Background
Stigma has toxic effects in many individuals (Thornicroft et al., 2022). Welfare stigmatization in the United States is prevalent and correlates with negative mental health outcomes including depression, poor self-esteem, anxiety, and suicidal thoughts and behavior (Bassuk et al., 1997; Lapham & Martinson, 2022; Pak, 2020; Petterson & Friel, 2001). According to Lapham and Martinson (2022), welfare stigmatization is a cause of health inequality (Hatzenbuehler, Phelan, & Link, 2013). Welfare stigma has been found to increase marginalization, which can create or sustain poverty (Lapham & Martinson, 2022; Thornicroft et al., 2022). Stigmatization happens when a poor person who seeks welfare such as public nutritional assistance is considered undeserving of support (Mettler, 2011) while someone else who may not be poor but seeks another form of welfare is considered worthy of the benefit (Horan & Austin, 1974). Association between poverty, violent, and property crimes has been noted in the literature (Vasconcelos et al., 2022), and such correlations may not be unconnected to beliefs that poor people are more likely to engage in violent crime and property crime. This belief exacerbates the stigmatization of poor people. This author explored how the prevalence of public nutritional assistance utilization, an indicator of poverty, associates with the prevalence of violent crime and property crime. Findings showed inverse relationships between public nutritional assistance and violent crime and property crime. Implications for mental health, social work education, social policy, and violence prevention are discussed.
Across the world, studies have highlighted possible associations among various forms of crime (Deane, Armstrong, & Felson, 2005; Lussier, LeBlanc, & Proulx, 2005; Steffensmeier & Ulmer, 2005; Sullivan, McGloin, Pratt, & Piquero, 2006). Han, Bandyopadhyay, and Bhattacharya (2013) in a study of the determinants of violent crime and property crime in England and Wales found that a decline in violent crime and property crime was dependent on high level of incarceration. Oana-Ramona et al. (2017) assessed how socioeconomic factors influenced levels of crime in Romania; they found that high levels of income inequality and geographical location were positive correlates of crime.
Until recently, crime rates in the United States were high, particularly between the 1960s and the 1990s (Desmond, Kikuchi & Morgan, 2010). It was after the 1990s that crimes began to decline. Many cities in the United States have continued to grapple with high crime rates. Reports indicate that property crimes from January to June 2020 declined by 7.8%. Although claims of crime decline in the United States have been partly supported empirically (Friedson & Sharkey, 2015), some form of crimes such as murder and vehicle theft had continued to increase. Specifically, vehicle theft, a form of property crime, increased by 6.2%, murder and nonnegligent manslaughter has increased by 14.8%, and aggravated assault increased by 4.6% (FBI, 2020). Increases in murder, motor vehicle theft, and arson in the first 6 months of 2020 (FBI, 2020) suggest that the relationship between the above forms of crime continues to be of significant concern.
Barnett and Mencken (2009) in a study of crimes in nonmetropolitan counties found that economic factors are positively related to violent crime and property crimes. Violent crime and property crimes negatively affect every age group across the globe and in the United States. Finkelhor, Turner, Ormrod, and Hamby (2009) revealed about 60.6% of young participants in their study (children and youth) had experienced one or more episodes of victimization in the year prior to the study. During the year of the study, about one-half of the study participants (46.3%) experienced physical assault, while 24.6% experienced one incidence of property crime. In the same study, 6.1% of the participants experienced an incidence of sexual victimization, while 25.3% of the participants had witnessed a violent crime or had been exposed to a form of victimization, including assault. In fact, 10.2% of the participants were victims of a form of injury, while 38.7% were direct victims of two or more injuries because of direct victimization, among other findings. These findings are in some ways like the findings of Turner, Finkelhor, Mitchell, and Jones (2020) in terms of infant victimization in a national sample. Hence, this concern is of importance to both the young and the adult population.
Prevalence, Trend, and Cost of Property Crimes in the United States
The study of growth and decline of violent crime and property crimes has become increasingly important. Despite reports of decline in crime rates according to some reports, there still were about 9,063,173 property crimes in 2011; that is, an estimated rate of 2908.7 per 100,000 residents, amounting to an estimated loss of $15.6 billion (FBI, 2011). Also, despite a 4.1% reduction in property crimes in 2013, a total of an estimated 8,632,512 occurred in 2013, which amounted to 2730.7 per 100,000 residents, with an estimated total cost of $16.6 billion. Specifically, 69.6% of the overall property crime in 2013 was larceny, burglary amounted to 22.3%, and vehicle theft amounted to 8.1% (FBI, 2013). Looking at the long-term trend of property crimes, one would notice a seemingly important decrease in the phenomenon. Particularly, property crimes in the United States declined to about 2.6% between 2014 and 2015. It still amounted to a loss of $14.3 billion. A 10-year trend also revealed a 20.2% decline in property crime between 2005 and 2015. Yet, the total number of property crimes that occurred within the period remained high. Currently, based on 2018 data, it is estimated that 7196.045 incidences of property crimes occurred in the United States, with a 6.3% decline as compared to the 2017 total estimate. The 10-year trend also showed a decline of 22.9% in property crimes. The estimated total cost of property crimes in 2018 was $16.4 billion (FBI, 2018).
Prevalence, Trend, and Cost of Violent Crimes in the United States
Violent crimes negatively impact the health, economic, and social well-being of individuals, families, governments, and institutions. According to the preliminary Uniform Crimes Report for 2020 (FBI, 2020), offenses such as nonnegligent manslaughter and murder increased by 14.8%, in the first 6 months of 2020, while aggravated assault also increased by 4.6%. In all, cities with less populations (<10,000) reported decreases in violent crimes (7.2%) while those with larger populations (100,000 to 249,000) had lower decline in violent crimes (0.3%). Despite the reported decreases in violent crimes for the time frame under consideration (first 6 months of 2020), a large portion of the US population continues to struggle with the direct and indirect consequences of violent crimes. It is specifically troubling that what might be considered the most heinous forms of violent crimes—murder and nonnegligent manslaughter—have continued to be on the increase. Although existing information on total crime estimates in the United States is not current, the total cost is estimated at $2.6 trillion. This amounts to $620 billion cash costs including $1.95 trillion towards losses to quality of life, with an uncertainty interval of 95% ($2.2 – $3.0 trillion, Miller et al., 2020). According to Miller et al. (2020), violent offenses resulted in 85% cost of crimes in the United States, with violent crimes accounting for 85% of the costs.
Violent Crime, Public Nutritional Assistance, and Property Crime
Scholars have highlighted the relationship between violent crime and property crime. For example, Tcherni, Davies, Lopes, and Lizotte (2016) have noted that “one indirect consequence of property crimes occurring online could be a drop in the violent crime rate” (p. 907). Also, examinations of patterns of criminal behavior have exposed some connections between violent crime and property crimes (Lussier et al., 2005; Shover, 1996). In fact, most offenders do have prior histories that are not limited to a category of crime (Piquero, Farrington, & Blumstein, 2003). A phrase captured by previous scholars “offense versatility” seems to connote the associations between and among different forms of crime (Deane et al., 2005; Shover, 1996; Steffensmeier & Ulmer, 2005; Sullivan et al., 2006). Although both violent crime and property crimes are offenses, the differences between the two are quite clear. But interestingly, studies assessing how violent crime predicts property crimes are not many. More so, studies investigating growth trajectories for property crimes based on violent crime, and their effects being mediated by a marker of prevalence of poverty within a geographical setting are scarce. In terms of public nutritional assistance, no study known to the author has explored its association with violence and crime. Public nutritional assistance is administered in the United States. According to US Food and Nutrition Service (2022), “SNAP provides nutrition benefits to supplement the food budget of needy families so they can purchase healthy food and move towards self-sufficiency.” Some show associations between hunger and violence and other forms of crime (Islam, 2016). It is also important to explore whether an intervention that is aimed at ameliorating hunger would negatively correlate crime.
Crime Trajectories in the United States (2011, 2013, 2015)
Reports of decline in property crimes have been noted both in the United States and internationally (Button & Cross, 2017; FBI, 2011, 2013, 2015, 2018; Tcherni et al., 2016; Van Dijk, Tseloni, & Farrell, 2012). In the United States, some decline in property crimes has been observed since the 1980s (Bureau of Justice Statistics, 1994; Walters, Moore, Berzofsky, & Langton, 2013). Violent crimes have also been observed as declining nationally since the 1990s (Truman & Planty, 2012). However, online crimes have increased (Caneppele & Aebi, 2017). Van Dijk et al. (2012) see the decline in property crimes because of the increase in cybercrimes. It is not easily dismissible because the online environment has increasingly created a new environment for human activities (Caneppele & Aebi, 2017), thereby possibly creating new forms of crime (Button & Cross, 2017), Levi (2017). Levi pondered whether some of the decline is due to the overlap between offenders and previous offenders outside the online environment, or as Tcherni et al. (2016) would suggest, due to an increase in online property crimes. Farrell and Birks (2018) reviewed available evidence related to the reasons for decline in crime and concluded that cybercrime is not responsible for the decline in property crimes. Rather, they suggested that crimes, crime decline, or rise are dependent-less trends resulting from wide-range adjustments to the structures upon which crime opportunities exist. The authors pointed to improvements in security as the reason for decline in physical crime, consistent with the findings of Kriven and Ziersch (2007) and Farrell et al. (2014).
Property and Violent Crime in the Study’s Setting
In 2011, 2013, and 2015, the setting of the study reported important trajectories in terms of violent crime and property crime incidences. In those years, property crime rates per 1,000 residents in the study’s location were 38.5, 33.5, and 31.8, respectively. Violent crime rates per 1,000 residents in the county for the same years were 3.9, 4.4, and 4.2, respectively. Based on the most recent crime statistics for the study’s setting, there was a 4.9% increase in property crime in 2019 and an 11.5% increase in violent crimes in the study’s setting, in 2019. Although the national trend in property and violent crimes seem to be declining, except for murder and aggravated assault and arson (FBI, 2020), the increasing trend of violent crime and property crimes in the study’s setting remained a concern.
Scholars of several backgrounds have applied several frameworks in the study of violent crime and property crime, and their correlates (Cohen & Felson, 1979 Pratt, Holtfreter, & Reisig, 2010). Many of these frameworks have their foundations in criminology, sociology, psychology, and economics. This study considers the empowerment perspective in exploring how poverty relates to violent crime and property crimes. This framework is used for two main reasons: First, it is a perspective that aligns with the field of social work. Second, the empowerment perspective has been used in the study of community-level factors across various disciplines (Webster & Perkins, 2000; Zimmerman & Rappaport, 1988); as such, it allows scholars and policy practitioners to assess individuals and their challenges from a strengths-based perspective. Third, it aligns with the grand challenges of social work, which include ending family violence, reducing extreme economic disequilibrium, and encouraging decarceration in a smart way. Fourth, it aligns with the 2022 educational policy and accreditation standards (Council on social work Education, CSWE, 2023), which recognizes the need to align social work curriculum to current challenges facing humanity including violence and social determinants of health. The empowerment perspective has been described as contextually situated and nonstatic (Perkins & Zimmerman, 1995).
Public nutritional assistance is an empowerment-based policy that is recognized in social work. It is largely aimed at empowering individuals that are poor whose benefits from receiving the assistance may be far beyond nutrition. This is partly demonstrated in Lord and Hutchisons’ (1993) study, which sought to understand the lived experience of disempowered community members and the stages they passed through to achieve empowerment. They found that nearly all the participants identified unresponsive services and systems (or resources) as being a source of their problems. The authors reported two types of resource-related problems. The first problem was described as systemic failures via “neglect” while the second problem was described as failure by “inappropriate interventions” (Lord & Hutchison, 1993, p. 8). Interestingly, factors which the authors identified as disempowering included poverty. As endorsed by Keifer (1984), empowerment is concerned with an individual’s capacity to feel capable and competent to participate in community, among other things.
This author adds that empowered individuals and communities are able to stand against stigma of all forms, especially poverty and mental illness. Two assumptions are important for this study, based on the empowerment perspective: (1) individuals are capable of being empowered and (2) community goals including safety will be achieved as a result of individuals being empowered and coming to work together (Haddad & Toney-Butler, 2023). Hence, the author hypothesizes that public nutritional assistance will have an inverse relationship with violent crime and property crime. Poverty is a key social determinant of mental health. Existing evidence suggests that poverty can be both a cause and effect of mental illness (Knifton & Inglis, 2020; Shim et al., 2014). Evidence suggests that social and economic factors do affect human development, behavior, and relationships (Allen, Balfour, Bell, & Marmot, 2014), and these outcomes have implications for the mental health for adults and children (Cooper & Steward, 2015). Evidence also shows that, despite all of the above, stigmatization of individuals with mental illness and those seeking assistance remains a problem (Turan et al., 2019) such that stigma is also a source of health inequality (Hatzenbuehler, 2013). Also, empowerment has been shown to negatively associate with crime and violence (Hockley & Barden, 2023), while stigmatization of people experiencing poverty may play a negative role in terms of self-image, traumatic response, and management at the individual level. We know that many individuals that are involved in crimes have violent and traumatic backgrounds. In a study by Jaggi, Mezuk, Watkins, and Jackson (2016), it was found that trauma exposure was associated with violence and crime. The study also found that mental health and its comorbidities were correlates of incarceration. These realities have necessitated calls for exploration of the linkages between trauma and criminal involvement from an empowering perspective. The main question for this study was: How does prevalence of public nutritional assistance, which is an indicator of empowerment relate to the prevalence of violent crime and property crime? The author used logistic regression to assess how prevalence of violent crime and prevalence of public nutritional assistance predicted property crime.
Methodology
Description of the Study Sample
This study was based on a secondary analysis of data from the Quality-of-Life, (City of Charlotte, 2019) project. Due to the nature of the data, individual-level descriptions were unavailable for analysis. Given this, the study was based on NPAs. The study’s setting had 4.9% increase in property crime in 2019 and an 11.5% increase in violent crimes in the setting, in 2019. Specifically, all forms of violent crimes showed an increase compared to 2018 data: homicide (n = 108, 89.5%), robberies (n = 2,000, 12.7%), and aggravated assaults increased by (n = 4,559, 11.2%) except rape, which recorded a decrease of 5.6%, with a total of 288 incidences. All forms of property crimes except arson (n =148, −5.1%) showed increase compared to 2018 data: burglary (n = 5,428, 0.3%), motor vehicle theft (n = 2,922, 2.1%), and larceny (n = 27,577, 6.2%) (City of Charlotte, 2019). The current increase in both violent crime and property crimes in the location of this study brings about the importance of investigating the relationships between violent crime and property crimes, and how that relationship might be linked with the growth or decrease in property crimes.
Kline (2015) also indicates a ratio of 20 cases to 1 item, while Schreiber et al. (2006) recommend 10 cases to 1 item ratio as enough sample size for studies involving factorial analysis and structural equations. Other scholars have also indicated lower ratios as acceptable. For example, Bentler and Chou (1987) accept a ratio of five cases to one item acceptable. The sample size for this analysis was 452. Five cases were removed from the analysis due to missing more than 70% responses within the dataset. The overall dataset had missing cases and maximum likelihood, a method that ensures all nonmissing data points were considered in the analysis as recommended by Little and Rubin (1987).
Data Sources and Descriptions
The dataset contained more than 80 variables at the NPA level, including violent crime and property crimes. The NPAs were treated as nominal and identification variables, property crime was treated as a continuous variable comprising the annual total for arson, attempted burglary and burglary, motor vehicle theft, and larceny, divided by the population and multiplied by 1,000. According to the Federal Bureau of Investigations’ (FBI) Uniform Crimes Report (FBI, 2011, 2013, 2015, 2019), offenses, such as larceny-theft, burglary, arson, and motor vehicle theft, are considered property crimes. Furthermore,
the object of the theft-type offenses is the taking of money or property, but there is no force or threat of force against the victims. The property crime category included arson because the offense involves the destruction of property; however, arson victims may be subjected to force declared the FBI (2018, p. 1). Property crimes have continued to plague the United States at alarming rates, despite observed decline in its prevalence based on recent estimates.
Similarly, violent crime was treated as a continuous variable comprising annual total for armed robbery, aggravated assault, homicide, armed robbery, rape and attempted rape, and negligent manslaughter. The crimes data were originally sourced from six coordinating police departments. Public nutritional assistance (PNA) was also applied as an independent variable, with violent crimes, to assess economic mediator roles between violent crime and property crimes. The variable PNA was derived by dividing the total percentage of the population receiving PNA by the total population. Job density at baseline was used to assess the mediational impact on the relationship between violent crime and property crime. Job density was obtained by dividing the number of total jobs by the total land area.
In conducting the logistic regression, the continuous variables were recoded into binary forms, using the mean as a cutoff between a high and low category.
Findings
Descriptive Analysis
Summary statistics were calculated for public nutritional assistance (PNA, an indicator of poverty), Job density (JD), property crime (PC), and violent crime (VC). The observations for VC had an average of 1.65 (SD = 1.07, SEM = 0.05). The observations for PC had an average of 3.75 (SD = 1.10, SEM = 0.05). The observations for PNA had an average of 2.44 (SD = 1.04, SEM = 0.05). The observations for JOBD had an average of 2.38 (SD = 6.85, SEM = 0.32). The summary statistics can be found in Table 1.
Summary statistics table for key variables (log transformed)
| Variable | M | SD | n | SEM |
|---|---|---|---|---|
| VC | 1.65 | 1.07 | 412 | 0.05 |
| PC | 3.75 | 1.10 | 452 | 0.05 |
| PN | 2.44 | 1.04 | 450 | 0.05 |
| JOBD | 2.38 | 6.85 | 452 | 0.32 |
Summary statistics were calculated for public nutritional assistance (PNA, an indicator of poverty), Job density (JD), property crime (PC), and violent crime (VC) for logistic regression purposes. The most frequently observed category of PC within the 0 category of PNA was 0 (n = 156, 68.12%). The most frequently observed category of PC within the 1 category of PNA was 1 (n = 158, 71.49%). The most frequently observed category of JOBD within the 0 category of PNA was 0 (n = 130, 56.77%). The most frequently observed category of JOBD within the 1 category of PNA was 1 (n = 116, 52.49%). The most frequently observed category of PNA within the 0 category of PNA was 0 (n = 229, 100.00%). The most frequently observed category of PNA within the 1 category of PNA was 1 (n = 221, 100.00%). The most frequently observed category of VC within the 0 category of PNA was 0 (n = 184, 80.35%). The most frequently observed category of VC within the 1 category of PNA was 1 (n = 181, 81.90%). Frequencies and percentages are presented in Table 1.
Bivariate Analysis
In this analysis, the author sought to answer the following question: Is there a statistically significant association between property crime and violent crime, while controlling for public nutritional assistance? A partial correlation analysis was conducted for property crime and violent crime while controlling for public nutritional assistance and filtered by job density. Cohen’s (1988) standard was applied in assessing the coefficients. According to Cohen (1988), the strength of coefficients or effect sizes range from small (0.02) to medium (0.15) to large (0.35). For this first analytical group filtered by low job density (0), findings showed that the relationship between violent crime and property crimes was statistically significant (r = 0.43, p < 0.001, 95% CI [0.32, 0.54]), with a medium effect size. This suggests that both violent crime and property crimes increased, controlling for Public Nutrition Assistance. For the high category of the filtered group (job density), a partial correlation analysis was conducted between property crime at wave 1 and violent crime, controlling for public nutritional assistance, and filtered by job density (1). For this first analytical group filtered by low job density (1), findings showed that the relationship between violent crime and property crimes was statistically significant (r = 0.53, p < 0.001, 95% CI [0.43, 0.62]), with a larger effect size compared to the first analytical group (job density, 0). This suggests that both violent crime and property crimes had a higher increase, controlling for public nutritional assistance.
Multivariate Analysis
The model was assessed based on an a priori of 0.05. The following equation was applied in the logistic regression:
Where , , , , .
The overall model was significant, , , indicating that public nutritional assistance and violent crime at the baseline had a significant effect on the odds of observing high prevalence (1 category) of property crime. McFadden’s R-squared was calculated to examine the model fit, where values greater than 0.2 was indicative of models with excellent fit (Rolfe, Bennette, & Louviere et al., 2000). The McFadden R-squared value calculated for this model was 0.39. The regression coefficient for the high category of public nutritional assistance at baseline was not significant, , , , indicating that high prevalence of public nutritional assistance (1 category) did not have a significant effect on the odds of observing high prevalence (1 category) of property crimes. The regression coefficient for violent crime (1 category) was significant, , , , indicating that for a one unit increase in high prevalence of violent crimes, the odds of observing high prevalence of property crime grew by approximately 1,070%. The interaction between public nutritional assistance and violent crime had a significant effect on property crime, , , . This suggests that moving from the low (0) to high (1) category of public nutritional assistance strengthened the relationship of high property crime to high violent crime. Table 2 summarizes the results of the regression model.
Frequency Table for PC, JOBD, PNA, and VC (recoded, binary)
| Variable | PNA | ||
|---|---|---|---|
| 0 | 1 | Missing | |
| PC | |||
| 0 | 156 (68.12%) | 58 (26.24%) | 2 (100.00%) |
| 1 | 56 (24.45%) | 158 (71.49%) | 0 (0.00%) |
| Missing | 17 (7.42%) | 5 (2.26%) | 0 (0.00%) |
| Total | 229 (100.00%) | 221 (100.00%) | 2 (100.00%) |
| JOBD | |||
| 0 | 130 (56.77%) | 105 (47.51%) | 1 (50.00%) |
| 1 | 99 (43.23%) | 116 (52.49%) | 1 (50.00%) |
| Missing | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) |
| Total | 229 (100.00%) | 221 (100.00%) | 2 (100.00%) |
| PNA | |||
| 0 | 229 (100.00%) | 0 (0.00%) | 0 (0.00%) |
| 1 | 0 (0.00%) | 221 (100.00%) | 0 (0.00%) |
| Missing | 0 (0.00%) | 0 (0.00%) | 2 (100.00%) |
| Total | 229 (100.00%) | 221 (100.00%) | 2 (100.00%) |
| VC | |||
| 0 | 184 (80.35%) | 40 (18.10%) | 0 (0.00%) |
| 1 | 45 (19.65%) | 181 (81.90%) | 2 (100.00%) |
| Missing | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) |
| Total | 229 (100.00%) | 221 (100.00%) | 2 (100.00%) |
Logistic regression results with public nutritional assistance and violent crime predicting property crime
| Variable | B | SE | 95% CI | p | OR | |
|---|---|---|---|---|---|---|
| (Intercept) | −1.70 | 0.21 | [−2.12, −1.28] | 63.34 | ** | |
| PNA | −0.47 | 0.57 | [−1.59, 0.64] | 0.69 | – | 0.62 |
| VC | 2.46 | 0.39 | [1.70, 3.22] | 40.27 | ** | 11.70 |
| PNA: VC | 1.61 | 0.69 | [0.25, 2.97] | 5.42 | * | 5.01 |
-
**, ,
-
*, ,
Note: , , . PNA = public nutritional assistance; VC = violent crime.
–, nonsignificance.
Discussions, Implications, and Limitations
This exploratory study utilized multiple methods to assess the overarching research question: How does public nutritional assistance (an indicator of poverty) relate to violent crime and property crimes? In a study of individuals receiving public benefits, Ciciurkaite and Brown (2023) found that, even during the COVID pandemic, people experiencing poverty, hunger, or food insecurity continued to face many forms of discrimination. Ciciurkaite and Brown (2023) found that experiences of discrimination that were related to food benefits did diminish the positive effects of public benefits that were meant to serve individuals that were experiencing poverty. Ciciurkaite and Brown’s findings are not unique as similar findings had been echoed by others (Thornicroft et al., 2022; Turan et al., 2019). Although the above findings provided a motivation for this exploration, it was obvious that not much is known about how baseline levels of violent crime may affect changes in property crime. This study makes an exploratory contribution by approaching the subject from an empowerment framework, given that discriminations and stigmas that are related to use of public benefits have been linked to poor mental health outcomes (Gearing, Brewer, & Washburn, 2023; Thornicroft et al., 2022; Turan et al., 2019). Findings from this study showed that public nutritional assistance was not a significant influencer of the relationships between violent crime and property crime. This finding is important as it contradicts the notion that aid or social support may increase crime and violence. Findings from partial correlation analysis support the assumption that public nutritional assistance was not a positive influencer of the positive relationship that was found between violent crime and property crime prevalence, except when modeled to interact with violent crime. These findings support the empowerment approach on which this study was framed (Haddad & Toney-Butler, 2023; Kieffer, 1984; Zimmerman, Israel, Schulz, & Checkoway, 1992; Zimmerman & Rappaport, 1988). They also support calls against stigmatization of individuals that use public nutritional assistance or that are suffering from hunger or food insecurity (Gearing et al., 2023).
By applying a logit approach to this analysis, the author was able to address the central question as to how baseline violent crime affected property crime. The logistic regression indicated that high prevalence of seeking public nutritional assistance (1 category) did not have a significant effect on the odds of observing high prevalence (1 category) of property crimes, which is similar to the findings that were derived from partial correlation. In fact, findings of the logistic regression suggest that prevalence of public nutritional assistance was negatively associated with property crime. What the findings showed is that violent crime is a predictor of property crime, which is consistent with what is known in the literature. Findings also suggest that moving from the low (0) to high (1) category of public nutritional assistance did strengthen the relationship of high property crime to high violent crime. But this finding must be carefully interpreted in that, it does not show that PNA has a causal effect on violent or property crime or is directly related to it and given that such is beyond the scope of this exploration, the author did not address it. Findings from this exploratory study are also important as they may open some avenue for conversations on the intersections between policy, interventions, and violent crime and property crime.
Limitations
Some limitations are worth mentioning. First, the study was exploratory in nature, given that not much had been done in this area—looking at the specific variables that were explored here, in combination. Second, due to the nature of the data (secondary) that were used in the analysis, certain adjustments were necessary, which may have affected the original dispositions of the data, and which may affect the outcomes and interpretations. For example, some of the variables had been recoded from a continuous form to a binary form. Some of the data also showed wide distributions compared to the others. It is no doubt that primary data collected for the purpose of a study like this would have been more appropriate. However, explorations of this nature are helpful, for feasibility reasons. Third, some important variables that could have been used in this study were not available for a few years. This impacted the years that were studied. It is also possible that public nutritional assistance as a variable could go either way in the direction of increase or decrease with the dependent variable, given that the interaction effect was statistically significant. However, the scope of this analysis did not allow for all these limitations to be addressed. Such aspects will be considered in the future. Despite the above limitations, the findings from this study are important for social work and other disciplines that are involved in human problem-solving.
Implications
A central implication of this study is the need to advocate for individuals that are receiving food aid in the United States, and to stand against stigmas that are addressed toward such individuals. It is also a call to keep boosting policies and interventions that have negative association with violence and crime. Stigmatization of food aid users in the United States has been linked to poor mental health outcomes (Thornicroft et al., 2022). In fact, to stigmatize individuals that are going through poverty or food insecurity can be an exacerbating factor for poor self-esteem and other negative internal psychological responses (Carrara, Fernandes, Bobbili, & Ventura, 2021). Studies continue to point to connections between mental illness, poverty, violence, and crime victimization (Ciciurkaite & Brown, 2023). Perpetrators of stigma against individuals that are utilizing public assistance, especially food assistance, ought to consider that there are many layers of problems that lead to seeking such assistances, and that stigmatization and discrimination do exacerbate those problems rather than solve those (Allen et al., 2014). Given what we know about the overrepresentation of individuals exposed to trauma, crime victims, and individuals with mental illness in the utilization of public aid, it is important that policy makers and agencies responsible for administering public nutritional assistance address and seek ways to prevent latent and manifest stigmatization (Knifton & Inglis, 2020; Shim et al., 2014). This is a call to policy makers, law enforcement personnel, mental health professionals, individuals, and agencies that deliver social aid to people experiencing poverty or food insecurity to approach the same from a trauma-informed and empowerment perspective. Hence, this call is both from a mental health and policy practice. The goal is to make public nutritional assistance to not be an inappropriate intervention that may end up inhibiting rather than helping solve a problem, like what was found by Lord and Hutchison (1993).
This study also has significant implications for social work education. Stigmatization is not completely eradicated in social work. Also, knowledge of how different kinds of violence and crime (not just family violence) affect individuals and communities is significantly lacking in the social work curriculum. Much of the curriculum currently focuses on addressing the outcomes of crime and violence rather than their prevention. Social work as a discipline stands against oppression and discrimination of all kinds. Hence, the field seeks to empower individuals and considers human functioning from a person-in-environment perspective. It is for this reason that the field has identified its grand challenges to include ending family violence, reducing extreme economic disequilibrium, and encouraging decarceration in a smart way (CSWE, 2023). Within this consideration, it does appear that the grand challenges ought to be updated to include all forms of violence, not just family violence. This is in recognition of the fact that family violence is very important. At the same time, other forms of violence are also related to family violence and may affect victims in similar or distinct ways—all of which can result in outcomes that are contrary to social work values and stance. Hence, there is a need to make it explicit in the social work curriculum, to study ways to prevent violence—both family violence and other forms of violence. It is also time to situate the educational policy and accreditation standards (CSWE, 2023) to educate social workers on the intersections between health disparities, especially mental health disparities, crime, violence, and stigmatization. This education will be successful if it requires all academic levels to interrogate stigma, public assistance, violence, and other forms of crime. Such academic requirement should be integrated into the implicit and explicit curriculum and should be monitored beyond the classroom and field practice. It should also be made an integral part of social work licensing and ought to be required for continuing education. This might help address the systemic failures that Lord and Hutchison found in their study in 1993, and which have yet to be addressed 20 years later.
References
Allen J., Balfour R., Bell R., & Marmot M. (2014). Social determinants of mental health. International Review of Psychiatry, 26(4), 392–407. https://doi.10.3109/09540261.2014.928270https://doi.10.3109/09540261.2014.928270
Barnett C., & Mencken C. (2009). Social disorganization theory and the contextual nature of crime in nonmetropolitan counties. Rural Sociology, 67(3), 372–393. https://doi.org/10.1111/j.1549-0831.2002.tb00109.xhttps://doi.org/10.1111/j.1549-0831.2002.tb00109.x
Bassuk E. L., Buckner J. C., Weinreb L. F., Browne A., Bassuk S. S., Dawson R., & Perloff J. N. (1997). Homelessness in female-headed families: childhood and adult risk and protective factors. American Journal of Public Health, 87(2), 241–248. https://doi.org/10.2105/AJPH.87.2.241https://doi.org/10.2105/AJPH.87.2.241
Bentler P., & Chou P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117. https://doi.org/10.1177/0049124187016001004https://doi.org/10.1177/0049124187016001004
Bollen K. A., & Patrick J. Curran P. J. (2006). Latent Curve Models: A Structural Equation Perspective. Hoboken, NJ: John Wiley and Sons, Inc.
Bureau of Justice Statistics. (1994). Criminal victimization in the United States: 1973–1992 trends. Washington, DC: Bureau of Justice Statistics.: Washington D.C.
Button M., & Cross C. (2017). Technology and fraud: The Fraudogenic consequences of the internet revolution. In M. Maguire & T. Holt (Eds.), (2021). The Routledge handbook of technology, crime, and justice. London: Routledge.
Caneppele S., & Aebi M. F. (2017). Crime drop or police recording flop? On the relationship between the decrease of offline crime and the increase of online and hybrid crimes. Policing, 13(1), 66–79. https://doi.org.10.1093/police/pax055https://doi.org.10.1093/police/pax055
Carrara B. S, Fernandes R. H. H., Bobbili S. J, & Ventura C. A., A. (2021). Health care providers and people with mental illness: an integrative review on anti-stigma interventions. International Journal of Social Psychiatry, 67: 840–853. https://doi.org/10.1177/00207640209858910.1016/j.apnu.2019.01.006https://doi.org/10.1177/00207640209858910.1016/j.apnu.2019.01.006
Caneppele S., & Aebi M. (2017). Crime drop or police recording flop? On the relationship between the decrease of offline crime and the increase of online and hybrid crimes Policing. https://doi.org/10.1093/police/pax05https://doi.org/10.1093/police/pax05
Ciciurkaite G., & Brown R. L. (2023). Disability, food insecurity, and nutritional assistance in the context of COVID-19 pandemic: Interrogating the role of perceived everyday discrimination. Stigma and Health. https://doi.org/10.1037/sah0000434https://doi.org/10.1037/sah0000434
City of Charlotte. (2019). Crime statistical summary for the year end 2019. Retrieved from https://charlottenc.gov/CMPD/Safety/Pages/CrimeStats.aspxhttps://charlottenc.gov/CMPD/Safety/Pages/CrimeStats.aspx
Cohen J. (1988). Statistical power analysis for the behavior sciences (2nd ed.). West Publishing Company.
Cohen L. E., & Felson M. (1979). Social change and crime rate trends: A routine activity approach. American sociological review, 588–608.
Comrey A. L., & Lee H. B. (2013). A first course in factor analysis. Psychology Press.
Cooper K., & Stewart K. (2015). Does money in adulthood affect adult outcomes. York: Joseph Rowntree Foundation.
Council on Social Work Education. (2023). Educational policy and accreditation standards. Retrieved from 2022-EPAS.pdf (cswe.org)cswe.org
Deane G., Armstrong P., & Felson R. (2005). An examination of offense specialization using marginal logit models. Criminology, 39(3). https://doi.org/10.1111/j.1745-9125.2005.00030.xhttps://doi.org/10.1111/j.1745-9125.2005.00030.x
Desmond S. A., Kikuchi G., & Morgan K. H. (2010). Congregations and crime: Is the spatial distribution of congregations associated with neighborhood crime rates? Journal for the Scientific Study of Religion, 49(1), 37–55. https://doi.org/10.1111/j.1468-5906.2009.01491.xhttps://doi.org/10.1111/j.1468-5906.2009.01491.x
Dooley D., & Prause J. (2002). Mental health and welfare transitions: Depression and alcohol abuse in AFDC women. American Journal of Community Psychology, 30(6), 787–813. https://doi.org/10.1023/A:1020253002468https://doi.org/10.1023/A:1020253002468
Farrell G., & Birks D. (2018). Did cybercrime cause the crime drop? Crime Science, 7, 8. https://doi.org/10.1186/s40163-018-0082-8https://doi.org/10.1186/s40163-018-0082-8
Farrell G., Tilley N., & Tseloni A. (2014). Why the crime drop? Crime and justice, 43(1), 421–490.
Federal Bureau of Investigations. (2019). Crime in the United States. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/topic-pages/property-crimehttps://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/topic-pages/property-crime
Federal Bureau of Investigations. (2011). Crime in the United States. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/property-crime/property-crimehttps://ucr.fbi.gov/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/property-crime/property-crime
Federal Bureau of Investigations. (2013). Crime in the United States. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2013/crime-in-the-u.s.-2013/property-crime/property-crime-topic-page/propertycrimemain_finalhttps://ucr.fbi.gov/crime-in-the-u.s/2013/crime-in-the-u.s.-2013/property-crime/property-crime-topic-page/propertycrimemain_final
Federal Bureau of Investigations. (2015). Crime in the United States. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2015/crime-in-the-u.s.-2015/offenses-known-to-law-enforcement/property-crimehttps://ucr.fbi.gov/crime-in-the-u.s/2015/crime-in-the-u.s.-2015/offenses-known-to-law-enforcement/property-crime
Federal Bureau of Investigations. (2019). Crime in the United States. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/topic-pages/property-crimehttps://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/topic-pages/property-crime
Federal Bureau of Investigations. (2020). Overview of preliminary uniform crime report, January–June, 2020. Retrieved from https://www.fbi.gov/news/pressrel/press-releases/overview-of-preliminary-uniform-crime-report-january-june-2020https://www.fbi.gov/news/pressrel/press-releases/overview-of-preliminary-uniform-crime-report-january-june-2020
Finkelhor D., Turner H., Ormrod R., & Hamby S. (2009). Violence, abuse, and crime exposure in a national sample of children and youth. Pediatrics, 124 (5), 1411–1423. https://doi.org/10.1542/peds.2009-0467https://doi.org/10.1542/peds.2009-0467
Friedson M., & Sharkey P. (2015). Violence and neighborhood disadvantage after the crime decline. The Annals of the American Academy of Political and Social Science, 660(1), 341–358. https://doi.org/10.1177/0002716215579825https://doi.org/10.1177/0002716215579825
Gearing R. E., Brewer K. B., Washburn M., Carr L. C., Burr S., Manning K., & Torres-Hostos L. R. (2023). Public stigma toward schizophrenia within Latino communities in the United States. Community Mental Health Journal. https://doi.org/10.1007/s10597-022-01075-whttps://doi.org/10.1007/s10597-022-01075-w
Haddad L. M., & Toney-Butler T. J. (2023). Empowerment. [Updated 2022 Oct 10]. In: StatPearls [Internet]. Treasure Island, (FL): StatPearls Publishing; 2023 Jan. Retrieved from: https://www.ncbi.nlm.nih.gov/books/NBK430929/https://www.ncbi.nlm.nih.gov/books/NBK430929/
Han L., Bandyopadhyay S., & Bhattacharya S. (2013). Determinants of violent and property crimes in England and Wales: A panel data analysis. Applied Economics, 45(34), 4820–4830. https://doi.org/10.1080/00036846.2013.806782https://doi.org/10.1080/00036846.2013.806782
Hatzenbuehler M. L., Phelan J. C., & Link B. G. (2013). Stigma as a fundamental cause of population health inequalities. American Journal of Public Health, 103(5), 813–821. https://doi.org/10.2105/AJPH.2012.301069https://doi.org/10.2105/AJPH.2012.301069
Hockley N., & Barden M. (2023). Empowering children to prevent violence. In School violence and primary prevention (pp. 709–717). Cham: Springer International Publishing.
Horan P. M., & Austin P. L. (1974). The social bases of welfare stigma. Social problems, 21(5), 648–657. https://doi.org/10.2307/799640https://doi.org/10.2307/799640
Intellectus Statistics [Online computer software]. (2020). Intellectus Statistics. Retrieved from https://analyze.intellectusstatistics.com/https://analyze.intellectusstatistics.com/
Islam M. (2016). Does hunger affect violent crime? A cross-country study of developing nations. https://doi.org/10.2139/ssrn.4033213https://doi.org/10.2139/ssrn.4033213
Jaäggi L. J., Mezuk B., Watkins D. C., & Jackson J. S. (2016). The relationship between trauma, arrest, and incarceration history among black Americans: Findings from the National Survey of American Life. Society and Mental Health, 6(3), 187–206. https://doi.org/10.1177/2156869316641730https://doi.org/10.1177/2156869316641730
Kieffer C. H. (1984). Citizen empowerment: A developmental perspective. Prevention in Human Services, 3(2–3), 9–36. https://doi.org/10.1300/J293v03n02_03https://doi.org/10.1300/J293v03n02_03
Kline R. B. (2015). Principles and practice of structural equation modeling. Guilford Publications.
Knifton L., & Inglis G. (2020). Poverty and mental health: policy, practice, and research implications. BJ Psych Bulletin, 44(5), 193–196. https://doi.org/10.1192/bjb.2020.78https://doi.org/10.1192/bjb.2020.78
Kriven S., & Ziersch E. (2007). New car security and shifting vehicle theft patterns in Australia. Security Journal, 20, 111–122. https://doi.org/10.1057/palgrave.sj.8350026https://doi.org/10.1057/palgrave.sj.8350026
Lapham J., & Martinson M. (2022). The intersection of welfare stigma, state contexts and health among mothers receiving public assistance benefits. SSM—Population Health, 18, 101117. https://doi.org/10.1016/j.ssmph.2022.101117https://doi.org/10.1016/j.ssmph.2022.101117
Levi M. (2017). Assessing the trends, scale, and nature of economic cyber-crimes: overview and issues. Crime, Law and Social Change, 76, 3–20. https://doi.org/10.1007/s10611-016-9645-3https://doi.org/10.1007/s10611-016-9645-3
Little R.J.A., Schenker N. (1995). Missing Data. In: Arminger G., Clogg C.C., Sobel M.E. (eds) Handbook of Statistical Modeling for the Social and Behavioral Sciences. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1292-3_https://doi.org/10.1007/978-1-4899-1292-3_
Lord J., & Hutchinson P. (1993). The process of empowerment: Implications for theory and practice. Canadian Journal of Community Mental Health, 12(1), 5–22. https://doi.org/10.7870/cjcmh-1993-0001https://doi.org/10.7870/cjcmh-1993-0001
Han Lu, Bandyopadhyay Siddhartha & Bhattacharya Samrat (2013) Determinants of violent and property crimes in England and Wales: a panel data analysis, Applied Economics, 45:34, 4820–4830. https://doi.org/10.1080/00036846.2013.806782https://doi.org/10.1080/00036846.2013.806782
Lussier P., Marc LeBlanc M., & Jean P. J. (2005). The generality of criminal behavior: a confirmatory factor analysis of the criminal activity of sex offenders in adulthood. Journal of Criminal Justice, 33, 177–189. https://doi.org/10.1016/j.jcrimjus.2004.12.009https://doi.org/10.1016/j.jcrimjus.2004.12.009
Mecklenburg County, the City of Charlotte, UNC Charlotte. (2020). Charlotte/Mecklenburg quality of life explorer. Retrieved from https://mcmap.org/qol/#58/https://mcmap.org/qol/#58/
Mettler S. (2011). The submerged state: How invisible government policies undermine American democracy. University of Chicago Press.
Miller T. R., Cohen M. A., Swedler D., Ali B., & Hendrie D. V. (2020, October 6). Incidence and costs of personal and property crimes in the United States, 2017. Retrieved from https://ssrn.com/abstract=3514296 or http://doi.org/10.2139/ssrn.3514296https://ssrn.com/abstract=3514296http://doi.org/10.2139/ssrn.3514296
Oana-Ramona L., Ana-Cristina N., Nicoleta-Claudia M., & Ayhan K. (2017). The effect of socioeconomic factors on crime rates in Romania: a macro-level analysis, Economic Research-Ekonomska Istraživanja, 30:1, 91–111. https://doi.org/10.1080/1331677X.2017.1305790https://doi.org/10.1080/1331677X.2017.1305790
Pratt T. C., Holtfreter K., & Reisig M. D. (2010). Routine Online Activity and Internet Fraud Targeting: Extending the Generality of Routine Activity Theory. Journal of Research in Crime and Delinquency, 47(3), 267–296. https://doi.org/10.1177/0022427810365903https://doi.org/10.1177/0022427810365903
Pa Neal. (1996). Great pretenders: Pursuits and careers of persistent thieves. Boulder, CO: Westview.
Pak T. Y. (2020). Welfare stigma as a risk factor for major depressive disorder: evidence from the supplemental nutrition assistance program. Journal of Affective Disorders, 260, 53–60. https://doi.org/10.1016/j.jad.2019.08.079https://doi.org/10.1016/j.jad.2019.08.079
Perkins D. D., & Zimmerman M. A. (1995). Empowerment theory, research, and application. American journal of community psychology, 23(5), 569–579.
Petterson S. M., & Friel L. V. (2001). Psychological distress, hopelessness, and welfare. Women & Health, 32(1–2), 79–99. https://doi.org/10.1300/J013v32n01_04https://doi.org/10.1300/J013v32n01_04
Piquero A. R., Farrington D. P., & Blumstein A. (2003). Criminal career paradigm: Background, recent developments, and the way forward. Int’l Annals Criminology, 41, 243.
Pratt T. C., Holtfreter K., & Reisig M. D. (2010). Routine online activity and internet fraud targeting: Extending the generality of routine activity theory. Journal of Research in Crime and Delinquency, 47(3), 267–96. https://doi.org/10.1177/0022427810365903https://doi.org/10.1177/0022427810365903
Rolfe J., Bennett J., & Louviere J. (2000). Choice modelling and its potential application to tropical rainforest preservation. Ecological Economics, 35(2), 289–302. https://doi.org/10.1016/S0921-8009(00)00201-9https://doi.org/10.1016/S0921-8009(00)00201-9
Schreiber J. B., Nora A., Stage F. K., Barlow E. A., & King J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of educational research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338\https://doi.org/10.3200/JOER.99.6.323-338
Shim R., Koplan C., Langheim F. J., Manseau M. W., Powers R. A., & Compton M. T. (2014). The social determinants of mental health: An overview and call to action. Psychiatric Annals, 44(1), 22–26. https://doi.org/10.3928/00485713-20140108-04https://doi.org/10.3928/00485713-20140108-04
Steffensmeier D. J., & Jeffery T. U. (2005). Confessions of a dying thief: Understanding criminal careers and illegal enterprise. New Brunswick, NJ: Aldine Transaction.
Sullivan C. L., Jean M. M., Travis C. P., & Alex R. P. (2006). Rethinking the ‘Norm’ of offender generality: investigating specialization in the short-term. Criminology, 44: 199–233. https://doi.org/10.1177/0022427807302664https://doi.org/10.1177/0022427807302664
Tabachnick B. G., & Fidell L. S. (2019). Using multivariate statistics. Pearson Education.
Tcherni M., Davies A., Lopes G., & Lizotte A. (2016). The dark figure of online property crime: Is cyberspace hiding a crime wave? Justice Quarterly, 33(5), 890–911. https://doi.org/10.1080/07418825.2014.994658https://doi.org/10.1080/07418825.2014.994658
Thornicroft G., Sunkel C., Aliev A. A., Baker S., Brohan E., Chammay R., … & Winkler P. (2022). The Lancet Commission on ending stigma and discrimination in mental health. Lancet, 400, 1438–1480. https://doi.org/10.1016/S0140-6736(22)01470-2https://doi.org/10.1016/S0140-6736(22)01470-2
Truman J. L., & Planty M. (2012). Criminal victimization 2011. Washington, D.C.: Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.
Turan J. M., Elafros M. A., Logie C. H., Banik S., Turan B., Crockett K. B., …... & Murray S. M. (2019). Challenges and opportunities in examining and addressing intersectional stigma and health. BMC Medicine, 17, 1–15. https://doi.org/10.1186/s12916-018-1246-9https://doi.org/10.1186/s12916-018-1246-9
Turner H., Finkelhor D., Mitchell L., & Jones M. (2020). Strengthening the predictive power of screening for adverse childhood experiences (ACEs) in younger and older children. Child Abuse and Neglect, 107, 104522. https://doi.org/10.1016/j.chiabu.2020.104522https://doi.org/10.1016/j.chiabu.2020.104522
US Food and Nutrition Service. (2022). Supplemental nutrition assistance program. Retrieved from https://www.fns.usda.gov/snap/supplemental-nutrition-assistance-programhttps://www.fns.usda.gov/snap/supplemental-nutrition-assistance-program
Van Dijk J. J. M., Tseloni A., & Farrell G. (2012). The international crime drop: New directions in research. New York, NY: Palgrave Macmillan.
Vasconcelos P. N., Zambroni de Souza A. C., Ribeiro P. F., Alencar P., & Balestrassi P. P. (2022). A multi-aspect dynamic system model to assess poverty traps. In: A. C. Zambroni de Souza, A.C., M. J. Verkerk, M.J., & P. F. Ribeiro, P.F. (Eds.), Interdisciplinary and social nature of engineering practices (Vol. 61). Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol 61. Cham: Springer, Cham. https://doi.org/10.1007/978-3-030-88016-3_18https://doi.org/10.1007/978-3-030-88016-3_18
Walters J. H., Moore A., Berzofsky M., & Langton L. (2013). Household Burglary 1994–2011. Washington, DC: Bureau of Justice Statistics: Washington D.C. (Report NCJ 241754).
Webster L., & Perkins D. D. (2000). Redressing structural violence against children: Empowerment-based interventions and research. In D. J. Christie, R. V. Wagner, & D. A. Winter (Eds.), Peace, conflict, and violence: Peace psychology for the 21st century (pp. 330–340). Englewood Cliffs, NJ: Prentice-Hall.
Zimmerman M. A., Israel B. A., Schulz A., & Checkoway B. (1992). Further explorations in empowerment theory: An empirical analysis of psychological empowerment. American Journal of Community Psychology, 20, 707–727. https://doi.org/10.1007/BF01312604https://doi.org/10.1007/BF01312604
Zimmerman M. A., & Rappaport J. (1988). Citizen participation, perceived control, and psychological empowerment. American Journal of Community Psychology, 16(5), 725–750. https://doi.org/10.1007/BF00930023https://doi.org/10.1007/BF00930023
Kingsley U. Chigbu is a Professor at School of Social Work, Morrison Family College of Health, University of St Thomas, 2115 Summit Avenue, St Paul, MN 55105, USA. He can be contacted at kchigbu@stthomas.edu.