Explaining Feelings of Safety in Honduras: Demographic

EXPLAINING FEELINGS OF SAFETY IN HONDURAS: DEMOGRAPHIC

VULNERABILITIES, PERCEPTIONS OF LOCAL ORDER, AND ORGANIZATIONAL

PARTICIPATION

A Thesis

Submitted to the Graduate School

of the University of Notre Dame

in Partial Fulfillment of the Requirements

for the Degree of

Master of Arts

Leslie MacColman

Erin McDonnell, Director

Graduate Program in Sociology

Notre Dame, Indiana

June 2016

Leslie MacColman

EXPLAINING FEELINGS OF SAFETY IN HONDURAS: DEMOGRAPHIC

VULNERABILITIES, PERCEPTIONS OF LOCAL ORDER, AND ORGANIZATIONAL

PARTICIPATION

Abstract

Leslie MacColman

Honduras has exceptionally high rates of crime and violence, a great deal of which occurs in low-income, urban neighborhoods. Despite this, little scholarly attention has been given to how safe (or unsafe) the residents of such neighborhoods feel and how their levels of fear vary based on demographic attributes, perceptions of disorder, social relationships, and participation in local organizations. In this thesis, I leverage survey data collected in eleven low-income neighborhoods from across Honduras. Using a series of multinomial logistic models, I show that perceptions of social disorder and community cohesion and prior victimization are strong predictors of fear and that these variables provide greater explanatory power than the demographic attributes commonly referred to in studies from the United States and other developed countries. I then turn to the issue of collective efficacy, showing that individuals with higher levels of participation in community organizations are less likely to express feelings of fear, with this effect being the strongest for state-oriented - rather than civic or religious - organizations. This research serves to extend and deepen scholarly understanding of fear of crime in the unique context of Central America.

To the baby I carry, who will soon come into the world.

May you grow up safely and multiply the love with which you are received.

CONTENTS

TABLES ...... v

ACKNOWLEDGMENTS ...... vi

CHAPTER 1: INTRODUCTION ...... 1

1.1 Introduction ...... 1

CHAPTER 2: EXPLAINING FEAR OF CRIME ...... 5

2.1 Understanding Fear of Crime ...... 5

2.2 Social and Demographic Vulnerabilities ...... 7

2.3 Perceptions of Neighborhood Disorder ...... 9

2.4 Personal Efficacy as Organizational Participation ...... 13

2.5 Relationship with Public Authorities ...... 18

CHAPTER 3: CASE BACKGROUND ON HONDURAS ...... 20

3.1 Patterns of Victimization ...... 20

3.2 Individual-Level Responses ...... 23

CHAPTER 4: DATA AND METHODS ...... 27

4.1 Data ...... 27

4.2 Dependent Variable ...... 28

4.3 Independent Variables ...... 28

4.4 Analytic Methods ...... 32

CHAPTER 5: RESULTS ...... 34

5.1 Model Results ...... 34

iii

CHAPTER 6: DISCUSSION ...... 46

6.1 Discussion...... 46

BIBLIOGRAPHY ...... 59

TABLES

TABLE 1: DESCRIPTIVE STATISTICS OF MAIN VARIABLES ...... 31

TABLE 2: CORRELATIONS ...... 33

TABLE 3: MLOGIT RESULTS OF MODELS 1 AND 2 ...... 38

TABLE 4: MLOGIT RESULTS OF MODELS 1 AND 2 ...... 39

TABLE 5: MLOGIT RESULTS OF MODELS 1 AND 2 ...... 40

TABLE 6: MLOGIT RESULTS OF MODELS 3 AND 4 ...... 43

TABLE 7: MLOGIT RESULTS OF MODELS 3 AND 4 ...... 44

TABLE 8: MLOGIT RESULTS OF MODELS 3 AND 4 ...... 45

TABLE A.1: FINDINGS ON THE DETERMINANTS OF FEAR OF CRIME ...... 51

ACKNOWLEDGMENTS

First, I wish to thank my advisor, Dr. Erin McDonnell. I appreciate her willingness to take a gamble on a student who – at the time - she barely knew and her confidence in my ability to complete this project. Under her tutelage, I found the right balance of patience and distance, encouragement, and urgent intervention in moments of technical or intellectual crisis. The steep learning curve has been well worth it, as it has moved me incrementally closer to the scholarly excellence that Erin exhibits and encourages in all of her students. The same gratitude extends to the other members of my committee, Dr. Kraig Beyerlein, whose charitable words gave me an emotional recharge at more than one critical juncture, and Dr. Ann Mische, whose unique mix of intellectual acuity, genuine humility, and overwhelming kindness continues to inspire me.

Second, I want to express deep thanks to my counterparts at the Fondo Hondureño de

Inversión Social (FHIS). Without their interest and willingness to share data, this work would have been impossible. In particular, I wish to thank Lic. Zunilda Martell for her leadership and support of my work. I am also deeply indebted to Oscar Matute Mandujano, whose professional competence, curiosity, and excellent sense of humor make him a pleasure to work with. It is my greatest hope that this thesis – and the related research projects that have emerged since – will, in some small way, strengthen the work done by FHIS and its partner organizations. Yet, I have no illusions; it is not academia that will transform the Honduran reality but the knowledge, vision, and perduring efforts of committed citizens on either side of the state / non-state divide. Indeed, it is the community leaders, whose identities are obscured behind statistics in the present account, who should be acknowledged as the most vital agents of change.

Lastly, I wish to thank Demián Gómez, my partner, my best friend, and the father of my child. Demián, nunca sabrás cuánto te quiero. I could never have come this far without you.

CHAPTER 1:

INTRODUCTION

1.1 Introduction

The impact of violence on everyday life in Honduras can hardly be overstated. In 2012,

Honduras reported a staggering homicide rate of 90.4 per 100,000 (UNODC 2013). A salient exemplar of the Central American ‘insecurity crisis,’ this murder rate was nearly four times the regional average (24 per 100,000) in the same year. Contextualized at the global scale, Honduras’

2012 homicide rate was approximately 14 times greater than the worldwide average (6.2 per

100,000) and nearly 20 times larger than that of the United States of America (4.7 per 100,000)

(ibid). Rapid and sustained growth in homicides rates over the past decade has earned Honduras the ignominious title of “most violent place on the planet” (Rivera, 2013). Yet, national averages belie great variability along geographic and socio-economic lines. In 2013, for example, 65% of homicides occurred in just 5% of urban municipalities (IUDPAS, 2014), such that acute violence is concentrated. While murder rates are an imperfect proxy for other types of violent and non- violent crime, available data indicate that they are often coupled (Serrano-Berthet & Lopez,

2011). This coupling points to the concentration of criminal activity in locales with particular social and economic characteristics; the dynamics surrounding this phenomenon are, as yet, poorly understood by researchers.

In an attempt to explain the myriad factors contributing to the current insecurity crisis

(and ultimately inform interventions to ameliorate it), a small but growing amount of research has looked at the causes of crime and violence in Honduras and other Central American countries

(Berg & Carranza, 2015; Bruneau, 2014; Cruz, 2011; L. del C. G. Rivera, 2011; L. G. Rivera,

2013). A parallel body of research has examined the effects of crime and violence. Scholars have

examined, for example, how victimization impacts support for democracy (Bateson, 2012;

Seligson & Booth, 2010), political participation (Alvarado, 2010; Bateson, 2012), trust in

institutions (Blanco, 2013; Corbacho, Philipp, & Ruiz-Vega, 2012), and economic indicators

(Berthet & Lopez, 2011). Far less attention, however, has been given to how crime and violence

affect the subjective wellbeing of Hondurans living in neighborhoods where they are most acute

(for one notable exception see: Hansen-Nord et al., 2014).

How do the residents of marginalized neighborhoods characterize their situation in terms of safety and security? Which individual-level attributes, context-relevant perceptions, and social relationships are most strongly associated with higher (or lower) feelings of safety? What, if anything, makes the residents of low-income neighbors feel secure in their homes? These are the questions that motivate the present study. The answers to these questions promise a more nuanced understanding of the variable impacts of the Central American insecurity crisis on different segments of ‘the urban poor,’ which otherwise runs the risk of being conceived of as a monolithic block. It may also help illuminate the relationship between perceptions of safety and other attitudes and behaviors which strengthen or diminish local social order.

The aim of this paper is twofold. First, on the basis of extant literature on fear of crime, which is largely based on research from developed countries, I aim to test the utility and transposability of standard individual-level predictors in a unique international context:

Honduras. This is important not only in the context of the aforementioned security crisis, but also because Latin American cities appear to have different relationships between poverty, social organization, and crime (Sampson, 2012: 167).

In order to examine how individual-level attributes affect fear of crime among the urban poor, I draw on survey data collected in eleven neighborhoods distributed throughout five municipalities throughout Honduras. Using a series of multinomial logistic models, I show that

perceptions of social disorder and prior experiences of victimization are strong predictors of fear

among the residents of low-income neighborhoods, and provide greater explanatory power than

the demographic attributes commonly referred to in studies from the United States and other

developed countries. Overall, increased perceptions of order and community cohesion are

associated with stronger feelings of personal security, which points to the importance of parochial

ties (R. J. Bursik & Grasmick, 1993) in shaping localized sense-making. Findings are less

conclusive with regards to the relationship between fear of crime and public order, since police

presence appears not to affect feelings of safety crime and increased public sector investment fails

to dramatically reduce feelings of fear.

The second goal of this paper is to expand scholarly understandings of how collective efficacy (Sampson, Raudenbush, & Earls, 1997) operates at the individual level and, in particular, whether participation in different types of religious, civic, and state-oriented organizations is differentially associated with fear of crime. This provides a necessary corrective to previous studies of fear of crime which have tended to confuse measures of social networks or perceptions of the generalized other with measures that better capture an individual’s willingness to intervene in community affairs. It also calls for deeper theorization of how the composition, function, and relational attributes of distinct types of organizations may influence the subjective security perceptions of their members. Using the same sample of individuals from low-income Honduran neighborhoods, I show that organizational participation is, on average, positively associated with feelings of safety. People with a greater number of active memberships are less likely to express fear, even after controlling for demographic, experiential, and perceptual attributes.

My subsequent disaggregation of organizational membership and participation into religious, civic, and state-oriented clusters yields less conclusive results. I show that participation in religious congregations and civic groups, like parent-teacher associations or sports clubs, does little to mitigate fear of crime, whereas membership in state-oriented groups, like elected neighborhood councils, is sometimes associated with lower levels of fear. These findings may

mean that individual-level participation in action oriented towards the public good and directly targeting public authorities enhances personal efficacy and, in turn, decreases fear of crime.

Alternately, the causal arrow may be reversed, and the cross-sectional data may be demonstrating the increased propensity of less fearful individuals to take on highly visible positions of leadership. In either case, the findings highlight the need for a more robust and sophisticated analysis of the relationship between fear of crime and organizational participation, not only in

Honduras but in other cities around the world.

CHAPTER 2:

EXPLAINING FEAR OF CRIME

2.1 Understanding Fear of Crime

Crime is a complex social phenomenon that negatively affects the lives of many individuals, particularly in urban areas. But, even more ubiquitous than crime itself, is the fear experienced by individuals, that they or someone they care about will become the victim of a crime. Fear is a subjective and affect-laden phenomenon. When an individual reports feeling afraid in their home or their neighborhood, this is best viewed as an emotional state, rather than a purely cognitive assessment of the risk of victimization (Scarborough, Like-Haislip, Novak,

Lucas, & Alarid, 2010). Fear is an “emotional response of dread or anxiety to crime or symbols that a person associates with crime” (Ferraro, 1995: 4). As such, it the product not only of space - territorially-bounded, cartographic units, like neighborhoods, streets, or dark alleys – but also of place – which includes the myriad cultural meanings ascribed to such units through individually- variable cognitive mapping processes (Benz, 2014; Gieryn, 2000).

Fear of crime and perceived risk of victimization are conceptually distinct (Ferraro, 1995) and studies have shown them to have different determinants (Kanan & Pruitt, 2002). Research indicates that, while fear is influenced by knowledge of criminal activity and personal risk assessments, it is also broader than this. It may “largely be a result of individuals’ perceptions of latent influences present in the surrounding environment rather than of manifest criminal activity”

(Ferguson & Mindel, 2007). Fear of crime thus varies by neighborhood, depending on the prevalence and severity of local crime patterns, and by individual, based on perceptions of common environmental cues. In the present study, I focus my attention on how safe people feel in

their homes, or in other words, their meaning-imbued emotional response to in the face of

proximate crime and violence.

Bursik and Grasmick's (1993) classic treatise, Neighborhoods and Crime: The

Dimensions of Effective Community Control, provides a rich theoretical framework for

understanding and explaining fear of crime in contemporary urban settings. Built on the tradition

of the Chicago school and, particularly, on Shaw and McKay’s theory of social disorganization

(Kubrin & Weitzer, 2003), Bursik and Grasmick’s ecological approach views neighborhoods as

“a complex system of friendship and kinship networks and formal and informal associational ties

rooted in family life and ongoing socialization processes" (Bellair, 1997: 677). In this model, fear

of crime is both a cause and an effect of perceived breakdowns in local order and shared

normative values about appropriate conduct. From the ecological perspective, fear of is seen as

the confluence of personal, social-relational, and contextual factors (R. J. Bursik & Grasmick,

1993; Snell, 2001a; Wyant, 2008). These factors interact in a patterned fashion, giving rise to

several clusters of variables commonly associated with a greater (or lesser) sense of personal

safety. As previously noted, research on fear of crime has relied disproportionately on data from

developed world cities (see: Appendix A). The degree of applicability of these findings and of the

ecological model to the Honduran context is, therefore, an open question.

For the sake of analytical clarity, and in order to link my analysis more explicitly to previous research, I refer to these explanatory clusters as (1) demographic and social vulnerabilities; (2) perceptions of disorder; (3) personal efficacy; and (4) relationship with public authorities. I discuss each of these clusters and review empirical findings about the effects of relevant variables on individual-level fear of crime. I endeavor to clarify and justify the definitions I employ. Yet, it is important to note that the analytical boundedness of these categories is deceptive, at best, since extant studies vary wildly in their operationalization of key concepts and display a noticeable lack of agreement on the differences among social capital, social networks, collective efficacy, willingness to intervene, informal social control, sense of

community, attachment to place, and social homogeneity as they apply to fear of crime (Taylor,

2002: 789).

2.2 Social and Demographic Vulnerabilities

Studies on fear of crime in cities of the developed world consistently indicate that some people are more likely than others to feel afraid of crime, based on their demographic and social characteristics. These characteristics include visible and, sometimes, invisible attributes which are posited to influence a person’s sense of vulnerability; they include sex, age, income, education, prior experiences (criminal victimization), and local social networks (household composition, length of residence in neighborhood).

Sex and age are directly related to vulnerability, insofar as they affect physical prowess and the ability to fend off potential assaults. Previous research has regularly shown elevated fear of crime among women (Box, Hale, & Andrews, 1988; Ferguson & Mindel, 2007; Villarreal &

Silva, 2006; Will & McGrath, 1995; Wyant, 2008; Zhao, Lawton, & Longmire, 2015) and among the elderly (Box et al., 1988; Lane & Meeker, 2000; Will & McGrath, 1995; Zhao et al., 2015).

Paradoxically, these attributes are not necessarily linked to higher risk, because victimization is more frequent among males than females and among youth than the elderly (R. J. Bursik &

Grasmick, 1993; Ferguson & Mindel, 2007). Outwardly visible demographic attributes, such as sex and age, are meaningful only within the specific, social contexts in which people perceive and interpret potential criminal threats, but have consistently emerged as reliable and transposable determinants of fear in cities of the developed world.

Other demographic attributes – such as social status - may be less outwardly visible but many nonetheless exert significant influence on an individual’s perceived vulnerability and resultant feelings of safety. A large body of literature, for example, indicates that educational attainment mitigates fear of crime, such that, all else being equal, individuals with higher levels of

education are less likely to report feeling afraid (Scarborough et al., 2010; Weinrath & Gartrell,

1996; Zhao et al., 2015). The prevailing interpretation of this finding has been that education

provides a buffer against feelings of powerlessness and offers access to resources that can be used

to reduce victimization risks or mitigate adverse consequences. Nonetheless, the posited link

between higher education and decreased fear is by no means infallible, with a handful of studies

showing the reverse effect (Rader, Cossman, & Porter, 2012) or no effect at all (Kanan & Pruitt,

2002).

Like education, income has sometimes been shown to mitigate fear of crime. Although having more cash and material possessions might, seemingly, make people attractive targets for criminals (increase their risk of victimization), income seems to augment individual’s “abilities to insulate themselves from crime, and consequently feel more secure about their surroundings”

(Kanan & Pruitt, 2002: 544). Physical attributes, like sex and age, and demographic attributes related to social status, like income, and education, act independently and in concert to shape a person’s subjective sense of vulnerability and resulting degree of fear. As shown by sophisticated multi-level path models, these variables are mutually implicated and interdependent, such that greater importance cannot be assigned to one or the other (Rader et al. 2012).

Prior experiences of criminal victimization have been shown to increase feelings of personal vulnerability and corresponding fear of crime (Box et al., 1988; Ferguson & Mindel,

2007; Villarreal & Silva, 2006; Weinrath & Gartrell, 1996; Zhao et al., 2015). Perceptions of personal insecurity may also be heightened by ‘vicarious’ or ‘indirect’ victimization, or knowledge that family members, friends, or neighbors have been victimized. Based on this observation, some researchers have speculated that individuals with a greater degree of ‘local connectedness’ might have higher perceptions of risk or feel greater fear, due to their increased knowledge of proximate victimization (Skogan, 1986; Villarreal & Silva, 2006).

Local connectedness means having a more relationships with family members and friends who reside in the same neighborhood. For many people, this is associated with residential stability, living in the same neighborhood for a long time as opposed to moving and being a homeowner as opposed to a renter. Following theories of indirect victimization, having larger or denser local social networks might increase and individual’s knowledge of local crime, leading to increased fear. Yet most research indicates that having a larger numbers of personal ties actually decreases fear of crime (Snell, 2001b), likely because individuals with broader social networks

have more resources with which to prevent victimization and/or mitigate its adverse effects. It

may also be because ‘social connectedness’ is associated with higher levels of trust and belief that

community-level social controls will deter criminal activity in the first place.

2.3 Perceptions of Neighborhood Disorder

Independent of demographic and social vulnerabilities, individual feelings of safety and

security are also influenced by visual signals of the breakdown in community consensus and

crime prevention capacities, which I refer to as perceptions of neighborhood disorder. Closely

following Bursik and Grasmick’s (1993) model, disorder (also called incivilities) refers to

“breaches of community standards that signal erosion of conventionally accepted norms and

values” (LaGrange, Ferraro, & Supancic, 1992) or “minor offenses such as prostitution and

disorderly conduct that destabilize local neighborhoods by creating public fear” (Zhao et al.,

2015). Bursik and Grasmick draw heavily on the idea of disorder as a ‘mediating condition’ to

explain why particular neighborhood-level characteristics - high rates of poverty, racial

heterogeneity, and residential instability - may lead to higher rates of crime. These characteristics

disrupt traditional institutions, such as the family, church, and schools that are essential for

informal social controls over youth, making youth more likely to roam the streets and come into

the criminal fray (McCrea, Shyy, Western, & Stimson, 2005). Disorder constitutes an indicator of

the lack of control, which, following Hunter’s (1985) classic formulation, Bursik and Grasmick

(1993) visualize as operating on three levels: private (close friends, family), parochial

(neighbors, acquaintances), and public (organizations, leaders). When controls on one or more of these three levels begin to break down, the neighborhood becomes subject to minor offenses

(disorder) which, in turn, generate an environment conducive to the commission of more serious crimes.

The concept of disorder, as employed by Bursik and Grasmick (1993) emerged out of the earlier tradition of social disorganization. In recent years, this concept has been partially rebranded under headers such as ‘collective efficacy’ and ‘social capital,’ each of which emphasizes slightly different aspects of the complex relationship between signals of breakdown in formal and informal controls and the capacity of neighborhood residents to counter them

(Taylor, 2002). Here, I privilege the concept of disorder (and its logical converse, order) over alternative constructs because it allows me to distinguish between individuals’ perceptions of their proximate neighborhood environment - the actions and motives of others within the community, particularly those which indicate breakdowns in social control – and their perceptions of their own capacity to act within this environment – by developing associational ties which may, over time, improve social control.

As an analytic construct, disorder has often been operationalized as a supra-individual / group-level attribute, that is, something ascribed to neighborhoods as a whole, based on the prevalence of particular behaviors and environmental conditions (Bursik, 1988; Villarreal &

Silva, 2006). It is relatively easy, however, to make a case for individual-level variations in perceptions of disorder, based on differences in exposure, values, and symbolic sense-making among neighborhood residents (Vogel & Meeker, 2001) and to argue that distinct perceptions of disorder lead to greater (or lesser) degrees of fear. Indeed, many authors have done so (Bursik &

Grasmick, 1993; Ferguson & Mindel, 2007; Kanan & Pruitt, 2002; Renauer, 2007; Scarborough et al., 2010). Such an approach recognizes that signals of disorder are symbolic and physical cues

which are, themselves, ambiguous, absent frames of contextual social meanings (Sampson, 2013).

Residents of the same neighborhood may interpret the same social and environmental cues in

distinct ways. Yet, individual variation aside, signals of disorder will tend to generate increased

perceptions of uncertainty, risk of victimization, and loss of control, resulting in higher levels of

fear.

The assertion that perceptions of increased disorder are associated with lower individual- level feelings of safety has found consistent empirical support (Gibson, Zhao, Lovrich, &

Gaffney, 2002; Kanan & Pruitt, 2002; McCrea et al., 2005; Scarborough et al., 2010; Zhao et al.,

2015). Studies from urban centers in the ‘developed world’ have shown that fear can result from purely physical cues, such as graffiti, overgrown weeds and shrubs, abandoned cars, and – most famously – broken windows, as well as social cues, like loud parties, public drunkenness, illegal drug use, or groups of truant teenagers. Most likely, perceptions of disorder are a combination of both.1 Expectations about ‘acceptable’ social behavior and environmental conditions are contextually and culturally variable. Thus, local interpretations of what constitutes ‘disorder’ may vary across settings (Sampson, 2013; Sampson & Raudenbush, 2004; Vogel & Meeker, 2001)

Yet, the concept of disorder itself is sufficiently broad to be fruitfully applied to the Honduran context.

Many studies from the US and other developed countries collapse indicators of social and environmental disorder into a single index (Ferguson & Mindel, 2007; Gibson et al., 2002;

1 Studies have operationalized ‘disorder’ in dozens of ways, including the following: rundown housing, empty buildings or lots, having the wrong kinds of people moving in, and the presence of neighbors who cause trouble (Kanan and Pruitt 2002); people being beaten up, drunk drivers, public drinking, groups of teenagers hanging out and harassing, youth gangs, use of illegal drugs, home break-ins, and robberies (Gibson, Zhao, Lovrich, and Gaffney 2002); measures of vandalism and cleanliness (McCrea, Shyy, Western, and Stimson 2005); drinking in public, speeding/ reckless driving, stealing of car registration stickers, gang presence, prostitution, loud music/parties, homelessness, begging, loitering, truancy, vandalism, garbage, litter, abandoned cars, illegally parked cars, rundown buildings and homes, overgrown weeds and shrubs, and graffiti (Scarborough, Like-Haislip, Novak, Lucas and Alarid 2010); and proximity of instances of loitering, driving while intoxicated, drunkenness, drug charges, gambling, liquor law violations, simple assault, vagrancy, vandalism, vice crimes, and runaways (Zhao, Lawton, and Longmire 2015).

McCrea et al., 2005; Renauer, 2007; Scarborough et al., 2010). Thus, even when low-level

criminal activity, like drug-dealing or gang presence are included (most often they are not), it is

impossible to gauge how such these phenomena affect peoples’ subjective sense of safety.

Presumably, the presence of street gangs or organized criminal groups constitutes a particularly

salient and unambiguous indicator of the breakdown of conventional norms and the loss of social

control in any neighborhood. Gangs may also evoke a distinct or deeper sense of fear, since gang

violence is often seen as random and threatening to unfortunate passers-by (Lane & Meeker,

2000). Thus, even in the absence of sufficient empirical study, perceived gang presence could be

expected to have a larger effect on fear than other forms of disorder for most urban residents.

Individual perceptions of disorder – defined as cues of the absence of effective social controls – are also linked to people’s generalized understandings of social cohesion, or the degree to which they know and trust the other members of their community. There is strong support for the idea that people who view their community as more cohesive tend to feel less afraid of crime, all else being equal. For example, previous research has found that decreased fear of crime is associated with: characterizing one’s neighborhood as somewhere were people ‘mostly help each other’ (Box et al., 1988); knowing more neighbors by name (Scarborough et al., 2010); asking neighbors to watch one’s home while away or being asked to do the same (Ferguson & Mindel,

2007); or characterizing one’s neighbors as ‘close-knit,’ ‘trustworthy’ or ‘willing to help’

(Abdullah, Marzbali, Bahauddin, & Tilaki, 2015; Renauer, 2007). Nonetheless, it is exceedingly difficult to compare studies inter alia, due the lack of common measures. Further, there is a certain degree of conceptual confusion between social cohesion and related concepts, such as

‘social integration’ and ‘collective efficacy’ (see: Gibson et al., 2002; Taylor, 2002).

Bursick and Grasmik (1993) emphasize the complementarity of private, parochial, and public controls in the co-production of local security. They see local-level relational networks - among family members, friends, and neighbors - as necessary but insufficient for the exercising public control, which they define as “the ability of a neighborhood to influence political and

economic decision making and to acquire externally based goods and services that may increase

its ability to control the level of crime in the area” (ibid 52). Public control involves not only kin

groups and informal networks, but also more formal neighborhood associations and external

actors, such as the police. Unfortunately, “there has not been sufficient empirical research to

ascertain whether informal, formal, or both levels of social control influence emotional fear of

crime” (Renauer 2007: 43), and, the police have been particularly neglected in relevant studies on

fear of crime.

Empirical studies on the relationship between fear of crime and perceptions of the police have yielded ambiguous results (Silverman & Della-Giustina, 2001). Some have found a significant, negative relationship between police satisfaction and fear of crime, such that citizens who view the police as effective or trustworthy report feeling less afraid (Box et al., 1988;

Karakus, McGarrell, & Basibuyuk, 2010; Zhao et al., 2015). Other studies find no relationship once demographic variables and other perceptions of disorder are controlled for (Ferguson &

Mindel, 2007; Scarborough et al., 2010). Renauer (2007) disaggregates perceptions of police and policing activities along several dimensions, finding perceived police effectiveness and fear of the police to be significant (albeit competing) predictors of fear of crime, while knowledge of specific police activities had no effect. Thus, despite the vital role of the police in establishing public order and preventing criminal activity, it is still unclear how residents’ perception of them affects subjective feelings of fear.

2.4 Personal Efficacy as Organizational Participation

Demographic attributes and prior experiences of victimization combined with perceptions of neighborhood disorder account for a great deal of variation in feelings of safety and security.

But, independent of these factors, individuals may also exhibit higher (or lower) degrees of fear based on their capacity to intervene in community affairs, taking concrete actions to make their

neighborhoods more cohesive, more livable, and ultimately safer. This is what I refer to as

personal efficacy and operationalize as participation in different types of local organizations.

Defined in this fashion, personal efficacy represents a more constrained version of the

neighborhood-level construct which is ubiquitous in prevailing ecological explanations for

variable rates of crime and fear of crime: collective efficacy.

As formulated in Sampson, Raudenbush, and Earl's (1997) classic article, collective efficacy is a neighborhood-level attribute which refers to “social cohesion among neighbors combined with their willingness to intervene on behalf of the common good” (918, emphasis mine). It operates at all levels of social control – private, parochial, and private – and relies on, but transcends the social bonds present in these realms. Research taking neighborhoods as the unit of analysis has shown consistently that higher levels of collective efficacy are associated with lower rates of crime and delinquency (Sampson, 2012). As noted by Taylor (2002), the commonly-accepted definition of collective efficacy is strikingly similar to Bursik and

Grasmick’s (1993) concept of social control but reflects a shift among researchers towards the mechanisms by which social control actually occurs. One key mechanism is the participation of

local residents in formal or informal organizations (organizational participation), which helps

foster shared norms, strengthen bonds of trust, and facilitate the solicitation of external resources

to solve crime-related problems.

There is a high level of scholarly consensus around the conceptual and empirical validity

of collective efficacy. However, the translation of this concept across theoretical models – to

explain fear of crime, rather than prevalence of crime – and the shift to a different unit of analysis

– individuals rather than neighborhoods – has yielded fuzzier results. One reason for this is the

variability with which efficacy is defined and operationalized. Many studies have focused

exclusively on the first element of Sampson, Raudenbush, and Earls’ (1997) definition, trust or

cohesion, while ignoring the second and arguably more crucial one, willingness to intervene.

Such studies tend to emphasize what I have called perceptions of disorder, measures of

generalized trust or, in some cases, expectations that neighbors will intervene. Most have failed to

take into account the individual’s personal commitment to action. Other studies (Kanan & Pruitt,

2002) have conflated collective efficacy with social networks, focusing on the quantity and

frequency of interpersonal contacts rather than actions to which they purportedly lead (Taylor,

2002). Personal ties may be associated with lower crime rates (Bellair, 1997; Sampson &

Raudenbush, 1999) but they cannot be expected to reduce crime – or fear of crime – unless they

coalesce in the form of concrete social action. Sampson (2013) himself has clarified that “dense

personal or even acquaintanceship ties may facilitate collective efficacy, but they are not posited

as sufficient and therefore do not form the definitional core of the concept” (20). In fact, in some

high-crime neighborhoods dense social ties may actually impede efforts to rid the area of drug or

gang-related crime (Sampson, 2012).

The prevailing psychological perspective is that self efficacy and beliefs about personal control “develop in accordance with lived experience and social context” (Shippee, 2012) and are thus shaped by a persons’ network of relationships with others. At the same time, personal efficacy is situated, rather than general (Sampson, 2012), and implies purposive problem-solving in relation to local insecurity. Like collective efficacy, personal efficacy places emphasis on

“capability for action to achieve an intended effect” and “engagement on the part of residents to solve problems” (Sampson, McAdam, MacIndoe, & Weffer-Elizondo, 2005: 676). For this reason, I suggest that it is helpful to focus on measures of a person’s organizational participation, given that such participation offers relatively unambiguous evidence of willingness to intervene in community affairs. Operationally, this means distinguishing between perceptions of cohesion and measures of actual participation in groups, clubs, and organizations oriented towards the exercise of informal social control and, more broadly, promotion of the public good. This type of engagement is linked not only to crime prevention, but also to fear of crime, insofar as the positive feedback loop between participation, social cohesion, and self efficacy helps residents feel more capable of achieving positive change, individually and collectively.

There are few studies on fear of crime which include measures of organizational participation (and do not combine them with perceptions of social cohesion). Several such studies indicate a positive relationship between ‘civic engagement’ and feelings of safety (Hale, 1996).

Caiazza (2005), for example, shows that civic participation, broadly construed to include congregation-based volunteer programs, arts, culture, and youth support groups, and neighborhood associations or neighborhood watch groups, is associated with increased senses of safety in one’s neighborhood. This effect is stronger for women than for men and for higher- income than lower-income individuals. Similarly, Ferguson and Mindel (2007) show that participation in Community Watch meetings leads to increased neighborhood satisfaction, which in turn decreases fear of crime. Other authors have, however, suggested that civic participation may be “detrimental to people’s perception of safety” (Van den Herrewegen 2010: 85, in De

Donder, De Witte, Buffel, Dury, & Verté, 2012).

It is an open question whether and to what extent organizational participation impacts perceptions of safety, particularly in low-income, high-crime environments. It seems plausible to assume that the ‘civic skills’ acquired in formal and informal associational life (debating and collective decision-making, negotiating, organizing, and community outreach) would impact the personal efficacy of group members and their willingness to intervene in public affairs. Civic engagement literature indicates that not all participation is created equal and that the diverse objectives, organizational structures, and internal dynamics of churches, service-based organizations, and advocacy groups may generate very different outcomes. But the variable impact of different types of civic engagement on fear of crime remains virtually unexplored (De

Donder et al., 2012).

Faith-based associational life is concentrated in churches, which are the most common civic organizations in the United States (as well as Honduras). Church participation is unlike other forms of participation because of its spiritual nature and focus on ‘other-worldly’ problems

(Driskell, Lyon, & Embry, 2008). This has led some researchers to assert that participation in

religious congregations may bolster self-esteem and self-efficacy or ‘provide existential certainty,

and thus a sense of meaning and purpose in life’ (Lim & Putnam, 2010). Congregation-based

participation may also encourage higher levels of non-religious civic activity, such as

volunteering with neighborhood groups or supporting social movements (Ammann, 2014;

Beyerlein & Hipp, 2006; Kwak, Shah, & Holbert, 2004; Schwadel, 2005). This is likely because

people who attend church often have larger social networks and higher degrees of social support

than non-churchgoers (Lewis, MacGregor, & Putnam, 2013). To the extent that church

participation leads to a greater sense of self efficacy, increased perceptions of community

cohesion, and greater capacity for action, it may also be associated with decreased fear of crime.

This effects of church-based participation may – or may not - hold for participation in non-religious civic organizations. Non-religious organizations include community-oriented sports or cultural clubs that organize to provide services for members and state-oriented advocacy groups that organize to improve the provision of public services. The personal connections forged through participation in such groups is likely to differ from those forged through religious participation, which Lin and Putnam (2010) have shown to have a particularly strong effect on personal wellbeing. On the other hand, non-religious groups, due to their ‘this-worldly’ focus, may do more than bolster people’s social support networks. They may offer specific opportunities to address community problems in a collective, purposive fashion and, in doing so, to encourage greater feelings of safety. It remains unclear how effects may differ between community-oriented groups, which operate largely on the level of parochial control, and state-oriented advocacy groups, which operate more expressly on the level of public control and retain a more political character.

One of very few studies that looks at the relationship between feelings of safety and civic engagement indicates that participation in political decision making processes have a stronger effect on feelings of safety than do other forms of participation. This study was carried out by De

Donder, De Witte, Buffel, Dury and Verté (2012) in Belgium and focused specifically on the

elderly population. It found that participation in voluntary associations was associated with higher

feelings of safety, as was increased membership in clubs and social organizations and

engagement in cultural activities. The strongest effect, however, was for political participation –

in this case, respondents’ assessment of their capacity to influence municipal policy. While

Belgium represents a distinct case than that of Honduras, these findings indicate there may be

significant differences in the influence of different forms of organization participation on feelings

of safety.

2.5 Relationship with Public Authorities

Critical to the theoretical model advanced by Bursik and Grasmick (1993) is the idea that

neighborhood-level social controls span multiple levels: private (close friends, family), parochial

(neighbors, acquaintances), and public (organizations, leaders). Breakdown at any one of these levels is sufficient to push neighborhoods towards increased disorder, leading to higher levels of crime and fear. The public level of control is distinct from others, insofar as it often involves relationships between neighborhood residents and external actors, including real-estate investors, non-profit organizations, or public authorities. Independent of community-level characteristics and dynamics of social control, the relationship between external actors and neighborhood leaders can have a profound influence on the effectiveness of community organizing efforts (Sampson et al., 1997; Skogan, 1986). Public authorities, for example, may encourage and support crime- prevention efforts – directly or indirectly – by providing neighborhoods with funding for social programs, investing in public infrastructure, or otherwise facilitating local work. Conversely, they may ‘abandon’ neighborhoods or relegate them the responsibility for crime prevention efforts beyond basic policing. While such variations at the level of public controls / public investments

have been largely ignored in quantitative studies of fear of crime, there is reason to believe that

they retain important explanatory power.

In summary, previous research on fear of crime, carried out mainly in cities of the United

States and other developed countries, shows that individual-level feelings are based on a combination of demographic, perceptual, experiential, and environmental characteristics. Almost universally, women are shown as being more afraid than men, as are individuals who perceive higher levels of neighborhood disorder or lower levels of social cohesion. Conversely, people with higher levels of personal efficacy – defined as pro-social activity in the form of religious or civic participation – appear also to have lower levels of fear. Finally, in relation to the level of public control, lower levels of fear are associated with improved relationships between local residents and public authorities.

CHAPTER 3:

CASE BACKGROUND ON HONDURAS

3.1 Patterns of Victimization

Global homicide statistics underscore the exponential growth of violence in Honduras in recent years, where the murder rate more than doubled between 2005 and 2010 alone (UNODC,

2011). In 2012, Honduras reported a staggering homicide rate of 90.4 per 100,000 (ibid). This was nearly four times the rate for Latin America (24 per 100,000), 14 times the worldwide average (6.2 per 100,000) and 20 times the average for the United States of America (4.7 per

100,000) (ibid). In 2013, the two largest urban centers in Honduras, San Pedro Sula and

Tegucigalpa, were respectively classified as the first and fifth most violent cities in the world.

The same year, San Pedro Sula registered a homicide rate of 318.3 homicides per 100,000 among men between the ages of 20 and 24 (B-Lajoie, D’Andrea, Rodriguez, Greenough, & Patel, 2014).

An overwhelming majority of Honduran homicide victims are young men (Berthet &

Lopez, 2011). In 2012, for example, 91.6% of homicide victims were male; within this group,

63% were between the ages of 15 and 34 (IUDPAS, 2013). Rates in subsequent years were nearly identical (IUDPAS, 2014, 2015). Homicide is the most dramatic form of interpersonal violence and does not necessarily follow the same patterns as non-lethal crimes. However, homicide data is often used as a proxy for insecurity and crime rates, in the absence of reliable statistics, due to victim underreporting and incomplete records (Berthet & Lopez, 2011; B-Lajoie et al., 2014).

Homicide is the most serious form of crime, but it is also among the rarest, with a much larger share of the population affected by non-lethal crimes against their person or their property.

Nationally representative LAPOP survey data from 2010 indicated that 14% of adults had been

the victims of some type of crime in the past year (Berthet & Lopez, 2011). Of these crimes,

approximately 3% were household burglaries (ibid). More recent data, collected by IUDPAS in

2014, indicates that victimization rates may be as high as 20.5% (IUDPAS, 2015). According to both official statistics and victimization surveys, the most common type of crime is armed robbery (IUDPAS, 2006, 2013).

National level statistics belie significant variation by geographic region. The northern part of the country has, by far, the highest rates of homicide – 99.8 per 1,000 people in 2014 – whereas the southern part of the country has a much lower rate – 19.3 per 1,000 people in 2014

(IUDPAS, 2015). The central region is also plagued by a high homicide rate – 73.2 per 1,000 people in 2014 – mainly due to crimes committed in the capitol city of Tegucigalpa (IUDPAS,

2015). Taken together, homicide and other forms of crime are more common in urban areas than rural areas.

Just as crime and violence are unequally distributed across the national territory, they are also disproportionately concentrated among the poor (Berthet & Lopez, 2011; Koonings & Kruijt,

2007; Rodgers, Muggah, & Stevenson, 2009). In a 2007 study of the homeless population in the capitol city of Tegucigalpa, 60% of homeless individuals reported being the victim of severe or moderate physical violence in the preceding 12 months (Doctors Without Borders 2017, in B-

Lajoie et al., 2014). A 2006 victimization survey, also from Tegucigalpa, showed that 62.3% of all armed robbery victims belonged to the lower class or the marginal class (29.4% and 32.9%, respectively). This contrasts starkly with the 8.9% victimization rate among the upper class

(IUDPAS, 2006).

Despite being less attractive targets for economically-motivated crimes, the poor are at higher risk of criminal victimization based on the neighborhoods in which they reside. In cities characterized by stark territorial segregation, the residents of burgeoning informal settlements often face overcrowding, lack of land tenure, degraded public spaces, unsafe environmental conditions, and limited access to safe drinking water, sewerage, electricity, and social services,

such as health care and education (Koonings & Kruijt, 2007). They are also places with a high prevalence of informal market activity, some licit, some illicit. Spatial divisions are mirrored by social divisions, with outsiders viewing settlement areas as ‘no trespassing zones,’ subject to an entirely different code of conduct (ibid).

In low-income Honduran neighborhoods, the small-scale commercial sale of marijuana, crack, and cocaine, as well as larger-scale drug trafficking activities are often associated with gangs. Gangs are a predominantly urban phenomenon and are more likely to emerge in poor neighborhoods (Miguel Cruz, 2010; Rodgers et al., 2009). In such neighborhoods, gangs find

‘local governance voids’ (Koonings & Kruijt, 2007) that offer ample space for economic and territorial expansion. They also find willing recruits among youth facing the compound challenges of poverty and unemployment. For such youth, crime and gang involvement offer “a fast and more stable source of income” (USAID & Proyecto Metas, 2013: 41). Good data is virtually non-existent, but some studies indicate that between 3% and 15% of youth in gang- affected communities end up affiliated with gangs (ibid).

There are three distinct types of gangs currently operating in Honduras. The first kind, local turf gangs (pandillas), is home-grown and has historical roots throughout Central America

(Rodgers et al., 2009). The second kind, large-scale street gangs (maras), is a more recent phenomenon, exported from Los Angeles in the 1970’s and 1980’s by itinerant migrants and deportees (ibid). As of 2011, there were estimated to be at least 900 maras operating in Central

America, with at least 70,000 members (World Bank 2011). The third type of gangs is affiliated with the international drug trade (bandas), managed by a sophisticated and highly-organized transnational network. Due to the paucity of quality data about the perpetrators of crime, it is impossible to know the proportion of crimes perpetrated by affiliates of gangs (Mateo, 2011).

Many analysts highlight the inter-connectedness of crime and gang activity (Bruneau, 2014;

Manwaring, 2005; Miguel Cruz, 2010). Others draw attention to the fact that, while certain types

of gangs may be associated with certain types of crimes, the simplistic association between gang membership and violence may be overblown (Rivera, 2011; Rodgers et al., 2009).

Given the proliferation of both crime and gangs, insecurity is among the foremost concerns of the Honduran population. In a nationally-representative survey carried out by UNDP in 2012, “63 percent of adult respondents reported that they would ‘always’ or ‘almost always’ cross the street if they saw a group of young people, and 54 percent would ‘always’ or ‘almost always’ be afraid of assault when seeing a group of young people on the corner” (UNDP 2012, cited in USAID & Proyecto Metas, 2013: 20). The generalized fear of the population is exacerbated by the seeming randomness of violence. Almost half of all homicides have no clear motive (IUDPAS, 2013) and the public is bombarded by sensationalistic media coverage of crime.

Even in the face of widespread agreement about the gravity of the security situation, there is limited consensus about how best to address this problem. In other contexts, high rates of delinquency and crime often increases demands for police presence. In Honduras, however, the police are highly distrusted. According to a 2012 UNDP survey, 69% of individuals “distrust” the police (USAID & Proyecto Metas, 2013). Indeed, police are often seen as corrupt or directly implicated in the criminal activities of gangs or narco-trafficking organizations. This intense distrust of the public security apparatus helps explain why recent crime victims are more likely to approve of taking the law into their own hands and are 9 percent less likely to believe that the rule of law should always be respected (Serrano-Berthet & Lopez, 2011).

3.2 Individual-Level Responses

The context of crime and violence described in the previous section exhibits both similarities and differences from developed world cities where most research on fear of crime has been carried out. As in other parts of the world, young men and the urban poor experience higher

rate of victimization than other groups and there is wide variation in both crime rates and

victimization patterns among geographic regions. Nonetheless, the ubiquitous, public presence of

different types of gangs – coupled with uncertainty about their role as criminal perpetrators – and

widespread distrust of the police make Honduras a unique case, as compared to many cities in the

developed world. It is therefore difficult to anticipate the extent to which individual-level fear

will be associated with attributes of demographic and social vulnerability, social embeddedness,

organizational participation, and relationships to public authorities. In this section, I review

available data about fear of crime in Honduras, contrasting it with the findings outlined in the

previous sections, and make predictions about how specific characteristics may increase or

decrease feelings of personal safety in low-income neighborhoods.

Demographic and social vulnerabilities - such as being female, elderly, or poor – are associated with increased feelings of fear in many developed world cities, and there are reasons to expect the same patterns in low-income Honduran neighborhoods. In this country, violence is viewed largely as the purview of men, with men constituting 97.5% of all aggressors (Gutierrez

Rivera 2011). Gangs are linked to hyper-masculine behavior and, as such, to the culture of chauvinism (machismo) that is common in Central America (Rodgers, Muggah, and Stevenson

2009; Briceño-León 2005). Public displays of violence acquire particular importance in the

adolescent years, when young men “are obliged to reaffirm themselves in a culture of masculinity

that exposes them to risk (Briceño-León 2005: 1641). This leads me to expect that females will,

on average, feel less safe than men. However, recent data from a nationally-representative public

opinion survey indicate that females are no less likely than men to report feelings of insecurity

(Pérez & Zechmeister, 2015). The same data show insignificant associations between fear and

other commonly-cited vulnerabilities, such as age, education level, and wealth. It is, therefore, an

open question as to whether these attributes will have explanatory power in the more

circumscribed sample of low-income urban neighborhoods that are the focus of the present study.

Among the urban Honduran poor, higher rates of criminal victimization and widespread exposure to violence diminishes individual feelings of safety. Reports of feeling “unsafe” when walking in one’s own neighborhood are, for example, more common among lower class individuals than upper class individuals (IUDPAS, 2006). As reported in one large-scale, qualitative study on youth and violence carried out in 2012 in nine communities in three

Honduras cities, “Youth from all subgroups and community marginalization levels expressed a generalized sense of fear and anxiety about security. Within these groups, marginalizations and insecurity appeared to be linked.” (USAID & Proyecto Metas, 2013: 32). In the same study, youth reported feeling unsafe in their homes but reticent to leave their homes for fear of being targets of crime or violence. Similarly, parents and community leaders in marginal communities reported fear that youth would not live to adulthood and powerlessness in the face of deeply- engrained, systemic violence (ibid).

National polls from Honduras show that prior criminal victimization is associated with

higher levels of fear (Pérez & Zechmeister, 2015) and there is reason to believe that this pattern

holds among the residents of low-income neighborhoods. The same national polls show a strong

association between fear and perceptions of local disorder, including neighborhood crime and

inadequate police presence (ibid). Nonetheless, these findings cannot automatically be extended

to the low-income neighborhoods that are the focus of this study. There are two reasons for this.

The first is the profound distrust of the police, common to many Hondurans, but particularly

acute among the urban poor. As described by USAID & Proyecto Metas (2013), in low-income

communities “High violence levels […] compounded with the fact that community members

view police as participants in organized crime – rather than protectors against organized crime –

make community members feel like they must be in continual fear for their lives, as anyone could

be the next victim.” (201). While police presence is a rare and sought-after commodity in such

neighborhoods, it does not necessarily guarantee order, due to known collusion between police

and criminal elements.

The second reason for which patterns of fear in low-income neighborhoods may differ from those at the national level is the complex social functions of gangs (maras) in such communities. On the one hand, the perceived presence of gangs might be interpreted as a sign of incivilities or breakdown of social norms (disorder). On the other hand, it is well known that gangs often afford protection to local residents or non-gang inhabitants of ‘their’ turf, such that the perceived presence of gangs may actually predict higher feelings of safety or have no effect at all. Considerations aside, I predict that for most residents of low-income communities the presence of gangs will be interpreted as a sign of social disorder and, following Bursik and

Grasmick (1993), will be associated with lower levels of personal safety and security.

According to recent, nationally-representative polling, the most significant predictor of fear of crime in Honduras is interpersonal trust (Pérez & Zechmeister, 2015). People who report trusting their neighbors or being part of a cohesive community are significantly less likely to express feelings of acute insecurity. These findings are consistent with those of developed world cities and are expected to hold in low-income Honduran communities where, arguably, trust is even more important because so much of social and economic life and territorial management is informal. Indeed, as noted by Sampson (2012), there is an unusually strong relationship between concentrated poverty and social cohesion in many Latin American cities, where “collective efficacy may serve something of a survival function by which residents band together to protect each other from the organized forces of drug violence” (167). Based on these considerations, I expect that increased appreciations of community unity will be associated with lower levels of fear. Similarly, I expect that individuals who demonstrate efficacy through participation in local organizations will, on average, feel less afraid than those who don’t. This may differ by type of organization (church, civic, state-oriented) but it is not clear which direction this will go.

CHAPTER 4:

DATA AND METHODS

4.1 Data

The data for this paper come from a survey administered in 2012 in eleven low-income, urban neighborhoods in five municipal districts of Honduras. The data was collected during the evaluation phase of an urban improvement and crime prevention program implemented over the previous six years with funding from the World Bank and the Government of Honduras. This program targeted neighborhoods with high rates of poverty, crime, and violence and financed infrastructure improvements, such as lighting, pavement, and stormwater and sewerage systems, as well as the rehabilitation of public spaces, such as parks. It also attempted to foment participation by channeling resources (mainly training) towards community organizations and at- risk subpopulations (mainly youth) and strengthen channels of communication between local organizations and municipal authorities. Each neighborhood received a different amount of investment and a different mix of social and infrastructure projects.

The survey was a random probability sample of 3,691 households from the eleven neighborhoods in question. In each locale, the number of households surveyed was proportional to population, with a low of 33 households in the smallest neighborhood and 1,041 households in the largest neighborhood. In each household, one respondent over the age of 18 answered a battery of approximately 120 questions covering a range of topics, including household composition, employment, education, health, time of residence in home and neighborhood, land tenure, access to public services, and satisfaction with service provision. Relevant to my areas of research interest, the survey also contained questions about feelings of safety, prior victimization,

perceptions of police and gang (mara) presence, and participation community organizations.

Beginning with the original sample then using listwise deletion to exclude respondents without

complete data on all my variables on interest left a base N of 2,814.

4.2 Dependent Variable

As the dependent variable, I focus on individual feelings of safety in the face of local crime and violence. Specifically, this variable is based on a question which asked respondents

“How safe do you feel in your home?” Possible responses included “Safe” (coded as 1), “A Little

Safe” (coded as 2), “Unsafe” (coded as 3) and “I don’t know” (coded as 4). Overall, this question has a response rate of 99%. Since the “I don’t know category” accounts for a relatively small proportion of the original sample and is difficult to interpret with respect to other categories, I exclude if from my analysis. Across the full sample, this question has a response rate of 99%, and showed a high degree of both within-neighborhood and between-neighborhood variation.

As compared to a more general question about how respondents feel “walking in their neighborhoods at night,” which is often taken as a proxy for fear of crime, my measure has the advantage of greater geographical and situational specificity. This serves to narrow the scope of potential responses and makes measurement more precise. While this measure could be criticized for conflating cognitive assessments of risk with emotional orientations, I sustain that the former can be safely subsumed within the latter and that, following Vaisey (2009), forced response survey questions can be an effective method for tapping into the taken-for-granted schemas that guide everyday action.

4.3 Independent Variables

Following the explanatory clusters laid out in the first section, my variables are grouped into four categories. The first category includes variables associated with demographic

vulnerabilities which, in previous research, have been shown to affect feelings of safety. These

are sex, age, household income (logged), household size, and years of residence in the

neighborhood (see Table 1 below). I also draw on the detailed victimization data from the survey

to create a measure of victimization in the home within the previous 12 months. This variable

accounts for three categories: attempted burglary, burglary without harm to household members,

and burglary with harm to household members. On the assumption that experiences of

victimization in the home will have a stronger effect on feelings of safety in this particular place,

the variable excludes other experiences of victimization that occurred elsewhere in the

respondent’s neighborhood or in other neighborhoods.

Measures of perceived disorder are social in character. The first is the perceived presence of gangs (maras) based on the question “Are there maras in this neighborhood?” While this could arguably be considered a neighborhood-level attribute (gangs are either present or absent in a given neighborhood), preliminary data analyses showed wide variability in individual respondent’s perceptions of gang presence within neighborhoods. This may be due to variability in exposure, based on the geographic location of respondents’ homes or areas of transit, or due to differential interpretations of visual cues (ie. groups of youth). Either way, the measure preserves this variability and, hence, the capacity to explain the relationship between feelings of safety and perceived social disorder. I focused on “yes” or “no” responses and excluded the 12.4% of the sample that responded “I don’t know.” Following the same logic, I include a measure of perceived police presence, based on the question “Do the preventative police patrol this neighborhood?” Again, I focus on “yes” or “no” responses and exclude the 2.6% of the sample that responded “I don’t know.”

Additionally, I include a measure of perceived neighborhood unity and cohesion using data from the following question: “Based on your knowledge of the community and your experience with community improvement projects, please rank the degree of unity among the residents of this neighborhood: (1) not united at all, (2) united at times when there are big

problems, (3) united most of the time, and (4) I don’t know” excluding the 5% of the sample that

responded “I don’t know.”

The third group of variables refers to organizational participation, which I employ as a proxy for personal efficacy and willingness to intervene on behalf of the public good. The first measure, participation and membership, is the sum total of organizations of which the respondent reports being a member or participating in activities. The following three measures break down this membership into discrete categories or types of organizations. Congregation membership and/or church attendance becomes a binary variable (0 = no, 1 = yes) since respondents are members of one church only. The other two variables remain sums. Civic participation includes reported membership or participation in the following groups: parent-teacher association

(asociación de padres de familia), youth group (club juvenile) and sporting association (club

deportivo). State-oriented participation includes reported membership or participation in the

following groups: neighborhood association (patronato), local emergency committee (comité de

emergencia local), water council (junta de agua), and security committee (comité de vigilancia/

seguridad).

The fourth category of interest, relationship with public authorities, is limited to a single

variable: program investment. This count measure ranges from 0 to 5 and captures the number of

government-supported infrastructure projects carried out in the neighborhood during the three

years preceding the survey. While individual projects varied in scope, scale, and cost – making

this a rather crude measure – it is a reasonable way to display between-neighborhood variability

in terms of their relationship with public authorities.

Control variables include dummies for each of the five municipal districts in which the

eleven neighborhoods are located. I also create dummy variables for each neighborhood in order

to identify the primary sampling unit and correct for clustered standard errors.

TABLE 1:

DESCRIPTIVE STATISTICS OF MAIN VARIABLES

Variable Coding Observations Mean Std. Dev. Min Max

Dependent Variable (1 = safe, 2 = a little Feelings of Safety 3601 1.534 0.664 1 3 safe, 3 = unsafe)

Independent

Variables Sex (0 = female, 1 = male) 3611 0.306 0.461 0 1 Age (in years) 3610 41.106 16.441 1 93 (household income, Household Income 3374 8.884 0.775 2.81 14.29 logged) (number of people in Household Size 3611 4.245 2.094 1 20 household) (years of residence in Years of Residence 3637 17.422 11.736 0 80 neighborhood) (victimized in home in Victimization in the last 12 months, 0 = 3627 0.0378 0.191 0 1 Home no, 1 = yes) Mara (gang) (0 = no or I don’t know, Presence in 3647 0.474 0.499 0 1 1 = yes) Neighborhood Police Presence in (0 = no, 1 = yes) 3546 0.549 0.498 0 1 Neighborhood (1 = not united, 2 = Perceived Unity of sometimes united, 3 = 3439 1.757 0.706 1 3 Community mostly united) (sum of all Participation and organizational 3691 0.816 0.940 0 6 Membership memberships) (sum of civic Civic Participation organizational 3691 0.109 0.332 0 2 memberships) (sum of state-oriented State-oriented organizational 3691 0.221 0.517 0 3 Participation memberships) Church Attendance (0 = no, 1 = yes) 3691 0.485 0.500 0 1 Number of community Program Investment 3691 3.499 1.985 0 5 projects (dummy variable for Municipality 3691 2.687 1.269 1 5 municipality)

4.4 Analytic Methods

Since dependent variable - feelings of safety – contains three discrete categories, I used a multinomial logistic regression to estimate the predicted probabilities of each outcome. The decision to use multinomial logit (mlogit) rather than ordered logistical (ologit) was motivated primarily by the theoretical assertion that “safe,” “a little safe,” and “unsafe” are qualitatively distinct and are unlikely to fulfill the proportional distance assumption imposed by ologit. This was confirmed by testing the parallel line assumption using five tests (Wolfe Gould, Brant, score, likelihood ratio, and Wald) all of which indicated that this assumption had been violated and that ordered logistic regression should not be used.

Because individual-level survey responses are clustered by neighborhood, the dataset contains 11 primary sampling units (PSUs). I therefore used Stata’s svyset command to correct for clustered standard errors by neighborhood. I used listwise deletion to ensure that all models were estimated using the same number of cases, yielding a final N of 2,814. I examined the correlations between all the variables in my models (see Table 2 below) and checked for collinearity among them. Variance inflation factors (VIFs) were between 1.02 and 1.45 for all variables, minimizing the risk of unreliable estimates of regression coefficients due to high correlations. Finally, I pre-tested the statistical significance of the individual variables included in the models using the Stata’s nestreg command. Incremental F-tests showed that the majority of variables significantly improved model fit, when added alone rather than in blocks.

TABLE 2:

CORRELATIONS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1. Feelings of Safety 1 2. Sex -0.014 1 3. Age 0.034 0.056 1 4. Years of Education -0.01 0.024 -0.439 1 5. Logged Household Income 0.006 0.082 -0.096 0.230 1 6. Household Size 0.045 -0.078 -0.036 -0.036 0.261 1

7. Years of Residence -0.041 0.014 0.352 -0.131 -0.014 0.067 1 33 8. Prior Victimization in Home 0.069 -0.011 -0.025 0.037 0.017 0.024 0.007 1 9. Perceived Gang Presence 0.192 -0.022 0.006 0.018 -0.027 0.007 0.051 0.055 1 10. Perceived Police Presence -0.18 0.038 0.037 -0.044 -0.048 -0.004 0.055 0.00 0.008 1 11. Perceived Unity -0.096 0.000 0.027 -0.01 -0.01 0.014 0.041 0.001 -0.066 0.058 1 12. Organizational Participation -0.098 -0.041 0.077 0.006 0.046 0.145 0.135 0.000 -0.009 0.125 0.226 1 13. Religious Affiliation -0.054 -0.096 0.103 -0.013 -0.005 0.11 0.121 0.014 0.026 0.129 0.144 0.708 1 14. Infrastructure Investment -0.017 -0.017 -0.027 -0.007 -0.074 -0.017 0.131 0.093 0.145 0.136 -0.05 -0.023 -0.032 1 15. Municipality 0.006 0.017 0.049 -0.062 0.09 0.036 -0.173 -0.011 -0.247 0.024 0.060 0.027 -0.048 -0.136 1 16. Neighborhood -0.097 0.069 -0.017 -0.015 0.105 -0.023 -0.223 -0.073 -0.267 0.038 0.083 -0.078 -0.143 -0.314 0.547 1

CHAPTER 5:

RESULTS

5.1 Model Results

Model 1 includes all the variables discussed in the demographic vulnerabilities section above: sex, age, level of education, years of residence in neighborhood, household size, and prior victimization in the home. Results are summarized in three tables, each one displaying the odds ratios and significance levels of these variables when the mlogit model is run with different base categories, “unsafe,” “a little safe,” and “safe,” respectively. Rather than discussing the three tables separately, I concentrate on results that are consistent across categories and those that differ. Model 1 and all subsequent models contain controls for municipal district which are omitted from the output.

Surprisingly, and counter to most previous findings, sex is not a significant predictor of fear. Women are no more likely than men to report feeling “unsafe” or “a little safe.” However, consistent with previous research, age has a significant and negative effect on feelings of safety.

While older individuals are no less likely to report feeling “a little safe” than “unsafe,” they are somewhat less likely to report feeling “safe.” Substantively, this result is large. Based on previous findings, I expected education household income to act as buffers again fear, such that individuals with more years of education and/or higher household incomes would, on average, report feeling safer. This is not the case, since education and income have no statistically significant effect in any of the categorical comparisons. In the case of income, this may be due to measurement error

– people are unable or unwilling to provide accurate figures on household earnings in the context of a door-to-door survey – or, conversely, this may be differences in target attractiveness – higher

income households in poor communities may actually be (or may perceive themselves to be) at a

higher risk of burglary.

I also expected that individuals who had lived in the neighborhood for longer would experience lower degrees of fear, due to the increased number and depth of their local social ties and overall degree of integration, and this is partially confirmed. There is a significant and positive relationship between years of residence and the likelihood of feeling “safe” as opposed to

“a little safe.” This relationship does not hold, however, when “unsafe” is used as the base category, leading me to interpret that deep, local social ties are enough to nudge people into greater feelings of safety, absent other perceived vulnerabilities. Such ties are not enough, however, to generate an emotional shift among respondents who, for whatever reason, report feeling “unsafe.” This interpretation seems to run contrary to the findings about household size, which I expected to have a positive relationship with feelings of safety on the same theoretical grounds. However, increased household size has a significant negative effect on the likelihood of feeling “safe” whether the base category is “unsafe” (-0.054) or “a little safe” (-.045). One possible explanation for this is the positive correlation between household size and household income; households with larger numbers of individuals are, more often than not, more acutely affected by poverty, such that the negative relationship between household size and feelings of safety may actually be tapping into the higher propensity of individuals from poor households to experience fear, all other things held constant.

The final variable in Model 1, prior victimization, produces the anticipated results: after controlling for other sources of demographic vulnerability, victimization has a strong, negative effect on feelings of safety. Individuals who have been victimized in their home in the 12 months prior to the survey, were far less likely to report feeling “safe” than “unsafe” (-0.827) or “a little safe” (-0.593). This relationship is similar to that found in other types of communities in other parts of the world and supports the idea that victimization heightens risk perceptions and emotional fear responses.

Model 2 retains the variables from the previous model and incorporates several new variables related to perceptions of local disorder: presence of gangs (maras), presence of police, and perceived unity of community. It also includes a raw measure of personal efficacy, the sum total of the organizations with which each individual is affiliated, either through formal membership or participation in activities. Finally, Model 2 includes one contextual variable, the number of state-led infrastructure projects carried out in the neighborhood under the auspices of the government program. I take this as a proxy for the community’s relationship with public authorities.

In Model 2, the same demographic vulnerabilities variables retain their significance. With the base set to “unsafe,” age is significantly and negatively associated with a decreased likelihood of feeling “safe” (-0.013) as is household size (-0.091) and prior victimization (-0.899). In fact, both the coefficient and the level of significance increase for these three variables in Model 2 as compared to Model 1. Again, there is no significant difference between individuals who report feeling “unsafe” and those who report feeling “a little safe.” When the base category is set to “a little safe” the findings are similarly consistent from one model to the next. Individuals who belong to larger household are significantly less likely to report feeling “safe” (-0.054) as are individuals who have been victimized in the past 12 months (-0.628). Similarly, an increase in years of neighborhood residence is positively associated with reports of feeling “safe” (0.016), although the coefficient size and significance level of this variable decreases with respect to

Model 1.

Model 2 also shows that perceptions of social disorder are, in many cases, significant predictors of feelings of safety. This is most apparent in the case of maras or gangs: the perceived presence of gangs (which varies among residents in the same neighborhoods) is strongly associated with decreased feelings of safety. When the base category is set to “unsafe,” respondents who believe that gangs are present are less likely to report feeling “a little safe” (-

.745) and even less likely to report feeling “safe” (-1.316). Similarly, when the base category is

set to “a little safe,” residents who perceive that gangs are present in their neighborhood are less

likely to report feeling “safe” (-.571) and more likely to report feeling “unsafe” (.745). In

contrast, perceptions of police presence have no significant effect in any of the mlogit models.

Holding constant residents’ views of gangs and other forms of social disorder, as well as their

demographic characteristics, the presence of the police does not make them any more likely to

feel safe. Perceptions of unity – or the idea that residents are able and willing to work together to

solve common problems – has a strong, positive effect on feelings of safety. With the base

category set to “unsafe” and all other variables held constant, residents who view their

community as more united are more likely to report feeling “a little safe” (.541) and “safe” (.523).

The contrast between “a little safe” and “safe,” is not, however, significant.

Organizational participation is shown to be significantly and positively associated with feelings of safety. All else held constant, individuals who report a higher level of affiliations and participation are more likely to report feeling “safe” (.375) than “unsafe.” There are no statistically significant contrasts, however, between “a little safe” and “safe” or between “unsafe” and “a little safe.” This may indicate that active membership and participation that buffer against feelings of insecurity. Alternately, since this is cross-sectional data, causal influences may run in the opposite direction, pointing towards the increased propensity of people who feel “safe” in their neighborhoods to participate in local organizations. This would contrast with people who feel less safe and, hence, choose to limit unnecessary movement and exposure.

Finally, with regards to my proxy for relationship with public authorities, the pattern is similar to that of organizational affiliation but more attenuated. Individuals living in neighborhoods with a more pronounced presence of municipal and national government agencies

– in the form of a higher number of infrastructure projects – are more likely to report feeling

“safe” (.087) than “a little safe.” However, there is no significant contrast between the other categories.

TABLE 3:

MLOGIT RESULTS OF MODELS 1 AND 2 (BASE CATEGORY SET AT “UNSAFE”)

Model 1 Model 1 Model 2 Model 2 “A Little “A Little How Safe do You Feel in Your Home? “Safe” “Safe” Safe” Safe” Sex -0.111 -0.084 -0.098 -0.079 (0.75) (0.90) (0.59) (0.83) Age in Years -0.007 -0.011 -0.009 -0.013 (1.78) (3.63)** (1.95) (3.79)** Years of Education 0.000 0.004 -0.001 0.004 (0.02) (0.17) (0.03) (0.14) Years of Residence in Neighborhood -0.002 0.017 -0.006 0.010 (0.20) (1.53) (0.96) (1.38) 38

Log of Total Household Income 0.056 0.046 0.082 0.090 (0.94) (0.84) (1.80) (1.54) Number of people in household -0.009 -0.054 -0.037 -0.091 (0.34) (2.51)* (1.39) (4.43)** Have You been Victimized in your Home in the past 12 months? -0.235 -0.827 -0.270 -0.899 (0.90) (2.30)* (1.09) (2.77)* Are there gangs (maras) in this neighborhood? -0.745 -1.316 (4.20)** (4.08)** Do the preventative police patrol this neighborhood? 0.433 0.827 (2.05) (2.16) Perceived Unity of Community 0.541 0.523 (3.35)** (2.65)* Sum of Membership and Participation in Organizations 0.250 0.375 (1.98) (2.56)* Number of World Bank Infrastructure Projects -0.112 -0.025 (2.09) (0.47) Constant 0.709 1.284 0.444 0.728 (1.11) (2.64)* (0.79) (1.25) N 2,814 2,814 2,814 2,814 * p<0.05; ** p<0.01

TABLE 4:

MLOGIT RESULTS OF MODELS 1 AND 2 (BASE CATEGORY SET AT “A LITTLE SAFE”)

Model 1 Model 1 Model 2 Model 2

How Safe do You Feel in Your Home? “Unsafe” “Safe” “Unsafe” “Safe” Sex 0.111 0.027 0.098 0.019 (0.75) (0.22) (0.59) (0.17) Age in Years 0.007 -0.003 0.009 -0.003 (1.78) (1.31) (1.95) (1.30) Years of Education -0.000 0.004 0.001 0.005 (0.02) (0.30) (0.03) (0.34) Years of Residence in Neighborhood 0.002 0.019 0.006 0.016 (0.20) (3.04)* (0.96) (2.68)* 39

Log of Total Household Income -0.056 -0.010 -0.082 0.008 (0.94) (0.11) (1.80) (0.09) Number of people in household 0.009 -0.045 0.037 -0.054 (0.34) (4.05)** (1.39) (4.20)** Have You been Victimized in your Home in the past 12 months? 0.235 -0.593 0.270 -0.628 (0.90) (3.10)* (1.09) (3.83)** Are there gangs (maras) in this neighborhood? 0.745 -0.571 (4.20)** (2.41)* Do the preventative police patrol this neighborhood? -0.433 0.394 (2.05) (1.48) Perceived Unity of Community -0.541 -0.018 (3.35)** (0.17) Sum of Membership and Participation in Organizations -0.250 0.125 (1.98) (1.50) Number of World Bank Infrastructure Projects 0.112 0.087 (2.09) (3.64)** Constant -0.709 0.575 -0.444 0.284 (1.11) (0.70) (0.79) (0.48) N 2,814 2,814 2,814 2,814 * p<0.05; ** p<0.01

TABLE 5:

MLOGIT RESULTS OF MODELS 1 AND 2 (BASE CATEGORY SET AT “SAFE”)

Model 1 Model 1 Model 2 Model 2 “A Little “A Little How Safe do You Feel in Your Home? “Unsafe” “Unsafe” Safe” Safe” Sex 0.084 -0.027 0.079 -0.019 (0.90) (0.22) (0.83) (0.17) Age in Years 0.011 0.003 0.013 0.003 (3.63)** (1.31) (3.79)** (1.30) Years of Education -0.004 -0.004 -0.004 -0.005 (0.17) (0.30) (0.14) (0.34) Years of Residence in Neighborhood -0.017 -0.019 -0.010 -0.016 (1.53) (3.04)* (1.38) (2.68)*

40 Log of Total Household Income -0.046 0.010 -0.090 -0.008 (0.84) (0.11) (1.54) (0.09) Number of people in household 0.054 0.045 0.091 0.054 (2.51)* (4.05)** (4.43)** (4.20)** Have You been Victimized in your Home in the past 12 months? 0.827 0.593 0.899 0.628 (2.30)* (3.10)* (2.77)* (3.83)** Are there gangs (maras) in this neighborhood? 1.316 0.571 (4.08)** (2.41)* Do the preventative police patrol this neighborhood? -0.827 -0.394 (2.16) (1.48) Perceived Unity of Community -0.523 0.018 (2.65)* (0.17) Sum of Membership and Participation in Organizations -0.375 -0.125 (2.56)* (1.50) Number of World Bank Infrastructure Projects 0.025 -0.087 (0.47) (3.64)** Constant -1.284 -0.575 -0.728 -0.284 (2.64)* (0.70) (1.25) (0.48) N 2,814 2,814 2,814 2,814 * p<0.05; ** p<0.01

Model 3 disaggregates the category of organizational affiliation into two broad categories: membership or participation in religious congregations and membership or participation in other non-religious organizations. The former includes affiliations with both

Catholic and Protestant or Evangelical churches, but does not differentiate between them. The latter includes affiliations with a wide variety of formal organizations aimed at improving the quality of life of members or of the neighborhood as a whole. All the demographic vulnerabilities variables employed in previous models (sex, age, years of education, years of residence, household income, household size, and prior victimization) and the geographic dummy variables are included in Model 3 and in Model 4 but omitted from the tables.

Model 3 shows that affiliations with different types of organizations are associated with variable perceptions of safety and security. Membership and participation in religious congregations, for example, fails to predict whether individuals report feeling “unsafe,” “a little safe,” or “safe,” since there are no significant differences among these categories. Non-religious affiliations, however, are consistently associated with increased feelings of safety. When the base category is set to “unsafe,” individuals affiliated with these types of organizations are significantly more likely to report feeling “safe” (.405). Likewise, when the base category is set to

“a little safe,” the same individuals are more likely to report feeling “safe” (0.276).

Model 4 builds on Model 3, further disaggregating the non-religious affiliation category into its 'civic' and 'political' components. Civic groups include parent-teacher associations, sports clubs, and youth groups, all of which aim mainly at providing benefits to their members and operate in relative autonomy from the state. State-oriented organizations include elected neighborhood councils, water councils, and citizen security committees, all of which incorporate logics of democratic representation, work for the benefit of the neighborhood as a whole, and require constant interface with state entities.

Model 4 shows that there is a difference between belonging to these two types of groups in terms of individual-level feelings of safety and that, in fact, their effects appear to point in

opposite directions. When the base category is set to “unsafe,” neither type of affiliation is significantly associated with feeling safe. However, based on the change in coefficients, it appears that the previous (significant and positive) relationship between non-religious affiliations and increased feelings of safety is being driven by state-oriented groups more than civic groups.

This is further evidenced by the results of the model when the base category is set to “a little safe.” Here, affiliation with state-oriented groups is significantly and positively associated with a higher likelihood of feeling “safe” (0.358) whereas affiliation with civic organizations is not. One interpretation of this finding is that the same individuals who, by virtue of participation in state- oriented organizations, have more information about the neighborhood and the perspective of local authorities, as well as more public exposure are more likely to feel safe. Due to the cross- sectional nature of the data, however, I cannot rule out the alternative interpretation that it is precisely those individuals who feel less afraid that are more likely to assume visible positions of leadership in their communities.

TABLE 6:

MLOGIT RESULTS OF MODELS 3 AND 4 (BASE CATEGORY SET AT “UNSAFE”)

Model 3 Model 3 Model 4 Model 4 “A Little “A Little How Safe do You Feel in Your Home? “Safe” “Safe” Safe” Safe” Are there gangs (maras) in this neighborhood? -0.747 -1.311 -0.747 -1.309 (4.17)** (4.14)** (4.18)** (4.14)** Do the preventative police patrol this neighborhood? 0.418 0.830 0.418 0.826 (1.97) (2.20) (1.96) (2.17) Perceived Unity of Community 0.543 0.523 0.542 0.526 (3.37)** (2.63)* (3.35)** (2.63)* Number of World Bank Infrastructure Projects -0.110 -0.026 -0.110 -0.024 (2.08) (0.48) (2.05) (0.43) Non-Religious Membership and Organizational Participation 0.130 0.405

43 (1.05) (2.99)*

Congregation Membership 0.433 0.296 0.429 0.287 (1.77) (1.33) (1.78) (1.31) Civic Membership and Organizational Participation 0.031 0.137 (0.13) (1.00) State-Oriented Membership and Organizational Participation 0.191 0.549 (0.72) (2.04) Constant 0.371 0.768 0.360 0.752 (0.64) (1.33) (0.62) (1.32) N 2,814 2,814 2,814 2,814 * p<0.05; ** p<0.01 TABLE 7:

MLOGIT RESULTS OF MODELS 3 AND 4 (BASE CATEGORY SET AT “A LITTLE SAFE”)

Model 3 Model 3 Model 4 Model 4

How Safe do You Feel in Your Home? “Unsafe” “Safe” “Unsafe” “Safe” Are there gangs (maras) in this neighborhood? 0.747 -0.564 0.747 -0.563 (4.17)** (2.48)* (4.18)** (2.47)* Do the preventative police patrol this neighborhood? -0.418 0.412 -0.418 0.407 (1.97) (1.61) (1.96) (1.57) Perceived Unity of Community -0.543 -0.020 -0.542 -0.017 (3.37)** (0.19) (3.35)** (0.15) Number of World Bank Infrastructure Projects 0.110 0.085 0.110 0.087 (2.08) (3.81)** (2.05) (4.00)** Non-Religious Membership and Organizational Participation -0.130 0.276 44 (1.05) (3.05)* Congregation Membership -0.433 -0.137 -0.429 -0.142 (1.77) (0.59) (1.78) (0.62) Civic Membership and Organizational Participation -0.031 0.106 (0.13) (0.68) State-Oriented Membership and Organizational Participation -0.191 0.358 (0.72) (3.18)** Constant -0.371 0.397 -0.360 0.393 (0.64) (0.72) (0.62) (0.71) N 2,814 2,814 2,814 2,814 * p<0.05; ** p<0.01 TABLE 8:

MLOGIT RESULTS OF MODELS 3 AND 4 (BASE CATEGORY SET AT “SAFE”)

Model 3 Model 3 Model 4 Model 4 “A Little “A Little How Safe do You Feel in Your Home? “Unsafe” “Unsafe” Safe” Safe” Are there gangs (maras) in this neighborhood? 1.311 0.564 1.309 0.563 (4.14)** (2.48)* (4.14)** (2.47)* Do the preventative police patrol this neighborhood? -0.830 -0.412 -0.826 -0.407 (2.20) (1.61) (2.17) (1.57) Perceived Unity of Community -0.523 0.020 -0.526 0.017 (2.63)* (0.19) (2.63)* (0.15) Number of World Bank Infrastructure Projects 0.026 -0.085 0.024 -0.087 (0.48) (3.81)** (0.43) (4.00)** Non-Religious Membership and Organizational Participation -0.405 -0.276 45 (2.99)* (3.05)* Congregation Membership -0.296 0.137 -0.287 0.142 (1.33) (0.59) (1.31) (0.62) Civic Membership and Organizational Participation -0.137 -0.106 (1.00) (0.68) State-Oriented Membership and Organizational Participation -0.549 -0.358 (2.04) (3.18)** Constant -0.768 -0.397 -0.752 -0.393 (1.33) (0.72) (1.32) (0.71) N 2,814 2,814 2,814 2,814 * p<0.05; ** p<0.01 CHAPTER 6:

DISCUSSION

6.1 Discussion

The ecological model of crime points to a complex relationship between neighborhood level victimization rates and the emotional responses of citizens, most notably the degree of fear they experience in their local environments. Fear of crime, as theorized by Bursik and Grasmick

(1993), is the effect of breakdowns in social order, exercised at the private, parochial, and public levels, but is also influenced by variable demographic attributes, such as gender, age, income, education, as well as prior experiences of victimization. It may, simultaneously, be the cause of further breakdown in social order, as individuals who experience fear withdraw from public spaces and active participation in public life. Understanding the relationship between fear of crime and individual-level characteristics and perceptions is, therefore, an important aspect of literature on crime. Unfortunately, much of our current understanding is based on studies carried out in the United States or other wealthy countries, and limited work has been done in different contexts, such as Latin America.

One major objective of this study was to expand knowledge on individual-level fear of crime in low-income, urban neighborhoods of Honduras, by examining the effects of multiple demographic, experiential, perceptual, and behavioral attributes related to vulnerabilities and subjective assessments of local order. Contrary to previous findings from the United States, it has shown that being female, having a lower level of education, and having a lower income are not significantly associated with feelings of safety. While being older is associated with a higher level

46 46

of personal fear, on the whole the findings indicate that presumed demographic vulnerabilities

have limited explanatory power in the Honduran neighborhoods examined. For example, I find

gender differences to be inconsequential. This finding stands in stark contrast with previous

studies on fear of crime and may be due to the unique characteristics of these locations,

specifically the high rates of gang violence directed at young men and their consequent anxiety,

captured in qualitative studies. While the inconsequentiality of individual-level demographics is

somewhat surprising, these findings are also consistent with those of LAPOP (2015) based on a

nationally-representative survey data.

I have shown that victimization and perceptions of social disorder are strongly associated with fear of crime in low-income neighborhoods of Honduras, as elsewhere. In particular, the presence of gangs is a strong, consistent predictor of perceived insecurity. Feelings of fear are partially mitigated by increased trust in neighbors and perceptions of community unity, as well as years of residence, which is presumably associated with higher numbers of local friends and acquaintances. This finding indicates that what Bursik and Grasmick (1993) denominate parochial controls play a powerful role in shaping the subjective sense-making of individuals who inhabit low-income Honduran neighborhoods, and that variability within this cluster of perceptual attributes offers far greater explanatory power than demographic attributes alone. Yet the theoretical supposition that increased police presence may be associated with greater perceptions of order and, hence, lower levels of fear is not supported by the findings. Instead, police presence is shown to have no effect on perceptions of insecurity. This is likely due to the extraordinarily high levels of distrust manifested by Hondurans, due to connections between police and gangs in many low-income neighborhoods. Yet this finding merits further study, through the disaggregation of perceived police presence and trust in police, as well the relationship between perceptions of police and feelings of fear among distinct sub-populations.

Findings are inconclusive as regards the relationship between individual-level feelings of fear and perceptions of public sector responsiveness to community problems, measured here as

47 47 the number of large-scale state-sponsored infrastructure projects. Investment is shown to have a

small, positive effect on feelings of safety in some cases, but this is inconsistent across models.

This may be a product of the crude measurement instrument used and its failure to adequately

capture variation in neighborhood-government relations across the sites surveyed. It may also be

due to small number of neighborhoods sampled or the single-level modeling strategy employed,

which necessitates the assignation of a higher-order attribute (a neighborhood-level measurement)

to individual persons, without accounting for variations in individual perceptions. Alternately, it

is possible that public sector responses to locally-articulated demands have less influence on

subjective feelings of safety in low-income Honduran neighborhoods than they do elsewhere.

Theoretically, this is improbable, given the presumed relationship between local order and

presence of public authorities. Thus, future research could explore this relationship in greater

detail, using more sophisticated, multi-level modeling strategies and better measures of public

sector influence.

The secondary objective of this study was to expand knowledge about the relationship between feelings of safety and what I have denoted personal efficacy and operationalized in the form of organizational participation. Highlighting the strong relationship between collective efficacy and crime at the neighborhood level, I have argued that measurement of this concept has been inadequate at the individual level and when used to measure fear of crime, rather than crime itself. By focusing on organizational participation - a behavior presumably linked to the construction of trust, shared normative orientations, and capabilities for collective action – my aim was to ascertain whether levels of personal efficacy are significantly associated with feelings of safety. My findings indicate that there is a positive relationship between the two: greater organizational participation is associated with lower fear of crime.

I have also gone a step further, positing that participation in different types of religious, civic, and state-oriented organizations might have different impacts on personal feelings of safety.

The results of the study offer provisional support for this hypothesis, showing that membership or

48 48 participation in member-focused religious congregations and civic groups, like parent-teacher

associations or sports clubs, has no significant association with fear of crime. This contrasts with

membership and participation in state-oriented organizations, characterized by attention to the

public good and close contact with public authorities, which I show to be associated with lower

feelings of fear in some cases. One tentative explanation for this finding is that personal efficacy

is bolstered through active participation in groups with a ‘this-worldly’ ethos and claims-making

directed at public authorities. This boost in personal efficacy, in turn, decreases fear. An

alternative and equally plausible explanation is that individuals who are less afraid are also more

likely to assume public positions of leadership. Unfortunately, the cross-sectional nature of this

data makes it impossible to ascertain the direction of this relationship. Yet this is something that

could and should be explored in future studies.

49 49 APPENDIX A:

PREVIOUS FINDINGS ON THE DETERMINANTS OF FEAR OF CRIME

50 50 Key: POS / NEG = positive vs. negative effect on fear of crime; (**) = significance level INSIG = insignificant effect; N/I = not included in analysis

TABLE A.1:

FINDINGS ON THE DETERMINANTS OF FEAR OF CRIME

51 Household Trust in Social Article, Author, Geographic Measurement Prior Statistical Size / Police / Disorder / Cohesion / Year of Purview of Data, Strategy: Fear of Female Age Education Income Victimi- Approach Marital Police Incivilities Collective Publication Year Collected Crime zation Status Presence Efficacy

Britain and Wales Explaining Fear of Binomial Question: How safe do - nationally POS POS NEG NEG POS Crime logistic you feel walking alone N/I N/I N/I N/I representative - (***) (***) (*) (***) (***) (Box et al., 1988) regression in this area after dark? 1984 Perceived Risk and Fear of Crime: Two-item index based Role of Social and USA - nationally on reported fear of OLS multiple POS NEG NEG Physical representative - personal crime and N/I N/I INSIG N/I INSIG INSIG regression (***) (*) (***) Incivilities 1990 reported fear of (LaGrange et al., property crime. 1992) TABLE A.1 (CONTINUED)

Household Trust in Social Article, Author, Geographic Measurement Prior Statistical Size / Police / Disorder / Cohesion / Year of Purview of Data, Strategy: Fear of Female Age Education Income Victimi- Approach Marital Police Incivilities Collective Publication Year Collected Crime zation Status Presence Efficacy Crime, Neighborhood Perceptions, and Question: Is there any OLS multiple the "Underclass": USA - nationally area right around here regression - POS POS NEG NEG POS The Relationship representative - where you would be N/I N/I N/I N/I individual level (***) (**) (***) (***) (*) between Fear of 1987 afraid to walk alone at analysis Cirme and Class night? Position (Will & McGrath, 1995) Victimization and Canada - OLS multiple Question: How safe is it Fear of Crime - Edmonton, regression - POS POS NEG POS to walk alone in your N/I INSIG N/I N/I N/I (Weinrath & Alberta – 1981, individual level (***) (***) (***) (***) neighborhood at night? 52 Gartrell, 1996) 1985 analysis Subcultural Stepwise OLS Diversity and the USA - Orange multiple Question: How often do Fear of Crime and POS NEG County, CA - regression - you worry about INSIG INSIG N/I N/I N/I N/I N/I Gangs (*) (*) 1995 individual level crime? (Lane & Meeker, & path analysis 2000) Modeling Fear of Crime and Perceived Victimization Question: Do you worry Binomial Risk: The USA - Nashville, about being the victim POS NEG POS POS logistic INSIG INSIG INSIG N/I INSIG (In)Significance TN - 1988 of a crime in your (*) (*) (**) (**) regression of Neighborhood neighborhood? Integration (Kanan & Pruitt, 2002) TABLE A.1 (CONTINUED)

53 Individual, social I feel safe walking and OLS multiple Australia - around this POS POS POS neighbourhood regression – in N/I N/I N/I N/I N/I INSIG Brisbane - 2003 neighborhood after (***) (***) (***) factors in blocks dark. perspective (McCrea et al., 2005) (In your neighborhood) Social Cohesion, does it seem to you Criminal that the risk is very Victimization, Multi-level large, large, medium, and Perceived model with Belo Horizonte, small, or very small of: POS NEG POS POS Risk of Crime in individuals INSIG INSIG INSIG N/I INSIG Brazil - 2002 1) being robbed?; 2) (**) (*) (***) (*) Brazilian nested within being harmed?; 3) Neighborhoods neighborhoods being kidnapped?; 4) (Villarreal & Silva, being gravely wounded 2006) or killed? TABLE A.1 (CONTINUED)

54 you feel afraid of being attacked or assaulted?; (4) In general, how often are you fearful of being the victim of a violent crime? Seven-item scale: How much do you worry about (a) being attacked while driving a car; (b) getting mugged; (c) getting Reducing Fear of beaten up, shot, or Crime: Citizen, USA - Portland, Multivariate POS NEG POS NEG knifed; (d) getting INSIG N/I INSIG N/I N/I Police, or OR - 2004 OLS regression (***) (*) (***) (***) murdered; (e) getting Government sexually assaulted; (f) Responsibility? getting burglarized (Renauer, 2007) while someone is home; (g) getting burglarized while no one is home? TABLE A.1 (CONTINUED)

55 Impacts neighborhood because (Wyant, 2008) you felt unsafe here?; (4) How satisfied are you with your personal safety in this neighborhood? TABLE A.1 (CONTINUED)

57 (Scarborough et al., neighborhood at night; 2010) (f) parking your car overnight on the street, and (g) visiting a neighborhood park/ playground? Two item scale: (1) Fear of crime How safe do you feel Turkey - among citizens of while alone at home?; nationally OLS multiple POS NEG NEG NEG POS NEG POS Turkey and (2) How safe do N/I N/I representative - regression (***) (*) (*) (*) (***) (***) (***) (Karakus et al., you feel while walking 2004 2010) alone at night in the neighborhood? Fear of crime and vulnerability: Nested logistic Using a national multilevel On average, how often sample of models with have you felt unsafe, if USA - nationally POS POS POS POS Americans to individual, ever, in your current INSIG N/I N/I N/I N/I representative (***) (***) (*) (*) examine two census tract, neighborhood within competing and county the last year? paradigms level data (Rader et al., 2012) TABLE A.1 (CONTINUED)

58 property damaged by vandals? Two-item scale: (1) How safe do you feel being outside and An Examination of alone in your the Micro-Level OLS multiple neighborhood at POS POS NEG POS NEG POS NEG USA - Houston N/I N/I Crime regression night?; and (2) How (**) (**) (**) (**) (***) (**) (**) (Zhao et al., 2015) safe do you feel being outside and alone in your neighborhood during the day? BIBLIOGRAPHY

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