What Is Wrong with Data on Women and Girls?
Mayra Buvinic and Ruth Levine1
(September 2015)
The Sustainable Development Goals (SDGs) have once again highlighted the importance of data as well as the inadequacy of
current data sources to guide policy, track progress and ensure accountability in the post-2015 era. Getting data on women
and girls ‘right’ is a major challenge for the ‘data revolution’ that has been called to address these data inadequacies.
Good data on women and girls (as on other population groups) is useful to diagnose the size and nature of the disadvantages
experienced by women and girls, both in absolute terms and in comparison with men and boys; identify the underlying causes
of these disadvantages; measure their consequences; and design effective policy. There is no evidence-based policy without
good evidence, and there is no accurate way to measure progress towards achieving the new SDGs without good evidence
on women and girls. Addressing the current deficits is a particularly high priority if the promise of “leaving no one behind” is to
be realized.
The limitations of data for development are well known and result from an insufficient culture of evidence-informed policy
making and low investments in domestic and international data systems, especially but not exclusively in low-income
countries. But problems with information about women and girls go beyond routine data gaps.
What is wrong with data on women and girls is rooted in intrinsic biases in measurement and attention resulting in bad data as
well as no data on critical dimensions of their lives, with costly consequences for them and society. In this article we examine
the reasons behind this bad data and no data and its consequences, and discuss some of the opportunities the ‘data
revolution’ offers to significantly improve data on women and girls globally.
Bad data
Having bad data is more insidious than having no data about the lives of women and girls-- bad data reinforces stereotypes
and guarantees the failure of policies in improving their lives. By bad data, we mean data that systematically misrepresents
reality, particularly in ways that make women appear to be more dependent and less productive than they actually are.
Bad data results from traditional sex role stereotypes – where the man is the producer and provider and the woman is the
reproducer and care-taker—that introduce systematic biases in the definition of concepts and measures and in the design of
surveys and questionnaires, especially when measuring women’s economic behavior. Inaccurate or bad data is particularly
a problem in low-income countries where gender data gaps are the greatest, because data capacity is also most limited in
these contexts. Bad data often results from someone other than the individual woman or girl responding on her behalf when
interviewers collect information on households and enterprises.
The sex role stereotype that the man in the family is the principal producer has influenced the collection of employment
1 Mayra Buvinic is a UN Foundation Senior Fellow working on Data2X. Ruth Levine is Director of the Global Development and Population Program at the William
and Flora Hewlett Foundation and Co-chair of the Data2X Technical Advisory Group.
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data privileging main jobs or activities over secondary, seasonal and unpaid jobs, and disproportionately favoring recording
formal sector jobs over informal occupations. Labor force surveys often limit recording to the main activity, restrict the jobs
recall period to 7 days, record unpaid activities only if it is the person’s main activity, and fail to adequately record non-wage
earnings. These survey features substantially undercount women’s market work, since they miss picking up the multiple
activities that predominate in women’s working lives, including having paid work as a secondary occupation (housewife being
the primary activity), doing seasonal and unpaid activities, and working in informal employment (that is, employment that lacks
legal or social protection, whether in informal enterprises, formal enterprises or households).
Uganda, which has a tradition of producing high quality labor force statistics, asked labor force questions differently 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 labor force increased from 78.3 to 86.6 percent when
the survey picked up secondary activities. 702,149 workers, the majority of them women, went missing when the survey only
recorded the main activity, and should have been added to the count, expanding the workforce from 6.5 to 7.2 million workers
(Fox and Pimhidzai 2013).
New data gathered by Women in Informal Employment: Globalizing and Organizing (WIEGO) and the International Labour
Organization (ILO), using direct measures of informal employment in 40 countries and indirect ones in 80 countries, confirms
the importance of this source of paid work for women. It shows that women predominate in informal employment outside of
agriculture in three world regions, share equal participation rates with men in two others, and have lower participation rates
than men only in the Middle East and North Africa (WIEGO and ILO 2013; Vanek et al. 2014).
Not surprisingly perhaps, the way employment questions are asked and whether the person herself or another family member
responds on her behalf affect the information obtained. An experiment in Tanzania showed that using a short rather than a
detailed employment module produced lower numbers for women but not for men. Women, who had reported they were
‘employed’ in the larger module, chose instead ‘domestic duties’ in the short module. Employment was higher when men
self-reported their main activity versus when someone else in the household reported on their behalf; for women, however,
there was no difference between the results of self- versus other-reporting (Bardasi et al. 2011).
The misconception that adult male household members are the main providers has biased the design of household
socioeconomic and agricultural surveys –surveys have been designed using the (male) head of household as the anchor for
the household roster, and other family members have been defined in relation to the (male) head. The assumption that men
should be heads of household, explicitly stated in many survey module instructions and held by enumerators and respondents
alike, undercounts women who fulfill this role and renders them invisible in the statistics.
The proportion of rural female-headed households doubled in Costa Rica and El Salvador and increased by more than 50
percent in Honduras and Nicaragua when an unbiased gender survey was carried out and contrasted with official statistics
(Parada 2002). This bad data on the household head is especially a problem in regions, such as Latin America and the
Caribbean, where female headship has historically represented a significant proportion of all households (about a quarter) and
in Africa, where their share of all households has risen sharply in the last two decades, to an average of 26 percent (Milazzo
and van de Walle 2015).
No data
What is not counted is not valued and vice versa. Generally, one finds no data especially on aspects of the lives of women
and girls that are not highly valued by society. Unpaid work in home production, time spent fetching fuel and carrying water,
housework, childcare and eldercare, all activities carried out mostly by women and girls, are part of a ‘care economy’ that
society undervalues and, therefore, does not count in official statistics.
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Other data gaps are more directly linked to women’s lower societal standing when compared to men’s. Gender gaps in health
data exemplify this cause of no data. Until recently, health research was done primarily on males; they defined the standard
and results were simply extrapolated to women without considering sex and gender differences in health conditions2. The
excuse often times was that women were an unwelcome extra source of variability in the data.
The convenience of considering the household as a unit in economic research and the reluctance to look inside the household
and into the privacy of family life, due to beliefs about the boundaries of public action and the real added complexity in
gathering intra-household data, discouraged gathering data on critical aspects of women’s and girls’ well-being linked to the
family, including intimate partner violence and gender inequalities in the distribution of household resources.
There is also no credible sex-disaggregated individual level poverty data, partly because of difficulty in tracking intra-household
allocation of work and resources between family members, as well as transfers and ‘spillovers’ to non-resident family
members.
Data impacts
Despite all the rhetoric, evidence-based policy making is a relatively new concept and the relationship between data and
public policy is still emerging and hard to prove. But there is a growing number of success stories where good data has
prompted public investment and action (Vaitla et al. 2015). Because of what is ‘wrong’ with data on women, however, it
is more difficult to identify cases where gender data can be directly linked to policies on their behalf. Exceptions include
pioneering efforts to gather data on intimate partner violence in Latin America and the Caribbean in the 1980s, which provided
the empirical rationale for the 1994 Inter-American Convention on Prevention, Punishment and Eradication of Violence against
Women (Larrain 1999). More recently, Demographic and Health Survey (DHS) modules on gender-based violence have
resulted in similar legislation in other world regions, and DHS sex-disaggregated data showing the disproportionate incidence
of HIV/AIDS in young women in Sub Saharan Africa has resulted in targeted programs for them (Vaitla et al. op cit).
In addition, the fact that most countries report sex-disaggregated schooling data led to the use of gender parity in education
as the most prominent indicator of gender equality in the Millennium Development Goal (MDG) framework. Indirectly, a focus
on this indicator induced investments and policy changes to get more girls into school. Since the MDGs were launched, DAC
donor investments in girls’ education have grown at an impressive annual average rate of 14 percent in the 2002-12 period-from USD $1.2 to $4.4 billion, which is significantly above the average growth rate of 6 percent for all other sector-specific aid
(Treussart and Piemonte 2014).
Consequences of bad data and no data
It is not difficult to discern the potential detrimental consequences of bad data and no data on women and girls. If women’s
productive work in enterprises and agriculture is significantly undercounted, it is no wonder that productive services and
resources bypass them. We now have data that more precisely estimates the costs of these data errors, and they are sizable.
Women-managed firms and farms are less productive than those managed by men, not because women are less able
entrepreneurs or farmers than men, but because they have less access to productive inputs. The value-added per worker is
between 6 and 35 percent lower in female-owned than male-owned enterprises (World Bank 2012). A male bias in agricultural
research and services, partly from “blind spots” or bad data regarding women’s work in agriculture, has translated into
2 Similarly, office air conditioning in the US has been set using men’s metabolic rates as the standard, with the result that temperatures are generally too cold for
women office workers, and energy efficiency is impaired (The New York Times, August 4, 2015) Available at http://www.nytimes.com/2015/08/04/science/chillyat-work-a-decades-old-formula-may-be-to-blame.html?_r=0
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average yields 20 to 30 percent lower for female versus male managed farms. It is estimated that if women farmers had the
same access to productive resources as men farmers, total agricultural output could be raised by 2.5 to 4 percent (O’Sullivan
et al. 2014).
Similarly, if female household headship is underreported, the observation that these households are overlooked in the
distribution of public resources and benefit less from anti-poverty programs than other household types should surprise no
one. In India’s Rural Public Work Scheme, the largest of its kind in the world, households with female heads obtained less
work than other households. In the state of Bihar, where a detailed survey was conducted, male-headed households got,
on average, 7 more days of work than female-headed households. The scheme should have reduced poverty in Bihar by
14 percentage points if jobs, including for female household heads, had not been rationed. It did only by 1 percentage point
(Dutta et al. 2014).
No data on women’s unpaid household work has fed the myth that women who do housework and home production have
free time available for training and other development interventions. High dropout rates from female participants are a typical
consequence of training programs designed on this false premise. For instance, a systematic review of more than twenty
business management programs in developing countries found a dropout rate of around 40 percent for women, higher than
the dropout rate exhibited by men (McKenzie and Woodruff 2014).
No official counting and the generalized undervaluing of unpaid care work have contributed to overlooking negative effects of
public services cutbacks on these (mostly female) workers and have depressed wages for care workers, male and female, in
the labor market (Folbre 2006).
Women have died as result of no data on female health conditions. In the US, heart research done on males has led to
misdiagnosis or under treatment of heart disease in women, who can develop symptoms that are noticeably different from
men’s, as well as higher female than male mortality from heart disease (Seils et al. 2001; Kim et al. 2008). In addition, disease
conditions that affect women only (for instance, diseases linked to reproduction affecting older women) have been largely
ignored in research and undertreated in clinical settings, despite the fact that cost- effective health technologies exist (Buvinic
et al. 2006).
The simplifying assumptions about households have underplayed or ignored household sources of deprivation and
disadvantage for women. Intimate partner violence adversely affects women’s physical and mental health and incurs
economy-wide costs through expenditures in services for victims and women’s decrease labor productivity and foregone
income. The costs to society of this violence have been estimated to be between 1.2 and 3.7 percent of a country’s annual
GDP (Duvvury et al. 2013). The consequences to society of the extreme deprivation and death suffered by infant girls in
cultures that favor male over female children are far-reaching. Ebenstein and Sharygin (2009), for instance, have forecasted
that the demographic imbalance created by the ‘missing’ girls in China, will contribute to perpetuating poverty for older single
men without family support systems, a growing cohort among the 22 million more men than women born between 1980 and
2000, unless state funding for old-age security increases dramatically.
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 advocates. The most notable advances in gender equality
and women’s rights have been in education and in sexual and reproductive health, both areas with better data availability,
while areas with bad data, such as economic participation, or no data, such as unpaid work and intimate partner violence,
have witnessed less progress. According to a UN Statistics Division (UNSD) survey of 126 countries, 80 percent regularly
produce sex-disaggregated statistics on education and 65 to 70 percent produce statistics on sexual and reproductive health
and fertility, but only 30-40 percent regularly produce statistics on informal employment, unpaid work and violence against
women (UN Statistics Division 2012).
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Opportunities and challenges
The ‘data revolution’ that has been called to support the SDGs provides a welcoming global framework to emphasize the
priority of and establish sound principles for capturing good data on women and girls, and one that should not be missed.
The production of good gender data needs to be mainstreamed in major initiatives linked to this ‘data revolution.’ In addition,
specialized, stand-alone gender data investments are needed.
Major data-related initiatives, such as the 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 emphasize
improving data sources on women and girls. It is especially important to take advantage of the international momentum to
improve registration of births and deaths spearheaded by WHO, the World Bank, the Government of Canada and others, and
extend it to cover marriage and divorce registration; accurately identify and correct gender related sources of CRVS underregistration; and embed a gender lens in the identity for development movement. Similarly, gender data needs to rise to the
top in current efforts to use ‘big data’ for development, including transactional and crowdsourcing data from mobile phones
and the Internet. These new sources of data provide exciting possibilities to obtain gender data with sufficient granularity and
in areas that are difficult to cover with more standard instruments (such as to gauge mental health from sentiment data or to
obtain real-time data in conflict settings), and questions of gender should be included in these ‘big data’ efforts from the start,
not as add-ons3.
Significant headway has been made recently on specialized international gender data initiatives. They show what can be done
and pave the way for intensified gender data efforts.
Producing nationally useful and internationally comparable data requires close partnership between international agencies
and between them and national partners. The recent resurgence of an inter-agency group on gender statistics, the IAEG-GS,
and the active agenda they spearhead on gender indicators and capacity building, provides a valuable venue for international
coordination, and is an important player moving forward.
Some of the recent international gender data initiatives are potentially groundbreaking in devising measures of work and
economic behavior that are free of gender biases. WIEGO and ILO’s work program on informal employment has made
significant headway in measuring women’s participation in informal employment outside of agriculture (see above). The ILO,
World Bank, and FAO, convened by Data2X, an initiative housed at the UN Foundation that builds partnerships for gender
data, have joined forces to improve measuring women’s work in subsistence agriculture as part of a program to pilot new
work and employment definitions issued by the 13th International Conference on Labor Statisticians in 2013. Evidence
on Data and Gender Equality (EDGE), a multi-agency collaboration implemented by UNSD and UN Women, is developing
measures and international guidelines on entrepreneurship and individual assets, including land and credit.
These advances notwithstanding, the task of producing better and useful data on women and girls is not trivial. Giving a
sense of the magnitude of the task ahead, a recent count yielded 28 policy relevant global gender data gaps across five
domains: health, education, economic opportunities, political participation and human security (Buvinic et al. 2014). One
third of the minimum set of 52 indicators proposed by the UN to track progress on gender issues cannot be generated
3 Data2X, a gender data initiative housed at the UN Foundation, has partnered with UNESCAP and UNECA to bring a gender lens to CRVS work in Asia and the
Pacific and in Africa, respectively, and with UN Global Pulse and academics to undertake gender ‘big data’ pilots.
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internationally because they either lack conceptual clarity, coverage, regular country production or international standards (UN
Statistics Division 2013). Only 3 of the 14 proposed Sustainable Development Goals indicators for the gender equality and
empowerment of women and girls goal are currently widely available. Filling these data gaps will require high-level political
commitment and earmarked resources for gender data.
If the ‘gender data revolution’ is to be successful, partnerships like those mentioned above, and with national statistical offices
and private sector ‘big data’ producers, need to coalesce quickly around the major, more urgent gender data gaps, leveraging
mainstream data initiatives and existing and new resources for data. These partnerships should strengthen gender data
analysis capacity so that the data is usable and demand for gender data for policy making and accountability at national and
international levels is real.
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