Ethnic, Educational, and Occupational Dimensions of Domestic Violence in Southern Nigeria.

Ethnic, Educational, and Occupational Dimensions of Domestic Violence in Southern Nigeria.

Omotoso T. Kehinde¹; Adewara O. Sunday^2*; Adeleke Oluwayemisi²

¹Department of Economics, University of Ilorin, Ilorin, Nigeria

²Department of Economics, Redeemer’s University, Ede, Nigeria

*Corresponding Author

DOI: https://doi.org/10.51244/IJRSI.2024.11120001

Received: 09 November 2024; Accepted: 19 November 2024; Published: 26 December 2024

ABSTRACT

Purpose: This study examined the effect of a married woman’s ethnicity, education, and occupation on domestic violence in Southern Nigeria. The specific goals are to determine the impact of ethnicity, education, and occupation on domestic violence in Southern Nigeria, and assess the determinants of domestic violence in Southern Nigeria.

Design/Methodology/Approach: The data from the 2018 Demographic Health Survey (DHS) was analysed using the logit and ordered logit regression techniques. The study considered married women aged between 15- 50 years in the Southern part of Nigeria.

Findings: Our findings show that the Ethnicity of a married woman has a positive effect on domestic violence in Southern Nigeria, In the region, being Yoruba, Hausa, and Ijaw reduces the odds of a woman suffering domestic violence. Education has a positive impact on domestic violence, indicating that, the more educated the partner is, the lesser the odds of a woman suffering domestic violence. Gainfully employed women are more prone to suffer domestic violence in Southern Nigeria, that is, having a job increases the odds of a woman suffering domestic violence.

Conclusion: At the end of the study, it was discovered that ethnicity and occupation have a positive effect on domestic violence, while education on the other hand is negatively related to domestic violence in Southern Nigeria.

Keywords: Domestic violence, Ethnic Dimensions, Occupational Dimensions, Educational Dimensions

INTRODUCTION

Domestic violence is an issue of global concern in many societies, and it has become a common occurrence in recent times. This is because domestic violence is abuse that is perpetrated against people in close relationships, often in a household environment like a home or family. Although several policies have been implemented to curb this prevalence, the challenge persists in both developed and developing climes. According to (Dahlberg& Krug, 2002; UNICEF, 2005), domestic violence occurs globally and women from all ethnic, educational, and occupational backgrounds experience domestic violence.

Furthermore, in some developing nations, particularly in West Africa, domestic violence is prevalent, justified, and accepted in some cultures, such as the Southern part of Nigeria. In Nigeria, reports reveal a “shockingly high” level of violence against women (Afrol News, 2007). Also, Amnesty International (2007) says that one-third of women are believed to have been victims of physical, sexual, and psychological violence executed primarily by their husbands, partners or fiancée and fathers, while girls are frequently forced into early marriage and are being punished if they attempt to escape from their husbands.

Similarly, according to World Health Organization research on violence against women, the lifetime prevalence of intimate partner violence among women who had ever been in a committed relationship was 30% globally and 37% in Africa. Statistics for Nigeria indicate the prevalence of intimate partner violence (IPV) ranges from 31% to 61% for psychological/emotional abuse, 20% to 31% for sexual violence, and 7% to 31% for physical assault. In addition, intimate partner violence prevalence ranged from 29% in the Southwest, 78.8% in the Southeast, and 41% in the South. Thus, implying that the increase affects physical and mental health such as physical injuries, depression, anxiety, and post-traumatic stress disorder. Also, it can affect the productivity of women, thus leading to loss of work period, increased medical expenses

Taking into consideration that intimate partner violence has increased in Southern Nigeria in recent times, irrespective of the women’s educational and occupational background, and has led to their untimely deaths. Also, very few studies have addressed the problem of domestic violence with a focus on the woman’s ethnicity, educational background, and occupation. Therefore, this study intends to examine the impact of a married woman’s ethnicity, educational level, and occupation on domestic violence.

LITERATURE REVIEW

Several theories have been propounded regarding domestic violence; the cycle of violence theory explains domestic violence as an abusive relationship where it has three distinct stages which are the phase of tension building, the acute or violent phase, and the honeymoon stage. Another theory is the dependency relation theory, where the abused is dependent on the abuser. Also, the resource theory was propounded by Goode in 1971 where it was stated that all social systems, including the family, depend on force or the fear of intimidation to some extent to perpetuate violence. This implies that the more social, personal, and financial resources someone can control, the more power they can convene. We also have the feminist theory which is based on gender and power inequalities in opposite-sex relationships. The theory posits that the core reasons for intimate partner violence result from living in a society where men are encouraged to act aggressively while encouraging women to abstain from violence. Lastly, the social learning theory by Albert Bandura in 1973 believed that violence occurs because of children’s exposure to it during their formative years. This implies that violence is learned through role models provided by the family, such as parents, siblings, relatives, and boyfriends/girlfriends, either directly or indirectly (i.e., witnessing violence), is reinforced in childhood, and continues in adulthood as a coping mechanism for stress or as a means of conflict resolution.

Several empirical studies have been carried out on the problem of domestic violence. Benebo et al (2018) examined the impact of individual and societal factors on intimate partner violence in Nigeria using cross-sectional data based on the Nigerian Demographic Health Survey (2013). Their findings revealed that one in four women experienced intimate partner violence in the country, however, women with higher status had fewer cases of violence. Community norms were found to increase intimate partner violence. Likewise, Ifeanyi-obi et al (2017) investigated the socio-economic factors that affect domestic abuse in rural women crop farmers in Nigeria’s Orlu agricultural zone. 80 rural women crop farmers were chosen using a multi-stage sampling approach and data was collected using structured interviews and both descriptive and inferential statistical methods. The study revealed that arguments over money, avoidance of household duties, disobedience to the partner, and discussing marital issues with friends were the leading causes of domestic violence. Also, they found that age, length of marriage, educational level, religion, size of their family, and the number of wives were critical factors that affect domestic violence.

The study of Oyediran and Feyisetan (2017) investigated the underlying factors affecting women’s exposure to domestic violence in Nigeria using multilevel logistic regression. They utilized the Nigeria Demographic and Health Survey (2013) and found that 15.2% of married or cohabiting women experienced intimate partner violence in the year before the survey. Also, household, and societal factors, including the woman’s age, the age difference between her and her spouse, the type of marriage she has, her education, her religion, her ethnicity, and the area of habitation had significant effects on IPV. Similarly, women’s status, and gender normative norms all independently and incrementally affect the likelihood that a woman will experience domestic violence.

Also, Ahinkorah et al (2018) investigated the relationship between women’s decision-making abilities and IPV among 18 Sub-Saharan African nations using the Demographic and Health Survey (2016). The results showed that women with the ability to make decisions were more likely to report ever experiencing IPV. Also, there was little chance of intimate partner violence among young women. Similarly, Compared to Muslim women, women who are members of other religious groups and Christians have higher rates of intimate partner violence. Women who have partners with no education were found to experience intimate partner violence than women whose partners have higher education, primary education, and secondary education. Compared to unemployed women, those who were working had higher odds of encountering domestic violence.

In 2020, Okorie et al examined how the culture of silence affects domestic violence and the trend of domestic violence between 2013 and 2016 in Orlu, Nigeria using a cross-sectional questionnaire and sampled 440 respondents (220 males and women). This study found that all 440(100%) respondents cited that disobeying or ‘talking back’ to the male partner was the primary cause of domestic violence. Likewise, unemployment, refusal to have sex, delay in serving meals, alcohol influence, suspicion of cheating, and disagreement over funds were crucial contributory factors of IPV. However, the study found that effective partner communication helped to reduce the cases of intimate partner violence.

Oluwole et al (2020) examined the prevalence and predictors of lifetime IPV among women in an Urban community in Lagos State Nigeria. 400 participants were sampled using a questionnaire and found a 73.3% lifetime prevalence of IPV. Also, factors that were significant predictors of IPV were found to be; a partner drinking alcohol, a partner having other sexual partners, a partner being employed, and witnessing parental violence. In a similar study, Larsen et al (2020) investigated attitudes regarding violence, the frequency of physical, sexual, and emotional intimate partner violence in Myanmar, as well as the link between wealth and intimate partner violence between 2015 and 2016. Their study revealed that the overall prevalence of intimate partner violence (physical, sexual, and emotional) was 20.6%. Less severe physical intimate partner violence was reported by 14.8% while 4.4% reported severe physical violence. The prevalence of sexual and emotional violence was 2.8% and 13.1% respectively. The results revealed that intimate partner violence against ever-married women in Myanmar was present, that many women justify beating, and that wealth and intimate partner violence are associated.

Theoretical Framework

The theoretical basis of this study is the resource learning theory propounded by Goode in 1971 and the social learning theory by Bandura in 1973. According to the resource learning theory, all social systems, including the family, depend on force or the fear of intimidation to some extent. The more social, personal, and financial resources someone can control, the more power they can convene. However, according to William Goode (1971), who propounded the theory, the more resources a person has, the less they will use force openly. He further explained that a husband who wants to be the dominant person in the family, but has little education, has a job low in prestige and income, and lacks interpersonal skills, may use violence to maintain the dominant position. Likewise, the social learning theory of domestic violence is a sociological theory that aims to explain domestic violence by examining how power and resources are distributed within intimate relationships. It implies that power and resource disparities between partners are the root causes of domestic violence.

Figure 1 below presents a model of feminist theory. It begins with the belief that systems exist that oppress and work against individuals. The model then shows that oppression is based on intersecting identities that can create discrimination and exclusion. Traditionally, husbands are assumed to be superior to their wives thereby giving them undue advantage to impose their wills on their wives and oftentimes oppress them.

The main concerns of the feminist theory are sexual discrimination, gender discrimination, racial discrimination, equality, difference, and others.

MODEL SPECIFICATION

This study aims to examine the impact of a married woman’s ethnicity, educational level, and occupation on domestic violence. In line with the social learning theory and the study of Marium (2014), this study will adapt and modify the study, and the functional form of the model is specified below as;

DV=

The OLS equation for the model is specified mathematically, incorporating the variables into the following function, we have;

……….. (2)

Where;

WAi= Women abuse; AGEi= Age; AGSi= Age square; HHSi= Household Size; WGTi= Weight; HGTi= Height; WRANKi= Wife rank; WTHi= Wealth; HEDUi= Husband’s education year; ETHi= Ethnicity; REGi= Religion; PWTRi= Pipe water; EDUi= Education level; OCCi= Occupation; POLYi= Polygamy; OCCi= Occupation.

ɛi = Error term

β0= Intercept and β1 – β14= Estimated coefficient of the independent variables.

Estimation Techniques

The logit and ordered logit Regression techniques are used for our analysis. Logit regression was used to analyse the likelihood of a binary outcome (Being abused domestically or not) occurring. This variable takes the value of 1 if a woman suffered domestic violence and 0 if otherwise.

Ordered logistic regression is an extension of binary logistic regression, normally used when the dependent variable is ordered. Since there are five types of domestic violence a married woman can suffer, these are ordered from a minimum of zero to a maximum of five.

Data and Sources

To account for the effect of religion and wealth on the possibility of being abused as a married woman in Nigeria, this study will use a secondary cross-sectional data set from the Demographic Health Survey (DHS) 2018 dataset for Nigeria.

Descriptive Analysis

Descriptive statistics describe the features of the study’s data. They include the mean, median, maximum, minimum, standard deviation, and number of observations per variable used in the study, as shown in the table below.

The mean is the average of all the data for each variable obtained by summing up the series and dividing by the number of observations. The median is the exact middle value of the analyzed data. The maximum is the data with the highest value of each variable. The minimum is the lowest of all data in use per variable. Standard deviation is a more accurate and detailed estimate of dispersion, which measures the variation of a data set from its meaning. The mean and median are used to measure central tendency, and the standard deviation measures the level of dispersion among the variables.

From the table below, there are a total number of 12,780 married women respondents for the study. There are five types of domestic violence a woman may suffer.

Table 1: Summary Statistics

Variable	Observation	Mean	Standard Deviation	Minimum	Maximum
Abuse	12780	0.50806	1.273536	0	5
Abuse probability	12780	0.169953	0.375606	0	1
Age	12780	36.20391	7.293338	16	49
Age2	12780	1363.912	528.3617	256	2401
Household size	12780	6.051487	2.521713	1	25
Education year	12780	8.981142	4.37563	0	20
Weight	12780	650.9002	149.7861	338	1489
Height	12780	1590.568	61.46431	1040	1863
Wife rank	12780	0.277543	0.718364	0	7
Wealth	12780	3.66831	1.135646	1	5
Polygamist	12780	0.160642	0.367214	0	1
Husband education year	12780	9.838106	4.329818	0	20
Religion	12780	1.956573	0.596629	1	4
Ethnic	12780	22.21667	33.35232	1	10
Pipe water	12780	0.622144	0.48487	0	1
Higher education	12780	0.231768	0.421978	0	1
Lower education	12780	0.391628	0.488133	0	1
Urban	12780	0.564476	0.495845	0	1
Husband job type	12780	4.803521	2.88089	0	9
Job type	11698	4.731749	1.996796	1	12
Yoruba	12780	0.276682	0.447376	0	1
Igbo	12780	0.415024	0.492745	0	1
Hausa	12780	0.009311	0.096049	0	1
Fulani	12780	0.005086	0.071138	0	1
Tiv	12780	0.713772	0.452015	0	1
Christian	12780	0.675039	0.525091	0	4
Muslim	12780	0.161894	0.43822	0	4
Catholic	12780	0.195931	0.396931	0	1

Source: Author’s computation

PRESENTATION AND DISCUSSION OF RESULTS

Table 2: Determinants of domestic violence in Southern Nigeria: Logit regression analysis

	(Model 1)	(Model 2)	(Model 3)
Variables	abuse1	abuse1	abuse1
	0.00382	0.0365
Age	(0.0311)	(0.0318)
Age2	4.89e-05	-0.000388
	(0.000425)	(0.000436)
Household size	0.0860***	0.0594***	0.0622***
	(0.00872)	(0.00924)	(0.00924)
Education year	-0.0448***	0.0462***	0.0445***
	(0.00649)	(0.0114)	(0.0115)
Weight	-0.00151***	-0.00124***	-0.00120***
	(0.000194)	(0.000200)	(0.000200)
Height	-0.00122***	-0.000783*	-0.000828**
	(0.000413)	(0.000421)	(0.000420)
Wife rank	0.107***	0.0381	0.0367
	(0.0303)	(0.0654)	(0.0654)
Poorer		0.300**	0.306**
		(0.126)	(0.126)
Middle		0.108	0.121
		(0.123)	(0.124)
Richer		0.0364	0.0481
		(0.127)	(0.127)
Richest		-0.638***	-0.620***
		(0.144)	(0.144)
Polygamous		0.337**	0.344***
		(0.132)	(0.132)
Husband’s education year	-0.00595	-0.0690***	-0.0684***
	(0.00635)	(0.0116)	(0.0116)
Muslim		0.0339	0.0331
		(0.0625)	(0.0625)
Fulani		0.276	0.264
		(0.305)	(0.305)
Hausa		-0.992***	-0.982***
		(0.381)	(0.380)
Igbo		0.138**	0.135**
		(0.0612)	(0.0612)
Yoruba		-0.767***	-0.768***
		(0.0798)	(0.0798)
Pipe water		0.0979*	0.0942*
		(0.0542)	(0.0542)
Higher education		-0.236***	-0.224***
		(0.0843)	(0.0843)
Lower education		0.698***	0.696***
		(0.0765)	(0.0765)
Urban		0.136**	0.136**
		(0.0535)	(0.0535)
Age group:
20-24			0.228
			(0.413)
25-29			0.235
			(0.399)
30-34			0.238
			(0.398)
35-39			0.335
			(0.397)
40-44			0.178
			(0.399)
45-49			0.373
			(0.399)
Wife’s job	0.456***	0.558***	0.579***
	(0.0887)	(0.0904)	(0.0904)
Husband’s job		-0.00492	-0.00559
		(0.0104)	(0.0104)
Constant	0.558	-1.217	-0.686
	(0.835)	(0.858)	(0.758)
Observations	12,780	12,780	12,780

Source: Author’s computation (Standard errors in parentheses), *** p<0.01, ** p<0.05, * p<0.1 represent variables significant at 99%, 95% and 90% respectively.

We considered six models using different independent variables based on theoretical and empirical studies to analyze domestic violence’s determinants in Southern Nigeria. Model 1 shows that a unit increase in household size will increase the odds of a woman suffering domestic violence by 0.0860; this variable is significant at a 99% level. A unit increase in education year of a woman reduces the odds of her suffering domestic violence from her husband by 0.0448. This variable is significant at the 99% level. A unit increase in weight reduces the odds of a woman suffering domestic violence by 0.00151, this variable shows a 99% level of significance. A unit increase in height reduces the odds of a woman suffering domestic violence by 0.00122 and it is significant at a 99% level, A unit increase in the rank of the wife increases the odds of her suffering domestic violence by 0.107, at a 99% level of significance. A woman residing in an urban area will increase the odds of her suffering domestic violence by 0.136 compared to a woman living in a rural area. An employed married woman has a higher odds of suffering domestic violence by 0.456 compared to an unemployed married woman.

In model 2, variable household size is still significant at 99. A unit increase in the year of education of a woman increases the odds of her experiencing domestic violence by 0.0462. However, a unit increase in weight reduces the odds of a woman suffering domestic violence by 0.00124. Likewise, a unit increase in height reduces the odds of a woman suffering domestic violence. A poorer married woman has the odds of suffering more domestic violence compared to a wealthy woman. Being polygamous also increases the odds of a woman suffering domestic violence by 0.337. Most of the variables in Model 1 retained their significance in Models 2 and 3 even when more independent variables were introduced to the models.

Table 3: Determinants of domestic violence in Southern Nigeria: Logit regression analysis

	(Model 4)	(Model 5)	(Model 6)
Variables	abuse1	abuse1	abuse1
Age	0.0348	0.0243	0.0249
	(0.0340)	(0.0340)	(0.0340)
Age2	-0.000324	-0.000192	-0.000201
	(0.000464)	(0.000464)	(0.000464)
Household size	0.0521***	0.0525***	0.0522***
	(0.00945)	(0.00949)	(0.00948)
Education year	0.0468***	0.0458***	0.0457***
	(0.0120)	(0.0122)	(0.0122)
Weight	-0.00119***	-0.00121***	-0.00118***
	(0.000209)	(0.000210)	(0.000210)
Height	-0.00107**	-0.00106**	-0.00104**
	(0.000435)	(0.000440)	(0.000441)
Wife rank	0.0434	0.0527	0.0577
	(0.0659)	(0.0664)	(0.0664)
Poorer	0.281**	0.237*	0.235*
	(0.129)	(0.131)	(0.131)
Middle	0.0551	-0.00866	-0.0304
	(0.127)	(0.129)	(0.129)
Richer	-0.0178	-0.0774	-0.101
	(0.132)	(0.133)	(0.134)
Richest	-0.844***	-0.889***	-0.914***
	(0.151)	(0.153)	(0.153)
Polygamous	0.382***	0.383***	0.369***
	(0.134)	(0.135)	(0.135)
Husband education year	-0.0534***	-0.0533***	-0.0534***
	(0.0122)	(0.0123)	(0.0123)
Muslim	0.0553	0.0736
	(0.0638)	(0.0649)
Fulani	0.745**
	(0.328)
Hausa	-0.898**
	(0.390)
Igbo	0.175***
	(0.0646)
Yoruba	-0.848***
	(0.0848)
Pipe water	0.0257	0.0108	0.0131
	(0.0568)	(0.0568)	(0.0570)
Higher education	-0.108	-0.103	-0.0968
	(0.0871)	(0.0875)	(0.0876)
Lower education	0.685***	0.682***	0.684***
	(0.0805)	(0.0812)	(0.0812)
Urban	0.106*	0.122**	0.131**
	(0.0559)	(0.0565)	(0.0566)
Husband’s Job Type:
People who do not know	1.073***	1.100***	1.097***
	(0.246)	(0.247)	(0.247)
Professional/technician	0.894***	0.960***	0.987***
	(0.299)	(0.300)	(0.301)
Clinical workers	1.106***	1.152***	1.154***
	(0.242)	(0.242)	(0.242)
Salesman	0.955***	0.998***	1.000***
	(0.239)	(0.239)	(0.240)
Skilled manual	0.828***	0.846***	0.837***
	(0.254)	(0.255)	(0.255)
Unskilled manual	1.042***	1.068***	1.068***
	(0.258)	(0.259)	(0.259)
Others	0.934***	0.963***	0.958***
	(0.241)	(0.241)	(0.241)
Wife’s Job Type:
Administrative and managerial workers	0.180	0.215	0.222
	(0.272)	(0.272)	(0.272)
Office workers	-0.0950	-0.0821	-0.101
	(0.265)	(0.265)	(0.265)
Sales and related workers	0.389***	0.403***	0.405***
	(0.127)	(0.127)	(0.127)
Service workers	0.335**	0.317**	0.315**
	(0.149)	(0.150)	(0.150)
Installation workers	3.138***	3.126***	3.117***
	(0.883)	(0.893)	(0.896)
Construction workers	0.258*	0.260*	0.254*
	(0.137)	(0.137)	(0.137)
Agriculture workers	0.712***	0.713***	0.718***
	(0.176)	(0.177)	(0.177)
Fulani		0.303	0.190
		(0.391)	(0.400)
Hausa		-1.299***	-1.415***
		(0.447)	(0.455)
Ibiobio		-0.150	-0.228
		(0.244)	(0.246)
Igala		–	–
Igbo		-0.188	-0.215
		(0.219)	(0.219)
Ijaw		-0.558**	-0.635***
		(0.237)	(0.239)
Yoruba		-1.226***	-1.324***
		(0.228)	(0.233)
Other ethnic groups		-0.286	-0.360
		(0.222)	(0.224)
People who do not know		0.158	0.0644
		(0.582)	(0.584)
Christian			0.157**
			(0.0693)
Islam			0.251**
			(0.125)
Traditionalist			0.348
			(0.331)
Constant	-1.650*	-1.072	-1.180
	(0.936)	(0.966)	(0.968)
Observations	11,691	11,593	11,593

Source: Author’s computation, Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 represent variables significant at 99%, 95% and 90% respectively.

Model 4, in Table 3 shows that a unit increase in household size increases the odds of a woman suffering domestic violence by 0.0521 in agreement with the earlier regressions in Table 2. A unit increase in education year increases the odds of a woman suffering domestic violence by 0.0468, at a 99% level of significance. A unit increase in weight reduces the odds of a woman suffering domestic violence by 0.00119, this variable is significant at 99%. A unit increase in height reduces the odds of a woman suffering domestic violence by 0.00107. A poorer married woman has the odds of suffering higher domestic violence compared to the poorest woman at a significant level of 0.28; this variable is significant at 95%. At a significance level of 99%, the richest woman has 0.844 less odds of suffering domestic violence compared to the poorest woman.

Being in a polygamous home increases the odds of a woman suffering domestic violence by 0.382 at a 99% significant level. A unit increase in the husband’s education year reduces the odds of a woman suffering domestic violence by 0.0534. A Fulani woman has higher odds of suffering domestic violence at a significant level of 0.745 compared to an Ekoi woman, this variable is significant by 95%. A Hausa woman has a less odd of 0.898 of suffering domestic violence compared to an Ekoi woman at a 95% significant level. Being an Igbo woman increases the odds of a woman suffering domestic violence by 0.175 compared to being an Ekoi woman. If a woman is less educated compared to her husband, her odds of suffering domestic violence increase by 0.685, a 99% significant level. Living in an urban area increases the odds of a woman suffering domestic violence by 0.106.

Being married to a professional/ technician increases the odds of a woman suffering domestic violence by 1.073, at a significance of 99%. If a woman is married to a cleric, her odds of suffering domestic violence increase by 1.106, at a significant 99% significant level. A woman whose husband is a sales manager has a higher odd of 0.955 to suffer domestic violence at a 99% significant level.

At a significant level of 99%, a woman whose job is sales has a higher odds of suffering domestic violence at 0.389. Being a service worker increases the odds of a woman suffering domestic violence by 0.335, this variable is 95% significant. Being a maintenance worker increases the odds of a woman suffering domestic violence by 3.138. Being a construction worker increases the odds of a woman suffering domestic violence by 0.258. Being a farmer increases the odds of a woman suffering domestic violence by 0.712, at a 99% significant level.

In models 5 and 6, most variables retained their signs and significant levels in Table 4 even after controlling for other relevant variables.

Ordered logit results are presented below in Tables 4 and 5 to further examine the determinants of domestic violence in southern Nigeria.

Table 4: Determinants of the degree of domestic violence in Southern Nigeria: (Ordered Logit regression analysis)

	(Model 1)	(Model 2)	(Model 3)
Variables	Domestic Violence	Domestic Violence	Domestic Violence
Age	0.0305		0.0232
	(0.0315)		(0.0336)
Age square	-0.000293		-0.000149
	(0.000429)		(0.000458)
Household size	0.0526***	0.0558***	0.0472***
	(0.00902)	(0.00904)	(0.00923)
Education year	0.0420***	0.0407***	0.0443***
	(0.0112)	(0.0113)	(0.0118)
Weight	-0.00128***	-0.00124***	-0.00121***
	(0.000199)	(0.000198)	(0.000207)
Height	-0.000651	-0.000691*	-0.000963**
	(0.000417)	(0.000417)	(0.000432)
Wife rank	-0.00200	0.000132	0.0115
	(0.0645)	(0.0644)	(0.0651)
Poor	0.227*	0.232*	0.199l
	(0.124)	(0.124)	(0.127)
Middle	0.0696	0.0819	0.0191
	(0.122)	(0.122)	(0.125)
Rich	0.0261	0.0388	-0.0251
	(0.126)	(0.126)	(0.130)
Richer	-0.660***	-0.644***	-0.858***
	(0.143)	(0.143)	(0.150)
Polygamist	0.375***	0.374***	0.405***
	(0.131)	(0.130)	(0.133)
Husband Education year	-0.0618***	-0.0612***	-0.0454***
	(0.0115)	(0.0115)	(0.0120)
Pipe water	0.0942*	0.0900*	0.0203
	(0.0534)	(0.0535)	(0.0562)
More educated	-0.180**	-0.169**	-0.0456
	(0.0831)	(0.0831)	(0.0859)
Less educated	0.687***	0.685***	0.680***
	(0.0753)	(0.0754)	(0.0794)
Urban	0.138***	0.138***	0.114**
	(0.0529)	(0.0529)	(0.0551)
Husband Job Type:
don’t know			1.060***
			(0.245)
Professional/ technician			0.989***
			(0.296)
Clinical			1.096***
			(0.240)
Sales man			1.031***
			(0.238)
Skilled manual			0.856***
			(0.253)
Unskilled manual			1.149***
			(0.257)
Other jobs			0.999***
			(0.240)
Wife’s Job Type:
Professional/ technician			0.0708
			(0.271)
Administrative and managerial workers			-0.165
			(0.263)
Office workers			0.372***
			(0.125)
Sales workers			0.287*
			(0.148)
Service workers			2.024***
			(0.518)
Installation workers			0.245*
			(0.136)
Construction Workers			0.750***
			(0.173)
Agricultural workers			-11.98
			(474.0)
Others			-12.12
			(1,164)
Wife employed	0.531***	0.551***
	(0.0901)	(0.0900)
Husband employed	0.00536	0.00446
	(0.0103)	(0.0103)
Muslim	0.0328	0.0318	0.0541
	(0.0612)	(0.0611)	(0.0623)
Fulani	0.256	0.251	0.690**
	(0.301)	(0.301)	(0.321)
Hausa	-0.937**	-0.923**	-0.832**
	(0.381)	(0.381)	(0.391)
Igbo	0.117*	0.114*	0.154**
	(0.0606)	(0.0606)	(0.0640)
Yoruba	-0.737***	-0.739***	-0.828***
	(0.0795)	(0.0795)	(0.0845)
Age group:
20-24		0.176
		(0.411)
25 – 29		0.121
		(0.398)
30 – 34		0.150
		(0.397)
35 – 39		0.246
		(0.396)
40 – 44		0.0959
		(0.398)
45 – 49		0.316
		(0.398)
Observations	12,780	12,780	11,698
Version	3	3	3
r2_p	0.0425	0.0426	0.0477
chi2	790.3	793.6	825.7
Ic	4	4	9
Rank	29	33	43

Source: Author’s computation, Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 represent variables significant at 99%, 95% and 90% respectively.

Model 1 shows that a unit increase in household size increases the odds of suffering a higher degree of domestic violence by 0,0526, at a significance of 99%. At a 99% significance level, a unit increase in the education year of a married woman increases the odds of suffering higher domestic violence by 0.0420. A unit increase in weight reduces the odds of a woman suffering a higher degree of domestic violence by 0.00128, which is significant at 99%. A unit increase in height increases the odds of a woman suffering a higher degree of domestic violence by 0.000963 and it is significant at 90%. A poor married woman has the odds of suffering a higher degree of domestic violence by 0.227 compared to the poorest woman, which is significant at 99%.

A richer married woman has the odds of suffering less domestic violence by 0.660 compared to the poorest woman, and this variable is significant at 99%. A polygamist has high odds of suffering a higher degree of domestic violence by 0.375, at a 99% significant level. At a 99% level of significance, a unit increase in the husband’s education year reduces the odds of his wife suffering a higher degree of domestic violence by 0.0618. Availability of piped water in the home increases the odds of suffering a higher degree of domestic violence by 0.0942, at a 90% significant level. A more educated woman has fewer odds of suffering a higher chance of domestic violence by 0.180 compared to a less educated woman. A less educated woman has high odds of suffering a higher chance of domestic violence by 0.687. At a 99% significance level, living in an urban area increases the odds of a married woman suffering a higher level of domestic violence by 0.138. An employed wife has greater odds of suffering higher domestic violence by 0.531, and this variable is significant at a 99% level. Being Hausa reduces the odds of a woman suffering a higher degree of domestic violence by 0.937 compared to others, and this variable is 99% significant. At a 90% significant level, being Igbo increases the odds of a woman suffering a higher degree of domestic violence by 0.117 compared to others. Being Yoruba reduces the odds of a woman suffering a higher degree of domestic violence by 0.737 compared to others.

Most of the variables retained their signs and levels of significance in models 2 and 3. This was like the regression results in Table 2 when the dependent variable was the probability of being abused as discussed earlier.

Table 5: Determinants of the degree of domestic violence in Southern Nigeria: Ordered Logit regression analysis

	(Model 4)	(Model 5)
Variables	Domestic violence	Domestic Violence
Age	0.0126	0.0143
	(0.0336)	(0.0337)
Age square	-1.24e-05	-3.54e-05
	(0.000458)	(0.000458)
Household size	0.0470***	0.0468***
	(0.00926)	(0.00923)
Edu year	0.0441***	0.0436***
	(0.0120)	(0.0120)
Weight	-0.00124***	-0.00120***
	(0.000208)	(0.000208)
Height	-0.000929**	-0.000915**
	(0.000436)	(0.000437)
Wife rank	0.0219	0.0257
	(0.0654)	(0.0653)
Poor	0.154	0.158
	(0.129)	(0.129)
Middle	-0.0458	-0.0753
	(0.127)	(0.127)
Rich	-0.0869	-0.121
	(0.131)	(0.132)
Richer	-0.945***	-0.910***
	(0.151)	(0.151)
Polygamist	0.399***	0.384***
	(0.133)	(0.133)
Husband Edu year	-0.0453***	-0.0448***
	(0.0121)	(0.0122)
Christian		0.220***
		(0.0681)
Islam		0.346***
		(0.124)
Traditionalist		0.317
		(0.323)
Fulani	0.512	0.331
	(0.380)	(0.390)
Hausa	-0.959**	-1.152**
	(0.444)	(0.453)
Ibibio	0.0314	-0.0835
	(0.235)	(0.237)
Igala	-16.14	-17.10
	(649.5)	(1,004)
Igbo	0.0546	0.0134
	(0.211)	(0.212)
Ijaw	-0.235	-0.349
	(0.229)	(0.232)
Kanuri	-16.78	-17.84
	(2,266)	(3,510)
Tiv	-16.17	-17.18
	(1,092)	(1,692)
Yoruba	-0.942***	-1.093***
	(0.220)	(0.226)
Other ethnic groups	-0.0183	-0.126
	(0.214)	(0.216)
People who don’t know	0.580	0.450
	(0.578)	(0.579)
pipe water	0.00543	0.00884
	(0.0562)	(0.0563)
More educated	-0.0417	-0.0295
	(0.0862)	(0.0864)
Less educated	0.681***	0.682***
	(0.0800)	(0.0801)
Urban	0.122**	0.134**
	(0.0557)	(0.0558)
Husband’s Job Type:
Don’t know	1.076***	1.069***
	(0.245)	(0.245)
Professional/technician	1.032***	1.076***
	(0.297)	(0.298)
Clinical workers	1.130***	1.134***
	(0.241)	(0.241)
Sales worker	1.066***	1.066***
	(0.238)	(0.239)
Skilled manual workers	0.871***	0.859***
	(0.253)	(0.253)
Unskilled manual workers	1.163***	1.164***
	(0.257)	(0.258)
Others	1.015***	1.006***
	(0.240)	(0.240)
Administrative workers	0.0967	0.119
	(0.271)	(0.271)
Office	-0.151	-0.180
	(0.263)	(0.264)
Sales and related workers	0.387***	0.386***
	(0.126)	(0.126)
Services workers	0.282*	0.278*
	(0.148)	(0.148)
Installation workers	1.968***	1.939***
	(0.521)	(0.521)
Construction workers	0.249*	0.237*
	(0.136)	(0.137)
Agriculture workers	0.760***	0.767***
	(0.174)	(0.174)
jobtype	-15.09	-15.95
	(2,251)	(3,478)
jobtype	-15.24	-16.21
	(5,551)	(8,597)
Muslim	0.0719
	(0.0632)
Observations	11,698	11,698
Version	3	3
r2_p	0.0513	0.0519
chi2	887.3	898.4
Ic	11	12
Rank	50	52

Source: Author’s computation, Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 represent variables significant at 99%, 95% and 90% respectively

Model 4, in Table 5 shows that a unit increase in household size increases the odds of a woman suffering a higher degree of domestic violence by 0.0470, at a significant level of 99%. A unit increase in the education year of a married woman increases the odds of a woman suffering a higher degree of domestic violence by 0.0441. A unit increase in weight reduces the odds of a married woman suffering domestic violence by 0.00124, at a significant level of 99%. A unit increase in height reduces the odds of a woman suffering from domestic violence by 0.000929, A richer woman has lower odds of suffering from domestic violence by 0.945 at a 99% significant level. A unit increase in the husband’s education year reduces a woman suffering domestic violence by 0.0453, at a 99% significant level.

Model 5 and 6 results further confirmed all the results in Table 3 under logit regressions. All the regression results in our ordered logit regression confirmed all the results and analysis of the probability of a married woman suffering domestic violence as presented in Tables 2 and 3 and as explained earlier.

CONCLUSION

In conclusion, this study examined the effect of ethnicity, education and occupation on domestic violence in Southern Nigeria using logit and ordered logit regression analysis. The findings revealed the existence of five distinct forms of domestic violence, with a significant portion of women, approximately one in ten, experiencing a combination of two out of these five forms. At the end of the study, it was discovered that ethnicity and occupation have a positive effect on domestic violence, while education on the other hand is negatively related to domestic violence in Southern Nigeria.

Therefore, it is recommended that the government strengthen laws, enact thorough legislation that defines domestic abuse, create legal safeguards for victims, and hold offenders accountable. Domestic abuse should be made illegal, restraining orders should be put in place, and victims should be given support and legal assistance. Also, the government should facilitate prevention and education activities for women by supporting them educationally, small-scale business financing, community enlightenment, and schools. Other initiatives may include eradicating domestic violence workshops, encouraging healthy partnerships, and teaching conflict resolution and non-violent communication techniques. Prevention initiatives should also focus on married women, children and adolescents to stop the cycle of violence

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