Examining fear of crime among Macao residents: Vulnerability,victimisation and situational factors

Abstract

This study examines fear of crime among residents of Macao using data from the 2022 Macao Victimisation Survey. Fear of crime is conceptualised as a multidimensional construct encompassing affective, cognitive and behavioural components. Analyses are based on regression models incorporating vulnerability, victimisation and situational factors. The study also assesses whether situational variables moderate the relationship between victimisation and fear of crime. Findings reveal both consistencies and deviations from prior research. Contrary to established studies, age and gender do not significantly influence fear of crime. However, the victimisation model remains robust, indicating that victimisation experiences are significantly associated with heightened fear. Community disorder and perceived crime rates are found to affect specific dimensions of fear of crime. Notably, situational variables do not exhibit statistically significant moderating effects on the relationship between victimisation and fear of crime. By offering contextual insights from Macao, an East Asian city with distinct socioeconomic and structural dynamics, this study contributes to the broader criminological discourse on fear of crime and highlights the need for a locally grounded theoretical framework.

Keywords

Fear of crime Macao vulnerability victimisation experience community disorder perceived crime rate moderating effect

Introduction

Fear of crime has long been acknowledged as a critical issue in criminological research due to its profound implications on individual well-being and life quality (Alfaro-Beracoechea et al., 2018; Alper and Chappell, 2012; Lorenc et al., 2014). Early research was predominantly situated in Western contexts, generating diverse theoretical frameworks and measurement approaches. Although recent scholarship has extended attention to Asian contexts (Hong et al., 2025; Jing et al., 2024; Liu and Wu, 2025; Nalla et al., 2011; Yirmibesoglu and Ergun, 2015), these contributions remain marginal when compared with the extensive body of Western research. Ranaweera (2024) further confirms this imbalance, noting that research in the United States, the United Kingdom and Australia account for a bulk of highly cited studies, while Asian perspectives remain underrepresented.

Within this broader context, the Greater China region offers an instructive case. Existing studies have largely focused on mainland China (Jing et al., 2024; Liu et al., 2009; Zhang et al., 2009), Hong Kong (Chui et al., 2013; Lee and Adorjan, 2016; Li et al., 2022; Mak and Jim, 2018) and Taiwan (Hebenton et al., 2010; Li, 2018; Lo et al., 2023). By contrast, Macao remains conspicuously absent. This omission limits the generalisability of prevailing frameworks: without empirical evidence from contexts such as Macao, where systematic victimisation surveys are lacking, the explanatory power of prevailing frameworks remains uncertain. As Hale (1996) warned, theoretical conclusions derived from the American context should not be uncritically transplanted across socio-cultural settings. Hence, social science research should prioritise the replicability and generalisability of theoretical constructs through systematic validation across diverse socio-cultural settings (Polit and Beck, 2010).

Macao presents an especially salient case for such inquiry. As a Special Administrative Region of China, it operates under the ‘one country, two systems’ governance framework, characterised by a distinctive socio-political and legal structure alongside a multicultural population. Its economy is uniquely dependent on gambling and tourism, which drive rapid financial circulation and large-scale population mobility. Geographically, it is a small urban territory with exceptionally high population density.¹ These socio-structural features raise considerations regarding the applicability of existing fear of crime frameworks, as residents’ perceptions of crime and disorder may be shaped by dense urban environments and rapid population flows. Moreover, in culturally hybrid environments, established models may be only partially applicable, as divergent cultural norms and distinctive institutional arrangements can generate fear trajectories different from those observed elsewhere.

Crime rates further underscore this complexity. As an established determinant of fear of crime (Barni et al., 2016; Franc et al., 2011), they merit particular scholarly attention in Macao. Prior research links gambling industries to elevated levels of urban crime (Adolphe et al., 2019; Gazel et al., 2001; Wheeler et al., 2008; Zabielskis, 2015). Following this logic, one would theoretically expect Macao to experience high crime levels, given its intensive gambling economy, substantial population movement and dense urban environment. Paradoxically, however, the actual recorded crime rate in Macao was 14.57 per 1,000 population in 2022 (Macao Security Bureau, 2023; Macao Statistics Census Bureau 2023), markedly lower than other major gambling hubs such as Las Vegas (35.41 per 1,000) (Neighborhood Scout, 2022). This discrepancy challenges prevailing assumptions and highlights the need to interrogate whether Western-derived frameworks of fear of crime are applicable in such a distinctive context.

Against this backdrop, this study draws upon data from the first large-scale Macao Victimisation Survey, conducted in 2022, to examine residents’ fear of crime. Specifically, it examines how established determinants such as vulnerability, victimisation experiences and situational conditions operate within Macao’s socio-cultural and institutional context. By assessing the transferability of Western frameworks, the study contributes both to the empirical understanding of fear of crime in Macao and to the broader project of cross-cultural validation and potential refinement of criminological theory.

Dimensions of fear of crime: conceptualisation and measurement

The conceptualisation of fear of crime has evolved significantly, shifting from early unidimensional approaches to more sophisticated, multidimensional frameworks. Initial research commonly defined fear of crime in overly generalised emotional terms, often characterising it as ‘an emotional reaction characterised by a sense of danger and anxiety’ (Garofalo, 1981: 854). Rather than capturing the multidimensionality of the construct, many early studies employed overly simplified measures, often reducing fear of crime to a single survey item. A widely used example was the question of whether respondents felt afraid to walk alone in their neighbourhood at night. However, this approach simplifies a complex socio-psychological phenomenon by representing it with only one item (Clemente and Kleiman, 1977; Garofalo, 1979; Lebowitz, 1975). While widely adopted, these measures have been increasingly criticised for lacking conceptual depth, thereby undermining theoretical precision and measurement validity (Gabriel and Greve, 2003; Jackson, 2005; LaGrange and Ferraro, 1987).

In response to these limitations, scholars began to reconceptualise fear of crime as a multidimensional construct, highlighting its distinct yet interrelated components (LaGrange and Ferraro, 1989; Miethe and Lee, 1984; Warr, 1984). DuBow et al. (1979) proposed a two-dimensional typology, classifying reactions into psychological (concern, judgement, emotional response) and behavioural (avoidance, protective, insurance, communicative, participatory) domains. Building on DuBow et al.’s work, LaGrange and Ferraro (1987) offered a more analytically precise classification by explicitly distinguishing between cognitive judgements, such as perceived risk and affective fear, thereby advancing both the conceptual clarity and operational reliability. These shifts marked an important move from simplistic, emotion-laden definitions towards more analytically sophisticated framework.

Gabriel and Greve (2003) further refined these concepts that recognise the reciprocal relationships among three core dimensions: affective, cognitive and behavioural. Specifically, they define these components as follows: ‘Affect: a corresponding affective experience; Cognition: the individual’s cognitive perception of being threatened; Behaviour: an appropriate motive or action tendency’ (Gabriel and Greve, 2003: 604). This formulation provided a coherent structure for disentangling overlapping concepts and aligning theoretical elaboration with empirical measurement.

Following the conceptual shift, scholars systematically incorporated behavioural and cognitive indicators into measurement frameworks (Franklin et al., 2008). Behavioural indicators have typically been operationalised through either general or specific approaches. General behavioural measures assess whether individuals have altered their routine activities in response to a diffuse sense of fear, exemplified by questions such as, ‘In general, have you limited or changed your activities in the past year because of crime?’ (Liska et al., 1988) More refined approaches subdivide behavioural responses into specific forms of avoidance (Hardyns and Pauwels, 2010), defensive behaviours (Rader et al., 2007) and protective behaviours (Woolnough, 2009), which helps to capture the heterogeneity of fear-induced actions. It should be noted that not all behaviours necessarily serve as valid indicators of fear. For example, some participatory behaviours may reflect factors other than fear, including collective efficacy or civic engagement (Luengas and Ruprah, 2008). This highlights the importance of selecting behavioural indicators that are both theoretically appropriate and contextually sensitive.

While behavioural responses capture actions taken due to fear, the cognitive dimension focuses on perceived risk, which has gained increasing attention (Ferraro, 1995; Jackson, 2011; Rader et al., 2007). This is often assessed using either hypothetical scenarios or direct estimations of future risk. For instance, respondents were asked whether they considered themselves likely to become victims of crime (Ferraro and LaGrange, 2017; Williams et al., 2000). In addition to assessing general perceptions of victimisation risk, the authors distinguished between different types of crime – such as property and personal victimisation – thereby employing a more context-specific approach to capture the cognitive dimension of fear of crime (Franklin et al., 2008; Guedes et al., 2018). These instruments reflect a deliberate effort to disentangle and independently assess the emotional, cognitive and behavioural components within the broader construct of fear of crime, thereby enhancing both conceptual clarity and measurement validity.

Despite these advancements, terminological inconsistencies persist in current academic research. As interdisciplinary research on fear of crime continues to expand, ongoing debates about conceptual boundaries and measurement standards are inevitable, underscoring the need for further theoretical synthesis and methodological refinement.

Indicators of fear of crime

To investigate why certain groups exhibit elevated levels of fear of crime, researchers have proposed a range of theoretical frameworks to assess the extent to which specific factors contribute to this variation. This section provides an integrative review of the existing literature, categorising explanatory variables into three primary types: vulnerability, victimisation experiences and situational context (Barton et al., 2017; Gabriel and Greve, 2003; Scarborough et al., 2010).

Vulnerability

Vulnerability, defined as an increased susceptibility to crime due to individual characteristics, is widely employed in fear of crime research (Henson and Reyns, 2015; Walklate, 2011). Killias (1990) identified three mechanisms, namely exposure to risk, seriousness of consequences and loss of control, through which it contributes to fear of crime. Given the methodological difficulties in directly measuring vulnerability, researchers often rely on sociodemographic indicators as proxies, such as age, gender, education and income (Lee et al., 2020; McCrea et al., 2005; Rader et al., 2012). Empirical studies typically categorise vulnerability into physical and social dimensions, providing a more nuanced understanding of how different population groups experience and respond to fear.

Physical vulnerability refers to inherent traits, such as gender or age, which constrain individuals’ capacity for self-protection (Cossman et al., 2016; Pantazis, 2000). Women, for instance, often report higher fear levels even in low risk situations (Scott, 2003; Snedker, 2015; Zhang et al., 2009). Ferraro (1995, 1996) posits the ‘shadow of sexual assault’ hypothesis, which suggests that women’s fear of sexual assault generalises to other types of crime, supported by studies such as Fisher and Sloan III (2003), Lane and Fox (2013), Mellgren and Ivert (2019) and Özascilar (2013). However, evidence is mixed. When models adjust for risk-related covariates, the association between fear of sexual assault and broader fear of crime often attenuates and may become non-significant (Hirtenlehner and Farrall, 2014), suggesting that the hypothesis is contingent on context and measurement.

Similarly, research on age and fear of crime yields mixed results. Some studies indicate that older adults report greater fear, often linked to declining physical capacity (Ceccato and Bamzar, 2016; Vauclair and Bratanova, 2017; Weinrath and Gartrell, 1996). In contrast, other studies suggest that younger populations actually report higher levels of fear than older adults (Kappes et al., 2013; LaGrange and Ferraro, 1989; Sacco and Nakhaie, 2001). Franklin et al. (2008) further found that age differences in fear vary by measurement: older adults report higher perceived risk, while younger adults express more worry about victimisation. As emphasised by recent findings from Hong et al. (2025), demographic influences on fear of crime should be interpreted with careful consideration of environmental contexts and specific crime types.

Social vulnerability emphasises the notion that individuals with fewer socioeconomic resources are less able to prevent victimisation and face greater challenges in recovery following victimisation (Skogan and Maxfield, 1981; Virtanen, 2017). Common indicators include income, education and marital status. Prior studies frequently demonstrate that individuals with lower socioeconomic status, such as the unemployed or low-income groups, report higher fear levels (McKee and Milner, 2000; Pantazis, 2000; Vieno et al., 2013). In addition, living alone or being unmarried may elevate perceptions of vulnerability (De Donder et al., 2005; Jang and Jung, 2025). Similarly, lower educational attainment has been associated with greater fear of crime (Macassa et al., 2023; Scarborough et al., 2010). However, evidence from China suggests a contrasting pattern: Liu et al. (2009) found that those with higher education may also report elevated fear. This indicates that previous findings on the relationship between education and fear of crime are not universally consistent.

Victimisation experiences

Victimisation experiences constitute another determinant in research on fear of crime. The conventional assumption is seemingly intuitive: individuals who have previously been victimised are expected to exhibit higher levels of fear (Balkin, 1979; Maier and DePrince, 2023; Podana and Krulichová, 2023).

However, this assumption has been challenged by some research suggesting a weak correlation between victimisation and fear of crime (Noble and Jardin, 2020; Wilcox et al., 2007). To account for these inconsistencies, scholars have examined the relationship from multiple perspectives. Noble and Jardin (2020) argue that such inconsistencies arise from treating victimisation as a single, homogeneous category. By distinguishing between different intensities of victimisation, they show that the type and consequences of incidents are systematically associated with varying levels of fear of crime. The timing of victimisation also helps to interpret it, as earlier incidents generally have a diminished impact on fear, whereas recent experiences exert a stronger and more lasting influence (Fisher and Sloan, 1995; Janssen et al., 2021). Fisher and Sloan (1995) demonstrated that individuals who had been victimised within the past year exhibited higher levels of fear compared with those whose victimisation occurred more than a year earlier.

The type of crime also shapes fear levels. Women who have experienced sexual harassment tend to report greater fear (Keane, 1995). Dull and Wint (1997) distinguished between property and personal crimes, noting that individuals with victimisation experiences were more fearful of property crime, whereas those without such experiences expressed greater fear of personal crime. However, Miethe and Lee (1984) noted that victimisation is a strong predictor of fear of violent crime, while it is less effective in predicting fear of property crime. Research differentiating among multiple dimensions of fear has further demonstrated that victimisation has varying impacts depending on the aspect of fear considered. For example, Hinkle (2015) found that victimisation significantly increases emotional fear but does not influence perceptions of safety or perceived risk.

Taken together, these findings suggest that fear varies systematically with the intensity, timing and type of victimisation, as well as with the dimension of fear under consideration. This highlights that the pathways through which victimisation shapes fear should not be treated simplistically, but instead require validation across diverse contexts, temporal settings and crime types.

Situational factors

Situational factors, especially those stemming from observable features of the external environment such as disorder or visible crime, have been recognised as key predictors of fear of crime (Ferraro, 1995; Hanslmaier, 2013; Lorenc et al., 2012). These indicators reflect broader situational conditions that may shape individual fear of crime.

Disorder typically refers to visible signs or incivility in the built environment, such as vandalism, graffiti, broken windows or loitering, which are often interpreted as indicators of social disorganisation and weak informal control mechanisms (Barton et al., 2017; Brunton- et al., 2011; Camacho Doyle et al., 2022; Jing et al., 2024). Grounded in ‘Broken windows theory’ (Wilson, 1982), disorder is understood as symbolic signals of declining social order, which may heighten residents’ fear of crime (Gabriel and Greve, 2003; Jackson, 2005; Sampson and Raudenbush, 2004). Innes (2004) further introduced ‘signal crimes’, emphasising that fear is socially constructed through the interpretation of incidents or environmental cues. This framework extends the broken windows perspective by demonstrating why specific manifestations of disorder disproportionately generate fear, even in contexts of low objective risk.

Anchored in this line of reasoning, ‘social disorganisation theory’ situates disorder within broader neighbourhood dynamics. Markowitz et al. (2001) proposed the ‘disorder–decline hypothesis’, describing a self-reinforcing feedback loop in which perceived disorder heightens fear, fear erodes neighbourhood cohesion and weakened cohesion subsequently increases victimisation risk. Collectively, these theoretical perspectives offer a cumulative framework that progressively links disorder to fear of crime, illustrating how visible signs of disorder serve as cues of declining social order, which, together with other indicators such as reduced collective efficacy, signals potential social breakdown and underlies residents’ fear of crime (Lewis, 2017; Lorenc et al., 2012; Skogan, 1992; Taylor and Hale, 2017).

In parallel, crime rates have been used as situational indicators. Individuals residing in high crime rate neighbourhoods tend to report greater fear (Franc et al., 2011). However, extant research noted a mismatch between actual crime trends and public sentiment, commonly referred to as the ‘fear–crime paradox’ (Balkin, 1979; Heath et al., 2001; Hipp, 2013). Walker-Peddakotla (2025), drawing on Gallup survey data, documented that despite sustained declines in violent crime, the public’s fear of crime in the United States has remained disproportionately high. This paradox underscores the need to distinguish between the official crime rate and the perceived crime rate. The latter denotes subjective assessments of the crime rate, shaped by mechanisms such as neighbourhood gossip, media reporting or vicarious victimisation (Skogan, 1986). Research suggests that individuals’ perceptions of crime rate may play a more significant role in influencing fear of crime than official crime rates (Ambrey et al., 2014; Baker et al., 1983).

Taken together, these indicators highlight the key factors shaping fear of crime and demonstrate how the analytical framework has progressively expanded with research (Ferraro and LaGrange, 2017; Hart et al., 2022). Building on these insights, Macao provides a valuable context for empirical analysis. Its demographic profile, characterised by a relatively high proportion of older adults and a female majority, together with its densely urbanised setting and the coexistence of low overall crime rates with concentrated risk factors, make it distinct from other contexts.² These features create favourable conditions for applying established frameworks to examine fear of crime in Macao.

The current study

Building upon prior scholarship on fear of crime, which has provided valuable conceptual and methodological guidance, this study applies these insights to the Macao context. To advance understanding in this unique setting, this study adopts three widely used explanatory frameworks: the vulnerability model, the victimisation model and the situational model.

In addition to examining the simple linear relationships among variables, recent studies have increasingly explored how interactions among different factors may influence fear of crime. For instance, by demonstrating that situational factors moderate the victimisation–fear relationship, Jing et al. (2024) underscored the complex mechanisms through which fear of crime is produced. Jing et al.’s model is particularly instructive for this study, as it stems from a context culturally proximate yet institutionally distinct from Macao and employs a measurement instrument comparable to the Macao Victimisation Survey. Building on their strategy, this study adopts a similar approach while improving the framework in two respects.

Whereas Jing et al. treated fear of crime as a unidimensional construct, we adopt a multidimensional conceptualisation that distinguishes among affective, cognitive and behavioural dimensions. This disaggregation allows for a more nuanced understanding of how different predictors influence distinct aspects of fear, acknowledging that emotional responses, perceived risk and defensive behaviours may be shaped by separate mechanisms (Camacho Doyle et al., 2022; Gabriel and Greve, 2003; Rader et al., 2007).

Another refinement involves the incorporation of perceived crime rates, enabling the analysis to capture residents’ subjective evaluations of the local environment. In Macao, although official data reported a 13.9% decline in crime from 2021 to 2022 (Macao Security Bureau, 2023), survey data from this study reveal a significant perceptual gap: only 12.83% of respondents reported perceiving a decline, whereas 35.72% believed crime had increased. This discrepancy illustrates the divergence between official crime rates and the public’s perceived crime rate (Heath et al., 2001; Walker-Peddakotla, 2025). Given that objective crime rates often do not capture the socially constructed and interpretive nature of fear, attempts to align official statistics with fear have been criticised for their limited explanatory power (Farrall et al., 2007; Lorenc et al., 2014; Young, 1988). Accordingly, this study incorporates perceived crime rates as another situational indicator, facilitating a more nuanced analysis of how subjective evaluations of the local environment contribute to the formation or emergence of fear.

In summary, this study pursues three objectives. First, it seeks to examine the predictors of fear of crime among Macao residents using the three models. Second, it investigates whether situational factors moderate the relationship between victimisation and fear. Third, it explores how these effects vary across the affective, cognitive and behavioural dimensions of fear, thereby advancing the theoretical and empirical precision of fear of crime research.

Methods

This section outlines the operationalisation of key variables, guided by established theoretical models of fear of crime. Variables are organised according to their function as dependent or independent predictors, with consideration of their theoretical relevance.

Data collection and sampling

The data were obtained from a 2022 Macao Victimisation Survey using a Computer-Assisted Telephone Interviewing system with random-digit dialling to ensure probabilistic sampling. The sampling frame covered the majority of residential landline numbers in the region, allowing for broad geographic and demographic coverage. The target population comprised Macao residents aged 18 years and above. A total of 1,102 respondents completed the survey through telephone interviews. Missing data were handled using listwise deletion, and then the final analytic sample consisted of 795 respondents (N = 795). The data supporting the findings of this study are available from the 2022 Macao Victimisation Survey project, managed by the Macao Society of Criminology and funded by the Macao Foundation. However, restrictions apply to the availability of these data, which were used under licence for this study and are not publicly accessible.

The questionnaire was adapted from the 4.0 version of the Latin America and the Caribbean Crime Victimisation Survey Initiative (LACSI) drafted and edited by the United Nations Office on Drugs and Crime (UNODC). As a standardised instrument endorsed by a specialised United Nations agency, this tool has been widely implemented across various national contexts and has demonstrated consistent applicability for cross-national crime victimisation studies.³ In this study, the original questionnaire was translated and revised to suit Macao’s specific cultural, geographic and social structure. This ensures the validity of the measurement while enhancing the international comparability of the findings. Table 1 summarises the descriptive statistics for all variables included in this study.

Table 1.

Frequency distribution statistics for independent variables (N = 795).

Variable	N (%)
Age
18–30	159 (20.00)
31–45	171 (21.51)
46–60	173 (21.76)
61–75	213 (26.79)
76–90	77 (9.69
>90	2 (0.25)
Gender
Female	483 (60.75)
Male	312 (39.25)
Paid job
Yes	666 (83.77)
No	129 (16.23)
Partner
Yes	513 (64.53)
No	282 (35.47)
Educational level
Non-formal education	30 (3.77)
Primary education	249 (31.32)
Middle education	186 (23.40)
High education	330 (41.51)
Household income
Low	317 (39.87)
Middle	445 (55.97)
High	33 (4.15)
Frequency of victimisation
0	699 (87.92)
1	80 (10.06)
2	11 (1.38)
3	4 (.50)
4	1 (.13)
Frequency of community disorder
0	455 (57.23)
1	250 (31.45)
2	63 (7.92)
3	16 (2.01)
4	3 (.38)
5	3 (.38)
6	5 (.63)
Perceived crime rate
Decrease	102 (12.83)
Stable	409 (51.45)
Increase	284 (35.72)

Variable measurement

This section details the operationalisation of both predictor variables and outcome variables. Specifically, we detail how vulnerability, victimisation experience and situational factors were operationalised, alongside the three distinct dimensions of fear of crime. These measurement decisions were guided by established theoretical frameworks and prior empirical research, ensuring both conceptual clarity and empirical relevance.

Dependent variable

The affective dimension of fear of crime was operationalised by the total number of locations within the respondents’ neighbourhood that they felt were unsafe. Respondents were asked to evaluate their feelings of insecurity across 14 predefined locations typically associated with safety concerns, supplemented by an open-ended question inviting them to specify any additional places where they felt unsafe. The cumulative count of these unsafe locations serves as an indicator of the breadth of perceived environmental insecurity, with higher scores reflecting a more pervasive sense of emotional fear across various spatial contexts.

The cognitive dimension was defined by the perceived risk of victimisation. Respondents were asked: ‘Do you believe that within the next twelve months, you may become a victim of a crime due to your activities or the places you usually visit?’ Responses were coded using a dichotomous variable (0 = No, 1 = Yes). This variable captures respondents’ perceived likelihood of victimisation, providing insight into how individuals cognitively assess the potential risks they face in their daily lives.

The third dependent variable reflects the behavioural dimension, measured through respondents’ engagement in defensive behaviour. Specifically, participants were asked whether they had undertaken any precautionary measures at home during the past 12 months to prevent or protect themselves from potential crime; responses were recorded on a dichotomous variable (0 = No, 1 = Yes). This measure reflects the behavioural coping strategy associated with fear of crime, operationalised through reported precautionary behaviours.

Independent variables

Vulnerability

Consistent with prior research, several demographic variables were employed as proxies for vulnerability, including age, gender, educational level, partner status, employment status and household income. Age was categorised into six ordinal groups. Gender was measured as a binary variable (0 = male, 1 = female). Educational attainment was treated as an ordinal variable ranging from 1 to 4, corresponding to the categories of no formal education, primary education (primary and middle school), secondary education (high school) and tertiary education (college level and above). Both partner status and employment status were coded as dummy variables (0 = absence, 1 = presence). Household income was assessed through a subjective question: ‘How would you rate your household’s total income level?’ Responses were categorised into three levels: low, middle and high.

Victimisation experiences (past 12 months)

Respondents were provided with a checklist of 14 crime types and asked whether they had experienced each within the past 12 months. Responses were coded dichotomously (1 = experienced, 0 = not experienced), and these responses were aggregated to form a continuous variable indicating the total number of victimisation experiences reported within the past year.

Community disorder

Perceptions of community disorder were measured using a checklist of 12 indicators of criminal and non-criminal incivilities, along with an open-ended option for respondents to specify any other signs of disorder. Respondents indicated whether they had noticed each item (0 = No, 1 = Yes). Responses were summed to construct a continuous variable, with higher scores indicating greater perceived disorder in the community.

Perceived crime rate

Respondents were asked: ‘Compared to 2021, how do you perceive the crime situation in Macao in 2022?’ Responses were coded on a three-point ordinal scale: 1 = decreased, 2 = stable and 3 = increased. This variable measures the respondents’ prediction of the overall crime rate in Macao.

Analysis strategy

This survey did not employ hierarchical sampling design (e.g. community or group-level nesting); therefore, no multilevel modelling was required. Instead, following the analytical strategy proposed by Scarborough et al. (2010), conventional regression techniques were applied to simultaneously examine the influence of individual level characteristics and contextual factors on multiple dimensions of fear of crime. To assess the effects of three types of indicators on the affective, cognitive and behavioural dimensions of fear of crime, a series of models were estimated. Specifically, a linear regression model was built to explore the affective dimension of fear, while logistic regression models were utilised for binary outcome variables corresponding to the cognitive and behavioural dimensions.

A sequential modelling strategy was adopted to assess the incremental explanatory power of each group of predictors. Model 1 included only vulnerability indicators as the baseline model. Models 2 through 4 sequentially incorporated victimisation experiences, community disorder and perceived crime rates, respectively. Model 5 introduced interaction terms between victimisation and situational variables (community disorder and perceived crime rate) to investigate whether victimisation experiences’ influence on fear of crime is moderated by situational factors. All interaction variables were mean-centred prior to inclusion to reduce multicollinearity and facilitate interpretation.

Multicollinearity diagnostics using variance inflation factors (VIFs) confirmed no severe multicollinearity, with all values well below the standard threshold of 10. To address potential heteroscedasticity and violations of classical regression assumptions, robust standard errors were applied throughout the analyses (Froot, 1989; Hauser and Kleck, 2017; Koenker and Bassett Jr, 1982).

Results

Preliminary statistics

Before specifying the regression models, we did a preliminary analysis. We followed Hinkle’s (2015) framework to explore the extent and variation of fear of victimisation among Macao residents. As summarised in Table 2, the mean scores for all three dependent variables were relatively low, indicating an overall limited fear response within the surveyed population. This is further supported by the frequency distributions in Table 3. A substantial majority of respondents exhibited no emotional fear, with 78.87% reporting zero unsafe locations, and only 2.77% identifying two or more. Similarly, perceived risk of victimisation was reported by only 9.69% of respondents, indicating that the vast majority did not feel at risk. The proportion of residents reporting defensive behavioural responses was the lowest among the three dimensions of fear, with only 2.89% of respondents indicating that they had engaged in defensive behaviours in the preceding 12 months.

Table 2.

Descriptive statistics for all variables in the models (N = 795).

Variable	Mean	SD	Min	Max
Age	2.85	1.29	1.00	6.00
Female	0.61	0.49	0.00	1.00
Paid job	0.84	0.37	0.00	1.00
Partner	0.65	0.48	0.00	1.00
Educational level	3.03	0.94	1.00	4.00
Low household income	0.40	0.49	0.00	1.00
Middle household income	0.56	0.50	0.00	1.00
High household income	0.04	0.20	0.00	1.00
Victimisation	0.15	0.45	0.00	4.00
Community disorder	0.61	0.91	0.00	6.00
Perceived crime rate	2.23	0.66	1.00	3.00
Affective fear	0.24	0.50	0.00	3.00
Cognitive fear	0.10	0.30	0.00	1.00
Defensive behaviour	0.03	0.17	0.00	1.00

SD = standard deviation.

Table 3.

Distribution of fear of crime (N = 795).

Variable	n (%)
Frequency of affective fear
0	627 (78.87)
1	146 (18.36)
2	20 (2.52)
3	2 (0.25)
Frequency of cognitive fear
No	718 (90.31)
Yes	77 (9.69)
Frequency of defensive behaviour
No	772 (97.11)
Yes	23 (2.89)

In terms of victimisation, Table 2 reveals a relatively low prevalence within the sample (M = 0.15, SD = 0.45 on a 0–4 count scale). Consistently, Table 4 shows that among those without victimisation experiences, 71.32% reported having no locations they considered fearful. Similarly, 80.25% perceived no risk of future victimisation, and 86.29% reported no behavioural fear. These patterns collectively underscore a consistent trend: fear of crime, across emotional, cognitive and behavioural dimensions, is markedly lower among non-victims, while repeated victimisation is associated with elevated levels of fear. These preliminary findings point to a potential relationship between victimisation and fear of crime that warrants further empirical investigation. To formally examine this association, we conducted a series of multivariate regression analyses, as detailed in the following section.

Table 4.

Cross-table between the victimisation and fear of crime (N = 795).

Variable		Victimisation (n (%))
Variable		0	1	2	3	4
Emotional fear	0	567 (71.32)	54 (6.79)	6 (0.75)	0 (0.00)	0 (0.00)
	1	112 (14.09)	24 (3.02)	5 (0.63)	4 (0.50)	1 (0.13)
	2	19 (2.39)	1 (0.13)	0 (0.00)	0 (0.00)	0 (0.00)
	3	1 (0.13)	1 (0.13)	0 (0.00)	0 (0.00)	0 (0.00)
Cognitive fear	0	638 (80.25)	69 (8.68)	8 (1.01)	2 (0.25)	1 (0.13)
Cognitive fear	1	61 (7.67)	11 (1.38)	3 (0.38)	2 (0.25)	0 (0.00)
Defensive behaviour	0	686 (86.29)	73 (9.18)	10 (1.26)	2 (0.25)	1 (0.13)
Defensive behaviour	1	13 (1.64)	7 (0.88)	1 (0.13)	2 (0.25)	0 (0.00)

n (%).

Affective dimension of fear of crime

Table 5 presents the results of a series of multivariate linear regression models for affective fear of crime. Model 1 includes only vulnerability-related indicators. Among these, educational level emerges as a consistent positive predictor of affective fear (β = 0.06, p < 0.05). Victimisation experiences within the past 12 months were added to the second model and were shown to be strongly predictive of an indicator of emotional fear (β = 0.13, p < 0.001). Model 3 adds situational factors, where community disorder displays a significant positive association (β = 0.10, p < 0.001), and perceived crime rate yields a marginally significant effect (β = 0.06, p < 0.05). In Model 4, interaction terms between victimisation and each situational factor are incorporated, but none of the interaction effects reach statistical significance. These results suggest that while individual and contextual factors contribute to affective fear, the interactive influence of victimisation and situational contexts is limited.

Table 5.

Linear regression of emotional fear.

Variable	Model 1	Model 2	Model 3	Model 4
Vulnerability
Age	−0.03	−0.02	−0.01	−0.01
Female	0.02	0.02	0.02	0.02
Education	0.06*	0.06*	0.06*	0.06*
Paid job	−0.10	−0.10	−0.12*	−0.12*
Partner	−0.02	−0.02	0.01	0.01
Middle household income	−0.08*	−0.07	−0.05	−0.06
High household income	0.01	0.04	0.04	0.04
Victimisation		0.13***	0.12**	0.11*
Situational factors
Community disorder			0.10***	0.10***
Perceived crime rate			0.06*	0.06*
Interactions
Victimisation × community disorder				0.02
Victimisation × perceived crime rate				0.06
Constant	0.26*	0.24	−0.01	−0.01
Adj. R²	0.03	0.05	0.08	0.08
F	4.65	6.38	9.21	8.29

p < 0.05; **p < 0.01; ***p < 0.001.

Cognitive dimension of fear of crime

Table 6 displays a series of multinomial logistic regression models with the cognitive dimension of fear as the dependent variable. In Model 1, educational level is positively associated with cognitive fear, indicating that individuals with higher levels of education report greater perceived risk (β = 0.32, p < 0.05). However, this effect becomes statistically non-significant once additional predictors are introduced in subsequent models. Model 2 incorporates victimisation experiences, which emerges as a significant predictor (β = 0.53, p < 0.05). In Model 3, after accounting for community disorder and perceived crime rate, the effect of victimisation remains significant (β = 0.49, p < 0.05). Perceived crime rate itself is another significant predictor, with higher perceived rates associated with increased cognitive fear (β = 0.64, p < 0.01). In Model 4, interaction terms between victimisation and situational factors are added; however, no significant moderating effects are observed. At this stage, the effect of victimisation also becomes non-significant, while perceived crime rate remains the sole significant predictor.

Table 6.

Multinomial logistic regression of cognitive fear of crime.

Variable	Model 1	Model 2	Model 3	Model 4
Vulnerability
Age	−0.06	−0.04	0.07	0.07
Female	0.31	0.33	0.29	0.32
Education	0.32*	0.29	0.29	0.32
Paid job	0.49	0.55	0.44	0.54
Partner	0.11	0.14	0.18	0.16
Middle household income	−0.49	−0.47	−0.43	−0.42
High household income	−1.78	−1.63	−1.68	−1.69
Victimisation		0.53*	0.49*	0.18
Situational factors
Community disorder			0.19	0.19
Perceived crime rate			0.64**	0.65**
Interactions
Victimisation × community disorder				0.54
Victimisation × perceived crime rate				0.22
Constant	−3.44***	−3.59***	−5.45***	−5.65***
Wald χ2	14.00	21.25	36.89	41.28
Log pseudolikelihood	−246.34	−243.49	−237.10	−234.86

p < 0.05; **p < 0.01; ***p < 0.001.

Behavioural dimension of fear of crime

Table 7 presents the multinomial logistic regression results for the behavioural dimension of fear of crime. In Model 1, paid job emerges as the only significant predictor: individuals engaged in a paid job are significantly less likely to report adopting defensive behavioural strategies (β = −1.24, p < 0.05). Model 2 adds victimisation experiences, which are strongly and positively associated with behavioural fear (β = 0.86, p < 0.001). The effect remains robust and slightly increases in magnitude across subsequent models. In Model 3, the inclusion of community disorder and perceived crime rate does show statistical significance. In Model 4, interaction terms between victimisation and contextual perceptions are introduced; however, no significant moderation effects are identified. Paid job and victimisation remain the consistently significant predictors throughout the model.

Table 7.

Multinomial logistic regression of behavioural fear of crime.

	Model 1	Model 2	Model 3	Model 4
Vulnerability
Age	0.01	0.08	0.08	0.10
Female	−0.08	−0.06	−0.10	−0.20
Education	0.52	0.39	0.39	0.36
Paid job	−1.24*	−1.17*	−1.22*	−1.21*
Partner	−0.70	−0.72	−0.74	−0.76
Middle household income	−0.18	−0.07	−0.10	−0.04
High household income	−0.22	0.22	0.19	0.25
Victimisation		0.86***	0.87***	0.99***
Situational factors
Community disorder			−0.23	−0.20
Perceived crime rate			0.11	0.24
Interactions
Victimisation × community disorder				−0.09
Victimisation × perceived crime rate				−0.41
Constant	−3.81*	−3.98	−3.99	−4.25
Wald χ2	20.49	34.68	35.56	37.95
Log pseudolikelihood	−95.43	−90.63	−90.27	−89.76

p < 0.05; **p < 0.01; ***p < 0.001.

In conclusion, the stepwise inclusion of theoretically relevant predictors enhanced model performance, indicating that both individual and situational factors are important for understanding the different facets of fear of crime. However, interaction effects between victimisation and contextual factors are not statistically supported, indicating their influence may operate in parallel rather than multiplicatively.

Discussion

As the first empirical investigation of fear of crime among Macao residents, this study identifies distinct associations between several predictors and specific dimensions of fear. Importantly, it reinforces the conceptual necessity of treating fear of crime as a multidimensional construct. These findings not only contribute to the understanding of residents’ fear in the unique Macao context but also challenge several well-established assumptions derived from Western and regional studies.

First, consistent with the fear–victimisation framework (Dammert and Malone, 2003; Garofalo, 1979; Hanslmaier, 2013; Shippee, 2012; Skogan, 1987), victimisation experience emerges as a robust predictor across all three dimensions of fear. From a social psychological perspective, victimisation experience is a potent negative stimulus that not only diminishes individuals’ subjective well-being but also heightens their fear of crime (Russo and Roccato, 2010). These results can be interpreted as cognitively rational, as they reflect risk perceptions and assessments based on personal experiences conveyed information regarding potential threats (Skogan, 1987).

Second, the findings suggest that situational factors exhibit a significant relationship with the emotional and affective dimensions of fear. To some extent, these results support social disorganisation theory. When signs of disorder or incivility emerge within a community, residents who perceive these signals may interpret them as precursors to crime, thereby heightening their fear or perceived risk of victimisation (Ferguson and Mindel, 2007). Residents who perceive higher levels of incivilities or local crime rates tend to report elevated fear irrespective of official statistics.

By contrast, neither perceived crime rate nor disorder showed significant effects on behavioural fear. This outcome appears closely linked to Macao’s residential context. The city’s high-density housing patterns mean that most residents live in apartment complexes equipped with standardised security measures, including controlled entry systems and on-site guards (Chen et al., 2025). In addition, substantial government investment in residential improvement, such as the Building Maintenance Grant Scheme (MOP 13,298,000) and the Common Area Maintenance Subsidy Scheme (MOP 20,233,000), further reduces the necessity for residents to implement additional household-level defensive strategies (Macau Special Administrative Region Financial Services Bureau, 2023).

Third, contrary to prior research, age and gender did not exhibit statistically significant associations with fear of crime. This unexpected result can be attributed to both institutional and social factors unique to Macao. Institutionally, the Macao government has placed significant emphasis on social welfare investments, which may provide a buffer against fear for potentially vulnerable groups (Macau Special Administrative Region Financial Services Bureau, 2023).⁴ Macao’s extensive welfare infrastructure, including universal healthcare, old-age pensions and social protection schemes, may mitigate perceived vulnerability among older adults.⁵ Furthermore, legal safeguards such as the Domestic Violence Law and enhanced victim support systems may contribute to a sense of gendered security (e.g. 22 rape and 8 sexual coercion cases reported in 2022) (Macao Government Social Work Bureau, 2022). Notably, Macao’s Gender Inequality Index (GII) was 0.06 in 2021, substantially lower than that of mainland China (0.192), Japan (0.083), and the global average (0.457), indicating a relatively equitable gender environment (Macao Government Social Work Bureau, 2021; United Nations Development Programme, 2022). These factors may jointly mitigate the salience of physical vulnerability in shaping fear of crime, thus explaining the nonsignificant effects of age and gender.

Fourth, the consistently low levels of fear across all three dimensions, as well as the non-significant moderating effects, may be partly attributed to Macao’s substantial governmental investment in public security and its relatively dense allocation of police resources (European Central Bank, 2025; Federal Reserve Bank of St. Louis, 2025; Lao and Xu, 2024; Macao Statistics Census Bureau 2023).⁶ Prior research indicates that higher levels of state investment in security and positive evaluations of police performance can enhance residents’ perceptions of safety and reduce fear of crime (Abbott et al., 2020; Hummelsheim et al., 2011; Vieno et al., 2013).

Given Macao’s extensive policing and public safety investment, the overall crime rate is low, resulting in a correspondingly limited prevalence of victimisation in the sample. At the same time, the city’s orderly urban ecology, characterised by minimal visible disorder and incivilities, restricted the range of situational factors. Together, the low prevalence of victimisation and the limited variability in situational cues help explain why the moderating effects of situational factors on fear of crime were not statistically significant. Similar findings have been reported in other studies (McNeeley and Stutzenberger, 2025; Roccato et al., 2011), underscoring both the statistical constraints and the complex socio-environmental dynamics underlying fear of crime.

Fifth, as a cross-cultural validation of theoretical frameworks, the relatively low levels of fear reported by Macao residents may be accounted for by both substantive cultural mechanisms and methodological factors. At the substantive level, collectivist and relational cultural orientations may exert a genuine buffering effect. In Chinese societies, individuals are embedded within dense family, kinship and community networks (Braithwaite, 1989; Kagitcibasi, 1997; Liu, 2024), which facilitate access to social support and mitigate stress and fear responses (Leung et al., 2007; Poulin et al., 2012). Together with Confucian norms emphasising harmony and collective responsibility, these structures may reduce fear of victimisation by framing social risks in terms of collective identity rather than individual vulnerability (Jiang and Song, 2024; Yun et al., 2010).

At the methodological level, the low levels of fear may also be partially attributable to measurement bias. Cultural orientations towards emotional regulation, which favour avoidance or indirect expression to preserve harmony (Liang and Bogat, 1994), combined with pressures of social desirability, in collectivist contexts (Hofstede, 1984; Smith, 2004; Zerbe and Paulhus, 1987), may discourage overt reporting of fear. As a result, residents may understate their fear in survey responses, producing attenuated estimates. Such social desirability-related distortions highlight the need for caution in interpreting the findings and point to the importance of adopting mixed-methods or indirect measurement strategies in future research to minimise potential bias.

Limitations and conclusion

While this study contributes to the literature on fear of crime, particularly by contextualising predictors within Macao’s unique setting, it is not without methodological and analytical limitations that warrant acknowledgement.

One important issue concerns the survey instrument. The questionnaire relied heavily on items developed within Western research paradigms, which may introduce subtle cultural or contextual mismatches for Macao’s population. Items such as fear of going out at night, perceived likelihood of victimisation, and the use of household security measures were operationalised as binary variables (0/1). Such dichotomous coding is inherently limited: it fails to capture nuanced gradations of fear (e.g. mild vs severe anxiety) and omits critical contextual details like the frequency of fear experiences or the intensity of security-related behaviours (Hardyns and Pauwels, 2010; Hart et al., 2022).

A further limitation relates to the use of single-item measures to assess fear of crime. Given the limitations commonly associated with single items, many scholars recommend the use of multi-item instruments to better capture the complexity of this construct (Gabriel and Greve, 2003; Jackson, 2005; LaGrange and Ferraro, 1987; Williams et al., 2000). Nevertheless, the reliance on single items in this study is justified by two considerations: the established reliability of the ICVS, and the current absence of validated multi-item scales specifically developed for Macao. Previous studies further demonstrate that single-item indicators can yield valid results, making them a practical option under such conditions (Alfaro-Beracoechea et al., 2018). Despite this justification, it must be acknowledged that single-item measures constrain the ability to capture the full range of fear of crime, particularly across its affective, cognitive and behavioural dimensions.

The analytical scope was limited to a single-level (individual-level) analysis, constrained by the availability of data. This limitation may obscure the influence of contextual factors, such as neighbourhood characteristics and socio-cultural dynamics, and prevent examination of cross-level effects. Consequently, the study cannot capture multilevel variation or interactions between individual and contextual predictors, which restricts the ability to draw robust inferences about the mechanisms linking context and fear of crime.

As the first large-scale victimisation survey in Macao, the study faced inherent data constraints, especially the absence of panel data. Consequently, the results derived from this cross-sectional analysis may preclude the examination of temporal dynamics and causal mechanisms.

In light of these limitations, future research should prioritise the development of contextually grounded and culturally sensitive measurement instruments that better reflect Macao’s socio-cultural, institutional and legal realities. Locally adapted scales would provide more accurate assessments of fear of crime, while refinements in technical design, including the use of multi-indicator rather than single-item approaches, would enhance measurement validity, at the same time adopting multi-level and longitudinal data to better capture changes over time and across neighbourhoods. Expanding sample sizes and increasing the temporal depth of data collection would also strengthen the reliability and external validity of findings, thereby improving the generalisability of conclusions.

The findings provide several implications for social governance in Macao. The paradox between residents’ subjective perceptions of crime and the decline reflected in official statistics highlights the importance of strengthening publication and law-enforcement transparency. Disseminating crime-related information through multiple channels and improving the visibility of crime-prevention efforts may therefore help reduce fear and enhance public trust (Duffy et al., 2008).

In addition, the results suggest that public spending on social welfare and security has shown some effectiveness. This is particularly relevant in the context of rapid population ageing. Beyond maintaining such expenditures, strengthening informal social control may also contribute to enhancing community collective efficacy. Initiatives such as expanding volunteer programmes and supporting neighbourhood associations (e.g. Macao Federation of Neighbourhood Associations; Macao Youth Volunteers Association) can provide additional social resources to mitigate risks associated with physical vulnerability (Macau Social Work Bureau, n.d.).

The influence of situational factors on the emotional and affective dimensions of fear also underscores the need to improve the order and stability of densely populated environments. Practical measures such as night patrols, surveillance installation, and the maintenance of clean and orderly public spaces may contribute to reducing perceived risks of victimisation (Ditton, 2000; Johnston, 2001; Kelling and Coles, 1997). Collectively, these strategies emphasise the importance of combining formal interventions with community-based approaches to address both the structural and perceptual dimensions of fear in Macao’s distinctive socio-cultural setting.

Footnotes

ORCID iDs

Yixuan Wang

Donna Soi Wan Leong

Jianhong Liu

Lichao Lu

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: We would like to thank the University of Macau for financial support, which was crucial for the completion of this research (Grant No. MYRG-GRG2023-00064-FLL).

Declaration of Conflicting Interests

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

Notes

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