READY TO MEASURE: PHASE II

Finding the Indicators to Monitor SDG Gender Targets

 

DRAFT FOR DISCUSSION

 

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1. INTRODUCTION

 

Achieving gender equality – and monitoring its progress – will require a significant improvement in the availability of data disaggregated by sex, age, and other important attributes. Currently, 23 percent of the 232 indicators across the Sustainable Development Goals (SDG), or 53 indicators in total, have a gender component to them. Access to high-quality gender data are critical to a country’s ability to compile each of these indicators needed to plan and implement SDGs; but how many of these indicators are available today?

 

The Ready to Measure (R2M) study, published in September 2015 and reproduced in March 2016, demonstrated that, while many of the proposed gender-related indicators are challenging to collect or not yet available in most countries, there are 20 indicators (16 identical to or closely related to SDG indicators and four additional, complementary indicators) that are “ready to measure.” The 20 R2M indicators provide valuable information on the economic status and social welfare of women. They measure outcomes for women and girls (rather than processes) and were selected on the basis of their internationally agreed definitions; availability from established databases or data collection instruments; wide coverage; and absence of built-in gender biases. While other SDG indicators are under development, the 20 indicators can help kick-start the measurement of a gender baseline for the SDGs.

 

The first phase of this project provided us with a general sense of what data are available and identified the principal instruments used to collect them. In the current phase, we have begun to consolidate the data for the 15 indicators from cross-country databases and initiated the tabulations required for the remaining 5. Where data are available from open data sources, they will be placed in a public, R2M database. The purpose of the R2M database is not to supplant other collections of gender indicators, but to provide readily accessible documentation of the R2M project. In the next phase of the R2M, we will provide the R2M database tools and documentation to other organizations interested in including these data in their gender data collection.

 

These 20 R2M indicators are the most robust indicators available to track and measure gender equality, but are not perfect. Fifteen of the indicators are currently available directly from international databases, but the remaining five will require additional compilation from individual household surveys. The process of assembling the R2M data has spotlighted issues such as data coverage and quality, interoperability, openness, and country capacity. It is notable that many gender indicators lack direct observations in the last five years. A number of the gaps are therefore filled using statistical models. Many of the international databases lack observations from high-income countries, although data are presumably available for many of them. And while the surveys needed to compile the five new indicators exist, access to them is often restricted. As countries are drawing detailed national plans for SDGs, the issues of data availability and openness will need to be highlighted. Also, as indicators for the SDGs – particularly those listed as Tier III – become formalized, an iterative and reflective process such as this one is important to fully understand trends behind data quality and highlighting areas for improvements.

 


2. DATA INSIGHTS


 

R2M Indicator 1:
Ratio of women to men living in poverty

 

Are women more likely than men to live in poverty? Although household surveys do not measure the consumption or income levels of men and women separately, we can ask whether more women or more men live in poor households. There are reasons to think that women are more likely to live in poverty. Women’s wages are often less than men’s for the same work, and women are more likely to take on low-wage or unpaid work, such as childcare, eldercare, or farm labor. Women are more likely than men to be the sole parent of household with children, and because women typically live longer than men, households are more likely to include older, dependent women.

 

Evidence of these effects can be found in data compiled for the Socio-Economic Database for Latin America and the Caribbean (SEDLAC). Results for the most recent years available from nationally representative surveys in 14 countries show, in every case, the proportion of women living in poor households exceeded that of men, and in six countries by more than 1 percentage point. In Mexico in 2012, the difference of 1.3 percentage points meant that there were almost 1 million more women than men living in poverty.

 

 

About the data

 

SDG 1 calls for the poverty headcount ratio to be disaggregated by sex, age, employment status and location. Although household income and expenditure surveys can be used to calculate the proportion of men and women living below the poverty line, this statistic rarely appears in country reports or international databases. The SEDLAC dataset includes calculations based on poverty lines of USD 2.50 and 4.00 measured at purchasing power parity. Although these lines are higher than the current international poverty line of USD 1.90, they are more typical of living standards in Latin America and the Caribbean.

 

Source: Socio-Economic Database for Latin America and the Caribbean (CEDLAS and The World Bank). Updated April 2017. Data downloaded on 21 June 2017 from SEDLAC, Statistics by Gender.

 


 

R2M Indicator 2:
Prevalence of stunting in children under five years of age

 

Childhood stunting affects approximately 162 million children under the age of five worldwide. Stunting is a consequence of malnutrition, first of the mother and then of the child. The consequences of stunting – diminished cognitive and physical development, reduced future earnings, and increased risk of nutrition-related diseases – affect boys and girls. In most countries, the rate of stunting is higher for boys than girls. In Pakistan, for example, the prevalence of stunting in boys in 2012 was 48.4 percent compared to 41.7 percent for girls. Rates of stunting for both sexes are higher, on average, in low-income and lower-middle-income countries. Further disaggregation by wealth quintiles within a country may reveal interesting trends on progress – or lack of progress – towards bridging the gap of stunting rates between the wealthy and poor.

 

 

About the data

 

Children who are more than two standard deviations below the World Health Organization’s (WHO) Child Growth Standards of height for age are considered to be stunted. This indicator is included in SDG Goal 2. Data on stunting are collected through surveys, principally UNICEF’s Multiple Indicator Cluster Surveys (MICS) and USAID’s Demographic and Health Surveys (DHS). Although standards of care for children should include regular measurements of height and weight, these data are not systematically compiled or reported in international databases. As a result, few recent observations are available for countries not covered by MICS or DHS programs.

 

Source: WHO database on Child Growth and Malnutrition. Data downloaded on 21 June 2017 from World Bank DataBank, World Development Indicators.

 


 

R2M Indicator 3:
Prevalence of anemia in women of reproductive age, pregnant and non-pregnant

 

While the risk of anemia is of concern to all women – particularly those in low-income countries – anemia is more prevalent among pregnant women. Anemia puts not only the woman at risk but also her child. Infants born to mothers with anemia are at greater risk of low birth weight, premature birth, or impaired cognitive development. In 2011, the most recent year for which data are available, anemia affected 40.4 percent of pregnant women and 35.8 percent of women of reproductive age in low-income countries. In high-income countries, the corresponding rates were 21.8 and 17.6 percent. Among pregnant women living in Southern Asia or Western Africa, the prevalence of anemia stood at over 50 percent.

 

 

About the data

 

The prevalence of anemia is not included in the SDGs, but it is an important indicator of women’s health and nutrition. The World Health Organization (WHO) has produced estimates of anemia in children and women of reproductive age. WHO estimates are based on statistical models using 232 nationally representative surveys that included measurements of hemoglobin levels over the period 1995-2011. WHO estimates have not been updated since 2011, but more recent data may be available from national surveys.

 

Source: World Health Organization, 2015, The Global Prevalence of Anaemia in 2011, Geneva. Data downloaded on 19 June 2017 from World Bank DataBank, Health Nutrition and Population Statistics.

 


 

R2M Indicator 4:
Maternal deaths per 100,000 live births

 

Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. The average risk to mothers of death from child birth is 50 times higher in low income countries than in high-income countries. Even within income groups there exist large differences in outcomes. Across the world, the highest ratios are found in Sub-Saharan Africa and Central America. Decreasing maternal mortality by three quarters between 1990 and 2015 was the target of goal 5 of the Millennium Development Goals (MDGs). While maternal mortality ratios have fallen since 2000, most countries are still short of the MDG target. To make further progress, all women need access to reproductive healthcare, and better data monitoring systems are needed to gather evidence for effective interventions.

 

 

About the data

 

The maternal mortality ratio is included in goal 3 of the SDGs. The data shown here are modeled estimates prepared by the United Nations Maternal Mortality Estimation Inter-Agency Group (MMEIG). Maternal deaths should be reported through vital statistics systems, but even in high-income countries with reliable vital registration systems, misclassification of cause of death can lead to serious underestimation. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.

 

Source: World Health Organization et. al., Trends in Maternal Mortality: 1990 to 2015, 2015. Data downloaded on 21 June 2017 from World Bank DataBank, Health Nutrition and Population Statistics.

 


 

R2M Indicator 5:
Under-five mortality rate

 

Mortality rates for different age groups are important indicators of health status. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Since 2000, the under-five mortality rates have decreased by nearly half. While this is a huge improvement, mortality rates remain very high in 83 low- and lower-middle-income countries.

 

Rates of under-five mortality are typically higher for boys than for girls (because of biological differences between the sexes) in countries where there is no significant discrimination against girls. In high-income countries the ratio of female to male under-five mortality rates falls between 0.79 and 0.88. In 2015 there were 25 countries with ratios higher than this range, suggesting that girls there may have unequal access to nutrition and healthcare. In two countries, Tonga and India, girls’ mortality rates exceeded boys’, but the difference has been falling over the last two decades. Higher child mortality for girls can be linked to socio-cultural barriers, such as dowry, that make young girls a greater economic burden than boys. In some places, having a girl may be disadvantageous because she is less likely to enter the work force. Further disaggregation of mortality rates by ethnic group and geography would reveal significant disparities in most countries.

 

 

About the data

 

The main sources of mortality data are civil registration systems and direct or indirect estimates based on sample surveys or censuses. The estimates used here are produced by the United Nations Interagency Group for Child Mortality Estimation (UN IGME) using all available data. Annual estimates of combined male and female mortality rates are available for most countries. Separate estimates of male and female rates are available every five years, the latest being 2015. The under-five mortality rate is included in the SDGs as indicator 3.2.1.

 

Source: UN IGME, Levels & Trends in Child Mortality: 2015 Report. Data downloaded on 18 June 2017 from World Bank DataBank, World Development Indicators.

 


 

R2M Indicator 8:
Modern Contraceptive Use

 

Poorly timed or inadequately spaced births increase the risks of complications during pregnancy for mothers and infants. Although cheap and easy methods of preventing unwanted pregnancies are available, 12 percent of women worldwide who are married or in union have and unmet need for contraceptive services. The use of modern contraceptive methods is especially low in Sub-Saharan Africa: in some countries, less than 10 percent of women report using modern contraception and as many as 34 percent more would like some method of birth control. Low rates of family planning reflect not only a lack of contraceptive services, but also a lack of education and opportunities for young women. Education and gender equality are a form of social contraception, empowering women to decide how many children she wants and when.

 

 

About the data

 

SDG indicator 3.7.1 specifies the proportion of women of reproductive age who have their need for family planning satisfied with modern methods. Data on the use of contraception and the unmet need for contraceptive services come from household surveys such as MICS and DHS. Although there is a relatively long historical record of data on contraceptive prevalence, determining unmet need is more difficult, requiring a more extensive set of survey questions and trust on the part of the subject. Therefore, data on unmet need are available only in recent years and mostly in low- and lower-middle income countries where surveys have been sponsored by international agencies. The data shown here are for 85 countries that have at least one measurement on each indicator between 2011 and 2015.

 

Source: United Nations, Department of Economic and Social Affairs, Population Division, Trends in Contraceptive Use Worldwide 2015. Data downloaded on 23 June 2017 from World Bank DataBank, World Development Indicators.

 


 

R2M Indicators 7 and 10:
Early marriage and adolescent birth rates

 

Adolescent (age 15-19) birth rates vary by region but are strongly correlated with early marriage (women who were married or in a union before age 18). Although rates of non-marital childbearing have increased, most adolescent births occur within marriages. Young mothers face greater risks for themselves and for their children. Early marriage also cuts short their opportunities for education and full participation in the labor market. The highest rates of early marriage and adolescent births are found in Sub-Saharan Africa, where eight countries have rates of adolescent marriage higher than 50 percent in the most recent year, and twenty had adolescent fertility rates exceeding 100 births per 1,000 women.

 

 

About the data

 

Adolescent birth rates are estimated from civil registration data or, in the absence of complete civil registration, from censuses and surveys using demographic models. Annual estimates are available for most countries. The adolescent birth rate is included in the SDGs as indicator 3.7.2. The SDGs further specify birth rates among 10-14-year-olds, but data for the younger age group are not available.

 

Source: United Nations Population Division, World Population Prospects. Data downloaded from the World Bank Data Bank, Population, Health, and Nutrition database on 14 June 2017.

 

Rates of early marriage are measured as the proportion of women aged 20-24 who were married or in union before age 18. Data are derived from national censuses and household surveys. One or more observations are available for 92 mostly low- and middle-income countries between 2011 and 2015. The SDGs, which include this indicator under goal 5, also specify rates of marriage before age 15, but these data are not available. Data on early marriage are shown for the most recent year available.

 

Source: UNICEF global databases, 2016. Data downloaded from World Bank DataBank Gender Statistics database on 14 June 2017.

 


 

R2M Indicator 12:
Proportion of seats held by women in national parliaments

 

In almost every country in the world, women are underrepresented in the lower house of their national legislatures. The exceptions are Rwanda (61%) and Bolivia (53%), with Cuba (49%) and Iceland (48%) as close runners-up. The gap between men and women has been closing: worldwide, women held only 13 percent of the seats in their national legislatures in 1990 and now hold 23 percent. It is notable that high-income countries do not perform better than developing countries on this indicator.

 

Women’s rights and well-being have been subject to intense political debate. Decisions made at the national level influence the situation at the community level. Women’s representation at the national level is important, both politically and symbolically, but women’s participation at all levels of decision making is crucial for empowering women and advancing gender equality in the social and economic domains.

 

 

About the data

 

Data on the number and proportion of seats held by women in national legislatures are reported annually by the Inter-Parliamentary Union (IPU). Data are for the lower house or for unicameral legislatures. Some countries maintain quotas, requiring a certain proportion of the seats go to women. The IPU also reports data on the number of seats held by women in upper houses and the number of women holding leadership positions within legislatures. While data on women ministers and parliamentarians are readily available at the national levels, there is growing interest in measuring and understanding women’s participation and leadership at the subnational levels

 

Source: Inter-Parliamentary Union, Women in Parliament in 2016: The year in review. Data downloaded from the World Bank DataBank, World Development Indicators database on 24 June 2017.

 


 

R2M Indicator 15:
Females employed as a ratio of the working-age female population (15 to 59)

 

Increased participation of women in the workforce can be a powerful driver of economic growth, increasing GDP and reducing family poverty. But in most countries, a smaller proportion of women than men are employed. Women’s employment rates are lowest in Afghanistan and Saudi Arabia, where traditional roles and cultural norms keep many women out of the workforce. Further disaggregation by age shows that women who are younger may be at higher risk of being unemployed. In both Northern Africa and the Arab States female youth unemployment is about double the rate of young men.

 

 

About the data

 

Data on characteristics of the labor force come from labor force surveys, other population surveys, and administrative records. This indicator is not included in the SDGs, but is included here because it is a basic indicator of the participation of women in the labor force. The data shown are ILO estimates, harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors.

 

Source: International Labour Organization, Key Indicators of the Labour Market (KILM). Data downloaded from the World Bank DataBank, World Development Indicators on 19 June 2017.

 


 

R2M Indicator 18:
Share of adult women with a formal financial account

 

Having a formal bank account is a starting point for inclusion in the financial system and the modern economy. For women, access to a bank account is empowering, giving them greater control over their own and their families’ resources. Globally, women are less likely than men to have access to a bank account or to use a mobile money service to make payments. In 2011 47 percent of women had an account and 54 percent of men did. By 2014, when 64 percent of men and 57 percent of women had an account at a financial institution, the gender gap had not narrowed. Differences between men and women are small in high-income countries, where more than 90 percent of people have an account.

 

 

About the data

 

Data on access to a formal bank account were collected by survey as part of the World Bank’s Global Financial Inclusion (Findex) database. The indicator measures the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution. The full R2M indicator includes accounts accessed through a mobile device, which is of increasing importance. A similar indicator in included as SDG 8.10.2 but without specifying sex disaggregation. Data are available for 2014 and 2011.

 

Source: FINDEX database and World Development Indicators. Data downloaded from the World Bank DataBank, World Development Indicators database on 18 June 2017.

 


3. WHAT NEEDS TO BE DONE

 

Gender data and the SDGs 

 

The resolution adopting the Sustainable Development Goals specified that the indicators used to monitor the 17 goals and 169 targets “… should be disaggregated, where relevant, by income, sex, age, race, ethnicity, migratory status, disability and geographic location, or other characteristics, in accordance with the Fundamental Principles of Official Statistics.” Of the 232 indicators proposed by the Inter-Agency and Expert Group on the SDGs (IAEG-SDG), 53 refer explicitly to women or girls or specify disaggregation by sex. (Table 1) A review of the indicator list shows that at least 34 more indicators could and should be disaggregated by sex. These include 15 additional health indicators for Goal 3 and 8 additional indicators for Goal 16. In most cases, if the underlying data are available, the reporting of sex-disaggregated values should entail little additional effort. But many of the SDG indicators still lack established standards and definitions, and others are available in only a few countries or at infrequent intervals. The IAEG-SDG has classified 82 of the 232 indicators as Tier I, well-defined and currently available; but 61 more are classified as Tier II – well defined but not generally available; and 84 as Tier III, lacking agreed definitions and methodologies. Five indicators are classified in multiple tiers. Among the 53 gender-related indicators, 15 are classified as Tier I and 3 more as a mix of Tier I and lower tiers.

 

The Ready to Measure project had its origins in the recognition that it will take time to build capacity and put in place the programs needed to produce the indicators needed to monitor the gender-related targets of the SDGs. In the meanwhile, we can move ahead with the 2030 Agenda if we make use of the data already available from international datasets and survey programs. The 20 Ready to Measure indicators form the kernel of an expanding effort to measure the gender dimension of sustainable development.

 

 

 

What can be measured now?

Fifteen of the 20 Ready to Measure indicators are reported in databases maintained by international organizations such as the World Bank, the United Nations, the World Health Organization, UNICEF, and the International Labour Organization. But the presence of an indicator in a database does not guarantee that useful data exist for every country or for every year. Table 2 summarizes the availability of data for each of the fifteen indicators over the period 2011-2015. Several lessons emerge.

 

  • Indicators with high coverage rates are usually the product of statistical models using direct observations, indirect observations (from recall or sisterhood methods), or covariates to extend or interpolate from survey data. The only exception is indicator 12, the proportion of seats in parliaments held by women, which is based on administrative statistics reported annually to the Inter-Parliamentary Union.
  • 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.
  • Indicators on the use of new technologies have only recently appeared in public databases. For example, since 2014 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.

 

 

Five of the Ready to Measure indicators are not regularly reported in national or international databases, although the data needed to calculate the indicators are often collected through the same instruments used to construct other better-known indicators. The remaining indicators and their most important data collections instruments are shown in table 3.

 

 

The feasibility of calculating R2M indicator 1 has been demonstrated by the data compiled in the SEDLAC database on women living below the poverty line in Latin American countries. As part of the Ready to Measure project, algorithms for calculating the five indicators from available surveys have been tested. The existence of many of these surveys is documented in the IHSN Gender Navigator. Because access to many surveys is restricted, these indicators can only be calculated with the cooperation of the data owners. Countries that control the required data sets should work with their development partners and other stakeholders to extract the full value of the data they possess.

 

Moving ahead: Strengthening national capacity for gender data

 

Good quality data puts the right information in the hands of those who make policies and those who monitor policies to improve the lives of women and girls. The R2M indicators demonstrate that for many countries the data already exist. But gaps remain, even for widely cited indicators. Statistical models can fill some of these gaps and, in some cases, improve the quality of the data. But models are only as good as the underlying data. To achieve the ambitious goals of the SDGs, regular data collection and production of statistics according to international standards is required.

 

National statistical offices should take the lead. In the middle of a data revolution, they can draw on an expanding data ecosystem to realize their own goals, embodied in SDG target 17.18: “…to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.” The critical elements necessary for strengthening statistical capacity and mainstreaming of gender statistics within statistical systems are:

 

  • a sustainable funding mechanism, both internationally and domestically resourced;
  • strong skill development to take advantage of innovative sources and methods;
  • partnerships and alliances inside and outside the national statistical office;
  • political support and commitment within country governments to support gender statistics as a core component of smart, gender-informed policy-making;
  • an organizational strategy for incorporating a gender statistics ethos into the statistical system.

 

To accelerate the implementation of gender equality within the SDGs, a comprehensive approach to improving the data as well as the statistical systems is necessary. Achieving gender equality is not sector-specific. Just as women participate in all human activities, gender data are needed from all sectors, from the environment to employment and from agriculture to finance. To achieve gender equality in the workplace, gender data on salaries or unpaid labor must be available. To achieve equal access to financial services, gender data on financial accounts and intra-household poverty must be available. And to achieve equal educational opportunity for boys and girls, gender data on child marriage and school enrollment must also be available. R2M offers a starting point but there is much work to be done to ensure gender data are improved and used to create a positive impact on the lives of girls and women around the world.

 


ANNEX: DATA COVERAGE

 

 

 

Acknowledgements

 

The Ready to Measure: Phase 2 project was conducted by a team from Open Data Watch, including Deirdre Appel, Shaida Badiee, Elettra Baldi, Amelia Pittman and Eric Swanson. The Data2X team provided valuable comments and substantive review, including Alba Bautista, Mayra Buvinic, Emily Courey-Pryor, Rebecca Furst-Nichols, Gayatri Koolwal and Nina Rabinovitch Blecker. In addition to the project team, we are grateful to Leo Tornarolli (Centro de Estudios Distributivos, Laborales y Sociales – Universidad Nacional de La Plata) for advice on data for R2M indicator 1.

 

DATA2X is a collaborative technical and advocacy platform dedicated to improving the quality, availability, and use of gender data in order to make a practical difference in the lives of women and girls worldwide. Data2X works with United Nations agencies, governments, civil society, academics, and the private sector to close gender data gaps, promote expanded and unbiased gender data collection, and use gender data to improve policies, strategies, and decision-making in support of gender equality. Learn more about Data2X at www.data2x.org

 

OPEN DATA WATCH is a non-profit, non-governmental organization that monitors progress and provides information and assistance to guide implementation of open data systems around the world, with emphasis on improving statistical systems to support the Sustainable Development Goals. An area of special focus is sex-disaggregated data and supporting the Data2X team. Open Data Watch has unparalleled experience in development data management and statistical capacity building in developing countries and is committed to making open data a reality in all countries and development agencies. Learn more about Open Data Watch at www.opendatawatch.com 

 

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