Posted: August 1st, 2023

The Association of Demographic Factors with the Factors of Economic

The Association of Demographic Factors with the Factors of Economic/Financial Abuse Against Women in Kuwait
Name
Institutional Affiliation
Abstract
Economic abuse is one of the forms of Intimate Partner Violence (IPV) that entails behaviors that control the woman’s capacity of acquiring, using, and maintaining economic resources hence putting limitations on their freedom of choice. This study will concentrate on the relationship between demographic factors with the factors of economic or financial abuse against women in Kuwait for deeper understanding. The factors of economic abuse considered were factor (A) financial exploitation, factor (B), occupational interference, factor (C) money shaming and factor (D) control spending. Demographic factors considered in the study were marital status, job title, age and district. The study established that a significant correlation does exist between a woman’s marital status and age for women living in Kuwait and their experience of economic abuse.The most significant findings in this research are that the divorced women are the most group suffered from economic/financial abuse among all factors (A, B, C, & D). Also, as age goes low, the economic/financial abuse goes high, which means that younger age women group is more likely/ more vulnerable to encounter economic/financial abuse. The findings illustrated the complex nature of economic abuse within Arabian culture, specifically in Kuwait and the fact that more research is required on the matter while reflecting on the dynamics of IPV.

Keywords: Intimate Partner Violence (IPV), Economic Abuse, Financial Abuse, Demographic Factors, Kuwait’s culture

Introduction
The United Nations’ statistics have reported that violence against women prevails at a rate of 70% globally (Alsalem, 2018). Nonetheless, these numbers have never been precise in respect to Kuwait because of social barriers. The Government of Kuwait, specifically through the Ministry of Social Affairs and Labor, surveyed in 2013 focusing on IPV within its society. Their findings indicated that 98.4% of them asserted the existence of emotional and verbal violence, 94.8% claimed financial abuse, and 94.1% claimed the presence of physical violence (Alsalem, 2018). Furthermore, statistical reports from the Ministry of Justice for the period between 2000 and 2009 demonstrated that the average number of reported violence cases against women was 368 yearly (Alsalem, 2018). This showed there was at least a single act of violence against women being reported in Kuwait each day of the year. These statistics demonstrate that the different forms of violence against women pervade Kuwait’s society, which is the most extreme form of gender inequality and is widely acknowledged as a violation to human rights.
Economic or financial abuse is primarily a power and control means within intimate relationships that has recently started to garner more attention (Postmus et al., 2020). Economic abuse laps with emotional violence in using coercive control but is constantly acknowledged as a category in itself. There is, however, a developing consensus on the definition of economic abuse, which is the control over the individual’s capacity of obtaining, using, or sustaining their access to financial resources as a way to reduce the victim’s ability to support oneself, threatening one’s economic security and potential to be self-sufficient or coerces the victim to rely on the perpetrator financially (Postmus et al., 2012). From this definition, a broad range of economic acts is admitted, including preventing access to the property, employment disruption that is outside of the home, depletion of savings and assets while creating debt and expenditure, among others. When the victim is limited in their access to resources, it creates a vicious circle such that their capacity to change the abusive experience is compromised (Sanders & Schnabel, 2006).
Economic abuse has considerable repercussions for women’s individual lives, social lives, and general well-being (Alsawalqa, 2020). The abuse reduces employment opportunities and stability, depletes their survival resources, and their living standards diminish. The economic dependence that arises from economic abuse becomes a significant obstacle for the victim that wishes to leave an abusive partner or even end the violent relationship (Postmus et al., 2020). The women capable of escaping the relationships are commonly impoverished, and the abuse is bound to continue even after leaving the intimate relationships. Apart from the physical, mental, and emotional repercussions experienced by the woman victim, economic abuse affects the community, specifically its economic well-being. Society will have its financial resources directed into handling medical and mental health expenses. It loses productivity, increases homelessness among women, and becomes a considerable challenge to their workforce participation (Center on Violence Against Women and Children, n.d.). The extensive adversarial effects of economic abuse highlight the importance of conducting more research on this subject.
Despite the abundant research on the different forms of abuse in intimate relationships, especially against women, limited studies have focused on economic abuse against women (Adams et al., 2011). Many scholars have stressed the importance of including financial abuse among the categories of intimate partner violence specifically due the nature of behaviors perpetrating the violence. To this effect, this research seeks to delve into the economic form of abuse against women. The study will concentrate on Kuwait’s society, a community rife with IPV that mainly comes from patriarchal norms and values.
This study will concentrate on the relationship between demographic factors with economic or financial abuse against women in Kuwait for deeper understanding. The experience of economic abuse will be associated to four factors specifically factor (A) financial exploitation, factor (B), occupational interference, factor (C) money shaming and factor (D) control spending. Demographic factors were considered in this study as they would denote a woman’s exposure to economic abuse within their intimate relationships. Understanding the characteristics of women victims experiencing financial abuse is fundamental in identifying the risk and protective factors that will prevent others from undergoing similar violent experiences.
Literature Review
According to Adams et al. (2008), economic abuse is defined as the deliberate pattern of control that particular parties will use to intercept another’s ability to obtain, utilize and sustain financial resources. Scholars have constantly been working on grouping the distinct forms of economic abuse. For example, Postmus et al. (2016) suggested that economic abuse entails the behaviors that are controlling, exploitative, or sabotaging a person’s economic resources, which encompasses access to employment. In literature, economic abuse will be used interchangeably with financial abuse. Also, the abuse could be termed as affecting the economic or financial security of the affected individuals or causing economic or financial insecurity. According to Sharp-Jeffs (2015), economic and financial abuse could be differentiated to state that the latter is part of the former and entails similar behaviors. Financial abuse will solely focus on individual money and finances and not the economic resources, including a place to lie, employment, and education. Notably, this research upholds the definition of financial abuse whereby the abuser tries to control the victim’s capacity to acquire, use, and maintain resources.
Financial Abusive Behaviors
One considerable method abusive partners use to interfere with the woman’s financial capacity is preventing them from gaining and sustaining employment opportunities. According to Brewster (2003), findings indicated that abusive partners would forbid, discourage and constantly hinder their female partners from working outside their homes. The mean man could sabotage their victim’s efforts of finding jobs by inflicting apparent injuries or refusing to provide childcare which prevents the woman from going to work. The abusive partners will also incorporate strategies that will prevent their wives from sustaining employment. According to Riger et al. (2001), these tactics include preventing adequate rest and sleep, failing to show care for their children, or constantly harassing them during their workdays. The effect of the work interference could reach severe levels to cause missed work days or hours that the victim could lose their job. Evidence has also suggested that the women victims are prevented from obtaining income and assets through other means. For instance, when the woman is employed, the partner may demand the woman’s paycheck and deny her from accessing her earned money; they will hinder the woman from receiving other support, including child support, public assistance, and any education-based financial aid help. The women are even restricted from obtaining assets by the partners, denying their credentials to the houses or cars that their earnings could have purchased. All these instances are tactics of abusive partners preventing their women from acquiring resources.
Other tactics used by perpetrators are to prevent the woman victim from using resources the women already own. These tactics are primarily about controlling the distribution of resources and also monitoring the utilization process. According to Adams et al. (2008), women start to report instances of their partners limiting their access to household resources, being denied access to money even for necessities being allocated a particular amount of money to be spent on household necessities alone. The abusive partners could hide jointly earned money, prevent their women from accessing the joint accounts or choose to lie or withhold information about their finances (Adams et al., 2008). The abusive partner will incorporate tactics that will control the partner’s capacity to use their own and shared financial resources.
Additionally, these abusive partners will also deliberately deplete the available economic resources for the women, limiting their options. According to Anderson et al. (2003), 38% of women have reported that their abusive partners have stolen their money through various ways, including their purses or wallet, ATM cards, or gambling. A woman in an abusive relationship finds it challenging to maintain their economic resources, especially when abusive partners engage in expenses. According to Brewster (2003), findings have shown that abusive men will steal, damage, and destroy the possessions and household items belonging to their partners, among other assets. All these tactics deplete the woman’s economic resources in two ways. The first is that the woman loses the resources they once had, and the second is that she incurs expenses of reinstating utilities, replacement, and repairing damages.
Looking into the various tactics that demonstrate economic abuse, it is evident that the husband has taken a superior position which has given him implied authority to govern the wife’s resources. A study of Australian practitioner’s perspectives of economic abuse in intimate relationships acknowledged that gender stereotypes were a significant driver of financial abuse. In the study, the practitioners pointed out that economic abuse was fostered by stereotypes that reinforced male entitlement and privilege while perpetuating men’s ideal of being the boss in relationships (Gendered Violence Research Network, 2020). Another research focused on African refugee families who had re-settled in Australia found that men losing their conventional breadwinner status after the settlements became a risk factor to IPV since some men tried to retain it within their families. While the study does not explicitly state out economic or financial abuse, it did highlight a number of cases in which the loss of breadwinner status led to men no longer feeling that they were responsible for contributing to household expenses hence leaving their female partners as providers for the families (Gendered Violence Research Network, 2020). It constantly becomes evident that traditionally gendered norms influence whether and the extent to which persons will undergo economic or financial abuse. It is common knowledge that Kuwait is primarily a patriarchal society that holds cultural beliefs that put the man at a higher position to the woman’s detriment.
The Association of Distinct Factors with Economic or Financial Abuse
The exposure of women to economic abuse has been affiliated with numerous factors. According to Kutin et al. (2017), women with education levels are more susceptible to suffering economic abuse. Notably, other studies such as Yount et al. (2016) indicate that women who attended more school years were more likely to report economic abuse than those with fewer school years. The partner’s education level is also significant such that educational differences between the partner and the woman have been highly affiliated with economic abuse. Concerning the income factor, Ozpinar et al. (2016) indicated that having more significant living standards mitigates the likelihood of economic abuse and any other kind of IPV that occurs concurrently. Many women who have reported economic abuse from their partners have a lower family income, or the woman is not working. Financial stress and financial resilience are also considerable factors related to economic abuse. Gursoy and Kara (2020) conducted a study with older adults to find that financially independent people were less susceptible to suffering economic abuse.
Other factors that have shown considerable correlations to economic abuse include age, marital status, and any history of violence. Concerning age, the women that have made financial abuse reports were primarily older such that the young women were less likely to report economic abuse. Notably, there has been a call for more research on young adults since some studies have indicated the group does experience economic abuse, negative economic conflict, and economic control. According to Gursoy and Kara (2020), single people are less exposed to economic abuse than married individuals. In addition, a significant correlation has been found between economic abuse and the mother’s history of violence. Women that have witnessed domestic violence, specifically against their mothers, are more likely to report economic abuse and other kinds of violence. The variables of parental abuse about the husband have also been affiliated with economic abuse. The people who experienced domestic violence from family members, especially in their younger years, consider intimate abuse regular in their marriage lives. Generally, a history of child abuse has increased the possibility of being exposed to economic abuse. There is a significant relationship between the structure of a woman’s family, the society’s socio-cultural and economic structure, and exposure to economic abuse. According to Gokkaya (2011), an increase in socio-cultural cohesion considerably mitigates the possibility of a woman being exposed to economic abuse, among other abuse forms. Decision-making, either economically or in family planning, is an essential factor that has been associated with economic abuse.
Generally, numerous factors have demonstrated significant relations with one’s exposure to economic abuse. Unfortunately, the abuse does have long-lasting impacts even when the victims are no longer experiencing the abuse. To this effect, it becomes clear why studying economic abuse in its various dimensions and occurrences is prudent when bright mitigation measures can be developed. This research aims to look into the relationship between distinct demographic factors and economic abuse to achieve an in-depth understanding.
Research Methodology

Data
The research used non-probability snowball sampling through distributing online surveys via social media platforms focusing on financial/economic abuse against women in Kuwait. A total sample of 4,808 respondents undertook the online survey questionnaire. These individuals would provide their demographic information in respect to one’s marital status, job title, age and district. The main objective is to determine how these demographic factors are associated with the identified factors of economic abuse that is financial exploitation, occupational interfering, money shaming and control spending.

Instrument
The semi-structured questionnaire was developed following the SEA-12 measurement to study the frequency of economic abuse among the research subjects in their intimate relationships. Adams et al. (2008) developed the original scale that comprised 28 items. The questions relating to economic control would assess the perpetrator’s efforts to dictate the women’s access to the money and use it. In contrast, exploitation items assessed the ways used by the abusers to take advantage of the woman financially (Adams et al., 2015). Appendix A outlines the 28 items used in this study to measure the prevalence of economic abuse in these participants. These 28 items used in the study measured the common types of economic abuse identified in this study: financial exploitation, occupational interfering, money shaming, and control spending. Notably, the SEA-12 fails to acknowledge the economic abuse that happens after separation.
Nonetheless, this study focused on whether economic abuse has happened during the duration of their intimate relationships. For each listed item in the questionnaire, it would be measured on the 3 point Likert-type scale ranging from never to always. These levels would be used to compare the level of abuse felt in every element.
Outcome Variable
The online questionnaire for the study was designed similar to the question papers used by the World Health Organization (WHO), specifically on their reflections on the domestic violence against women (Directorate General on the Status of Women [DGSW], 2014). In this case, the focus was primarily on economic abuse, and hence questions to be asked would be used in measuring financial abuse. Subcategories of economic abuse were used in developing these questions. These subcategories focused on how the behaviors fostered economic abuse either by preventing acquiring, using or maintaining resources. The subcategories also considered economic control, economic exploitation, and employment sabotage. These questions included: my husband holds my bank card and doesn’t allow me to use it freely, my husband takes my bank card if he doesn’t like the way I spend money, my husband doesn’t have sex with me if I refuse to give him money or my bank card and I hide from my husband my bank balance and income sources fearing of his greed or control over it among others.
The questions used to measure economic abuse were used to develop the dependent variable. Two dichotomous variables would be used to measure economic abuse. If one experienced the stipulated experience in the question, the respondent would respond with the code, Yes, and if not, they would denote No. (1=Yes, 0=No).
Dependent Variables
The study’s independent variables are related to the demographic factors of the women respondents in the study’s survey. This included the respondent’s age, marital status, employment status, and governorate in Kuwait. Each of these factors would be divided into respective categories; nominal variables were used for observing the effects of the classes in each of the variables to be used in the regression analysis.
Statistical Analysis
Survey statistics were deployed to represent the complex sampling design and weights. The analyses would then follow and be carried out in steps. First, a weighted analysis was carried out. The initial state entailed obtaining frequency and percentages depending on one’s status concerning their demographic factors. Second, Pearson’s correlation coefficient would be used to measure the correlation between the variables. A correlation between the variables indicated that as one variable changes in value, the other variable or rather factor would change to a particular direction. Third, multivariate regression analyses were conducted since the study has one dependent variable with multiple independent variables. Multivariate Analysis of Variance (MANOVA) is to be incorporated with the Pillai’s Trace, Wilks’ Lambda, Hotelling’s Trace, and Roy’s Largest Root are the four tests of significance to be displayed in the multivariate tests table. These four approaches will be used to calculate the F value for MANOVA (Giri & Priya, 2017). Hence, they will all test whether the vector regarding the groups’ means is from a similar sampling distribution or not. These analyses will aid in understanding the relationships among the variables present in the data and understand the correlation between independent and dependent variables.
Fourth, the Analysis of Variance (ANOVA) was incorporated to test if the survey results were significant. It entailed testing groups to determine if there is a difference between them. In this case, the different demographic factors are tested to determine which has a more significant association or correlation to economic abuse. This is in consideration of four factors of economic abuse that is (PC: Principal Component) PC1: Financial Exploitation, PC2: Occupational Interfering, PC3: Money Shaming and PC4: Control Spending.

Results
Demographic Characteristics of the Study Sample
The most frequently observed category of marital status was Married (n = 4245, 88%). The most frequently observed category of job title was government sector (n = 2797, 58%). The most frequently observed category of age was 24 – 29 (n = 1147, 24%). The most frequently observed category of the district was Al-Asimah (n = 987, 21%). A substantial percentage of the respondents (81.68%) reported not believing that they were economically abused. In the 18.30% of respondents that believed they were economically or financially abused, most of them (14.52%) indicated not wanting to speak to the social worker, and only 3.79% responded yes to wanting to talk to the social worker. Frequencies and percentages are presented in Table 1.
Table 1: Frequency Table for Nominal Variables
Variable n % Cumulative %
Marital Status
Married 4245 88.29 88.29
Divorced 452 9.40 97.69
Widowed 111 2.31 100.00
Job Title
unemployed 1161 24.15 24.15
Government Sector 2797 58.17 82.32
Private Sector 193 4.01 86.34
Business Owner 140 2.91 89.25
Retired 517 10.75 100.00
Age
18 – 23 945 19.65 19.65
24 – 29 1147 23.86 43.51
30 – 35 867 18.03 61.54
36 – 41 557 11.58 73.13
42 – 47 513 10.67 83.80
48 – 53 424 8.82 92.62
54 and above 355 7.38 100.00
Governorate
Jahra 696 14.48 14.48
Hawalli 630 13.10 27.58
Farwaniya 921 19.16 46.73
Mubarak Al-Kabeer 676 14.06 60.79
Ahmadi 892 18.55 79.35
Al-Asimah 987 20.53 99.88
Based on the provided international definition of economic/financial abuse, do you think you are economically/financially abused?
Yes 880 18.30 18.30
No 3927 81.68 99.98
If yes, would you like to speak with a social worker or a social therapist?
Yes 182 3.79 3.79
No 698 14.52 18.30
Not included 3927 81.70 100.00

The Correlation Between the Factors of Economic Abuse
In this study, the researcher conducted a factor analysis with the scale items being loaded into four factors primarily factor (A) financial exploitation, factor (B) occupational interfering, factor (C) money shaming, and factor (D) control spending. The correlation analysis between the factors demonstrated all factors have a positive linear correlation. Notably, the relationship between financial exploitation and occupational interference demonstrated a stronger relationship to mean that they are more likely to occur together. A coefficient of 0.752 was demonstrated which makes the relationship highly significant in this study. Conversely, the relationship between money shaming and control spending demonstrated the lowest correlation. Its coefficient of 0.469 is also statistically significant for the study. Additionally, the p-values were used to measure the significance of the empirical analysis. The p-values of * p < .05, ** p < .01, and *** p < .001 were demonstrated which are very low p-values. These low p values are evidence that the null hypothesis which is that the different factors have no relationship is to be rejected. The alternate hypothesis of a linear relationship existing between the factors of economic abuse is hence accepted which can be used to study the population. Below is a table of the correlation coefficients for the different factors.

Table 2: Correlation

Variable factorA factorB factorC factorD
1. factorA —
2. factorB 0.752 *** —
3. factorC 0.560 *** 0.573 *** —
4. factorD 0.715 *** 0.634 *** 0.469 *** —
* p < .05, ** p < .01, *** p < .001
Interpreting the Output:
Table 3: Multivariate Tests

Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
Intercept Pillai’s Trace 0.466 972.302b 4.000 4455.000 0.000 0.466
Wilks’ Lambda 0.534 972.302b 4.000 4455.000 0.000 0.466
Hotelling’s Trace 0.873 972.302b 4.000 4455.000 0.000 0.466
Roy’s Largest Root 0.873 972.302b 4.000 4455.000 0.000 0.466
Marital_Status Pillai’s Trace 0.017 9.674 8.000 8912.000 0.000 0.009
Wilks’ Lambda 0.983 9.691b 8.000 8910.000 0.000 0.009
Hotelling’s Trace 0.017 9.707 8.000 8908.000 0.000 0.009
Roy’s Largest Root 0.015 16.222c 4.000 4456.000 0.000 0.014
Job_Title Pillai’s Trace 0.006 1.613 16.000 17832.000 0.057 0.001
Wilks’ Lambda 0.994 1.615 16.000 13610.887 0.057 0.001
Hotelling’s Trace 0.006 1.616 16.000 17814.000 0.056 0.001
Roy’s Largest Root 0.005 5.112c 4.000 4458.000 0.000 0.005
Age Pillai’s Trace 0.012 2.254 24.000 17832.000 0.000 0.003
Wilks’ Lambda 0.988 2.257 24.000 15542.848 0.000 0.003
Hotelling’s Trace 0.012 2.260 24.000 17814.000 0.000 0.003
Roy’s Largest Root 0.009 6.689c 6.000 4458.000 0.000 0.009
District Pillai’s Trace 0.007 1.562 20.000 17832.000 0.052 0.002
Wilks’ Lambda 0.993 1.562 20.000 14776.513 0.052 0.002
Hotelling’s Trace 0.007 1.562 20.000 17814.000 0.052 0.002
Roy’s Largest Root 0.004 3.220c 5.000 4458.000 0.007 0.004

Table 4: Tests of between-subject effects

Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Corrected Model factorA 144.582a 341 0.424 3.012 0.000 0.187
factorB 206.021b 341 0.604 2.561 0.000 0.164
factorC 153.127c 341 0.449 2.344 0.000 0.152
factorD 134.918d 341 0.396 2.651 0.000 0.169
Intercept factorA 427.270 1 427.270 3035.778 0.000 0.405
factorB 574.736 1 574.736 2436.501 0.000 0.353
factorC 492.847 1 492.847 2573.110 0.000 0.366
factorD 389.122 1 389.122 2607.267 0.000 0.369
Marital_Status factorA 8.121 2 4.060 28.849 0.000 0.013
factorB 11.964 2 5.982 25.360 0.000 0.011
factorC 3.001 2 1.500 7.834 0.000 0.004
factorD 4.467 2 2.234 14.966 0.000 0.007
Job_Title factorA 0.401 4 0.100 0.713 0.583 0.001
factorB 0.822 4 0.206 0.871 0.480 0.001
factorC 1.508 4 0.377 1.969 0.096 0.002
factorD 1.270 4 0.317 2.127 0.075 0.002
Age factorA 1.913 6 0.319 2.266 0.035 0.003
factorB 3.809 6 0.635 2.692 0.013 0.004
factorC 1.545 6 0.257 1.344 0.234 0.002
factorD 5.529 6 0.921 6.174 0.000 0.008
District factorA 1.653 5 0.331 2.348 0.039 0.003
factorB 1.482 5 0.296 1.257 0.280 0.001
factorC 1.817 5 0.363 1.897 0.091 0.002
factorD 0.798 5 0.160 1.070 0.375 0.001
a. R Squared = .187 (Adjusted R Squared = .125), b. R Squared = .164 (Adjusted R Squared = .100), c. R Squared = .152 (Adjusted R Squared = .087), d. R Squared = .169 (Adjusted R Squared = .105)

Table 3 and Table 4 above will be fundamental in interpreting the output of the association of demographic variables with the factors of economic abuse. Table 3 below demonstrates the outcomes of the MANOVA analyses conducted to obtain respective F values and the p-values. This study will consider the Pillai’s test approach and the Wilk’s lambda approach as they have proven to produce robust results in both balanced and unbalanced number of variations (Ates et al., 2019). Considering the Pillai’s Test approach for the factor (A) financial exploitation, its respective value is 0.017 with an F value of 9.674 and a p-value of 0.000 which is significant at a 5% level. This is a significant result to indicate that a difference does exist between the different levels of this independent variable. Factor (C) money shaming demonstrated a similar significance level of 0.000 within its Pilla’s trace value of 0.012 and an F value of 2.254. The Pillai’s trace value for factor (B) occupational interfering was 0.006 with an F-value of 1.613 and a p-value of 0.057. While this is not a significant outcome considering that the p-value is higher than the 5% level, this remains a trend of significance that could change with an increase in the sample cases. The same outcome is evident for factor (D) control spending since Pillai’s trace value was 0.007 with an F value of 1.562 and a p-value of 0.052.
Considering the Wilk’s Lambda for factor (A) financial exploitation, its value is 0.98, with an F value of 9.69 and a p-value of 0.000. Following the alpha level of 0.05, its significant F value demonstrated significant differences with other factors that are in a linear relationship with it. The same interpretation applies to factor (C) money shaming which had a Wik’s Lambda value of 0.988, F value of 2.257 and a p-value of 0.000. In respect to factor (B) occupational interfering and factor (D) control spending, the p-values were slightly higher than the set optimum of 0.05 demonstrating not statistically significant means.
The results from the Tests of Between Subjects Effects Table 4 would demonstrate whether significant differences are present in the means for each individual dependent variable. The outcomes of the ANOVAS compare the means of the factors of economic abuse, specifically financial exploitation, occupational interfering, money shaming and control spending. According to the values in the marital status row of the table and relying on the standard α of 0.05, subjects in the independent variable categories demonstrated significant results. The values were specifically financial exploitation (F=28.849, p= 0.000), occupational interfering(F=25.360, p= 0.000), money shaming (F= 7.834, p= 0.000) and control spending (F= 14.966, p= 0.000). Similar statistically significant differences were demonstrated in the age row as the results were financial exploitation (F=2.266, p= 0.035), occupational interfering (F=2.692, p= 0.013) and control spending with the values (F= 6.174,p= 0.000). No statistically significant relationship was between one’s age and the factor of money shaming (F=1.344, p= 0.234. The variable job title would demonstrate no statistically differences in all the factors of economic abuse since their p values surpassed the standard α of 0.05. No statistically differences would be demonstrated between the district factor and the factors of economic abuse, namely occupational interfering (F= 1.257, p= 0.280), money shaming(F= 1.897, p= 0.091) and control spending (F=1.070, p=0.375). Notably, the interaction between district and financial exploitations demonstrated significant mean difference (F= 2.348, p= 0.039).
The differences in the dependent variable scores provide a mathematical explanation for the different canonical variate scores. From the inspection, it is evident that the demographic factors of marital status and age have a relationship with the factors of economic abuse, hence the former are substantial contributing factors to one’s experience of economic abuse. Age did not exhibit any relationship with money shaming as a form of economic abuse thus cannot be considered a contributing factor. . The demographic factors, job title and one’s district did not exhibit a relationship with all factors of economic abuse except for district and the fourth factor of economic abuse, control spending.
ANOVA Factor Analysis
The researcher ran a series of ANOVA tests as illustrated by Table 5 in respect to the two demographic factors, marital status and age as they were the only significant variables established in the multivariate analysis. The null hypothesis here is that there is no interaction between factors and the alternate hypothesis is that there is a significant interaction between the factors. It is evident that the two factors have a clear significant interaction as the p-value is <0.001 which is less than 5% (α) hence the null hypothesis is rejected. The variable marital status is statistically significant compared to that of age as it demonstrated a p-value less than 0.05. There is a significant difference in the main effect of marital status compared to age.
Table 5: ANOVA – factors
Cases Sum of Squares df Mean Square F p η²p
Marital_Status 49.599 2 24.799 166.018 < .001 0.065
Age 1.684 6 0.281 1.879 0.080 0.002
Marital_Status ✻ Age
5.281 12 0.440 2.946 < .001 0.007
Residuals 714.923 4786 0.149
Note. Type III Sum of Squares

Table 6: Descriptives – factor (A)
Marital_Status Age Mean SD N
Divorced 18 – 23 1.481 0.560 47
24 – 29 1.501 0.534 104
30 – 35 1.495 0.531 118
36 – 41 1.578 0.554 55
42 – 47 1.643 0.641 65
48 – 53 1.674 0.685 34
54 and above 1.631 0.596 29
Married 18 – 23 1.237 0.409 896
24 – 29 1.210 0.388 1037
30 – 35 1.173 0.324 745
36 – 41 1.191 0.335 488
42 – 47 1.200 0.328 428
48 – 53 1.204 0.366 362
54 and above 1.116 0.243 288
Widowed 18 – 23 2.000 1.414 2
24 – 29 1.523 0.695 5
30 – 35 1.596 0.455 4
36 – 41 1.473 0.563 14
42 – 47 1.265 0.409 20
48 – 53 1.176 0.356 28
54 and above 1.170 0.361 38

The descriptive table above provided important descriptive statistics including the mean, standard deviation, and number of respondents for the independent variables marital status and age in respect to the financial exploitation factor (A). It is evident from the table that the divorced respondents had a higher association with the financial exploitation factor of economic abuse due to the higher mean with the widowed category demonstrating the lowest association. The divorced respondents between ages 48 to 53 years, married respondents between ages 18 to 23 years and the widowed respondents between ages 18-23 years demonstrated that highest means to the relationship between this fact and economic abuse.

Table 7: ANOVA – factor (B)
Cases Sum of Squares df Mean Square F p η² p
Marital_Status 74.520 2 37.260 152.352 < .001 0.060
Age 3.392 6 0.565 2.311 0.031 0.003
Marital_Status ✻ Age
4.907 12 0.409 1.672 0.066 0.004
Residuals 1170.491 4786 0.245

Note. Type III Sum of Squares
The ANOVA table for the data measured in respect to factor (B) occupational interfering is outlined in Table 7. The marital status* age effect demonstrated that the main effect of this factor of economic abuse is not statistically significant (F=1.672, p= 0.066).
Table 8: Descriptives – factorB
Marital_Status Age Mean SD N
Divorced 18 – 23 1.906 0.649 47
24 – 29 1.763 0.607 104
30 – 35 1.797 0.647 118
36 – 41 1.818 0.602 55
42 – 47 1.846 0.673 65
48 – 53 1.935 0.701 34
54 and above 1.828 0.618 29
Married 18 – 23 1.398 0.488 896
24 – 29 1.378 0.483 1037
30 – 35 1.373 0.460 745
36 – 41 1.394 0.478 488
42 – 47 1.380 0.482 428
48 – 53 1.371 0.476 362
54 and above 1.293 0.425 288
Widowed 18 – 23 2.000 1.414 2
24 – 29 1.840 0.805 5
30 – 35 1.750 0.526 4
36 – 41 1.757 0.556 14
42 – 47 1.490 0.634 20
48 – 53 1.300 0.473 28
54 and above 1.300 0.500 38

Considering the means in different categories, a higher mean to the association with the factor occupational interfering has been noted for the divorced that are between 48-53 years, the married that are between 18 to 23 years and the widowed between the years of 18 to 23 years.
Table 9: ANOVA – factor (C)
Cases Sum of Squares df Mean Square F p η² p
Marital_Status 35.517 2 17.758 88.759 < .001 0.036
Age 4.397 6 0.733 3.663 0.001 0.005
Marital_Status ✻ Age
6.806 12 0.567 2.835 < .001 0.007
Residuals 957.159 4784 0.200

Note. Type III Sum of Squares.
The ANOVA table for the data measured in respect to factor (C) money shaming is outlined in Table 9. The marital status* age effect demonstrated that a statistically significant relationship with the money shaming factor exists due to the values(F=2.835, p=<.001)

Table 10: Descriptives – factorC
Marital_Status Age Mean SD N
Divorced 18 – 23 1.876 0.685 47
24 – 29 1.617 0.527 104
30 – 35 1.535 0.567 118
36 – 41 1.627 0.626 55
42 – 47 1.608 0.606 65
48 – 53 1.721 0.626 34
54 and above 1.615 0.472 29
Married 18 – 23 1.389 0.452 896
24 – 29 1.334 0.431 1036
30 – 35 1.309 0.399 745
36 – 41 1.307 0.409 488
42 – 47 1.354 0.454 428
48 – 53 1.392 0.484 362
54 and above 1.222 0.363 288
Widowed 18 – 23 1.833 1.179 2
24 – 29 1.400 0.325 5
30 – 35 1.667 0.561 4
36 – 41 1.774 0.568 14
42 – 47 1.281 0.500 19
48 – 53 1.280 0.428 28
54 and above 1.189 0.334 38
Considering the descriptive statistics related to factor C age, a higher mean to the association with economic abuse was demonstrated by the divorced between 18-23 years, the married between 48-53 years and the widowed between 18-23 years.
Table 11: ANOVA – factor (D)
Cases Sum of Squares df Mean Square F p η² p
Marital_Status 29.552 2 14.776 93.511 < .001 0.038
Age 7.380 6 1.230 7.784 < .001 0.010
Marital_Status ✻ Age
4.977 12 0.415 2.625 0.002 0.007
Residuals 756.239 4786 0.158

Note. Type III Sum of Squares
The ANOVA table for the data measured in respect to factor (D) control spending is outlined in Table 11. The marital status* age effect demonstrated a statistically significant relationship with the respondent’s district due to the values (F=2.625, p= 0.002)

Table 12: Descriptives – factorD
Marital_Status Age Mean SD N
Divorced 18 – 23 1.418 0.608 47
24 – 29 1.388 0.577 104
30 – 35 1.415 0.586 118
36 – 41 1.424 0.659 55
42 – 47 1.487 0.702 65
48 – 53 1.363 0.577 34
54 and above 1.322 0.515 29
Married 18 – 23 1.203 0.431 896
24 – 29 1.162 0.380 1037
30 – 35 1.133 0.349 745
36 – 41 1.123 0.347 488
42 – 47 1.134 0.355 428
48 – 53 1.110 0.334 362
54 and above 1.032 0.144 288
Widowed 18 – 23 1.833 1.179 2
24 – 29 1.533 0.869 5
30 – 35 1.917 0.918 4
36 – 41 1.500 0.624 14
42 – 47 1.483 0.671 20
48 – 53 1.167 0.369 28
54 and above 1.061 0.267 38

Considering the descriptive statistics related to factor (D) control spending, a higher mean to the association with economic abuse was demonstrated by the divorced between 42-47 years, the married between 18-23 years and the widowed between 30-35 years.

Assignment help – Discussion
In the present study, the focus was looking at the association of different demographic factors with factors of economic abuse among women in Kuwait. The research findings indicated that a woman’s age and marital status are significant contributing factors to whether one experiences financial or economic abuse from their intimate partners. Nonetheless, the findings did confirm Adams et al. (2008) findings who indicated that 99% of women above 18 years had experienced astounding economic abuse at some point in their intimate relationships. This meant that almost every woman that has been with a male partner had had the latter control their use or access to economic resources.
The most significant finding was that the most disadvantaged group was the divorced women category as they experienced all forms of financial abuse as outlined by the considered factors. Additionally, a decrease in age demonstrated an increase in the experiences of economic or financial abuse. Considering the marital status factor, the different subcategories of divorced, married, and widowed demonstrated distinct means. The widowed respondents between the age of 18 to 23 years had the highest standard of 2.000 to indicate experiencing higher levels of economic abuse. The married women in the same age bracket also demonstrated a higher relationship with financial abuse. At the same time, the divorced category showed women between 48 to 523 years experienced the highest rates of economic abuse. While Kutin et al. (2017) found that women in all age groups were at a higher risk of experiencing economic abuse, this research specifically noted that the women in their younger years were more at risk of experiencing economic abuse in their intimate relationships than the older ones. The younger women have higher chances of being abused due to the lack of understanding of whether or not they are undergoing the abuse while being economically abused. Most of the older ones could have better understood themselves and their partners, including characters, hence strengthening the mutual recognition of each other’s needs. To this effect, these women may consider some of their husband’s behaviors as not economically abusive but instead, their partners doing what they are supposed to do.
Early marriage could also be associated with economic abuse from husbands, consistent with other studies such as Erulkar (2013), who indicated that women who entered into marriages in their early years are more likely to be victimized. In Kuwait, the legal age of marriage does vary but it is currently at 15 for girls. This is a young age for the girl who has no experience of life. It is hence possible for them to be economically abused by their husbands. Notably, the divorced individuals did demonstrate a higher correlation to economic abuse. Therefore, there are two extremes on the spectrum : those that are getting into the union and also those that have left the union.
Regarding the second demographic factor of the woman’s job title, the findings demonstrated no statistically significant correlation between the factors of economic abuse and their job title. Regarding one’s job title, previous studies such as Krishnan et al. (2010) indicated that IPV is more likely to happen among working women than those not working. Nonetheless, our results of the job title not being significant demonstrated the need for a better understanding of the relationship between a job title and the likelihood of abuse, specifically economic abuse. The fact that a woman’s job title does not determine whether they will experience economic abuse or not is attributed to the intergenerational transmission of perception related to violence. The patriarchal norms and values adopted in Kuwait’s society entails husbands being abusive to their wives in different ways, will have women consider these behaviors as the norm and have them replicated in their intimate relationships. Notably, Abramsky et al. (2019) indicated that the relationship between the woman’s economic status and their risk of abuse could be explained with consideration of contextual factors such as the economic hardship within the household, the women’s economic contribution, the marital years, the woman’s characteristics such as age and childhood abuse experiences among others.
On the final demographic factor, district, it demonstrated a statistically significant correlation with the control spending factor only. This could also be attributed to other contextual factors such as the difference in distribution factors within the populations, including age, how partner violence has been defined and how willing the respondents were to experience violence
.This study had its limitations. First, the data collected was on the experiences of economic abuse based on self-reported information given by the respondents. Therefore, the data was subject to bias, cultural values, and how willing the respondents were in reporting experiencing economic abuse. Also, this research was a cross-sectional study such that it was not possible to determine the causal relationship between the demographic factors identified and financial abuse.
Conclusion
Economic abuse is one of the forms of IPV that entails behaviors that control the woman’s capacity of acquiring, using, and maintaining economic resources hence putting limitations on their freedom of choice. This abuse commonly occurs with other abusive conduct that amplifies economic abuse, including physical and mental abuse. This research is the first to establish the correlation between demographic factors and economic abuse among women in Kuwait. It also contributes to the limited existing literature on economic abuse among women in the Arabian environment.
This research did establish that a significant correlation does exist between a woman’s marital status and age with the four factors of economic abuse for the women living in Kuwait. The most significant findings in this research are that the divorced women are the most suffered from economic/financial abuse among all factors. Also, as age goes low, the economic/financial abuse goes high, which means that younger age women group is more likely/ more vulnerable to encounter economic/financial abuse
These results illustrate the complex nature of economic abuse within Arabian culture, specifically in Kuwait. Also, the research pointed out the importance of attaining a better understanding of the relationship between one’s job title and economic abuse since the study did not find a clear association between them. Financial abuse requires more attention and consideration within the reflections on the dynamics of IPV. This is because more awareness of the concept among Kuwait women needs to be increased so that the victims could acknowledge any abusive behaviors they are experiencing that limit their ability to become economically independent.

References
Abramsky, T., Lees, S., Stöckl, H., Harvey, S., Kapinga, I., Ranganathan, M., … & Kapiga, S. (2019). Women’s income and risk of intimate partner violence: secondary findings from the MAISHA cluster randomised trial in North-Western Tanzania. BMC public health, 19(1), 1-15.
Adams, A. E., Beeble, M. L., & Gregory, K. A. (2015). Evidence of the construct validity of the Scale of Economic Abuse. Violence and Victims, 30(3), 363-376.
Adams, A. E., Sullivan, C. M., Bybee, D., & Greeson, M. R. (2008). Development of the scale of economic abuse. Violence against women, 14(5), 563-588.
Adams, A. E., Sullivan, C. M., Bybee, D., & Greeson, M. R. (2011). Development of the scale of economic abuse. Companion reader on violence against women, 51-74.
Alsalem, Fatima. (2018). Attitudinal Survey on Violence Against Women In Kuwait. AnalytiKs Center for Public Opinion Research and Social Media Analysis.
Al Sawalqa, R. O. (2020). Economic abuse of women in Amman, Jordan: A quantitative study. SAGE Open, 10(4), 2158244020982616.
Anderson, M. A., Gillig, P. M., Sitaker, M., McCloskey, K., Malloy, K., & Grigsby, N. (2003). “Why doesn’t she just leave?”: A descriptive study of victims reported impediments to her safety. Journal of Family Violence, 18, 151-155.
Ateş, C., Kaymaz, Ö., Kale, H. E., & Tekindal, M. A. (2019). Comparison of test statistics of nonnormal and unbalanced samples for multivariate analysis of variance in terms of type-I error rates. Computational and mathematical methods in medicine, 2019.
Brewster, M. P. (2003). Power and control dynamics in pre-stalking and stalking situations. Journal of Family Violence, 18, 207-217.
Center on Violence Against Women and Children. (n.d.). Economic Abuse Fact Sheet. Rutgers University: School of Social Work.
Directorate General on the Status of Women (DGSW). (2014). National Research on Domestic Violence against Women in Turkey. Hacettepe University Institute of Population studies, ICON-Institute Public Sector GmbH and BNB: Turkish Republic Prime Ministry Directorate General on the Status of Women.
Erulkar, A. (2013). Early marriage, marital relations and intimate partner violence in Ethiopia. International perspectives on sexual and reproductive health, 6-13.
Gendered Violence Research Network. (2020). Understanding Economic and Financial Abuse in Intimate Partner Relationships. University of New South Wales.
Giri, I., & Priya, C. (2017, Mar 14). Interpreting multivariate analysis with more than one dependent variable. Knowledge Tank; Project Guru. https://www.projectguru.in/multivariate-dependent-variable/
Gokkaya, VB (2011). ECONOMIC VIOLENCE AGAINST WOMEN IN TURKEY.
Gürsoy, M. Y., & Kara, F. (2020). Prevalence of violence against older adults and associated factors in Çanakkale, Turkey: A cross‐sectional study. Geriatrics & gerontology international, 20(1), 66-71.
Krishnan, S., Rocca, C. H., Hubbard, A. E., Subbiah, K., Edmeades, J., & Padian, N. S. (2010). Do changes in spousal employment status lead to domestic violence? Insights from a prospective study in Bangalore, India. Social science & medicine, 70(1), 136-143.
Kutin, J., Russell, R., & Reid, M. (2017). Economic abuse between intimate partners in Australia: prevalence, health status, disability and financial stress. Australian and New Zealand journal of public health, 41(3), 269-274.
Özpinar, S., Horasan, G. D., Baydur, H., & Canbay, T. (2016). Factors affecting the views and experiences of women living in the city centre of Manisa, Turkey, regarding domestic violence. Australian journal of primary health, 22(5), 466-471.
Postmus, J. L., Hoge, G. L., Breckenridge, J., Sharp-Jeffs, N., & Chung, D. (2020). Economic abuse as an invisible form of domestic violence: A multicountry review. Trauma, Violence, & Abuse, 21(2), 261-283.
Postmus, J. L., Plummer, S. B., & Stylianou, A. M. (2016). Measuring economic abuse in the lives of survivors: Revising the Scale of Economic Abuse. Violence against women, 22(6), 692-703.
Postmus, J. L., Plummer, S. B., McMahon, S., Murshid, N. S., & Kim, M. S. (2012). Understanding economic abuse in the lives of survivors. Journal of interpersonal violence, 27(3), 411-430.
Riger, S., Ahrens, C., & Blickenstaff, A. (2001). Measuring interference with employment and education reported by women with abusive partners: Preliminary data. In K. D. O’Leary & R. D. Maiuro (Eds.), Psychological abuse in violent domestic relations (pp. 119-133). New York: Springer.
Sanders, C. K., & Schnabel, M. (2006). Organizing for economic empowerment of battered women: Women’s savings accounts. Journal of Community Practice, 14(3), 47-68.
Sharp-Jeffs, N. (2015). Money matters: Research into the extent and nature of financial abuse within intimate relationships in the UK.
Yount, K. M., Krause, K. H., & VanderEnde, K. E. (2016). Economic coercion and partner violence against wives in Vietnam: a unified framework?. Journal of interpersonal violence, 31(20), 3307-3331.

Appendix A
The financial/economic abuse against women scale
Item Question
Q7R1 My husband holds my bank card and doesn’t allow me to use it freely.
Q7R2 My husband takes my bank card if he doesn’t like the way I spend money.
Q7R3 My husband provocatively investigates with me for every amount of money I spend.
Q7R4 My husband makes excuses and problems in order to get my bank card.
Q7R5 My husband degrades/mocks me when he spends money on me, and that makes me feel insulted.
Q7R6 My husband degrades/mocks me that he spends more money on me than my family did.
Q7R7 When my husband degrades/mocks me too much for spending money on me, I stop asking for money even for necessary needs.
Q7R8 My husband doesn’t provide me with the basic needs such as food, clothing and adequate housing.
Q7R9 I feel like my husband is deliberately forgetting his wallet so I can pay him when we go out.
Q7R10 I feel that my husband is taking advantage of me in his many requests for necessities so that I can buy them for him.
Q7R11 I feel my husband is exploiting me by seducing me to travel at my expense.
Q7R12 My husband asks me to borrow money for him from the bank.
Q7R13 My husband asks me to borrow money for him from my family or friends.
Q7R14 My husband asks me to join a collaboration group so he can gain money.
Q7R15 My husband becomes nice only when I receive a financial reward or when my salary is close to being deposited.
Q7R16 My husband is not satisfied with the simple gifts I give him and gets upset; he always pushes me to buy him expensive gifts.
Q7R17 My husband uses my money to pay living bills or rent.
Q7R18 My husband is paying off his debts with my money.
Q7R19 My husband is having fun with his friends with my money.
Q7R20 My husband uses my money and/or properties according to his whims and without consulting with me or taking my opinion.
Q7R21 My husband doesn’t have sex with me if I refuse to give him money or my bank card.
Q7R22 I hide from my husband my bank balance and income sources fearing of his greed or control over it.
Q7R23 My husband pressures or urges me to leave work and sit at home.
Q7R24 My husband doesn’t want me to work in business or to have another source of income.
Q7R25 My husband is trying very hard to discourage or prevent me from looking for a job.
Q7R26 My husband wants me to be financially dependent on him.
Q7R27 My husband doesn’t allow me to develop myself professionally, such as taking courses or obtaining certain licenses that would improve my financial situation.
Q7R28 My husband doesn’t allow me to continue my education, which would improve my financial situation.

The international definition of economic/financial abuse is a form of violence that some husbands may perpetrate against their wives through the husband’s control or control over the wife’s economic/financial resources such as income and property and/or preventing or withholding future financial gain opportunities in order to reduce the wife’s ability to support herself and force her to rely on him financially.

Order | Check Discount

Tags: Masters Essays, PSY Papers, PSYC, Psychology Assignment, Psychology Dissertations