Gender statistics are not only sex-disaggregated data. They allow compilation and analysis of such data, recognizing that gender-based factors have an impact on well-being. They cut across various domains, reflecting gender issues - the economic, social and political roles of women and men (and girls and boys) in a society. Gender statistics not only elicit outcomes, but also the needs and capabilities by sex across important policy areas. There has been heightened emphasison framing these questions appropriately, as policymakers work towards narrowing gender gaps in such areas as education, work and health, as well as a better understanding of how these outcomes are interrelated.
Because gender data cuts across sectors, a growing number of international and national statistical agencies favorintegrating (or “mainstreaming”) gender intoall stages of data collection and production of official statistics. Back in 1995, the Platform for action at the UN World Conference on Women recommended strengthening capacity to produce gender statistics and reinforce gender mainstreaming in policy-formulation, implementation and monitoring for giving a clearer understanding of the contributions women make to national development. Since 2007, the United Nations Statistical Division’s Global Gender Statistics Programme has also worked to develop countries’ capacity to collect and present reliable gender statistics and indicators to better inform policymaking. This includes an effort to mainstream gender within national statistical organizations (NSOs), and in so doing help reduce different sources of bias that often hamper the interpretation of gender statistics — including underreporting,and stereotypes and other cultural factors that affect data collection.
Gender statistics provide valuable information on disparities for women relative to men across domains. Hence the main objectives in gender data collection are to:
- Quantify women’s vulnerability and disadvantage by measuring levels in female wellbeing in absolute terms (rather than in comparison with males), in addition to measuring sex and genderdifferences and inequalities.
- Measure progress or changes in women’s conditions in absolute terms (changes in levels) and in comparison with men’s (changes in sex and gender disparities) by tracking trends over time.
- Quantify and explain women’s participation in society and their contributions to development.
- Assess the outcome and impact of development interventions on women’s capabilities and the realization of opportunities — on their wellbeing and participation in society.
NEED TO IMPROVE GENDER STATISTICS
Despite the importance of gender statistics, data is still lacking on girls’ and women’s activity and well-being across multiple domains such as health, education, economic opportunities, political participation, and human security (cf. Data 2X initiative in Tools below).
Better vital registration data is needed to gather accurate information on maternal deaths, including causes of death by age, in high mortality low-income countries that do not currently collect and report this data (a main reason being that very large samples are needed to collect valid data on maternal mortality). Maternal mortality rates remain unacceptably high in these countries and accurate recording of rates and conditions leading to maternal deaths are key information for building evidence-based policy. More and better information is also needed on maternal morbidity.
But women’s health issues go beyond maternal conditions. There is substantial evidence that women lose more healthy life to disability compared to men, including excess disease burdens that are unrelated to motherhood (such as Alzheimer’s disease, dementia, osteoarthritis, and other disabilities). Data collection efforts need to address largely unreported causes of women’s excess disease burdens and parse out the contributions of sex and gender, and their interaction, in the etiology, onset, progression and prevention of these conditions.Adolescent health – including information on the social determinants of healthy behaviors among adolescent girls – also stands on its own as a data gap with important implications for policy.
Additional important data gaps in women’s health are data on violence against women and mental health. These issues are both broad, affect large numbers of girls and women, and despite their impact are severely limited in data. A push to close these data gaps may create a snowball effect, where more data increases the visibility of these issues and provides an impetus for women to seek help and for service providers to offer more treatment options.
Relatedly, more reliable data on women’s utilization of maternal and non-maternal health services (under utilization that has been documented in many poor countries) would go a long way towards the design of better health interventions for girls and women.
Improving educational outcomes ensures that students, male and female, reap the social and economic returns to education, and may have a multiplier effect on enrollment. Having internationally comparable measures of learning outcomes disaggregated by sex should drive gender data efforts in this domain. Current measures of education quality across countries are largely based on inputs and are not sufficient to assess learning outcomes.
A second data gap is better information on socially excluded girls – due to race, ethnicity, or disability – who are likely to suffer the double disadvantage of gender and social exclusion, resulting in lower enrollment levels and in poor learning outcomes for those who do enroll.
The third gap is adolescent girls’ transition from education to the workforce, as well as what happens to the large numbers of young women in developing countries who fail to make this transition. This information will allow for targeted policymaking to improve the relevance of schooling for the most disadvantaged girls in the educational system and to help with their incorporation into the workforce.
Having quality sex disaggregated data on informal sector work and informal sector enterprises is needed; these are domains where women are overrrepresented in work and enterprises that are not accurately or officially counted. Understanding women’s experience in these areas requires having detailed data on their unpaid work, including reliable time use data, types and extent of informal employment, as well as entrepreneurial activities. Other data gaps include earnings disparities and shadow earnings for women, female labor migration (including age and other demographic characteristics, reasons for migrating, remittances sent and working conditions), employment mobility (that is, on those who are looking to move to paid work in the formal sector, and those who are transitioning from home and subsistence production into market work), asset ownership, and access to financial services. Better measurement of women’s assets and financial constraints is essential to understanding their economic empowerment, but very few existing national surveys record this information at the individual level.
Data in the agricultural sector, particularly on women’s stake in on-farm activities and conditions in agricultural informal employment, rounds out the list of gender data gaps in economic opportunities. Measuring women’s agricultural productivity and the factors determining this productivity, including access to land and agricultural resources, is essential for the design of gender-informed agricultural policies.
Very limited data exists on the gender aspects of conflict, so better data collection overall in this area, including sex-disaggregated data on war-related mortality and morbidity,forcible displacement, adaptive responses to conflict, and conflict-related violence, is key.
There is also scant data on women’s leadership roles in peace and security efforts, while this information is basic to adequately monitor the implementation of UN Resolution 1325 at national levels.
According to 2012 estimates by the UN Statistical Division, close to 80% of countries worldwide regularly produce sex-disaggregated statistics on mortality, labor force participation, and education and training. But less than a third of countries produce important gender statistics on informal employment, entrepreneurship, violence against women and unpaid work.
The lack of data has been a major obstacle in gauging gender differences and inequalities, designing policies and programs to promote girls’ and women’s advancement and expand their opportunities in society, and assessing the outcome and impact of related development interventions.And this is evenmore severe in developing countries where data capacity is most limited. The consequences of this lack of information are also worse in many of these countries, where women’s disadvantages are greater, as is the need to devise effective evidence-based solutions to address these disadvantages – for the sake of both women and society.Closing gender data gaps can also result in having more useful information on both women and men, for the development of better policies benefiting all.In the case of women, in particular, bad data can result from having someone other than the individual woman or girl responding on her behalf when interviewers collect information on households or enterprises.
IDENTIFYING GENDER DATA GAPS AND SOURCES
Based on need (severity and disparities in outcomes affecting women), country coverage, and policy relevance, 26 gender data gaps resulted from the mapping exercise led by the Data 2X initiative (see full document p. 9 in Tools below). The mapping exercise considered three main sources of gender data:
- censuses and micro-level surveys that provide information on individuals, households and enterprises;
- data generated institutionally, including facility-generated service and administrative records (through hospitals, schools, civil registries, tax authorities etc.) as well as policies, laws, and regulations that are developed through the political process;
- big data, which has yet to be mined for gender issues.
BRIDGING DATA GAPS
Integrating gender in the NSDS
In developing countries, mainstreaming gender into the National Statistical System will be greatly facilitated if it is taken into account at the very beginning of the design of a country’s NSDS, since the NSDS provides the linkage between statistical strategies and national development policy frameworks, specific national sectoral policies including gender-related policies or international demand (MDGs or other international initiatives).
The importance of gender statistics should be well understood and acknowledged (See ACKNOWLEDGMENT). Various international initiatives provide valuable support in understanding and reinforcing acknowledgement of the issue. In recent years, a few agencies have taken steps to systematically identify specific gaps in gender data that countries and national statistical offices should focus on to improve policies directed towards women. The UN Inter-Agency Expert Group on Gender Statistics (IAEG-GS) has compiled a “minimum set” of 52 quantitative indicators for gender statistics and divided them into three tiers, according to their conceptual clarity, international standards and regular production. The IAEG-GS is continuing to develop and propose modifications of these indicators as well as their classification, and a number of international agencies are also introducing variants of the minimum set. This includes UN Women which, as part of the post-2015 development framework put forth a set of proposed indicators on women’s empowerment in June 2013. Through the Evidence and Data for Gender Equality (EDGE) project, UNSD and UN Women also collaborate on developing methodological guidelines to collect data on physical and financial assets disaggregated by sex; the same exercise will be done for entrepreneurship. Data 2X, an initiative launched in 2012 as a partnership between the William and Flora Hewlett Foundation, the United Nations Foundation, and the US Government, seeks to identify and propose ways to resolve policy-relevant data gaps.
During the NSDS design process, it could be decided in the Roadmap (see ROADMAP) that whatever the statistical production, the gender dimension must be taken into account. The gender perspective would thus be accounted for systematically in all the design steps (See DESIGN STEPS)and increase the chances of the gender dimension being incorporated in data collection and production. “Engendering” specific sectoral strategies such as agriculture will also be very important in integrating the gender perspective at all levels.
Improvements in terms of availability, quality and use of existing survey data can be facilitated through the Accelerated Data Program (see below "Tools").
The various ways to analyze existing data can be further exploited to provide richer information on girls and women. Surveys covering different topics can be supplemented with one another, if they cover the same time period and context, to add data complexity and help tackle broader questions that may be difficult to address with just one source. Correlating data on outcomes with women’s age, ethnicity, marital status, income, and other socioeconomic characteristics is important, as indicators of women’s status can vary substantially by these variables. Existing databases can also be enriched by adding specific modules with new questions to an existing survey instrument and sampling frame.
Strong and permanent advocacy (see PROMOTION) on crucial sources of gender information, such as Civil and Vital registrations (important source of information on occurrence and characteristics of vital events), Population and Housing censuses (the capacity to produce gender statistics will be strengthened if gender aspects are mainstreamed into these censuses) and Time Use surveys - Labor Force surveys (the contribution of women is not captured properly in the framework of the System of National Accounts) will also be very important.