Intrahousehold Conflict Effects on Domestic Violence in Rwanda: Evidence from the Demographic and Health Survey 2019-2020

Intrahousehold Conflict Effects on Domestic Violence in Rwanda: Evidence from the Demographic and Health Survey 2019-2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intrahousehold Conflict Effects on Domestic Violence in Rwanda: Evidence from the Demographic and Health Survey 2019-2020 Jean de Dieu Harerimana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3781618/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Domestic violence affects approximately one-third of women globally and presents unique challenges in Rwanda. This study examined the link between domestic violence and household conflicts, focusing on infertility, unintended pregnancies, and the impact of premarital pregnancies. The survey uses data from the 2019/20 Rwanda Demographic and Health Survey and addresses the influence of Rwandan cultural norms, the prevalence of child marriage, and societal attitudes toward violence. Methodology This study analysed data from the 2019-20 Rwanda Demographic and Health Survey using a two-stage sampling process. Regression discontinuity design (RDD) and logistic regression were used to evaluate variables such as domestic violence incidence and age, as well as control variables such as education, marital status, and occupational status. Results The findings indicate increased risks of domestic violence as women approaching the legal marriage age of 21 years face increased risks of domestic violence, with 35.1% greater likelihoods of physical violence and 14.8% greater odds of sexual violence. Polygamous marriages significantly increase the risk of sexual violence by 26.5%. Early forced sexual encounters intensify the likelihood of physical and sexual violence by 67.5% and 129.7%, respectively. Conclusion This study highlights the increased vulnerability to physical and sexual violence linked to early sexual encounters and polygamous marriage. These findings, diverging from global trends, underscore the necessity of employing Rwanda-specific strategies. Moreover, to effectively address domestic violence, it is important to consider cultural dynamics, socioeconomic status, and matrimonial education, including sex education and bargaining power, for both parties. Intrahousehold conflict domestic violence premarital motherhood Figures Figure 1 1 Background Domestic violence is a widespread problem on a global scale, impacting approximately one-third of women worldwide. Its effects are especially significant in developing nations, where unique challenges are present in Sub-Saharan Africa and, specifically, in Rwanda ( 1 – 3 ). This study aimed to explore the connections between domestic violence and intrahousehold conflict-related issues such as infertility or unintended pregnancies in Rwanda, as well as their implications for public health ( 4 – 6 ). These intersections contribute to heightened susceptibility to abuse, leading to increased maternal mortality rates, disability, and infectious illnesses ( 7 , 8 ). Rwanda has experienced fluctuations in domestic violence cases over time. While there was a decline in the early 2000s, there was an alarming increase in societal acceptance of such violence ( 9 – 13 ). This study seeks to examine the complex factors contributing to this trend, with a specific focus on conflicts within households as a significant contributor to domestic violence in Rwanda. This risk is further worsened by issues such as premarital pregnancies ( 14 , 15 ) Despite well-known protective factors such as education and financial independence, the connection between intrahousehold conflict and child maltreatment remains a critical issue that requires comprehensive intervention. The cultural and societal norms in Rwanda significantly influence attitudes toward domestic violence. Traditional beliefs about male power and the aftermath of war and genocide play key roles in the acceptability and occurrence of family violence( 16 ). This study explored these aspects by examining how cultural, psychosocial, and economic factors impact intimate partner violence among ever-married women( 17 ). Legal policies, which include setting the legal age for marriage at 21 years, are crucial for addressing domestic violence issues. Nonetheless, it is essential to further investigate the implementation and effectiveness of these policies, especially concerning marital conflicts related to premarital motherhood ( 18 , 19 ). This study aimed to investigate the relationship between intrahousehold conflict, which is specifically related to premarital pregnancies, and domestic violence in Rwanda. This study drew on data from the 2019/20 Demographic and Health Survey to perform a microeconometric examination of domestic violence in Rwanda. The survey revealed that approximately 22% of married women in Rwanda have encountered domestic violence, with 14% reporting instances of sexual violence by their partners. Compared to regional statistics, these numbers underscore the impact of cultural, historical, and socioeconomic factors in sub-Saharan Africa( 20 – 23 ). The prevalence of child marriage is another significant aspect to consider according to UNICEF data, which indicate that 7% of girls in Rwanda are wed before they reach the age of 18. It is imperative to comprehend the complexities associated with early marriages and their correlation with domestic violence. The Rwandan scenario following the Genocide in 1994 provides distinctive insights into how conflict relates to long-term societal violence—akin to post-conflict societies such as Bosnia or Cambodia. This study aims to offer thorough insights into the factors contributing to domestic violence in Rwanda and to provide a basis for policy and intervention strategies. This study seeks to address gaps in the current body of research by examining understudied aspects, such as the impact of premarital pregnancies and perceptions about marital status, on experiences of violence. Furthermore, this study provides insights into how existing legal frameworks influence efforts to combat domestic violence. 2 Methodology 2.1 Data sources This study utilizes the Rwanda Demographic and Health Survey (DHS) 2019-20 for its empirical analysis( 24 ). The dataset is known for its methodological rigor and comprehensive coverage, making it an ideal choice. A representative sample of married women aged 15–49 in Rwanda was obtained through a meticulous two-stage sampling process. The first stage involved identifying clusters within enumeration areas across Rwanda to ensure geographic and socioeconomic representation. In the second stage, households within these clusters were systematically selected, including long-term residents and overnight visitors, to create a demographic profile that was inclusive. The DHS's robust sampling methodology and participant selection ensure data reliability, which is crucial for evaluating the extent and determinants of domestic violence. However, it is important to note that the cross-sectional nature of the survey and the reliance on self-reported data, especially on sensitive issues such as domestic violence, can introduce certain biases. These biases can impact longitudinal inferences and response accuracy. Nonetheless, the thoroughness of the DHS dataset aligns closely with the study's objectives to explore intrahousehold conflict-related factors associated with domestic violence in Rwanda. 2.2 Selection of the variables Outcome Variable The primary focus of this study was to measure the incidence of domestic violence within households. This measurement was obtained by using responses from the domestic violence module of the DHS, which includes questions about physical, emotional, and sexual violence. The study specifically pays attention to instances of violence related to marital conflict, including those related to premarital motherhood. Running Variable : Age of Married Women: The age of the surveyed women serves as a running variable, with a particular emphasis on the age at which they entered marriage. This approach is important for understanding the impact of early marriage and the presence of prelegal or premarital pregnancies, which are factors associated with increased risks of domestic violence. Age will be analysed in granular units, such as years and months, to facilitate a more precise analysis using regression discontinuity design (RDD) ( 25 , 26 ). Control variables The selection of control variables is crucial for comprehending the multifaceted nature of domestic violence within households. These variables include the educational attainment of women, as research has shown that education influences awareness of and responses to domestic violence ( 27 ). The number of dependents in a household is also considered, recognizing its potential impact on household dynamics and stress levels ( 10 , 28 ). The occupational status of women is included to examine how financial independence affects domestic violence situations( 28 , 29 ). Access to healthcare facilities is another critical variable, as it can significantly influence the ability to seek help in cases of violence ( 19 , 30 ). Geographical location is considered to account for varying cultural norms and resource availability between urban and rural areas( 16 , 31 ). Women's perceptions of family planning and personal experiences with violence are also crucial, as they can contribute to marital conflicts and shape attitudes toward violence ( 32 ). Collectively, these control variables, grounded in recent literature, provide a comprehensive framework for analysing the complex interplay of factors contributing to domestic violence in the Rwandan context. 2.3 Analytical approach and empirical strategy This study used a quantitative methodology to examine the association between household conflict and domestic violence among married women in Rwanda. This study utilized regression discontinuity design coupled with logistic regression to investigate the potential impact of demographic factors and early childbirth on the likelihood of experiencing domestic violence. Moreover, RDD was used to identify distinct discontinuity criteria, such as age at first childbirth or duration of marriage before giving birth, in Rwanda to specifically assess the causal effects of intrahousehold conflict on domestic violence incidents. Hence, the RDD estimating equation is expressed as follows: $${Y}_{i}=\alpha +\beta {D}_{i}+{\gamma }_{1}{X}_{i}+{{\gamma }_{2}\left({X}_{i}-c\right)}^{+}+\delta {Z}_{i}+{ϵ}_{i}$$ where \({Y}_{i}\) is the outcome variable representing the incidence of domestic violence for woman i . Di ​ is a binary treatment indicator equal to 1 if individual i 's characteristic (e.g., duration of given birth between 15 and 49 years) falls under the cut-off c and 0 otherwise. $${D}_{i}=\left\{\begin{array}{c}1, If a woman given birth before 21 years\\ 0, Otherwise\end{array}\right.$$ Xi ​ represents the running variable (e.g., age at marriage or duration of marriage). ( Xi ​− c ) + is the distance from the cut-off, applied only when Xi ​ is above the cut-off c , allowing for a differential impact on either side of the cut-off. Zi ​ includes other covariates or control variables relevant to the Rwandan context, such as educational level, employment status, or demographic factors. α , β , γ 1​, γ 2​, and δ are the parameters to be estimated. ϵi ​ is the error term. Logistic regression, a widely used statistical method for categorical outcomes, was used to determine the associations between domestic violence and a set of explanatory variables. These variables were selected based on a comprehensive review of the relevant literature and theoretical foundations. The logistic regression equation can be described as follows: $$\text{l}\text{o}\text{g}\left(\frac{\text{P}({\text{Y}}_{\text{i}}=1)}{1-P({Y}_{i}=1)}\right)={\beta }_{0}+{\beta }_{1}{X}_{1i}+{\beta }_{2}{X}_{2i}+\dots +{\beta }_{k}{X}_{ki}$$ Here: \({Y}_{i}\) is the outcome variable representing the occurrence of domestic violence for woman i . \({X}_{1i}, {X}_{2i},\dots , {X}_{ki}\) are the independent variables and could include factors such as the individual's age, education level, employment status, exposure to intrahousehold conflict, and other relevant socioeconomic or demographic factors. \({\beta }_{0}\) is the intercept, and \({\beta }_{1}, {\beta }_{2},\dots ,{\beta }_{k}\) are the coefficients to be estimated for each independent variable. \(\text{P}\left({\text{Y}}_{\text{i}}=1\right)\) is the likelihood of domestic violence occurring for woman i . The integration of RDD in this framework enabled a deeper comprehension of the causal dynamics involved in the correlation between intrahousehold conflict and the incidence of domestic violence. This analytical method sought to offer detailed perspectives on the complex interactions of different factors contributing to the incidence of domestic violence, thus laying solid groundwork for comprehending and tackling this critical matter within the Rwandan context. 3 Results 3.1 Descriptive evidence The present study showed that as married women approach the legal marriage age of 21, the risk of sexual and physical violence significantly increases. This age appears to be a turning point, with sexual violence risks rising before the age of 21 and then declining, while physical violence risks sharply rise at this age ( 33 ). These findings indicate that the transition to legal marriage eligibility has a significant impact on women's vulnerability to domestic violence ( 34 ). Therefore, the following tables aim to provide a clearer description of the factors contributing to intrahousehold conflicts and the determinants or risk factors associated with domestic violence. 3.2 The association between sociodemographic status and domestic violence During the analysis, we examined the factors that influence domestic violence, specifically physical violence (Table 1 ) and sexual violence (Table 2 ). We compared married women who had children before and after the age of 21. The results showed significant correlations with domestic violence; however, the influencing factors varied. These factors included early sexual experiences, dynamics within marriages, and economic conditions. The findings indicate that early sexual encounters, polygamous marriages, decision-making dynamics within households, past traumatic experiences, and economic factors all play important roles in predisposing women to sexual violence. Both Table 1 and Table 2 present clear patterns that provide insights into the prevailing circumstances. Notably, the age at first sexual encounter stands out. Women who initiated sexual activity before the age of 15 years consistently reported higher rates of both physical violence (41.8% for those who had children younger than 21 years and 35.7% for those older than 21 years) and sexual violence (22% and 14.3%, respectively). This suggests that a vulnerability trajectory persists from early sexual initiation into later stages of life. Polygamous marriages also emerge as a concern. Among these women, 26.5% reported a significantly higher rate of sexual violence, regardless of when they had children. This disparity is evident when comparing the figures to those from non-polygamous unions, highlighting the unique risks faced by women in polygamous settings. Furthermore, household wealth, as an indicator of economic status, offers specific insights. Women from the poorest households consistently reported higher instances of both physical and sexual violence. For sexual violence, the rates were 18.5% for those who had children younger than 21 years and 15.3% for those older than 21 years, with a clear decline as household wealth increased. Table 1 demonstrates the statistical significance (P = 0.000) of age at first sexual experience for physical violence. Higher percentages of physical violence were reported among those who had their first sexual encounter before the age of 15 (41.8% for those who had children younger than 21 and 35.7% for those older than 21), between the ages of 16–18 (42.7% and 40.7%), and between 18–20 (35.9% and 35%) than among those who began sexual activity at the age of 21 or older (27.2% and 26.4%). The difference in the likelihood of getting married and having a first child was also statistically significant. For those who give birth before the age of 21, the p value is 0.043; for those who give birth after the age of 21, the p value is 0.051. A higher percentage of physical violence was reported among couples who had been married for 2 or more years. This was followed by couples who had been married for 1–2 years and then those who had been married for less than 6 months or 7–11 months. The rates of physical violence were significantly different for individuals with polygamous marriages, with a higher rate of violence among women with polygamous marriages than among those with monogamous marriages. The person who makes large household purchases is also a significant factor. When the husband makes decisions alone or with others, women face the highest rate of violence. This is followed by women who make decisions alone, and the lowest rate of violence is found in couples who make joint decisions. The factor of experiencing forced sex as a child (before the age of 15) was also significant. Those who have experienced such trauma report higher rates of violence than those who have not. Table 1 Bivariate analysis of marital status in women who experienced physical violence Given Birth < 21 (N = 2193) Given Birth After 21 (N = 1795) Factors % CI P Value % CI P Value Age at first sex 34.6 [32.4,36.8] 0.000 33.1 [30.6,35.6] 0.000 < 15 years 41.8 [32.8,51.4] 35.7 [26.9,45.6] 16–18 years 42.7 [38.5,47.0] 40.7 [35.9,45.7] 18–20 Years 35.9 [31.5,40.6] 35 [30.0,40.4] 21 + Years 27.2 [24.4,30.3] 26.4 [23.3,29.8] Marriage at first birth interval 38.9 [36.5,41.4] 0.043 35.2 [32.6,37.8] 0.051 < 6 months 34.9 [27.8,42.8] 28.3 [21.6,36.2] 7–11 Months 35.9 [32.1,39.9] 33.2 [29.3,37.4] 1–2 Years 38.8 [34.3,43.5] 35.2 [30.4,40.3] 2 + years 44.7 [39.5,50.0] 40.8 [35.4,46.4] Polygamous marriage 34 [31.5,36.6] 0.001 34 [31.5,36.6] 0.001 No 32.6 [30.0,35.3] 32.6 [30.0,35.3] Yes 49.7 [39.7,59.7] 49.7 [39.7,59.7] Person who decides large household purchases 34 [31.5,36.7] 0.000 34 [31.5,36.7] 0.000 Wife Only 51.9 [42.7,60.9] 51.9 [42.7,60.9] Couple 28.8 [26.0,31.9] 28.8 [26.0,31.9] Husband alone, other 43.5 [38.2,48.9] 43.5 [38.2,48.9] Forced to have sex as child (before age 15) 34.5 [32.3,36.7] 0.044 33 [30.5,35.6] 0.065 No 34.1 [31.9,36.4] 32.7 [30.2,35.3] Yes 46.6 [34.6,59.1] 46.7 [32.0,62.1] Wife listens to radio at least once a week 34.6 [32.4,36.8] 0.000 33.1 [30.6,35.6] 0.000 No 40.2 [36.6,43.9] 38.6 [34.8,42.5] Yes 30.4 [27.7,33.2] 29.3 [26.3,32.5] Wife believes IPV is justified 34.6 [32.4,36.8] 0.000 33.1 [30.6,35.6] 0.000 No 27.8 [24.8,30.9] 25.9 [22.5,29.5] Yes 41.1 [38.1,44.1] 40 [36.8,43.3] Wife's employment 34.6 [32.4,36.8] 0.043 33.1 [30.6,35.6] 0.318 Unemployed 29 [24.3,34.2] 29.9 [24.8,35.6] self-employed agriculture 34.3 [31.1,37.7] 32.5 [29.0,36.1] Employed 37.1 [33.5,40.8] 35.1 [30.9,39.6] Number of children under 5 34.6 [32.4,36.8] 0.883 33.1 [30.6,35.6] 0.201 No child 33.8 [30.0,37.9] 29.6 [25.0,34.7] 1–2 child 34.9 [32.3,37.7] 34.4 [31.5,37.3] 3–7 child 33.6 [23.9,45.0] 30 [20.6,41.6] Female Headed 34.6 [32.4,36.8] 0.105 33.1 [30.6,35.6] 0.275 Male 33.5 [30.8,36.2] 33.6 [30.9,36.4] Female 37.2 [33.6,41.0] 30 [24.5,36.1] Age of husband/partner 34 [31.5,36.6] 0.130 34 [31.5,36.6] 0.13 < 21 Years 19 [4.7,52.9] 19 [4.7,52.9] 21–29 yrs 27.6 [22.0,34.0] 27.6 [22.0,34.0] 30–39 yrs 34.6 [30.9,38.6] 34.6 [30.9,38.6] 40–49 yrs 35.9 [30.8,41.4] 35.9 [30.8,41.4] 50 + Year 37.8 [31.0,45.2] 37.8 [31.0,45.2] Residence 34.6 [32.4,36.8] 0.004 33.1 [30.6,35.6] 0.082 Urban 27.7 [23.2,32.7] 27.6 [21.6,34.5] Rural 35.9 [33.5,38.4] 34.1 [31.4,36.9] Age at first birth 36.4 [34.1,38.7] 0.000 34.8 [32.3,37.5] 0.000 Aged 21 and above 32.3 [29.8,35.0] 31.5 [28.6,34.5] Less than 21 years 44.4 [40.3,48.6] 41.8 [37.0,46.7] Household wealth 34.6 [32.4,36.8] 0.000 33.1 [30.6,35.6] 0.000 Poorest 43 [38.4,47.6] 41.2 [36.0,46.7] Poorer 41.1 [36.3,46.1] 38.9 [33.8,44.3] Middle 34.5 [29.5,39.8] 34.5 [29.2,40.3] Richer 29.7 [25.2,34.6] 28.7 [24.2,33.7] Richest 22.6 [18.5,27.2] 21.4 [16.6,27.1] The wife's beliefs about intimate partner violence (IPV) being justified also play a significant role. Those who believe it is justified face higher rates of violence than those who do not. In terms of wife's employment, the significance of the difference in employment status varies depending on whether the birth occurred before or after the age of 21. Compared with unemployed women, employed women face slightly greater rates of violence. The influence of the number of children under 5 years old was not statistically significant (P = 0.883 for before 21 and P = 0.201 for after 21). However, women without children experience slightly less violence than women with children. In terms of residence, urban women faced lower levels of violence (27.7% before 21 and 27.6% after 21) than did rural women (35.9% before 21 and 34.1% after 21), with significance levels of P = 0.004 and P = 0.082, respectively. Age at first birth was a statistically significant factor (P = 0.000), with higher rates of violence observed among those with births occurring before 21 years than among those with births occurring after 21 years. Finally, the poorest households experience the highest levels of violence, with the rate decreasing as household wealth increases. This difference was statistically significant, with a P value of 0.000. Age at first sexual intercourse was a significant factor (P = 0.005) for women who gave birth before 21 years of age. Among women who had their first sexual encounter before the age of 15, 22% reported the highest rate of violence (compared to 14.3% for those who gave birth after 21). The rate of violence decreases as the age at first sex increases, but it is noteworthy that even those who first had sex at 21 years or older still reported substantial rates of violence (13.1% before 21 and 12.3% after 21). The intervals between marriage and first birth, although not significantly different between the two groups (P = 0.571 before 21 and P = 0.827 after 21), were not statistically significant. However, there is a striking difference in the rates of violence for women in polygamous marriages, with a high rate of 26.5% regardless of the age at which they gave birth. Table 2 shows that factors such as the wife's perspective on refusing sex, decision-making power in large household purchases, and experience of forced sex during childhood all have significant P values, indicating their importance in understanding the dynamics of sexual violence. Importantly, women who are solely responsible for making decisions about large household purchases, as well as those in households where decisions are made by the husband alone or by others, report higher rates of violence (29.6% and 21%, respectively). A total of 25.7% of those who experienced forced sex during childhood reported having committed violence before 21, and 23.3% of those who had given birth after 21. The data presented further reveal disparities related to household wealth. Women from economically disadvantaged households reported higher prevalence rates of violence (18.5% before the age of 21 and 15.3% after the age of 21). On the other hand, these rates decrease as household wealth increases. Table 2 Bivariate analysis of marital status in women who experienced sexual violence Given Birth < 21 (N = 2193) Given Birth After 21 (N = 1795) Factors % CI P Value % CI P Value Age at first sex 14.7 [13.1,16.5] 0.005 13 [11.3,14.9] 0.567 < 15 years 22 [15.4,30.4] 14.3 [9.0,21.8] 16–18 years 17.6 [14.6,21.1] 14.6 [11.5,18.3] 18–20 Years 12.1 [9.5,15.4] 11.8 [8.8,15.5] 21 + Years 13.1 [10.9,15.8] 12.3 [10.0,15.1] Marriage at first birth interval 16.7 [14.9,18.8] 0.571 14 [12.1,16.0] 0.827 < 6 months 18 [12.5,25.2] 13.9 [9.0,20.8] 7–11 Months 15.2 [12.4,18.5] 13.2 [10.5,16.3] 1–2 Years 16.7 [13.7,20.3] 13.8 [10.7,17.6] 2 + years 18.4 [14.9,22.6] 15.4 [11.8,19.9] Polygamous marriage 13.5 [11.8,15.5] 0.000 13.5 [11.8,15.5] 0.000 No 12.4 [10.7,14.3] 12.4 [10.7,14.3] Yes 26.5 [19.1,35.4] 26.5 [19.1,35.4] Person who decides large household purchases 13.5 [11.8,15.5] 0.000 13.5 [11.8,15.5] 0.000 Wife Only 29.6 [21.7,38.9] 29.6 [21.7,38.9] Couple 9.2 [7.6,11.2] 9.2 [7.6,11.2] Husband alone, other 21 [16.9,25.8] 21 [16.9,25.8] Forced to have sex as child (before age 15) 14.7 [13.1,16.5] 0.021 12.7 [11.1,14.5] 0.038 No 14.1 [12.5,15.9] 12.4 [10.8,14.3] Yes 25.7 [15.5,39.6] 23.3 [13.0,38.2] Wife listens to radio at least once a week 14.7 [13.1,16.5] 0.000 13 [11.3,14.9] 0.001 No 18.6 [15.9,21.5] 16.3 [13.6,19.3] Yes 11.9 [10.0,14.0] 10.8 [8.9,13.0] Wife believes IPV is justified 14.7 [13.1,16.5] 0.074 13 [11.3,14.9] 0.060 No 13.1 [10.9,15.6] 11.3 [9.1,14.0] Yes 16.3 [13.9,19.0] 14.6 [12.4,17.2] Wife's employment 14.7 [13.1,16.5] 0.015 13 [11.3,14.9] 0.268 Unemployed 12 [8.9,16.0] 10.7 [7.5,15.0] self-employed agriculture 13 [10.8,15.5] 12.4 [10.2,15.1] Employed 17.5 [14.7,20.8] 14.6 [11.7,18.2] Number of children under 5 14.7 [13.1,16.5] 0.651 13 [11.3,14.9] 0.87 No child 15.7 [12.7,19.3] 13.8 [10.6,17.7] 1–2 child 14.2 [12.4,16.3] 12.7 [10.8,14.9] 3–7 child 16.6 [9.6,27.3] 13.4 [7.1,23.9] Female Headed 14.7 [13.1,16.5] 0.002 13 [11.3,14.9] 0.664 Male 13 [11.2,15.0] 12.8 [11.0,14.8] Female 18.9 [15.6,22.7] 13.9 [9.8,19.4] Age of husband/partner 13.5 [11.8,15.5] 0.114 13.5 [11.8,15.5] 0.114 < 21 Years 0 0 21–29 yrs 9.3 [6.2,13.6] 9.3 [6.2,13.6] 30–39 yrs 14.4 [11.9,17.3] 14.4 [11.9,17.3] 40–49 yrs 13.8 [10.5,18.0] 13.8 [10.5,18.0] 50 + Year 16.7 [11.9,23.0] 16.7 [11.9,23.0] Residence 14.7 [13.1,16.5] 0.488 13 [11.3,14.9] 0.302 Urban 13.4 [9.9,17.9] 10.5 [6.7,16.3] Rural 15 [13.2,17.0] 13.4 [11.7,15.4] Age at first birth 15.5 [13.8,17.4] 0.033 13.8 [12.0,15.9] 0.899 Aged 21 and above 14.1 [12.2,16.3] 13.7 [11.6,16.2] Less than 21 years 18.3 [15.1,22.0] 14 [10.9,17.8] Household wealth 14.7 [13.1,16.5] 0.000 13 [11.3,14.9] 0.000 Poorest 18.5 [15.1,22.5] 15.3 [11.8,19.6] Poorer 18.6 [15.0,22.9] 17.2 [13.4,21.9] Middle 15.9 [12.2,20.6] 15.6 [11.8,20.3] Richer 10.5 [7.8,14.0] 9.9 [7.1,13.8] Richest 9.3 [6.7,12.8] 6.8 [4.3,10.4] 3.3 Risk factors for physical and sexual violence among married women The analysis presented in Table 3 reveals the key factors contributing to physical and sexual violence among married women after giving birth. The findings indicate that women in polygamous marriages are 51.6% more likely to experience physical violence and 82.6% more likely to experience sexual violence. Conversely, when women are involved in making important financial decisions for the household, the risk of physical violence decreases by 79.4%, and the risk of sexual violence decreases even more significantly by 124%. Furthermore, the evidence suggests that experiencing forced sexual encounters before the age of 15 increases the likelihood of experiencing physical violence in marriage by 67.5% and sexual violence by 129.7%. This highlights the long-lasting effects of early traumatic experiences on intimate relationships. Additionally, economic stability, as indicated by household wealth, is inversely related to the risk of violence. As household wealth increases, the risk of physical violence decreases by 66.9%, and the risk of sexual violence decreases by 66.1%. However, believing in intimate partner violence among married women leads to a 53.1% increase in the risk of enduring physical violence. Table 3 Risk factors for physical and sexual violence among married women Physical Violence Sexual violence Factors Coef Std. Err Coef Std. Err Polygamous marriage No Yes 0.516** 2.69 0.826*** 0.223 Person who decides large household purchases Wife Only Couple -0.794** 0.199 -1.24*** 0.233 Husband alone, other -0.22 0.216 -0.382 0.246 Forced to have sex as child (before age 15) No Yes 0.675* 0.352 1.297*** 0.379 Wife believes IPV is justified No Yes 0.531*** 0.112 Household wealth Poorest Poorer -0.012 0.159 0.293 0.224 Middle -0.113 0.163 0.193 0.234 Richer -0.268 0.164 -0.198 0.278 Richest -0.669*** 0.186 -0.661** 0.613 Constant -0.193 0.218 -1.09*** 0.239 P value: *10% **5% ***1%. Full logistic regression model 3.4 Effects of intrahousehold conflicts on domestic violence Based on previous studies, we examined various factors, including the household's wealth quintile, home size, partner's age and education level, and polygamy status, to determine their potential impact on domestic violence ( 35 ). The results of the study showed a significant association between intrahousehold conflicts and domestic violence. Table 4 clearly illustrates that women who experienced intrahousehold conflicts were at a greater risk of facing domestic violence than women who did not. Regarding age at motherhood, the study showed that married women who gave birth before the age of 21 had a lower risk of physical violence (11.9%) and sexual violence (8.4%) than did those who gave birth later. On the other hand, women who started motherhood after the age of 21 faced an increased risk. Specifically, they had a 35.1% greater chance of experiencing physical violence and a 14.8% greater risk of experiencing sexual violence than women who had children at a younger age. It is important to note that these findings considered all the factors that contribute to domestic violence among married women. Table 4 Effects of intrahousehold conflicts on domestic violence Physical violence Sexual violence Coef. Std. Err Coef. Std. Err Married women with birth Given birth before 21 -0.119** 0.053 -0.084*** 0.0321 Given birth after 21 0.351*** 0.0103 0.148*** 0.0078 Ratio of the average -0.339** 0.148 -0.571*** 0.211 4 Discussion This study provides a comprehensive analysis of the factors contributing to domestic violence, with a particular focus on the Rwandan context. One important finding is that the risk of domestic violence significantly increases as women approach the legal marriage age of 21. Interestingly, the study reveals a unique trend in which the risk of sexual violence increases before turning 21 and then decreases, while the risk of physical violence sharply increases at this age. However, these findings differ from previous research by Dhamija and Roychowdhury, which suggested that getting married at an older age serves as a protective factor against physical violence ( 34 ). The impact on sexual violence is also unclear, with some studies indicating a decline and others finding no significant effect. The study also explored the relationship between early sexual experiences and domestic violence. Notably, women who had sexual encounters before the age of 15 years reported higher rates of both physical (41.8% and 35.7%) and sexual violence (22% and 14.3%). This finding suggested that early sexual activity is a risk factor for later violence. These findings align with research conducted in India, Peru, Ghana, Nigeria, and Punjab, which also demonstrated a strong association between young age at marriage or early sexual experiences and an increased risk of domestic violence against women worldwide( 36 ). Another significant finding is that women in polygamous marriages experience a greater rate of sexual violence (26.5%) than women in nonpolygamous unions. This indicates the unique risks associated with polygamy and polyandry. Supporting this observation, research has consistently shown that women in polygamous marriages experience a range of negative psychological and social outcomes, such as depression( 37 ), lower education and economic status, and engagement in risky sexual behavior( 38 ). Furthermore, the study examines the relationship between economic status and domestic violence. This study establishes a clear correlation between economic disadvantage, as indicated by household wealth, and higher instances of domestic violence. Women from the poorest households reported higher rates of both physical and sexual violence. This relationship is influenced by various factors, including neighborhood economic disadvantage, individual economic distress, education, attitudes toward wife-beating, and control by husbands ( 39 , 40 ). 5 Conclusion The study's findings present a complex picture that differs from global trends in several ways. Factors such as the age of marriage, early sexual experiences, economic status, and societal attitudes toward polygamy and intimate partner violence (IPV) all contribute to domestic violence patterns. However, these factors manifest differently in Rwanda, likely due to unique cultural, socioeconomic, and legal contexts. For instance, the impact of motherhood before the age of 21 or women's employment on domestic violence may not align with global patterns, possibly due to differing societal norms or economic dynamics. Additionally, the urban‒rural divide in experiences of violence and the influence of early forced sex on later violence might be perceived or reported differently in Rwanda. These discrepancies highlight the need for context-specific approaches to understanding and addressing domestic violence. It is crucial to consider local cultural, economic, and social factors when developing public health strategies and interventions. To address domestic violence in Rwanda, a multifaceted approach is necessary. This includes integrating mental health services that focus on trauma-informed care, providing training for healthcare providers regarding mental health issues related to domestic violence, and implementing comprehensive sex education in schools and community workshops. Additionally, it is important to develop educational programs that promote healthy marital and conjugal relationships, empower women to enhance their bargaining power in marriages, and address the unique challenges of polygamous relationships. These efforts should be supported by collaboration across sectors and continuous monitoring and evaluation to adapt to evolving needs and feedback. Abbreviations CI Confidence Interval DHS Demographic and Health Survey RDD Regression Discontinuity Design Declarations Ethics approval The procedures were performed in accordance with the appropriate guidelines of the Demographic and Health Surveys (DHS) program. The International Review Board of DHS program data archivists waived the requirement for informed consent. Upon submitting the consent form to the DHS Program, permission to download the dataset was granted for this study. The dataset was kept confidential and anonymized to ensure its privacy and was not shared or transferred to any other entities. Consent for publication Not Applicable Competing interests I declare no conflict of interest. Funding No funding was obtained from public, commercial, or not-for-profit sectors for this study. Availability of data and materials The manuscript has fully used DHS Datasets and free available upon request from their website https://dhsprogram.com/ Author and Affiliation D-Hause Co., Rwanda Harerimana Jean de Dieu (HJD) Author' contributions HJD conceptualized the study, drafted the background and literature review. conducted the analysis, drafted the results and the discussion. HJD reviewed several drafts and suggested additional revisions. HJD is responsible for submitting the manuscript. HJD reviewed and approved the final version of the manuscript. 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Jewkes RK, Levin JB, Penn-Kekana LA. Gender inequalities, intimate partner violence and HIV preventive practices: Findings of a South African cross-sectional study. Soc Sci Med. 2003;56(1):125–34. Fernández M. Cultural Beliefs and Domestic Violence. Ann N Y Acad Sci. 2006 Nov 5;1087(1):250–60. Mukashema I, Sapsford R. Marital Conflicts in Rwanda: Points of View of Rwandan Psycho-sociomedical Professionals. Procedia Soc Behav Sci. 2013;82. Liu G. How premarital children and childbearing in current marriage influence divorce of Swedish women in their first marriages. Demogr Res. 2002 Aug 27;7:389–406. Maguele MS, Taylor M, Khuzwayo N. Evidence of sociocultural factors influencing intimate partner violence among young women in sub-Saharan Africa: a scoping review. BMJ Open. 2020 Dec 7;10(12):e040641. Finnoff K. Intimate partner violence, female employment, and male backlash in Rwanda. The Economics of Peace and Security Journal. 2012 Jul 1;7(2). La Mattina G. Civil conflict, domestic violence and intrahousehold bargaining in postgenocide Rwanda. J Dev Econ. 2017;124. Kattari SK, Atteberry-Ash B, Collins C, Kattari L, Harner V. Increased Prevalence, Predictors, and In-Group Differences of Forced Sex and Physical Dating Violence among Trans/Gender Diverse Youth. In 2021. Available from: https://api.semanticscholar.org/CorpusID:235681292 Howard AL, Pals S, Walker B, Benevides R, Massetti GM, Oluoch RP, et al. Forced Sexual Initiation and Early Sexual Debut and Associated Risk Factors and Health Problems Among Adolescent Girls and Young Women — Violence Against Children and Youth Surveys, Nine PEPFAR Countries, 2007–2018. MMWR Morb Mortal Wkly Rep. 2021 Nov 26;70(47):1629–34. NISR. Rwanda Demographic and Health Survey 2019-2020: key indicators report. Vol. 53, Demographic and Health Surveys. 2020. Angrist JD, Pischke JS. Mostly harmless econometrics: An empiricist’s companion. Mostly Harmless Econometrics: An Empiricist’s Companion. 2008. Frölich M, Sperlich S. Impact Evaluation: Treatment Effects and Causal_Analysis. Cambridge University Press. 2019. Ghimire DJ, Axinn WG, Smith-Greenaway E. Impact of the spread of mass education on married women’s experience with domestic violence. Soc Sci Res. 2015 Nov;54:319–31. Stack RJ, Meredith A. The Impact of Financial Hardship on Single Parents: An Exploration of the Journey From Social Distress to Seeking Help. J Fam Econ Issues. 2018;39(2). Haobijam S, Singh KA. Socioeconomic Determinants of Domestic Violence in Northeast India: Evidence From the National Family Health Survey (NFHS-4). J Interpers Violence [Internet]. 2021;37:NP13162–81. Available from: https://api.semanticscholar.org/CorpusID:232407794 Naidoo S, Sartorius B, de Vries H, Taylor M. Prevalence and Risk Factors Associated with Forced-Sex Among South African High School Students. J Community Health. 2017 Oct 15;42(5):1035–43. Machariah LW, Iteyo C. Socio-Cultural Dynamics Influencing Domestic Violence in Nyeri County, Kenya. African Journal of Empirical Research. 2023;4(2). Anttila-hughes J, Stopnitzky Y, Tolonen A. Seasonality of Attitudes on Violence. 2017;2017. Kargar Jahromi M, Jamali S, Koshkaki AR, Javadpour S. Prevalence and Risk Factors of Domestic Violence Against Women by Their Husbands in Iran. Glob J Health Sci. 2015 Sep 28;8(5):175. Roychowdhury P, Dhamija G. The Causal Impact of Women’s Age at Marriage on Domestic Violence in India. Fem Econ. 2021 Jul 3;27(3):188–220. Camargo Freile IE, Flórez Lozano KC, Sarmiento Crespo CA, Vecchio Camargo CM, Rodríguez Acosta SM, Florez-Garcia V, et al. Risk of violence from a current or former partner: Associated factors and classification in a nationwide study in Colombia. PLoS One. 2022 Dec 22;17(12):e0279444. Fan S, Koski A. The health consequences of child marriage: a systematic review of the evidence. BMC Public Health. 2022 Feb 14;22(1):309. Shaiful Bahari I, Norhayati MN, Nik Hazlina NH, Mohamad Shahirul Aiman CAA, Nik Muhammad Arif NA. Psychological impact of polygamous marriage on women and children: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2021 Dec 13;21(1):823. Mabaso MLH, Malope NF, Simbayi LC. Sociodemographic and behavioural profile of women in polygamous relationships in South Africa: a retrospective analysis of the 2002 population-based household survey data. BMC Womens Health. 2018 Dec 2;18(1):133. Benson ML, Fox GL, DeMaris A, Wyk JA Van. Neighborhood Disadvantage, Individual Economic Distress and Violence Against Women in Intimate Relationships. J Quant Criminol [Internet]. 2003;19:207–35. Available from: https://api.semanticscholar.org/CorpusID:141834737 Tenkorang EY, Owusu AY, Yeboah EH, Bannerman R. Factors Influencing Domestic and Marital Violence against Women in Ghana. J Fam Violence. 2013 Nov 22;28(8):771–81. Additional Declarations No competing interests reported. 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Its effects are especially significant in developing nations, where unique challenges are present in Sub-Saharan Africa and, specifically, in Rwanda (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This study aimed to explore the connections between domestic violence and intrahousehold conflict-related issues such as infertility or unintended pregnancies in Rwanda, as well as their implications for public health (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These intersections contribute to heightened susceptibility to abuse, leading to increased maternal mortality rates, disability, and infectious illnesses (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRwanda has experienced fluctuations in domestic violence cases over time. While there was a decline in the early 2000s, there was an alarming increase in societal acceptance of such violence (\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This study seeks to examine the complex factors contributing to this trend, with a specific focus on conflicts within households as a significant contributor to domestic violence in Rwanda. This risk is further worsened by issues such as premarital pregnancies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Despite well-known protective factors such as education and financial independence, the connection between intrahousehold conflict and child maltreatment remains a critical issue that requires comprehensive intervention.\u003c/p\u003e \u003cp\u003eThe cultural and societal norms in Rwanda significantly influence attitudes toward domestic violence. Traditional beliefs about male power and the aftermath of war and genocide play key roles in the acceptability and occurrence of family violence(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This study explored these aspects by examining how cultural, psychosocial, and economic factors impact intimate partner violence among ever-married women(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Legal policies, which include setting the legal age for marriage at 21 years, are crucial for addressing domestic violence issues. Nonetheless, it is essential to further investigate the implementation and effectiveness of these policies, especially concerning marital conflicts related to premarital motherhood (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This study aimed to investigate the relationship between intrahousehold conflict, which is specifically related to premarital pregnancies, and domestic violence in Rwanda.\u003c/p\u003e \u003cp\u003eThis study drew on data from the 2019/20 Demographic and Health Survey to perform a microeconometric examination of domestic violence in Rwanda. The survey revealed that approximately 22% of married women in Rwanda have encountered domestic violence, with 14% reporting instances of sexual violence by their partners. Compared to regional statistics, these numbers underscore the impact of cultural, historical, and socioeconomic factors in sub-Saharan Africa(\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of child marriage is another significant aspect to consider according to UNICEF data, which indicate that 7% of girls in Rwanda are wed before they reach the age of 18. It is imperative to comprehend the complexities associated with early marriages and their correlation with domestic violence. The Rwandan scenario following the Genocide in 1994 provides distinctive insights into how conflict relates to long-term societal violence\u0026mdash;akin to post-conflict societies such as Bosnia or Cambodia.\u003c/p\u003e \u003cp\u003eThis study aims to offer thorough insights into the factors contributing to domestic violence in Rwanda and to provide a basis for policy and intervention strategies. This study seeks to address gaps in the current body of research by examining understudied aspects, such as the impact of premarital pregnancies and perceptions about marital status, on experiences of violence. Furthermore, this study provides insights into how existing legal frameworks influence efforts to combat domestic violence.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eThis study utilizes the Rwanda Demographic and Health Survey (DHS) 2019-20 for its empirical analysis(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The dataset is known for its methodological rigor and comprehensive coverage, making it an ideal choice. A representative sample of married women aged 15\u0026ndash;49 in Rwanda was obtained through a meticulous two-stage sampling process. The first stage involved identifying clusters within enumeration areas across Rwanda to ensure geographic and socioeconomic representation. In the second stage, households within these clusters were systematically selected, including long-term residents and overnight visitors, to create a demographic profile that was inclusive.\u003c/p\u003e \u003cp\u003eThe DHS's robust sampling methodology and participant selection ensure data reliability, which is crucial for evaluating the extent and determinants of domestic violence. However, it is important to note that the cross-sectional nature of the survey and the reliance on self-reported data, especially on sensitive issues such as domestic violence, can introduce certain biases. These biases can impact longitudinal inferences and response accuracy. Nonetheless, the thoroughness of the DHS dataset aligns closely with the study's objectives to explore intrahousehold conflict-related factors associated with domestic violence in Rwanda.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection of the variables\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eOutcome Variable\u003c/strong\u003e \u003cp\u003eThe primary focus of this study was to measure the incidence of domestic violence within households. This measurement was obtained by using responses from the domestic violence module of the DHS, which includes questions about physical, emotional, and sexual violence. The study specifically pays attention to instances of violence related to marital conflict, including those related to premarital motherhood.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRunning Variable\u003c/b\u003e: Age of Married Women: The age of the surveyed women serves as a running variable, with a particular emphasis on the age at which they entered marriage. This approach is important for understanding the impact of early marriage and the presence of prelegal or premarital pregnancies, which are factors associated with increased risks of domestic violence. Age will be analysed in granular units, such as years and months, to facilitate a more precise analysis using regression discontinuity design (RDD) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eControl variables\u003c/strong\u003e \u003cp\u003eThe selection of control variables is crucial for comprehending the multifaceted nature of domestic violence within households. These variables include the educational attainment of women, as research has shown that education influences awareness of and responses to domestic violence (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The number of dependents in a household is also considered, recognizing its potential impact on household dynamics and stress levels (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The occupational status of women is included to examine how financial independence affects domestic violence situations(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAccess to healthcare facilities is another critical variable, as it can significantly influence the ability to seek help in cases of violence (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Geographical location is considered to account for varying cultural norms and resource availability between urban and rural areas(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Women's perceptions of family planning and personal experiences with violence are also crucial, as they can contribute to marital conflicts and shape attitudes toward violence (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Collectively, these control variables, grounded in recent literature, provide a comprehensive framework for analysing the complex interplay of factors contributing to domestic violence in the Rwandan context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analytical approach and empirical strategy\u003c/h2\u003e \u003cp\u003eThis study used a quantitative methodology to examine the association between household conflict and domestic violence among married women in Rwanda. This study utilized regression discontinuity design coupled with logistic regression to investigate the potential impact of demographic factors and early childbirth on the likelihood of experiencing domestic violence. Moreover, RDD was used to identify distinct discontinuity criteria, such as age at first childbirth or duration of marriage before giving birth, in Rwanda to specifically assess the causal effects of intrahousehold conflict on domestic violence incidents. Hence, the RDD estimating equation is expressed as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${Y}_{i}=\\alpha +\\beta {D}_{i}+{\\gamma }_{1}{X}_{i}+{{\\gamma }_{2}\\left({X}_{i}-c\\right)}^{+}+\\delta {Z}_{i}+{ϵ}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{i}\\)\u003c/span\u003e \u003c/span\u003e is the outcome variable representing the incidence of domestic violence for woman \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eDi\u003c/em\u003e​ is a binary treatment indicator equal to 1 if individual \u003cem\u003ei\u003c/em\u003e's characteristic (e.g., duration of given birth between 15 and 49 years) falls under the cut-off \u003cem\u003ec\u003c/em\u003e and 0 otherwise.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${D}_{i}=\\left\\{\\begin{array}{c}1, If a woman given birth before 21 years\\\\ 0, Otherwise\\end{array}\\right.$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eXi\u003c/em\u003e​ represents the running variable (e.g., age at marriage or duration of marriage).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(\u003cem\u003eXi\u003c/em\u003e​\u0026minus;\u003cem\u003ec\u003c/em\u003e)\u003csup\u003e+\u003c/sup\u003e is the distance from the cut-off, applied only when \u003cem\u003eXi\u003c/em\u003e​ is above the cut-off \u003cem\u003ec\u003c/em\u003e, allowing for a differential impact on either side of the cut-off.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eZi\u003c/em\u003e​ includes other covariates or control variables relevant to the Rwandan context, such as educational level, employment status, or demographic factors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eα\u003c/em\u003e, \u003cem\u003eβ\u003c/em\u003e, \u003cem\u003eγ\u003c/em\u003e1​, \u003cem\u003eγ\u003c/em\u003e2​, and \u003cem\u003eδ\u003c/em\u003e are the parameters to be estimated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eϵi\u003c/em\u003e​ is the error term.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eLogistic regression, a widely used statistical method for categorical outcomes, was used to determine the associations between domestic violence and a set of explanatory variables. These variables were selected based on a comprehensive review of the relevant literature and theoretical foundations. The logistic regression equation can be described as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{l}\\text{o}\\text{g}\\left(\\frac{\\text{P}({\\text{Y}}_{\\text{i}}=1)}{1-P({Y}_{i}=1)}\\right)={\\beta }_{0}+{\\beta }_{1}{X}_{1i}+{\\beta }_{2}{X}_{2i}+\\dots +{\\beta }_{k}{X}_{ki}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{i}\\)\u003c/span\u003e \u003c/span\u003e is the outcome variable representing the occurrence of domestic violence for woman \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({X}_{1i}, {X}_{2i},\\dots , {X}_{ki}\\)\u003c/span\u003e \u003c/span\u003e are the independent variables and could include factors such as the individual's age, education level, employment status, exposure to intrahousehold conflict, and other relevant socioeconomic or demographic factors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e \u003c/span\u003eis the intercept, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}, {\\beta }_{2},\\dots ,{\\beta }_{k}\\)\u003c/span\u003e\u003c/span\u003eare the coefficients to be estimated for each independent variable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\left({\\text{Y}}_{\\text{i}}=1\\right)\\)\u003c/span\u003e \u003c/span\u003e is the likelihood of domestic violence occurring for woman \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe integration of RDD in this framework enabled a deeper comprehension of the causal dynamics involved in the correlation between intrahousehold conflict and the incidence of domestic violence. This analytical method sought to offer detailed perspectives on the complex interactions of different factors contributing to the incidence of domestic violence, thus laying solid groundwork for comprehending and tackling this critical matter within the Rwandan context.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive evidence\u003c/h2\u003e \u003cp\u003eThe present study showed that as married women approach the legal marriage age of 21, the risk of sexual and physical violence significantly increases. This age appears to be a turning point, with sexual violence risks rising before the age of 21 and then declining, while physical violence risks sharply rise at this age (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). These findings indicate that the transition to legal marriage eligibility has a significant impact on women's vulnerability to domestic violence (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Therefore, the following tables aim to provide a clearer description of the factors contributing to intrahousehold conflicts and the determinants or risk factors associated with domestic violence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The association between sociodemographic status and domestic violence\u003c/h2\u003e \u003cp\u003eDuring the analysis, we examined the factors that influence domestic violence, specifically physical violence (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and sexual violence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We compared married women who had children before and after the age of 21. The results showed significant correlations with domestic violence; however, the influencing factors varied. These factors included early sexual experiences, dynamics within marriages, and economic conditions. The findings indicate that early sexual encounters, polygamous marriages, decision-making dynamics within households, past traumatic experiences, and economic factors all play important roles in predisposing women to sexual violence.\u003c/p\u003e \u003cp\u003eBoth Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present clear patterns that provide insights into the prevailing circumstances. Notably, the age at first sexual encounter stands out. Women who initiated sexual activity before the age of 15 years consistently reported higher rates of both physical violence (41.8% for those who had children younger than 21 years and 35.7% for those older than 21 years) and sexual violence (22% and 14.3%, respectively). This suggests that a vulnerability trajectory persists from early sexual initiation into later stages of life.\u003c/p\u003e \u003cp\u003ePolygamous marriages also emerge as a concern. Among these women, 26.5% reported a significantly higher rate of sexual violence, regardless of when they had children. This disparity is evident when comparing the figures to those from non-polygamous unions, highlighting the unique risks faced by women in polygamous settings. Furthermore, household wealth, as an indicator of economic status, offers specific insights. Women from the poorest households consistently reported higher instances of both physical and sexual violence. For sexual violence, the rates were 18.5% for those who had children younger than 21 years and 15.3% for those older than 21 years, with a clear decline as household wealth increased.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates the statistical significance (P = 0.000) of age at first sexual experience for physical violence. Higher percentages of physical violence were reported among those who had their first sexual encounter before the age of 15 (41.8% for those who had children younger than 21 and 35.7% for those older than 21), between the ages of 16–18 (42.7% and 40.7%), and between 18–20 (35.9% and 35%) than among those who began sexual activity at the age of 21 or older (27.2% and 26.4%). The difference in the likelihood of getting married and having a first child was also statistically significant.\u003c/p\u003e \u003cp\u003eFor those who give birth before the age of 21, the p value is 0.043; for those who give birth after the age of 21, the p value is 0.051. A higher percentage of physical violence was reported among couples who had been married for 2 or more years. This was followed by couples who had been married for 1–2 years and then those who had been married for less than 6 months or 7–11 months. The rates of physical violence were significantly different for individuals with polygamous marriages, with a higher rate of violence among women with polygamous marriages than among those with monogamous marriages.\u003c/p\u003e \u003cp\u003eThe person who makes large household purchases is also a significant factor. When the husband makes decisions alone or with others, women face the highest rate of violence. This is followed by women who make decisions alone, and the lowest rate of violence is found in couples who make joint decisions. The factor of experiencing forced sex as a child (before the age of 15) was also significant. Those who have experienced such trauma report higher rates of violence than those who have not.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate analysis of marital status in women who experienced physical violence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGiven Birth \u0026lt; 21 (N = 2193)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGiven Birth After 21 (N = 1795)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 15 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.8,51.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.9,45.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16–18 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[38.5,47.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[35.9,45.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18–20 Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.5,40.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.0,40.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21 + Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[24.4,30.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[23.3,29.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage at first birth interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[36.5,41.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[32.6,37.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[27.8,42.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[21.6,36.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7–11 Months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.1,39.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[29.3,37.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–2 Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[34.3,43.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.4,40.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 + years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[39.5,50.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[35.4,46.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolygamous marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.5,36.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[31.5,36.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[30.0,35.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.0,35.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[39.7,59.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[39.7,59.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerson who decides large household purchases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.5,36.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[31.5,36.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife Only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[42.7,60.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[42.7,60.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[26.0,31.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.0,31.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband alone, other\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[38.2,48.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[38.2,48.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForced to have sex as child (before age 15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.3,36.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.5,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.9,36.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.2,35.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[34.6,59.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[32.0,62.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife listens to radio at least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[36.6,43.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[34.8,42.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[27.7,33.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.3,32.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife believes IPV is justified\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[24.8,30.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[22.5,29.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[38.1,44.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[36.8,43.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife's employment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[24.3,34.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[24.8,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eself-employed agriculture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.1,37.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[29.0,36.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[33.5,40.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.9,39.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of children under 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo child\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[30.0,37.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[25.0,34.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–2 child\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.3,37.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[31.5,37.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3–7 child\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[23.9,45.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[20.6,41.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale Headed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[30.8,36.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.9,36.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[33.6,41.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[24.5,36.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of husband/partner\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.5,36.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[31.5,36.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 21 Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[4.7,52.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[4.7,52.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21–29 yrs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[22.0,34.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[22.0,34.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30–39 yrs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[30.9,38.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.9,38.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49 yrs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[30.8,41.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.8,41.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50 + Year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[31.0,45.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[31.0,45.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[23.2,32.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[21.6,34.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[33.5,38.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[31.4,36.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[34.1,38.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[32.3,37.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAged 21 and above\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[29.8,35.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[28.6,34.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 21 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[40.3,48.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[37.0,46.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[32.4,36.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[30.6,35.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[38.4,47.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[36.0,46.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[36.3,46.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[33.8,44.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[29.5,39.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[29.2,40.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[25.2,34.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[24.2,33.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[18.5,27.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[16.6,27.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe wife's beliefs about intimate partner violence (IPV) being justified also play a significant role. Those who believe it is justified face higher rates of violence than those who do not. In terms of wife's employment, the significance of the difference in employment status varies depending on whether the birth occurred before or after the age of 21. Compared with unemployed women, employed women face slightly greater rates of violence.\u003c/p\u003e \u003cp\u003eThe influence of the number of children under 5 years old was not statistically significant (P = 0.883 for before 21 and P = 0.201 for after 21). However, women without children experience slightly less violence than women with children. In terms of residence, urban women faced lower levels of violence (27.7% before 21 and 27.6% after 21) than did rural women (35.9% before 21 and 34.1% after 21), with significance levels of P = 0.004 and P = 0.082, respectively. Age at first birth was a statistically significant factor (P = 0.000), with higher rates of violence observed among those with births occurring before 21 years than among those with births occurring after 21 years. Finally, the poorest households experience the highest levels of violence, with the rate decreasing as household wealth increases. This difference was statistically significant, with a P value of 0.000.\u003c/p\u003e \u003cp\u003eAge at first sexual intercourse was a significant factor (P = 0.005) for women who gave birth before 21 years of age. Among women who had their first sexual encounter before the age of 15, 22% reported the highest rate of violence (compared to 14.3% for those who gave birth after 21). The rate of violence decreases as the age at first sex increases, but it is noteworthy that even those who first had sex at 21 years or older still reported substantial rates of violence (13.1% before 21 and 12.3% after 21). The intervals between marriage and first birth, although not significantly different between the two groups (P = 0.571 before 21 and P = 0.827 after 21), were not statistically significant. However, there is a striking difference in the rates of violence for women in polygamous marriages, with a high rate of 26.5% regardless of the age at which they gave birth.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that factors such as the wife's perspective on refusing sex, decision-making power in large household purchases, and experience of forced sex during childhood all have significant P values, indicating their importance in understanding the dynamics of sexual violence. Importantly, women who are solely responsible for making decisions about large household purchases, as well as those in households where decisions are made by the husband alone or by others, report higher rates of violence (29.6% and 21%, respectively). A total of 25.7% of those who experienced forced sex during childhood reported having committed violence before 21, and 23.3% of those who had given birth after 21. The data presented further reveal disparities related to household wealth. Women from economically disadvantaged households reported higher prevalence rates of violence (18.5% before the age of 21 and 15.3% after the age of 21). On the other hand, these rates decrease as household wealth increases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate analysis of marital status in women who experienced sexual violence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGiven Birth \u0026lt; 21 (N = 2193)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGiven Birth After 21 (N = 1795)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 15 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.4,30.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[9.0,21.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16–18 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[14.6,21.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.5,18.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18–20 Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[9.5,15.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[8.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21 + Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.9,15.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.0,15.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage at first birth interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[14.9,18.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[12.1,16.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.5,25.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[9.0,20.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7–11 Months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.4,18.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.5,16.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–2 Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.7,20.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.7,17.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 + years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[14.9,22.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.8,19.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolygamous marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[11.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.7,14.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.7,14.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[19.1,35.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[19.1,35.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerson who decides large household purchases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[11.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife Only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[21.7,38.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[21.7,38.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[7.6,11.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[7.6,11.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband alone, other\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[16.9,25.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[16.9,25.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForced to have sex as child (before age 15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.1,14.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.5,15.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.8,14.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.5,39.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[13.0,38.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife listens to radio at least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.9,21.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[13.6,19.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.0,14.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[8.9,13.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife believes IPV is justified\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.9,15.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[9.1,14.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.9,19.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[12.4,17.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife's employment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[8.9,16.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[7.5,15.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eself-employed agriculture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.2,15.1]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[14.7,20.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.7,18.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of children under 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo child\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.7,19.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.6,17.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–2 child\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.4,16.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.8,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3–7 child\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[9.6,27.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[7.1,23.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale Headed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[11.2,15.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.0,14.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.6,22.7]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[9.8,19.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of husband/partner\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[11.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.8,15.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 21 Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21–29 yrs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[6.2,13.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[6.2,13.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30–39 yrs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[11.9,17.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.9,17.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49 yrs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.5,18.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.5,18.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50 + Year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[11.9,23.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.9,23.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[9.9,17.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[6.7,16.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.2,17.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.7,15.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.8,17.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[12.0,15.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAged 21 and above\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.2,16.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.6,16.2]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 21 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.1,22.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[10.9,17.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[13.1,16.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.3,14.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.1,22.5]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.8,19.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.0,22.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[13.4,21.9]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[12.2,20.6]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[11.8,20.3]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[7.8,14.0]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[7.1,13.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[6.7,12.8]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[4.3,10.4]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk factors for physical and sexual violence among married women\u003c/h2\u003e \u003cp\u003eThe analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals the key factors contributing to physical and sexual violence among married women after giving birth. The findings indicate that women in polygamous marriages are 51.6% more likely to experience physical violence and 82.6% more likely to experience sexual violence. Conversely, when women are involved in making important financial decisions for the household, the risk of physical violence decreases by 79.4%, and the risk of sexual violence decreases even more significantly by 124%.\u003c/p\u003e \u003cp\u003eFurthermore, the evidence suggests that experiencing forced sexual encounters before the age of 15 increases the likelihood of experiencing physical violence in marriage by 67.5% and sexual violence by 129.7%. This highlights the long-lasting effects of early traumatic experiences on intimate relationships. Additionally, economic stability, as indicated by household wealth, is inversely related to the risk of violence. As household wealth increases, the risk of physical violence decreases by 66.9%, and the risk of sexual violence decreases by 66.1%. However, believing in intimate partner violence among married women leads to a 53.1% increase in the risk of enduring physical violence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors for physical and sexual violence among married women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePhysical Violence\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSexual violence\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Err\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolygamous marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.516**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.826***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerson who decides large household purchases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife Only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.794**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.24***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband alone, other\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.382\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForced to have sex as child (before age 15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.297***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWife believes IPV is justified\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.531***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.268\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.669***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.661**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.193\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.09***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eP value: *10% **5% ***1%. Full logistic regression model\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Effects of intrahousehold conflicts on domestic violence\u003c/h2\u003e \u003cp\u003eBased on previous studies, we examined various factors, including the household's wealth quintile, home size, partner's age and education level, and polygamy status, to determine their potential impact on domestic violence (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The results of the study showed a significant association between intrahousehold conflicts and domestic violence. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e clearly illustrates that women who experienced intrahousehold conflicts were at a greater risk of facing domestic violence than women who did not.\u003c/p\u003e \u003cp\u003eRegarding age at motherhood, the study showed that married women who gave birth before the age of 21 had a lower risk of physical violence (11.9%) and sexual violence (8.4%) than did those who gave birth later. On the other hand, women who started motherhood after the age of 21 faced an increased risk. Specifically, they had a 35.1% greater chance of experiencing physical violence and a 14.8% greater risk of experiencing sexual violence than women who had children at a younger age. It is important to note that these findings considered all the factors that contribute to domestic violence among married women.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of intrahousehold conflicts on domestic violence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePhysical violence\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSexual violence\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Err\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried women with birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiven birth before 21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.119**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.084***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiven birth after 21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.351***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.148***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatio of the average\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.339**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.571***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e "},{"header":"4 Discussion","content":"\u003cp\u003eThis study provides a comprehensive analysis of the factors contributing to domestic violence, with a particular focus on the Rwandan context. One important finding is that the risk of domestic violence significantly increases as women approach the legal marriage age of 21. Interestingly, the study reveals a unique trend in which the risk of sexual violence increases before turning 21 and then decreases, while the risk of physical violence sharply increases at this age. However, these findings differ from previous research by Dhamija and Roychowdhury, which suggested that getting married at an older age serves as a protective factor against physical violence (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The impact on sexual violence is also unclear, with some studies indicating a decline and others finding no significant effect.\u003c/p\u003e\u003cp\u003eThe study also explored the relationship between early sexual experiences and domestic violence. Notably, women who had sexual encounters before the age of 15 years reported higher rates of both physical (41.8% and 35.7%) and sexual violence (22% and 14.3%). This finding suggested that early sexual activity is a risk factor for later violence. These findings align with research conducted in India, Peru, Ghana, Nigeria, and Punjab, which also demonstrated a strong association between young age at marriage or early sexual experiences and an increased risk of domestic violence against women worldwide(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother significant finding is that women in polygamous marriages experience a greater rate of sexual violence (26.5%) than women in nonpolygamous unions. This indicates the unique risks associated with polygamy and polyandry. Supporting this observation, research has consistently shown that women in polygamous marriages experience a range of negative psychological and social outcomes, such as depression(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), lower education and economic status, and engagement in risky sexual behavior(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the study examines the relationship between economic status and domestic violence. This study establishes a clear correlation between economic disadvantage, as indicated by household wealth, and higher instances of domestic violence. Women from the poorest households reported higher rates of both physical and sexual violence. This relationship is influenced by various factors, including neighborhood economic disadvantage, individual economic distress, education, attitudes toward wife-beating, and control by husbands (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe study's findings present a complex picture that differs from global trends in several ways. Factors such as the age of marriage, early sexual experiences, economic status, and societal attitudes toward polygamy and intimate partner violence (IPV) all contribute to domestic violence patterns. However, these factors manifest differently in Rwanda, likely due to unique cultural, socioeconomic, and legal contexts. For instance, the impact of motherhood before the age of 21 or women's employment on domestic violence may not align with global patterns, possibly due to differing societal norms or economic dynamics. Additionally, the urban‒rural divide in experiences of violence and the influence of early forced sex on later violence might be perceived or reported differently in Rwanda.\u003c/p\u003e\u003cp\u003eThese discrepancies highlight the need for context-specific approaches to understanding and addressing domestic violence. It is crucial to consider local cultural, economic, and social factors when developing public health strategies and interventions. To address domestic violence in Rwanda, a multifaceted approach is necessary. This includes integrating mental health services that focus on trauma-informed care, providing training for healthcare providers regarding mental health issues related to domestic violence, and implementing comprehensive sex education in schools and community workshops. Additionally, it is important to develop educational programs that promote healthy marital and conjugal relationships, empower women to enhance their bargaining power in marriages, and address the unique challenges of polygamous relationships. These efforts should be supported by collaboration across sectors and continuous monitoring and evaluation to adapt to evolving needs and feedback.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegression Discontinuity Design\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedures were performed in accordance with the appropriate guidelines of the Demographic and Health Surveys (DHS) program. The International Review Board of DHS program data archivists waived the requirement for informed consent. Upon submitting the consent form to the DHS Program, permission to download the dataset was granted for this study. The dataset was kept confidential and anonymized to ensure its privacy and was not shared or transferred to any other entities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was obtained from public, commercial, or not-for-profit sectors for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript has fully used DHS Datasets and free available upon request from their website https://dhsprogram.com/ \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor and Affiliation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-Hause Co., Rwanda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarerimana Jean de Dieu (HJD) \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJD conceptualized the study, drafted the background and literature review. conducted the analysis, drafted the results and the discussion. HJD reviewed several drafts and suggested additional revisions. HJD is responsible for submitting the manuscript. HJD reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Harerimana Jean de Dieu ([email protected])\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHanington L, Heron J, Stein A, Ramchandani P. Parental depression and child outcomes \u0026ndash; is marital conflict the missing link? Child Care Health Dev. 2012 Jul 19;38(4):520\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eEze-Ajoku E, Fakeye O, Atanda A, Sosina OA. Economic Empowerment and Tolerance of Domestic Violence Among Married Women: A Cross-Sectional Study. J Interpers Violence. 2022 Mar 29;37(5\u0026ndash;6):NP2719\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eChristaki V, Orovou E, Dagla M, Sarantaki A, Moriati S, Kirkou G, et al. 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MMWR Morb Mortal Wkly Rep. 2021 Nov 26;70(47):1629\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eNISR. Rwanda Demographic and Health Survey 2019-2020: key indicators report. Vol. 53, Demographic and Health Surveys. 2020.\u003c/li\u003e\n\u003cli\u003eAngrist JD, Pischke JS. Mostly harmless econometrics: An empiricist\u0026rsquo;s companion. Mostly Harmless Econometrics: An Empiricist\u0026rsquo;s Companion. 2008.\u003c/li\u003e\n\u003cli\u003eFr\u0026ouml;lich M, Sperlich S. Impact Evaluation: Treatment Effects and Causal_Analysis. Cambridge University Press. 2019.\u003c/li\u003e\n\u003cli\u003eGhimire DJ, Axinn WG, Smith-Greenaway E. Impact of the spread of mass education on married women\u0026rsquo;s experience with domestic violence. Soc Sci Res. 2015 Nov;54:319\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eStack RJ, Meredith A. The Impact of Financial Hardship on Single Parents: An Exploration of the Journey From Social Distress to Seeking Help. J Fam Econ Issues. 2018;39(2).\u003c/li\u003e\n\u003cli\u003eHaobijam S, Singh KA. Socioeconomic Determinants of Domestic Violence in Northeast India: Evidence From the National Family Health Survey (NFHS-4). J Interpers Violence [Internet]. 2021;37:NP13162\u0026ndash;81. Available from: https://api.semanticscholar.org/CorpusID:232407794\u003c/li\u003e\n\u003cli\u003eNaidoo S, Sartorius B, de Vries H, Taylor M. Prevalence and Risk Factors Associated with Forced-Sex Among South African High School Students. J Community Health. 2017 Oct 15;42(5):1035\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eMachariah LW, Iteyo C. Socio-Cultural Dynamics Influencing Domestic Violence in Nyeri County, Kenya. African Journal of Empirical Research. 2023;4(2).\u003c/li\u003e\n\u003cli\u003eAnttila-hughes J, Stopnitzky Y, Tolonen A. Seasonality of Attitudes on Violence. 2017;2017.\u003c/li\u003e\n\u003cli\u003eKargar Jahromi M, Jamali S, Koshkaki AR, Javadpour S. Prevalence and Risk Factors of Domestic Violence Against Women by Their Husbands in Iran. Glob J Health Sci. 2015 Sep 28;8(5):175.\u003c/li\u003e\n\u003cli\u003eRoychowdhury P, Dhamija G. The Causal Impact of Women\u0026rsquo;s Age at Marriage on Domestic Violence in India. Fem Econ. 2021 Jul 3;27(3):188\u0026ndash;220.\u003c/li\u003e\n\u003cli\u003eCamargo Freile IE, Fl\u0026oacute;rez Lozano KC, Sarmiento Crespo CA, Vecchio Camargo CM, Rodr\u0026iacute;guez Acosta SM, Florez-Garcia V, et al. Risk of violence from a current or former partner: Associated factors and classification in a nationwide study in Colombia. PLoS One. 2022 Dec 22;17(12):e0279444.\u003c/li\u003e\n\u003cli\u003eFan S, Koski A. The health consequences of child marriage: a systematic review of the evidence. BMC Public Health. 2022 Feb 14;22(1):309.\u003c/li\u003e\n\u003cli\u003eShaiful Bahari I, Norhayati MN, Nik Hazlina NH, Mohamad Shahirul Aiman CAA, Nik Muhammad Arif NA. Psychological impact of polygamous marriage on women and children: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2021 Dec 13;21(1):823.\u003c/li\u003e\n\u003cli\u003eMabaso MLH, Malope NF, Simbayi LC. Sociodemographic and behavioural profile of women in polygamous relationships in South Africa: a retrospective analysis of the 2002 population-based household survey data. BMC Womens Health. 2018 Dec 2;18(1):133.\u003c/li\u003e\n\u003cli\u003eBenson ML, Fox GL, DeMaris A, Wyk JA Van. Neighborhood Disadvantage, Individual Economic Distress and Violence Against Women in Intimate Relationships. J Quant Criminol [Internet]. 2003;19:207\u0026ndash;35. Available from: https://api.semanticscholar.org/CorpusID:141834737\u003c/li\u003e\n\u003cli\u003eTenkorang EY, Owusu AY, Yeboah EH, Bannerman R. Factors Influencing Domestic and Marital Violence against Women in Ghana. J Fam Violence. 2013 Nov 22;28(8):771\u0026ndash;81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intrahousehold conflict, domestic violence, premarital motherhood","lastPublishedDoi":"10.21203/rs.3.rs-3781618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3781618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDomestic violence affects approximately one-third of women globally and presents unique challenges in Rwanda. This study examined the link between domestic violence and household conflicts, focusing on infertility, unintended pregnancies, and the impact of premarital pregnancies. The survey uses data from the 2019/20 Rwanda Demographic and Health Survey and addresses the influence of Rwandan cultural norms, the prevalence of child marriage, and societal attitudes toward violence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analysed data from the 2019-20 Rwanda Demographic and Health Survey using a two-stage sampling process. Regression discontinuity design (RDD) and logistic regression were used to evaluate variables such as domestic violence incidence and age, as well as control variables such as education, marital status, and occupational status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings indicate increased risks of domestic violence as women approaching the legal marriage age of 21 years face increased risks of domestic violence, with 35.1% greater likelihoods of physical violence and 14.8% greater odds of sexual violence. Polygamous marriages significantly increase the risk of sexual violence by 26.5%. Early forced sexual encounters intensify the likelihood of physical and sexual violence by 67.5% and 129.7%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the increased vulnerability to physical and sexual violence linked to early sexual encounters and polygamous marriage. These findings, diverging from global trends, underscore the necessity of employing Rwanda-specific strategies. Moreover, to effectively address domestic violence, it is important to consider cultural dynamics, socioeconomic status, and matrimonial education, including sex education and bargaining power, for both parties.\u003c/p\u003e","manuscriptTitle":"Intrahousehold Conflict Effects on Domestic Violence in Rwanda: Evidence from the Demographic and Health Survey 2019-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-23 16:24:42","doi":"10.21203/rs.3.rs-3781618/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c73bcc54-5551-4378-a13c-f98f127a903b","owner":[],"postedDate":"December 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-16T09:29:47+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-23 16:24:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3781618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3781618","identity":"rs-3781618","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}