Gender data are essential. How else are we going to monitor progress in the wellbeing of women and girls?

Kudos to the Bill and Melinda Gates Foundation for their strong commitment to the importance of metrics and their new user-friendly and visually stunning data publication, Goalkeepers: The Stories Behind the Data 2017, which they have promised to make an annual publication.

This year’s report tracks progress in women’s reproductive health by measuring progress in access to family planning and in reducing maternal mortality, but most of the 18 indicators included in the report are aggregate measures, representing averages of men’s and women’s outcomes. As we are only too aware, there is a dearth of gender data. In the future, we hope that better gender data and better coverage will allow for tracking of women’s work—both paid and unpaid—and their contributions to the economy, their participation in politics and public policy, and their wealth and well-being as individuals, not just as members of households. All are dimensions that go beyond women’s traditional roles as child bearers and mothers, and provide a more well -rounded picture of their lives. They are critical components for measuring progress, but these data are not there right now. Indeed, Goalkeepers found so little data for its one sex-disaggregated indicator—a measure of ownership of agricultural land—that they couldn’t report results.

Below, we provide background on data shortage, and outline three key steps that countries and the international community should take in order to produce better data and close gender gaps.

The holes in gender data

The need for more finely disaggregated data to monitor the progress of excluded groups is one of the statistical challenges of the Sustainable Development Goals, which remind us that we must “leave no one behind” in our efforts to achieve the 2030 development goals. The SDGs also call for disaggregation (“where relevant”) by sex, and it is a glaring irony that data on women and girls, who, after all, constitute the majority of human beings, are absent or infrequently recorded in the national and international databases used to measure the SDGs.

The holes in gender data happen because there are no measures for many events in women’s lives, because the measures that exist are bad (read biased) or not well defined, or because they are measured infrequently or not at all. Of the 232 indicators included in the SDG framework, 53 refer explicitly to women or girls or specify disaggregation by sex. Among these, only 15 are well defined and generally available (classified as Tier I).

Significant investments are needed in gender data—both in producing more and better data, and in using these data for policymaking. This is a tall order, and one that may need quite a bit of heavy lifting, especially in poor countries with few resources for statistics and data. But there are easier, less resource-intensive initial steps to be taken.

Ready to Measure

Where to begin? Some indicators are—or should be—“ready to measure.” Data2X, an initiative of the UN Foundation, along with Open Data Watch last year proposed a list of 20 such gender indicators. Sixteen of these indicators are the same or similar to SDG indicators; four more broaden the traditional view of women’s roles by adding measures of economic status measures. See the original Ready to Measure report for documentation on the sources of these indicators. The recently released Ready to Measure Phase II report compiles the data from international databases and household surveys. This database and all accompanying metadata are available online.

While all of these indicators are, indeed, ready to measure, large gaps remain. The table below (cited in R2M-II) summarizes the availability of data for the 16 R2M indicators found in international databases.

Table 1. Data Availability for 16 Ready to Measure Indicators, 2011-2015

Ready to Measure Indicator Number of countries with at least one observation 2011–2015 Countries with 3+ obs. 2011–2015
1. Ratio of women to men living below the international and national poverty lines 14 0
2. Prevalence of stunting in children under 5 years of age by sex 67 5
3. Prevalence of anemia in women of reproductive age* 189 0
4. Maternal deaths per 100,000 live births* 109 9
5. Under-five mortality rate* by sex 193 0
6. HIV Prevalence* by sex 105 105
7. Adolescent birth rate* 194 194
8. Contraceptive prevalence, modern methods 101 11
10. Individuals using the Internet by sex 83 0
11. Women aged 20-24 who were married or in a union before age 18 83 6
12. Proportion of seats held by women in national parliaments 192 188
15. Employment to population ratio* by sex 187 187
16. Employed persons who are own-account workers* by sex 186 186
17. Non-agricultural wage employment* by sex 187 187
18. Women with an account at a financial institution 142 0
20. Children under 5 whose births have been registered with a civil authority by sex 43 2

*Modeled estimates by international organizations

 

The four remaining measures in the R2M report, which can be compiled from existing surveys, are:

  1. completion rates of women by school stage
  2. share of women among mobile telephone owners
  3. share of young women who are not in school nor looking for work
  4. growth rate in adult women’s share of household earned income among the bottom 40 percent of the population

The current state of gender data

The effort to gather the R2M indicators has revealed new or confirmed old observations on the state of development data:

  • Indicators with high coverage rates are usually the product of statistical models using limited direct observations, indirect observations (from recall or sisterhood methods), or covariates to extend or interpolate values from survey data.
  • Gender data collected through surveys sponsored by bilateral and multilateral agencies in developing countries, such as MICS, DHS, and LSMS, are more likely to be included in international databases. For example, coverage rates for data on anemia, HIV, and contraceptive prevalence are higher for low- and lower-middle-income countries than for upper-middle and high-income countries.
  • Many microdata sets from surveys are not publicly available, even from those sponsored by international organizations. Therefore, it is not possible to confirm the statistics derived from them or to use them to construct new indicators
  • Indicators on the use of new technologies have only recently appeared in public databases. For example, since 2014 only a single observation on use of the Internet by women is available for 83 countries
  • Data collection and publication schedules for most indicators are not known and their future continuity is uncertain.

Next steps

We have identified at least three steps, all doable if countries and the international community are committed to “leaving no one behind” by 2030:

1) Commit to the R2M indicators and close key gender data gaps

A first next step is to help countries collect full information on the R2M indicators or a similar set on a regular basis. Concurrently, countries need to commit themselves to begin closing gender data gaps in key areas that are most important to them. The international community should support country efforts, particularly for the poorest countries.

2) Parity is not always equality

The Goalkeepers report shows substantial progress in reducing child mortality. The R2M report shows the most recent sex-disaggregated mortality rates and discusses how to read the data. In this case, parity does not mean that girls do better than boys. On the contrary—for biological reasons, infant boys have higher mortality rates and, therefore, the “normal” acceptable ratio in countries with no discrimination against girls is a ratio favorable to girls. A parity ratio of 1 (same number of infant boys and infant girls dying) means that girls are in trouble—dying at higher rates than they should. Research on what sex differentials mean and how they arise needs to go along with any effort to improve the collection of gender data.

3) Measure women’s poverty: A challenge long overdue

Lastly, the international community should invest in identifying, testing, and agreeing on robust measures for a few core gender data applicable globally. One outstanding problem is measuring women’s poverty, separate from household poverty. The often-cited claim that 70 percent of the poor are women is false, and, hopefully, well discredited by now. However, we do not have an adequate response to this very core question.

R2M includes data for Latin America on the proportions of women and men living in poor households (compared to the totals for each sex), and shows that women are overrepresented among those living in poverty in most countries. This gets the closest perhaps to an objective measure, with a big caveat that it does not capture inequalities within the household that most often (and across cultures) benefit male over female household members.

The World Bank, building on its household survey capacity and their work on a multidimensional poverty measure, should lead an international effort involving UNSD and other relevant UN agencies, as well as academics and women’s advocates, to devise robust, doable, and simple measures of women’s poverty.

We are now almost 20 percent of the way toward the SDG target date of 2030, still we lack regular observations on at least 70 percent of the proposed gender indicators. The Millennium Development Goals taught us two lessons: first, setting quantified targets and rigorously monitoring progress can galvanize action; second, databases do not build themselves. If we are to honor the SDG goal of achieving gender equality and empowering women and girls and keep our commitment to leave no one behind, then there must be an equally strong commitment to gathering and publishing the data needed to build effective programs and measure outcomes.

About the Authors

Mayra Buvinic is a senior fellow at both the Center for Global Development and the United Nations Foundation. Previously, she was director for gender and development at the World Bank.

Eric Swanson is director of research at Open Data Watch. Previously he was senior adviser in the Development Data Group of the World Bank.

Authors:

Eric Swanson