Fear of Crime in an Island Paradise

Abstract

Numerous empirical studies have examined fear of crime. Key theoretical constructs include age, gender, vulnerability, marital status, social cohesion, social incivilities, and perceptions of police. While these constructs have extensive empirical support from cross-sectional and longitudinal projects, they focused on Western liberal democratic nations. Little research exists on fear of crime and its correlates within smaller, island nation-states. The current study (N = 480) examines (a) the prevalence of fear of crime within the Maldives and (b) the extent to which previous theoretical constructs can be generalized to other population areas. Findings demonstrate levels of fear of crime in the Maldives consistent with Western liberal democratic societies but that only certain previous theoretical constructs are associated with variations in fear of crime.

Keywords

fear of crime social incivility perceptions of police social cohesion Maldives

Much has been written on fear of crime, exploring the correlates of fear, such as gender, age, income, and social incivilities (Hale, 1996; Lorenc et al., 2012; Skogan & Maxfield, 1981), as well as the impacts of fear of crime, such as indirect victimization and the breakdown of social cohesion (Covington & Taylor, 1991; Foster, Knuiman, Hooper, Christian, & Giles-Corti, 2014). Recently, research on this construct has emerged in non-Western contexts such as Ghana, China, and Turkey (Adu-Mireku, 2002; Karakus, McGarrell, & Basibuyuk, 2010; Liu, Messner, Zhang, & Zhuo, 2009). However, its construct and measurement are highly debated. While most research on fear of crime has focused on the British Crime Survey or similar forms of data collection, many question whether fear is truly captured in these measures (Lee, 2013). Critics claim that fear needs to be better operationalized, that surveys often fail to capture situational and social factors that contribute to fear, and that responses are more likely to represent general worry, a process, rather than fear, an event (Farrall, 2004; Farrall, Bannister, Ditton, & Gilchrist 1997; Hough, 2004). Attempts to correct these issues through qualitative data collection, anchoring vignettes and fear mapping, hold promise for the future of fear of crime research (Doran & Burgess, 2011; King, Murray, Salomon, & Tandon, 2004; Sutton & Farrall, 2005). However, fear of crime research also suffers from a lack of non-Western, nonglobal north, participant populations. It is useful to investigate the typically used correlates of fear of crime in these populations to determine whether additional constructs or operational issues can be identified.

This study contributes to the international literature on the fear of crime through an examination of several key risk factors associated with the fear of crime in the Republic of Maldives (Maldives). Fear of crime is particularly important to investigate in the Maldives for two reasons. First, as mentioned, most theorizing and empirical research on fear of crime has involved large liberal Western democratic societies. Therefore, it is not evident whether findings can be generalized to smaller, more rural indigenous island population locations. Second, many of the correlates of fear of crime are present in the Maldives, including a rising rate of crime and relative poverty-based social disorganization (Human Rights Commisson of Maldives [HRCM], 2011). Accordingly, this study examines two interrelated issues. First, to what degree do people of the Maldives fear or worry about crime? Second, to what extent do traditional, albeit often debated, theoretical constructs, validated within larger liberal Western democratic countries, apply to smaller unique, predominantly indigenous islander populations? Fear of crime continues to be correlated with quality of life and potential negative social and physical ramifications (Lorenc et al., 2012). Thus, to improve quality of life and better guide policy, it is useful to continue research of fear of crime in new and potentially different contexts.

Related Research

Much of the literature connects fear of crime to overall quality of life, including psychological, physiological, and behavior changes (Box, Hale, & Andrews, 1988; Garofalo & Laub, 1978; Green, Gilbertson, & Grimsley, 2002; Mirrlees-Black & Allen, 1998; Møller, 2016). Fear of crime can encourage social isolation and reduce interpersonal interaction contributing to poor mental health as well as impede trust and social cohesion (Stafford, Chandola, & Marmot, 2007). Fear of crime also impedes trust and social cohesion, erodes the social fabric and connectedness of the community, and negatively affects perceptions of safety (Saville, 2009; Skogan, 1986). Furthermore, when the public believes there is a significant and uncontrolled crime problem, negative stereotypes and support for punitive crime prevention measures also emerge (Sprott, Webster, & Doob, 2013). This is important because public opinion typically determines the legislation and policy of these countries’ criminal justice systems. Accordingly, fear of crime not only impacts the quality of life among individuals but also has potential negative broader policy criminal justice ramifications.

Despite extensive literature on the fear of crime construct, there is no consensus concerning its definition and operationalization (Farrall et al., 1997; Wyant, 2008). Initial studies often used a single-measure survey question to measure fear of crime, that is, “How safe do you feel, or would you feel, being alone in your neighborhood at night?” (LaGrange & Ferraro, 1987, p. 699). This question does not necessarily address fear, ignore context, and provide a static response about what is essentially a process (Farrall, 2004; Hough, 2004), resulting in capturing “formless¹” fear and overstating fear in general (Farrall et al., 1997; Ferraro, 1995; Mirrlees-Black & Allen, 1998). Additional issues include whether the measure of fear involves a specific moment or a more pervasive/general measure of worry about crime (Farrall, 2004; Gabriel & Greve, 2003; Hough, 2004; Williams, McShane, & Akers, 2000), that is, the question capturing beliefs and perceptions of anticipated fear in particular situations or worry in general (Rountree & Land, 1996; Skogan & Maxfield, 1981). Recent surveys have added several additional questions regarding time of day and worry about specific types of victimization and created alternative scales to acknowledge the multidimensional construct (Killias & Clerici, 2000; Rader, 2004; Rountree & Land, 1996; Williams et al., 2000).

Two broad and multilevel sets of correlates are often utilized concerning the fear of crime: individual and ecological. Individual-level correlates of fear of crime include gender, age, marital status, income, and unemployment. Ecological-level correlates include signs of disorder, neighborhood characteristics, and levels of crime—these correlates are often placed within the context of social disorganization theory and/or collective efficacy (Gibson, Zhao, Lovrich, & Gaffney, 2002; Sampson, Raudenbush, & Earls, 1997; Weisburd, Hinkle, Famega, & Ready, 2011).

Most of the research has consistently shown that women are more afraid of crime than men, despite a lower risk of victimization in both Western and non-Western contexts (Adu-Mireku, 2002; Cossman & Rader, 2011; Liu et al., 2009; Stanko, 1995). The standard explanation for gender differences and the fear of crime remain contentious (Schafer et al., 2006), largely because more recent research suggests that this discrepancy between fear and risk is more appropriately explained by women’s greater risk of the most serious forms of domestic violence and sexualized crimes (Fox, Nobles, & Piquero, 2009). Furthermore, some qualitative research has determined that men are constrained by masculinity norms, which encourage them to respond with socially desirable answers, rather than answering survey questions honestly regarding fear (Sutton & Farrall, 2005).

The elderly, again despite having the lowest risk of crime victimization, are often found as more fearful (Greve, 1998; Killias & Clerici, 2000; Yin, 1982). Some suggest this is a result of actual vulnerability, that is, their inability to defend themselves resulting from age-related physical vulnerabilities in particular situations, such as walking alone at night, encountering groups of youth in public places, and experiencing a crime, especially unarmed robbery, sexual crime, and an assault (Greve, 1998). In addition, the elderly’s disproportionate fear of crime may be related to their distance from the community, particularly their physical isolation (Farrall et al., 1997; Fattah & Sacco, 1989). However, these findings have been debated too. Chadee and Ditton (2003), in a replication study of Ferraro and LaGrange (1992), found that in Trinidad, age was not associated with fear of crime, and the elderly were the least afraid. Debate around the correlation between age and fear of crime may also be a result of the interdependency between social and physical vulnerability (Rader, Cossman, & Porter, 2012).

Vulnerability is not merely physical (age and gender) but also social. Individuals with a lower income have higher fear of crime, as living in less secure areas increases vulnerability to certain criminal events, and physical or financial loss can be more damaging (Franklin, Franklin, & Fearn, 2008; Hale, 1996). Individuals living in poverty are unable to pay to protect their property, often cannot afford insurance, are more reliant on public services such as public transit that can put them in harm’s way, and cannot afford to move out of high crime areas (Pantazis, 2000). Partnership status can also be considered a form of vulnerability whether an individual is isolated. Partnered or married individuals report fear of crime less often, likely because an additional adult present can potentially offer physical protective assistance (Baumer, 1978; Mesch, 2000). However, recent research has examined the role of fear of victimization within an intimate partner relationship that could counter this narrative (Broll, 2014; Rader, 2009).

Ecological variables, including social capital and policing policies (Perkins & Taylor, 2002), can have mediating effects on key individual-level variables (Scarborough, Like-Haislip, Novak, Lucas, & Alarid, 2010). For example, Brunton-Smith and Sturgis (2011) compared perception data with other “objective” data sources and found that signs of disorder, neighborhood structural characteristics, and amount of police-recorded crime directly and independently effect individual-level variables related to the fear of crime. Accordingly, ecological correlates of fear of crime include whether crime in the neighborhood is perceived as increasing, the level of neighborhood cohesion, the presence of incivilities, and whether the police are not helpful and/or effective.

Social disorganization theory associates neighborhood crime rates with the sociodemographics of these neighborhoods and has been central to most studies focused on the ecological correlates of fear of crime (LaGrange, Ferraro, & Supancic, 1992; Markowitz, Bellair, Liska, & Liu, 2001; Robinson, Lawton, Taylor, & Perkins, 2003; Shaw & McKay, 1942; Skogan & Maxfield, 1981; Taylor, 2001; Wyant, 2008). When key stabilizing community goals, especially safety, are not addressed, other stabilizing social structures (e.g., schools, parks/recreation, and businesses) deteriorate. Once incivilities or signs of physical and social disorder become common place, residents experience persistently higher levels of fear of crime (Coleman, 1990). More specifically, social (such as teenagers loitering in an area) and physical (such as litter and graffiti) incivilities can lead residents to believe that “no one cares,” thereby increasing feelings of vulnerability to criminal victimization and consequent reactive social isolation. The latter then perpetuates a feedback loop, reinforcing the fear of crime both individually and collectively within the neighborhood (Kelling & Coles, 1997; Link, Kelly, Pitts, Waltman-Spreha, & Taylor, 2014; Wilson & Kelling, 1982). However, in Trinidad, no major differences were found in fear of crime between high crime and disorder areas and low crime areas (Chadee & Ditton, 1999). Thus, disorder levels and fear of crime in non-Western contexts still require exploring.

A neighborhood with high collective efficacy may reduce social disorganization, increase social cohesion, and increase the likelihood that residents will intervene on behalf of the common good and the belief that others will reciprocate (Sampson et al., 1997). In effect, high levels of neighborhood trust and cohesion enhance the expectation that the neighborhood/community collective network will act to positively affect the goals of the members regarding crime (Portes, 1998). Socially organized neighborhoods are usually characterized by high levels of collective efficacy, which, in turn, are related to residents’ higher levels of social capital, that is, personal connections and group networks among people who promote norms of trust and reciprocity and act as a protective factor for fear of crime (Putnam, 2000; Sacco & Nakhaie, 2007).

A related neighborhood risk factor for fear of crime is negative perceptions of police. Lack of confidence in the police role has been associated with higher fear of crime (Mesko & Klemencic, 2007). Recent changes in police policies (e.g., a shift away from foot patrols/community policing to far less frequent and visible police squad car patrols) contribute to explaining this negative relationship (Jackson & Bradford, 2009). In effect, traditional policing most commonly emphasizes operational strategies, patrolling areas, responding to calls for assistance, and solving crime; while in many major urban and metropolitan areas, policing resources are shifting to highly specialized units such as organized crime, emergency response teams, white-collar crime, and, more recently, antiterrorism (Kraska & Kappeler, 1997; Murray, 2005; Tankebe, 2013). This all contributes to some communities feeling less served by their police services, susceptible to fear of and worry about crime.

The study takes place in the Maldives, and thus it is important to understand that Maldivian context. The Maldives is an island nation comprised of approximately 1,200 coral islands crossing the Equator in the Indian Ocean, approximately 750 km to the southwest of India and Sri Lanka—188 of these islands are officially populated. According to the latest census in 2014, the total population was an estimated 344,000 people (Ministry of Health, 2016). Because one third of the country’s population live in the capital city of Malé, the country’s resources are centralized within the capital city. The land area of Malé is approximately 1.95 km² (Statistical Yearbook, 2016). The country is largely an Islamic republic, and its economy depends primarily on tourism (BBC News, 2016). In recent years, the Maldives have suffered increases in illegal drug use and trafficking, political unrest, and terrorism (Burke & Rasheed, 2015).

In 2015, the crime rate in the Maldives was 3,985 crimes per 100,000 population. This is down from 5,111 per 100,000 population in 2013, the year the data were collected (Statistical Yearbook, 2016).² The property³ and violent⁴ crime rates for the Maldives in 2013 were 2,183 and 813 crimes per 100,000 population, respectively (Statistical Yearbook, 2016). Consequently, the Maldives is demonstrating a reduction in the crime rate like that of the rest of the Western world in recent years (Farrell, Tilley, & Tseloni, 2014). Moreover, the overall crime rate for the Maldives is less than the United Kingdom in 2015, at 6,864 per 100,000 population⁵ (Flatley, 2016).⁶ However, changes may emerge in Maldives, as more illegal drugs enter the country and the proportion of youth increases resulting from a declining infant mortality rate (Ministry of Health, 2016).

Data and Method

The present study is based on a survey conducted in 2013. It involved a purposive convenience sample with a target population of residents of the Maldives. The survey was designed to investigate both the prevalence and the correlates of fear of crime within this unique population.⁷ Key geographic regions of the Maldives were selected in order to collect a wide demographic sample. Malé was selected from the capital (Central) region. The islands of Seenu, Hithadhoo, and Gaaf Alif, Kolamaafushi, were selected from the Southern region and Raa, and Meedhoo were selected from the Northern region. The selection of islands was based on the availability of nongovernmental organizations (NGOs) who volunteered to distribute and administer the research questionnaire. Within each of the three Maldivian islands, NGOs associated with the research project visited every household and provided each resident(s) with the opportunity to participate in the study. No incentives were provided for their participation. Questionnaires were administered by face-to-face interviews and lasted approximately 20 min.⁸ The total sample consists of 480 participants, with 216 participants from Malé, 115 from Gaaf Alif, 99 from Seenu, and 50 from Gaa—see the Appendix for details regarding the sample participants.

Outcome Variables

The outcome variables in the current study relate to both the fear of crime and the worry about crime. Fear of crime was measured using a 5-point Likert-type scale (1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, and 5 = strongly disagree) considering the three following statements:
I feel safe to walk alone in this area after dark,

I feel safe to walk alone in this area during the day, and

I feel safe when I am alone in my own home at night.⁹

These three statements were then reverse coded to capture fear of crime in a dichotomous manner: disagree and strongly disagree were coded to 1, whereas strongly agree, agree, and neither agree nor disagree were coded to 0. Additionally, a composite fear of crime variable (ordinal α = .89) was generated by adding the values of the three base fear of crime variables together.

Worry about crime was measured on the same 5-point Likert-type scale considering the six following worry-specific statements:
I’m worried about my home being broken into,

I’m worried about being robbed when I’m in my neighborhood,

I’m worried that someone might steal my vehicle (motorbike/cycle),

I’m worried about being raped,

I’m worried about being physically attacked, and

I’m worried about being insulted or pestered in the neighborhood.

Similar to the fear of crime variables, these six statements were dichotomized such that strongly agree and agree were coded as 1 and strongly disagree, disagree, and neither agree nor disagree were coded as 0. A composite worry about of crime variable was generated (ordinal α = .94) by adding the values of the three base fear of crime variables together.

The frequencies for the various outcome variables are shown in Table 1. In general, just under one third of those surveyed had no fear of crime, a further one third had 1 item related to fear (most often fear of walking during the night), and the final third of those surveyed were fearful of two or all of the contexts relating to fear and safety in their neighborhood. With regard to worry, just under 20% of those surveyed had no worries with regard to the six forms of victimization. However, more than 40% of those surveyed were worried about five or six of the forms of victimization. As such, levels of worry about crime are notably greater than actual fear.

Table 1.
Frequencies, Outcome Variables.

Dependent Variables Value Percentage of Cases

Fear, general 0 32.9

1 32.5

2 26.9

3 7.7

Worry, general 0 19.6

1 4.4

2 7.7

3 11.9

4 14.4

5 29.0

6 13.1

Fear, walking during night Agree/strongly agree 49.8

Fear, walking during day Agree/strongly agree 24.8

Fear, home alone Agree/strongly agree 34.8

Worry, burglary Agree/strongly agree 67.5

Worry, robbery Agree/strongly agree 65.4

Worry, stolen vehicle Agree/strongly agree 61.5

Worry, rape (women) Agree/strongly agree 27.5 (59.2)

Worry, assault Agree/strongly agree 63.5

Worry, pestered Agree/strongly agree 51.0

With regard to the specific forms of fear and worry, one half of those surveyed reported fear walking alone at night, one quarter reported fear walking alone during the day, and just over one third reported fear being home alone at night. For worry about crime, 60% or more of those surveyed worried about burglary, robbery, stolen vehicles, rape (for women), and assault. Just over 50% of those surveyed reported being worries about being pestered in their neighborhood.

Predictor Variables

In order to identify any predictable relationships for the presence of fear of and worry about crime, we use a set of individual-level and ecological-level variables based on the related research discussed earlier.¹⁰ The individual-level variables relate to gender, age, marital status, low income, and unemployment. Gender is measured in a dichotomous fashion (female = 1), age and age-squared are included to account for any potential changes in the effect of age on fear of and worry about crime, marital status is measured in a dichotomous fashion (married = 1), as is unemployment (unemployed = 1), and low income is measured dichotomously, defined as those living with a monthly wage less the 10,000 Maldivian rufiyaa, the average income in the Maldives. Length of time in current neighborhood is a control and measured dichotomously (less than 1 year = 1). With reference to Table 2, it can be seen that 46% of the sample is female, with an average age of 31 years. Of those surveyed, 62% are married, 95% have lived in their neighborhood for more than 1 year, only 8%are unemployed, but 75% make less than average income.

Table 2.
Descriptive Statistics, Predictor Variables.

Independent Variables Minimum Maximum Mean

Gender 0 1 0.46

Age 18 70 31.13

Married 0 1 0.62

Neighborhood, <1 year 0 1 0.05

Low income 0 1 0.75

Unemployed 0 1 0.08

Crime increasing 0 1 0.59

Neighborhood cohesion 0 3 1.95

Incivilities 0 8 4.49

Police not helpful/effective 0 2 1.14

The ecological-level variables include crime is increasing in my neighborhood, neighborhood cohesion, the presence of incivilities, and the police are not helpful and/or effective. Crime is increasing is measured using a 5-point Likert-type scale (1 = strongly agree, 2 = agree, 3 = neutral, 4 = agree, and 5 = strongly disagree), dichotomized such that strongly agree or agree equals 1. Neighborhood cohesion is measured using the following three components:
How often do you talk with your neighbors;

When I do a favor for a neighbor, I trust my neighbor to return the favor; and

The area I live in feels like a “real home.”

The first component is measured as daily, weekly, fortnightly, monthly, less than once a month, and never; this variable was dichotomized with daily and weekly equaling 1. The latter two components were measured using the above-mentioned 5-point Likert-type scale, dichotomized such that strongly agree or agree equals 1. A neighborhood cohesion composite variable was then calculated by summing these three dichotomous variables. Incivilities was measured considering eight dichotomous variables summed into a composite incivilities variable that considered the presence of (strongly agree or agree) noisy neighbors, problem teenagers, rubbish, vandalism, graffiti, people using drugs, people dealing drugs, and drunks. And finally, police are not helpful and/or effective was the composite of two dichotomous variables (strongly agree or agree): I believe the police in my area is doing a good job in controlling crime and I believe the police do everything they can to help people.

As shown in Table 2, 59% of those surveyed believe that crime is increasing, and there are more than four incivilities present, on average. Neighborhood cohesion is moderately high, on average, as is the belief that the police are not helpful or effective. The Spearman’s (nonparametric) correlations are presented in Table 3. Although there are many statistically significant correlations, none are greater than .50, leaving no a priori concern for multicollinearilty in the subsequent analyses.

Table 3.
Nonparametric (Spearman’s) Correlations, Predictor Variables.

Independent Variables X1 X2 X3 X4 X5 X6 X7 X8 X9 X10

Gender, X1 1 −.02 .12* .02 .19** −.07 −.09* −.05 .11* −.07

Age, X2 1 .48 .07 −.17 −.12** −.09* .06 .11* −.06

Married, X3 1 −.08 −.13 −.17 −.20 .095 .17 −.19

Neighborhood, <1 year, X4 1 −.20 .10* .07 −.12* .02 −.03

Low income, X5 1 .05 −.04 .17 −.01 .13

Unemployed, X6 1 .12 −.04 .01 .05

Crime increasing, X7 1 −.28 −.16 .38

Neighborhood cohesion, X8 1 .05 −.09

Incivilities, X9 1 −.30**

Police not helpful/effective, X10 1

p < .05. p < .01.

Analytic Strategy

Because of the nature of the outcome variables, discrete choice models are estimated in all cases. For the composite variables representing fear of crime and worry about crime, we estimate multinomial logistic regression models. For both cases, the values of zero (no fear or worry) are the baseline values such that the models shown predict some level of fear of crime or worry about crime relative to that baseline. With regard to the specific forms of fear of crime and worry about crime, binary logistic regression models are estimated because of the dichotomous nature of the outcome variables. All estimated parameters, shown subsequently are odds ratios for ease of interpretation.

Results

Multinomial Results

The multinomial logistic regression results for fear of crime are reported in Table 4. Immediately evident is the relative lack of individual-level variables that are statistically significant for predicting various levels of fear of crime. As expected, gender is statistically significant for all levels of fear of crime, increasing as the levels of fear increase. Age and age-squared are only statistically significant for the presence of one type of fear: Initially as people age, there is a decrease in fear, but fear begins to increase with successive years. Although marriage does reduce the various levels of fear in all cases, as expected, it is only statistically significant when individuals are fearful in all three contexts: walking during the night, walking during the day, and being home alone. All other individual-level variables are not statistically significant.

Table 4.
General Fear of Crime, Multinomial Regression Results.

Independent Variables 1 2 3

Gender 2.21 5.83* 7.46*

Age 0.88* 0.99 0.86

Age-squared 1.01* 1.00 1.00

Married 0.90 0.95 0.29

Neighborhood, <1 year 1.07 0.59 1.88

Low income 1.00 0.68 0.97

Unemployed 0.67 1.14 0.97

Crime increasing 1.99 2.74* 2.48

Neighborhood cohesion 0.82 0.51* 0.77

Incivilities 1.12 1.204* 1.25

Police not helpful/effective 1.23 1.919* 3.87*

Ga 0.35* 0.73 0.06*

Seenu 1.30 4.666* 1.07

Raa 21.36* 49.08* n/a

Note. Nagelkerke pseudo-R ² = .434.

p < .01. p < .05. p < .10.

The ecological-level variables all have their expected positive or negative effect and are statistically significant in most cases. When crime is perceived to be increasing in one’s area, this increases the expected outcome of fear of crime, the increased presence of neighborhood cohesion reduces the fear of crime, increases in the presence of incivilities increases fear, and increases in the belief that police are either not helpful and/or effective increases fear. Moreover, when statistically significant, the magnitude of the odds ratios increase as the level of fear increases, similar to gender, as would be expected. And finally, the regional dummy variables indicate that Ga has lower levels of fear than the capital region whereas Seenu and, particularly, Raa have greater levels of fear.

The multinomial logistic regression results for worry about crime are presented in Table 5. Although each level of the worry about crime retains at least three statistically significant variables, far fewer of the predictor variables are statistically significant when compared to the fear of crime model. Gender is statistically significant for four of the six categories—there were no females who worried about all six crime types, hence the n/a value in that cell. Being married increased the probability worrying about one type of crime; although initially counterintuitive, this result indicates that being married is associated with a low level of worry (only one crime type), most often a property-related crime. The presence of low income increases worry about crime, as expected, but when unemployment is statistically significant, it reduces the probability of worry about crime. As such, in this model, individual-level variables as a whole, when statistically significant, do not correspond well with theoretical expectations.

Table 5.
General Worry About Crime, Multinomial Regression Results.

Independent Variables 1 2 3 4 5 6

Gender 1.05 4.73* 6.59* 6.12* 4.06* n/a

Age 0.92 0.92 0.97 1.05 0.91 0.83

Age-squared 1.00 1.00 1.00 1.00 1.00 1.00

Married 4.62** 0.73 0.65 0.90 1.23 0.81

Neighborhood, <1 year 1.06 0.36 0.43 0.70 0.95 0.76

Low income 1.36 0.67 1.38 2.51* 2.61 1.11

Unemployed 2.07 0.80 0.99 0.15 0.41 0.37

Crime increasing 2.06 0.99 1.80 0.63 0.45 0.70

Neighborhood cohesion 1.45 1.10 1.30 0.95 1.30 1.01

Incivilities 0.94 1.00 1.09 1.28* 1.31* 1.64*

Police not helpful/effective 0.78 0.89 1.25 1.23 0.95 1.19

Ga 0.24 0.10* 0.29 0.09* 0.05* 0.94

Seenu 1.29 1.28 2.50 2.57 1.44 9.50*

Raa 0.11 0.01* 0.02* 0.012* 0.001*** 0.13*

Note. Nagelkerke pseudo-R ² = .651.

p < .01. p < .05. p < .10.

Turning to the ecological-level variables, it is curious that the belief that crime is increasing reduces the worry about crime. However, increased levels of incivilities only become statistically significant when the level of worry about crime becomes high. Neither neighborhood cohesion nor police helpful/effective are statistically significant for worry about crime. And finally, aside from the highest level of fear in Seenu, levels of the worry about crime are greater in the capital region of Maldives.

Binomial Results

The binomial logistic regression results for the individual fear of crime responses are shown in Table 6. Generally speaking, the results are consistent with the composite variable for the fear of crime, including the relative lack of individual-level predictors, but there are some notable results. As with the composite results, gender is statistically significant, so women are more fearful of crime than men. Interesting is the magnitude of the odds ratios for the different types of fear. As would be expected, gender is a stronger predictor of fear walking alone at night than during the day, but it has its greatest impact on the fear of being home alone. Being married only has a statistically significant effect (decreasing fear) regarding walking alone during the day. Low income and being unemployed only have statistically significant effects for being home alone, decreasing and increasing fear of crime, respectively.

Table 6.
Fear of Crime, Binary Logistic Regression Results.

Independent Variables Dark Day Home

Gender 2.81*** 1.65* 4.06***

Age 0.96 0.96 1.03

Age-squared 1.00 1.00 1.00

Married 0.96 0.56* 0.74

Neighborhood, <1 year 0.98 1.00 1.17

Low income 1.25 0.85 0.53

Unemployed 0.73 0.81 2.43

Crime increasing 1.69** 1.90* 1.91*

Neighborhood cohesion 0.65* 0.92 0.75

Incivilities 1.20* 1.10* 1.05

Police not helpful/effective 1.35 2.09* 1.80*

Ga 0.49* 0.10* 1.48

Seenu 2.64* 0.30* 3.46*

Raa 0.15* 24.74* 3.24*

Nagelkerke pseudo-R ² .356 .441 .306

*p < .01. **p < .05. p < .10.

Turning to the ecological-level variables, when statistically significant, they all have their expected relationship. Areas with crime perceived to be increasing have greater of fear, neighborhood cohesion decreases fear, incivilities increase fear, and police not being helpful and/or effective also increase fear. With regard to incivilities, this predictor is only statistically significant for walking alone during the night or day; this makes sense given that incivilities, by definition, occur outside of the home. The regional variables show that Ga still has lower levels of fear than the capital region, with Seenu and Raa generally having more fear than the capital region as well.

The final set of results refers to the specific worry about crime logistic regression models (Table 7). Overall, each model has more statistically significant variables with varying effects for the different types of worry. This provides strong support for not considering “worry about crime” as a general category but being specific with regard to the various types of worry—the disaggregate results for fear of crime were generally consistent with the composite variable but also showed more instructive disaggregate results.

Table 7.
Worry About Crime, Binary Logistic Regression Results.

Independent Variables Burglary Robbery Steal Vehicle Assault Pester Rape

Gender 2.07 2.48* 1.14 3.52* 2.18*

Age 1.03 0.96 0.92 0.99 1.03 0.72*

Age-squared 1.00 1.00 1.00 1.00 1.00 1.01*

Married 1.09 0.98 1.00 0.85 0.80 1.24

Neighborhood, <1 year 1.68 1.13 0.56 1.09 1.65 1.30

Low income 1.76 2.22* 2.08** 1.64* 1.23 0.62

Unemployed 0.52 0.55 0.78 0.42 0.77 1.69

Crime increasing 0.56 0.66 0.56 0.82 0.47* 0.74

Neighborhood cohesion 1.17 0.87 0.86 1.13 1.23* 0.61*

Incivilities 1.19* 1.19* 1.2* 1.29* 1.24* 1.11

Police not helpful/effective 1.11 1.16 1.18 0.91 0.81 1.10

Ga 0.49* 0.30* 0.60 0.18* 0.54 2.00

Seenu 3.59* 1.07 2.38* 0.99 1.11 0.89

Raa 0.07* 0.05* 0.09* 0.05* 0.14* 1.00

Nagelkerke pseudo-R ² .386 .37 .306 .435 .339 .251

p < .01. p < .05. p < .10.

For the individual-level variables, gender and low income were the most consistently statistically significant. Gender was statistically significant for all crime types except for vehicle theft, with the greatest impact being for assault—the model for rape only included females so gender was not included as a variable in this model. Low income was statistically significant for all crime types except for pestering and rape, increasing worry about crime. Age and age-squared were only statistically significant for rape with an initial decrease in worry followed by a moderate increase in subsequent years. And being unemployed led to decreased worry about assault; this may be because the unemployed are expected to spend more time in the relatively protective environment of the home (Cohen & Felson, 1979).

With regard to the ecological-level variables, perceived increases in an area’s crime unexpectedly leads to decreases in the worry about burglary, stolen vehicle, and pestering. Neighborhood cohesion increases the worry about pestering but decreases the worry about rape. This former result is unexpected with no obvious explanation. Incivilities, as expected, increase the probability of worry about all crime types aside from rape. The helpfulness and/or effectiveness of the police has no statistically significant effect on worry about crime. And aside from rape, those who live in Gaa and Raa are generally less worried about crime than those who live in the capital region, whereas those who live in Seenu are more worried about burglary and stolen vehicle than those who live in the capital region.

Discussion

The study intended to answer two main research questions. What is the amount of fear of crime in the Maldives, and do the correlates of fear of crime in Western and developed countries also exist in different cultural and social climates? The amount of fear of crime in the Maldives is generally consistent with other findings. The consistency of fear of crime could be a result of similar crime problems in the Maldives (HRCM, 2011). It could also reflect an issue with the operationalization of the question that instead captures “formless fear” and overestimating fear, although efforts were made to reduce this issue.

The second question, regarding the consistency of fear of crime correlates, resulted in some interesting findings. Being female remains as a significant correlate. This finding is not surprising, as it is the most consistent finding across the fear of crime literature (Franklin & Franklin, 2009; Stanko, 1995) and in non-Western studies (Adu-Mireku, 2002; Karakus et al., 2010; Liu et al., 2009). Although some research has investigated the social desirability of fear of crime respondents (Sutton & Farrall, 2005), the consistency of concern found across crime types may be reflective of a genuine gender difference. In the Maldives, which rank 95th of 135 countries in gender equality for women, this fear could also be a product of the current rank of women in this area (Asian Development Bank, 2014). Although the Maldives is making great strides to increase women’s equality in education and the workforce, more work needs to bridge the gender gap to reduce victimization as well as fear of crime for women. Furthermore, more research is necessary into analyzing social desirability issues in these contexts.

Income also often emerged as a significant variable. Less than 1% of the population in the Maldives lives in absolute poverty, and while there still are income disparities, this may not translate into the isolation and fear that is commonly found for poor individuals in Western countries (United Nations Development Program [UNDP], 2012). However, it may contribute to perceived vulnerability and increased concern about property crime.

Other individual-level variables do not consistently emerge as statistically significant in this analysis. Marital status decreases or has no statistically significant relationship with fear or worry about crime except for one instance. In this latter case, being married increases the probability of being worried about one crime type: property-related crime. However, as stated earlier, this initially counterintuitive result is consistent with the literature because it states that married persons are more likely to only be worried about this crime type (Cohen & Felson, 1979). Age was insignificant, a finding consistent with other non-Western fear of crime research (Karakus et al., 2010; Liu et al., 2009). This research could contribute to the growing body of Western and non-Western fear of crime literature demonstrating that individual-level fear of crime variable may not be useful.

Alternatively, theoretical ecological variables (perceptions of police, social incivilities, and neighborhood cohesion) were significantly associated with fear of crime when controlling for individual characteristics. This would suggest that the role of ecological variables is robust, a consistent finding in the literature (Gainey, Alper, & Chappell, 2011; Ross & Jang, 2000). When social disorganization theory is applied to the conditions in the Maldives, high levels of disorganization could be considered present. Due to the inequitable distribution of resources between the capital city and the rest of the Maldives, relative poverty, gender inequality, and unemployment, the crime rate is higher in the capital city relative to the other areas (Department of National Planning & Ministry of Finance and Treasury, 2011; UNDP, 2012). This is consistent with the Western literature though differs from some non-Western literature (Adu-Mireku, 2002; Hwang, 2006) and demonstrates the importance of addressing community cohesion and relationships with the policing when addressing fear of crime in the Maldives. Furthermore, perceptions of increasing crime were associated with decreases in fear. Furthermore, some residents in some locations are more fearful than others. Thus, research needs to consider not only the type of crime individuals perceive as increasing to help explain this anomaly but also types of crime prevalent in certain areas that could be contributing to different levels of fear. These findings would suggest that ecological-level variables continue to be significant contributors to fear of crime and quality of life, and further investigation into these variables, as well as better operationalization, in Western and non-Western contexts is a useful direction for fear of crime research.

Limitations

The findings of the current study need to be interpreted within the context of their limitations. First, issues of generalizability to other non-Western populations are raised, given the use of a convenience sample on a geographically dispersed population (i.e., the Maldives consists of over 200 islands). However, the sampling method is the result of the challenges of collecting data in this kind of geographic location, and the large sample size does allow adequate statistical power to examine the unique social structure in the different island populations of the Maldives. Second, the operationalization of the questions for fear and worry of crime are problematic, considering recent research in this area. However, these measures are still reflective of the British Crime Survey and allow for an analysis of fear of crime in the Maldives that is consistent with other measures. Third, not all variables that have been tested for fear of crime were available for testing in this study. Fourth, and finally, the research was initiated by a member of the police services. While there are concerns with the validity of data collection connected with persons in power, all attempts were made to ensure confidentiality and include nonpolice partners. Furthermore, we must acknowledge the realities of collecting data in a non-Western location, and that issues of power and surveillance could also be a problem with government run crime surveys.

Conclusion

The effects of fear of crime on individuals and the community are real and are often similar if not the same as criminal victimization. Thus, it is important to continue to measure fear of crime in new areas and to determine whether the same variables that explain fear of crime in Western or developed countries can be generalized to different social and cultural settings. In the case of the Maldives, the amount of fear of crime is generally consistent with other nations. Furthermore, gender and income emerge as significant individual-level variables. Interestingly, ecological variables are consistently significant in the findings. This could have implications for policy and future fear of crime research. Perhaps, considering the issues with measurement of individual-level variables, more investigation is needed into ensuring accurate capture of ecological-level variables and exploring these correlates as the best possible routes for addressing fear of crime and in turn quality of life.

Dependent Variables	Value	Percentage of Cases
Fear, general	0	32.9
	1	32.5
	2	26.9
	3	7.7
Worry, general	0	19.6
	1	4.4
	2	7.7
	3	11.9
	4	14.4
	5	29.0
	6	13.1
Fear, walking during night	Agree/strongly agree	49.8
Fear, walking during day	Agree/strongly agree	24.8
Fear, home alone	Agree/strongly agree	34.8
Worry, burglary	Agree/strongly agree	67.5
Worry, robbery	Agree/strongly agree	65.4
Worry, stolen vehicle	Agree/strongly agree	61.5
Worry, rape (women)	Agree/strongly agree	27.5 (59.2)
Worry, assault	Agree/strongly agree	63.5
Worry, pestered	Agree/strongly agree	51.0

Independent Variables	Minimum	Maximum	Mean
Gender	0	1	0.46
Age	18	70	31.13
Married	0	1	0.62
Neighborhood, <1 year	0	1	0.05
Low income	0	1	0.75
Unemployed	0	1	0.08
Crime increasing	0	1	0.59
Neighborhood cohesion	0	3	1.95
Incivilities	0	8	4.49
Police not helpful/effective	0	2	1.14

Independent Variables	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
Gender, X1	1	−.02	.12*	.02	.19**	−.07	−.09*	−.05	.11*	−.07
Age, X2		1	.48**	.07	−.17**	−.12**	−.09*	.06	.11*	−.06
Married, X3			1	−.08	−.13**	−.17**	−.20**	.095	.17**	−.19**
Neighborhood, <1 year, X4				1	−.20**	.10*	.07	−.12*	.02	−.03
Low income, X5					1	.05	−.04	.17**	−.01	.13**
Unemployed, X6						1	.12**	−.04	.01	.05
Crime increasing, X7							1	−.28**	−.16**	.38**
Neighborhood cohesion, X8								1	.05	−.09
Incivilities, X9									1	−.30**
Police not helpful/effective, X10										1

Independent Variables	1	2	3
Gender	2.21***	5.83***	7.46***
Age	0.88*	0.99	0.86
Age-squared	1.01*	1.00	1.00
Married	0.90	0.95	0.29**
Neighborhood, <1 year	1.07	0.59	1.88
Low income	1.00	0.68	0.97
Unemployed	0.67	1.14	0.97
Crime increasing	1.99**	2.74***	2.48
Neighborhood cohesion	0.82	0.51***	0.77
Incivilities	1.12**	1.204***	1.25**
Police not helpful/effective	1.23	1.919***	3.87***
Ga	0.35***	0.73	0.06***
Seenu	1.30	4.666***	1.07
Raa	21.36***	49.08***	n/a

Independent Variables	1	2	3	4	5	6
Gender	1.05	4.73***	6.59***	6.12***	4.06***	n/a
Age	0.92	0.92	0.97	1.05	0.91	0.83
Age-squared	1.00	1.00	1.00	1.00	1.00	1.00
Married	4.62**	0.73	0.65	0.90	1.23	0.81
Neighborhood, <1 year	1.06	0.36	0.43	0.70	0.95	0.76
Low income	1.36	0.67	1.38	2.51*	2.61**	1.11
Unemployed	2.07	0.80	0.99	0.15**	0.41	0.37
Crime increasing	2.06	0.99	1.80	0.63	0.45**	0.70
Neighborhood cohesion	1.45	1.10	1.30	0.95	1.30	1.01
Incivilities	0.94	1.00	1.09	1.28***	1.31***	1.64***
Police not helpful/effective	0.78	0.89	1.25	1.23	0.95	1.19
Ga	0.24**	0.10***	0.29**	0.09***	0.05***	0.94
Seenu	1.29	1.28	2.50	2.57	1.44	9.50***
Raa	0.11**	0.01***	0.02***	0.012***	0.001***	0.13*

Independent Variables	Dark	Day	Home
Gender	2.81***	1.65*	4.06***
Age	0.96	0.96	1.03
Age-squared	1.00	1.00	1.00
Married	0.96	0.56*	0.74
Neighborhood, <1 year	0.98	1.00	1.17
Low income	1.25	0.85	0.53**
Unemployed	0.73	0.81	2.43**
Crime increasing	1.69**	1.90*	1.91***
Neighborhood cohesion	0.65***	0.92	0.75**
Incivilities	1.20***	1.10*	1.05
Police not helpful/effective	1.35**	2.09***	1.80***
Ga	0.49***	0.10***	1.48
Seenu	2.64***	0.30***	3.46***
Raa	0.15***	24.74***	3.24***
Nagelkerke pseudo-R ²	.356	.441	.306

Independent Variables	Burglary	Robbery	Steal Vehicle	Assault	Pester	Rape
Gender	2.07***	2.48***	1.14	3.52***	2.18***
Age	1.03	0.96	0.92	0.99	1.03	0.72***
Age-squared	1.00	1.00	1.00	1.00	1.00	1.01***
Married	1.09	0.98	1.00	0.85	0.80	1.24
Neighborhood, <1 year	1.68	1.13	0.56	1.09	1.65	1.30
Low income	1.76**	2.22***	2.08**	1.64*	1.23	0.62
Unemployed	0.52	0.55	0.78	0.42**	0.77	1.69
Crime increasing	0.56**	0.66	0.56**	0.82	0.47***	0.74
Neighborhood cohesion	1.17	0.87	0.86	1.13	1.23*	0.61***
Incivilities	1.19***	1.19***	1.2***	1.29***	1.24***	1.11
Police not helpful/effective	1.11	1.16	1.18	0.91	0.81	1.10
Ga	0.49***	0.30***	0.60**	0.18***	0.54**	2.00
Seenu	3.59***	1.07	2.38***	0.99	1.11	0.89
Raa	0.07***	0.05***	0.09***	0.05***	0.14***	1.00
Nagelkerke pseudo-R ²	.386	.37	.306	.435	.339	.251

Footnotes

Appendix A

Table A1.

Descriptive Statistics for the Survey Data.

Variables	Malé	Seenu	Gaaf	Raa	Total	Percentage (%)
Age
18–24 years	37	29	52	30	148	30.8
25–44 years	155	52	52	17	276	57.5
45 years and above	24	18	11	3	56	11.7
Sex
Male	111	51	65	30	257	53.5
Female	105	48	50	20	223	46.5
Marital status
Single/divorced/separated	61	51	47	25	184	38.3
Married	155	48	68	25	296	61.7
Residential stability
<1 year	14	8	4	0	26	5.4
1 year to <5 years	36	5	11	0	52	10.8
5 years to <10 years	17	15	7	0	39	8.1
10 years+	149	71	93	50	363	75.6
Employment status
Working	167	53	74	25	319	66.5
Not working	49	46	41	25	161	33.5
Income
Less than MVR 5,000	58	52	60	39	209	43.5
MVR 5,000–9,999	91	18	37	4	150	31.3
MVR 10,000–14,999	27	18	15	1	61	12.7
MVR 15,000–29,999	40	9	3	6	46	9.6
MVR 30,000+	12	2	0	0	14	2.9

Abbreviation: MVR: Maldivian Rufiyaa.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

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