[null, {"document_url": "https://link.springer.com/article/10.1007%2Fs10896-009-9288-6", "unformatted_src": "https://media.springernature.com/w306/springer-static/cover/journal/10896/25/3.jpg", "src": "https://media.springernature.com/w306/springer-static/cover/journal/10896/25/3.jpg"}, null]


Journal of Family Violence 
April 2010, Volume 25, Issue 3, pp 247–257 | Cite as

Domestic Violence and Female Genital Mutilation in Kenya: Effects of Ethnicity and Education



This paper investigates domestic violence against women, including definitions of ‘domestic violence;’ and investigates “Female Genital Mutilation.” Data for this paper are from three national household surveys in Kenya: ‘Demographic & Health Survey’ (2003), Afrobarometer (2003), and ‘Work, Attitudes, & Spending’ (2004). Previous research in many countries has found convincing evidence of a tendency for domestic violence to be less common in households where the respondent and/or spouse have more education. This paper adds a new factor: the respondent’s mother’s education also seems relevant to prevalence of GBV (perhaps because of childhood socialization). This pattern applies to both experience of violence, and attitudes to such violence. There also appears to be a strong link between ‘Female Genital Mutilation’ (female circumcision) and mother’s education level. In each case, more education is associated with less violence.


Gender-based violence Female genital mutilation Education Ethnic groups Kenya

Appendix: regression results

To assess whether or not the mother’s education significantly affects behavior, even after controlling for other variables, logistic regression is used for this appendix. Of the three surveys used in this paper, DHS was chosen for regression—as well as being the largest sample of these three surveys, it is the only survey with data on the respondent’s mother’s education. Logistic regression (unlike OLS regression, for example) is suitable for a zero/one dummy variable. In Table 8, there are two regressions; both use experience of (one type of) GBV as the dependent variable. This dependent variable is coded as 1 if the female respondent reported that she had been punched by her husband, or zero if she said she had not (it is not possible to assess how accurate such responses are; but DHS interviewers are careful not to ask if her husband or anyone else is within earshot of the interview). Regression 1 and Regression 2 both include education, husband’s age, respondent’s ethnicity, and region of the household. Regression 2 is identical to Regression 1 except that the second regression adds an extra explanatory variable: respondent’s mother’s education. To ensure comparability between Regression 1 and Regression 2, both samples are restricted to households for which the respondent’s mother’s education level is reported.
Table 8

Regression results

DHS. ** indicates statistically significant at the 1% level; * represents significant at 5%

In Table 8, each ethnic group is compared with the reference group, Kikuyu (the largest sample in DHS). Table 8 indicates that in Regression 1, the Maasai and Somali ethnic groups both differ significantly at the 5% level from the Kikuyu. However, this is not a persuasive test: using the 5% significance level, we would expect one in twenty of the (nine) ethnic group coefficients to be significant, even if there were no causal relationship. A more reliable approach is to use the overall significance level reported in logistic regression: this measures whether there is a significant difference between ethnic groups as a whole. No coefficient is reported for this test, but a statistical significance level is (shown as an asterisk, if statistically significant; or no asterisk, if not statistically significant, in Table 8). In Table 8, the difference between ethnic groups is statistically significant in Regression 1 but not statistically significant in Regression 2. This tells us that adding one extra variable to Regression 2, mother’s education, can explain (at least a large amount of) the apparent difference between ethnic groups in Regression 1. It may be helpful, in future research, to add other data to regression analysis, such as respondent’s husband’s mother’s education.

Table 8 indicates that the respondent’s mother’s education seems to have a statistically significant impact on the respondent’s risk of being punched by her husband, as shown by the two asterisks in the ‘mother’s education’ row of Regression 2. This is consistent with the fact that Fig. 6 appears more successful than Fig. 5, in explaining respondent’s risk of experiencing GBV. Note also the first row of coefficients in Table 8, which indicate that husband’s education is statistically significant in both Regression 1 and Regression 2; this indicates that the more educated a husband is, the less likely he is to punch his wife.



© Springer Science+Business Media, LLC 2009



Simister, J.G. J Fam Viol (2010) 25: 247. https://doi.org/10.1007/s10896-009-9288-6



