The role of income inequality on factors associated with male physical Intimate Partner Violence perpetration: A meta-analysis

Aggression and Violent Behavior 48 (2019) 116–123

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Aggression and Violent Behavior journal homepage: www.elsevier.com/locate/aggviobeh

The role of income inequality on factors associated with male physical Intimate Partner Violence perpetration: A meta-analysis

T

⁎

Chelsea M. Spencer , Marcos Mendez, Sandra M. Stith Kansas State University, United States of America

A B S T R A C T

This study examines the influence of income inequality on risk markers for Intimate Partner Violence (IPV) in countries with low and high income inequality measured by the GINI index. Examining male perpetration of IPV, we used meta-analytic procedures to learn if income inequality moderated the strength of the relationship between well-established risk markers and IPV. We found that young age, relationship dissatisfaction, violence towards non-family members, and emotional abuse perpetration were significantly stronger risk markers for countries with high income inequality than for countries with low income inequality. We also found that having experienced trauma was a significantly stronger risk marker for countries with low income inequality than for countries with high income inequality. We also ran additional analyses between high and low income inequality countries excluding research conducted in the United States. Here we found that perpetrating emotional abuse, relationship dissatisfaction, and witnessing IPV in family of origin were all significantly stronger risk markers in high income inequality countries compared to low income inequality countries Our findings suggest that income inequality impacts risk markers for male IPV perpetration.

1. Introduction Intimate Partner Violence (IPV) against women is an important international health problem with serious consequences for families and serious economic costs (García-Moreno, Jansen, Ellsberg, Heise, & Watts, 2005). A review of over 50 studies in 35 countries indicated that between 10% and 52% of women reported that they had experienced physical IPV at some point in their lives (Alhabib, Nur, & Jones, 2010). Despite the high rate of IPV throughout the world, research on the subject has been primarily located in the West (Jeyaseelan et al., 2004). There has been a number of studies examining factors associated with IPV that aim to provide a greater understanding of IPV, which can allow us to understand who may be at a greater risk of IPV perpetration or victimization, as well as ways to prevent IPV or intervene when IPV is present. Previous meta-analyses examining factors associated with IPV have examined specific types of associated factors, such as mental health factors (Author et al., 2019; Birkley, Eckhardt, & Dykstra, 2016; Oram, Trevillion, Khalifeh, Feder, & Howard, 2014), substance use (Author et al., 2018a, 2018b; Devries et al., 2014), HIV infection (Li et al., 2014), relationship factors (Author Author et al., 2008), and family-of-origin factors (Author et al., 2015). Other meta-analyses have examined factors associated with IPV in a variety of populations, such as same-sex relationships (Author et al., 2017; Buller, Devries, Howard, & Bacchus, 2014), and military populations (Author et al., 2016a, 2016b). The results of these studies conclude that IPV is a multi-faceted phenomenon that cannot be pinpointed to a single factor that would

⁎

cause IPV perpetration. These studies have also found that there have been differences between populations in regards to factors associated with IPV. This suggests that further research should continue to explore differences among populations in factors associated with IPV in order to gain a greater understanding of potential population differences and cultural differences related to IPV, which may allow for more targeted intervention and prevention efforts. The goal of this study is to further explore factors associated with male IPV perpetration by examining the role of income inequality on factors associated with IPV. 1.1. Social disorganization theory Social disorganization theory has been widely used to examine how neighborhood level factors impact delinquency and crime rates (Kingston, Huizinga & Elliot, 2009; Shaw & McKay, 1969). Social disorganization theory is rooted in the notion that social structures that impact a location's level of desirability make it difficult for residents of the neighborhood to come together to create common goals, which then creates higher levels of social disorganization and weakened community attachments (Shaw & McKay, 1969). Social disorganization then leads to “the inability of local communities to realize the common values of their residents or solve commonly experienced problems,” such as crime and violence within the community (Kornhauser, 1978, p. 63). Due to the disorganization among the community, Cullen (1994) points out the importance of examining social supports, such as community networks, social networks, and confiding partners, when conducting

Corresponding author. E-mail address: [email protected] (C.M. Spencer).

https://doi.org/10.1016/j.avb.2019.08.010 Received 20 September 2018; Received in revised form 29 April 2019; Accepted 16 August 2019 Available online 22 August 2019 1359-1789/ © 2019 Elsevier Ltd. All rights reserved.

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research guided by social disorganization theory. Although social disorganization theory was originally focused on delinquency rates and crime, the theory has been expanded to focus specifically on IPV, noting the importance of examining community level factors and their relationship with IPV (Bensen, Wooldredge, Thistlethwaite, & Fox, 2004; Browning, 2002; Morgan & Jasinski, 2017). Of particular interest in this study is the role of income inequality on social disorganization. Wilson (1987) and Sampson (1986) suggest that income inequality leads to an increase in social disorganization because communication across groups in disparate income categories becomes more difficult. This also leads to an inability for members of a community to be able to come together, or makes social cohesion of a community more difficult, leading to an inability to define common interests among community members to enforce social control (Osgood & Chambers, 2000). Chamberlain and Hipp (2015) expanded social disorganization theory by examining income inequality between a neighborhood and nearby neighborhoods, and found that income inequality provided a more robust explanation in understanding variations in crime rates than solely examining economic disadvantages of a single neighborhood. They suggested that researchers need to account for the larger context of neighboring areas, rather than solely the neighborhood of interest (Chamberlain & Hipp, 2015). In this study, we use social disorganization theory to guide our decision to examine the role of income inequality on the relationship between known factors associated with IPV and IPV perpetration. In this study, we seek to determine if there are significant differences in strength of associated factors for IPV perpetration between high income inequality and low income inequality countries.

studies were found examining the influence of income inequality (SanzBarbero et al., 2015) on overall rates of IPV. One study found that the level of income inequality in several regions of Spain (measured by the Gini Index) is a significant predictor of IPV. A second study of more than 20,000 British people found that deprivation of a population as measured by indexes of economic, educational, and social class all predicted female physical violence victimization (Khalifeh, Hargreaves, Howard, & Birdthistle, 2013). Other studies have explored the relationship between income inequality and factors commonly associated with IPV (e.g., low education and problems with alcohol, Author et al., 2004). Sylwester (2002) found that countries investing more resources in education were able to reduce income inequality. A study looking at pre-teens and teenagers found that countries with higher income inequality experienced higher levels of alcohol consumption and higher episodes of drunkenness (Pickett et al., 2005). What is missing from the literature is a study which examines how income inequality affects the strength of the relationship of factors associated with IPV in the context of high or low income inequality.

1.2. Income inequality and violence

2. Method

Income inequality is considered a factor that contributes to social disorganization within a community and has also been found to be related to acts of violence within a community. Income inequality is a worldwide issue. For example, a study examining a world-wide population found that income inequality can be understood by looking at class division. Milanovic and Yitzhaki (2006) reported that 11% of the population of the world is considered rich, 11% middle class, and 78% poor. Wilkinson (2004) reported that increased levels of violence in different cultures, measured by rates of homicide, are linked to higher levels of income inequality. He suggested that income inequality provides a social environment in which stress and anxiety may be experienced at a greater level among the less privileged, resulting in increased violence. The theory that income inequality increases stress in the population has received empirical support by a study of 35,000 individuals in 30 countries (Layte, 2011). Even though data consistently has shown the existence of income inequality, the question as to how income inequality negatively affects society still warrants our attention. Two earlier studies also looked at the influence income inequality had on violence in society (Muller, 1985a; Weede, 1981). These authors theorized that income inequality causes general discontent in the population which, in turn, may increase levels of violence. In a more recent study, Pickett, Mookherjee, and Wilkinson (2005) explored the influence income inequality had on homicide rates. They found that income inequality was related to homicide rates while average income was not related to homicide rates. Similarly, rates of female homicide were significantly lower in countries with lower rates of income inequality. Thus, income inequality may not only have an effect on overall discontent and violence, but it may also decrease levels of social cohesion, which in turn may increase levels of general violence (Vanderende, Young, Dynes, & Sibley, 2012). This suggests that examining how income inequality is related to IPV, specifically, is important to further our understanding of the connection between violence and income inequality. Although previous studies have examined the influence of income inequality on intimate partner homicide (e.g., Chon, 2016), only two

2.1. Measures

1.3. The present study In this study, we use social disorganization theory to guide our research exploring how the country's level of income inequality may impact the strength of various associated factors on male IPV perpetration. In this study, we answer the following research question: Does the strength of various factors associated with IPV significantly differ between countries identified as having high income inequality and countries identified as having low income inequality?

2.1.1. Income inequality The data set for income inequality coefficients (Gini index) used in this study is from the World Bank Development Indicator. The Gini Index measures the distribution of income and consumption expenditure among individuals or households and how much it deviates from an equal perfect distribution (World Bank, 2017). A Gini index of 0 represents perfect equality while an index of 1 implies perfect inequality. All coefficients available from 2001 to most recent data available (2014) were averaged in every country with the exception of Kenya and Iraq which only had one Gini coefficient for that period of time. Next, the aggregate Gini index scores were then dichotomized into low income inequality and high income inequality so that half of the countries used in the analysis were located in the low income inequality group and the other half were located in the high income inequality group. However, four countries were excluded because the studies in our data did not contain a usable effect size (i.e., they examined a factor that did not have enough effect sizes for us to use in the analysis). 3. Method for meta-analysis 3.1. Literature search Studies for this meta-analysis were identified through multiple stages, following standard procedures for gathering data for the use of a meta-analytic review (Borenstein, Hedges, Higgins, & Rothsteine, 2014; Card, 2012). A total of 367 studies and 1492 effect sizes were used in this analysis. Locating these studies occurred through a series of database searches over time in order to continually update the current data set. First, database searches (e.g., Sociological Abstracts, Medline, PsychLit, ERIC, Social Sciences Citation Index, and Social Sciences Abstracts) were utilized to search for studies conducted between 1980 and 2000. In this search, the key words used were related to the 117

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in the second round of screening. The majority of these articles were excluded because they did not examine male perpetration of physical IPV (73.6%). These studies either examined other forms of IPV (emotional, sexual, or undifferentiated), physical IPV victimization, or female perpetration of physical IPV. We also excluded studies that did not provide statistical information to calculate a univariate or bivariate effect size (10.5%), examined adolescent or college dating violence (9.8%), examined homicide as opposed to violence (3.9%), examined special populations (1.7%) or were not written in English (0.5%). A total of 367 studies yielding 1492 effect sizes were ultimately included in the analysis.

intimate relationship (intimate partner, spousal/spouse, family, couple, marital) and violence (abuse, violence, aggression). In addition to the database searches, the reference lists of all studies found were also examined for the inclusion of additional studies. Next, studies from 2001 to 2012 were located through database searches (e.g., Proquest, PsychInfo, Web of Science) using specific search terms regarding intimate partners (e.g., marital, husband, wife, intimate partner, spouse), violence (e.g. aggression, abuse, batter, maltreatment, violence, domestic violence), and associated factors (e.g. factor, predictor, correlate, risk, pathway). The data was updated a third time, examining studies from 2012 to 2014, using database searches (e.g. Proquest) using the same search terms as used previously. A final search, using the same methods as before, was conducted in order to locate additional studies published between January 2014 and May 2016.

3.3. Coding procedures We used the recommended coding procedures for meta-analyses (Card, 2012; Hunter & Schmidt, 2004). A team of graduate students were trained by research team leaders to use a 37-item code sheet to gather pertinent information from each study used in the meta-analysis. Over 75% of the articles were cross-coded by two research team members, with an overall agreement rate of 96.2%. When those involved in coding the articles found discrepancies between their coding, they would meet with one another in order to come to an agreement on the correct answer. If the two research team members could not agree upon the correct answer, they would then meet with a team leader in order to understand the data at a deeper level (Hawkins, Blanchard, Baldwin, & Fawcett, 2008).

3.2. Inclusion criteria Studies were included in the current meta-analysis if: (1) the outcome variable in the study was male physical IPV perpetration, (2) the study included statistical information that allowed for the calculation of one or more bivariate effect sizes for male IPV perpetration, (3) the study was written in English, and (4) the sample was composed of adults in romantic relationships (as opposed to adolescent or college dating violence). The literature search that was conducted in three phases yielded a total of 25,138 studies for further examination (See Fig. 1). Of the 25,138 studies obtained, 24,668 of these studies were located through the database searches and 470 were located through reference list searches. We found a total of 5535 duplicates, which were removed from the sample of potential studies to be included in the meta-analysis. In the first round of screening based on the inclusion criteria listed above, a total of 16,986 studies were removed based on clearly not fitting the inclusion criteria. This left a total of 2593 studies to examine

3.4. Statistical approach The effect sizes for associated factors for male IPV perpetration among high income inequality and low income inequality groups were analyzed using Comprehensive Meta-Analysis software V. 3.0 (Borenstein et al., 2014). A random-effects model was used in this

Fig. 1. Study selection flow chart. 118

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Table 1 Information on countries used in the meta-analysis. Country

Average GINI index

Number of studies included in analysis

Table 2 Strength of factors associated with male IPV perpetration for high income inequality and low income inequality, sorted by strength of associated factor.

Sample size from country included in analysis

High income inequality countries Bolivia 0.54 Brazil 0.56 China 0.42 Dominican 0.49 Republic Haiti 0.60 Israel 0.41 Kenya 0.49 Malawi 0.43 Mexico 0.48 Nicaragua 0.44 Nigeria 0.44 Peru 0.49 Rwanda 0.51 South Africa 0.64 Sri Lanka 0.40 Thailand 0.40 Turkey 0.40 Uganda 0.44 United States 0.41

2 1 6 1

26,145 504 6921 588

1 3 1 1 2 2 3 3 1 6 1 1 3 1 276

2275 1500 208 8385 15,074 7216 1655 31,357 921 9892 417 619 2444 1743 286,977

Low income inequality countries Albania 0.31 Australia 0.35 Cambodia 0.35 Canada 0.34 Ethiopia 0.32 Holland 0.29 India 0.34 Indonesia 0.35 Iran 0.38 Iraq 0.29 Jordan 0.34 Pakistan 0.31 Spain 0.35 Syria 0.36 Ukraine 0.27

1 5 1 27 2 1 5 1 3 1 2 1 2 1 1

1039 810 1707 40,871 730 237 13,399 765 2072 500 693 176 374 411 1116

Low income inequality associated factor

k

Mean r

Associated factor

k

Caused injury

3

0.69⁎⁎⁎

12

0.64⁎⁎⁎

Previously violent towards partner Victim of emotional abuse Perpetrate sexual abuse

5

0.55⁎⁎⁎

Previously violent towards partner Caused injury

12

0.56⁎⁎⁎

4

0.50⁎⁎⁎

90

0.53⁎⁎⁎

3

0.45⁎

4

0.44⁎⁎⁎

Perpetrate emotional abuse Borderline PD

18

0.43⁎⁎⁎

30

0.40⁎⁎⁎

6

0.38⁎⁎⁎

23

0.37⁎⁎⁎

Child abuse in FOO Drug use Trauma PTSD

7 10 4 3

0.33⁎⁎⁎ 0.32⁎⁎⁎ 0.30⁎⁎⁎ 0.29⁎⁎

51 41 12 15

0.27⁎⁎⁎ 0.34⁎⁎⁎ 0.34⁎⁎⁎ 0.33⁎⁎⁎

Controlling behaviors Approval of violence Prior arrest Substance use Alcohol use

9 3 3 6 40

0.28⁎⁎⁎ 0.26⁎⁎ 0.25⁎ 0.25⁎⁎⁎ 0.24⁎⁎⁎

7 49 12 19 75

0.33⁎⁎⁎ 0.26⁎⁎⁎ 0.27⁎⁎⁎ 0.27⁎⁎⁎ 0.26⁎⁎⁎

Depression Anger Violent towards nonfamily members Witness IPV in FOO Jealousy Mental health issues Self-esteem Relationship dissatisfaction Income

4 9 5

0.24⁎ 0.23⁎⁎⁎ 0.20⁎⁎⁎

Perpetrate emotional abuse Victim of emotional abuse Perpetrate sexual abuse Controlling behaviors Witness IPV in FOO Anger Borderline PD Violent towards nonfamily members Approval of violence Child abuse in FOO Jealousy Prior arrest Relationship dissatisfaction Ptsd Drug use Substance use

26 101 12

0.26⁎⁎⁎ 0.24⁎⁎⁎ 0.22⁎⁎⁎

13 9 3 3 12

0.20⁎⁎⁎ 0.16⁎⁎ 0.14 −0.11 0.12⁎⁎⁎

Alcohol use Income Mental health issues Depression Young age

302 56 14 50 85

0.22⁎⁎⁎ −0.22⁎⁎⁎ 0.22⁎⁎ 0.21⁎⁎⁎ 0.15⁎⁎⁎

7

−0.12

38

−0.15⁎⁎⁎

9 9 12 3 5

−0.08 0.08⁎ −0.04 0.01 0.01

Length of relationship Education Self-esteem Trauma Marital status Employment

67 9 3 18 32

−0.14⁎⁎⁎ −0.11⁎ 0.06 −0.05 −0.04

Education Young age Employment Length of relationship Marital status

analysis in order to aggregate mean effect sizes for both between-study variance and within-study variance to account for real population differences between studies (Card, 2012). Studies were combined into two groups: those where the samples lived in countries with high income inequality and those where the samples lived in countries with low income inequality. We then compared the strength of 29 factors associated with male IPV perpetration between high income inequality and low income inequality groups. Meta-analyses can be conducted with as few as two or three effect sizes (Cumming, 2012), so in this study, we chose to include associated factors in this analysis if they had a minimum of 3 effect sizes to analyze for high income inequality and low income inequality groups. Additionally, we removed the United States from the sample and compared 12 factors associated with male IPV perpetration between high income and low income inequality groups.

High income inequality Mean r

Note: k = number of effect sizes; r = point estimate of effect size; Boldface indicates statistically significant. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

within the intimate relationship, which included previously violent towards partner (r = 0.64, p < .001), previously caused injury to partner (r = 0.56, p < .001), emotional abuse perpetration (r = 0.53, p < .001), emotional abuse victimization (r = 0.44, p < .001), and sexual abuse perpetration (r = 0.40, p < .001). Other significant factors associated with male IPV perpetration in high income inequality countries included (See Table 2 for details): controlling behaviors, witnessing IPV in family of origin, anger, borderline personality disorder, violent towards non-family members, approval of violence, experiencing child abuse in family of origin, jealousy, prior arrest, relationship dissatisfaction, post-traumatic stress disorder, drug use, substance use, alcohol use, income, mental health issues, depression, young age, length of relationship, education, and self-esteem.

4. Results Table 1 includes the countries which had at least three effect sizes for at least one risk factor, their level of income inequality as measured by the GINI index, the number of studies included in the analysis from that country, and the sample size included in the analysis.

4.2. Low income inequality The strongest factors associated with male IPV perpetration for low income inequality countries were also related to prior violence in the intimate relationship, including previously caused injury to partner (r = 0.69, p < .001), previously violent towards partner (r = 0.55,

4.1. High income inequality The strongest factors associated with male IPV perpetration for high income inequality countries were all connected with prior violence 119

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Table 3 (continued)

Table 3 Testing the strength of associated factors for high income inequality and low income inequality separated by individual-level risk markers and social-level risk markers. Associated factor

k

Individual-level factors Alcohol use High income inequality 302 Low income inequality 40 Anger High income inequality 41 Low income inequality 9 Approval of violence High income inequality 7 Low income inequality 3 Borderline personality disorder High income inequality 12 Low income inequality 6 Depression High income inequality 50 Low income inequality 4 Drug use High income inequality 101 Low income inequality 10 Education High income inequality 67 Low income inequality 9 Employment High income inequality 12 Low income inequality 32 Income High income inequality 56 Low income inequality 7 Mental health problems High income inequality 14 Low income inequality 3 Prior arrest High income inequality 19 Low income inequality 3 PTSD High income inequality 26 Low income inequality 3 Self-esteem High income inequality 9 Low income inequality 3 Substance use High income inequality 12 Low income inequality 6 Trauma High income inequality 3 Low income inequality 4 Young Age High income inequality 85 Low income inequality 9 Relational-level factors Child abuse in family of origin High income inequality 61 Low income inequality 7 Controlling behaviors High income inequality 23 Low income inequality 9 Jealousy High income inequality 12 Low income inequality 9 Length of relationship High income inequality 38 Low income inequality 3 Marital status High income inequality 18 Low income inequality 5 Perpetrator of emotional abuse High income inequality 90 Low income inequality 18 Perpetrator of sexual abuse High income inequality 30 Low income inequality 3

Mean r

95% CI

Qb

Associated factor

k

Mean r

Previously caused injury to partner High income inequality 12 0.56⁎⁎⁎ Low income inequality 3 0.69⁎⁎⁎ Previously violent towards partner High income inequality 12 0.64⁎⁎⁎ Low income inequality 5 0.55⁎⁎⁎ Relationship dissatisfaction High income inequality 75 0.26⁎⁎⁎ Low income inequality 12 0.12⁎⁎⁎ Victim of emotional abuse High income inequality 4 0.44⁎⁎⁎ Low income inequality 4 0.50⁎⁎⁎ Violent towards non-family members High income inequality 15 0.33⁎⁎⁎ Low income inequality 5 0.20⁎⁎⁎ Witness IPV in FOO High income inequality 60 0.30⁎ Low income inequality 12 0.22

p-value

95% CI

Qb

p-value

[0.43, 0.67] [0.46, 0.83]

1.19

0.28

[0.53, 0.73] [0.34, 0.72]

0.72

0.40

[−0.29, −0.24] [−0.19, −0.05]

15.95

0.00

[0.39, 0.48] [0.44, 0.55]

2.64

0.10

[0.26, 0.39] [0.09, 0.30]

4.01

0.04

[0.01, 0.54] [−0.42, 0.71]

0.06

0.81

0.22⁎⁎⁎ 0.24⁎⁎⁎

[0.20, 0.24] [0.19, 0.28]

0.36

0.55

0.34⁎⁎⁎ 0.23⁎⁎⁎

[0.29, 0.39] [0.11, 0.34]

3.15

0.08

0.33⁎⁎⁎ 0.26⁎⁎

[0.23, 0.43] [0.10, 0.40]

0.68

0.41

0.34⁎⁎⁎ 0.38⁎⁎⁎

[0.26, 0.42] [0.26, 0.48]

0.21

0.64

0.21⁎⁎⁎ 0.24⁎

[0.16, 0.27] [0.05, 0.42]

0.09

0.77

0.24⁎⁎⁎ 0.32⁎⁎⁎

[0.20, 0.27] [0.20, 0.42]

1.84

0.17

−0.14⁎⁎⁎ −0.08

[−0.17, −0.10] [−0.16, 0.01]

1.67

0.19

−0.04 −0.04

[−0.09, 0.01] [−0.11, 0.03]

0.00

0.98

−0.22⁎⁎⁎ −0.12

[−0.30, −0.14] [−0.33, 0.10]

0.79

0.38

0.22⁎⁎ 0.14

[0.09, 0.35] [−0.19, 0.44]

0.23

0.63

0.27⁎⁎⁎ 0.25⁎

[0.18, 0.35] [0.05, 0.43]

0.02

0.88

0.26⁎⁎⁎ 0.29⁎⁎

[0.20, 0.33] [0.11, 0.45]

0.07

0.79

p < .001), emotional abuse victimization (r = 0.50, p < .001), sexual abuse perpetration, (r = 0.45, p < .001), and emotional abuse perpetration (r = 0.43, p < .001). Other significant factors associated with IPV perpetration for low income inequality countries included (see Table 2 for more details): borderline personality disorder, experiencing child abuse in family of origin, drug use, trauma, post-traumatic stress disorder, controlling behaviors, approval of violence, prior arrest, substance use, alcohol use, depression, anger, violent towards non-family members, witnessing IPV in family of origin, jealousy, relationship dissatisfaction, and young age.

−0.11⁎ −0.11

[−0.21, −0.00] [−0.28, 0.06]

0.00

0.99

4.3. Comparing high income inequality and low income inequality countries

0.22⁎⁎⁎ 0.25⁎⁎⁎

[0.15, 0.29] [0.14, 0.35]

0.17

0.68

0.06 0.30⁎⁎⁎

[−0.05, 0.17] [0.20, 0.40]

10.09

0.00

0.15⁎⁎⁎ 0.08⁎

[−0.18, −0.13] [−0.14, −0.01]

5.06

0.02

0.31⁎ 0.23

[0.02, 0.56] [−0.57, 0.81]

0.04

0.85

0.37⁎⁎⁎ 0.28⁎⁎⁎

[0.28, 0.45] [0.13, 0.42]

0.95

0.33

0.27⁎⁎⁎ 0.16⁎⁎

[0.18, 0.35] [0.07, 0.26]

2.55

0.11

−0.15⁎⁎⁎ 0.01

[−0.20, −0.09] [−0.17, 0.19]

2.87

0.09

−0.05 0.01

[−0.14, 0.06] [−0.17, 0.18]

0.27

0.61

0.53⁎⁎⁎ 0.43⁎⁎⁎

[0.50, 0.56] [0.34, 0.51]

5.44

0.02

0.40⁎⁎⁎ 0.45⁎

[0.30, 0.49] [0.13, 0.69]

0.11

Note: k = number of effect sizes; r = point estimate of effect size; CI = confidence interval; Qb = heterogeneity of between-group differences with k-1 degrees of freedom. Boldface indicates statistically significant. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

We compared the strength of 29 factors associated with male IPV perpetration for countries with low income inequality versus countries with high income inequality. Of these 29 associated factors, we found five that were significantly different in strength between high income inequality countries and low income inequality countries [See Table 3]. We found that young age (Qb = 5.06, p < .05), relationship dissatisfaction (Qb = 15.95, p < .01), violence towards others (Qb = 4.01, p < .05), and emotional abuse perpetration (Qb = 5.44, p < .05) were significantly stronger associated factors for high income inequality countries than for low income inequality countries. Two of the factors associated with IPV perpetration that were stronger for high income inequality countries were relational-level factors (relationship dissatisfaction and emotional abuse perpetration) and two were individual-level factors (young age and history of violence towards others). We also found that having experienced trauma (Qb = 10.09, p < .01) was a significantly stronger factor associated with male IPV perpetration for low income inequality countries than high income inequality countries. Trauma was considered an individual-level factor. 4.4. Comparing high income inequality and low income inequality countries without the United States included In order to account for the high number of studies in the high income inequality group that were conducted in the United States we also ran the comparative analyses between high income inequality and low income inequality while excluding studies conducted in the United States. We were able to compare the strength of 12 factors associated

0.74

120

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be the most important factor to examine when assessing for, or researching, IPV in countries with both high income inequality and low income inequality. Although research has highlighted the importance of examining community level variables when studying IPV (Bensen et al., 2004; Browning, 2002; Morgan & Jasinski, 2017), our results suggest that associated factors within the intimate relationship, specifically related other forms of violence, may be most important when assessing for physical IPV in a relationship. When comparing the strength of factors associated with male IPV perpetration between high income inequality and low income inequality countries, this study found five out of 29 associated factors for male IPV perpetration significantly differed in strength between countries with low income inequality and high income inequality. We also found that when excluding studies conducted in the United States, we found that three out of 12 associated factors for male IPV perpetration significantly differed in strength. It is important for us to note that although significant differences were found, the majority of factors associated with male IPV perpetration did not significantly differ, suggesting that income inequality may play a role in these associated factors, but there are other relational level factors that should be examined in relation to IPV. We did find that young age, relationship dissatisfaction, violence towards others, and emotional abuse perpetration were significantly stronger associated factors for high income inequality countries than for low income inequality countries. We also found that having experienced some form of trauma was a significantly stronger associated factor for IPV perpetration in low income inequality countries than high income inequality countries. When excluding the studies conducted in the United States, we found that relationship dissatisfaction, emotional abuse perpetration, and witnessing IPV in family of origin were significantly stronger factors associated with IPV perpetration for high income inequality countries than low income inequality countries. Income inequality has been described as a factor associated with social disorganization (Chamberlain & Hipp, 2015). It may be that there are some specific factors associated with IPV perpetration that are exacerbated when income inequality is present. When examining the significant differences between high income inequality countries and low income inequality countries, there is a pattern that the factors that had a significantly stronger relationship with IPV perpetration in high income inequality countries were related to social support, the relationship, and community. This ties in well with social disorganization theory, which would suggest that in areas with high income inequality, there may be more social disorganization, leading to a lack of community support or social cohesion, making these factors more strongly related to male physical IPV perpetration. We found that young age was a stronger factor associated with male IPV perpetration in countries with higher income inequality (only when including the US) compared to low income inequality countries. Since this significant difference was no longer present after excluding studies conducted in the United States, this may suggest that the sample from the United States played the largest role in this difference and warrants further investigation of how young age in the United States may play a role in male IPV perpetration. There has been research that has examined income inequality's impact on older populations (Vauclair et al., 2015), but research examining how income inequality impacts younger individuals is scarce. We also found that perpetrating emotional abuse and relationship dissatisfaction were both factors more strongly associated with male IPV perpetration in countries with higher income inequality versus in lower income inequality countries. This pattern was still found when removing the studies conducted in the United States. Wilkinson (2004) suggested that high levels of income inequality provides an environment where stress and anxiety may be experienced at a greater level among the less privileged. It is possible that perpetration of emotional abuse and relationship dissatisfaction in the context of high income inequality, and the stress associated with income inequality, may

Table 4 Testing the strength of associated factors for high income inequality (excluding studies conducted in the United States) and low income inequality separated by individual-level risk markers and social-level risk markers. Associated factor Individual-level factors Alcohol use High income inequality Low income inequality Drug use High income inequality Low income inequality Education High income inequality Low income inequality Employment High income inequality Low income inequality Income High income inequality Low income inequality Young age High income inequality Low income inequality

k

Mean r

95% CI

Qb

p-value

60 40

0.26⁎⁎⁎ 0.24⁎⁎⁎

[0.21, 0.30] [0.18, 0.29]

0.25

0.620

7 10

0.28⁎⁎⁎ 0.32⁎⁎⁎

[0.15, 0.41] [0.20, 0.42]

0.14

0.704

6 9

−0.03 −0.07

[−0.15, 0.10] [−0.18, 0.02]

0.44

0.508

9 12

−0.03 −0.04

[−0.10, 0.05] [−0.10, 0.03]

0.04

0.838

5 7

−0.15⁎⁎ −0.11⁎

[−0.24, −0.05] [−0.19, −0.02]

0.41

0.524

9 10

0.05⁎⁎ 0.06⁎⁎⁎

[0.02, 0.08] [0.03, 0.09]

0.07

0.798

0.38⁎⁎⁎ 0.28⁎⁎⁎

[0.23, 0.51] [0.12, 0.42]

0.87

0.351

−0.10 0.01

[−0.24, 0.06] [−0.20, 0.22]

0.69

0.407

0.01 −0.03

[−0.21, 0.15] [−0.16, 0.18]

0.10

0.753

0.64⁎⁎⁎ 0.43⁎⁎⁎

[0.50, 0.76] [0.34, 0.52]

5.83

0.015

0.24⁎⁎⁎ 0.12⁎⁎

[0.15, 0.32] [0.04, 0.19]

4.48

0.034

0.36⁎⁎⁎ 0.20⁎⁎⁎

[0.28, 0.43] [0.12, 0.27]

8.23

0.004

Relational-level factors Controlling behaviors High income inequality 9 Low income inequality 9 Length of relationship High income inequality 6 Low income inequality 3 Marital status High income inequality 5 Low income inequality 5 Perpetrator of emotional abuse High income inequality 4 Low income inequality 18 Relationship dissatisfaction High income inequality 6 Low income inequality 12 Witness IPV in FOO High income inequality 9 Low income inequality 13

Note: k = number of effect sizes; r = point estimate of effect size; CI = confidence interval; Qb = heterogeneity of between-group differences with k-1 degrees of freedom. Boldface indicates statistically significant. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

with male IPV perpetration for countries with low income inequality versus countries with high income inequality, excluding the United States. We did not find any significant differences between any of the individual-level factors (alcohol use, drug use, education, employment, income, and young age; See Table 4). However, we did find that half of the relational-level factors did significantly differ between high income inequality countries and low income inequality countries. Perpetrating emotional abuse (Qb = 5.83, p < .05), relationship dissatisfaction (Qb = 4.48, p < .05), and witnessing IPV in family of origin (Qb = 8.23, p < .01) were all significantly stronger risk markers in high income inequality countries compared to low income inequality countries. However, controlling behaviors, length of relationship, and marital status did not significantly differ between high income inequality and low income inequality groups. 5. Discussion For both high income inequality countries and low income inequality countries, factors related to prior violence in the intimate relationship were the strongest associated factors with male IPV perpetration. This suggests that previous violence within a relationship may 121

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amplify relationship discord, which has been linked to an increase in the occurrence of IPV (Author et al., 2004). When excluding the studies that were conducted in the United States from the analysis, we also found that witnessing IPV in the family of origin was a significantly stronger risk marker for high income inequality countries compared to low income inequality countries. O'Keefe (1998) found that in a sample of adolescents, witnessing community violence differentiated adolescents who witnessed parental violence and also perpetrated dating violence from adolescents who witnessed parental violence but did not perpetrate dating violence. This highlights how social cohesion may play a role in protecting against someone witnessing IPV in their own families and then later perpetrating violence themselves. In this study, we found that violence towards non-family members was a significantly stronger associated factor for IPV for high income inequality countries than it was for low income inequality countries. This finding may highlight the connection between social disorganization and IPV. Income inequality has been considered an aspect that influences social disorganization (Sampson, 1986; Wilson, 1987), and social disorganization has been connected to crime and delinquency rates (Shaw & McKay, 1969). Violence towards non-family members would be an aspect of crime and delinquency, which was more strongly related to male IPV perpetration in high income inequality countries. This may suggest that in areas where social disorganization is more prevalent, aspects of delinquency related to social disorganization may be more strongly related to IPV. We also found that factors related to social and intimate relationships were significantly stronger in high income inequality countries, which supports Cullen's (1994) notion of the importance of examining social supports when examining social disorganization. Finally, one factor associated with male physical IPV perpetration which differentiated high income inequality and low income inequality countries was trauma. In this study, we found that trauma was more strongly related to IPV in low income inequality countries than high income inequality countries. With the small number of studies examining trauma, we believe that this result should be interpreted cautiously. Research has linked experiencing traumatic events to IPV perpetration (Maguire et al., 2015; Roberts, McLaughlin, Conron, & Koene, 2011; Whitfield, Anda, Dube, & Felitti, 2003). One possible explanation for this finding is that people that live in unequal societies may be more resilient to trauma. Future research could examine how income inequality impacts possible resiliency to trauma, as this may explain why this was the only significantly stronger risk marker for low income inequality countries than for high income inequality countries.

few studies included in the analysis were influenced more by community or local income inequality than country-level income inequality. We believe that future research would greatly benefit from continued exploration of factors associated with IPV on a community level to further assess the impact of social disorganization on IPV. Further research is also needed to fully understand the connection between income inequality and IPV. Weede (1981) suggested that in addition to studying income inequality, other inequality markers could be studied, including indicators of people's standards of justice and legitimacy, as well as opportunities to move up the social ladder or to organize and express conflicts of interests should also be considered. These factors could also play an important part in the social disorganization of a community. Not only may unequal distribution of income be an important predictor of IPV, but it is possible that unequal distribution of privilege may be associated with increased rates of IPV. A limitation of the meta-analytic part of the study is that much of the research available on factors associated with male IPV perpetration published in English was conducted in the US Since the US was included in the high income inequality group, the number of studies in that group are higher than in the low income inequality group. We did attempt to address this limitation by running additional analyses excluding the United States. Future research on IPV in non-Western countries may aid in gaining a greater understanding of IPV around the globe.

6. Limitations and suggestions for future research

References

A number of limitations exist in the data that was available to us. First, there are some limitations in the way income inequality is measured. Most researchers agree that wealth is more unevenly distributed than income (DeSilver, 2015), however, income inequality (measured by the Gini Index) is the most widely used measure of income inequality. Also, income inequality measures can vary by country depending on impact of taxes and other transfer payments. Furthermore, income inequality may not consider economic resources such as credit availability, government assistance, or family wealth (DeSilver, 2015). Researchers have conducted comparative evaluations to establish the quality of income inequality databases (Galbraith, Choi, Halbach, Malinowska, & Zhang, 2015) and recognize the benefit-cost trade-off of each data set (Jenkins, 2015). In addition, even though indices of income inequality do not change rapidly from year to year, there is a lack of consistency between countries on years income inequality is reported. Future research should address this issue. A strong limitation of this study was that we were only able to examine income inequality on a country-level basis, rather than on neighborhood or local area basis. It is also possible that countries with

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7. Conclusion Even though there were limitations to the data, this was the first study to look at world-wide data to determine the impact of income inequality on factors associated with male IPV perpetration using social disorganization theory to guide the study. Associated factors related to previous violence in the intimate relationship were of the strongest for both high income inequality and low income inequality countries, suggesting the continued importance of examining previous violence in the intimate relationship when addressing IPV. When comparing the strength of factors associated with male IPV perpetration, we found that four factors (young age, relationship satisfaction, violence towards others, and emotional abuse perpetration) were more strongly related to IPV in countries with higher income inequality than in countries with low income inequality. The data suggests that high income inequality may account for differences in factors associated with male IPV perpetration. Only one factor (having experienced trauma) was more strongly associated with male IPV perpetration in countries with lower income inequality than with high income inequality.

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