Gender Considerations in Monitoring and Evaluation

Gender Integration Checklist

This practical guide explains the steps to integrate and address gender considerations throughout the phases of a project.

Why collect sex-disaggregated and gender-sensitive data?
In many contexts, women, men, girls, and boys have different needs and face different social, economic, and cultural barriers that can affect their ability to access and benefit from services and programs equitably. The best way to plan for gender considerations is by conducting a gender analysis to identify the gender inequalities, constraints, opportunities, and unintended consequences that can contribute to or affect how target beneficiaries access and benefit from a service or program, as well as issues that may affect program outcomes. By identifying important gender-related issues before a program starts or early on in implementation, implementers can foresee and address gender-related issues proactively.

Collecting and analyzing sex-disaggregated data is an important part of every gender analysis. But even if a program is unable to conduct a thorough gender analysis, sex-disaggregated data is a powerful tool to identify quantifiable differences between women and men and between girls and boys. It is ideal to begin collecting sex-disaggregated data at the very beginning of a program monitoring activity. But sex-disaggregated data is still useful at any stage of a program--meaning it’s never too late to start collecting it. Without sex-disaggregated data, vital information is missed about the existing differences and gaps between girls, boys, women, and men, and important opportunities to adapt programs to meet their unique needs to improve outcomes can be overlooked.

Disaggregated data is also important to provide evidence to local partners such as Ministries of Health and donors of gender-related barriers in health care to advocate for the value of a gender-focused approach. In quality improvement, we aim to identify and address barriers to health care and access to services to support improvement efforts. It is critical for improvement efforts to disaggregate data by sex and develop gender-sensitive indicators, to view males’ and females’ access to, utilization of, and retention in services separately and to identify and address any issues one group is facing which causes them to be less likely to access or benefit from services. This is critical to improving outcomes, because if a portion of the population is not able to access services equitably, improvement past a certain point will not be possible until these barriers are addressed and all members of a community have equal access to, and utilization of, services. The USAID ASSIST Project routinely collects and analyzes relevant improvement-related data separately for women, men, girls, and boys.

An illustrative list of indicators disaggregated by sex utilized by the USAID ASSIST Project includes:

  • The proportion of people living with HIV (PLHIV)(female/male) identified in the community
  • The proportion of clients on antiretroviral therapy (ART) (female/male) seen in the past month who have shown clinical improvement
  • The number of orphans and vulnerable children (girls/boys) accessing children’s centers for psychosocial wellbeing
  • The proportion of HIV patients screened for TB  (female/male)
  •  Percentage of HIV-positive people (female/male) assessed for nutritional status

Analyzing sex-disaggregated data
Collecting sex-disaggregated data does not alone result in a gender-sensitive intervention. Once collected, it’s vital that the sex-disaggregated data are continually analyzed over time. The analysis of sex-disaggregated data can inform staff about whether the proportion of females to males identified, enrolled or accessing a service is representative of the population as a whole, and whether males and females are retained in care or responding to the intervention in the similar numbers.

When gaps in access, retention or outcomes are identified, it’s extremely important to do further research to determine what is causing the gap, and to make adjustments to the project in order to implement an intervention which benefits males and females equitably. One example which highlights how sex-disaggregated data can lead to improvement is in reviewing the proportion of male and females on ART showing clinical improvement, to determine whether males and females are showing different levels of clinical improvement. This can inform us about whether a group of people are not benefiting equally from the program.  More research would then be done to figure out why that is and to then propose a change to test to overcome the barrier or issue causing poorer outcomes.

Another example that highlights how sex-disaggregated data can lead to improvement is to analyze the proportion of females and males living with HIV in the community. The goal of this identification is to then link PLHIV with treatment. If, for example, 70% of the PLHIV identified in the community are male, and only 30% are female, then it’s important to look at other data sources to determine the ratio of the male to female HIV burden, to see whether the ratio of females to males identified is representative of the community. If it’s not, then it suggests that there is an issue with the identification process, and it should be reviewed and adapted accordingly in order to overcome the issues that prevent one sex from being identified and linked to treatment.

Through the USAID ASSIST Project, our team uses quality improvement methods to find innovative and workable solutions to address gender-related issues affecting health outcomes. This is done by evaluating the different conditions of women and men, including changes over time, and digging deeper to further define the root problems of these differences. We work with local implementing partners to develop local solutions, response options and changes to test and evaluate, to monitor outcomes by sex, and to characterize these results through quantitative measures.

Why are gender-sensitive indicators important?
Gender-sensitive indicators, which measure changes in the status and role of men and women over time, are central to the monitoring and evaluation of programs. It is important to construct, measure, and analyze indicators to monitor progress in achieving program aims. Gender-sensitive indicators can be used to assess the impact of changes or interventions that address gender-related barriers in care. These indicators are designed to measure changes in the status and role of men and women over time.

An illustrative list of gender-sensitive indicators utilized by the USAID ASSIST Project includes:

  • Proportion of male partners who are tested for HIV at their partner’s antenatal care visit
  • Proportion of female partners who attend at least one education session or clinic visit with a male partner prior to undergoing voluntary medical male circumcision
  • Proportion of male partners who participate in postpartum family planning counseling sessions
  • Proportion of women who report that their partner accompanied them for at least one antenatal care visit during their pregnancy

Gender-sensitive indicators have the special function of pointing out how far and in what ways development programs have met their gender objectives and achieved results related to gender equity. It can also alert staff to any unintended consequences of an improvement effort by showing if the any aspects of the program benefit one gender group more than another or create or increase negative results for one social group.

How to develop gender-sensitive indicators
The best way for a program to develop gender-sensitive indicators is to utilize findings from a gender analysis or gender assessment to identify gender-related issues or constraints that may affect the program and to design indicators to track those issues over time. In developing gender-sensitive indicators, it’s important to formulate measures that demonstrate removal of gender-based constraints, establish realistic separate targets for women and men, and check assumptions. We can determine how information can be obtained and clarify areas where more information is needed. Gender-sensitive indicators should capture quality and not just quantity: for example, not just measuring attendance but also true participation and decision-making, or examine the quality of jobs, rather than simply numbers of women employed.

To develop gender-sensitive indicators:

  1. Identify gender issues within a specific context of the program or activity.
  2. Formulate measures that demonstrate the mitigation/removal of gender-based constraints or the change in the relationship or roles of males and females over time.
  3. Establish realistic targets. Separate targets for males and females and by relevant age groups. Check assumptions: Would an intervention targeted to vulnerable children and families benefit all families equally? Instead of “Increase vulnerable children and families family income by 25%,” consider “Increase child-headed households’ income by 25%.” It’s also important to track girl-headed households and boy-headed households separately.
  4. Clarify where more information is needed, and determine how this information can be obtained. Have you conducted a gender analysis? Interviewed both men and women?
  5. Indicators should capture quality, not just quantity. Avoid counting bodies – capture true participation and decision-making power. Gender-sensitive indicators aim to assess increases in access and equality. For example, when measuring impact and increases in equality, the quality of jobs newly available to women is more important than the number employed.
  6.  Aim to measure changes in the levels of inequality. Measure proportions instead of numbers; compare proportions of males and females. Compare proportions over time to the proportions expected (if available).For example, in an area where the HIV prevalence is expected to be 60% men, 40% women, an activity should not aim for 50/50 access, but rather 60/40 access. To assess the level of inequality, measure actions instead of knowledge. For example, instead of the number of men who recognize the danger signs of pregnancy and birth complications; measure the % of men who allocate household resources for emergency transport.

Resources to learn more

To learn more about how to use an improvement approach to identify and respond to gender-related gaps using sex-disaggregated data collection and analysis, view step-by-step instructions including actions that program staff should take, an explanation of how to achieve the action and specific examples.

For more information about practical steps to integrate gender considerations into program implementation and improvement and for real examples of how sex-disaggregated and gender-sensitive data can help you to understand how to improve care and outcomes, see the HCI Implementation Guide for Integrating Gender in Improvement Activities.

To see how the USAID ASSIST Project tracks sex-disaggregated improvement data, view the ASSIST multi-facility sex-disaggregated database and ASSIST multi-region sex-disaggregated database tools, designed to store and pool indicator data from facilities, and create run charts of the pooled indicator data. They automatically update the time scale of run charts depending on how many months or weeks of data have been collected. Feel free to alter and use, as needed.

A two-hour Gender M&E eLearning Course, designed by MEASURE Evaluation and hosted by the Global Health eLearning Center, is now available to the public. It is designed to strengthen develop practitioners' understanding of gender and  M&E. This course is very relevant for improvement teams who will be designing improvement aims and indicators, as well as improvement M&E teams.

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