Locating Dimensions of Women’s Empowerment in Family Planning in Burkina Faso

By Tayler Nelson

Women’s “empowerment,” defined by Naila Kabeer[1] as “the expansion of people’s ability to make strategic life choices in a context where this ability was previously denied to them,” has been shown[2] to be associated with greater birth spacing, lower fertility, and lower rates of unplanned pregnancy. Yet scholars disagree[3] on how to measure women’s empowerment, and meanings of empowerment can shift across geographic and cultural contexts.

IPUMS PMA’s family planning surveys include variables that can help researchers investigate dimensions of women’s empowerment in family planning. All samples include indicators of women’s knowledge about family planning methods. Many survey rounds dig deeper, collecting data that can be used by researchers and policymakers.

The Burkina Faso 2018 Round 6 survey includes a range of variables measuring family planning attitudes, beliefs, and decision-making dynamics that relate to women’s empowerment. I used a weighted polychoric factor analysis[4] to investigate women’s empowerment in family planning in Burkina Faso. Factor analysis can help researchers reduce a large number of observed variables by identifying similar response patterns among observed variables and grouping them into a smaller set of underlying variables, or factors. Through analyzing how variables are grouped and the strength and signs of coefficients within these groups, researchers can glean insight into which sets of observed variables might be best at measuring an unobserved construct such as women’s empowerment.

After reviewing the literature on women’s empowerment in family planning, I selected nineteen PMA variables to capture dimensions of women’s empowerment in family planning in Burkina Faso. These included social context variables (URBAN, WEALTHQ, EDUCATTGEN), whether the woman is a current/recent family planning user (FPCURRECUSER), whether the woman has heard of family planning on television (FPTVHR) or radio (FPRADIOHR), and belief and attitude variables that use a Likert scale to measure how much the woman agrees with a particular statement about family planning (SAFEDISCKID, SAFEDISCFP, CONFLICTFP, DAMRELFP, NEGOTIATEKIDS, BELIEFCARRYPREG, BELIEFDAUPREG, AGREESPACE, AGREELIMIT, AGREECONTR, AGREEFP, AGREEPARTFP). I also combined two variables that measure who is the primary decision-maker in deciding to use (FPDECIDER) or not to use (DECNOFPUSE) family planning into a single ordinal variable (DECIDER) that reports how much input a woman has in the couple’s family planning decisions. I excluded not in universe (NIU) and missing cases as well as females who were not married or partnered; my analytical sample contained 1,686 women. All analyses applied appropriate sampling weights.

I retained three factors[5] and analyzed which characteristics or beliefs relate to each underlying factor by examining variable groupings and factor loadings. Table 1 displays all factor loadings above 0.3 for each variable. Looking at high factor loadings can help researchers identify underlying factors. I have marked moderate and strong factor loadings[6] in the table with an asterisk(*).

Table 1: Factor Loadings (promax rotations)

VARIABLE DESCRIPTION F1 F2 F 3
URBAN Urban/rural status .94 *
WEALTHQ Wealth score quintile .87 *
EDUCATTGEN Highest level of schooling attended .66 *
FPCURRECUSER Current or recent FP user .32
DECIDER Women’s level of say in decisions about whether to use/not use FP
SAFEDISCKID Safe to discuss when to have children w/ partner .66 *
SAFEDISCFP Safe to discuss FP w/ partner  .71 *
CONFLICTFP Conflict in relationship if used FP -.50
DAMRELFP Delaying or limiting children would deteriorate relationship w/ partner -.44
NEGOTIATEKIDS Able to negotiate w/ partner when to stop having children .65 *
BELIEFCARRYPREG Thinks a woman should not get pregnant if child still on her back .60 *
BELIEFDAUPREG Thinks a woman should not get pregnant if daughter is pregnant .48
AGREESPACE Agrees with couple that uses FP to space births .45 .44
AGREELIMIT Agrees with couple that uses FP to limit births .52
AGREECONTR Agrees with man/woman that uses contraception .34
AGREEFP Agrees with couples that use FP .59 .39
AGREEPARTFP Partner agrees with couples that use FP .69 *
FPTVHR Heard about FP on TV .66 * .38
FPRADIOHR Heard about FP on the radio .33

Factor 1 includes the majority of attitude and belief variables. I looked at similarities and differences between variables and their factor loadings[7] to interpret this factor. Variables with high factor loadings all relate to good spousal communication around family planning. For instance, SAFEDISCFP, which reports how much the respondent agrees that it is safe to discuss family planning with her partner, has the highest factor loading. Variables related to spousal conflict, such as CONFLICTFP, have weak but negative factor loadings. Together, these results indicate that Factor 1 might be a latent measure of spousal communication. Alternative interpretations are possible: for example, it might be that this factor simply represents support for family planning, and variables with high factor loadings are the most useful observed indicators of this factor.

For Factor 2, the high factor loadings for URBAN and WEALTHQ suggest that this factor indicates socioeconomic status. The variable for whether the woman has heard about family planning on TV loads moderately highly on this factor; this seems logical assuming TV ownership is a reflection of socioeconomic status.

For Factor 3, the factor loadings are relatively small except for the loading for the belief that women should not get pregnant if she is still carrying a child on her back. In line with a Burkina Faso 2014 report that found popular acceptance of family planning for spacing rather than limiting births and stigma around women who have children too closely together, this factor might indicate adherence to traditional Burkina Faso beliefs around family planning. AGREESPACE, which reports the respondent’s agreement with couples using contraception to space the birth of their children, also loads on this factor. This factor may indicate value of traditional beliefs or more explicit support for child spacing.

This factor analysis indicates several dimensions of empowerment for understanding family planning in Burkina Faso and suggests a handful of important variables (those with high factor loadings) users may be interested in including in analyses of female empowerment. It also highlights other areas to explore. For example, I was surprised that DECIDER was not strongly associated with any single factor (though iterations of this analysis that measured joint decision-making showed high factor loading alongside the other socioeconomic status variables in Factor 2). Researchers may want to use PMA data to investigate whether spousal communication, socioeconomic status, and geographically-specific traditional beliefs remain important factors in family planning variables across different countries and years, or look further into how decision-making might align with these factors.

Get the Stata code to replicate this analysis: Stata_synatax_ipums.txt

[1] Kabeer, Naila. 2001. “Reflections on the Measurement of Women’s Empowerment.” In: Discussing Women’s Empowerment: Theory and Practice. Stockholm: SIDA: Swedish International Development Cooperation Agency.

[2] Upadhyay, Ushma D. et al. 2014. “Women’s Empowerment and Fertility: A Review of the Literature,” Social Science & Medicine 115: 111-120.

[3] Prata, Ndola et al. 2017. “Women’s Empowerment and Family Planning: A Review of the Literature,” Journal of Biosocial Science 49(6): 713-743.

[4] I used Stata’s polychoric function in order to include a mix of binary and ordinal variables in my analysis.

[5] I used Eigenvalues and scree plots to determine how many factors to retain.

[6] I am defining moderate and strong factor loadings as those loadings of .60 and greater.

[7] I looked for common conceptual threads between the highest factor loadings (asking myself, ‘What latent factor might all of these variables be measuring?’). I also looked at weak and negative factor loadings to help interpret this factor.

New survey data from IPUMS PMA allows for exploration of factors in child nutrition status

By Devon Kristiansen

Last month, when IPUMS PMA released data from nine countries, including the most recent person level and service delivery point level surveys on family planning, we also released data on a new topic for Performance Monitoring for Action (PMA) – nutrition.  PMA conducted two survey rounds each in Burkina Faso and Kenya (2017 and 2018) in both in people’s homes (households) and where they received care and medical services (service delivery points).  Household surveys contained questions about the diet and nutritional status of children under 5 and women between 10 and 49 years, antenatal care and advice received by currently or recently pregnant women, and other household and demographic questions.  Service delivery points were surveyed for medical equipment and services relating to malnutrition and anthropometric monitoring.

A key factor for nutrition status of young children in the low and middle-income country (LMIC) context is incidence of diarrhea.  Diarrhea prevents the uptake of nutrients into the child’s body and causes dehydration. According to the World Health Organization1, diarrhea is the leading cause of malnutrition and second leading cause of death for children under 5 globally.  A well-established association in the nutrition literature is the presence of livestock on the homestead and incidence of diarrhea in young children, due to fecal contamination of water and food sources2, 3.

The newly-released IPUMS PMA Nutrition data confirm past findings regarding the presence of livestock and diarrheal disease incidence.  This blogpost is a brief, informal exploration of one type of research question these data can provide.  In the Burkina Faso and Kenya 2017 Nutrition rounds, 26.9% of children under 2 years old living on a homestead with livestock present had experienced diarrhea in the past 2 weeks, compared to 20.2% of young children living on homesteads without livestock.  The difference between these prevalence rates are statistically significant.

The richness of IPUMS PMA Nutrition data allow researchers to further study the nuances of this effect, and test other hypotheses related to child nutrition.   For example, I looked to see if there was a significant correlation between the presence of livestock and diarrhea in young children after controlling for urban-rural status and examined the impact of possible mitigating factors.

It’s important to note the differences in how ‘urban’ and ‘rural’ are defined in different countries. For example, Burkina Faso’s definition of urban is a locality of more than 10,000 people with sufficient socio-economic and administrative infrastructures. In contrast, Kenya’s definition of urban is municipalities, town councils, and other urban centers with 2,000 or more inhabitants.  The comparability tab on IPUMS PMA makes it easier to identify these differences and take them into consideration when comparing data across countries and surveys.

I found that higher wealth, secondary education or higher of the child’s mother, treatment of drinking water by boiling, and the presence of hand sanitation facilities in the household may have protective effects on children’s health, that is, these factors are associated with a lesser probability of diarrhea in young children.

Looking at the occurrence of diarrhea in young children and factors expected to mitigate it revealed unexpected results. Surprisingly, access to protected drinking water sources and treated water was positively associated with children experiencing diarrhea.  Perhaps families do not take additional precautions when they perceive their drinking water source to be safe.

Also surprising was the finding that the mother’s educational level only seemed to have a protective impact when it was a post-secondary level. Primary and middle school educational levels among mothers was actually associated with greater diarrhea incidence when compared to mothers with less education.

The results I describe are the result of an informal look into IPUMS PMA’s new Nutrition data, and they hold so much potential for more research.  There are more than 1000 new variables that have been added to IPUMS PMA from the Nutrition module data.

Like the family planning core surveys, household and service delivery point data can be linked together by the variable (the primary sampling unit).  For more information on how to link person data and facility data, see our user note.

We hope you are able to leverage these data in your own research!

As always – use it for good! 

 

1 https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease

2Mosites, E. M., Rabinowitz, P. M., Thumbi, S. M., Montgomery, J. M., Palmer, G. H., May, S., … & Walson, J. L. (2015). The relationship between livestock ownership and child stunting in three countries in Eastern Africa using national survey data. PLoS One, 10(9).

3Kaur, M., Graham, J. P., & Eisenberg, J. N. (2017). Livestock ownership among rural households and child morbidity and mortality: an analysis of demographic health survey data from 30 sub-Saharan African countries (2005–2015). The American journal of tropical medicine and hygiene, 96(3), 741-748.