World leaders have committed to gender equality with the Sustainable Development Goals, but we currently lack the data that is required to ensure this target is met.
By Mayra Buvinic & Ruth Levine
8 April 2016 - The “gender gap” is an issue that the United Nations (UN) has pledged to tackle with its new Sustainable Development Goals (SDGs). A commitment to deliver gender equality and the empowerment of women and girls is fifth on the list of 17 ambitions. Detailed targets include an end to discrimination, violence and sexual exploitation, early and forced marriage, and genital mutilation.
These practices – all harmful, many illegal – are some of the more shocking examples of gender disparity and rightfully attract the most condemnation. But inequality can also be more subtle, and thus harder to account for.
A particular form of bias is manifested in the way we measure – or fail to measure – aspects of people's lives. For many of the development outcomes covered by the SDGs, information about current conditions is not disaggregated by sex, obviating any possibility of understanding gender differences. For others, gender bias is engrained in the measurement process.
Consider, for example, the labour force surveys that reinforce sex-role stereotypes: the male as breadwinner, the female as housekeeper. These surveys often ask only about a person's primary economic activity. In so doing, they vastly underestimate the economic contribution of women, for whom paid work can often be a secondary occupation (with “housewife” being considered the primary activity). As a result, decision-makers who depend on these surveys have little understanding of how women add value to the economy.
To achieve gender equality and female empowerment, we need robust information about the lives of women and girls. We need to understand the size and nature of the gender gap. We need to identify the underlying causes of inequality, measure its consequences, design effective policy solutions and have adequate data to monitor progress.
No data, bad data
An absence of information about aspects of women's lives constitutes one gap in gender data. For example, goal 16 of the SDGs speaks to the importance of peaceful and inclusive society, and establishes a target for civic participation. However, for most countries of the world there are no sources of data that measure differences between male and female participation in civil society organisations, or in local advisory or decision-making bodies. That is just one instance in which we would likely understand current conditions and progress differently if the data permitted us to distinguish between men's and women's experiences.
Having no data is bad enough, but substandard data is arguably more insidious, particularly when the data systematically misrepresent reality in such a way as to make women appear to be more dependent and less productive than they actually are.
When Uganda revised its question about labour force participation in two contiguous surveys in 1992–93 – recording the main activity in one case, while expanding questions to cover secondary activities in the other – the percentage of working-age Ugandans in the labour force increased from 78% to 87%. These additional workers – 702 000 of them, the majority women – went unacknowledged in the first survey that asked only about primary activities.1
Surveys can also be designed in ways that further minimise the role of women in family and economic life. For example, many socioeconomic and agricultural surveys of households are constructed using the (male) head of household as the anchor for the household roster, and other family members are defined in relation to the (male) head. The assumption that men are most often the heads of household – a view explicitly stated in many survey module instructions, and held by enumerators and respondents alike – undercounts women who fulfil this role. For instance, interviewer instructions for the Demographic and Health Surveys (used in more than 85 countries) and the Multiple Indicator Cluster Surveys (60 countries) say: “A household head is a usual resident member of the household acknowledged by the other members of the household as the household head. This person may be acknowledged as the head on the basis of age (older), sex (generally, but not necessarily, male), economic status (main provider), or some other reason”.2,3
What does this mean in practice? In 2002, surveys that were unbiased by gender assumptions were carried out in several Central American countries. The results suggested that the proportion of female-led households in rural areas was more than twice that counted by official sources in Costa Rica and El Salvador, and more than 50% higher in Honduras and Nicaragua.4
The consequences
It is not difficult to discern drawbacks of non-existent and substandard data on the lives of women and girls. If the number of female-led households is underreported, these households may be overlooked in the distribution of productive resources and may receive fewer benefits from anti-poverty programmes, especially those that target the head of household.
In all 16 countries in a rural database, female-headed farm households had less access to fertilisers and other agricultural inputs than male-headed ones, were less likely to have received credit in the last year, and were less likely to have land titles and own agricultural land.5 Research suggests that female-managed firms and farms are less productive than those managed by men, not because women are less able entrepreneurs or farmers, but because they have less access to productive inputs. However, equal access to inputs in itself may not be enough, and to close the gender gap in agricultural productivity, agricultural policy will have to explicitly acknowledge the existence of female farmers as separate from male farmers, and design policy to address their different needs.6
Limited data on unpaid household work has also fed the myth that housewives have free time available for training and other development interventions. It is therefore typical for projects designed on this false premise to see high dropout rates from female participants. For instance, travel time to attend classes and childbearing demands predicted the low retention rates in a business training programme for women microentrepreneurs in Lima, Peru. Only 42% of the roughly 2000 women who started the training of three sessions a week, three hours each, over three months, attended at least half of the training.7 Conversely, successful retention rates in a programme training young women for entrepreneurial and wage jobs in Monrovia, Liberia, were partly attributed to the provision of a stipend for trainees’ childcare needs.8
Lastly, the lack of data on women and girls has hampered the ability to influence policy, track progress and demand accountability. Data can be a powerful tool in the hands of women's advocates. The most notable advances in gender equality and women's rights have been in education and in sexual and reproductive health, both areas where better data is available. Meanwhile, areas with poor data, such as economic participation, or no data, such as unpaid work, have seen less progress.
According to a UN Statistics Division survey of 126 countries, 80% regularly produce sex-disaggregated statistics on education and 65–70% produce statistics on sexual and reproductive health and fertility, but only 30–40% regularly produce statistics on informal employment, unpaid work and violence against women.9
Data by itself does not bring about improvements, but it provides the evidence necessary to prompt policy-makers into action, to generate investments and to demonstrate the effectiveness of interventions. In the previous round of international goal-setting – the Millennium Development Goals (MDGs) – one of the only measures that was available in sex-disaggregated form across many countries was school enrolment. As a result, gender parity in education became the most prominent indicator of gender equality in the MDG framework, and gender equality became synonymous with girls’ education.
Indirectly, a focus on this indicator induced investments and policy changes to get more girls into school. Since the MDGs were launched in the year 2000, donor investments in girls’ education have grown at an annual average rate of 14% between 2002 and 2012 – from $1.2 billion to $4.4 billion – which is significantly above the average growth rate of 6% for all other sector-specific aid.10
What makes for “good evidence” on women and girls?
Good evidence on women and girls, above all, is of high quality – that is, data is reliable, valid and representative, and free of gender biases. Good evidence also has good coverage, including country coverage and regular country production, and is comparable across countries in terms of concepts, definitions and measures. Lastly, good evidence on women and girls has the desirable features of complexity, where data from different domains in women's lives (for instance, health and employment) can be cross-referenced and cross-tabulated, and granularity, where the data can be disaggregated into smaller units by race and ethnicity, age and geographical location, as well as sex.
Source: Buvinic et al.12
Opportunities and challenges
The “data revolution” (see Significance, October 2015, page 24) that has been called for to support the SDGs provides a welcome global framework within which to establish sound principles for capturing improved data on women and girls. It is an opportunity that should not be missed. The production of robust gender data needs to be mainstreamed in major initiatives linked to this data revolution. In addition, specialised, stand-alone data investments are needed.
Major initiatives – such as the international movement to strengthen national-level civil registration and vital statistics (CRVS), the basic building blocks for population-based national data – should pay attention to gender data issues and place an emphasis on improving sources of data on women and girls. It is especially important to take advantage of the international momentum provided by the SDGs to improve registration of births and deaths – a push spearheaded by the World Health Organization, the World Bank, the government of Canada and others – and extend this registration data to cover marriage and divorce registration. The work in this area needs to accurately identify and correct gender related sources of CRVS under-registration, and to ensure that there is attention to potential sources of gender bias in capturing civil events: for instance, the disincentive that unwed mothers in many countries have to register their newborns because of legal, cultural or social obstacles.
Some recent international initiatives have devised measures of work and economic behaviour that are free of gender biases. The International Labour Organization (ILO), in collaboration with a group representing the interests of informal workers – Women in Informal Employment: Globalizing and Organizing (WIEGO) – has made significant headway in measuring women's participation in informal employment outside agriculture. In doing so, it has not only helped to make visible a fuller set of economic activity among women, but also provided a more comprehensive view of how the poorest people around the world make a living.11 In addition, the ILO, World Bank, Food and Agriculture Organization and Data2X, a gender data partnership coordinated by the UN Foundation, have joined forces to improve measures of women's work in subsistence agriculture as part of a programme to pilot new work and employment definitions issued by the 19th International Conference of Labour Statisticians in 2013. Meanwhile, Evidence on Data and Gender Equality (EDGE), a multi-agency collaboration implemented by the UN Statistical Division and UN Women, is developing measures and international guidelines on entrepreneurship and individual assets, including land and credit.
Data can be a powerful tool in the hands of women's advocates. Without it, the ability to influence policy, track progress and demand accountability has been hampered
These are major advances, and herald a new realisation that improving the way we measure is integral to making social, economic and environmental progress. But this is a big job: a recent count yielded 28 policy-relevant gender data gaps across five global domains – health, education, economic opportunities, political participation and human security.12 One-third of the minimum set of 52 indicators proposed by the UN to track progress on gender issues cannot be generated internationally because they lack either conceptual clarity, coverage, regular country production or international standards.13 Indeed, only three of the 14 proposed SDG indicators for gender equality and the empowerment of females are currently widely available.
Filling these data gaps will require high-level political commitment, technical advances and earmarked resources for larger investments than have been made to date. For every political exhortation about the importance of improving the lot of women and girls, we need a comparable demand by leaders for gender-specific information about not only health and education, but also work, personal security and freedom, and protection from environmental harms. We need statisticians, demographers, computer scientists and others who work with quantitative data to understand the particular challenges that gender-specific questions pose, and to apply their talents to overcoming data collection and analysis obstacles. We also need greater financial investments in data collection and use, earmarked to fill the gaps that prevent us from understanding and addressing gender inequality. None of this will be easy, but it is all essential to realise the potential of the Sustainable Development Goals.
http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2016.00899.x/full
Извор: WUNRN – 08.09.2016
