Abstract
Background
The importance of women empowerment has been conceptualized not only as fundamental and core to fulfilling human rights, but also a basic requirement for enhancing women’s contributions to the development process in their national economies. The concept of women empowerment can consider several aspects of a woman including at personal, economic, social-cultural or community, and multidimensional levels. Kenya has implemented several policies and legal frameworks to support women’s empowerment. However, there is limited up-to-date research that provides an in-depth examination of the factors associated with the women’s empowerment in Kenya, for the four different dimensions of empowerment. This paper explores the determinants of women empowerment among married women in Kenya.
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
We observed a high proportion of women empowered at a personal (74%) and social-cultural levels (81%) while low proportions of 22% and 18% are empowered at the economic and multidimensional scales respectively. Out of all women who are economically empowered, larger proportions are also empowered at the personal (87%) and social-cultural (92%) scales, while 80% are empowered in all the three dimensions of empowerment. Women’s characteristics such as being older, attaining formal education, being in employment, and having an employed partner were positively associated with women’s empowerment. On the other hand, women who live in rural areas and those who justify norms associated with beating were negatively associated with women’s empowerment.
Conclusions
Finds from this study show that economic empowerment plays an important role in the formation of personal and social-cultural empowerment. There is therefore a need for policy reforms to improve the economic conditions of the households and to give special emphasis on the education of women, promote women’s employment opportunities and access to resources.
Keywords: Women empowerment, Personal empowerment, Economic empowerment, Socio-cultural empowerment, Married women
Background
The global development plan of today places a greater emphasis on women’s empowerment, which is closely associated with several 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]. Several scholars have defined women empowerment as the process through which individuals attain “the ability to make choices” under conditions in which choice was previously denied [2, 3]. 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 could make choices about own wellbeing [4]. In this paper, we conceptualize women empowerment following Kabeer’s definition [2] (p. 437), who states that “women’s empowerment is about the process by which those who have been denied the ability to make strategic life choices acquire such an ability”. This can consider several aspects of a woman including at personal, economic, social-cultural or community, and multidimensional levels [5–8].
Personal empowerment relates to taking control of individual own life-decisions. Decision-making authority is frequently used to measure the bargaining power of women [9]. 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 [10, 11]. 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 [12]. Access to 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 [13, 14]. 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 [15]. 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 [15, 16].
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. For instance, according to the 2022 Kenya Demographic and Health Survey report [17], 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. To the best of our knowledge, there is limited current research that provides an in-depth examination of the factors associated with the women’s empowerment in Kenya, for the different dimensions of empowerment, independently.
In this paper, we analyzed a national representative sample dataset from the 2022 Kenya Demographic and Health Survey (KDHS) to determine the factors associated with the different women empowerment dimensions of personal, economic, social-cultural and multidimensional empowerment.
Methods
Study setting and population
We analyzed data from a nationally representative population-based cross-sectional household survey – the 2022 KDHS which had a sample of 42,022 households [17]. Detailed description of the methodology for the 2022 KDHS is available elsewhere [17]. During preparation of this paper, we analyzed sample data of 18,312 women that met the inclusion criteria of married women aged 15–49 years.
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 multidimensionally empowered if all the three levels of personal, economic and social-cultural empowerment were fulfilled. Measures for women’s empowerment vary considerably in the scholarly literature [8, 18]. In this paper, we follow the approach adopted by Abbas et al. [19] to operationalize each of the four women empowerment dimensions as a binary variable resulting from the combination of two or more indicators. Specifically, a woman who responded “yes” on all 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 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 [20]. 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 [21] 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 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 considering 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 using 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 descriptive statistics of women, 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, compared to 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. Similarly, we observe higher proportions of empowerment for those residing in urban versus rural areas, and this increases with increasing educational attainment. Not surprisingly, we observe higher proportions of empowered employed women compared to those who are not currently working, and this is consistent across all the four empowerment dimensions. Intuitively, these observations are not unexpected. Women with large families (five or more living children) had lower proportions of empowerment compared to those with smaller families. Furthermore, we observe higher proportions of empowered women whose partners are educated and employed 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, religion and ethnicity. 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.
Proportion of empowered women across the four dimensions of empowerment by women’s background characteristics
| Women Characteristics | No. of Women | Personal | Social-cultural | Economic | Multidimensional |
|---|---|---|---|---|---|
| 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%) |
| Muslims | 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%) |
Intersectionality of different forms of empowerment
Figure 1 illustrates the intersectionality of the different dimensions of women empowerment by relative size. Overall, a large proportion of women are empowered at a personal (74%, n = 13,560) and social-cultural levels (81%, n = 14,833) while only 22% (n = 4,023) and 17.7% (n = 3,237) are empowered at the economic and multidimensional scales respectively. To note, however, is that 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 (n = 4,023), larger proportions are also empowered at the personal (87%; n = 3,505) and social-cultural (92%; n = 3,703) scales, while 80% (n = 3,237) 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.
Fig. 1.
Intersectionality of different forms of women empowerment
Analytical results
Table 2 shows the odds ratios, variances and their 95% confidence intervals of four mixed effects logistic regression models, in which all variables of interest are included. For all fixed effects, statistical significance at p-value < 0.05 for the adjusted odds ratio (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.
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) |
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 and 35–39 years are 2.5 and 1.6 times more likely than for those of women aged 15–19 years, and these are statistically significantly different. A similar trend is observed for educated women – those who have attained secondary and post-secondary levels are 1.6 and 2.8 times more likely to be empowered at the personal level, when compared to those without education. Similarly, women who are employed are 1.39 times more likely to be empowered at personal level 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. For instance, 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. 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. Similarly, women whose partners are educated to post-secondary level are 3.9 times more likely to be social-culturally empowered compared to those whose partners are not educated. Furthermore, employed women and those whose partners are employed are respectively 1.3 and 1.8 times more likely to be empowered at the social-cultural level when to compared to their counterparts who or whose partners do not work. On the hand, however, women who stay in rural areas and those who stay in female-headed households are about 0.7 times 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 or primary to secondary levels are respectively 2.65 and 1.43 times more likely to be economically empowered than those without formal education. Women whose partners are employed are 2.47 times more likely to be economically empowered compared to those whose partners are not employed. Not surprisingly, employed women are about 8.89 times more economically empowered than those who are not employed. 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. 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 being 3.79, 2.13 and 1.90 more likely to be multidimensionally empowered than those without any formal education. Women whose are employed are 7.63 times and those whose partners are employed are 2.43 times 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. 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 norms.
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. This indicates greater heterogeneity among women across different counties, than within the same county. 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) 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 considers access to information that helps one to make life-choices. Multidimensional empowerment considers all the three forms of empowerment.
Overall, results showed that about eight in ten women are independently empowered at the personal and social-cultural levels. Factors such as being older, attaining formal education, being in employment, and having an employed partner were positively associated with women’s personal and social-cultural empowerment. These observations are consistent with similar studies in developing economies. For instance, a recent analysis of demographic health survey data from 38 developing economies showed that women’s characteristics of higher education, husband higher education, husband employment status and household wealth are positively associated with women’s self-awareness, decision-making, self-esteem and self-confidence [22]. 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 and social-cultural levels. In rural areas, opportunities to increase women’s agency, self-esteem and decision-making are likely to be limited as compared to urban areas [3, 19, 22], all of which have been shown to be associated with women’s acceptance of wife-beating norms in developing countries [23, 24].
We observed low proportions of about two in ten women who are independently empowered on the economic and multidimensional levels. This is not a surprising result because literature shows that, in sub-Saharan Africa, women’s economic empowerment is generally low [25].
One the interesting findings in our analysis is the dominant coexistence of economic empowerment of women with dimensions of personal and social-cultural empowerment. That is, out of all women who are economically empowered, at least eight in ten women were also personally and or socio-culturally empowered. This indicates that economic empowerment plays an important role in the formation of personal and social-cultural empowerment. 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 [22, 26].
The findings indicate that higher odds of women’s economic empowerment are associated with increasing levels of education. This is in agreement with findings of [27, 28] 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. 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 [29].
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 [28, 30] who revealed that women’s empowerment and involvement in household decision-making depends on their employment status. The results also agree with those of [26] which demonstrated how women’s empowerment is increased when they are employed.
Having a partner who is employed is 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 [31, 32].
We observed that women who justify wife-beating are less likely to be empowered on all dimensions. Indeed, literature shows that women’s attitude towards wife-beating is a proxy for perception of their status and one indicator of women’s empowerment [33]. This is because women who believe wife-beating is a justifiable practice, are more likely to be unaware of their rights, have low self-esteem, accept intimate partner violence and hence have a less sense of empowerment [23, 24, 33].
Study limitations
Interpretation of results from this study need to be made considering some limitations. First is that, due to the cross-sectional nature of DHS surveys, we cannot establish causality of the factors associated with women empowerment due to temporal associations. Secondly, the outcome variables are defined on a binary scale for a rather complex phenomenon – women empowerment. Thirdly, the covariates and items used to compute the outcome variables of women empowerment were assessed by self-reports implying that the possibility of social desirability bias cannot be excluded. Fourth is the reliance on observed covariates (but not on unmeasured covariates) and the lack of qualitative data to contextualize the findings. For instance, there may be possibility of cross variable interactions, but these have not been explored in the present paper due to limited qualitative knowledge of potential theoretical hypotheses for such interactions. As such, there remains unanswered questions on the depth of evidence for determinants of the different dimensions of women empowerment. We acknowledge these gaps that could be explored for other studies. However, a key strength of our study is that we analyzed a large and nationally representative dataset, implying that the findings are generalizable to the entire country and other similar settings.
Conclusions and policy implications
The empowerment of women is a basic requirement for enhancing women’s contributions to the development process in their national economies. Findings from this study show 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. We recommend policy reforms to improve the economic conditions of the households and to give special emphasis on the education of women as an investment to build empowered human resources. Further, policies that promote women’s employment opportunities, education and access to resources will no doubt have an important role to play in the implementation of strategies for empowering women. Although we have studied the different forms of empowerment independently, it is important to note that different dimensions of women empowerment function concurrently [34] 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 [27, 28, 35]; or due to political, economic, and cultural norms [30, 36]. For instance, on the political front, presence of strong policy frameworks that support women in supporting their rights over the family issues [37]. Similarly on the economic front, women who are economically empowered are likely to improve the wellbeing of family members and the community at large [38]. Moreover, strong political and economic systems are known to influence individual’s and community social-cultural norms [39, 40].
Acknowledgements
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.
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. All authors revised the manuscript for quality, consistency and accuracy. All authors read and approved the final manuscript.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Data availability
The datasets analyzed during preparation of this paper are available in the DSH Program repository, available at http:/www.dhsprogram.com or directly at https:/dhsprogram.com/data/available-datasets.cfm.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
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.
There was no interaction with human subjects during preparation of this manuscript.
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Footnotes
Publisher’s Note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed during preparation of this paper are available in the DSH Program repository, available at http:/www.dhsprogram.com or directly at https:/dhsprogram.com/data/available-datasets.cfm.