Economic Empowerment and Tolerance of Domestic Violence Among Married Women: A Cross-Sectional Study

Abstract

We evaluated whether markers of economic empowerment are associated with a tolerant attitude toward spousal physical violence (SPV) among employed married women in Nigeria. Cross-sectional analyses of responses to the 2013 Nigeria Demographic Health Survey by a nationally representative sample of 3,999 women aged 15 to 49 years who reported being employed and married. Tolerance for SPV was defined as supporting statements with justifications for wife-beating. Logistic regression assessed the associations of reporting tolerance for SPV with educational attainment and interspousal equivalency in income, controlling for previous exposure to domestic abuse. The prevalence of tolerance for SPV among the sample was 37%. Women with tertiary education had lower odds of tolerance for SPV relative to their counterparts without formal education (adjusted odds ratio [aOR] = 0.22, 95% confidence interval [CI] = [0.12, 0.40], p < .0001). Compared with women with similar income levels as their partners, women who either earned more (aOR = 2.77, 95% CI = [1.36, 5.62], p = .005) or earned less income relative to their spouses (aOR = 1.93, 95% CI = [1.14, 3.26], p = .02) had higher odds of tolerance for SPV. Odds of tolerance for SPV were also higher among women reporting previous spousal abuse than among their counterparts without such a history (aOR = 1.55, 95% CI = [1.14, 2.12], p = .006). A history of nonspousal abuse was associated with lower odds of tolerance for SPV (aOR = 0.56, 95% CI = [0.37, 0.84], p = .005). Lower educational attainment and interspousal differences in income may contribute to tolerance of SPV. Efforts to increase economic empowerment should be combined with education to recognize cultural norms that foster SPV and build skills to exit violent relationships.

Keywords

domestic violence contextual factors women empowerment attitudes to violence tolerance

Background

Domestic violence is of public health importance globally and is a common form of harm against women (World Health Organization [WHO], 2012). It is a violation of human rights, and its elimination is a priority of the United Nations’ Sustainable Development Goals. Globally, one in three women has experienced an instance of physical or sexual domestic violence in their lifetime (WHO, 2017). Domestic violence may take the form of physical violence (light shoving, slapping, kicking, and beating), sexual abuse (forced sexual acts and coercive behaviors in sexual acts), or psychological/emotional abuse (Archer, 2002; Basile & Saltzman, 2002; Capaldi et al., 2012). Among survivors of domestic violence, numerous health problems, from sexually transmitted diseases to gynecological and mental health issues, can arise (Campbell et al., 1998).

The risks of subjection to domestic violence are moderated by the partners’ contextual characteristics, behavioral patterns, relationship structure, and modalities of spousal interaction (Basile & Saltzman, 2002). Economic empowerment—typically measured by educational attainment, employment, and income security—may be protective for the risk of domestic violence and may engender an attitude of disapproval toward such acts (Dalal, 2011; Vyas & Watts, 2009). It is well documented that cultural norms about the distribution of control between men and women and accepted gender roles may influence the acceptance and prevalence of domestic violence (Michau, 2007). Power dynamics within a marital relationship may be influenced by multiple factors, including the distribution of decision-making authority within the household and relative contribution to income (Thomas et al., 2002). Few studies have focused on the impact of interspousal incongruence in age, education, and income on exposure to and tolerance of physical violence within a marital relationship. These studies found that the risk of recent spousal violence was higher in marital situations where women reported greater educational attainment or differences in age compared with their partners (Ackerson et al., 2008; Adebowale, 2018; Izugbara, 2018).

Although interspousal equivalency on socioeconomic status measures has been advocated as a strategy to reduce spousal physical violence (SPV), cultural norms that dictate the power dynamics within marital relationships may foster unhealthy attitudes toward SPV even among economically empowered women (Bolis & Hughes, 2015). To the best of our knowledge, no prior study has examined how economic empowerment of married women and interspousal income equivalency modifies perspectives on the acceptability of SPV in the Nigerian context.

This study explores observed attitudes toward tolerance of domestic spousal abuse among married women in Nigeria. The focus is on physical abuse, a major cause of injury in women (Gracia & Herrero, 2006; Kyriacou et al., 1999; Thompson et al., 2003) and plausibly more apparent than psychological and emotional abuse as representative of domestic abuse.

Nigerian Cultural Context: Gender Roles and Factors Associated With the Prevalence of SPV

Tackling attitudes toward SPV is critical for reducing the occurrence of such abuse among women in contexts where power is tilted toward men. Nigeria, the most populous West African country, exemplifies this context. Traditionally, patriarchal views of male superiority over the female sex dominate, but the status of women is improving (Arua, 2017; Nwosu, 2012). About one in four women aged 15 to 49 years has experienced physical violence (National Population Commission/Nigeria & ICF International, 2014). Historically, low educational attainment and low household socioeconomic status have been associated with a higher prevalence of intimate partner violence (IPV) in rural Nigeria (Antai & Antai, 2008). The 2013 Nigeria Demographic Household Survey (NDHS) reported that among married women, 51% stated that their partner was a perpetrator of SPV. Given the limited data on the prevalence of spousal physical abuse in Nigeria, developing and targeting interventions for victims of abuse have been difficult.

This work contributes to the existing body of knowledge and policy on domestic violence in Nigeria by investigating the influence of interspousal differences in education and earnings, as well as previous exposure to domestic violence on tolerance for SPV. Our aim is that these findings will shed more light on how differences in perceived and substantive economic power within a marital relationship may foster acceptance and normalization of violence and improve the design of policy interventions for identifying women who may be at risk of SPV.

Theoretical Framework

The literature on domestic violence indicates that the factors that predispose to violence within relationships and attitudes toward violence are similar (Bucheli & Rossi, 2016). The perpetration of violence can affect the justification of violence. In turn, attitudes toward violence could determine the use of violence within a relationship. These factors are a complex interplay of macrolevel and microlevel factors. Beyer et al. provided a conceptual model that draws on previous research using the socioecological framework proposed by Heise (Beyer et al., 2015; Heise, 1998). The model links factors at the interpersonal and family; neighborhood and community; policy systems and social levels that interact in contributing to violence. These factors reinforce one another within and across levels, and we design our study using this conceptual model. Our focus in this study is on the individual and interpersonal factors that shape attitudes toward violence. This framework is aligned with the United States Centers for Disease Control and Prevention’s four-level, social-ecological model of violence that delineates the interplay among individuals, relationship, community, and societal factors (Dahlberg & Krug, 2006); and we examine how the individual and interpersonal factors (i.e., relationship factors) influence attitudes to SPV. The conceptual framework for this study draws not only from the above, but also from Flood and Pease’s work on factors influencing attitudes toward violence against women (Flood & Pease, 2009).

Method

Data

Data are from the 2013 NDHS, a household survey administered at 5-year intervals by the Monitoring and Evaluation to Assess and Use Results Demographic and Health Surveys (MEASURE DHS) in low- and middle-income countries. The sampling frame was nationally representative of the Nigerian population. The unweighted sample size for all women in the survey was 38,948, and the response rate among eligible women was 97%. The population of interest are married women between the ages of 15 and 49 years residing in Nigeria who are currently employed. These are women in our survey sample, who report being employed and married, and were typical members of the selected households or spent the night in the household before the survey. Our final sample consisted of 3,999 married and employed female respondents aged 15 to 49 years with nonmissing values for the outcome and all covariates used for adjustment in regression models.

Outcome Measure

The primary outcome, tolerance for SPV, was operationalized as a binary variable indicating whether the respondent provided an affirmative response to any of the five provided justifications for wife-beating (see Appendix A). Hence, tolerance for SPV was zero if the respondent provided a negative response to all five provided justifications for wife-beating. Furthermore, respondents who answered “I don’t know” to any question were categorized as tolerant of SPV; the authors classified such responses as a failure to renounce SPV in absolute terms. For the multinomial model, the outcome measure is defined as the count of affirmative responses across the five provided justifications for wife-beating.

Independent Variables

The primary predictor variables were educational attainment and income level relative to the respondent’s spouse. Educational attainment was defined as the highest level of formal education (no formal education, primary, secondary, or tertiary) reported by the respondent. The other primary predictor, the interspousal difference in income, categorized responses as: respondent earns more than her spouse; respondent earns less than her spouse; no difference.

Demographic characteristics and relationship factors reported by respondents were employed as covariates to account for potential confounding and possibly investigate their respective direct effect on the outcome in our study. Demographic characteristics include the respondent’s age group, the geopolitical region of residence, and religion. Relationship factors comprised the type of union (polygamy vs. monogamy), presence of interspousal difference in the highest level of education (primary, secondary, and tertiary levels) attained, the respondent’s level of involvement in household decision-making, and the household wealth index as a proxy measure of poverty level. The level of involvement in decision-making was defined for the married woman as full, partial, or not responsible for the household’s decisions regarding the respondent’s health care, making large household purchases, and visits to family or relatives.

Statistical Analysis

We evaluated the associations between the respondent’s demographic characteristics and relationship factors, respectively, on the outcome using bivariate chi-square tests. The analytic model was a multivariable logistic regression of the binary outcome, tolerance for SPV, on all demographic characteristics, relationship factors, and variables indicating previous experience of spousal violence from the current spouse, and prior experience of physical abuse from the respondent’s social circle (family or social associates).

In sensitivity analyses, we modified the original regression model, which was based on a complete case data set, by using multiple imputation (Little & Rubin, 2019) to examine the influence of missing data on findings. The imputation approach averaged the results from 10 separate regression models with inferred values for missing instances of potential confounders of theoretical interest—interspousal educational difference, interspousal income difference, religion, involvement in household decisions, and type of relationship. For each imputed data set, we applied sequential regression multivariate imputation (Raghunathan et al., 2001) using all variables (type of location, marital status, household wealth index, age, geopolitical zone, and variables quantifying spousal and nonspousal experience of violence) that appeared in the complete data model and had no missing values. Furthermore, to assess the impact of the exposure variables on increasingly tolerant attitudes toward domestic violence, we fit a multinomial regression model on the outcome of the number of affirmative responses provided to the five questions on justifications for wife-beating, using the complete case dataset. All analyses were conducted in Stata version 14 (StataCorp, College Station, TX). Two-sided p values <.05 based on Wald tests were considered statistically significant.

Results

Characteristics of Sample Respondents by Attitudes Toward SPV

Table 1 provides descriptive characteristics of the study sample categorized by general attitude toward SPV. A lower proportion of married Christian women reported at least one justification for SPV compared with Muslims and practitioners of other religions (33% vs. 40% and 53%, respectively). Women in older age groups were less likely to report tolerance for SPV relative to women younger than 18 years of age. Higher levels of household wealth index and level of education were also associated with a lower tolerance for SPV. Compared with counterparts in rural locations, a smaller proportion of women in urban areas expressed justification for SPV (43% vs. 28%, respectively).

Table 1.

Attitudes Toward Spousal Physical Violence Among Study Samples.

Variables	At Least One Justification for Beating (n = 1,468)	No Justification for Beating (n = 2,531)
Respondent’s religion, n (%)
Christian	646 (32.68)	1,331 (67.32)
Islam	804 (40.44)	1,184 (59.56)
Others	18 (52.94)	16 (47.06)
Respondent’s age group in years, n (%)
15–18	85 (50)	85 (50)
19–30	849 (38.38)	1,363 (61.62)
31–40	453 (33.26)	909 (66.74)
41–50	81 (31.76)	174 (68.24)
Household wealth index, n (%)
Poorest	319 (50.32)	315 (49.68)
Poorer	340 (47.75)	372 (52.25)
Middle	293 (40.92)	423 (59.08)
Richer	294 (32.27)	617 (67.73)
Richest	222 (21.64)	804 (78.36)
Respondent’s level of education, n (%)
No formal education	653 (47.11)	733 (52.89)
Primary	344 (38.52)	549 (61.48)
Secondary	404 (30.86)	905 (69.14)
Tertiary or higher	67 (16.3)	344 (83.7)
Type of location, n (%)
Rural	1,014 (42.73)	1,359 (57.27)
Urban	454 (27.92)	1,172 (72.08)
Difference in the income level between respondent and partner, n (%)
No difference	66 (27.05)	178 (72.95)
Respondent earns less than spouse	1,335 (37.27)	2,247 (62.73)
Respondent earns more than spouse	67 (38.73)	106 (61.27)
Respondent’s level of involvement in household decision-making, n (%)
Joint responsibility	675 (30.42)	1,544 (69.58)
No responsibility	681 (45.34)	821 (54.66)
Full responsibility	112 (40.29)	166 (59.71)
Difference in educational attainment between respondent and partner, n (%)
No difference	881 (36.74)	1,517 (63.26)
Respondent has lower attainment than spouse	439 (37.62)	728 (62.38)
Respondent has higher attainment than spouse	148 (34.1)	286 (65.9)
Geopolitical zone, n (%)
North Central	272 (34.74)	511 (65.26)
North East	254 (59.35)	174 (40.65)
North West	446 (41.11)	639 (58.89)
South East	128 (42.11)	176 (57.89)
South South	185 (31.52)	402 (68.48)
South West	183 (22.54)	629 (77.46)
Type of relationship, n (%)
Monogamy	1,136 (34.99)	2,111 (65.01)
Polygamy	332 (44.15)	420 (55.85)
Previous experience of spousal violence, n (%)
Yes	310 (46.83)	352 (53.17)
Never	1,158 (34.7)	2,179 (65.3)
Previous experience of nonspousal violence, n (%)
No	1,181 (36.26)	2,076 (63.74)
Yes	287 (38.68)	455 (61.32)

Among married couples with interspousal differences in the income level—regardless of which spouse earned more—the proportion of women reporting tolerance for SPV was higher than among women in households where the partners had equivalent income levels. Similarly, women in households with an imbalance between spouses in household decision-making were more likely to report tolerance for SPV relative to their counterparts from households in which partners had joint involvement. Women who were more educated than their spouses were slightly less likely to report tolerance for SPV than women with similar or less education than their spouses. Furthermore, there was a higher likelihood of tolerance for SPV among women in polygamous unions, and women who had experienced physical violence from their current spouse or other members of their community.

Factors Associated With Tolerance for SPV

Table 2 presents the unadjusted and adjusted odds ratios (aOR) from the multivariable logistic model. After controlling for confounders, living in rural areas was associated with increased odds of tolerance for SPV relative to urban areas (aOR = 1.44, 95% confidence interval [CI] = [1.03, 2.00], p = .03). There was a significant impact of geopolitical zones on tolerance for SPV (p < .01). Compared with the North Central geopolitical zone, respondents residing in the North Eastern zone (aOR = 1.71, 95% CI = [1.11, 2.65], p = .02) had significantly greater odds of SPV tolerance, whereas their counterparts in the North Western (aOR = 0.69, 95% CI = [0.50, 0.96], p = .03) and the South Western (aOR = 0.55, 95% CI = [0.38, 0.81], p = .002) geopolitical zones were significantly less likely to report tolerance for SPV. Respondents with at least a tertiary level of education had significantly lesser odds of reporting tolerance for SPV relative to their counterparts with no formal education (aOR = 0.22, 95% CI = [0.12, 0.40], p < .001).

Table 2.

Estimated Odds Ratio of Tolerant Attitude Toward Spousal Physical Violence.

Covariates	Unadjusted Odds Ratio (95% CI)	Adjusted Odds Ratio (95% CI)
Type of location (reference: Urban)
Rural	2.33 [1.87, 290]	1.44 [1.03, 2.00]
Geopolitical zone (reference: “North Central”)
North East	1.81 [1.20, 2.74]	1.71 [1.09, 2.69]
North West	0.67 [0.51, 0.88]	0.69 [0.50, 0.96]
South East	0.80 [0.47, 1.37]	1.77 [0.96, 3.25]
South South	0.39 [0.24, 0.64]	0.65 [0.36, 1.18]
South West	0.31 [0.22, 0.44]	0.55 [0.38, 0.81]
Respondent’s highest level of education attained (reference: “No formal education”)
Primary	0.77 [0.59, 0.999]	0.87 [0.63, 1.21]
Secondary	0.52 [0.40, 0.68]	0.76 [0.49, 1.19]
Tertiary	0.13 [0.09, 0.19]	0.22 [0.12, 0.40]
Difference in income level between respondent and partner (reference: “None”)
Respondent earns more than spouse	2.85 [1.49, 5.44]	2.77 [1.36, 5.62]
Respondent earns less than spouse	2.28 [1.36, 3.82]	1.93 [1.14, 3.26]
Previous experience of spousal violence (reference: “Never”)
Yes	1.64 [1.24, 2.18]	1.55 [1.14, 2.12]
Previous experience of nonspousal violence (reference: “Never”)
Yes	0.81 [0.60, 1.08]	0.56 [0.37, 0.84]

Note. CI = confidence interval.

We observed a significant effect of interspousal differences in income on tolerance for SPV (p = .02). Women who reported earning higher (aOR = 2.77, 95% CI = [1.36, 5.62], p = .005) or lower income than their spouse (aOR = 1.93, 95% CI = [1.14, 3.26], p = .02) had greater odds of reporting tolerance for SPV than women who earned similar amounts to their spouse. Previous experience of abuse from one’s current spouse was associated with greater odds (aOR = 1.55, 95% CI = [1.14, 2.12], p = .006) of reporting tolerance for SPV. Contrasting women with a history of nonspousal abuse to women without such prior experiences, we found significantly lower odds of reporting tolerance for SPV among the latter group (aOR = 0.56, 95% CI = [0.37, 0.84], p = .004).

Patterns of missingness in critical variables are presented in Appendix B, and the analyses using multiple imputation to account for missing data found similar associations as the primary regression analysis (Appendix C). Relative to women with similar income levels as their spouses, women who either earned more (aOR = 2.40, 95% CI = [1.19, 4.87], p = .02) or earned less income than their spouses (aOR = 1.80, 95% CI = [1.06, 3.08], p = .03) had higher odds of tolerance for SPV. Odds of tolerance for SPV were also higher among women reporting previous spousal abuse than among their counterparts without such history (aOR = 1.56, 95% CI = [1.18, 2.05], p = .002). A history of nonspousal abuse was associated with lower odds of tolerance for SPV (aOR = 0.55, 95% CI = [0.38, 0.79], p = .001). Higher educational attainment than one’s spouse and the level of involvement in household decision-making were not significantly associated with tolerance for SPV.

Using the multinomial model (Appendix D), we perceived a general consistency in the direction of effects for significant associations and the variables that had a significant impact on the outcome compared with our findings based on the logistic model. We observed a strong effect of education for almost all levels of the reported tolerance for SPV.

Discussion

In our analysis, we observed a significant relationship between tolerance for SPV and socioeconomic indicators, including the level of education among married women in Nigeria. Furthermore, women who had experienced SPV from their current partners were more likely to report some justification for this violence. This finding supports prior research demonstrating that previous exposure to violence may reduce sensitivity toward violence and lead to normalization of this form of abuse (Flood & Pease, 2009). Conversely, we found that women who experienced nonspousal abuse were less likely to justify SPV. In combination with previous studies, our results suggest that tolerance for spousal abuse may depend on the source of past violence. However, while we observed an opposite effect for women who had experienced nonspousal abuse, we note that we were not able to differentiate between women that experienced family abuse as the source of nonspousal abuse from women who experienced abuse from people outside of their family and the degree of this non-SPV which may help explain the results. Hence, further studies are needed to explore how the source or perpetrator of past violence, and the period the abuse occurred (childhood vs. adult experience) may help explain the differing attitudes toward violence based on exposure.

While we report regional (geopolitical zone) differences in observed tolerance for SPV, clarifying mechanisms for the directionality of these effects is difficult, due to the diversity among regions in the level of socioeconomic indicators such as urbanicity, average educational level, and cultural norms. More studies are needed to isolate the geopolitical influences on attitudes toward SPV.

We found that women with the highest educational attainment were less likely than their counterparts without formal education to report tolerance for SPV. This result is in line with studies showing that increased access to education for women can lead to an improvement in attitudes toward domestic violence (Flood & Pease, 2009; Marium, 2014). One global study of 39 low- and middle-income countries concluded that wife-beating was deemed acceptable among residents of rural locations and with limited formal education, while women with higher education and socioeconomic status were less likely to endorse domestic violence (Shiraz, 2016).

When examining the role of relationship factors on SPV tolerance, our analysis showed that formal education was protective against tolerance to SPV only at the tertiary level. Income differences between spouses expressed a more consistent association with tolerance for SPV; married women reported higher tolerance for violence in situations where there was any difference in earnings. The significance of income differences when compared with educational differences may be an artifact of the Nigerian context, where cultural norms about the head of a family entrench authority in the male spouse regardless of income or educational differences. Besides, more importance and power are attributed to the financial breadwinner compared with the spouse with the higher educational attainment. Although female employment, which is associated with higher income, may reduce domestic violence tolerance because it confers a degree of financial empowerment and reduces marital dependence (Akinyemi et al., 2017; Etuk et al., 2012), yet a woman who earns more than her spouse may be very cautious not to offend him, lest he asserts his position as the head of the home through violence (Qasim & Asubaro, 2019). Equally, a male spouse may resort to financial deprivation, resource control, or physical abuse to assert dominance and superiority over their female partner, who earns less than he does. In this situation, the female spouse may develop a tolerance for violence as a survival skill. These scenarios illustrate the status inconsistency perspective, a sociological theory indicating that violence is more likely to occur when an individual’s status is inconsistent with new norms or when standard norms that govern the family structure become ambiguous (Yick, 2001). The status inconsistency theory is derived from the resource theory of power (Goode, 1971; Mogford, 2011). It implies that if the husband lacks educational, income, or prestige resources, he may resort to violence to assert power in the family. Differences between the spouses in income and educational attainment represent a disruption of traditional hierarchical roles and distribution of power in intimate relationships that can result in violence (O’Brien, 1971; Rodríguez-Menés & Safranoff, 2012; Yick, 2001).

The findings of this study have broad implications for addressing domestic violence. Attitudes toward domestic violence are vital and should be addressed, as prior work has shown significant associations between tolerant attitudes and a higher probability of experiencing domestic violence (Vyas & Jansen, 2018). Furthermore, attitudes toward violence shape the response of individuals who experience violence. Favorable dispositions to domestic violence could result in less empathy toward subjects and victim-blaming, leading to difficulties in reporting or exiting violent relationships (Fawole et al., 2005). It has also been documented that in cases where women report to police authorities, they are frequently blamed for provoking the abuse—underlining the need for sensitization of law enforcement to appropriate management of these sensitive situations (Fawole et al., 2005). Also, the existence of some regional laws regarding domestic violence propagates victim-blaming. For example, Section 55 of the Penal Code applicable in Northern Nigeria permits the husband to correct his wife by beating or similar means insofar as this does not result in grievous harm (Chika, 2012). This reality, compounded by the cultural landscape in some parts of Nigeria, makes domestic violence more acceptable and challenging to address.

While the economic empowerment of women is important, our results indicate that its real benefit for refining attitudes toward SPV may be more nuanced. We note that while economic empowerment that is attained by increasing educational opportunities alone is beneficial, economic empowerment achieved solely through financial means may prove detrimental by increasing tolerance to SPV, thereby leading to a higher likelihood of experiencing it. Furthermore, cultural norms that come in effect when economic empowerment is attained solely through financial means may supersede the protective effect of a woman’s education on tolerance for SPV, leading to normalization of spousal abuse. Even as opportunities for women’s educational advancement improve globally, it is crucial to track societal attitudes on the acceptability of spousal abuse. Hence, a multidisciplinary approach that includes culturally sensitive interventions that address underlying practices around power-sharing between couples and is adapted to the varying contexts and relationship modalities will help reduce domestic violence. This approach is especially important where the power balance deviates from the expected cultural norm.

This analysis has several limitations. First, responses in the NDHS may be subject to recall bias, misrepresentation, social desirability bias, or reluctance to provide information on abuse that may have previously occurred (van Giezen et al., 2005; Yoshihama & Gillespie, 2002). Second, the survey does not provide the magnitude of differences in spousal income, which would have been useful to examine how levels of interspousal income disparity affect tolerance of domestic violence. Third, the study is limited by the lack of qualitative analysis, which would have provided more insight into our quantitative findings. Finally, we do not account for the male partner’s perception of spousal violence and how this may influence a woman’s tolerance of violence. Although physical and emotional abuse are usually studied together, we do not account for emotional abuse, as we were unable to identify how it was defined within the survey. Future research on domestic violence in Nigeria should consider why women who have higher socioeconomic status may still have tolerant attitudes toward domestic violence. Research should also include the effect of social media information campaigns on tolerance for domestic violence given the increasing prevalence of internet access among the Nigerian populace.

Ethical Considerations

Gender-based violence of any kind is a sensitive issue and thus protecting participants’ confidentiality is critical. It is essential to ensure that safety and confidentiality are prioritized during the study (WHO, 2001). This study is a secondary analysis of a de-identified publicly available dataset. Participants involved in the DHS provided informed consent as part of the data collection. The Federal Ministry of Health of Nigeria provided Institutional Review Board’s approval for the DHS. Administrative permission was obtained from the DHS program to access the data used in this study.

Footnotes

Appendix A

Responses to Justification of Wife-Beating.

Response	Beating Justified if Wife Goes Out Without Telling Husband	Beating Justified if Wife Neglects the Children	Beating Justified if Wife Argues With Her Husband	Beating Justified if Wife Refuses to Have Sex With Her Husband	Beating Justified if Wife Burns Food
No	2,943	2,933	4,396	3.077	3,369
Yes	990	1,558	1,002	848	566
I don’t know	41	54	38	47	38

Appendix B

Table 1.

With Patterns of Missingness.

Variables	Overall	Excluded From the Analysis	Included in the Complete Case Analysis
	Overall	Excluded From the Analysis	At Least One Justification for Beating	No Justification for Beating
	(n = 5,762)	(n = 1,763)	(n = 1,468)	(n = 2,531)
Type of location
Rural	3,697 (64.2%)	1,324 (75.1%)	1,014 (69.1%)	1,359 (53.7%)
Urban	2,065 (35.8%)	439 (24.9%)	454 (30.9%)	1,172 (46.3%)
Woman’s level of education
Higher	519 (9.0%)	108 (6.1%)	67 (4.6%)	344 (13.6%)
No education	2,185 (37.9%)	799 (45.3%)	653 (44.5%)	733 (29.0%)
Primary	1,356 (23.5%)	463 (26.3%)	344 (23.4%)	549 (21.7%)
Secondary	1,702 (29.5%)	393 (22.3%)	404 (27.5%)	905 (35.8%)
Difference in the level of education between partners
No gap	3,388 (58.8%)	990 (56.2%)	881 (60.0%)	1,517 (59.9%)
Woman higher Ed	574 (10.0%)	140 (7.9%)	148 (10.1%)	286 (11.3%)
Woman lower Ed	1,755 (30.5%)	588 (33.4%)	439 (29.9%)	728 (28.8%)
Missing	45 (0.8%)	45 (2.6%)	0 (0%)	0 (0%)
Difference in income between partners
Earns less	4,637 (80.5%)	1,055 (59.8%)	1,335 (90.9%)	2,247 (88.8%)
Earns more	213 (3.7%)	40 (2.3%)	67 (4.6%)	106 (4.2%)
Earns the same	291 (5.1%)	47 (2.7%)	66 (4.5%)	178 (7.0%)
Missing	621 (10.8%)	621 (35.2%)	0 (0%)	0 (0%)
Religion
Christian	2,734 (47.4%)	757 (42.9%)	646 (44.0%)	1,331 (52.6%)
Islam	2,929 (50.8%)	941 (53.4%)	804 (54.8%)	1,184 (46.8%)
Others	63 (1.1%)	29 (1.6%)	18 (1.2%)	16 (0.6%)
Missing	36 (0.6%)	36 (2.0%)	0 (0%)	0 (0%)
Age categories
15–18	239 (4.1%)	69 (3.9%)	85 (5.8%)	85 (3.4%)
19–30	3,053 (53.0%)	841 (47.7%)	849 (57.8%)	1,363 (53.9%)
31–40	2,048 (35.5%)	686 (38.9%)	453 (30.9%)	909 (35.9%)
41–50	422 (7.3%)	167 (9.5%)	81 (5.5%)	174 (6.9%)
Geopolitical zones
North Central	1,058 (18.4%)	275 (15.6%)	272 (18.5%)	511 (20.2%)
North East	833 (14.5%)	405 (23.0%)	254 (17.3%)	174 (6.9%)
North West	1,642 (28.5%)	557 (31.6%)	446 (30.4%)	639 (25.2%)
South East	406 (7.0%)	102 (5.8%)	128 (8.7%)	176 (7.0%)
South South	843 (14.6%)	256 (14.5%)	185 (12.6%)	402 (15.9%)
South West	980 (17.0%)	168 (9.5%)	183 (12.5%)	629 (24.9%)
Wealth index of households
Poorest	1,068 (18.5%)	434 (24.6%)	319 (21.7%)	315 (12.4%)
Poorer	1,159 (20.1%)	447 (25.4%)	340 (23.2%)	372 (14.7%)
Middle	1,045 (18.1%)	329 (18.7%)	293 (20.0%)	423 (16.7%)
Richer	1,227 (21.3%)	316 (17.9%)	294 (20.0%)	617 (24.4%)
Richest	1,263 (21.9%)	237 (13.4%)	222 (15.1%)	804 (31.8%)
Type of relationship
Monogamy	4,137 (71.8%)	890 (50.5%)	1,136 (77.4%)	2,111 (83.4%)
Polygamy	1,625 (28.2%)	873 (49.5%)	332 (22.6%)	420 (16.6%)
Level of involvement
Full responsibility	398 (6.9%)	120 (6.8%)	112 (7.6%)	166 (6.6%)
Joint responsibility	3,099 (53.8%)	880 (49.9%)	675 (46.0%)	1,544 (61.0%)
No responsibility	2,258 (39.2%)	756 (42.9%)	681 (46.4%)	821 (32.4%)
Missing	7 (0.1%)	7 (0.4%)	0 (0%)	0 (0%)
Previous experience of spousal violence
Has experienced spousal violence	1,987 (34.5%)	1,325 (75.2%)	310 (21.1%)	352 (13.9%)
Never experienced spousal experience	3,775 (65.5%)	438 (24.8%)	1,158 (78.9%)	2,179 (86.1%)
Previous experience of nonspousal violence
No	3,666 (63.6%)	409 (23.2%)	1,181 (80.4%)	2,076 (82.0%)
Yes	881 (15.3%)	139 (7.9%)	287 (19.6%)	455 (18.0%)
Missing	1,215 (21.1%)	1,215 (68.9%)	0 (0%)	0 (0%)

Appendix C

Table 2.

With Imputed Values Showing the Estimated Odds of Having a Tolerant Attitude Toward Domestic Violence.

Covariates	Odds Ratio	[95% Confidence Interval]
Type of location (reference: Urban)
Rural	1.3591	[0.9862, 1.8730]
Respondent’s religion (reference: Christianity)
Islam	1.1681	[0.8653, 1.5769]
Others	1.6723	[0.7468, 3.7449]
Difference in age between partners	1.0296	[0.9229, 1.1486]
Respondent’s age group in years (reference: 15–18)
19–30	0.8088	[0.5285, 1.2376]
31–40	0.6601	[0.4162, 1.0470]
41–50	0.9911	[0.5315, 1.8481]
Respondent’s level of education (reference: No formal education)
Primary	0.8408	[0.6233, 1.1340]
Secondary	0.7568	[0.5070, 1.1298]
Higher	0.2120	[0.1188, 0.3785]
Geopolitical zone (reference: North Central)
North East	1.5494	[1.0539, 2.2779]
North West	0.6414	[0.4723, 0.8711]
South East	1.7902	[1.0126, 3.1650]
South South	0.6447	[0.3827, 1.0862]
South West	0.5229	[0.3616, 0.7562]
Household wealth index (reference: Poorest)
Poorer	1.0826	[0.8049, 1.4561]
Middle	0.9105	[0.6065, 1.3667]
Richer	0.7676	[0.4690, 1.2563]
Richest	0.6763	[0.3848, 1.1886]
Type of relationship (reference: Monogamy)
Polygamy	1.1016	[0.8565, 1.4168]
Respondent’s level of involvement in household decision-making (reference: None)
Full responsibility	1.5640	[0.9247, 2.6453]
Joint responsibility	0.9233	[0.7602, 1.1214]
Difference in income level between respondent and partner (reference: None)
Respondent earns more than spouse	2.4034	[1.1856, 4.8720]
Respondent earns less than spouse	1.8041	[1.0571, 3.0790]
Difference in educational attainment between respondent and partner (reference: None)
Respondent has higher attainment than spouse	0.9966	[0.7133, 1.3924]
Respondent has lower attainment than spouse	1.0415	[0.8075, 1.3433]
Previous experience of spousal violence (reference: None)
Yes	1.5589	[1.1852, 2.0504]
Previous experience of nonspousal violence (reference: None)
Yes	0.5497	[0.3835, 0.7880]
Intercept	0.8018	[0.2736, 2.3497]

Appendix D

Count of Reasons Justifying SPV	Odds Ratio	[95% Confidence Interval]
Count of Reasons Justifying SPV	(Reference Outcome)	[95% Confidence Interval]
1
Type of location (reference: Urban)
Rural	1.1970	[0.6628, 2.1620]
Respondent’s religion (reference: Christianity)
Islam	1.4261	[0.7415, 2.7425]
Others	0.3337	[0.0612, 1.8180]
Difference in age between partners	0.9349	[0.7740, 1.1293]
Respondent’s age group in years (reference: 15–18)
19–30	0.7671	[0.3401, 1.7305]
31–40	0.5461	[0.2311, 1.2906]
41–50	1.0607	[0.3164, 3.5557]
Respondent’s level of education (reference: No formal education)
Primary	0.8139	[0.4543, 1.4583]
Secondary	1.1975	[0.5675, 2.5269]
Higher	0.5458	[0.2072, 1.4374]
Geopolitical zone (reference: North Central)
North East	1.5839	[0.7215, 3.4773]
North West	0.9372	[0.4957, 1.7717]
South East	1.7136	[0.6810, 4.3123]
South South	0.4616	[0.1976, 1.0785]
South West	0.6872	[0.3327, 1.4197]
Household wealth index (reference: Poorest)
Poorer	0.7912	[0.3716, 1.6845]
Middle	0.5904	[0.2712, 1.2855]
Richer	0.7848	[0.3499, 1.7599]
Richest	0.6596	[0.2415, 1.8016]
Type of relationship (reference: Monogamy)
Polygamy	1.1048	[0.6109, 1.9981]
Respondent’s level of involvement in household decision-making (reference: None)
Full responsibility	1.6604	[0.6828, 4.0380]
Joint responsibility	0.8349	[0.5236, 1.3313]
Difference in income level between respondent and partner (reference: None)
Respondent earns more than spouse	6.5990	[1.5023, 28.9868]
Respondent earns less than spouse	3.7470	[1.0464, 13.4179]
Difference in educational attainment between respondent and partner (reference: None)
Respondent has higher attainment than spouse	1.2946	[0.6505, 2.5764]
Respondent has lower attainment than spouse	1.3906	[0.8824, 2.1913]
Previous experience of spousal violence (reference: None)
	1.8293	[1.1262, 2.9714]
Previous experience of nonspousal violence (reference: None)
	0.3651	[0.2133, 0.6249]
Intercept	0.0786	[0.0146, 0.4248]
2
Type of location (reference: Urban)
Rural	1.5098	[0.9369, 2.4332]
Respondent’s religion (reference: Christianity)
Islam	1.0858	[0.5382, 2.1906]
Others	1.2447	[0.2919, 5.3067]
Difference in age between partners	0.8996	[0.7229, 1.1194]
Respondent’s age group in years (reference: 15–18)
19–30	0.8557	[0.4086, 1.7919]
31–40	0.8809	[0.3723, 2.0840]
41–50	2.6913	[0.9860, 7.3459]
Respondent’s level of education (reference: No formal education)
Primary	0.5413	[0.3042, 0.9631]
Secondary	0.6194	[0.3258, 1.1774]
Higher	0.1092	[0.0374, 0.3184]
Geopolitical zone (reference: North Central)
North East	2.3273	[1.0672, 5.0752]
North West	0.9807	[0.5215, 1.8444]
South East	2.7138	[0.8570, 8.5930]
South South	1.1722	[0.4019, 3.4188]
South West	0.6791	[0.3115, 1.4803]
Household wealth index (reference: Poorest)
Poorer	1.6954	[0.9101, 3.1585]
Middle	1.2680	[0.5411, 2.9713]
Richer	1.3420	[0.6498, 2.7716]
Richest	1.5852	[0.6711, 3.7445]
Type of relationship (reference: Monogamy)
Polygamy	0.9572	[0.5791, 1.5823]
Respondent’s level of involvement in household decision-making (reference: None)
Full responsibility	0.7265	[0.2849, 1.8527]
Joint responsibility	1.1077	[0.6839, 1.7942]
Difference in income level between respondent and partner (reference: None)
Respondent earns more than spouse	4.1259	[1.2381, 13.7490]
Respondent earns less than spouse	3.5950	[1.4996, 8.6184]
Difference in educational attainment between respondent and partner (reference: None)
Respondent has higher attainment than spouse	0.7804	[0.4035, 1.5097]
Respondent has lower attainment than spouse	0.8452	[0.5459, 1.3085]
Previous experience of spousal violence (reference: None)
	1.2830	[0.7671, 2.1461]
Previous experience of nonspousal violence (reference: None)
	0.7974	[0.4483, 1.4182]
Intercept	0.0312	[0.0060, 0.1627]
3
Type of location (reference: Urban)
Rural	1.2954	[0.6215, 2.7000]
Respondent’s religion (reference: Christianity)
Islam	0.9734	[0.4868, 1.9464]
Others	0.5881	[0.1196, 2.8926]
Difference in age between partners	0.8288	[0.6358, 1.0805]
Respondent’s age group in years (reference: 15–18)
19–30	0.7202	[0.2390, 2.1704]
31–40	0.5943	[0.1676, 2.1069]
41–50	0.8323	[0.1662, 4.1669]
Respondent’s level of education (reference: No formal education)
Primary	0.9296	[0.4520, 1.9118]
Secondary	0.5652	[0.1982, 1.6119]
Higher	0.1871	[0.0395, 0.8864]
Geopolitical zone (reference: North Central)
North East	2.4457	[1.0735, 5.5717]
North West	0.6372	[0.3359, 1.2088]
South East	4.4086	[1.5889, 12.2318]
South South	1.4943	[0.5380, 4.1500]
South West	1.9483	[0.9709, 3.9098]
Household wealth index (reference: Poorest)
Poorer	0.9591	[0.5346, 1.7208]
Middle	0.6965	[0.3304, 1.4683]
Richer	0.4013	[0.1232, 1.3076]
Richest	0.3935	[0.1267, 1.2224]
Type of relationship (reference: Monogamy)
Polygamy	0.8431	[0.5036, 1.4115]
Respondent’s level of involvement in household decision-making (reference: None)
Full responsibility	1.2626	[0.5588, 2.8527]
Joint responsibility	0.7780	[0.4887, 1.2385]
Difference in income level between respondent and partner (reference: None)
Respondent earns more than spouse	0.4316	[0.0851, 2.1890]
Respondent earns less than spouse	2.1815	[0.7906, 6.0196]
Difference in educational attainment between respondent and partner (reference: None)
Respondent has higher attainment than spouse	1.3630	[0.6406, 2.9001]
Respondent has lower attainment than spouse	1.0924	[0.6778, 1.7607]
Previous experience of spousal violence (reference: None)
	0.9900	[0.5735, 1.7090]
Previous experience of nonspousal violence (reference: None)
	0.3613	[0.1778, 0.7344]
Intercept	0.2234	[0.0261, 1.9114]
4
Type of location (reference: Urban)
Rural	1.1676	[0.6378, 2.1375]
Respondent’s religion (reference: Christianity)
Islam	0.9616	[0.4571, 2.0232]
Others	1.0470	[0.1645, 6.6629]
Difference in age between partners	1.1444	[0.8633, 1.5170]
Respondent’s age group in years (reference: 15–18)
19–30	0.6330	[0.2467, 1.6240]
31–40	0.4788	[0.1803, 1.2715]
41–50	0.3508	[0.0806, 1.5265]
Respondent’s level of education (reference: No formal education)
Primary	1.3802	[0.7065, 2.6963]
Secondary	0.9054	[0.3555, 2.3061]
Higher	0.2004	[0.0428, 0.9394]
Geopolitical zone (reference: North Central)
North East	1.1319	[0.4871, 2.6305]
North West	0.6212	[0.2808, 1.3742]
South East	1.7291	[0.5615, 5.3246]
South South	0.4672	[0.1495, 1.4599]
South West	0.3803	[0.1342, 1.0777]
Household wealth index (reference: Poorest)
Poorer	1.0651	[0.5150, 2.2025]
Middle	0.6989	[0.2922, 1.6717]
Richer	0.4021	[0.1365, 1.1841]
Richest	0.3882	[0.0789, 1.9088]
Type of relationship (reference: Monogamy)
Polygamy	1.8932	[1.0314, 3.4750]
Respondent’s level of involvement in household decision-making (reference: None)
Full responsibility	3.2325	[1.0262, 10.1826]
Joint responsibility	1.3857	[0.7465, 2.5720]
Difference in income level between respondent and partner (reference: None)
Respondent earns more than spouse	1.5030	[0.3059, 7.3840]
Respondent earns less than spouse	1.1071	[0.3229, 3.7956]
Difference in educational attainment between respondent and partner (reference: None)
Respondent has higher attainment than spouse	0.8868	[0.3803, 2.0678]
Respondent has lower attainment than spouse	1.1636	[0.6456, 2.0973]
Previous experience of spousal violence (reference: None)
	3.0133	[1.4697, 6.1783]
Previous experience of nonspousal violence (reference: None)
	0.8178	[0.3710, 1.8025]
Intercept	0.1377	[0.0114, 1.6616]
5
Type of location (reference: Urban)
Rural	2.0212	[1.1708, 3.4890]
Respondent’s religion (reference: Christianity)
Islam	1.3039	[0.8225, 2.0671]
Others	2.0514	[0.5931, 7.0946]
Difference in age between partners	1.1812	[0.9903, 1.4090]
Respondent’s age group in years (reference: 15–18)
19–30	0.8385	[0.4444, 1.5821]
31–40	0.5841	[0.2913, 1.1713]
41–50	0.9312	[0.3488, 2.4856]
Respondent’s level of education (reference: No formal education)
Primary	0.9117	[0.5338, 1.5571]
Secondary	0.7047	[0.3585, 1.3852]
Higher	0.1460	[0.0480, 0.4444]
Geopolitical zone (reference: North Central)
North East	1.5330	[0.8558, 2.7459]
North West	0.5813	[0.3614, 0.9350]
South East	0.7831	[0.2733, 2.2437]
South South	0.4847	[0.1873, 1.2542]
South West	0.2077	[0.0996, 0.4333]
Household wealth index (reference: Poorest)
Poorer	1.0380	[0.6250, 1.7241]
Middle	0.9997	[0.5986, 1.6695]
Richer	0.8463	[0.4030, 1.7773]
Richest	0.6635	[0.2356, 1.8688]
Type of relationship (reference: Monogamy)
Polygamy	1.1163	[0.7774, 1.6030]
Respondent’s level of involvement in household decision-making (reference: None)
Full responsibility	1.3222	[0.5936, 2.9450]
Joint responsibility	0.7826	[0.5549, 1.1038]
Difference in income level between respondent and partner (reference: None)
Respondent earns more than spouse	3.5011	[1.3028, 9.4089]
Respondent earns less than spouse	1.1874	[0.5278, 2.6717]
Difference in educational attainment between respondent and partner (reference: None)
Respondent has higher attainment than spouse	1.2427	[0.6367, 2.4255]
Respondent has lower attainment than spouse	0.9280	[0.6119, 1.4074]
Previous experience of spousal violence (reference: None)
	1.3705	[0.8382, 2.2409]
Previous experience of nonspousal violence (reference: None)
	0.6564	[0.3797, 1.1348]
Intercept	0.2656	[0.0629, 1.1223]

Note. SPV = spousal physical violence.

Authors’ Note

The views expressed in the submitted article are those of the authors and not an official position of the Johns Hopkins Bloomberg School of Public Health or Morgan State University.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ezinne Eze-Ajoku

Olukayode A. Sosina

Dataset

The datasets analyzed for the current study are available in the Measure DHS program repository.

Author Biographies

Ezinne Eze-Ajoku, MBBS, MPH, DrPH, is a physician with a background in health policy. Her research focuses on improving the implementation of health care policies using principles of organizational theory and behavior.

Oludolapo Fakeye, MA, PhD, is a senior policy analyst at The Hilltop Institute at the University of Maryland, Baltimore County. He is a health services researcher whose work evaluates the health and expenditure outcomes of health services payment and delivery initiatives targeted at individuals with chronic medical conditions, substance use disorders, and other high utilizers of health services.

Adejare Atanda, BDS, MPH, CPH, is a doctoral candidate at Morgan State University, School of Community Health and Policy. Trained primarily as a dentist, he holds an MPH and has federal, state and local U.S. experience as an epidemiologist and is familiar with the analysis of population health data from EHRs and electronic surveillance systems.

Olukayode A. Sosina, MSc, is a doctoral candidate at the Johns Hopkins School of Public Health. He received his MSc in biostatistics at the Harvard School of Public Health and his BSc in mathematics and statistics with First Class Honors from the University of Lagos, Nigeria. His research focuses on methods that investigate the biological mechanisms underlying genome-wide association signals.

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