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Counted and Visible Toolkit

Counted and Visible toolkit

The Counted and Visible: Toolkit to Better Utilize Existing Data from Household Surveys to Generate Disaggregated Gender Statistics (Counted and Visible Toolkit) provides a compilation of tools and mechanisms used by several countries to produce evidence to inform gender-responsive policies and catalyze actions to leave no one behind. The selected countries are linked to UN Women’s global gender data programme, Women Count.

The toolkit was developed by UN Women, in collaboration with the Intersecretariat Working Group on Household Surveys (ISWGHS), and benefited from the outcomes of the Counted and Visible global conference in 2020.

Explore the toolkit

Introduction

 

The toolkit has the following objectives:

  1. Provide guidance on mechanisms and tools
  2. Present methodologies and assessment exercises
  3. Present statistical management and coordination mechanisms
  4. Provide insights, learnings, and challenges

The toolkit was created in response to the need for tools and methodologies for data disaggregation and for achieving the full potential of household surveys for SDG monitoring. It is aimed at gender data producers and users within national statistical systems by providing firsthand experiences of countries across regions, particularly national statistical offices.

 
 

 

What the Counted and Visible Toolkit does

  1. Takes a holistic approach to capacity development

    The toolkit includes strategies and mechanisms on coordination, communication, advocacy and use of disaggregated gender statistics using existing data from household surveys to ensure institutionalized and sustainable gender data production.

  2. Showcases country-led experiences

    Initiatives highlighted are those that have been directly implemented by national statistical systems to ensure the Toolkit “talks” to their counterparts in other countries with similar contexts.

  3. Utilizes existing data from household surveys

    Recognizing the constraints of conducting new household surveys, the toolkit shows how to achieve the full potential of existing household surveys and produce statistics that leave no one behind.

  4. Serves as a living document

    UN Women and the Intersecretariat Working Group on Household Surveys (ISWGHS) consider this as a dynamic document that will be periodically updated as other countries use and adapt the Counted and Visible Toolkit to their country-specific needs.

  5. Uses examples from Women Count countries

    Since the list is not exhaustive, we welcome suggestions for country examples that may be added

What the Counted and Visible Toolkit does not do

  1. Prescribe gender data production processes

    While this toolkit serves as a guidance, it is not intended as the final word on how existing data from household surveys can be better utilized to produce disaggregated gender statistics. Further, it does not require all stages be undertaken by users and in a sequential manner, and the stages outlined are not exclusive to the process of producing disaggregated gender statistics using existing data from household surveys.

  2. Provide recommendations towards rationalizing existing household survey systems

    While the toolkit focuses on extracting more value from the existing household survey system, its rationalization, coordination, and harmonization is beyond the scope of this Toolkit and more aligned with the overarching work of the ISWGHS.

 

Use the tabs on your left to navigate across the five stages of the toolkit:

  • Stage 1: Commitment
    Outlines the commitment of NSS leadership to producing disaggregated gender statistics to leave no one behind
  • Stage 2: Prioritization
    Covers the process of identifying national priority gender equality indicators (NPGEIs)
  • Stage 3: Production
    Focuses on the development of methodologies and data production of select NPGEIs
  • Stage 4: Assessment
    Outlines the assessment and publication of results
  • Stage 5: Advocacy and use
    Specifies the importance of dissemination, advocacy and use of disaggregated gender statistics

Stage 1: Commitment

 

Leadership is essential to producing disaggregated gender statistics to leave no one behind. Commitment from the leadership of national statistical systems creates greater political will to develop and use statistics, advances gender-responsiveness in the NSS, and ultimately has the power to inform gender-responsive statistical policies. Through effective leadership there can be greater advocacy, coordination and investment in household surveys and gender statistics.

 
 

 

Engaging stakeholders

Engaging stakeholders is key to generating commitment and ensuring that disaggregated gender statistics are produced, used, and institutionalized into a gender-responsive NSS. Here are a few points to keep in mind:

  • Shared leadership between the NSO and national women’s machinery

    Ensuring co-ownership and engagement of key actors in the NSS – particularly the major gender data producers and users – is critical in the production and use of disaggregated gender statistics. The process needs to be user-oriented.

  • A participatory and coordinated approach

    The system for gender statistics is highly decentralized, in that gender data is collected and produced not only the NSO but also by sectoral ministries and other major data producers. It thus requires engaging a broad range of core political and public actors. So, NSS-wide consultations should involve different sectors and local governments as well as users and data producers of gender statistics beyond government institutions.

Stakeholder cooperation

Cooperation with data users and stakeholders is one of the important dimensions of sustainable and effective production of disaggregated gender statistics. Effective cooperation does not only improve production, but more importantly, strengthens trust and understanding among stakeholders, forges new networks, and creates an informed community. Building stakeholder cooperation can be achieved through user-producer dialogues, bringing stakeholders together in consultations, and trainings to build the capacity of the NSS.

Coordination of the NSS

Active coordination is required within the NSS to get the commitment, engagement, and participation of gender data users and stakeholders. Here are some ideas to improve coordination from the Toolkit:

  • Designation of gender statistics focal points or establishment of a gender statistics unit within the NSO or other key actor of the NSS
  • Formation of an inter-agency group or task force on gender statistics within the NSS
  • Legislation, statistical policies and agreements on the utilization of existing household surveys to generate disaggregated gender statistics
  • Development of a multi-year work program for the generation of disaggregated gender statistics
  • Web portal for gender statistics
 
 

 

Cameroon

As a major step for engendering the statistics process in the country, the Chief Statistician of Cameroon pushed for the creation of a gender statistics unit within the National Institute of Statistics (NIS) – a clear demonstration of the NIS leadership’s commitment of this initiative. This led to the creation of a Permanent Working Group on Gender Statistics in 2019, led by the Chief Statistician and serving as the gender statistics think thank within the NIS. It is tasked to coordinate gender-sensitive statistical activities within the NIS as well as the broader NSS. Key achievements from this working group includes the identification and adoption of a minimum set of priority gender indicators and the production of policy briefs.

UN Women supported a statistical legislation that was passed in 2020 to regulate the statistical activities in the country, updating the law in place since 1991. As gender statistics contribute to the country’s official statistics, the legislation implicitly provides a basis for data collection needed for disaggregated gender statistics. The law also calls for the national government to provide the necessary budget for the statistical operations of the country.

 

Georgia

Led by the National Statistics Office of Georgia (GEOSTAT), the country has been building a gender-responsive statistical system through a National Strategy on Gender Statistics and establishing the Inter-Agency Working Group on Gender Data (IAWG-GD). The Working Group serves as the country’s mechanism for cooperation and coordination of gender statistics. It also helped identify the National Priority Gender Equality Indicators (NPGEIs), which will inform the level of disaggregation of gender statistics that will be compiled on a regular basis using existing data from household surveys and other data sources. Importantly, the Working group contributed to the assessment and development of new policies, particularly the National Action Plans on combating violence against women and domestic violence, human rights, and Security Council Resolutions on women, peace, and security.

 

Albania

In Albania, the Inter-Agency Working Group on Gender Statistics (IAWG-GS), led and coordinated by the Institute of Statistics (INSTAT), plays an important role in improving access, dissemination, and communication of gender data. Key achievements of the group include updating the country’s priority gender indicators using existing data from household surveys, censuses and administrative data. More importantly, the creation of the IAWG-GS boosted user-producer dialogues, resulting in concrete actions to improve gender statistics production. In addition, through the Working group, INSTAT has been able to improve technical capacities for data production and increase awareness and statistical literacy of gender data.

 
 

 

Engaging stakeholders

  • Framework and Implementation Guidelines for Assessing Data and Statistical Capacity Gaps for Better Gender Statistics [PARIS21]
  • Integrating a Gender Perspective into Statistics [UN Statistics Division]
  • Making data count for all: Good practices in integrating gender in national statistical systems [UN ESCAP, UN ECE, and UN ESCWA]

Stakeholder cooperation

  • UN Women Asia and the Pacific. Training Syllabus: Curriculum on Gender Statistics Training. Module 4: User-Producer Dialogue. Women Count Programme. Bangkok, Thailand. December 2020.

Coordination of the NSS

 
  • The initiative requires strong commitments of institutions and actors in national statistical systems (NSSs) working on gender equality and women’s empowerment (GEWE). Leadership and commitment by national statistical offices (NSOs) and national women’s machineries are indispensable elements to ensure stakeholders’ engagement and cooperation throughout the process.
  • Gender data production process should be user-oriented rather than product-oriented.

Stage 2: Prioritizations

 

The Inter-Agency and Expert Group on Gender Statistics (IAEG-GS) has established a minimum set of gender indicators to provide a basis for monitoring progress on gender equality on the global level and guide the development of regional and national indicators.

Guided by the global framework, each country’s national statistical system must identify a National Priority Gender Equality Indicator (NPGEI) framework. This process calls for prioritizing which indicators must be disaggregated and by which dimensions, considering resources available and national priorities.

 

The NPGEI framework guides the development and production of gender-specific indicators in various thematic areas. Guided by the global framework, the NPGEIs of each country should reflect its prevailing characteristics such as political structure, living standards, and culture, as well as its priorities and commitments such as the SDG indicators and national gender equality and women’s empowerment indicators.

The framework should be developed and owned by the country and used to improve monitoring of national progress on gender equality. Ideally, the NSO and national women’s machinery take co-leadership roles in developing the framework.

An effective national framework that reflects statistical priorities and gender equality commitments will:

  • Clearly specify key gender indicator requirements
  • Guide the development needs of gender statistics
  • Guide development partners on areas of support required for gender statistics
  • Assist in meeting the country’s global and regional reporting requirements.
 
 

 

Viet Nam

Viet Nam’s NPGEIs were endorsed in 2019. During its development it followed the guidelines of the EPIC tool developed by ESCAP, which includes two stages and the following steps:

Stage I: Preparing for the analysis

  • Step 1: Identify EPIC team members or potential members to form a team for analysis
  • Step 2: Identify a policy document for the analysis
  • Step 3: Identifying sections of policy documents
  • Step 4: Read and familiarize definition of issues for action (IA), target groups (TGs), core concepts (CCs)
  • Step 5: Identify/prepare frameworks for review: national, regional and global indicator sets

Stage II: Carrying out the analysis

  • Phase 1 (Step 6-7-8): Identify of IA and TGs and establishing linkages across various dimensions of development through CCs
  • Phase 2 (Step 9-10-11): Towards the development of national indicators set
  • Phase 3 (Step 12): Identification of additional IA and TGs for future planning and consideration

Senegal

Senegal aimed contextualize the SDGs by adapting the targets and indicators to the country context. Moreover, it was deemed essential that disaggregated data are available to ensure the reliability of the planning, monitoring and evaluation processes of policies at the local level. This is even more necessary given existing legal frameworks on decentralization, wherein the government has decided to give more responsibilities to local authorities to promote better economic and social development. Presently, the country is using the same framework in updating and replicating the process and has focused on three policy-relevant indicators – education, informal work, and violence against women, under the leadership and coordination of the National Agency of Statistics and Demography of Senegal (ANSD, Agence Nationale de Statistique et de la Démographie).

United Republic of Tanzania

In the United Republic of Tanzania, the Department of Social and Demographic Statistics of the Office of the Chief Government Statistician (OCGS) in Zanzibar developed, for the first time, the Zanzibar SDG Gender Indicators Report, with support from the Women Count programme. The report covers detailed disaggregated gender statistics using existing data from household surveys, as well as censuses and administrative records to derive disaggregated priority gender indicators. The report provides valuable evidence to inform gender-responsive decision-making in Zanzibar.

 
  • Framework and Implementation Guidelines for Assessing Data and Statistical Capacity Gaps for Better Gender Statistics [PARIS21]
  • Global Minimum Set of Gender Indicators [UN Statistics Division UNSD)]
  • Gender-specific indicators in the SDG monitoring framework [UN Women and UN Statistics Division]
  • Tools for assessment of policy-relevant gender indicators:
    • Every Policy Is Connected (EPIC): A generic tool for policy-data integration Working Paper Series (SD/WP/09/September) [UN ESCAP]
    • Advanced Data Planning Tool (ADAPT) [PARIS21]
  • Regional gender indicators frameworks:
    • Core set of gender indicators for Asia and the Pacific [UN ESCAP]
    • Work of the secretariat and partners on mainstreaming gender in environment statistics [UNESCAP]
    • Minimum Set of Gender Indicators for Africa [UN Women East and Southern Africa Regional Office]
    • Prioritized set of indicators for regional statistical follow-up to the SDGs in Latin America and the Caribbean [UN ECLAC]
    • Indicators of Gender Equality [UN ECE]
 
  • The basis for the selection of gender statistics to be disaggregated should be guided by the country’s national priority gender equality indicators (NPGEIs) framework, which should reflect the expressed needs and priorities of the country as well as its prevailing characteristics such as political structure, living standards, and culture.
  • For greater ownership, the framework should be developed by countries themselves – both gender data producers and users. Ideally, the NSO and national women’s machinery take co-leadership and shared roles in undertaking this activity.

Stage 3: Production

 

This stage focuses on the methodologies that can be used to generate disaggregated gender statistics based on existing data from household surveys, and where computation and data analysis can be done to leave no one behind.

Microdata, metadata, and statistical software are needed before applying the analysis.

 

 

  • Microdata

    The required microdata depends on the indicator to be measured, and often, individual-level data is needed (rather than household level) to better reflect inequalities experienced by women and girls. The microdata should include the parameters of the sampling design structure of the survey.

  • Metadata

    Metadata provides the definition and method of computations, recommended data sources and potential limitations, and usually correspond to the microdata.

  • Statistical software

    STATA, SPSS, and SAS are the most commonly used software packages that can handle and analyse survey data. All three are licensed software that require fees for its usage. But there is also an open-source package called R, which uses opensource code to provide algorithms for generating disaggregated gender statistics using survey data.

 
 

 

Identification of the subdomain of estimation

In estimating gender statistics with multiple levels of disaggregation, the estimates in a ‘smaller’ domain or subdomain need to be identified. Subdomains can be geographical or formed by a cross-classification of certain population attributes. Generally, subdomains are identified depending on the purpose of estimation like an indicator or a policy to which the disaggregated gender equality statistics will be used.

National household surveys are designed with specific domains of estimation, which have a sufficient number of observations to produce relatively reliable estimates. In producing disaggregated gender statistics, the estimates will be obtained from ‘smaller’ domains or subdomains.

 

Direct estimation of disaggregated gender equality indicators

In direct estimation, only the collected or observed data in the subdomain are used in the computation. In other words, the direct estimator is a weighted total of the observations from all sampling units in the subdomain collected or obtained through a nation-wide survey. The survey sampling weight is the reciprocal of the probability of inclusion of an observational unit in a nation-wide survey and is computed based on the sampling design of the survey.

Note: Sampling weights should be included in the estimation process as the estimates should reflect the achieved weighted values in accordance to the sample survey sampling procedure used in conducting the national survey. The incorporation of the sampling weights ensures the accuracy of the estimates calculated. However, the estimator used does not guarantee that the estimates are precise as this can only be established using the observations gathered for the variable of interest.

 
 

 

Mongolia

NSO Mongolia and UN Women jointly analyzed the indicator, ‘Proportion of people who did not complete more than six years of education (or those who are education-poor)’ using Mongolia’s Multiple Indicator Cluster Survey (MICS) 2014–15. Computations show that the likelihood of being education-poor increases if women and girls identify with ethnic minorities, religious minorities and live in a poor household. These factors compound to create substantially deprived groups of women. NSO Mongolia also used its MICS 2018 to estimate the indicator, ‘Proportion of women who married as children’. Undertaking a gender and intersectionality analysis, smaller subdomains were also considered in estimating the indicator, such as the proportion of child marriage among women aged at least 18 years residing in urban areas by wealth index quintile.

See annex 5 in the Toolkit’s executive summary.

 

Pakistan

The case of Pakistan shows how Geographic Information System (GIS) data can be used to highlight the relationship between gender-based deprivations and location-based inequality from the intersection of geography with other group-based inequalities. The GIS modules of Pakistan’s Demographic and Health Survey (DHS) 2017 were used to capturing spatially segregated socio-economic disadvantage of women and girls. The study indicates that overlapping inequalities, for example, those based on gender, ethnicity, geography, and wealth, can and often do produce a form of disadvantage that is acute and distinct, leaving women and girls facing these overlapping forms of discrimination worse off than other groups in society.

See annex 2 in the Toolkit’s executive summary.

 

Iraq

In 2019, UN Women implemented a programme on ‘Strengthening the Resilience of Syrian Women and Girls and Host Communities in Iraq, Jordan and Turkey’, mainly funded by the EU Regional Trust Fund for the Syria Response. Building on this experience, UN Women collaborated with FAO to produce a gender-sensitive resilience index based on FAO’s Resilience Measurement Analysis (RIMA) Model.

This gender-sensitive resilience capacity index (RCI) measures changes in programme beneficiaries’ resilience and whether this is the same for all women across communities of origin (that is, host communities, refugees and internally displaced people (IDPs)). These were generated through repeated surveys with the same group of women at different points in time.

For the resilience profile of programme beneficiaries in Iraq, as of May 2020, see annex 1 in the Toolkit’s executive summary.

 
  • Training Syllabus: Curriculum on Gender Statistics Training [UN Women Asia and the Pacific]. Women Count Programme. Bangkok, Thailand. September 2020.
  • Guidelines for Producing Statistics on Asset Ownership from a Gender Perspective [UNSD].
  • Technical Report on Entrepreneurship from a Gender Perspective: Lessons Learned from the EDGE Project [UNSD].
  • Guide to Producing Statistics on Time Use: Measuring Paid and Unpaid Work [UNSD].
  • FAO Resilience Measurement Analysis Model (RIMA).
  • Gender Sensitive Resilience Capacity Index [UN Women].
  • Improving data availability on vulnerable populations of women and girls
    • Turning promises into action: Gender equality in the 2030 Agenda for Sustainable Development (Chapter 4) [UN Women].
    • Women with Disabilities
      • Realizing the rights of girls with disabilities through inclusive statistics [UNICEF].
      • Second National Study on Disability [University of Chile].
      • Disability statistics: Senegal experience [Senegal NSO/ANSD].
    • Age and Gender
      • Fertility among very young adolescents: Data, methods, trends, and challenges [UN Population Division (UNPD)].
      • Bridging the data gap: Women 50 years and older [UN East and Southern Africa].
      • Measuring children’s time use: Methodological challenges [UNICEF].
    • Migrants, refugees, and displaced persons
      • Experience of Jordan on improving migration data [Jordan NSO/DOS].
      • Statistical estimation of migration flows and patterns to leave no one behind [Moldova NSO/NBS].
      • Women and internal conflict in Colombia [Government of Colombia].
      • Using an intersectional approach to unpack some basic questions about international migration [UNPD].
    • Multidimensional poverty
      • Measuring women’s empowerment with multidimensional indices [International Food Policy Research Institute].
      • Operationalizing the Leave No One Behind Principle using multi-level disaggregation analysis to monitor the SDGs from a gender perspective [UN Women].
      • Missing Figures: Who is being left behind? [INEGI].
      • Data for understanding inequality and intersectionality: The Individual Deprivation Measure [International Women’s Development Agency].PPT / Video
      • Child poverty measurement and gender discrimination [UNICEF].
  • Disaggregated gender-specific SDG indicators
    • Spotlight on SDG 11: Harsh realities: Marginalized women in cities of the developing world [UN Women and UN Habitat].
    • The Impact of Marriage and Children on Labour Market Participation [UN Women and ILO].
    • Spotlight on SDG 1: Gender differences in poverty and household composition through the life cycle [UN Women and World Bank].
  • Data sources for the generation of disaggregated gender statistics
    • Scaling up best practices in intra-household individual-disaggregated survey data collection: LSMS+ Program [World Bank].
    • ILO Survey Catalogue
    • Multiple Indicators and Cluster Survey [UNICEF].
    • The DHS Program: Demographic and Health Surveys
    • IPUMS Census and Survey Data
    • Potential of combining multiple data sources for enhanced compilation of gender statistics [ADB].
    • Good practices on gender data disaggregation and household surveys
      • Colombia NSO/DANE
      • NSO Mongolia
    • Strengthening administrative systems to close gender data gaps [UNICEF].
    • Non-traditional data sources
      • How community-led data can lead to action [Development Initiatives].
      • Looking at gender, LNOB and non-traditional data sources from the demand side [Equal Measures 2030].
      • Filling the Gaps: Does non-official data hold any promise? [INEGI].
 
  • Existing data from household surveys can be further tapped to produce disaggregated NPGEIs
    • that is, beyond traditional gender statistics usually from standard statistical tables with limited disaggregation specifications.
  • Extraction of new disaggregated gender statistics using existing data from household surveys also calls for capacity development activities within the NSOs and NSSs to ensure institutionalization, transfer as well as sharing of knowledge across key actors.

Stage 4: Assessmint

 

Statistical data and outputs should be assessed and validated and systems should be developed to do this regularly. Quantitative and qualitative assessments should both be done when producing disaggregated gender statistics and how these should inform the publication of results.

When assessing the quality of disaggregated gender statistics produced from existing data from household surveys, consider the following:

  • Existing surveys are easily accessible, but availability of indicators is limited to data that has been collected.
  • Accessing existing surveys assumes that the data collection methodologies are implemented uniformly by all enumerators and understood similarly by respondents.
  • Surveys are designed specifically to give estimates to a certain population.
  • Since the latest surveys might have been conducted a long time ago, the information available may not be relevant in the current context.
 
 

 

Quantitative Assessment

A quantitative assessment involves evaluating the statistical properties of the estimates. The Counted and Visible Toolkit focuses on the following properties:

  • Sufficiently accurate, as measured by the bias

    Accuracy refers to the closeness of estimates to the true value of the indicator. A large bias may be due to sampling error, non-sampling-error, or both. Non-sampling errors cover all types of errors from all sources such as response errors, coverage errors, and errors linked to data collection and processing. See the annex.

  • Sufficiently precise, as measured by the standard error (SE)

    Precision is a measure of closeness of the estimates to each other. SE is computed by dividing the square root of the ratio of the variance of the estimates and the number of observations used in the estimation. This can be automatically generated in statistical analysis software like STATA. In the generation of disaggregated gender statistics, the estimates are expected to be less precise as more disaggregation is applied in the data since the sample size was computed for large domain of estimation, like national or regional level. [See statistical formula and practical application using STATA.]

  • Sufficiently reliable, as measured by the coefficient of variation (CV)

    Reliability of the estimates measure variability of the value of the estimates through the CV. Mathematically, it is expressed in percent and computed as ratio of the standard error of the estimate and value of the estimate. Thus, the smaller the value of the CV, the better. There are no internationally agreed standards or recommendations as to the “acceptable” values of CV for a certain type of estimator. [See statistical formula and practical application using STATA.]

Qualitative Assessment

A qualitative assessment is more of a practical evaluation of the estimates to assess how the estimates are perceived, mainly by users and stakeholders of the estimates generated, to provide the true picture of reality.

While quantitative assessments are usually done by data producers, qualitative assessment usually involves prospective users and subject experts. For example, an assessment of disaggregated survey results on local level estimates of poverty among women calls for the involvement of local officials (e.g., planning and development officers, county, or village leaders) with knowledge of the local area. The insights of specialists will be useful both for producers by ensuring the accuracy, precision, and reliability of the estimates, and for promoting the use of estimates.

Some ways to conduct these assessments include:

  • Workshops and focus group discussions involving prospective users and subject experts.
  • Consultations with colleagues familiar with the sub-population group of interest, wherein concerned population are asked to assess whether the estimates under study match with their expected results.

Publication of results

Data producers are encouraged to present estimates along with corresponding CVs. Since cut-offs of quantitative assessments vary from country to country and in some cases, from survey to survey, here are some suggestions for when estimates do not meet defined thresholds:

  1. Include caveats in the publication of results to be transparent about the reliability of the estimates. Standard errors and confidence intervals may also be presented as supplemental indicators. In practice, CV thresholds vary country to country and in some cases, from surveys to surveys. Some literature regards a measure of CV less than 10 percent as highly acceptable while a CV with value between 10 and 20 percent as still acceptable. For CV values ranging between 20 and 33 percent, estimates are regarded as less acceptable but still sufficiently reliable and should be used with caution. For those greater than 33 percent, caveats should be provided in terms of the level of reliability of these estimates.
  2. Provide information on the sampling design and explanations on why certain estimates do not meet criteria as it can help planning of future surveys and studies.
 
 

 

Mongolia

Disaggregated gender statistics were generated for nine gender indicators using Mongolia’s MICS 2018 for child marriage estimates using the wealth index and type of residence as disaggregating variables: one indicator used only one disaggregation variable; four indicators used two variables; and the remaining four used three variables. Examining the reliability of the estimates as measured by their corresponding CVs, only five of eight estimates are relatively reliable. Two estimates with CVs greater than 10 but less than 20 percent are relatively sufficiently reliable but should be used with caution. On the other hand, a caveat should be provided when publishing the remaining indicator given a CV of 30 percent.

See Annex 5 of the toolkit executive summary.

 

Philippines’ Small Area Poverty Estimates

The then Philippines National Statistical Coordination Board (NSCB; now part of the Philippine Statistics Authority (PSA))4 employed small area estimation (SAE) techniques based on the Elbers, Lanjouw and Lanjouw (ELL) methodology developed by the World Bank. Almost two decades ago, this generated – for the first time – intercensal small area estimates of poverty of all 1,622 cities and municipalities (as of 2000). Given the nature of drilling down to very low levels of geographic disaggregation, the SAE exercise called for the examination of the reliability of these small area estimates. Coefficients of variation as well as standard errors and confidence intervals were made available to all users, published along with the release of the small area estimates. This practice was institutionalized in all succeeding SAE exercises undertaken by the PSA. Further, this good practice of integrating and institutionalizing publication of measures of precision and reliability of estimates were also applied in the release of official estimates of poverty, directly generated from results of the Family Income and Expenditures Survey (FIES).

See Annex 4 of the toolkit executive summary.

 
 

 

Quantitative Assessment

  • Multilevel disaggregation analysis to monitor the SDGs from a Leave No One Behind perspective [UN Women].
  • Introduction to Small Area Estimation Techniques: A Practical Guide for National Statistics Offices [ADB].
  • Compilation of tools and resources for data disaggregation [IAEG-SDGs and UNSD].
  • Practical Guidebook on Data Disaggregation for the SDGs [ADB].
  • STATA Manual. Proportion Command.
  • STATA Manual. Svy Command.

Publication of results

  • Disaggregation of the SDG indicators related to food and agriculture [FAO].
  • User Guide for the Survey of Household Spending 2017. Household Expenditures Research Paper Series [Statistics Canada].
  • Healthy People 2010 Criteria for Data Suppression [CDC, Healthy People 2010]

Philippines’ Small Area Poverty Estimates

  • 2015 Family Income and Expenditure Survey. Quezon City, Philippines [PSA].
  • 2012 Municipal and City Level Poverty Estimates [PSA].
 
  • Estimates of “smaller” domains or subdomains (e.g., geographic or specific sub-population group) may not necessarily have been considered when the household survey was earlier designed. Hence, there is a need to examine whether disaggregated gender statistics produced are sufficiently precise and reliable as measured by the standard error and coefficient of variation in estimating the true value of the indicator. As there is currently no internationally agreed standards or recommendations regarding sufficient levels of precision and reliability, these vary across countries considering their level of statistical development and maturity.
  • Towards increasing statistical appreciation of gender data users, data producers are also encouraged to present these data quality measures along with other relevant information that will be helpful for users to understand and evaluate the estimates themselves.
  • The initiative should be undertaken within the framework of “nothing about us without us.” Thus, qualitative assessments should involve the specific population of interest themselves as well as other prospective users and those also knowledgeable in the type of disaggregation being studied.

Stage 5: Advocacy and use

 

Gender statistics will only be valuable to users if they are “easily found and accessible, and if users find them relevant and easy to understand.” Yet, there are cases when users do not know what gender statistics they need or where to find them; do not know how to use gender statistics tools and data platforms; or simply do not understand what the data producers are communicating.

Targeted dissemination and communication are key to overcome these challenges. The distinction between dissemination and communication is important: dissemination is a phase in statistical processes where data produced by statistical agencies are released to the public. It is a once-way approach focused on making information available to target users, through tools that they can easily utilize, and methods they can easily understand. Communication, on the other hand, is a two-way exchange between data producers and users that includes activities that improve the overall awareness and appreciation of users for gender statistics. Communication activities are mindful of and tailored to the needs of users, thereby ensuring the take-up and use of the statistics.

 
 

 

Communication and advocacy plan

A communication and advocacy plan should be developed, highlighting the importance of using gender statistics to inform gender-responsive policymaking. Having a well-developed plan signals the move from traditional dissemination towards more holistic communication and advocacy. Importantly, it should also reflect deeper engagement on gender and intersecting inequalities to better address them in statistical and policy work.

The plan should outline key components of dissemination and communication such as:

  • Communication and advocacy team.

    The team should include, among others: specialists on statistics, gender policy, and communication, who can serve as messengers.

  • Target users.

    Gender data users vary in terms of the types of gender data they need, their statistical literacy, and their interest and use of gender statistics. Communication tactics should recognize these distinctions and be tailored to these different users.

  • Key messages

    Messages should be driven by users’ needs and demands.

  • Tools

    There is a lot of guidance on tools and media that can help convey the messages, such as data stories, infographics, social media objects, and data visualization – see the training modules below, from UN Women and SPC in particular

  • Timeline and frequency

    Identifying the timeline of a release will aid in planning both in the processing of the data as well as outreach efforts leading up to and beyond the release.

  • Communication and advocacy activities. These are covered in more detail below.

Communication and advocacy activities

Communication and advocacy are essential to ensure that gender statistics not only reach their target audience but are used to drive policy change to advance gender equality. This includes:

  • Promotion of statistical products and events.

    This can be done via various channels, including the NSS website, social media assets, and e-mail distribution lists. Relationships with media outlets should be nurtured to encourage them to cover new data releases and incorporate the new data in their stories. PARIS 21 and Women Count designed an e-learning course to identify points of cooperation and collaboration between journalists and statisticians.

  • Advocating for the use of gender statistics.

    It is essential to maintain good relationships with users, and encourage their use of gender statistics through activities such as training courses for statistics literacy, generating user-friendly data visualizations, data fact sheets and creating opportunities to build dialogue between users and producers of data.

  • Monitoring use.

    A monitoring plan should systematically get updated on quantitative use data such as downloads of statistical products, attendance at events, and use of gender statistics. Qualitative use cases should also be pursued to track the depth of usage. The Women Count programme developed a guidance to systematically monitor the use of new gender data that came out of nearly 50 rapid gender assessments (RGAs) on the impact of COVID-19

 
 

 

Colombia

In November 2020, Colombia released its first edition of Women and Men: Gender Gaps in Colombia. The launch was opened by the Vice President of Colombia, Marta Lucía Ramírez, demonstrating the highest-level government support, and received wide attention online. Less than a month since the launch of the report, Colombia’s NSO committed to institutionalizing the report as an annual publication, and to producing regional reports to inform local decision-and policy-making.

The success of this publication highlights the role of partnerships in communicating gender statistics:

  • Partnerships with other government stakeholders. The publication was jointly produced and launched by the NSO, the Presidential Advisory Office on Gender Equality (CPEM), and UN Women Colombia. High-level government commitment ensured the wide uptake of the publication.
  • Partnership with media. The launch was organized in partnership with El Espectador, a national media outlet in Colombia with 4 million Facebook followers. The event was covered in at least 47 publications across the country.
  • Partnership with UN Women Colombia. The UN Women Country Representative in Colombia is a staunch advocate of communicating gender statistics, and strongly supported the launch activities.

Kenya

In August 2020, Kenya launched its first ever Women’s Empowerment Index (WEI). UN Women, through the Women Count programme and in partnership with Kenya National Bureau of Statistics (KNBS) and UNICEF, co-led the production and launch of Kenya’s WEI. WEI is a major milestone in the country’s monitoring of SDG 5 as the first comprehensive and systematic measure for women’s and girls’ empowerment in Kenya. The WEI launch received significant mainstream media coverage.

The success of the WEI uptake was the result of targeted communication to a broad audience:

  • Dedicated communications capacity. A Communications Consultant recruited by the Women Count programme in East and Southern Africa, provided expertise in developing key messages and packaging the publications finding to target broader audiences. This tailoring of the product made it more accessible for media coverage.
  • Companion assets - Women Count developed a set of infographics as a companion to the report to help visualize and summarize complex data. These infographics were used across social media and resulted in a wider uptake.

Uganda

In updating its NPGEIs in 2019, the Uganda Bureau of Statistics (UBOS), in collaboration with other Ministries, Departments and Agencies (MDAs) embarked on reprocessing existing census, survey and administrative data to provide disaggregated statistics on select NPGEIs, particularly those related to SDG Tier 1 indicators. As a result, reporting of gender indicators in Uganda’s Voluntary National Reviews (VNRs) have increased by 150% in a span of four years: from 11 in 2016 to 28 in 2020.

The increased usage of the NPGEIs in national reporting are due to improvements in disseminatation:

  • Dissemination plan. Uganda developed its own dissemination plan for gender statistics, which included an expanded social media presence, increasing awareness of UBOS data.
  • Creation of URL shortcuts to facilitate access. One of the simplest tactics was the use of a URL shortener to provide users with simple and accessible links to NPGEI publications,.
  • Gender Statistics Portal. The UBOS Gender Statistics Portal is a one-stop center for gender statistics in the country, providing access to gender statistics under different themes, along with news and upcoming events.
 
 

 

Communication and advocacy plan

  • Training module on Communicating Gender Statistics (Module 10) [UN Women]
  • Guidance on Communicating gender Statistics [UNECE]
  • Guide to gender statistics and their presentation [The Pacific Community (SPC)]
  • A Three-Part Guide Series on Making Data Meaningful [UNECE]
  • Communicating with the Media: A guide for statistical organizations [UNECE]
  • Best Practices in Designing Websites for Dissemination of Statistics [UNECE]
  • Dissemination of Microdata Files: Principles, Procedures, and Practices [IHSN]

Communication and advocacy activities

  • E-Learning Course on Communicating Gender Statistics [PARIS21 and UN Women]
  • Regional Workshop on Communicating gender data promoting better use and delivering impactful messages [UN Women]
  • Every child counts: Using gender data to drive results for children [UNICEF]
 
  • There is no single formula to produce disaggregated gender statistics that are relevant to the users, easy to find, easy to understand, and ensure uptake and use. It is composed of different good practices not just in disseminating statistical products but also in communicating and advocating the use of disaggregated gender statistics and tailor-fitting them to the target users, influencers and movers of the GEWE agenda in the country.
  • Investments are needed – human, information and communications technology, financial – in undertaking this initiative.

 


 

Abbreviations and acronyms

CPEM
Presidential Council for Women Equality/Colombia’s Ministry of Women
CV
Coefficient of variation
DANE
National Administrative Department of Statistics of Colombia/Colombia’s national statistical office
EPIC
‘Every Policy is Connected’ (tool developed by ESCAP)
GIS
Geographic Information System
GSS
Gender statistical system
INSTAT
Albania’s Institute of Statistics
ISWGHS
Intersecretariat Working Group on Household Surveys
KNBS
Kenya National Bureau of Statistics
MDA
Ministries, Departments and Agencies
MICS
Multiple Indicators Cluster Survey
NPGEI
National priority gender equality indicators
NSDS
National Strategy for the Development of Statistics
NSO
National statistical office
NSS
National statistical system
RGA
Rapid Gender Assessment
SAE
Small area estimation
SDG
Sustainable Development Goals
SE
Standard Error
UBOS
Uganda Bureau of Statistics/Uganda’s national statistical office
VNR
Voluntary National Review
WEI
Women’s Empowerment Index

Gender statistics defined

Gender statistics are defined by the sum of the following characteristics:

  1. Data are collected and presented by sex as a primary and overall classification;
  2. Data reflect gender issues;
  3. Data are based on concepts and definitions that adequately reflect the diversity of women and men and capture all aspects of their lives;
  4. Data collection methods take into account stereotypes and social and cultural factors that may induce gender bias in the data.

Gender statistics are more than data disaggregated by sex. The characteristics listed above are useful in differentiating between sex‐disaggregated statistics (the first characteristic above) and gender statistics (the other three). Sex-disaggregated statistics are simply data collected and tabulated separately for women and men. Disaggregating data by sex does not guarantee, for example, that the data collection instruments involved in the data production were conceived to reflect gender roles, relations and inequalities in society. Further, some statistics that incorporate a gender perspective are not necessarily disaggregated by sex, such as statistics on violence against women and girls, maternal mortality rate, fertility rate, among others.

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