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\def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2021-05-15 (revised: 15 May 2021)} \def\TheID{10.34257/GJHSSEVOL21IS1PG45\makeatother } \def\TheDate{2021-05-15} \title{The Effect of City Conditions on Youth Violence in Mexico a Spatial Econometric Analysis by Metropolitan Area} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 4\p@ plus2\p@ minus2\p@ 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\setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Gloria L. Mancha-Torres} \author[2]{Jose N. Martinez} \author[3]{Ernesto Aguayo-Téllez} \renewcommand\Authands{ and } \date{\small \em Received: 15 April 2021 Accepted: 1 May 2021 Published: 15 May 2021} \maketitle \begin{abstract} Using Mexico?s Social Cohesion Survey for the Prevention of Violence and Crime (ECOPRED 2014) and the Mexican Intercensal Population Survey (CONTEO 2015) this paper analyses the characteristics and environment factors that influence the violent behavior of young people aged 14 to 24 years within the 47 largest Mexican cities. The existence of spatial correlation between Mexican cities is corroborated and after controlling for it, it is found that factors related to addictions (drug use by young people and their families) and a violent environment around the young (being bullied, robbed, or having violent friends, neighbors, coworkers or classmates) are positively related to the percentage of young people who shout, hit objects, hit people, carry weapons, or have been arrested. Public policies to reduce the use of drugs and to improve the environment where young people live, mainly in their neighborhoods, schools and jobs, will have a direct effect on reducing the violent behavior of young people. And given the confirmed existence of spatial effects, coordinated efforts between nearby cities could multiply the impact of such public policies. \end{abstract} \keywords{youth, violence, crime prevention, spatial econometrics.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \begin{textblock*}{10cm}(1.05cm,3cm) {{\textit{CrossRef DOI of original article:}} \underline{10.34257/GJHSSEVOL21IS1PG45}} \end{textblock*}\let\tabcellsep& \section[{Introduction}]{Introduction}\par or decades, most Latin American countries have suffered high rates of violence and insecurity, to the extent that violence (intentional and unintentional) is the leading cause of death in the 15 to 50 years old population group \hyperref[b52]{(Trucco and Ullmann, 2015)}. For many years, Mexico seemed to be isolated from the wave of violence in Latin America (violence in Mexico used to be focalized in few states and it was associated to "account adjustments" between organized crime groups). However, after 2008, violence in Mexico increased greatly in almost all states {\ref (Banxico, 2012)}.\par Mexico is now in a crisis of violence. For example, between 2017 and 2018, the intentional homicide rate in the country increased 14\% to reach the alarming level of 27 deaths per 100,000 inhabitants; climbing from the 19 to the 16 place of the list of 230 countries by intentional homicide rate of the United Nations Office on Drugs and Crime (UNOCD, 2018).\par The problem is higher at the state and city levels. Some Mexican states present extremely high homicide rates; higher than the most violent countries in the world like El Salvador that recorded in 2017 a crime rate of 61.8 deaths per 100,000 inhabitants. In 2018, the Mexican state of Colima recorded a homicide rate of 95.41 and the state of Baja California recorded a homicide rate of 89. {\ref 44 (IEP, 2019)}. To understand the seriousness of this numbers, it is relevant to mention that according to the World Health Organization (WHO), a homicide rate higher than 10 per 100,000 inhabitants can be considered an epidemic of violence. In 2018, 23 of the 32 Mexican states recorded homicide rates higher than 12 (IEP, 2019). In addition, UNODC {\ref (2011)} shows that the increase in the violence in Mexico is related to the geographical area, that is, the most populated cities have higher homicide rates than the rest of the country.\par Beyond homicides, violence in Mexico has escalated in all areas. For example, the average rate of crimes committed with violence in Mexico increased 25.4\% between 2015 and 2018, and the country´s average rate of domestic violence increased 38\% during the same period of time (IEP, 2019). In the same sense, Mexico´s rate of crime prevalence in 2017 averaged 29,746 victims per 100,000 inhabitants; being the State of Mexico \hyperref[b45]{(46,} {\ref 705)}, Mexico City \hyperref[b41]{(43,} {\ref 069)} and Baja California \hyperref[b34]{(36,} {\ref 578)}, the states with the highest crime prevalence rates {\ref (ENVIPE, 2018)}.\par Violence and crime have negative effects and cost for individuals, society and the economy. For example in 2017, according to the National Survey of Victimization and Perception on Pubic Security (ENVIPE for its acronym in Spanish), in economic terms, the total cost of insecurity in Mexican households amounted 299.6 billion pesos or 1.65\% of GDP. Similarly, household´s spending on preventive measures against crime registered 89.1 billion pesos or 0.5\% of GDP. Other studies report higher estimated costs: The Institute for Economics and Peace (2018) reported that, in 2017, the economic impact of violence in Mexico was 249 billion dollars or 21\% of Mexican GDP.\par In addition to economic costs, violence and crime generate other types of costs and consequences that can affect victims and criminals for the rest of their lives, especially if they are young. Among the negative consequences for young people who are violent and/or who participate in crime activities are: low educational achievement, problematic social relationships, compromised mental health, loss of health, higher victimization rates, and physical harm \hyperref[b14]{(Fagan \& Catalano, 2012;} {\ref Buvinic, Morrison \& Orlando, 205;} {\ref Londoño \& Guerrero, 1999)}. Also, violence and crime can contribute to substantially raise morbidity and mortality rates, particularly among young people \hyperref[b25]{(Guerrero, 2008;}\hyperref[b24]{Griffin et al, 1999;} {\ref WHO, 2002)}. Following {\ref INEGI (2016b)}, violence is one of the main causes of mortality in Mexico and following {\ref WHO (2016)} it causes one in three deaths of adolescent boys in Latin America.\par Literature agrees that both violence and crime in young people are phenomena caused by multiple factors such as: individual, family, friends, school and neighborhood characteristics \hyperref[b26]{(Herrenkohl et al, 2004;} {\ref WHO, 2002;}\hyperref[b31]{Jiménez, 2005)}. Specifically for Mexico, {\ref Mancha (2017)} finds that two types of factors increase the likelihood of young individuals (14 to 18 years old) to commit violent acts: factors related to addictions (drugs or alcohol) and violence inside home or in the near environment. On the other hand, using a different definition of a violent profile, \hyperref[b41]{Mancha et al. (2019)} argue that violence in Mexico is transmitted among young individuals mainly throughout their near environment (neighborhood, school, and workplace) rather than by their own individual characteristics.\par Crime economics argues that factors such as poverty, inequality, low levels of education and high crime rates make individuals prone to participate in criminal activities \hyperref[b6]{(Becker, 1995)}. \hyperref[b7]{Case and Katz (1991)}, and \hyperref[b19]{Freeman (1996}\hyperref[b20]{Freeman ( , 1999) )} have strongly documented that variables such as poverty, unemployment, education, age, and income inequality are important factors in the propensity of an individual to commit violent acts. Thus, if illiteracy, poverty, inequality and crime predominate in a specific city, it is likely to affect individual´s propensity to commit violent and crime acts. And if, in turn, this city is surrounded by other similar cities, there may also be a spatial effect on the violent behavior of individuals.\par The concept of influence between regions leads to the use of spatial dependence models, where the idea of adjacency or proximity is key as it is possible that the characteristics of a region affect the characteristics of regions that share a border or that are a certain distance \hyperref[b12]{(Elffers, 2003)}.\par The objective of this paper is to study the impact the different environmental factors that could affect the violent behavior of young individuals (14 to 24 years old) in Mexico, including city level factors, such as poverty, unemployment, or lack of access to social security. Furthermore, the use of city level observations allows us to inquire into the spatial interaction of city characteristics and youth violence. That is, cities with high (or low) levels of youth violence or youth delinquency increase (or decrease) the levels of youth violence or youth delinquency in neighboring cities and vice versa. If the hypothesis of spatial interaction is corroborated, it would be necessary to use econometric methodologies to control for the spatial autocorrelation in order to obtain unbiased estimators of the effects of the city´s environment on youth violence in Mexico.\par In addition, this paper aims to contribute to increase the knowledge of youth violence in Mexico. The results will allow us to have empirical evidence for the design and targeting of public policies that reduce youth violence and increase the welfare of the Mexican youth. \section[{II.}]{II.} \section[{Background}]{Background}\par The issue of youth and the problems young people face (lack of opportunities of employment and education, unemployment, informality, teen pregnancy, lack of access to social security, violence, among others) have begun to be relevant among researchers and policy makers in Mexico. On the issue of youth violence and youth crime, it is observed that young Mexicans are more frequently involved in violent and criminal acts, including drug trafficking, organized crime, kidnapping, and homicides. In addition, the age of those who participate in these kinds of acts has been decreasing during the last years.\par Although public opinion agrees that the participation of young people in violent and crime acts is becoming more frequent in Mexico, statistical information to corroborate such believes is very limited, without continuity or very recent, and usually dispersed in different (and not always comparable) databases. Also, in many cases, the age of the offender or the victim is not among the data, so it becomes difficult to conduct studies that focus on young people. This lack of data is one of the causes of the very limited number of academic research from a quantitative approach to the issue of violence and youth in Mexico {\ref (Cortéz, 2015;}\hyperref[b23]{González, 2014)}. Most existing studies on violence and youth in Mexico are of a qualitative nature and with a sociological, psychological or case study approach. The research presented in this paper aims to collaborate to the understanding of violence and youth in Mexico from a quantitative point of view, based on information on physical and socioeconomic characteristics and with a public policy approach.\par In addition to the lack of data, the fact that violence is a complex issue (there exist numerous forms of violence: gender, sexual, physiological, political, and social, among others) and the fact that in the literature there is not consensus about its definition have contributed to the limited exploration and research of the youth and violence topic in Mexico.\par International literature in different disciplines of the social science that have studied the problem of violence and its causes and consequences among young people have not found an unique response that allows us to explain the phenomenon. Rather, it has been found that violence is a multidimensional problem, where individual, family, friends, and environmental characteristics, as well as experiences and exposure to violence all have an impact on youth violent behavior \hyperref[b26]{(Herrenkohl et al, 2004;} {\ref OMS, 2002;}\hyperref[b31]{Jiménez, 2005)}. What is a fact is that violence has been associated with youth over time. According to some theories, violence is inherent in youth since the beginning of civilizations as a form of survival, reproduction or rite {\ref (Potts \& Hayden, 2010)}.\par Given that youth is a period of preparation for life in which individuals accumulate capacities and form social networks so that they can be properly inserted into the social fabric as adults; it could be said that the adequate inclusion in society depends on what is done or not done during this stage of life {\ref (Hopenhayn, 2008)}. Thus, if a person does not respect the law and social norms when young, she/he will hardly do so as an adult; if a young person falls into addiction, violence or crime problems, it gets very difficult to reverse the process as an adult.\par Also, the behavior of young people can have an effect of network economies. For example, if many young people are involved in crime activities and other young people observe that the social cost of been involved in such activities is low, there exist a kind of "contagion" towards new young people to get involved in such crime activities. For example, if being dishonest or corrupt has a low moral hazard, more and more young people will accept and abide this mentality. If, on the contrary, young people are formed in environments of values, healthy relationships, and respect, where work and education are important tasks to achieve a better future, and where the moral hazard of committing violent acts is high, both individual and social benefits will be obtained. Hence, young people can contribute to trigger economic growth and social development in their locality and their country {\ref (Mancha, 2017)}.\par All these indicate the need to find a way to reduce the likelihood of young people to start their participation in violent and criminal activities. To do so, it is necessary to carry out empirical studies that allow us to identify the contributing factors as well as the mechanisms and instruments that help us prevent the participation of young people in violent and criminal acts. Moreover, it is necessary to generate more information and data at the city and neighborhood level that allow us to understand the phenomenon and to design targeted public policies aimed at reducing youth violence and crime. \section[{III.}]{III.} \section[{Literature Review}]{Literature Review}\par Violence is the result of a complex network of factors, none of which has an explanatory power on its own and since they are related to each other, it is important to recognize them and know that they do not always occur simultaneously or influence in the same way \hyperref[b13]{(Escotto, 2015)}.\par Literature constantly argues that young people exposed to violence (either as witnesses or as victims) are at great risk of developing aggressive and violent behavior patterns. \hyperref[b18]{Fehon (2007)} and \hyperref[b52]{Soto and Trucco (2015)} state that domestic violence (including having drug or alcohol abusing parents or having very strict parents), as well as violent neighborhoods are risk factors for triggering violent behavior in the young. \hyperref[b18]{Fehon (2007)} and \hyperref[b0]{Aizer (2009)} complement that the exposure of young people to violence and its relationship to problems such as depression, anxiety, post-traumatic stress, alcohol or drug use, aggressive behavior, crime, low academic performance or suicide is frequently documented. And, \hyperref[b18]{Fehon (2007)} remarks that the continued exposure to violence, whether in the home, neighborhood, school, or media promotes among young people the belief that aggression and violence are normal and acceptable behaviors, thereby increasing its potential for aggressive responses.\par Although it has been shown that the structural characteristics of the family (such as size or income) are also important to determine a violent behavior of young people, there is evidence that parental practices have a greater impact on the problem \hyperref[b35]{(Kotchick \& Forehand, 2002;} {\ref Stern \& Smith, 1995)}. Fagan (2013), \hyperref[b16]{Fagan et al. (2011)} and \hyperref[b27]{Hoeve et al. (2009)} show, using experimental evaluations, that parental practices affect the degree to which adolescents engage in substance use, crime and violence. In addition, \hyperref[b18]{Fehon (2007)} documents that young people exposed to high levels of violence in their community but living in families with a high degree of cohesion, structure, effective parental practices and strong family ties are less likely to engage in violent behavior than young people who live in more dysfunctional families. Some authors agree that the economic and social environment in which young people live is one of the main modelers of their behavior (Jencks \& Mayers, 1990; \hyperref[b7]{Case \& Katz, 1991;} {\ref Plotnick \& Hoffman, 1995)}. {\ref Brown (1990)} and Brown, Clasen and Eicher(1986) argue that friends, access to resources, distractors, opportunities, exposure to inequality or violence, etc. provoke in the young people behavioral patterns, both positive and negative, that are reflected both in the decisions they make, and in the activities they carry out.\par With respect to the environment of the city, various studies highlight the importance of city characteristics such as poverty, inequality, home ownership, as well as the presence of adequate places to promote a healthy development of children and youth (sport areas, schools, health clinics, shopping centers) or places that are a source of bad influence (bars or night clubs), are factors that contribute to determining violent and criminal behavior \hyperref[b34]{(Kelly, 2000;}\hyperref[b38]{Levitt \& Lochner, 2001;} {\ref Glaeser et al., 1996;}\hyperref[b5]{Anselin et al., 2000;}\hyperref[b36]{Lagrange, 1999)}.\par Akerlof (1997), Akerlof \& Kranton (2000), Crane (1991) and Glaeser and Scheinkman (2001) remark that individuals make decisions not only based on their own preferences, but also considering the preferences of their reference group. {\ref Wilson (1987} {\ref Wilson ( , 1996))}, Massey and Denton (1993), Granovetter (1995) and Jargowsky (1997) argument that if the habitants of a neighborhood show little commitment to work or study, individuals in particular also tend to show little commitment to such activities. And Kain (1968), Offner and Saks (1971), {\ref Leonard (1987)}, {\ref Ellwood (1986)} and Raphael (1998), using spatial mismatch models, add that individuals who live in segregated neighborhoods work and study less because they have less access to jobs and educational opportunities.\par Finally, studies examining the spatial distribution of crime \hyperref[b5]{(Anselin et al., 2000;} {\ref Kakamu et al., 2000;}\hyperref[b36]{Lagrange, 1999)} have shown that certain urban land uses and city characteristics are associated with criminality clusters known as "hot spots". Lagrange (1999) finds a marked concentration of criminal incidence in areas close to other areas that have high levels of unemployment. \hyperref[b12]{Elffers (2003)} adds that spatial effects are relevant when studying the phenomenon of crime since crime also depends in part on the characteristics of nearby neighborhoods and communities. The author stipulates that the probability of being a criminal is not only determined by family and neighborhood characteristics, but also by the proximity to other communities with high criminal levels.\par In Mexico, quantitative studies on violence and youth are limited. Some studies focus on the relationship between victimization and youth and others between crime and youth, but there are practically no studies that quantitatively review the relationship between a violent environment and the consequent violent behavior of young people. One reason for this lack of research is that until 2014, there was not a database that allowed exploring statistically the relationship between violence and youth. In An early work that related qualitatively the environment and the juvenile delinquency in Mexico is Jimenez {\ref (2005)}. The author argues that the growth of youth violence is linked to the poor economic performance of the country and to the interrelation between poverty and education. In addition, the author suggests that reducing delinquency among young people requires the commitment of the State to ensure a decent life for young people and their families.\par Recently, Jusidman et al. (2016) find that the violence growth in Mexico is related, among other things, to the concentration of young people in poorly planned urban environments without infrastructure and without sufficient services. The authors argue that the extensive and unequal growth of cities in Mexico have favored the emergence of youth violence. The authors state that confined people in small spaces inside and outside their homes and the lack of public spaces such as parks, sport fields or green areas have generated "a crisis of coexistence and social cohesion" in Mexico. This lack of social cohesion reduces the possibilities for people to live together and to create bonds of trust as a form of social capital. \section[{IV.}]{IV.}\par The Data The ECOPRED 2014 was created with the objective of measuring the risk factors and the situations of exposure to violence and crime that increase the possibility that young population aged 12 to 29 in Mexico replicate similar situations in society later. These factors include the individual context of young people; development and relationships within their families; the influence and interaction with friends and peers; and relationships among members of a community, as well as the social context in general. ECOPRED 2014 is a household oriented survey and is divided in two questionnaires: one applied to the head of household and other applied to the young.\par ECOPRED 2014 contains information on young people and their families, friends and neighbors from 997,754 households in 47 cities in Mexico. Information includes characteristics, habits and behavior of the young people, their relationships within and outside their family, the influence and interactions with friends and colleagues in the neighborhood, work and school, the relationships with other members of the community and their perspective of the social context in general. The population that lives in the 47 cities considered in the study is 41 million, which represents around 35 percent of the total population in Mexico. ECOPRED 2014 does not contain information on income, expenditure, job occupation or other socioeconomic characteristics of the family. Figure 1 displays the geographical location of the 47 Mexican cities considered in the ECOPRED 2014. The lines that connect them represent cities that are less than 300 kms (187 miles) away of each other.\par CONTEO 2015 is the intercensal survey carried out to upgrade the socio-demographic information of the Mexican population between the 2010 and 2020 population censuses. The 2015 intercensal survey gathered information from 6.1 million households. Such a sample size allows us to make inferences about socioeconomic, demographic, and physical characteristics of households at the municipality level and for each one of the Mexican towns larger than 50,000 inhabitants. Information such as average incomes, unemployment rates, fertility rates, or access to water are obtained from this survey. It is important to notice that with the information provided by the ECOPRED 2014 it is not possible to know whether a young individual is a criminal or not, but is possible to identify violent behaviors and factors that could be associated with such violent behaviors. Despite its restrictions, this database allows us to conduct a cross-sectional study to inquire into the effects of the characteristics of the neighborhood and the city on the average violent behavior of young people in Mexico. This study is bounded to people between 14 and 24 years old, because there is evidence in the literature that it is in the adolescence and early youth when people begin to engage in violent and criminal activities \hyperref[b17]{(Farrington, 2003} V. \section[{Descriptive Statistics}]{Descriptive Statistics}\par Since there is no consensus in the literature on the definition of violence and even less on the definition of youth violence, we adopted the operational definition of \hyperref[b56]{Wright and Fagan (2013)}. \hyperref[b56]{Wright and Fagan (2013)} define violent acts such as: throwing objects at someone, hitting someone, carrying a weapon or attacking with a weapon, being involved in a gang fight, and committing robbery. With the information that is possible to obtain from the ECOPRED 2014 and seeking to keep this operational definition, we use the following city average variables for young individuals aged 14 to 24, as proxies for youth violence for the 47 selfrepresented cities considered in ECOPRED 2014: 1) percentage of young people shouting at others (in situations of discussion or conflict); 2) percentage of young people hitting objects (to calm down); 3) percentage of young people hitting people (in situations of discussion or conflict); 4) percentage of young people carrying weapons such as knifes or guns (as a method of defense or to be respected); and 5) percentage of young people who have been arrested.\par To make sure that all five variables used as proxy of committing violent acts are different from each other, simple correlations between these variables were obtained. As we can see in Table \hyperref[tab_2]{2} reports descriptive statistics for the five variables used as proxy of committing violent acts. In average, for the whole sample of 47 cities, 8.4 percent of young people shout at others, 8.9 percent hits objects to calm down, 4.5 percent hit other people, 3.1 percent carry weapons such as knifes or guns and 6.8 percent have been arrested. It is interesting to notice that a higher percentage of young people report being arrested than carrying weapons or hitting other people. Been arrested could result from other violence risk activities such as drinking alcohol or using drugs. The three cities with the highest percentages of young people who report shouting are Gustavo A. Madero (inside Mexico City) (13.7\%), Guadalajara (12.6\%) and Aguascalientes (11.9\%) and the three cities with the lowest percentages are Tijuana (6.1\%), Tlaxcala (4.4\%) and Veracruz (3.8\%). The three cities with the highest percentages of young people who report hitting objects are Mexicali (13.5\%), Uruapan (12.3\%) and Guadalajara (11.9\%) and the three cities with the lowest percentages are Ecatepec (part of Mexico City´s Metropolitan Area) (5.8\%), Tlaxcala (5.7\%) and Ciudad Juárez (5.7\%). The three cities with the highest percentages of young people who report hitting other people are Aguascalientes (10\%), Morelia (9.3\%) and Nuevo Laredo (9\%) and the three cities with the lowest percentages are Zacatecas (1.7\%), Ciudad Obregón (1.5\%) and Tijuana (1.2\%). The three cities with the highest percentages of young people who report carrying weapons are León (7.1\%), Hermosillo (4.9\%) and Tehuacán (4.8\%) and the three cities with the lowest percentages are Acapulco (1.4\%), Tuxtla Gutiérrez (1.4\%) and Culiacán (1.1\%). Finally, the three cities with the highest percentages of young people who report being arrested are León (14.3\%), Aguascalientes (12.6\%) and Manzanillo (12.2\%) and the three cities with the lowest percentages are Acapulco (3.1\%), Nezahualcoyotl (part of Mexico City´s Metropolitan Area) (2.6\%) and Villahermosa (2.1\%). The cities of Guadalajara, Aguascalientes and León appear at least two times at the top three of the lists. On the other hand, the cities of Tijuana, Tlaxcala and Acapulco appear at least two times at the bottom of the lists.\par The last column of table 2 reports a measure of spatial autocorrelation that is widely used in the literature to know the spatial concentration of a certain variable: the I-Moran Chi-2 statistic \hyperref[b8]{(Cliff \& Ord, 1973)}. Although the I-Moran statistic does not indicate magnitude, it can be noticed that four of the five dependent variables have a significant global spatial autocorrelation.\par From the questionnaires of the ECOPRED 2014 we selected 15 independent variables (X) that represents city´s average characteristics of the young people (14 a 24 years), their families, their friends, their neighbors, their coworkers and their classmates; and from the CONTEO 2015 database we obtained 6 independent variables (Z) that account for social and economic conditions of the city. Some other variables available in ECOPRED 2014 and CONTEO 2015 were not considered due to the high collinearity with the variables already included or the high number of missing observations. Appendix Table \hyperref[tab_1]{A1} presents a brief description of each variable selected.\par Table \hyperref[tab_3]{3} shows the descriptive statistics of such independent variables. The top section describes the city level variables obtained from ECOPRED 2014. The average city has 51 percent of young men, 61 percent of their youth live in a home with both parents, 62 percent study and only 38 percent work fulltime. With respect to youth activities, 49 percent report exercise regularly, 26 percent report ever getting drunk, 8 percent report having used a soft drug such as marijuana, and 3 percent report having used hard drugs such as cocaine. 29 percent of young people declare being bullied and 28 percent declare being robbed. With respect to their environment, 38 percent of young people admit that at home their family push, shut or criticize, 3 percent admit that in their house there is at least a person who dinks, smokes, uses drugs or bets a lot, 47 percent report having at least a friend who drinks alcohol, smokes, takes drugs, commits violent acts, carries weapons or has been arrested, 49 percent declares that in their neighborhood there is graffiti, people drinking in the streets or people playing music at a very high volume, and 43 percent comment that in their school or work there is physical or verbal violence or there is alcohol or drug use. The bottom section of table 3 lists the city level independent variables obtained from CONTEO 2015. The first five variables presented are official numbers calculated by the Mexican Council for the Evaluation of Social Development Policy (CONEVAL) and the last variable was calculated directly from the CONTEO 2015 database. The average city has 42 percent of its population living with an income below CONEVAL´s poverty line, 16 percent of its 3 to 15 years old population do not attend a formal education center (education gap), 29 percent of its population do not have access to any public or private health care or health insurance, 50 percent do not have access to social security, and 10 percent do not have access to basic services (water, drainage, electricity and gas). For a better description of CONEVAL´s multidimensional poverty measures please refer CONEVAL (2010). The average city has an unemployment rate of 4 percent.\par In order to verify the absence of multicollinearity in the regressions, simple correlations between independent variables were obtained and the statistical VIF tests were performed. In general, correlations are low. The average correlation is 0.28 and the highest correlations is 0.65 between the percentage of young people who report that in their neighborhood there is graffiti, people drinking in the streets, or people playing music at a very high volume (Bad neighbors) and the percentage of young people being robbed (Robbed). In the VIF tests, the values for all the independent variables do not exceed 6 and have an average value of 3.72. VIF values are not high, since it is considered that there is evidence of multicollinearity when the VIF value is greater than 10. In order to save space, correlation coefficients and VIFs are not presented but can be provided by the authors upon request. \section[{VI.}]{VI.} \section[{Empirical Strategy}]{Empirical Strategy}\par According to \hyperref[b12]{Elffers (2003)}, the propensity of an individual to commit a violent act can be explained by the characteristics of the social and economic environment in which people live and by the characteristics of the surrounding regions. Therefore, the rates of violence and criminality in a city depend on the characteristics of the city and its inhabitants, as well as on the influence of nearby cities.\par In order to estimate the impact of certain factors on the probability that a young person between 14 and 24 years old commits a violent act, we specify the following linear regression model corrected by a possible spatial dependence of the data: \section[{Y=X?+Z?+?}]{Y=X?+Z?+?}\par To consider the possible spatial autocorrelation or spatial dependence of the data, we assume that geographically located observations are related to nearby observations following certain patterns. Thus, to handle the possible correlation of the data, the use of spatial econometric techniques is needed. It is worth to mention that if the existence of spatial autocorrelation is identified, if it is not controlled, there is a risk of obtaining biased estimators, which would lead us to make erroneous inferences \hyperref[b11]{(Dubin, 1998)}.\par Hence, the relationship between the percentage of young people committing violent acts within a city and the own characteristics of the city should be measured considering that the percentage of young people committing violent acts depends also on the percentage of young people committing violent acts in nearby cities as well as other spatially correlated unobservable characteristics; as follows: where Y is one of the five city proxies for youth violence: 1) percentage of young people shouting at others; 2) percentage of young people hitting objects; 3) percentage of young people hitting people; 4) percentage of young people carrying weapons such as knifes or guns; and 5) percentage of young people who have been arrested; X is the vector of characteristics of the young people of the city, including their families, friends, neighbors, coworkers and classmates listed in table \hyperref[tab_3]{3} and explained in Appendix Table \hyperref[tab_1]{A1}, Z is the vector of characteristics of the city such as poverty measures or unemployment, and ? is an error term.\par ?? = ?????? + ???? + ???? + ?? where ?? = ?????? + ?? and ?? ? ??(0, ?? 2 ?? ?? )\par If it is thought that spatial dependence is inherent only to the dependent variable (observable factors), then we have a model known as SAR or Spatial Autoregressive Model, where ?=0. If it is thought that spatial dependence exists only through the error term, that is, when there are omitted, non-measurable or unobservable variables that relate to nearby regions, then we have a model known as SEM or Spatial Error Model, where ?=0.\par The first step when applying spatial econometrics is to define what is considered as a neighbor city, because this definition will allow us to construct a spatial weights matrix (W) to observe spatial correlation patterns. There is not an optimal distance in the literature to consider two cities as neighbors. We experimented with different distances; however, distances lower than 200 kilometers decreased considerably the number of defined neighbor cities reducing the capacity of the model to capture spatial effects; and distances over 400 kilometers increased the number of neighbor cities to almost all cities reducing also the capacity of the model to capture spatial effects. Hence, in this paper we consider a city as a neighbor city if the geographical distance between two cities does not exceed 300 kilometers (187 miles). We also used the inverse distance between cities to construct the spatial weights matrix, however, we didn´t find a good fit with this measure.\par Once the spatial weights matrix (W) is defined, the next step is to define if the spatial autocorrelation in the model is due to observable factors (SAR), factors inherent to the error term (SEM), or both. Two popular statistics used in the literature to choose the spatial econometric model that best explains the spatial behavior of the data are the I-Moran Chi-2 statistic and the individual statistical significance tests for the spatial dependence parameters ? and ? \hyperref[b4]{(Anselin, Bera \& Florax, 1996)}.\par Table \hyperref[tab_5]{4} presents the I-Moran statistics and the individual dependence parameters significance tests for our definition of spatial influence: 300 kilometers. Based on the results in Table \hyperref[tab_5]{4} we can conclude that there is spatial autocorrelation and that in the case of the violence definitions of shouting, hitting objects and arrested the most appropriate spatial autocorrelation model is the SEM model; while in the case of the violence definitions of hitting people and weapons we decided to include both SEM and SAR spatial effect components in the model. The empirical strategy considers the regression estimation of two models. The first model ignores the presence of spatial autocorrelation (OLS) while the second model controls for the spatial behavior of the data (SEM or SEM+SAR, depending on the violence definition). \section[{VII.}]{VII.} \section[{Results}]{Results} \section[{Table 5 displays the regression results without controlling for the presence of spatial autocorrelation (OLS) while table 6 displays the regression results}]{Table 5 displays the regression results without controlling for the presence of spatial autocorrelation (OLS) while table 6 displays the regression results}\par controlling for the spatial behavior of the data (SEM or SEM+SAR) for the five definitions of youth violence: 1) percentage of young people shouting at others; 2) percentage of young people hitting objects; 3) percentage of young people hitting other people; 4) percentage of young people carrying weapons such as knifes or guns; and 5) percentage of young people who have been arrested.\par The first thing to note is that the five OLS regressions present considerably fewer numbers of significant variables and relatively lower R-square where ? is a scalar and represents the spatial dependence parameter, and W is a matrix of spatial weights. The error vector ? follows a spatial autoregressive process, where ? represents the parameter of spatial dependence of the errors. Finally, u is a vector of residuals that are distributed normally. It is important to mention that the term WY generates a regression bias, so it must be estimated by Maximum Likelihood instead of OLS {\ref (Anselin, 1998;}\hyperref[b37]{Lee, 2004)}.\par statistics. Also, OLS coefficients are consistently smaller and the F-statistic for hitting people is not statistically significant. These differences confirm that if we do not control for the presence of spatial autocorrelation, we will obtain biased estimated coefficients, as well as relatively lower goodness of fit. For example, with respect to the violent act of shouting, the OLS estimated coefficient of Bad neighbors in table \hyperref[tab_7]{5} indicates, with a statistical significance of 90\%, that an increase in one percentage point in the percentage of young people who report that in their neighborhood there is graffiti, people drinking in the streets, or people playing music at a very high volume increases the percentage of young people shouting at others in 0.10 percentage points. On the other hand, coefficient of Bad neighbor in the spatial autocorrelation model (SEM) presented in table 6 presents, with a better statistical significance of 95\%, a more conservative effect of 0.08.\par Given that due to the presence of spatial autocorrelation the SEM and SEM+SAR models present unbiased and more efficient estimators, in the following lines we will discuss the results obtained by the spatial corrected models, leaving the results of the OLS model as a benchmark. The first two rows of table 6 presents the estimated coefficient of the parameters of spatial dependence: ?-lambda for the SEM model and ?-rho for the SAR model. All coefficients are statistically significant for the five regressions, corroborating the presence of spatial autocorrelation. As we can see in table 6, after controlling for the presence of spatial autocorrelation, having a higher percentage of young men in a city is negatively related to the percentage of young people shouting at others and hitting other people. On the other hand, the percentage of young people living with both parents is positively related to the percentage of young people shouting at others, hitting other people, carrying weapons, and been arrested. Also, the percentages of young people studying and working fulltime are negatively related to the percentage of young people shouting at others but positively related to the percentage of young people carrying weapons and/or been arrested.\par These first results are counterintuitive; we may expect young men to be more violent and young people living with both parents, working fulltime or studying to be less violent. Although individual characteristics do play a role in the violent behavior of young people, these counterintuitive results may indicate that other characteristics of the environment play a more active way on the aggregated behavior of young people.\par The percentage of young people who practice sports regularly is negatively related to the percentage of young people hitting objects and carrying weapons.\par As expected, the percentage of young people who have ever got drunk is positively related to the percentage of young people hitting other people, using weapons and been arrested. The percentage of young people who consume or have used soft drugs such as marijuana, solvents or hallucinogenic mushrooms is positively related with the percentage of young people shouting at others or been arrested. And the percentage of young people who consume or have used hard drugs such as cocaine, amphetamines or crack is strongly positively related with the percentage of young people hitting objects; however, this last variable is negatively related to the percentage of young people hitting others and been arrested.\par In the same sense, the percentage of young people who report that in their house there is a person who drinks, smokes, uses drugs or bets a lot (Vicious family) is positively related to the percentage of young people using weapons or been arrested. And the percentage of young people who report having friends that drink alcohol, smoke or use drugs (Bad friends) is positively related to the percentage of young people shouting at others or hitting other people.\par Although some of the results are counterintuitive (negative coefficients), most estimated coefficients of all variables related to a hostile environment where young people live (Bullied, Robbed, Violent family, Bad friends, Bad neighbors, and Bad school) are positively related to the percentage of young people committing violent acts. For example, the percentage of young people who report that in their neighborhood there is graffiti, people drinking in the streets, or people playing music at a very high volume (Bad neighbors) is positively related to the percentage of young people shouting at others, hitting objects, carrying weapons and been arrested. And for example, a ten percentage points increase in the percentage of young people been bullied increases the percentage of young people hitting objects in 1.8 percentage points, and a ten percentage points increase in the percentage of young people been robbed increases the percentage of young people hitting other people in 3 percentage points.\par Finally, the city´s poverty rate, measured by CONEVAL as the percentage of families with income below the welfare line is positively related to the percentage of young people hitting other people, carrying weapons and been arrested. However, contrary to expectations, COVEVAL´s educational gap (Education gap), lack of access to health services (Health), lack of access to social security (Social Security), and lack of access to basic housing services (Services), and the unemployment rate, which are negative indicators (i.e., the bigger the number, the more bad it is), are negatively related to the percentage of people carrying weapons, hitting other people, or been arrested. \section[{VIII.}]{VIII.} \section[{Conclusions}]{Conclusions}\par Using data from ECOPRED 2014 and CONTEO 2015 and after controlling for the existence of spatial correlation between the 47 Mexican main cities, this paper studies some of the risk factors and characteristics of the Mexican cities that make young people (from 14 to 24 years old) to develop violent behaviors. We define violent behavior as the execution of the violent acts of shouting at others, hitting objects, hitting people, carrying weapons and have been arrested.\par Previous literature of youth violence in Mexico has determined risk factors associated to the propensity of young people to commit violent acts using microdata and conventional econometric analysis. However, this paper incorporates the use of spatial econometrics and corroborates the existence of spatial dependence on youth violence among Mexican cities. It should be remembered that if the spatial correlation is not controlled, there is a risk of obtaining biased results and invalid inferences.\par It is found that, after controlling for the existence of spatial dependency among Mexican cities, there are two main types of risk factors associated with the percentage of young people committing violent acts: one has to deal with drug additions of the young people and their families and the other with a violent environment close to them.\par In the case of the risk factors related to addictions, it is found that the percentage of young people consuming alcohol and drugs and the percentage of young people who report that in their house there is somebody who consumes alcohol or drugs are positively related to having larger groups of young people committing violent acts. In this way, a public policy aimed at preventing the participation of young people in violent acts should consider addressing the problem of additions (both for young people and for their families).\par The other risk factor consistently found in the results is related to the violent environment where young people live. It is found that the percentage of young people who have ever been bullied or robbed and the percentage of young people who report having violent friends, neighbors, coworkers or classmates are positively related to larger percentages of young people committing violent acts. These findings allow us to affirm that violence generates violence and that violence can be considered an epidemic (this is, it is contagious!).\par Thus, a public policy that seeks to prevent the participation of young people in violent acts must also focus on combating violence in public places (streets, schools, workplaces) through specific campaigns and programs that promote reporting violent acts to the authorities and that ensure a punishment for those who commit any reported type of violence. Internationally it has been found that family-focused programs such as "schools for parents", "safe schools" and "drug free schools" have good results in decreasing the percentage of young people participating in violent acts.\par Public policies focusing on improving the environment where young people live, mainly in the use of drugs and alcohol, will have a direct effect on reducing the violent behavior of young people. In addition, since the existence of spatial effects has been corroborated, individual efforts made by cities to reduce the violence of their youth, in addition to having an effect on their own youth, will have an effect on the violent behavior of youth in nearby cities. Therefore, joint efforts of neighboring cities to fight youth violence could have greater impacts than individual efforts made by single cities.\par Finally, it is worth remembering, as discussed in the literature section, that public prevention policies, designed and implemented at the local level, by municipal authorities or by the community, are usually more effective than policies designed from the center of the country and implemented in a generalized way. Likewise, a public policy regarding the prevention of youth violence must be consistent over time and for this it is necessary to have technical capabilities, political will, as well as long-term human and financial resources since the results of an effective violence prevention policy are not visible in the short term.\begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-2.png} \caption{\label{fig_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-3.png} \caption{\label{fig_1}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_0}Table 1 ,}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.25549132947976877\textwidth}P{0.39797687861271674\textwidth}P{0.09335260115606936\textwidth}P{0.06387283236994219\textwidth}P{0.034393063583815026\textwidth}P{0.0049132947976878606\textwidth}} \tabcellsep \multicolumn{5}{l}{Shouting Hitting objects Heating people Weapons Arrested}\\ Shouting\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Hitting objects\tabcellsep 0.3621\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \\ Hitting people\tabcellsep 0.3625\tabcellsep 0.1136\tabcellsep 1\tabcellsep \tabcellsep \\ Weapons\tabcellsep 0.2449\tabcellsep 0.4418\tabcellsep 0.4183\tabcellsep 1\tabcellsep \\ Arrested\tabcellsep 0.1665\tabcellsep 0.3458\tabcellsep 0.1629\tabcellsep 0.5562\tabcellsep 1\end{longtable} \par {\small\itshape [Note: Source: Own estimations with data from ECOPRED 2014.]} \caption{\label{tab_1}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.15\textwidth}P{0.065\textwidth}P{0.0625\textwidth}P{0.075\textwidth}P{0.1975\textwidth}P{0.22\textwidth}P{0.08\textwidth}} Variable\tabcellsep \multicolumn{3}{l}{Obs Mean Std Dev}\tabcellsep Max\tabcellsep Min\tabcellsep I-Moran\\ Shouting\tabcellsep 47\tabcellsep 8.4\%\tabcellsep 0.0201\tabcellsep Madero (13.7\%)\tabcellsep Veracruz (3.8\%)\tabcellsep 2.69*\\ Hitting objects\tabcellsep 47\tabcellsep 8.9\%\tabcellsep 0.0183\tabcellsep Mexicali (13.5)\tabcellsep Cd. Juárez (5.7\%)\tabcellsep 3.17*\\ Hitting people\tabcellsep 47\tabcellsep 4.5\%\tabcellsep 0.0219\tabcellsep Aguascalientes (10\%)\tabcellsep Tijuana (1.2\%)\tabcellsep 2.72*\\ Weapons\tabcellsep 47\tabcellsep 3.1\%\tabcellsep 0.0113\tabcellsep León (7.1\%)\tabcellsep Culiacán (1.1\%)\tabcellsep 2.26\\ Arrested\tabcellsep 47\tabcellsep 6.8\%\tabcellsep 0.0271\tabcellsep León (14.3\%)\tabcellsep Villahermosa (2.1\%)\tabcellsep 3.88**\end{longtable} \par {\small\itshape [Note: Source: Own estimations with data from ECOPRED 2014.Statistical significance level: ** 95\%; * 90\%.]} \caption{\label{tab_2}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.2031168831168831\textwidth}P{0.0574025974025974\textwidth}P{0.10597402597402597\textwidth}P{0.14129870129870128\textwidth}P{0.10376623376623376\textwidth}P{0.10376623376623376\textwidth}P{0.13467532467532467\textwidth}} Variable\tabcellsep Obs.\tabcellsep Mean\tabcellsep Std. Dev.\tabcellsep Min\tabcellsep Max\tabcellsep I-Moran\\ Males\tabcellsep 47\tabcellsep 0.51\tabcellsep 0.029\tabcellsep 0.45\tabcellsep 0.58\tabcellsep 0.00\\ Both parents\tabcellsep 47\tabcellsep 0.61\tabcellsep 0.044\tabcellsep 0.53\tabcellsep 0.70\tabcellsep 4.35**\\ Study\tabcellsep 47\tabcellsep 0.62\tabcellsep 0.059\tabcellsep 0.46\tabcellsep 0.72\tabcellsep 0.00\\ Work\tabcellsep 47\tabcellsep 0.38\tabcellsep 0.057\tabcellsep 0.27\tabcellsep 0.55\tabcellsep 0.00\\ Sports\tabcellsep 47\tabcellsep 0.49\tabcellsep 0.051\tabcellsep 0.38\tabcellsep 0.60\tabcellsep 0.83\\ Drunk\tabcellsep 47\tabcellsep 0.26\tabcellsep 0.049\tabcellsep 0.17\tabcellsep 0.34\tabcellsep 10.19***\\ Soft drugs\tabcellsep 47\tabcellsep 0.08\tabcellsep 0.028\tabcellsep 0.04\tabcellsep 0.15\tabcellsep 1.27\\ Hard drugs\tabcellsep 47\tabcellsep 0.03\tabcellsep 0.011\tabcellsep 0.01\tabcellsep 0.05\tabcellsep 0.12\\ Bullied\tabcellsep 47\tabcellsep 0.29\tabcellsep 0.042\tabcellsep 0.20\tabcellsep 0.37\tabcellsep 0.31\\ Robbed\tabcellsep 47\tabcellsep 0.28\tabcellsep 0.066\tabcellsep 0.18\tabcellsep 0.47\tabcellsep 45.12***\\ Violent family\tabcellsep 47\tabcellsep 0.38\tabcellsep 0.047\tabcellsep 0.27\tabcellsep 0.50\tabcellsep 1.85\end{longtable} \par \caption{\label{tab_3}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.85\textwidth}} Year 2021\\ 52\\ Volume XXI Issue I Version I\\ E )\\ (\\ Global Journal of Human Social Science -\\ © 2021 Global Journals\end{longtable} \par \caption{\label{tab_4}}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.85\textwidth}} Source: Own estimations with data from ECOPRED 2014 and CONTEO 2015.\\ Statistical significance level: *** 99\%; ** 95\%; * 90\%.\end{longtable} \par \caption{\label{tab_5}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.160539629005059\textwidth}P{0.19995784148397977\textwidth}P{0.15623946037099493\textwidth}P{0.11037099494097807\textwidth}P{0.1118043844856661\textwidth}P{0.1110876897133221\textwidth}} \tabcellsep \multicolumn{5}{l}{Shouting Hitting Objects Hitting People Weapons Arrested}\\ Males\tabcellsep -0.1162\tabcellsep 0.1840*\tabcellsep -0.215\tabcellsep 0.0233\tabcellsep 0.0131\\ Both parents\tabcellsep 0.1678*\tabcellsep 0.0456\tabcellsep 0.134\tabcellsep 0.078\tabcellsep 0.1233\\ Study\tabcellsep 0.0252\tabcellsep 0.014\tabcellsep -0.0141\tabcellsep -0.0028\tabcellsep 0.0074\\ Work\tabcellsep -0.0617\tabcellsep -0.0214\tabcellsep 0.0054\tabcellsep 0.0156\tabcellsep 0.1123\\ Sports\tabcellsep -0.0191\tabcellsep -0.1112*\tabcellsep 0.0065\tabcellsep -0.0374\tabcellsep 0.1176\\ Drunk\tabcellsep -0.018\tabcellsep 0.0213\tabcellsep 0.0912\tabcellsep 0.0803*\tabcellsep 0.0678\\ Soft drugs\tabcellsep 0.1189\tabcellsep -0.0346\tabcellsep 0.0241\tabcellsep 0.0343\tabcellsep 0.2486\\ Hard drugs\tabcellsep -0.3207\tabcellsep 0.4008\tabcellsep -0.2757\tabcellsep 0.1205\tabcellsep 0.0849\\ Bullied\tabcellsep 0.036\tabcellsep 0.1438\tabcellsep 0.1243\tabcellsep 0.0222\tabcellsep 0.1466\\ Robbed\tabcellsep -0.1295\tabcellsep -0.1264*\tabcellsep 0.1790*\tabcellsep 0.0249\tabcellsep -0.1147\\ Violent family\tabcellsep -0.0096\tabcellsep 0.0804\tabcellsep 0.0577\tabcellsep 0.0515\tabcellsep -0.0152\\ Vicious family\tabcellsep 0.4385\tabcellsep -0.0253\tabcellsep -0.4395\tabcellsep 0.1848\tabcellsep 0.1374\\ Bad friends\tabcellsep 0.1055\tabcellsep 0.0941\tabcellsep 0.1779\tabcellsep 0.0088\tabcellsep -0.0466\\ Bad neighbors\tabcellsep 0.1032*\tabcellsep 0.0932*\tabcellsep -0.0959\tabcellsep 0.0056\tabcellsep 0.0928\\ Bad school\tabcellsep 0.0044\tabcellsep -0.1263\tabcellsep -0.0597\tabcellsep -0.0686\tabcellsep -0.1135\\ Poverty\tabcellsep 0.0662\tabcellsep 0.0708\tabcellsep 0.0355\tabcellsep 0.0639*\tabcellsep 0.0523\\ Education gap\tabcellsep 0.0664\tabcellsep -0.0491\tabcellsep -0.1666\tabcellsep -0.0738\tabcellsep -0.0073\\ Health\tabcellsep 0.0062\tabcellsep 0.0111\tabcellsep -0.1272\tabcellsep -0.0092\tabcellsep -0.0642\\ Social Security\tabcellsep 0.054\tabcellsep 0.0144\tabcellsep 0.1401*\tabcellsep 0.0112\tabcellsep -0.0346\\ Services\tabcellsep -0.0416\tabcellsep 0.0729\tabcellsep 0.0485\tabcellsep 0.0249\tabcellsep -0.044\\ Unemployment\tabcellsep -0.7708\tabcellsep -0.4381\tabcellsep 0.281\tabcellsep -0.5981*\tabcellsep -0.4195\\ Constant\tabcellsep -0.0615\tabcellsep -0.0896\tabcellsep -0.0845\tabcellsep -0.0566\tabcellsep -0.11\\ N\tabcellsep 47\tabcellsep 47\tabcellsep 47\tabcellsep 47\tabcellsep 47\\ F\tabcellsep 2.0787**\tabcellsep 2.3579**\tabcellsep 1.6044\tabcellsep 2.5732**\tabcellsep 2.2312**\\ adj\textunderscore R2\tabcellsep 0.33\tabcellsep 0.3827\tabcellsep 0.2163\tabcellsep 0.418\tabcellsep 0.3598\\ \tabcellsep \multicolumn{5}{l}{Source: Own estimations with data from ECOPRED 2014 and CONTEO 2015.}\\ \tabcellsep \tabcellsep \multicolumn{4}{l}{Statistical significance level: *** 99\%; ** 95\%; * 90\%.}\end{longtable} \par \caption{\label{tab_7}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.14107883817427386\textwidth}P{0.1751728907330567\textwidth}P{0.11168741355463348\textwidth}P{0.1616528354080221\textwidth}P{0.12755878284923927\textwidth}P{0.13284923928077455\textwidth}} \tabcellsep \multicolumn{3}{l}{Shouting Hitting Objects Hitting People}\tabcellsep Weapons\tabcellsep Arrested\\ \tabcellsep (SEM)\tabcellsep (SEM)\tabcellsep (SEM+SAR)\tabcellsep (SEM+SAR)\tabcellsep (SEM)\\ ?-lambda\tabcellsep -2.6788***\tabcellsep -5.2538***\tabcellsep -2.0454**\tabcellsep -6.7515***\tabcellsep -6.0074***\\ ?-rho\tabcellsep \tabcellsep \tabcellsep -0.4049**\tabcellsep -0.2538**\tabcellsep \\ Males\tabcellsep -0.1920***\tabcellsep 0.2412***\tabcellsep -0.2072**\tabcellsep 0.0313\tabcellsep 0.0961\\ Both parents\tabcellsep 0.1220**\tabcellsep 0.0145\tabcellsep 0.1247*\tabcellsep 0.0828***\tabcellsep 0.1241**\\ Study\tabcellsep -0.0846*\tabcellsep -0.0219\tabcellsep -0.0383\tabcellsep 0.0346*\tabcellsep 0.1172***\\ Work\tabcellsep -0.1475***\tabcellsep -0.0052\tabcellsep 0.0118\tabcellsep 0.0229\tabcellsep 0.1109**\\ Sports\tabcellsep -0.0421\tabcellsep -0.1062***\tabcellsep -0.0369\tabcellsep -0.0465**\tabcellsep 0.0377\\ Drunk\tabcellsep -0.081\tabcellsep 0.0232\tabcellsep 0.2223**\tabcellsep 0.0969***\tabcellsep 0.1827***\\ Soft drugs\tabcellsep 0.2849**\tabcellsep -0.0924\tabcellsep 0.066\tabcellsep 0.0883\tabcellsep 0.4838***\\ Hard drugs\tabcellsep -0.2013\tabcellsep 0.5294***\tabcellsep -0.7990**\tabcellsep -0.1007\tabcellsep -0.4747*\\ Bullied\tabcellsep 0.0529\tabcellsep 0.1876***\tabcellsep 0.1066\tabcellsep 0.0618*\tabcellsep 0.2060***\\ Robbed\tabcellsep -0.1993***\tabcellsep -0.1377***\tabcellsep 0.3073***\tabcellsep 0.034\tabcellsep -0.2439***\\ Violent family\tabcellsep 0.004\tabcellsep 0.0185\tabcellsep 0.1182\tabcellsep 0.0483\tabcellsep -0.0421\\ Vicious family\tabcellsep 0.3077\tabcellsep 0.2842\tabcellsep -0.8138***\tabcellsep 0.2238**\tabcellsep 0.6862***\\ Bad friends\tabcellsep 0.1172*\tabcellsep 0.0579\tabcellsep 0.2482***\tabcellsep 0.033\tabcellsep -0.1424**\\ Bad neighbors\tabcellsep 0.0871**\tabcellsep 0.0654*\tabcellsep -0.1300***\tabcellsep 0.0359*\tabcellsep 0.1520***\\ Bad school\tabcellsep 0.1305**\tabcellsep -0.0541\tabcellsep -0.0731\tabcellsep -0.1092***\tabcellsep -0.1776***\\ Poverty\tabcellsep -0.0115\tabcellsep 0.0323\tabcellsep 0.0945*\tabcellsep 0.1312***\tabcellsep 0.1551***\\ Education gap\tabcellsep 0.0693\tabcellsep -0.0106\tabcellsep -0.2809***\tabcellsep -0.1155***\tabcellsep 0.079\\ Health\tabcellsep -0.0658\tabcellsep 0.0678\tabcellsep -0.1646***\tabcellsep 0.0087\tabcellsep 0.0763\\ Social Security\tabcellsep 0.0563\tabcellsep -0.0463\tabcellsep 0.1967***\tabcellsep 0.0056\tabcellsep -0.1557***\\ Services\tabcellsep 0.0157\tabcellsep 0.0345\tabcellsep 0.0973**\tabcellsep 0.0242*\tabcellsep -0.0847***\\ Unemployment\tabcellsep -0.1759\tabcellsep -0.4757\tabcellsep -0.1341\tabcellsep -0.9913***\tabcellsep -1.0481**\\ Constant\tabcellsep 0.1038\tabcellsep -0.0519\tabcellsep -0.1285\tabcellsep -0.1169***\tabcellsep -0.1732***\\ N\tabcellsep 47\tabcellsep 47\tabcellsep 47\tabcellsep 47\tabcellsep 47\\ chi2\tabcellsep 257.55***\tabcellsep 655.76***\tabcellsep 105.58***\tabcellsep 986.36***\tabcellsep 3579.18***\\ psd\textunderscore R2\tabcellsep 0.5242\tabcellsep 0.5518\tabcellsep 0.5418\tabcellsep 0.6354\tabcellsep 0.5414\\ \tabcellsep \multicolumn{5}{l}{Source: Own estimations with data from ECOPRED 2014 and CONTEO 2015.}\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Statistical significance level: *** 99\%; ** 95\%; * 90\%.}\end{longtable} \par \caption{\label{tab_8}Table 6 :}\end{figure} \footnote{© 2021 Global Journals © 2021 Global Journals} \footnote{© 2021 Global Journals} \backmatter \subsection[{Appendix}]{Appendix} \begin{bibitemlist}{1} \bibitem[Guadalajara]{b42}\label{b42} \textit{}, México Guadalajara . 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