Empowering women economically is more important than personal and socio-cultural empowerment. Analysis of 2022 Kenya Demographic and Health Survey

Empowering women economically is more important than personal and socio-cultural empowerment. 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Analysis of 2022 Kenya Demographic and Health Survey Boaz Nabimanya, Edison Mayanja, Miria Kyarikunda, Dianah Nkamusiima, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4138861/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Nov, 2025 Read the published version in BMC Women's Health → Version 1 posted 21 You are reading this latest preprint version Abstract Background Empowering women economically may boost household income, economic growth, the adoption of healthcare services, and the elimination of poverty. This means that when women are economically empowered, they are also personally and socio- culturally empowered. Studies have revealed that women economic empowerment is still low, particularly in developing countries like Kenya. This paper explores the determinants of women empowerment among married women in Kenya. Understanding women’s empowerment is necessary to overcoming poverty, achieving economic development and gender equality. Methods We analyzed secondary data from the 2022 Kenya Demographic and Health Survey. For the final analysis, we used a weighted sample of 18,312 currently married women. All frequencies and percentages in the results section are weighted. At the multivariate stage of analysis, the effect of explanatory variables on women empowerment was investigated using multilevel mixed effects logistic regression model. We computed adjusted Odds Ratio (AOR) with 95% confidence interval (95% CI). Variables with a P-value of less than 0.05 in the multi variable binary logistic regression analysis were considered statistically significant predictors of the outcome variable. Results Out of all women who are economically empowered, 80% are empowered in all the three dimensions of empowerment. Out of 18, 312 women, 61% are both personally and social-culturally empowered, 20% are social-culturally and economically empowered, while 19% are personally and economically empowered. This indicates that economic empowerment plays an important role in the formation of personal and social-cultural empowerment. Conclusions Generally, women empowerment in our study was low (17.7%). It is highly affected by socio demographic and economic characteristics of women and husbands’ characteristics. This study indicates that educating women, improving their economic status through employment opportunities, empowering women to be head of household will enhance their economic empowerment. Women empowerment personal empowerment economic empowerment socio-cultural empowerment married women Figures Figure 1 Background The global development plan of today places a greater emphasis on women's empowerment, which is closely associated with a number of development outcomes. The fifth Sustainable Development Goal (SDG-5) focuses on achievement of gender equality and empowerment of all women and girls by 2030 [ 1 ]. Despite the existing large body of work on the theory, conceptualization and operationalization of the women empowerment, there is no universal definition or indicator for achievement of empowerment [ 2 ]. Whereas several scholars have defined women empowerment variably, there is a general agreement that it refers to the process through which individuals attain “the ability to make choices” under conditions in which choice was previously denied [ 3 , 4 ]. As per the World Bank, empowerment refers to an individual's ability to make intentional choices and translate them into desired outcomes. For women this can happen if they have the ability to make choices about own wellbeing [ 5 ]. Understanding women’s empowerment is necessary to overcoming poverty, achieving economic development and gender equality. Women empowerment is conceptualized to be multidimensional in nature taking into account several facets including at personal, economic, social-cultural or community, and multidimensional levels [ 2 , 6 , 7 ]. Personal empowerment relates to taking control of individual own life-decisions. Decision-making authority is frequently used to measure the bargaining power of women [ 8 ]. Typically, "who" makes a certain decision is examined in order to gauge participation in decision-making. Women's empowerment and bargaining power are frequently measured by the degree to which they engage in intra-household decision-making processes, either alone or in conjunction with their spouses [ 9 , 10 ]. On the other hand, social-cultural empowerment refers to all elements that encompass components, circumstances, and influences that mold a person's personality and may have an impact on his or her behavior, attitude, choices, and actions [ 11 ]. Information has been shown to be a valuable resource for shaping norms, ideas, values, attitudes, behaviors, habits, and life styles of individuals that result from social, educational, religious, and cultural upbringing [ 12 , 13 ]. While information is necessary for empowering everyone, several studies have shown that in some specific communities, women have limited access to information platforms such as radios, television [ 14 , 15 ]. Lastly, women’s economic empowerment refers to a process that changes the lives of women and girls from one in which they have little agency and little power to one in which they have access to resources and economic advancement [ 16 ]. Women who are economically empowered have more access to financial services, employment, property and other productive assets, skill development, and market knowledge, among other economic resources and possibilities [ 16 , 17 ]. All three dimensions of women empowerment function concurrently [ 18 ] and are jointly influenced by several factors at individual, household and societal/community levels including a woman’s age, education, marital status, religion, residence, number of children and health [ 19 – 21 ]; or due to political, economic, and cultural norms [ 22 , 23 ]. For instance, on the political front, presence of strong policy frameworks that support women in supporting their rights over the family issues [ 24 ]. Similarly on the economic front, women who are economically empowered are likely to improve the wellbeing of family members and the community at large [ 25 ]. Moreover, strong political and economic systems are known to influence individual’s and community social-cultural norms [ 26 , 27 ]. Kenya has implemented several policies and legal frameworks to support women’s empowerment, such as the Sexual Offences Act 2006, the Prevention against Domestic Violence Act 2015, the Policy on Eradication of FGM 2019, and the National Policy on Gender and Development 2019. Despite these government’s efforts, there is still low women empowerment in the country. According to the 2022 Kenya Demographic and Health Survey report [ 28 ], 34% of currently married women do not make decisions about their own health care, major household purchases and visits to their family or relatives, either by themselves or jointly with their husband. This shows low personal empowerment for women, which has been shown to be associated with domestic violence, mismanagement of family income, and poverty [ 29 ]; and, poor health, disparities in allocation of household resources, medical care and education [ 30 ]. While women's involvement in decision-making may boost household economic growth, the adoption of healthcare services, and the elimination of poverty, studies have revealed that women's autonomy in making decisions is low, particularly in developing nations [ 31 , 32 ]. In this paper, we analyzed a national representative sample dataset from the 2022 Kenya Demographic and Health Survey (KDHS) in order to determine the factors associated with the different women empowerment dimensions of personal, economic, social-cultural and multidimensional empowerment. Methods Study setting We analyzed data from a nationally representative population-based cross-sectional household survey – the 2022 KDHS [ 28 ]. Data collection took place between 17th February to 31st July 2022. The 2022 KDHS employed a two-stage stratified sample design. At first stage, equal probability selection method was used to select 1,692 clusters independently from each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households was used as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, for some clusters that had fewer than 25 households, all households from these clusters were selected into the sample. Detailed description of the methodology for the 2022 KDHS is available elsewhere [ 28 ]. Study population The 2022 KDHS had 42,022 households. We considered only 7,663 households that met the inclusion criteria of married women aged 15–49 years. Data for the resulting sample of 18,312 married women was analyzed during preparation of this paper. Dependent variables The primary study variable of interest is women empowerment conceptualized at four levels, namely; personal, economic, social-cultural and multidimensional empowerment. Firstly, personal empowerment is measured using two indicators for a woman having the power to make decisions related seeking care for own health and visitations to her family members and relatives. Secondly, economic empowerment is measured using three indicators for a woman having a say the use of her own income/earnings, purchase of large household properties such as land or house equipment, and having a say on use of her husband’s or partner’s income or earnings. Thirdly, social-cultural empowerment is measured in terms of a woman’s access to information on a daily or weekly basis through print media, radio or television. Lastly, a woman was considered to be multidimensionally empowered if all the three levels of personal, economic and social-cultural empowerment were fulfilled. For all the four dimensions, a woman’s empowerment was measured on a binary scale. Specifically, a woman who responded “yes” on all of the items for each level of empowerment is considered empowered, otherwise a “no” is assigned. Independent variables : We considered several individual-, household-level variables available in the questionnaire including; religion (catholic, protestant, evangelical churches, Africa instituted churches, muslim and others), highest education level of household head and respondent (none, primary, secondary, higher), working status (employed, not employed), sex of household head (male, female), age of respondent (categorized in 5-year groups), type of residence (urban, rural), ethnicity (embu, kalenjini, kamba, kikuyu, kisii, luhya, somali, taita-taveta, luo, maasaai, meru, swahili, others), number of children living, and whether the respondent was currently residing with her husband/partner (stay together, stays elsewhere). Women’s justification of wife beating is also included as an explanatory variable – indicating a latent variable for social norms at the community level. Specifically, respondents were asked whether or not beating one’s wife was justified under five circumstances, namely if she: (a) goes out without telling her husband, (b) neglects the children, (c) argues with her husband, (d) refuses to have sex with her husband, and (e) burns the food. A woman who agreed that a man is justified in hitting or beating his wife in one or more of the five scenarios is scored a “yes”, else a “no” to imply justification of wife-beating norms. Data analysis All data management and analysis was implemented in STATA version 15.0 [ 33 ] [REF]. Percentages of women who were empowered at all the four different dimensions of personal, economic, social-cultural and multidimensional levels were studied separately. We use the proportioned and positioned Venn diagrams to visually examine the relative overlap of the different dimensions of women empowerment. This was achieved through the use of the pvenn2 command in STATA [ 34 ] which ensures that each of the proportions of the different dimensions of women’s empowerment (the circles, the outside rectangle, and the set intersections) is proportional to the population value. We compute basic descriptive statistics in form of frequencies and percentages to understand distributional differences between variables of interest and the four dimensions of women empowerment i.e., the primary dependent variables. Background characteristics are summarized according to whether or not women had attained personal, economic, social-cultural or multidimensional empowerment. We present weighted estimates of proportions for categorical variables. We use the Pearson’s chi-square test to examine whether there are differences in proportions of empowered women versus those who are not empowered. We assessed independent associations between respondents’ sociodemographic characteristics and attainment of the different levels of empowerment using a multilevel mixed effects logistic regression model with identifiers for counties and clusters as random variables to account for variation between counties and clusters respectively. After taking into account individual-level fixed effects, county random effects help to determine how much variation in women empowerment between counties, while cluster random effects help us to determine the variation in women empowerment between different clusters within counties. We fit a separate multivariable mixed effects logistic regression model for each dependent variable to identify explanatory factors for the different dimensions of women empowerment. This is achieved through the use of the svy:melogit command in STATA, which takes the sample design into account and provides inferences for the entire study population. For each dependent variable, a multivariable adjusted model included all explanatory variables irrespective of statistical significance. All tests are two tailed and a p-value < 0.05 is considered significant to facilitate interpretation and inferences. As such, we present the results as adjusted odds ratios (aOR) for fixed effects and variances of the two random effects with corresponding 95% Confidence Intervals. Results Descriptive Characteristics Table 1 shows frequencies and percentages of empowered women aged 15-49 years, across all the four dimensions of empowerment. Overall, of all the 18,312 married women whose data was analyzed, we observe higher proportions of women who were empowered at personal (74%) and social-cultural (81%) levels. This, however, was not the case for economic (22%) and multidimensional (17.7%) empowerment. In terms of age, we observe lower proportions of empowerment for younger women when compared to their older counterparts across all the four dimensions of empowerment. Intuitively, this is not an unexpected observation. Similarly, we observe higher proportions of empowerement for those residing in urban versus rural areas, and this increases with increasing educational attainment. With regard to religion, we do not observe clear differences in proportions of empowered women, except for the Muslims whose proportions are low compared to other religions. In terms of ethnicity, the Masai, Kalenjini, and Somali women are less empowered when compared to other ethnicities. In cases when the head of the household was male, there were more empowered women for social-cultural, economic and multidimensional levels. Additionally, women with large families (five or more living children) had lower proportions of empowerment compared to those with smaller families. Not surprisingly, we observe that employed women are more empowered compared to those who are not currently working, and this is consistent across all the four empowerment dimensions. Furthermore, women whose partners are educated and employed are more empowered than their counterparts. Specifically, on the multidimensional scale, 20% of women whose husbands are currently working are empowered compared to 3.4% of those whose husbands are not working. On the other hand, there seems to be no differences in proportions of empowered women, with respect to whether they live together with their partners or not. Lastly, we observe high proportions of empowered women among those who did not justify norms of wife beating compared to those who justify wife-beating norms. TABLE 1 HERE Intersectionality of different forms of empowerment Figure 1 illustrates the intersectionality of the different dimensions of women empowerment by relative size. Almost all women who are empowered at a personal level are also social-culturally empowered. However, only a small proportion of married women who are empowered at both personal and social-cultural levels. On the centrally, almost all women who are economically empowered are also empowered at personal and social-cultural levels. More specifically, out of all women who are economically empowered, 80% are empowered in all the three dimensions of empowerment. Out of 18, 312 women, 61% are both personally and social-culturally empowered, 20% are social-culturally and economically empowered, while 19% are personally and economically empowered. This indicates that economic empowerment plays an important role in the formation of personal and social-cultural empowerment. FIGURE 1 HERE Analytical results Table 2 shows the odds ratios, variances and their 95% confidence intervals of the four mixed effects logistic regression models, in which all variables of interest were included. For all fixed effects, statistical significance at p-value < 0.05 for the aOR estimates is fulfilled when the associated 95% CI does not contain a 1. In general, explanatory variables of age, area of residence, education of the woman, employment status of the woman and her partner, woman’s employment status, and justification of wife-beating norms are significantly associated with women’s empowerment at personal, economic, social-cultural and multidimensional levels. Personal empowerment: At the personal level, older women seem to be more empowered when compared to younger ones. For instance, the odds of being personally empowered for women aged 45 – 49 years (aOR=2.45, 95% CI [1.79, 3. 36]) are higher than for those aged 35 – 39 years (aOR=1.59, 95% CI [1.17, 2.16]), and these are statistically significantly higher than for those of women aged 15-19 years. A similar trend is observed for educated women – those who have attained secondary and post-secondary levels are 1.6 (aOR=1.61, 95% CI [1.30, 2.01]) and 2.8 (aOR=2.8, 95% CI [2.25, 3.48]) times more likely to be empowered at the personal level, when compared to those without education. Similarly, women who are employed have higher odds of being personally empowered (aOR= 1.39, 95% CI [1.25, 1.55]) when compared to those who are not employed. On the other hand, women who live in rural areas and those who justify norms associated with beating have lower odds of being empowered at the personal level. Specifically, women who accepted wife beating as a social norm are 0.6 times less likely to be personally empowered compared to those who do not accept (aOR=0.60, 95% CI [0.52, 0.70]). Religion, ethnicity, family size, and education of a woman’s partner are not significantly associated with women’s personal-level empowerment. Social-cultural empowerment: We observe similar trends for factors associated with social-cultural and personal empowerment. Specifically, higher odds of social-cultural empowerment appear to be increasing with increasing age, higher education levels for both women and their partners, as well as for employed women and their partners. . For instance, women with post-secondary education are about 6 times more likely to be social-culturally empowered than those who are not educated (aOR=6.20, 95% CI [3.90, 9.86]). Similarly, women whose partners are educated to post-secondary level are 3.86 times more likely to be social-culturally employed compared to those whose partners are not educated (aOR=3.86, 95% CI [2.78,5.34]). Furthermore, employed women (aOR=1.3, 95% CI [1.12, 1.51]) and those whose partners are employed (aOR=1.79, 95% CI [1.45, 2.20]) are more likely to be empowered at the social-cultural level when to ther counterparts who or whose partners do not work. On the hand, however, women who stay in rural areas (aOR=0.67, 95% CI [0.54, 0.84]) and those who stay in female-headed households (aOR= 0.65, 95% CI [0.51, 0.85]) are less likely to be social-culturally empowered when compared to their respective counterparts. Religion, ethnicity, whether a woman stays with her partner or not, and justification of wife-beating norms are not significantly associated with women’s social-cultural empowerment. Economic empowerment: In general, only three factors are positively associated with women’s economic empowerment, namely, educational achievement, being employed, and their partner’s employment status. More specifically, women who have attained post-secondary education level are 2.65 times more likely to be economically empowered than those with no education (aOR=2.65, 95% CI [1.96, 3.58]). Women with primary and secondary levels of education have slightly lower odds at 1.43 times to be economically empowered when compared to those without education. Women whose partners are employed are 2.47 times more likely to be economically empowered compared to those whose partners are not employed (aOR=2.47, 95% CI [1.76, 3.47]). Not surprisingly, employed women are about 9 times more economically empowered than those who are not employed (aOR= 8.89, 95% CI [6.6, 11.97]). On the other hand, women who stay in rural areas are 0.77 times less likely to be economically empowered compared to those who stay in urban areas (aOR=0.77, 95% CI [0.66,0.90]). The factors of age, religion, ethnicity, sex of the household head, family size, whether a woman stays with her partner or not, and justification of wife-beating norms are not statistically significantly associated with women’s economic empowerment. Multidimensional empowerment: We observe that age of a woman, educational attainment, employment status of the woman, and employment status of the woman’s partner are positively and statistically significantly associated with multidimensional empowerment. Overall, all other age groups are about 2 times more likely to be multidimensionally empowered when compared to those aged 15-19 years, and there appears to be no clear-cut trend. There seems to be some trend in terms of women’s educational attainment with those who have attained post-secondary, secondary and primary levels education having 3.79, 2.13 and 1.90 odds to be multidimensionally empowered than those with no education. Women whose are employed are 7.63 times (aOR=7.63, 95% CI [5.64, 10.33]) and those whose partners are employed are 2.43 times (aOR=2.43, 95% CI [1.68, 3.51]), more likely to be multidimensionally empowered than those who or whose partners are not working, respectively. On the other hand, women residing in rural areas, with large family size, and those who justification of wife beating norms are less likely to be empowered at the multidimensional scale when compared to their respective counterparts. For instance, women who stay in rural areas are 0.76 less likely to be multidimensionally empowered compared to those who stay in urban areas (aOR=0.76, 95% CI [0.66, 0.89]). Further, women who accept the social norm of wife beating are 0.83 times likely to be multidimensionally empowered compared to those who do not accept the wife beating norm (aOR=0.83, 95% CI [0.71, 0.97]). County and cluster variance effects: We observe a relatively large variability of women who are empowered at the personal level between counties, than those between clusters within counties. The same trend is observed for social-cultural empowerment. However, this variance is slightly larger at the social-cultural than at the personal empowerment level, potentially indicating the wide social-cultural differences between counties in Kenya. For economic and multidimensional empowerment, we observe relatively small variances for both county and cluster variables. Discussions We analyzed a national representative sample dataset from the 2022 Kenya Demographic and Health Survey (KDHS) in order to determine the factors associated with different dimensions of women empowerment, namely; personal, economic, social-cultural and multidimensional empowerment. Whereas personal and economic empowerment are measured using indicators related to decision-making power of the woman, social-cultural empowerment takes into account access to information that helps one to make life-choices. Multidimensional empowerment takes into account all the three forms of empowerment. Overall, there were higher proportions of women who were empowered at the personal and social-cultural levels. However, the findings reveal that only 22% and 17.7% of married women were empowered at the economic and multidimensional scales respectively. This primary finding indicates the dominant coexistence of economic empowerment of women with dimensions of personal and social-cultural empowerment. In other words, the study findings reveal that 78% of married were not economically empowered. This shows that women who are personally and socio-culturally empowered are not necessary economically empowered. However, almost all women who were economically empowered, were also personally and socio-culturally empowered. This could be due to the absence of skills training, job search assistance and social restrictions that are not binding [ 16 ]. Economic empowerment involves increasing a woman’s access to resources or earnings and increases. This necessitates creating conditions for women to make choices and take up opportunities. For instance, education and employment of women have been documented elsewhere to have a direct influence on their economic empowerment [ 35 , 36 ]. This is consistent with the findings in this study. The findings indicate that the odds of women’s economic empowerment increase with increasing levels of education, this is in agreement with findings [ 19 , 20 ] who revealed that women with higher levels of education are more likely to participate in choosing their own medical treatment and decision-making at household level respectively. This association is consistent with other studies in the literature in Africa [ 20 ]. Women with higher levels of education are more likely to be in a better position to have paid employment, and they are also more likely to possess the knowledge required to negotiate their involvement in household decisions that leads to improved economic empowerment [ 37 ]. This suggests that the ministry of education and other program managers could empower women through increasing the number of women enrolled in education and achieving higher levels of education, which is a crosscutting concern. Our findings reveal that the odds of women empowerment are more for those women who were currently working as compared to those who were not currently working. This concurs with findings of [ 20 , 22 ] who revealed that women's empowerment and involvement in household decision-making depends on their employment status. In additional the results also agree with those of [ 36 ] which demonstrated how women's empowerment is increased when they are employed. This is because working women will be able to pay for their own health care as well as other significant expenses, which in turn restricts women's ability to participate in decisions about their own health care, household purchases, and travel to see friends and relatives. Our findings show that having a partner who is employed as highly associated with economic empowerment. This is not unexpected and indeed the importance of engaging men at the household, community and policy levels, in interventions on women’s economic empowerment is gaining increasing recognition amongst development practitioners. By engaging men, there is an intentionality to deliberately question gender norms and power dynamics, so that they can embrace better co-operation and sharing of activities at the household level – an approach to women's economic empowerment [ 38 , 39 ]. Conclusions Given that over 80% of all married women who were economically empowered, were also multidemensionally empowered, indicates the importance of economic empowerment. This shows that there is a need for a more integrated and comprehensive approach to women’s economic empowerment that addresses the societal norms and structural barriers to women’s full participation in economic activities. Declarations Ethical statement This paper utilized secondary data of the Demographic Health Survey program, and permission to use these publicly available data was obtained from http://www.dhsprogram.com before data download and subsequent statistical analysis. As such, no ethical reviews and approvals were required before or during preparation of the present manuscript. Consent for publication This is not applicable during preparation of this paper. This paper is based on secondary data of the Demographic Health Survey program. There was no interaction with human subjects during preparation of this manuscript. Availability of data and materials The datasets analyzed during preparation of this paper are available in the DSH Program repository, available at http://www.dhsprogram.com Acknowledgments The authors are very grateful to the MEASURE DHS International Program that provided them with the necessary dataset used for the study. In addition, the authors are very grateful to the financial support extended by African Population and Health Research Center (APHRC) to attend research workshops that resulted into this article. We are also grateful to staff of Department of Economics and Statistics of Kabale University, and members of the Gender Pathways working group at APHRC for their insights during the two writing workshops in Kabale and Naivasha respectively. Competing interest The authors declare that they have no conflicts of interest. Funding No funding Authors' contributions BN, MK, WRK, and DTK conceptualized the paper. BN, MK, DN, WRK and DTK drafted the original manuscript. DTK and EM conducted the data management and statistical analysis. DTK and SKM reviewed and edited the draft manuscript. DTK and SKM securing funding that facilitated two writing workshops – 1 in Kabale, Uganda and 1 in Nairobi, Kenya –, which enabled development of this paper. 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Tables Table 1: Proportion of empowered women across the four dimensions of empowerment by women’s background characteristics Women Characteristics No. of Women Personal Social-cultural Economic Multi-dimensional Overall 18,312 13,560 (74.0%) 14,833 (81.0%) 4,023 (22.0%) 3,237 (17.7%) Age groups 15-19 590 336 (56.9%) 381 (64.6%) 43 ( 7.3%) 25 ( 4.2%) 20-24 2,764 1,848 (66.9%) 2,233 (80.8%) 484 (17.5%) 358 (13.0%) 25-29 4,014 2,968 (73.9%) 3,317 (82.6%) 953 (23.7%) 760 (18.9%) 30-34 3,556 2,653 (74.6%) 2,846 (80.0%) 797 (22.4%) 663 (18.6%) 35-39 3,348 2,512 (75.0%) 2,682 (80.1%) 783 (23.4%) 636 (19.0%) 40-44 2,281 1,833 (80.4%) 1,891 (82.9%) 531 (23.3%) 447 (19.6%) 45-49 1,759 1,410 (80.2%) 1,483 (84.3%) 432 (24.6%) 348 (19.8%) Place of residence Urban 6,627 5,224 (78.8%) 5,806 (87.6%) 1,869 (28.2%) 1,587 (23.9%) Rural 11,685 8,336 (71.3%) 9,027 (77.3%) 2,154 (18.4%) 1,650 (14.1%) Woman’s education level No education 3,038 1,978 (65.1%) 996 (32.8%) 166 ( 5.5%) 71 ( 2.3%) Primary 7,340 5,276 (71.9%) 6,287 (85.7%) 1,507 (20.5%) 1,128 (15.4%) Secondary 5,160 3,890 (75.4%) 4,849 (94.0%) 1,211 (23.5%) 1,009 (19.6%) Higher 2,774 2,416 (87.1%) 2,701 (97.4%) 1,139 (41.1%) 1,029 (37.1%) Religion Catholic 3,106 2,388 (76.9%) 2,728 (87.8%) 735 (23.7%) 616 (19.8%) Protestant 6,015 4,554 (75.7%) 5,431 (90.3%) 1,517 (25.2%) 1,251 (20.8%) Evangelical churches 3,951 2,849 (72.1%) 3,536 (89.5%) 995 (25.2%) 793 (20.1%) African instituted churches 1,464 1,142 (78.0%) 1,281 (87.5%) 351 (24.0%) 278 (19.0%) Moslem 2,995 2,056 (68.6%) 1,273 (42.5%) 242 ( 8.1%) 158 ( 5.3%) Other denominations 781 571 (73.1%) 584 (74.8%) 183 (23.4%) 141 (18.1%) Ethnicity Embu 234 171 (73.1%) 220 (94.0%) 74 (31.6%) 60 (25.6%) Kalenjin 3,756 2,671 (71.1%) 3,150 (83.9%) 644 (17.1%) 526 (14.0%) Kamba 1,568 1,334 (85.1%) 1,395 (89.0%) 506 (32.3%) 439 (28.0%) Kikuyu 2,355 1,839 (78.1%) 2,291 (97.3%) 763 (32.4%) 670 (28.5%) Kisii 996 793 (79.6%) 920 (92.4%) 285 (28.6%) 226 (22.7%) Luhya 2,255 1,714 (76.0%) 2,129 (94.4%) 480 (21.3%) 395 (17.5%) Luo 2,832 2,150 (75.9%) 2,313 (81.7%) 518 (18.3%) 426 (15.0%) Maasai 462 204 (44.2%) 370 (80.1%) 92 (19.9%) 58 (12.6%) Meru 1,239 953 (76.9%) 922 (74.4%) 327 (26.4%) 228 (18.4%) Mijikenda - Swahili 911 619 (67.9%) 631 (69.3%) 236 (25.9%) 148 (16.2%) Somali 1,459 944 (64.7%) 278 (19.1%) 34 ( 2.3%) 11 ( 0.8%) Taita-Taveta 202 141 (69.8%) 187 (92.6%) 56 (27.7%) 45 (22.3%) Other 43 27 (62.8%) 27 (62.8%) 8 (18.6%) 5 (11.6%) Sex of household head Male 14,066 10,281 (73.1%) 11,581 (82.3%) 3,134 (22.3%) 2,526 (18.0%) Female 4,246 3,279 (77.2%) 3,252 (76.6%) 889 (20.9%) 711 (16.7%) Number of living children No children 945 656 (69.4%) 765 (81.0%) 219 (23.2%) 177 (18.7%) One - two 7,006 5,232 (74.7%) 6,152 (87.8%) 1,805 (25.8%) 1,502 (21.4%) Three - four 6,097 4,615 (75.7%) 5,161 (84.6%) 1,396 (22.9%) 1,137 (18.6%) Five or more 4,264 3,057 (71.7%) 2,755 (64.6%) 603 (14.1%) 421 ( 9.9%) Employment status Not employed 8,400 5,802 (69.1%) 5,831 (69.4%) 425 ( 5.1%) 329 ( 3.9%) Employed 9,912 7,758 (78.3%) 9,002 (90.8%) 3,598 (36.3%) 2,908 (29.3%) Partner's education level No education 2,779 1,857 (66.8%) 977 (35.2%) 195 ( 7.0%) 105 ( 3.8%) Primary 6,656 4,784 (71.9%) 5,587 (83.9%) 1,380 (20.7%) 1,041 (15.6%) Secondary 5,373 4,011 (74.7%) 4,916 (91.5%) 1,275 (23.7%) 1,045 (19.4%) Higher 3,504 2,908 (83.0%) 3,353 (95.7%) 1,173 (33.5%) 1,046 (29.9%) Residence of partner Living together 14,776 10,763 (72.8%) 11,954 (80.9%) 3,259 (22.1%) 2,618 (17.7%) Staying elsewhere 3,536 2,797 (79.1%) 2,879 (81.4%) 764 (21.6%) 619 (17.5%) Partner's employment status Not employed 2,517 1,839 (73.1%) 1,224 (48.6%) 131 ( 5.2%) 85 ( 3.4%) Employed 15,795 11,721 (74.2%) 13,609 (86.2%) 3,892 (24.6%) 3,152 (20.0%) Justification of wife beating norms Not Justified 11,321 8,935 (78.9%) 9,714 (85.8%) 2,866 (25.3%) 2,430 (21.5%) Justified 6,991 4,625 (66.2%) 5,119 (73.2%) 1,157 (16.5%) 807 (11.5%) Table 2: Odds ratios, variances estimates and their 95% confidence intervals and variances for explanatory variables of the four dimensions of women empowerment Women Characteristics Personal Social-Cultural Economic Multi-dimensional Constant 2.91 (1.44, 5.89) 1.16 (0.49, 2.71) 0.02 (0.01, 0.06) 0.01 (0.00, 0.03) Age group (Ref = 15-19) 20-24 1.08 (0.82, 1.41) 1.43 (1.00, 2.05) 1.75 (1.13, 2.70) 1.97 (1.18, 3.29) 25-29 1.38 (1.05, 1.81) 1.66 (1.19, 2.32) 1.64 (1.05, 2.54) 2.10 (1.21, 3.65) 30-34 1.55 (1.13, 2.12) 1.95 (1.37, 2.78) 1.50 (0.95, 2.38) 2.12 (1.17, 3.82) 35-39 1.59 (1.17, 2.16) 2.15 (1.46, 3.17) 1.34 (0.85, 2.09) 1.92 (1.06, 3.49) 40-44 2.21 (1.63, 3.01) 2.80 (1.75, 4.48) 1.25 (0.76, 2.08) 1.91 (1.05, 3.48) 45-49 2.45 (1.79, 3.36) 2.52 (1.75, 3.63) 1.49 (0.93, 2.38) 2.19 (1.22, 3.93) Residence area (Ref = Urban) Rural 0.79 (0.66, 0.93) 0.67 (0.54, 0.84) 0.77 (0.66, 0.9) 0.76 (0.66, 0.89) Highest education level (Ref = None) Primary 1.37 (1.14, 1.64) 2.49 (1.94, 3.19) 1.43 (1.15, 1.78) 1.90 (1.38, 2.61) Secondary 1.61 (1.30, 2.01) 3.79 (2.72, 5.27) 1.43 (1.08, 1.91) 2.13 (1.50, 3.01) Higher 2.80 (2.25, 3.48) 6.20 (3.90, 9.86) 2.65 (1.96, 3.58) 3.79 (2.62, 5.50) Religion (Ref = Catholic) Protestant 0.94 (0.81, 1.09) 0.94 (0.76, 1.17) 1.00 (0.89, 1.13) 1.02 (0.90, 1.16) Evangelical churches 0.86 (0.73, 1.01) 1.04 (0.81, 1.35) 1.07 (0.89, 1.29) 1.06 (0.89, 1.27) African instituted churches 0.94 (0.72, 1.24) 0.91 (0.65, 1.26) 1.09 (0.86, 1.39) 0.97 (0.68, 1.37) Moslem 0.85 (0.61, 1.19) 0.93 (0.63, 1.38) 1.03 (0.70, 1.51) 0.89 (0.73, 1.08) Other denominations 0.91 (0.67, 1.25) 0.66 (0.42, 1.02) 1.06 (0.66, 1.70) 0.98 (0.59, 1.62) Ethnicity (Ref = Embu) Kalenjin 0.85 (0.55, 1.33) 0.61 (0.29, 1.26) 0.79 (0.57, 1.1) 0.87 (0.54, 1.38) Kamba 0.86 (0.60, 1.23) 0.87 (0.45, 1.70) 1.09 (0.71, 1.67) 1.07 (0.65, 1.74) Kikuyu 1.13 (0.77, 1.65) 1.43 (0.69, 2.96) 1.07 (0.77, 1.48) 1.12 (0.74, 1.69) Kisii 0.98 (0.70, 1.39) 0.61 (0.27, 1.36) 1.08 (0.71, 1.65) 0.98 (0.58, 1.64) Luhya 0.84 (0.50, 1.42) 1.01 (0.51, 2.01) 0.91 (0.68, 1.21) 0.95 (0.61, 1.46) Luo 0.85 (0.51, 1.44) 1.02 (0.49, 2.10) 0.87 (0.67, 1.14) 0.92 (0.61, 1.40) Maasai 0.44 (0.26, 0.74) 1.05 (0.58, 1.87) 0.96 (0.67, 1.38) 0.85 (0.54, 1.34) Meru 0.85 (0.42, 1.71) 0.64 (0.32, 1.30) 1.04 (0.72, 1.51) 0.94 (0.55, 1.62) Mijikenda-Swahili 0.63 (0.34, 1.19) 0.49 (0.25, 0.94) 1.27 (0.81, 1.99) 1.00 (0.63, 1.59) Somali 0.61 (0.30, 1.28) 0.46 (0.17, 1.25) 0.3 (0.1, 0.91) 0.15 (0.04, 0.55) Taita-Taveta 0.60 (0.37, 0.96) 1.09 (0.50, 2.38) 0.99 (0.55, 1.76) 0.94 (0.47, 1.87) Other 0.87 (0.32, 2.36) 0.44 (0.12, 1.56) 1.94 (0.62, 6.15) 1.87 (0.45, 7.78) Sex of household head (Ref = Male) Female 1.03 (0.83, 1.27) 0.65 (0.51, 0.85) 0.97 (0.82, 1.14) 0.96 (0.78, 1.19) Number of children (Ref = None) One-two 1.04 (0.81, 1.32) 0.93 (0.66, 1.32) 0.81 (0.62, 1.06) 0.77 (0.58, 1.02) Three-Four 0.99 (0.75, 1.31) 0.73 (0.51, 1.05) 0.76 (0.56, 1.03) 0.69 (0.48, 1.00) Five and above 0.91 (0.70, 1.18) 0.58 (0.40, 0.85) 0.7 (0.51, 0.98) 0.60 (0.42, 0.86) Employment Status (Ref = Not employed) Employed 1.39 (1.25, 1.55) 1.3 (1.12, 1.51) 8.89 (6.6, 11.97) 7.63 (5.64, 10.33) Education level of partner (Ref = None) Primary 0.98 (0.81, 1.20) 1.80 (1.40, 2.31) 0.82 (0.60, 1.13) 0.90 (0.64, 1.26) Secondary 0.94 (0.76, 1.15) 2.64 (1.98, 3.52) 0.83 (0.59, 1.18) 0.94 (0.65, 1.35) Higher 1.03 (0.83, 1.28) 3.86 (2.78, 5.34) 0.84 (0.57, 1.22) 0.96 (0.65, 1.41) Partner’s residence (Ref = stay together) Stays elsewhere 1.24 (1.01, 1.52) 1.11 (0.82, 1.5) 0.86 (0.71, 1.05) 0.89 (0.69, 1.14) Partner's employment status (Ref = Not employed) Employed 0.72 (0.57, 0.90) 1.79 (1.45, 2.20) 2.47 (1.76, 3.47) 2.43 (1.68, 3.51) Justification of wife-beating (Ref = Not justified) Justified 0.60 (0.52, 0.70) 0.87 (0.72, 1.06) 0.92 (0.78, 1.07) 0.83 (0.71, 0.97) Random Variables Counties 0.43 (0.29, 0.64) 0.57 (0.29, 1.14) 0.13 (0.08, 0.22) 0.14 (0.09, 0.23) Clusters 0.20 (0.14, 0.29) 0.23 (0.14, 0.36) 0.14 (0.08, 0.23) 0.11 (0.05, 0.25) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4138861","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283349487,"identity":"52e1d060-2987-4aea-860f-232cf28ac0bf","order_by":0,"name":"Boaz Nabimanya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACAwYGNoYEMJP/+48PQIqNnXgtDAaSM0BamInRAmNL84AoQlrMpZufPXiYY5dncPxAgrHNr23yfMwMjB8+5uDWYjnnmLlB4rbkYoMzCQeSc/tuG7YxMzBLztyGx2E3EswkErcxJ244kNhwOLfnNiNQCxszL14t6d+AWuoTN5x/zNhs2XPbnggtOSBbDiduuJHGzMzw43YiQS2WM3LKgX45njjzxhs2xt6G28ltzIzNeP1iLpG+7eHPbdWJfedz2Bh+/LltO7+9+eCHj3i0wIHCASDB2AZiMjYQoR4I5MHq/hCneBSMglEwCkYWAAAl11ZIx4yk6AAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Economics and Statistics, Kabale University, Kabale, Uganda.","correspondingAuthor":true,"prefix":"","firstName":"Boaz","middleName":"","lastName":"Nabimanya","suffix":""},{"id":283349488,"identity":"b8879cb8-020d-4e66-819f-e32a6e65eabe","order_by":1,"name":"Edison Mayanja","email":"","orcid":"","institution":"Department of Economics and Statistics, Kabale University, Kabale, Uganda.","correspondingAuthor":false,"prefix":"","firstName":"Edison","middleName":"","lastName":"Mayanja","suffix":""},{"id":283349489,"identity":"b4ec5dfd-c3b1-4aae-8b37-7fbf7daa51b1","order_by":2,"name":"Miria Kyarikunda","email":"","orcid":"","institution":"Department of Economics and Statistics, Kabale University, Kabale, Uganda.","correspondingAuthor":false,"prefix":"","firstName":"Miria","middleName":"","lastName":"Kyarikunda","suffix":""},{"id":283349490,"identity":"e3a302ed-c06f-4381-9b44-aae2ce2645d7","order_by":3,"name":"Dianah Nkamusiima","email":"","orcid":"","institution":"Department of Economics and Statistics, Kabale University, Kabale, Uganda.","correspondingAuthor":false,"prefix":"","firstName":"Dianah","middleName":"","lastName":"Nkamusiima","suffix":""},{"id":283349491,"identity":"1f5e82bc-cc93-4610-af66-113fcd6d6ab2","order_by":4,"name":"Willy Rwamparagi Kagarura","email":"","orcid":"","institution":"Department of Economics and Statistics, Kabale University, Kabale, Uganda.","correspondingAuthor":false,"prefix":"","firstName":"Willy","middleName":"Rwamparagi","lastName":"Kagarura","suffix":""},{"id":283349492,"identity":"c37e8b6a-ec87-4509-832f-62451dad7433","order_by":5,"name":"Sylvia Kiwuwa-Muyingo","email":"","orcid":"","institution":"African Population and Health Research Center","correspondingAuthor":false,"prefix":"","firstName":"Sylvia","middleName":"","lastName":"Kiwuwa-Muyingo","suffix":""},{"id":283349493,"identity":"50f99a84-7dd9-4313-b944-37b588b61ee9","order_by":6,"name":"Damazo T. Kadengye","email":"","orcid":"","institution":"Department of Economics and Statistics, Kabale University, Kabale, Uganda.","correspondingAuthor":false,"prefix":"","firstName":"Damazo","middleName":"T.","lastName":"Kadengye","suffix":""}],"badges":[],"createdAt":"2024-03-20 17:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4138861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4138861/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-04082-7","type":"published","date":"2025-11-04T15:56:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53558786,"identity":"a9e082a0-749c-4157-9924-26d0ec3b82ad","added_by":"auto","created_at":"2024-03-27 13:10:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64369,"visible":true,"origin":"","legend":"\u003cp\u003eIntersectionality of different forms of women empowerment\u003c/p\u003e","description":"","filename":"Figure1IntersectuionalityofWENabimanyaB.png","url":"https://assets-eu.researchsquare.com/files/rs-4138861/v1/b8c9a65cc1c6d374a5a46c7a.png"},{"id":95563905,"identity":"d7a55b8c-b33c-455d-8f29-137df0f7cc10","added_by":"auto","created_at":"2025-11-10 16:02:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4138861/v1/dc17ad6f-0cc8-48ef-8171-d19db4d2f304.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empowering women economically is more important than personal and socio-cultural empowerment. Analysis of 2022 Kenya Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global development plan of today places a greater emphasis on women's empowerment, which is closely associated with a number of development outcomes. The fifth Sustainable Development Goal (SDG-5) focuses on achievement of gender equality and empowerment of all women and girls by 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the existing large body of work on the theory, conceptualization and operationalization of the women empowerment, there is no universal definition or indicator for achievement of empowerment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Whereas several scholars have defined women empowerment variably, there is a general agreement that it refers to the process through which individuals attain \u0026ldquo;the ability to make choices\u0026rdquo; under conditions in which choice was previously denied [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As per the World Bank, empowerment refers to an individual's ability to make intentional choices and translate them into desired outcomes. For women this can happen if they have the ability to make choices about own wellbeing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Understanding women\u0026rsquo;s empowerment is necessary to overcoming poverty, achieving economic development and gender equality. Women empowerment is conceptualized to be multidimensional in nature taking into account several facets including at personal, economic, social-cultural or community, and multidimensional levels [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePersonal empowerment relates to taking control of individual own life-decisions. Decision-making authority is frequently used to measure the bargaining power of women [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Typically, \"who\" makes a certain decision is examined in order to gauge participation in decision-making. Women's empowerment and bargaining power are frequently measured by the degree to which they engage in intra-household decision-making processes, either alone or in conjunction with their spouses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the other hand, social-cultural empowerment refers to all elements that encompass components, circumstances, and influences that mold a person's personality and may have an impact on his or her behavior, attitude, choices, and actions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Information has been shown to be a valuable resource for shaping norms, ideas, values, attitudes, behaviors, habits, and life styles of individuals that result from social, educational, religious, and cultural upbringing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While information is necessary for empowering everyone, several studies have shown that in some specific communities, women have limited access to information platforms such as radios, television [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lastly, women\u0026rsquo;s economic empowerment refers to a process that changes the lives of women and girls from one in which they have little agency and little power to one in which they have access to resources and economic advancement [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Women who are economically empowered have more access to financial services, employment, property and other productive assets, skill development, and market knowledge, among other economic resources and possibilities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll three dimensions of women empowerment function concurrently [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and are jointly influenced by several factors at individual, household and societal/community levels including a woman\u0026rsquo;s age, education, marital status, religion, residence, number of children and health [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; or due to political, economic, and cultural norms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For instance, on the political front, presence of strong policy frameworks that support women in supporting their rights over the family issues [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly on the economic front, women who are economically empowered are likely to improve the wellbeing of family members and the community at large [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, strong political and economic systems are known to influence individual\u0026rsquo;s and community social-cultural norms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKenya has implemented several policies and legal frameworks to support women\u0026rsquo;s empowerment, such as the Sexual Offences Act 2006, the Prevention against Domestic Violence Act 2015, the Policy on Eradication of FGM 2019, and the National Policy on Gender and Development 2019. Despite these government\u0026rsquo;s efforts, there is still low women empowerment in the country. According to the 2022 Kenya Demographic and Health Survey report [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], 34% of currently married women do not make decisions about their own health care, major household purchases and visits to their family or relatives, either by themselves or jointly with their husband. This shows low personal empowerment for women, which has been shown to be associated with domestic violence, mismanagement of family income, and poverty [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; and, poor health, disparities in allocation of household resources, medical care and education [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While women's involvement in decision-making may boost household economic growth, the adoption of healthcare services, and the elimination of poverty, studies have revealed that women's autonomy in making decisions is low, particularly in developing nations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this paper, we analyzed a national representative sample dataset from the 2022 Kenya Demographic and Health Survey (KDHS) in order to determine the factors associated with the different women empowerment dimensions of personal, economic, social-cultural and multidimensional empowerment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eStudy setting\u003c/strong\u003e \u003cp\u003eWe analyzed data from a nationally representative population-based cross-sectional household survey \u0026ndash; the 2022 KDHS [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Data collection took place between 17th February to 31st July 2022. The 2022 KDHS employed a two-stage stratified sample design. At first stage, equal probability selection method was used to select 1,692 clusters independently from each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households was used as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, for some clusters that had fewer than 25 households, all households from these clusters were selected into the sample. Detailed description of the methodology for the 2022 KDHS is available elsewhere [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy population\u003c/strong\u003e \u003cp\u003eThe 2022 KDHS had 42,022 households. We considered only 7,663 households that met the inclusion criteria of married women aged 15\u0026ndash;49 years. Data for the resulting sample of 18,312 married women was analyzed during preparation of this paper.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDependent variables\u003c/strong\u003e \u003cp\u003eThe primary study variable of interest is women empowerment conceptualized at four levels, namely; personal, economic, social-cultural and multidimensional empowerment. Firstly, personal empowerment is measured using two indicators for a woman having the power to make decisions related seeking care for own health and visitations to her family members and relatives. Secondly, economic empowerment is measured using three indicators for a woman having a say the use of her own income/earnings, purchase of large household properties such as land or house equipment, and having a say on use of her husband\u0026rsquo;s or partner\u0026rsquo;s income or earnings. Thirdly, social-cultural empowerment is measured in terms of a woman\u0026rsquo;s access to information on a daily or weekly basis through print media, radio or television. Lastly, a woman was considered to be multidimensionally empowered if all the three levels of personal, economic and social-cultural empowerment were fulfilled. For all the four dimensions, a woman\u0026rsquo;s empowerment was measured on a binary scale. Specifically, a woman who responded \u0026ldquo;yes\u0026rdquo; on all of the items for each level of empowerment is considered empowered, otherwise a \u0026ldquo;no\u0026rdquo; is assigned.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIndependent variables\u003c/em\u003e: We considered several individual-, household-level variables available in the questionnaire including; religion (catholic, protestant, evangelical churches, Africa instituted churches, muslim and others), highest education level of household head and respondent (none, primary, secondary, higher), working status (employed, not employed), sex of household head (male, female), age of respondent (categorized in 5-year groups), type of residence (urban, rural), ethnicity (embu, kalenjini, kamba, kikuyu, kisii, luhya, somali, taita-taveta, luo, maasaai, meru, swahili, others), number of children living, and whether the respondent was currently residing with her husband/partner (stay together, stays elsewhere). Women\u0026rsquo;s justification of wife beating is also included as an explanatory variable \u0026ndash; indicating a latent variable for social norms at the community level. Specifically, respondents were asked whether or not beating one\u0026rsquo;s wife was justified under five circumstances, namely if she: (a) goes out without telling her husband, (b) neglects the children, (c) argues with her husband, (d) refuses to have sex with her husband, and (e) burns the food. A woman who agreed that a man is justified in hitting or beating his wife in one or more of the five scenarios is scored a \u0026ldquo;yes\u0026rdquo;, else a \u0026ldquo;no\u0026rdquo; to imply justification of wife-beating norms.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData analysis\u003c/strong\u003e \u003cp\u003eAll data management and analysis was implemented in STATA version 15.0 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] [REF]. Percentages of women who were empowered at all the four different dimensions of personal, economic, social-cultural and multidimensional levels were studied separately. We use the proportioned and positioned Venn diagrams to visually examine the relative overlap of the different dimensions of women empowerment. This was achieved through the use of the \u003cem\u003epvenn2\u003c/em\u003e command in STATA [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] which ensures that each of the proportions of the different dimensions of women\u0026rsquo;s empowerment (the circles, the outside rectangle, and the set intersections) is proportional to the population value.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWe compute basic descriptive statistics in form of frequencies and percentages to understand distributional differences between variables of interest and the four dimensions of women empowerment i.e., the primary dependent variables. Background characteristics are summarized according to whether or not women had attained personal, economic, social-cultural or multidimensional empowerment. We present weighted estimates of proportions for categorical variables. We use the Pearson\u0026rsquo;s chi-square test to examine whether there are differences in proportions of empowered women versus those who are not empowered. We assessed independent associations between respondents\u0026rsquo; sociodemographic characteristics and attainment of the different levels of empowerment using a multilevel mixed effects logistic regression model with identifiers for counties and clusters as random variables to account for variation between counties and clusters respectively. After taking into account individual-level fixed effects, county random effects help to determine how much variation in women empowerment between counties, while cluster random effects help us to determine the variation in women empowerment between different clusters within counties. We fit a separate multivariable mixed effects logistic regression model for each dependent variable to identify explanatory factors for the different dimensions of women empowerment. This is achieved through the use of the \u003cem\u003esvy:melogit\u003c/em\u003e command in STATA, which takes the sample design into account and provides inferences for the entire study population. For each dependent variable, a multivariable adjusted model included all explanatory variables irrespective of statistical significance. All tests are two tailed and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered significant to facilitate interpretation and inferences. As such, we present the results as adjusted odds ratios (aOR) for fixed effects and variances of the two random effects with corresponding 95% Confidence Intervals.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDescriptive Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows frequencies and percentages of empowered women aged 15-49 years, across all the four dimensions of empowerment. Overall, of all the 18,312 married women whose data was analyzed, we observe higher proportions of women who were empowered at personal (74%) and social-cultural (81%) levels. This, however, was not the case for economic (22%) and multidimensional (17.7%) empowerment. In terms of age, we observe lower proportions of empowerment for younger women when compared to their older counterparts across all the four dimensions of empowerment. Intuitively, this is not an unexpected observation. Similarly, we observe higher proportions of empowerement for those residing in urban versus rural areas, and this increases with increasing educational attainment. With regard to religion, we do not observe clear differences in proportions of empowered women, except for the Muslims whose proportions are low compared to other religions. In terms of ethnicity, the Masai, Kalenjini, and Somali women are less empowered when compared to other ethnicities. In cases when the head of the household was male, there were more empowered women for social-cultural, economic and multidimensional levels. Additionally, women with large families (five or more living children) had lower proportions of empowerment compared to those with smaller families. Not surprisingly, we observe that employed women are more empowered compared to those who are not currently working, and this is consistent across all the four empowerment dimensions. Furthermore, women whose partners are educated and employed are more empowered than their counterparts. Specifically, on the multidimensional scale, 20% of women whose husbands are currently working are empowered compared to 3.4% of those whose husbands are not working. On the other hand, there seems to be no differences in proportions of empowered women, with respect to whether they live together with their partners or not. \u0026nbsp; Lastly, we observe high proportions of empowered women among those who did not justify norms of wife beating compared to those who justify wife-beating norms.\u003c/p\u003e\n\u003cp\u003eTABLE 1 HERE\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntersectionality of different forms of empowerment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e illustrates the intersectionality of the different dimensions of women empowerment by relative size. Almost all women who are empowered at a personal level are also social-culturally empowered. However, only a small proportion of married women who are empowered at both personal and social-cultural levels. On the centrally, almost all women who are economically empowered are also empowered at personal and social-cultural levels. More specifically, out of all women who are economically empowered, 80% are empowered in all the three dimensions of empowerment. Out of 18, 312 women, 61% are both personally and social-culturally empowered, 20% are social-culturally and economically empowered, while 19% are personally and economically empowered. This indicates that economic empowerment plays an important role in the formation of personal and social-cultural empowerment.\u003c/p\u003e\n\u003cp\u003eFIGURE 1 HERE\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalytical results\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the odds ratios, variances and their 95% confidence intervals of the four mixed effects logistic regression models, in which all variables of interest were included. For all fixed effects, statistical significance at\u0026nbsp;p-value \u0026lt; 0.05 for the\u0026nbsp;aOR estimates is fulfilled when the associated 95% CI does not contain a 1. In general,\u0026nbsp;explanatory variables of age, area of residence, education of the woman, employment status of the woman and her partner, woman’s employment status, and justification of wife-beating norms are significantly associated with women’s empowerment at personal, economic, social-cultural and multidimensional levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePersonal empowerment:\u0026nbsp;\u003c/em\u003eAt the personal level, older women seem to be more empowered when compared to younger ones. For instance, the odds of being personally empowered for women aged 45 – 49 years (aOR=2.45, 95% CI [1.79, 3. 36]) are higher than for those aged 35 – 39 years (aOR=1.59, 95% CI [1.17, 2.16]), and these are statistically significantly higher than for those of women aged 15-19 years. \u0026nbsp;A similar trend is observed for educated women – those who have attained secondary and post-secondary levels are 1.6 (aOR=1.61, 95% CI [1.30, 2.01]) and 2.8 (aOR=2.8, 95% CI [2.25, 3.48]) times more likely to be empowered at the personal level, when compared to those without education. Similarly, women who are employed have higher odds of being personally empowered (aOR= 1.39, 95% CI [1.25, 1.55]) when compared to those who are not employed. On the other hand, women who live in rural areas and those who justify norms associated with beating have lower odds of being empowered at the personal level. \u0026nbsp; Specifically, \u0026nbsp;women who accepted wife beating as a social norm are 0.6 times less likely to be personally empowered compared to those who do not accept (aOR=0.60, 95% CI [0.52, 0.70]). Religion, ethnicity, family size, and education of a woman’s partner are not significantly associated with women’s personal-level empowerment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSocial-cultural empowerment:\u0026nbsp;\u003c/em\u003eWe observe similar trends for factors associated with social-cultural and personal empowerment. Specifically, higher odds of social-cultural empowerment appear to be increasing with increasing age, higher education levels for both women and their partners, as well as for employed women and their partners. . For instance, women with post-secondary education are about 6 times more likely to be social-culturally empowered than those who are not educated (aOR=6.20, 95% CI [3.90, 9.86]). Similarly, women whose partners are educated to post-secondary level are 3.86 times more likely to be social-culturally employed compared to those whose partners are not educated (aOR=3.86, 95% CI [2.78,5.34]). \u0026nbsp; Furthermore, employed women (aOR=1.3, 95% CI [1.12, 1.51]) and those whose partners are employed (aOR=1.79, 95% CI [1.45, 2.20]) are more likely to be empowered at the social-cultural level when to ther counterparts who or whose partners do not work. On the hand, however, women who stay in rural areas (aOR=0.67, 95% CI [0.54, 0.84]) and those who stay in female-headed households (aOR= 0.65, 95% CI [0.51, 0.85]) are less likely to be social-culturally empowered when compared to their respective counterparts. Religion, ethnicity, whether a woman stays with her partner or not, and justification of wife-beating norms are not significantly associated with women’s social-cultural empowerment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEconomic empowerment:\u0026nbsp;\u003c/em\u003eIn general, only three factors are positively associated with women’s economic empowerment, namely, educational achievement, being employed, and their partner’s employment status. More specifically, women who have attained post-secondary education level are 2.65 times more likely to be economically empowered than those with no education (aOR=2.65, 95% CI [1.96, 3.58]). Women with primary and secondary levels of education have slightly lower odds at 1.43 times to be economically empowered when compared to those without education. \u0026nbsp;Women whose partners are employed are 2.47 times more likely to be economically empowered compared to those whose partners are not employed (aOR=2.47, 95% CI [1.76, 3.47]). Not surprisingly, employed women are about 9 times more economically empowered than those who are not employed (aOR= 8.89, 95% CI [6.6, 11.97]). On the other hand, women who stay in rural areas are 0.77 times less likely to be economically empowered compared to those who stay in urban areas (aOR=0.77, 95% CI \u0026nbsp;[0.66,0.90]). The factors of age, religion, ethnicity, sex of the household head, family size, whether a woman stays with her partner or not, and justification of wife-beating norms are not statistically significantly associated with women’s economic empowerment. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMultidimensional empowerment:\u0026nbsp;\u003c/em\u003eWe observe that age of a woman, educational attainment, employment status of the woman, and employment status of the woman’s partner are positively and statistically significantly associated with multidimensional empowerment. Overall, all other age groups are about 2 times more likely to be multidimensionally empowered when compared to those aged 15-19 years, and there appears to be no clear-cut trend. There seems to be some trend in terms of women’s educational attainment with those who have attained post-secondary, secondary and primary levels education having 3.79, 2.13 and 1.90 odds to be multidimensionally empowered than those with no education. Women whose are employed are 7.63 times (aOR=7.63, 95% CI [5.64, 10.33]) and those whose partners are employed are 2.43 times (aOR=2.43, 95% CI [1.68, 3.51]), more likely to be multidimensionally empowered than those who or whose partners are not working, respectively. On the other hand, women residing in rural areas, with large family size, and those who justification of wife beating norms are less likely to be empowered at the multidimensional scale when compared to their respective counterparts. \u0026nbsp;For instance, women who stay in rural areas are 0.76 less likely to be multidimensionally empowered compared to those who stay in urban areas (aOR=0.76, 95% CI [0.66, 0.89]). Further, women who accept the social norm of wife beating are 0.83 times likely to be multidimensionally empowered compared to those who do not accept the wife beating norm (aOR=0.83, 95% CI \u0026nbsp;[0.71, 0.97]).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCounty and cluster variance effects:\u0026nbsp;\u003c/em\u003eWe observe a relatively large variability of women who are empowered at the personal level between counties, than those between clusters within counties. The same trend is observed for social-cultural empowerment. However, this variance is slightly larger at the social-cultural than at the personal empowerment level, potentially indicating the wide social-cultural differences between counties in Kenya. For economic and multidimensional empowerment, we observe relatively small variances for both county and cluster variables.\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eWe analyzed a national representative sample dataset from the 2022 Kenya Demographic and Health Survey (KDHS) in order to determine the factors associated with different dimensions of women empowerment, namely; personal, economic, social-cultural and multidimensional empowerment. Whereas personal and economic empowerment are measured using indicators related to decision-making power of the woman, social-cultural empowerment takes into account access to information that helps one to make life-choices. Multidimensional empowerment takes into account all the three forms of empowerment.\u003c/p\u003e \u003cp\u003eOverall, there were higher proportions of women who were empowered at the personal and social-cultural levels. However, the findings reveal that only 22% and 17.7% of married women were empowered at the economic and multidimensional scales respectively. This primary finding indicates the dominant coexistence of economic empowerment of women with dimensions of personal and social-cultural empowerment. In other words, the study findings reveal that 78% of married were not economically empowered. This shows that women who are personally and socio-culturally empowered are not necessary economically empowered. However, almost all women who were economically empowered, were also personally and socio-culturally empowered. This could be due to the absence of skills training, job search assistance and social restrictions that are not binding [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEconomic empowerment involves increasing a woman\u0026rsquo;s access to resources or earnings and increases. This necessitates creating conditions for women to make choices and take up opportunities. For instance, education and employment of women have been documented elsewhere to have a direct influence on their economic empowerment [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This is consistent with the findings in this study.\u003c/p\u003e \u003cp\u003eThe findings indicate that the odds of women\u0026rsquo;s economic empowerment increase with increasing levels of education, this is in agreement with findings [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] who revealed that women with higher levels of education are more likely to participate in choosing their own medical treatment and decision-making at household level respectively. This association is consistent with other studies in the literature in Africa [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Women with higher levels of education are more likely to be in a better position to have paid employment, and they are also more likely to possess the knowledge required to negotiate their involvement in household decisions that leads to improved economic empowerment [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This suggests that the ministry of education and other program managers could empower women through increasing the number of women enrolled in education and achieving higher levels of education, which is a crosscutting concern.\u003c/p\u003e \u003cp\u003eOur findings reveal that the odds of women empowerment are more for those women who were currently working as compared to those who were not currently working. This concurs with findings of [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] who revealed that women's empowerment and involvement in household decision-making depends on their employment status. In additional the results also agree with those of [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] which demonstrated how women's empowerment is increased when they are employed. This is because working women will be able to pay for their own health care as well as other significant expenses, which in turn restricts women's ability to participate in decisions about their own health care, household purchases, and travel to see friends and relatives.\u003c/p\u003e \u003cp\u003eOur findings show that having a partner who is employed as highly associated with economic empowerment. This is not unexpected and indeed the importance of engaging men at the household, community and policy levels, in interventions on women\u0026rsquo;s economic empowerment is gaining increasing recognition amongst development practitioners. By engaging men, there is an intentionality to deliberately question gender norms and power dynamics, so that they can embrace better co-operation and sharing of activities at the household level \u0026ndash; an approach to women's economic empowerment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eGiven that over 80% of all married women who were economically empowered, were also multidemensionally empowered, indicates the importance of economic empowerment. This shows that there is a need for a more integrated and comprehensive approach to women\u0026rsquo;s economic empowerment that addresses the societal norms and structural barriers to women\u0026rsquo;s full participation in economic activities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper utilized secondary data of the Demographic Health Survey program, and permission to use these publicly available data was obtained from http://www.dhsprogram.com before data download and subsequent statistical analysis. As such, no ethical reviews and approvals were required before or during preparation of the present manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent\u003c/strong\u003e \u003cstrong\u003efor publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable during preparation of this paper. This paper is based on secondary data of the Demographic Health Survey program. There was no interaction with human subjects during preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The datasets analyzed during preparation of this paper are available in the DSH Program repository, available at http://www.dhsprogram.com\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are very grateful to the MEASURE DHS International Program that provided them with the necessary dataset used for the study. In addition, the authors are very grateful\u0026nbsp;to the financial support extended by African Population and Health Research Center (APHRC) to attend research workshops that resulted into this article. We are also grateful to staff of Department of Economics and Statistics of Kabale University, and members of the Gender Pathways working group at APHRC for their insights during the two writing workshops in Kabale and Naivasha respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBN, MK, WRK, and DTK conceptualized the paper. BN, MK, DN, WRK and DTK drafted the original manuscript. DTK and EM conducted the data management and statistical analysis. DTK and SKM reviewed and edited the draft manuscript. DTK and SKM securing funding that facilitated two writing workshops – 1 in Kabale, Uganda and 1 in Nairobi, Kenya –, which enabled development of this paper. All authors revised the manuscript for quality, consistency and accuracy. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution Adopted by the General Assembly on 25 September 2015 [Internet]. United Nations Sustainable Development. 2015 [cited 2023 Aug 16]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sdgs.un.org/2030agenda\u003c/span\u003e\u003cspan address=\"https://sdgs.un.org/2030agenda\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDery F, Bisung E, Dickin S, Dyer M. 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Engaging Men for Women\u0026rsquo;s Economic Empowerment: Overview of the Evidence. 2023; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/10986/40418\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/10986/40418\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e License: CC BY-NC 3.0 IGO.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Proportion of empowered women across the four dimensions of empowerment by women\u0026rsquo;s background characteristics \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eWomen Characteristics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo. of Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003ePersonal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003eSocial-cultural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003eEconomic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003eMulti-dimensional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e18,312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,560 (74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e14,833 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,023 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,237 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAge groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e336 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e381 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e43 ( 7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e25 ( 4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e20-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,848 (66.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,233 (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e484 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e358 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,968 (73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,317 (82.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e953 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e760 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,653 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,846 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e797 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e663 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,512 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,682 (80.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e783 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e636 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e40-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,833 (80.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,891 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e531 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e447 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003e45-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,410 (80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,483 (84.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e432 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e348 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,224 (78.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,806 (87.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,869 (28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,587 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e11,685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e8,336 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,027 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,154 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,650 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eWoman\u0026rsquo;s education level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,978 (65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e996 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e166 ( 5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e71 ( 2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,276 (71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,287 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,507 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,128 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,890 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,849 (94.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,211 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,009 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,416 (87.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,701 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,139 (41.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,029 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eCatholic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,388 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,728 (87.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e735 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e616 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eProtestant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,554 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,431 (90.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,517 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,251 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eEvangelical churches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,849 (72.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,536 (89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e995 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e793 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eAfrican instituted churches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,142 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,281 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e351 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e278 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eMoslem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,056 (68.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,273 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e242 ( 8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e158 ( 5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther denominations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e571 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e584 (74.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e183 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e141 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eEmbu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e171 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e220 (94.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e74 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e60 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eKalenjin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,671 (71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,150 (83.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e644 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e526 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eKamba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,334 (85.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,395 (89.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e506 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e439 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eKikuyu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,839 (78.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,291 (97.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e763 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e670 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eKisii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e793 (79.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e920 (92.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e285 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e226 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eLuhya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,714 (76.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,129 (94.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e480 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e395 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eLuo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,150 (75.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,313 (81.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e518 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e426 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaasai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e204 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e370 (80.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e92 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e58 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eMeru\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e953 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e922 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e327 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e228 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eMijikenda - Swahili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e619 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e631 (69.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e236 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e148 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eSomali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e944 (64.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e278 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e34 ( 2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e11 ( 0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eTaita-Taveta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e141 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e187 (92.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e56 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e45 (22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e27 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e27 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e8 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eSex of household head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e14,066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e10,281 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e11,581 (82.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,134 (22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,526 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,279 (77.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,252 (76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e889 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e711 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of living children\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e656 (69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e765 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e219 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e177 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eOne - two\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,232 (74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,152 (87.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,805 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,502 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eThree - four\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,615 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,161 (84.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,396 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,137 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eFive or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,057 (71.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,755 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e603 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e421 ( 9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e8,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,802 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,831 (69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e425 ( 5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e329 ( 3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,758 (78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,002 (90.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,598 (36.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,908 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003ePartner\u0026apos;s education level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,857 (66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e977 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e195 ( 7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e105 ( 3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,784 (71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,587 (83.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,380 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,041 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,011 (74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,916 (91.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,275 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,045 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,908 (83.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,353 (95.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,173 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,046 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eResidence of partner\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eLiving together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e14,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e10,763 (72.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e11,954 (80.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,259 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,618 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eStaying elsewhere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,797 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,879 (81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e764 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e619 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003ePartner\u0026apos;s employment status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,839 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,224 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e131 ( 5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e85 ( 3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e15,795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e11,721 (74.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,609 (86.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,892 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,152 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eJustification of wife beating norms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eNot Justified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e11,321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e8,935 (78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,714 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,866 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,430 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"bottom\"\u003e\n \u003cp\u003eJustified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.636363636363637%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,625 (66.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.757575757575758%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,119 (73.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.848484848484848%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,157 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.727272727272727%\" valign=\"bottom\"\u003e\n \u003cp\u003e807 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Odds ratios, variances estimates and their 95% confidence intervals and variances for explanatory variables of the four dimensions of women empowerment\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.67484662576687%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eWomen Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003ePersonal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003eSocial-Cultural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003eEconomic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003eMulti-dimensional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.67484662576687%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.91 (1.44, 5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16 (0.49, 2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02 (0.01, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01 (0.00, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eAge group (Ref = 15-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003e20-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.08 (0.82, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.43 (1.00, 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.75 (1.13, 2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.97 (1.18, 3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.38 (1.05, 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.66 (1.19, 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.64 (1.05, 2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.10 (1.21, 3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.55 (1.13, 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.95 (1.37, 2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.50 (0.95, 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.12 (1.17, 3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.59 (1.17, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.15 (1.46, 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.34 (0.85, 2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.92 (1.06, 3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003e40-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.21 (1.63, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.80 (1.75, 4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.25 (0.76, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.91 (1.05, 3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003e45-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.45 (1.79, 3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.52 (1.75, 3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.49 (0.93, 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.19 (1.22, 3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.67484662576687%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eResidence area (Ref = Urban)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.79 (0.66, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.67 (0.54, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.77 (0.66, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.76 (0.66, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eHighest education level (Ref = None)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.37 (1.14, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.49 (1.94, 3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.43 (1.15, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.90 (1.38, 2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.61 (1.30, 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.79 (2.72, 5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.43 (1.08, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.13 (1.50, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.80 (2.25, 3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.20 (3.90, 9.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.65 (1.96, 3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.79 (2.62, 5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eReligion (Ref = Catholic)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eProtestant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.81, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.76, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00 (0.89, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.02 (0.90, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eEvangelical churches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86 (0.73, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.04 (0.81, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.07 (0.89, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.06 (0.89, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eAfrican instituted churches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.72, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.91 (0.65, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.09 (0.86, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.97 (0.68, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eMoslem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85 (0.61, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93 (0.63, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (0.70, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.89 (0.73, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther denominations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.91 (0.67, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.66 (0.42, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.06 (0.66, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 (0.59, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.67484662576687%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eEthnicity (Ref = Embu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eKalenjin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85 (0.55, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.61 (0.29, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.79 (0.57, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.87 (0.54, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eKamba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86 (0.60, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.87 (0.45, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.09 (0.71, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.07 (0.65, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eKikuyu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.13 (0.77, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.43 (0.69, 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.07 (0.77, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.12 (0.74, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eKisii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 (0.70, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.61 (0.27, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.08 (0.71, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 (0.58, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eLuhya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.84 (0.50, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.01 (0.51, 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.91 (0.68, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95 (0.61, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eLuo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85 (0.51, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.02 (0.49, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.87 (0.67, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.92 (0.61, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaasai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.44 (0.26, 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.05 (0.58, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.96 (0.67, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85 (0.54, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eMeru\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85 (0.42, 1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.64 (0.32, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.04 (0.72, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.55, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eMijikenda-Swahili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.63 (0.34, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.49 (0.25, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.27 (0.81, 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00 (0.63, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eSomali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.61 (0.30, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.46 (0.17, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3 (0.1, 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.15 (0.04, 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eTaita-Taveta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.60 (0.37, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.09 (0.50, 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99 (0.55, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.47, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.87 (0.32, 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.44 (0.12, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.94 (0.62, 6.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.87 (0.45, 7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eSex of household head (Ref = Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (0.83, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.65 (0.51, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.97 (0.82, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.96 (0.78, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of children (Ref = None)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eOne-two\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.04 (0.81, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93 (0.66, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.81 (0.62, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.77 (0.58, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eThree-Four\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99 (0.75, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.73 (0.51, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.76 (0.56, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.69 (0.48, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eFive and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.91 (0.70, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.58 (0.40, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7 (0.51, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.60 (0.42, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eEmployment Status (Ref = Not employed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.39 (1.25, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3 (1.12, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.89 (6.6, 11.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.63 (5.64, 10.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eEducation level of partner (Ref = None)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 (0.81, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.80 (1.40, 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.82 (0.60, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.90 (0.64, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.76, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.64 (1.98, 3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.83 (0.59, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.65, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (0.83, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.86 (2.78, 5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.84 (0.57, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.96 (0.65, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003ePartner\u0026rsquo;s residence (Ref = stay together)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eStays elsewhere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.24 (1.01, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.11 (0.82, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86 (0.71, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.89 (0.69, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003ePartner\u0026apos;s employment status (Ref = Not employed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.72 (0.57, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.79 (1.45, 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.47 (1.76, 3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.43 (1.68, 3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eJustification of wife-beating (Ref = Not justified)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eJustified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.60 (0.52, 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.87 (0.72, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.92 (0.78, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.83 (0.71, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eRandom Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eCounties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.43 (0.29, 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.57 (0.29, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.13 (0.08, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.14 (0.09, 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.7607361963190185%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.914110429447852%\" valign=\"bottom\"\u003e\n \u003cp\u003eClusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.20 (0.14, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.257668711656443%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.23 (0.14, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.33128834355828%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.14 (0.08, 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.865030674846626%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.11 (0.05, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Women empowerment, personal empowerment, economic empowerment, socio-cultural empowerment, married women","lastPublishedDoi":"10.21203/rs.3.rs-4138861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4138861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEmpowering women economically may boost household income, economic growth, the adoption of healthcare services, and the elimination of poverty. This means that when women are economically empowered, they are also personally and socio- culturally empowered. Studies have revealed that women economic empowerment is still low, particularly in developing countries like Kenya. This paper explores the determinants of women empowerment among married women in Kenya. Understanding women\u0026rsquo;s empowerment is necessary to overcoming poverty, achieving economic development and gender equality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed secondary data from the 2022 Kenya Demographic and Health Survey. For the final analysis, we used a weighted sample of 18,312 currently married women. All frequencies and percentages in the results section are weighted. At the multivariate stage of analysis, the effect of explanatory variables on women empowerment was investigated using multilevel mixed effects logistic regression model. We computed adjusted Odds Ratio (AOR) with 95% confidence interval (95% CI). Variables with a P-value of less than 0.05 in the multi variable binary logistic regression analysis were considered statistically significant predictors of the outcome variable.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of all women who are economically empowered, 80% are empowered in all the three dimensions of empowerment. Out of 18, 312 women, 61% are both personally and social-culturally empowered, 20% are social-culturally and economically empowered, while 19% are personally and economically empowered. This indicates that economic empowerment plays an important role in the formation of personal and social-cultural empowerment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGenerally, women empowerment in our study was low (17.7%). It is highly affected by socio demographic and economic characteristics of women and husbands\u0026rsquo; characteristics. This study indicates that educating women, improving their economic status through employment opportunities, empowering women to be head of household will enhance their economic empowerment.\u003c/p\u003e","manuscriptTitle":"Empowering women economically is more important than personal and socio-cultural empowerment. Analysis of 2022 Kenya Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 13:10:26","doi":"10.21203/rs.3.rs-4138861/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-04T11:13:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T07:00:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6168641212708456545835059517509030723","date":"2025-05-31T18:22:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289721515272399237349184595858204437892","date":"2025-05-24T08:22:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338407583936933161542814782940712052731","date":"2025-04-24T07:39:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170661109026345669222858344085346835879","date":"2025-02-18T23:12:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161623715740222983792768896321014525771","date":"2025-02-18T11:15:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-09T09:38:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224317869060782840741632660904129340503","date":"2025-01-09T09:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11684169423361383527539751831434660296","date":"2024-10-15T02:26:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104021998090667791692140543809128351331","date":"2024-10-14T13:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150414030193971303703465825572829746694","date":"2024-10-12T13:40:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259605277853739581231924994547098547476","date":"2024-08-17T22:03:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313129451864538739131994397424842529166","date":"2024-08-14T03:58:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72750695369478944896922626986706811968","date":"2024-05-21T08:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193564283181219980330677675149881199804","date":"2024-05-21T01:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-21T01:03:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-05T14:05:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-25T01:05:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-25T01:05:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2024-03-20T17:32:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10e7afc3-1157-4532-8460-03e1d37a802d","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T15:58:43+00:00","versionOfRecord":{"articleIdentity":"rs-4138861","link":"https://doi.org/10.1186/s12905-025-04082-7","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-11-04 15:56:56","publishedOnDateReadable":"November 4th, 2025"},"versionCreatedAt":"2024-03-27 13:10:26","video":"","vorDoi":"10.1186/s12905-025-04082-7","vorDoiUrl":"https://doi.org/10.1186/s12905-025-04082-7","workflowStages":[]},"version":"v1","identity":"rs-4138861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4138861","identity":"rs-4138861","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}