from Russian LFS; Higher School of Economics, WP BRP 177; Higher School of Economics: Moscow, Russia, 2017. [Google Scholar]
  80. Bruno, G.S.; Marelli, E.; Signorelli, M. The rise of NEET and youth unemployment in EU regions after the crisis. Comp. Econ. Stud. 2014, 56, 592–615. [Google Scholar] [CrossRef]
  81. Giret, J.F.; Guégnard, C.; Olivier, J. School–to–work transition in France: The role of education in escaping long–term NEET trajectories. Int. J. Lifelong Educ. 2020, 39, 428–444. [Google Scholar] [CrossRef]
  82. Ralston, K.; Everington, D.; Feng, Z.; Dibben, C. Economic inactivity, not in employment, education or training (NEET) and scarring: The importance of NEET as a marker of long–term disadvantage. Work Employ. Soc. 2022, 36, 59–79. [Google Scholar] [CrossRef]
  83. Pain, R.H. Elderly women and fear of violent crime: The least likely victims? A reconsideration of the extent and nature of risk. Br. J. Criminol. 1995, 35, 584–598. [Google Scholar] [CrossRef]
  84. Lane, J.; Gover, A.R.; Dahod, S. Fear of violent crime among men and women on campus: The impact of perceived risk and fear of sexual assault. Violence Vict. 2009, 24, 172–192. [Google Scholar] [CrossRef]
  85. Felaco, C.; Parola, A. Subjective well–being and future orientation of NEETs: Evidence from the Italian sample of the European Social Survey. Soc. Sci. 2022, 11, 482. [Google Scholar] [CrossRef]
  86. André, G.; Crosby, A. The “NEET” category from the perspective of inequalities: Toward a typology of school–to–work transitions among youth from lower class neighborhoods in the Brussels region (Belgium). J. Youth Stud. 2023, 26, 1387–1401. [Google Scholar] [CrossRef]
  87. Caroleo, F.E.; Rocca, A.; Neagu, G.; Keranova, D. NEETs and the process of transition from school to the labor market: A comparative analysis of Italy, Romania, and Bulgaria. Youth Soc. 2022, 54, 109S–129S. [Google Scholar] [CrossRef]
Table 1. Variable definitions and sources.
Table 1. Variable definitions and sources.
VariableDefinitionSource
NEETIncidence of young NEETs aged 15–24 (not employed and not in education) on the respective populationISTAT
VIOLENTHomicides per 100,000 inhabitantsISTAT—BES a on Ministry of the Interior data
THEFTSHome burglaries per 1000 familiesISTAT—BES a elaborations on ISTAT citizen security survey and on Ministry of the Interior data
YURPercentage of people looking for work aged 15–24, in the labor force of the corresponding age groupISTAT—territorial indicators for development policies
HCPercentage of the population aged 25–64 with tertiary education (ISCED b 5–8)EUROSTAT
POVERTYRisk of poverty, percentage valuesISTAT—BES a on Eu-Silc data c
GDPlog of gross domestic product per capita at 2015 values (euros)ISTAT
DROPOUTPopulation aged 18–24 years with at most a middle school certificate, who have not completed a professional training course recognized by the region and lasting more than 2 years, and who do not attend school courses or participate in training activities (percentage)EUROSTAT
SCVolunteer activities (percentage)ISTAT—BES a, survey: aspects of daily life
a Benessere E sostenibilità (wellness and sustainability) report. b International standard classification of education. c Statistics on income and living conditions.
Table 2. Summary statistics.
Table 2. Summary statistics.
VariableRegionsObs.MeanStd. Dev.MinMax
NEETAll31517.1466.616.534
Center-North19513.0813.1346.520.5
South12023.7525.3189.234
VIOLENTAll3150.7790.58403.9
Center-North1950.5740.29501.6
South1201.1110.76103.9
THEFTSAll31510.6264.4082.122.7
Center-North19512.3674.3513.522.7
South1207.7972.7172.114
YURAll31529.74513.4295.365.1
Center-North19522.5049.5745.349
South12041.51110.04817.165.1
HCAll31515.79510.3339.426.1
Center-North19516.7923.3031026.1
South12014.1742.3079.420.5
POVERTYAll31518.62110.333544.6
Center-North19511.4853.144521.8
South12030.2176.82517.344.6
GDPAll31528,241.267806.2215,844.4845,875.21
Center-North19533,389.074975.3923,366.8545,875.21
South12019,876.082539.1015,844.4826,003.16
DROPOUTAll31515.7675.1156.732.4
Center-North19514.0413.9986.726.3
South12018.5745.4917.432.4
SCAll31510.5064.6254.427.3
Center-North19512.6674.5535.527.3
South1206.9941.6034.411.2
Source: our elaborations on EUROSTAT and ISTAT data.
Table 3. Robust two-stage least squares estimation results (all Italian regions).
Table 3. Robust two-stage least squares estimation results (all Italian regions).
Dependent Variable: NEET(1)(2)
YUR0.0202 ***0.0107 ***
(0.002)(0.0021)
HC0.0290 ***0.0228 ***
(0.0055)(0.0045)
POVERTY0.00320.0021
(0.0053)(0.0036)
GDP0.01460.0144
(0.2223)(0.1757)
DROPOUT0.0158 ***0.0158 ***
(0.0034)(0.0031)
SC0.00160.0009
(0.0058)(0.0042)
VIOLENT0.2370 ***
(0.0707)
THEFTS 0.0341 ***
(0.0072)
Obs.315315
N2121
R20.640.68
Underidentification test (p value)5.944 (0.051)10.866 (0.004)
Hansen’s J test p value0.8740.508
Source: our elaborations on EUROSTAT and ISTAT data. Note: *** statistically significant at the 1% level. Standard errors robust to heteroskedasticity are given in parentheses.
Table 4. Robust two-stage least squares estimation results (central-northern regions).
Table 4. Robust two-stage least squares estimation results (central-northern regions).
Dependent Variable: NEET(3)(4)
YUR0.0247 ***0.0181 ***
(0.0025)(0.0055)
HC0.0208 ***0.0187 ***
(0.0061)(0.0043)
POVERTY0.00550.0029
(0.0048)(0.005)
GDP0.36920.147
(0.3191)(0.2868)
DROPOUT0.0123 ***0.0118 ***
(0.0026)(0.0033)
SC0.00170.0001
(0.0063)(0.0045)
VIOLENT0.2108
(0.1447)
THEFTS 0.017
(0.013)
Obs.195195
N1313
R20.740.77
Underidentification test (p value)6.819 (0.033)9.457 (0.009)
Hansen’s J test p value0.8870.399
Source: our elaborations on EUROSTAT and ISTAT data. *** statistically significant at the 1% level. Standard errors robust to heteroskedasticity are given in parentheses.
Table 5. Robust two-stage least squares estimation results (southern regions).
Table 5. Robust two-stage least squares estimation results (southern regions).
Dependent Variable: NEET(5)(6)
YUR0.0146 ***0.0096 ***
(0.0031)−0.0016
HC0.0375 ***0.0189 ***
(0.01)−0.0047
POVERTY−0.00040.0002
(0.0065)−0.0034
GDP−0.3094 **0.0635
(0.1473)−0.1441
DROPOUT0.0203 ***0.0180 ***
(0.0055)−0.0038
SC0.000−0.0018
(0.0062)(0.0034)
VIOLENT0.1839 ***
(0.0683)
THEFTS 0.0357 ***
(0.0131)
Obs.120120
N88
R20.540.71
Underidentification test (p value)4.000 (0.135)4.831 (0.089)
Hansen’s J test p value0.5880.611
Source: our elaborations on EUROSTAT and ISTAT data. Note: ** statistically significant at the 5% level; *** statistically significant at the 1% level. Standard errors robust to heteroskedasticity are given in parentheses.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Odoardi, I.; D’Ingiullo, D.; Di Nucci, A.; Quaglione, D. Exploring the Influence of Crime on NEET Rates: A Regional Analysis of Italy. Merits 2024, 4, 132-145. https://doi.org/10.3390/merits4020010

AMA Style

Odoardi I, D’Ingiullo D, Di Nucci A, Quaglione D. Exploring the Influence of Crime on NEET Rates: A Regional Analysis of Italy. Merits. 2024; 4(2):132-145. https://doi.org/10.3390/merits4020010

Chicago/Turabian Style

Odoardi, Iacopo, Dario D’Ingiullo, Ada Di Nucci, and Davide Quaglione. 2024. "Exploring the Influence of Crime on NEET Rates: A Regional Analysis of Italy" Merits 4, no. 2: 132-145. https://doi.org/10.3390/merits4020010

APA Style

Odoardi, I., D’Ingiullo, D., Di Nucci, A., & Quaglione, D. (2024). Exploring the Influence of Crime on NEET Rates: A Regional Analysis of Italy. Merits, 4(2), 132-145. https://doi.org/10.3390/merits4020010

Article Metrics

Back to TopTop