Bridging the Gap: Mapping Gender Data Availability in Asia and the Pacific assesses the availability of 98 gender indicators, their disaggregations, and their frequency of observation in international and national databases and publications. It reports on the availability of gender data in Armenia, Bangladesh, Mongolia, Philippines, and Samoa, and with the assistance of our partners at the UN Economic and Social Commission for Asia and the Pacific (ESCAP), it documents the availability of statistical indicators to support gender development plans in the five countries.
Gender data are indicators of the status and welfare of women and girls or, when sex-disaggregated, indicators of pertinent differences between men and women. These indicators — if produced regularly and to a high standard — can be used to develop and implement policies and monitor results, delivering on commitments to achieve equality and opportunities for women.
In 2018, Data2X and Open Data Watch conceived a study that would offer national statistical offices, international statistical systems, development partners, and others involved in measuring and monitoring the progress of the world’s women and girls a more complete understanding of where gaps in gender data exist, why such gaps occur, and what can be done to fill them. The resulting technical report, Bridging the Gap: Mapping Gender Data Availability in Africa (provided insights into those questions and moved the development community one step closer to producing high-quality and policy-relevant gender indicators to inform better decisions. Open Data Watch and Data2X then expanded the research scope to Latin America and the Caribbean, with assistance from our partners at the UN Economic Commission for Latin America (ECLAC) (Data2X and Open Data Watch 2020). This study builds on the experience of the previous studies but shifts the geographic focus to Asia and the Pacific.
The 98 indicators selected for this study come from the list of Sustainable Development Goal (SDG) indicators or were recommended by UN Women to supplement the SDGs. Data gaps were examined in four dimensions: availability, granularity, timeliness, and adherence to standards. Using official national and international sources, study assessors recorded whether the indicators exist in any form, whether they were sex-disaggregated, and whether there were additional advised disaggregations such as geographic location, age, income level, or disability status. Indicators were checked for adherence to international standards, how recently they were produced, and their frequency.
The availability of gender indicators was assessed at the international and national level from 2010 to 2020. Data in international databases have been reported by countries and reviewed by custodian agencies. They generally, but not always, follow international standards for the computation and presentation of the indicators. Data in national databases may follow methodologies different from those in international sources but may still provide useful information for citizens and governments. Further investigation was conducted on the microdata sources —censuses, surveys, or administrative records—used to produce the most recent estimates of the indicators. Their associated metadata reveals what instruments are being used to produce gender indicators. It may also reveal underutilized data resources or the need for higher frequency data collection. By better understanding the production and availability of gender data at these three levels, we can draw specific lessons on how to fill gender data gaps.
Large gaps remain in the statistical record. The study revealed that 46 percent of gender-relevant indicators are missing or lack sex-disaggregated data at the national level, and 53 percent of gender-relevant indicators are missing or lack sex-disaggregated data at the international level. In international databases, 26 percent of the indicators lack any sex-disaggregation and 27 percent are missing data entirely. In national databases, there are fewer missing observations (21 percent), but a greater proportion (25 percent) lack sex-disaggregation. This persistence of relatively large gaps in both international and national databases points to the need for a coordinated effort to improve data collection and adopt common standards for the compilation of indicators.
The study looks at the availability of gender data across six development domains: health, education, economic opportunity, political participation, human security, and the environment. None of the six domains assessed have more than 77 percent availability of sex-disaggregated indicators, showing that even where data availability is highest, significant gender data gaps exist. The education domain has the highest proportion of sex-disaggregated data, and the environment domain has the lowest proportion of sex-disaggregated data — with only four percent at the national level.
The assessments in East Asia and the Pacific were carried out before the full impact of the COVID-19 pandemic had been felt. The pandemic is expected to slow data collection in many countries worldwide. Postponed surveys and censuses or delays due to staff working from home will have an impact on the future availability and timeliness of many gender indicators.
Data availability varies between international and national databases as well as between countries themselves. There are data with sex disaggregation or are female specific for 47 percent of gender indicators in international databases and for 54 percent in national databases. In national databases, Armenia and Samoa produced the fewest sex-disaggregated indicators (46 and 48 percent, respectively), while the Philippines produced the most (62 percent). The frequency of observations is highest in the Philippines — where there was an average of 3.6 observations per indicator — and lowest in Samoa, with an average of only 1.5 observations per indicator over the 11-year period. Variations in data availability and capacity to fill data gaps shows that countries make difficult choices about their data production because of resource limitations.
Administrative sources are a potential source of high-quality sex-disaggregated information — providing insight into the lives of women and girls that cannot be achieved through surveys. However, to play this role, improved documentation and increased accessibility is required. Many of the indicators studied here still depend upon national or internationally sponsored sample surveys. However, these data sources, while of high quality, carry with them the limitations of any survey exercise: they are expensive, intermittent, and cannot provide resolution at small scale.
The results of this study document gaps in datasets needed to sustain progress toward gender equality, but even if these gaps are filled, the data still need to be used in decision-making processes and incorporated in government policies if they are to make a difference in people’s lives. Going beyond the previous Bridging the Gap assessments, this study also evaluates national gender policies on how well they include data in their planning and decision-making processes. Our findings show countries could improve their planning and decision-making process by either creating new plans or updating old plans with specific targets tied to measurable indicators. Further, providing easy access to these data through open data portals would increase public awareness and provide important evidence of progress towards targets and goals.
In addition to the results of the assessments and the findings described in this report, the study has produced an expansive dataset that will be used to inform further research and analysis about gender data availability and accessibility. A companion volume documents the study methodology.
Data gaps are voids in our knowledge of the world and the people and communities who live in it. They limit our ability to understand the world as it is and to plan for change. In the case of gender data, these gaps limit our knowledge of the status and well-being of women and girls in countries around the world. Just as gender data are essential for designing and monitoring programs to improve the well-being of women and girls, knowledge of the location and persistence of gender data gaps is needed to design programs and mobilize resources for filling those gaps.
The terms gender data and gender indicators are used interchangeably in this report to refer to indicators that are defined uniquely for women or that provide sex-disaggregated data. In addition, disaggregations other than sex, such as age, location, refugee status, or disability may also be defined for some indicators. This study reports on the availability of 98 gender indicators, their disaggregations, and their frequency of observation in international and national databases and publications in five countries from the Asia and the Pacific region. Data2X and Open Data Watch conducted this study to provide a quantitative assessment of the availability of statistical indicators that are relevant to measuring the living conditions of women and girls. The study also documents the microdata sources (censuses, surveys, and administrative records) used to construct the 91 gender indicators included in the Sustainable Development Goals (SDGs).
The study results show that, on average, sex-disaggregated data are available for 52.7 percent of the SDG gender indicators in national databases of the five countries studied. These gaps are extensive but not uniformly distributed. Some indicators are available for every country from 2010 to 2020. But other indicators occur only sporadically, and large gaps exist in every country’s gender statistics. Using the results of this study, we can identify which countries and indicators have the largest gaps and suggest methods for filling them.
In 2014, Data2X published the first comprehensive report on the availability of gender indicators, Mapping Gender Data Gaps (Buvinic et. al. 2014). The study included some of the 52 indicators that comprised the Minimum Set of Gender Indicators proposed by the United Nations Inter-Agency and Expert Group on Gender Statistics (UNSC 2013). The study found that, “globally, close to 80 percent of countries regularly produce sex-disaggregated statistics on mortality, labor force participation, and education and training. Less than a third of countries disaggregate statistics by gender on informal employment, entrepreneurship, violence against women, and unpaid work.”
Following the publication of Mapping Gender Data Gaps, Data2X and Open Data Watch (2016) identified a set of 20 gender indicators that were “ready to measure,” meaning that the indicators were available or the necessary microdata sources existed to construct them. The study drew on the World Bank’s Gender Data Navigator (GDN) to identify the surveys with sufficient data for constructing the indicators (World Bank n.d.). Notwithstanding the availability of survey and administrative data, many gaps in these and other gender indicators persist.
To provide a more complete tabulation of gaps in gender data, Open Data Watch (ODW) and Data2X undertook a study of an expanded set of indicators in 15 low- and middle-income countries in Sub-Saharan Africa (Data2X and Open Data Watch 2019). The study examined the availability of 104 gender indicators in national and international databases from 2010 to 2018. It recorded the years in which the indicators were available, their disaggregation (by sex or other specified characteristics), and information derived from their metadata (where available) on the sources of the underlying data. It was also noted whether the published indicators conformed to international standards including frequency and timeliness.
In the African countries studied, 48 percent of the gender indicators were missing or lacked sex-disaggregated data at both international and national levels. In international databases, 22 percent of the indicators lacked any sex-disaggregation and 26 percent were missing data entirely. In national databases there were more missing observations (35 percent) but a smaller proportion (13 percent) lacked sex-disaggregation. Indicators were classified into six development domains. The health domain had the highest proportion of sex-disaggregated data, with 73 percent of the indicators sex-disaggregated at the international level. The environment domain had the lowest proportion of sex-disaggregated data.
In 2019, Open Data Watch and Data2X expanded the scope to Latin America, with the guidance of the UN Economic Commission for Latin America and the Caribbean (Data2X and Open Data Watch 2020). This study followed the same methodology as Bridging the Gap: Mapping Gender Data Availability in Sub-Saharan Africa, however, the selected indicators were revised to include 12 additional indicators from the SDGs, while indicators from the pre-SDG era were removed. A total of 93 gender-relevant indicators were included in the study of five Latin American and the Caribbean countries from 2010 to 2019. The research went a step farther by intersecting current gender plans and other national development plans in the five countries with relevant indicators in the study.
In the five Latin American countries studied, 30 percent of the indicators lacked sex-disaggregated data in international databases and 25 percent more were missing data entirely. In national database there were more missing observations (31 percent) but a smaller proportion (22 percent) lacked sex-disaggregation. Indicators were classified into six development domains. The education domain had the highest proportion of sex-disaggregated data, with 58 percent of the indicators in national databases sex-disaggregated. The environment domain had the lowest proportion of sex-disaggregated data — with only 7 percent of indicators in national databases sex-disaggregated.
This study builds on the results from previous publications but shifts the geographic focus to Asia and the Pacific. It reports on the availability of gender data in Armenia, Bangladesh, Mongolia, Philippines, and Samoa. The countries were selected in consultation with the UN Economic Commission and Social Commission for Asia and the Pacific (UN ESCAP). The countries are more diverse in terms of income and statistical capacity than those included in the Latin America and Sub-Saharan Africa studies. They include a small island developing state (Samoa); two landlocked developing countries (Armenia and Mongolia); and a least developed country (Bangladesh).
The study uses a revised list of gender indicators, including eight additional SDG indicators for which methodologies have become available. The assessments follow the same methodology used for the Africa study, but the period has been extended from 2010–2018 in Africa and 2010–2019 in Latin America, to 2010–2020 in Asia and the Pacific (see Bridging the Gap: Methodology Report (Data2X and Open Data Watch 2021)). As in the previous studies in Africa and in Latin America, this study has produced a precise audit of the publicly available gender indicators for the selected countries. In doing so, it provides a blueprint for filling the gaps in these and similarly situated countries.
In 2017, the United Nations Economic and Social Commission for Asia and the Pacific (UN ESCAP) published the Regional Roadmap for Implementing the 2030 Agenda for Sustainable Development in Asia and the Pacific. In this report, opportunities for regional cooperation include the development of regional and national strategies for the development of statistics, along with the development of capacity building initiative in Member States to increase the availability of disaggregated and timely data for SDG monitoring (ESCAP 2017).
In addition to working with countries in statistical capacity building so that no one is left behind, ESCAP has developed initiatives for mainstreaming data in policies. Recently ESCAP has led initiatives centered around connecting policymakers and data producers. Every Policy is Connected (EPIC), a tool developed by ESCAP that “facilitates conversation between policymakers and data producers based on agreed development principles to articulate the demands of policies for disaggregated data in the context of the 2030 Agenda for Sustainable Development” (ESCAP 2018). In Armenia and in the Philippines, the EPIC tool was piloted to connect gender data with the national gender policy demands, while also strengthening the responsiveness of their national statistical systems.
In March 2016, the Inter-Agency and Expert Group on the Sustainable Development Goal Indicators (IAEG-SDGs) submitted its proposed list of some 232 indicators (IAEG-SDGs 2016). The indicator list was subsequently partitioned into three “tiers”: indicators with an agreed methodology and reported by a majority of countries were assigned to Tier I; indicators with an agreed methodology but less well reported were assigned to Tier II; and indicators lacking an agreed methodology were assigned to Tier III. At subsequent meetings of the IAEG-SDGs, the tier classification has been revised and indicators have been promoted to higher tiers as new methodologies were proposed or more data became available. As of the 51st session of the UN Statistics Commission (March 2020), Tier III indicators have been upgraded to Tier II or dropped. (UN Statistics Division 2020).
The Inter-Agency and Expert Group on Gender Statistics (IAEG-GS 2019) identified a “minimum set” of 52 SDG indicators that are “specifically or largely targeted” at women or girls. UN Women noted that “a less restrictive criteria where all indicators that are relevant for women and girls and can be disaggregated by sex are included would yield a greater listing of gender-relevant indicators.” UN Women has also proposed a set of supplemental, non-SDG indicators to ensure that there exists at least one indicator for each of the 17 SDGs (UN Women 2018).
In the current Bridging the Gap study in Asia and the Pacific, there are 98 SDG and non-SDG indicators in this study. Of these, 32 SDG indicators are from UN Women’s “minimum set,” 59 are additional SDG indicators that could be disaggregated by sex, and nine are supplemental non-SDG indicators. For more information about indicator sources and selection, see the Bridging the Gap Methodology Report (Data2X and Open Data Watch 2021).
The study recorded the availability of data in international and national databases. International databases included the United Nations Global SDG Database (UNSD n.d.), the World Bank’s Gender Data Portal, and those of the specialized agencies of the United Nations. National databases included data portals and publications available online that report official statistics of the country. These data portals and publications are housed in websites of national statistics offices or other relevant ministries and agencies. For each indicator and country, the study assessors noted whether the indicator was available with sex-disaggregation and other disaggregations required by the SDGs; the number of observations available between 2010 and 2020; and the location of metadata describing the sources and methods used to construct the indicator.
|2. LEVEL OF DISAGGREGATION
Each indicator was assessed on whether it was fully disaggregated or if it lacked one or more required disaggregations. Indicators that lacked sex-disaggregation were recorded separately.
|3. TIMELINESS AND FREQUENCY
Indicators were assessed for their timeliness and frequency. Timeliness was measured from the date of the most recent observation and frequency by the number of observations available from 2010 to 2020.
|4. Adherence to standards
Adherence to international standards is documented in the inventory of metadata recorded as part of the assessments. Indicators whose descriptions did not match their SDG definition or their description in the UN Women’s Turning Promises into Action were classified as “non-conforming” with their disaggregations recorded.
Examples of non-conforming indicators include the following:
The Bridging the Gap studies in Sub-Saharan Africa, Latin America and the Caribbean, and Asia and the Pacific link gender indicators to their microdata sources and provide a summary data page with a description of each SDG indicator, documentation of the indicator produced by each country, and its microdata sources. Metadata reviewed during the indicator assessments were used to identify the censuses, surveys, or administrative records used to construct the indicators found in national databases. Survey questionnaires were examined as needed to clarify sources and the availability of disaggregations.
Data quality depends on many factors: whether the data were properly collected and recorded; in the case of the survey data, whether the sample frame was well constructed and of sufficient size; and whether the construction of the indicator conformed to recognized standards and definitions. In this study, indicators available in national and international databases were assessed for the adherence to international standards as described by their SDG methodology or, for non-SDG indicators, as defined by UN Women.
For each indicator and each country, study assessors noted whether data for the selected indicators were available in one or more years between 2010 and 2020, whether the indicators were sex-disaggregated, and whether other disaggregations specified in their original description were included. The results were recorded separately for data found in international and national databases. The international databases studied are those maintained by designated custodian agencies such as the WHO, UNICEF, International Labour Organization (ILO), or the World Bank and the SDG Global Database. National data covered by the study included databases in online data retrieval systems such as data portals, online publications of national statistical offices or other government agencies, or nationally published research findings.
Indicators that fully conformed to their standard methodology and included all prescribed disaggregations were classified as:
- Available with all disaggregations
- Available but applicable only to women
Indicators that conformed to their standard methodology but lacked one or more prescribed disaggregations were classified as:
- Available and sex-disaggregated but lacking other disaggregations)
- Available, applicable only to women, but lacking other disaggregations
- Available but lacking sex-disaggregation
Non-conforming indicators that were judged to be similar to or plausible proxies for the specified gender indicators were classified as:
- Non-conforming but sex-disaggregated
- Non-conforming and applicable only to women
- Non-conforming and lacking sex-disaggregation
Indicators with no observations over the 11-year period were classified as missing or “no data.”
Table 1 shows the distribution of all 98 indicators in national databases. From a gender data perspective, indicators classified as conforming with sex-disaggregation can be considered high quality. We emphasize the availability of conforming indicators because they facilitate cross-country comparisons and may provide more consistent measurements over time. Non-conforming indicators may be of lower quality although they can provide gender-relevant information, and they may also be easier to produce and more suitable for setting and monitoring national policies.
Table 1: Availability of indicators in national databases by country (% of total)
|Fully disaggregated data available||17||28||32||22||17||23.3|
|Female only data with complete disaggregations||12||12||12||13||9||11.8|
|Sex-disaggregated available lacking other disaggregations||4||6||4||6||8||5.7|
|Female only data available lacking other disaggregations||2||7||4||4||1||3.7|
|Subtotal: Conforming with sex-disaggregation||35||53||52||6||36||44.5|
|Non-conforming data with sex-disaggregation||6||1||4||11||10||6.6|
|Non-conforming data applicable to females only||4||2||2||5||2||3.1|
|Subtotal: Non-conforming with sex-disaggregation||10||3||6||16||12||9.6|
|Conforming but lacking sex-disaggregation||22||25||21||13||5||17.6|
|Non-conforming data lacking sex-disaggregation||8||1||3||7||16||7.1|
|Subtotal: Missing or lacking sex-disaggregation||54||44||42||38||52||45.9|
Figure 1 shows the proportion of indicators available in international and national databases. The difference between national and international databases is that the former includes more non-conforming indicators, while international databases have a slightly higher proportion of indicators with complete disaggregation but also a larger proportion of indicators that lack sex-disaggregation. As previously noted, the use of non-conforming indicators is neither good nor bad. Countries may choose to report an indicator that differs from the international standard because it better suits their policy needs. Both national and international databases have a small share of indicators that lack some specified disaggregation but include sex-disaggregation. In total, 54 percent of the possible indicators are available in national databases with sex-disaggregated data; only 47 percent are available in international databases.
Figure1: Availability of data in international and national databases
The previous tabulations counted any indicator that had at least one observation from 2010 to 2020. But scattered observations are not as useful as a continuous time series, particularly for trends in gender equality and progress toward the SDGs. Long lags before data become available further diminish their relevance. The study assessments noted the individual years for which data are available and the total number of published observations over the 11-year period.
A lag of one year in the availability of annual data for most indicators is common. Some high frequency data (such as quarterly or monthly estimates of economic output or unemployment rates) may be more rapidly available, and the increasing use of data portals linked to administrative and survey databases may help to decrease the lag time of even complex social indicators. However, the COVID-19 pandemic is expected to slow data collection in many countries. Postponed surveys and censuses or delays due to staff working from home will have an impact on future data availability and timeliness.
Figure 2 shows the distribution of the first and last year data are available for all 98 indicators in the national databases of the five study countries (countries with earlier data series were recorded as beginning in 2010). On average, 21.2 percent of all indicators lack any data. Of the available indicators, 23.9 percent have an initial observation in 2010, but another 42.5 percent lack any observations before 2015. There is a pronounced surge in data availability from 2015 onwards, which may reflect the efforts to provide data at the end of the MDG period and establish baselines for the SDG period. Still, there are large lags: 20.5 percent of observations stop before 2016, and about half of all indicators are three to four years old.
Figure 2: First and last years of data availability in national databases
Most gender indicators should be reported annually. A complete series should, therefore, include eleven observations. But not all indicators are measured annually. Censuses are typically carried out once in a decade. Household surveys that collect data on income, consumptions, or the welfare of individuals occur sporadically but ideally every two to four years. Labor force surveys should occur annually, and administrative data, such as education data or crime statistics, are event driven but should be reported at least annually. Where necessary, values may be interpolated between measured points or extrapolating from time trends, but these points should be appropriately labeled.
Table 2 summarizes the number of observations available for all 98 indicators in national and international databases. The counts shown here are based on all indicators, including those that lack sex-disaggregation and non-conforming indicators. The observations of many indicators are sparse. There is no country where half of the available indicators have more than three observations in their national or international databases. In national databases, 21 percent of all indicators, on average, lack any data over the period; the count of missing data was slightly higher in international databases. For indicators with available data, national databases typically had fewer observations than international databases.
Table 2: Observations available, by country, 2010-2020
|National databases||International databases|
|Countries||Indicators with no data||Indicators with 1 to 3 observations||Indicators with more than 3 observations||Indicators with no data||Indicators
with 1 to 3 observations
with more than 3 observations
The term “data frequency” suggests that indicators follow a regular schedule—published annually, biennially, or at some regular interval. Some do, but in practice, many do not. Therefore, we use the term “data density” to describe the number of observations available in a given period.
Large differences were found in the availability and density of data between indicators and between countries. For example, complete annual data for SDG 5.5.1 (Proportion of seats held by women in national parliament) were only available from the SDG Global Database or from the Interparliamentary Union. In national databases, data are more sporadic. The Philippines published two observations from 2016 to 2019, while Armenia and Bangladesh published nine observations between 2010 and 2018. This indicator is derived from administrative records of the country and therefore should be complete and available in national databases.
Table 3 shows the average number of observations and the range of years available. On national databases, no country has five or more observations on indicators with any available data. On average, the Philippines has the greatest density, with about 4.4 observations for indicators with data. The range of years available in these countries is slightly greater: on average, data series begin in 2013 and extend to 2017. Samoa, with the lowest data density, lacks extended time series: over the period a typical series begins in 2013 and ends in 2015.
International databases show a more even distribution of data. The largest difference is between Armenia with 5.5 observations on indicators with data and Samoa with 4.3. Behind the averages, there are differences in the indicators available, but these results suggest that countries could make more data available simply by publishing the observations already available in international databases.
Table 3: Average indicator density and range of years, 2010-2020
Average number of observations for indicators with data
Average beginning year
Average final year
Average number of observations per indicator with data
Average beginning year
Average final year
The IAEG-SDGs classifies indicators as Tier II if they have an agreed methodology but are available in fewer than half of the countries of the world. Indicators were classified as Tier III if they lacked an agreed methodology, but efforts by the custodian agencies since 2015 have added methodologies to Tier III indicators promoting them to Tier II. The updated tier classification table maintained by the IAEG-SDGs now contains 130 Tier I indicators, 97 Tier II indicators, and 4 indicators that have multiple tiers (different components of the indicator are classified into different tiers) (IAEG-SDGs 2020). Tier II indicators in the five study countries are less likely to be available with sex-disaggregation. Based on an earlier (July 2020) classification, 59 percent of the Tier II indicators were missing or lacked sex-disaggregation in national databases compared to 37 percent of the Tier I indicators. Tier II indicators also have a lower density. They averaged 2.7 observations for indicators with at least one observation, while Tier I indicators averaged 3.8 observations over the period 2009 to 2020.
As with the Bridging the Gap studies in Sub-Saharan Africa and in Latin America and the Caribbean, each of the 98 indicators have been classified into one of six development domains. Five of the domains are based on Buvinic et al. (2014), with the environmental domain added to the Bridging the Gap studies. The economic domain covers indicators pertaining to the labor force, poverty, income and expenditure, social security, and access to ICT. The education domain covers education facilities and indicators of students’ progress through school. The environment category includes indicators on the physical characteristics of households, public infrastructure, and indicators of natural disasters. The health domain covers indicators on mortality, morbidity, reproductive and child health, nutrition, and access to health care. The human security domain covers indicators on violence against women, homicides, human trafficking, and conflict-related deaths. The public participation domain includes birth registration, women in public service and in managerial positions, bribery by public officials, and perceptions of decision making. A few indicators might arguably be classified in another domain. For example, mortality rates attributed to unsafe water or sanitation are included in the environment domain rather than health. But there are few other ambiguous classifications. The full list of indicators with their domain classifications is included in Annex table 1.
Health is the largest domain with 28 indicators, followed by economy and human security. The count of indicators in each domain is shown in Table 4.
Table 4: Number and share of indicators in each domain
|Domain||Number of indicators||Share (%)|
The quality and availability of gender indicators differs by domain. Table 5 shows the average availability of indicators in national databases by domain. Public participation has the fewest gender indicators (7) but has the highest share of conforming, sex-disaggregated indicators. The domains with the next highest share are health and education with sex-disaggregated data for 56 and 55 percent, respectively, of their conforming indicators. The education domain, which has the highest proportion of non-conforming indicators, adds another 20 percent that are sex disaggregated. After health and education, 49 percent of conforming economic indicators are sex disaggregated. Less than 40 percent of human security indicators are available with sex-disaggregated data, and the environment domain, with the smallest proportion of available indicators has no conforming indicators with sex-disaggregation.
Table 5: Average availability of indicators in national databases by domain (%)
|Indicator availability||Economy||Education||Environment||Health||Human security||Public participation|
|Fully disaggregated data available||38.1||26.7||0.0||28.6||12.6||17.1|
|Female only data with complete disaggregations||0.0||11.7||0.0||20.0||13.7||28.6|
|Sex-disaggregated available lacking other disaggregations||8.6||15.0||0.0||3.6||2.1||8.6|
|Female only data available lacking other disaggregations||1.9||1.7||0.0||3.6||9.5||2.9|
|Subtotal: Conforming with sex-disaggregation||48.6||55.0||0.0||55.7||37.9||57.1|
|Non-conforming data with sex-disaggregation||7.6||20.0||3.6||6.4||1.1||0.0|
|Non-conforming data applicable to females only||2.9||1.7||0.0||0.7||10.5||0.0|
|Subtotal: Non-conforming with sex-disaggregation||10.5||21.7||3.6||7.1||11.6||0.0|
|Conforming data lacking sex-disaggregation||17.1||5.0||50.9||19.3||6.3||11.4|
|Non-conforming data lacking sex-disaggregation||8.6||8.3||14.5||3.6||6.3||5.7|
|Subtotal: Missing or lacking sex-disaggregation||41.0||23.3||96.4||37.1||50.5||42.9|
The availability of sex-disaggregated indicators in each domain differs between national and international databases. Figure 3 shows the proportion of indicators with sex-disaggregated data available. To simplify this presentation, all sex-disaggregated indicators are grouped together. The greatest difference between national and international databases occurs in the human security domain, where national databases have substantially more sex-disaggregated indicators. The smallest difference occurs in the public participation domain, where national and international databases are tied. The environment domain, with only 11 gender-relevant indicators, also has the smallest proportion with sex-disaggregated data and the largest relative difference between international and national databases.
Figure 3: Proportion of indicators with sex-disaggregated data by domain (%)
In the following sections we explore some of the sources of gaps and differences between national and international databases in each domain.
The 21 economic opportunity indicators included in this study are, except the labor force participation rate, part of the SDG monitoring framework. They provide an important but limited view of women’s economic roles and barriers to their full participation in the labor force. They consist primarily of measures of income or expenditures collected through household surveys and labor force indicators collected through surveys and administrative records. Other indicators measure the use of the internet by men and women and participation in the banking system. Missing from this set, however, are measures of the status of migrant women, earnings differentials, or access to childcare (Grantham 2020).
Figure 4 shows the number of indicators available in the national databases of each country. These include both sex-disaggregated and non-disaggregated indicators for which at least one observation was available.
Figure 4: Number of economic indicators available in national databases, 2010-2020
Data collected at the household level are generally not available with sex-disaggregation because of the difficulty of assigning shared resources to individuals. Sex-disaggregated measures of poverty rates or other indicators of household income or expenditure are rarely available. An exception is the measure of the employed population below the international poverty line, the so-called working poor, calculated according to the ILO’s methodology. Three of the countries in the study reported poverty rates for men and women at the international poverty line and all five reported rates measured at national poverty lines.
One indicator is unavailable with sex-disaggregation in national or international databases:
- Proportion of people living below 50 percent of median income (10.2.1)
Two economic indicators are available with sex-disaggregation in national and international databases for all five countries:
- Unemployment rate (8.5.2)
- Labor force participation rate (not in SDGs)
The remaining 18 economic indicators are available in the national databases of one or more countries, although some are produced by non-conforming methodologies. Sex-disaggregated economic indicators are less available in international databases. For example, Samoa only has four sex-disaggregated indicators and the Philippines only has eight.
Education measures of school enrollment, progress, and completion generally come from administrative records that are sometimes supplemented by surveys or censuses that record whether children are attending (as opposed to enrolled in) school. Measures of learning outcomes may be based on school exams, but more sophisticated measures of numeracy, literacy, or other competencies require specialized assessments. Measures of the facilities, learning materials, and teaching staff are also of importance for the quality of education. The SDGs include only one gendered facility indicator: the availability of single-sex sanitation facilities.
Figure 5 shows the number of education indicators available in the national databases of each country. These include both sex-disaggregated and non-disaggregated indicators. No country has a complete set of the 12 education indicators with sex disaggregation, but the Philippines and Bangladesh come closest. The Philippines lacks sex-disaggregated data for two SDG indicators: proportion of children and young people achieving a minimum proficiency level in reading and mathematics (4.6.1) and proportion of teachers with the minimum required qualifications (4.c.1). Bangladesh lacks sex-disaggregated data for indicator 4.c.1 and has no data for 4.6.1.
Figure 5: Education indicators in national databases, 2010-2020
Because these indicators reflect the structure of national (or local) education systems and national standards for educational achievement, they may not conform to international standards. As previously noted, only 55 percent of the education indicators are conforming and sex-disaggregated, while another 22 percent are non-conforming and sex-disaggregated. The remaining 23 percent are missing entirely or lack sex-disaggregation.
The assessment results show a mixed pattern. Only literacy and numeracy rates (4.6.1) are available in the national databases of all five countries, four of which were classified as non-conforming. Only a single instance of the indicator was found (without sex-disaggregation) in international databases. This may reflect reporting problems or the rejection of non-conforming indicators by international compilers
Environment indicators in this study were selected because their data could plausibly be disaggregated by sex. All of them deal with the built environment: adequacy of housing, access to water, sanitation, and transportation services, and exposure to indoor pollution and natural disasters. This is not to say that the condition of the natural environment does not have a differential impact on men and women; however, indicators of resource use or environmental degradation are not measurable with sex-disaggregation. UN Women has suggested some supplemental indicators for the environmental goals (UN Women 2018) that capture women’s activities, such as the proportion of women and men working in fisheries or sex-disaggregated statistics on household fuel collection and forest conservation activities. These indicators were not included in the study set because they lack an agreed methodology.
No environment indicator is available in all countries. As shown in Figure 6, even including indicators without sex-disaggregation, many countries lack any data for many of the 11 environment indicators in their national databases. Similar results are shown in ESCAP’s working paper on Mainstreaming gender in environment statistics for the SDGs and beyond: Identifying priorities in Asia and the Pacific, where there are sufficient data but not from a gender perspective. This means there are sufficient environmental indicators that are available with a historical trend but without sex disaggregation; insufficient data that lack availability, historical trends, and sex disaggregation; and no data (ESCAP 2019).
Figure 6: Environment indicators available in national databases, 2010-2020
The environment is the domain with the least availability of sex-disaggregated indicators. Nine indicators are unavailable or lack sex-disaggregation in either national or international databases:
- Number of deaths, missing persons, and directly affected persons attributed to disasters (1.5.1)
- Proportion of population using safely managed drinking water services (6.1.1)
- Proportion of population using safely managed sanitation services (6.2.1)
- Proportion of women with access to clean cooking fuel (non-SDG)
- Proportion of population with primary reliance on clean fuels and technology (7.1.2)
- Proportion of the rural population who live within 2 km of an all-season road (9.1.1)
- Proportion of urban population living in slums, informal settlements, or inadequate housing (11.1.1)
- Proportion of population that has convenient access to public transport (11.2.1)
- Average share of the built-up area of cities that is open space for public use for all (11.7.1)
Many of the indicators identified as being capable of sex-disaggregation are collective goods, facilities, or services shared by all household members. Like other indicators recorded at the household level, it is difficult to differentiate access or use by individuals. However, it is still possible to calculate the proportion of women living in households that share or have access to the facility or service. Similarly, surveys or administrative data that include the age or disability status of household members could be used to provide average measures.
The SDGs include 26 indicators of women’s health spread across five goals. They fall into three broad groups: measures of undernourishment or food insecurity, including stunting in children; measures of disease incidence, prevalence, and mortality rates of mothers and children; and measures of reproductive health and agency. We included two supplemental indicators recommended by UN Women for a total of 28 — 18 of which are included in Goal 3 (“Ensure healthy lives and promote well-being for all at all ages”). The others fall under Goals 2, 4, 5, and 8. Eight of the 28 health indicators are specific to women; the remaining 20 apply to both males and females.
As shown in Figure 7, the Philippines’ national databases provide at least one observation on 27 indicators, and only three of the indicators lack sex disaggregation. Armenia has 26 indicators with data, although half lack sex disaggregation.
Figure 7: Health indicators available in national databases, 2010-2020
One health indicator was unavailable or lacked sex-disaggregation in national and international databases of all five study countries:
- Prevalence of undernourishment (2.1.1)
This indicator is generally available in the SDG Global Database or from the FAO for other countries in the region, although without sex-disaggregation.
There were six health indicators that are female-specific or have sex-disaggregated data in national and international databases for all five countries:
- Prevalence of stunting (2.2.1)
- Prevalence of malnutrition (2.2.2)
- Proportion of births attended by skilled health personnel (3.1.2)
- Proportion of women of reproductive age who have their need for family planning satisfied with modern methods (3.7.2)
- Adolescent birth rate (3.7.2)
- Age-standardized prevalence of current tobacco use (3.a.1)
There are 19 human security indicators, the majority of which record experiences of violence or perceptions of danger, 11 of which fall under SDG 16 (“Peaceful and inclusive societies…”); four fall under Goal 5 (“Gender equality”) that refer specifically to women and girls; three under Goal 10 (“Reduced inequalities”); and one under Goal 11 (“Sustainable cities and communities”). Data for these indicators, particularly those concerning sexual violence, are difficult to collect, requiring carefully planned and administered individual surveys. Administrative records, such as police reports, may also be used, but these are often incomplete or unreliable.
As shown in Figure 8, Bangladesh has the most complete set of human security indicators in its national databases, where 15 indicators have sex-disaggregated data, and one indicator lacks disaggregation. Samoa, with 10 available indicators, reports sex-disaggregated data for only six.
Figure 8: Human security indicators available in national databases, 2010-2020
For all five countries, two indicators are either not available or lack sex-disaggregation in both national and international databases:
- Number of people who died or disappeared in the process of migration towards an international destination (10.7.3)
- Proportion of the population who have experienced a dispute in the past two years and who accessed a formal or informal dispute resolution mechanism, by type of mechanism (16.3.3)
Indicator 10.7.3 and 16.3.3 were included during the 51st session of UN Statistics Commission’s 2020 comprehensive review (IAEG-SDGs 2020). Data are not available for all five countries in both databases. In the international databases, this indicator relies on IOM’s Missing Migrant Project (MMP), where the completeness of data varies from country to country (UNSD 2020). Only two countries provide data for indicator 16.3.3 in their national databases, but neither is sex-disaggregated; there are no data available in international databases. Like indicator 10.7.3, indicator 16.3.3 is a newly added indicator, which may explain the gaps in both.
Only one indicator is available with sex-disaggregation in national and international databases for all countries:
- Women aged 20–24 years who were married or in union before age 15 and age 18 (5.3.1)
Typically, household surveys such as the Demographic Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS) have adequate modules to collect data for this indicator. All five countries have conducted DHS or MICS surveys from 2010 to present.
Public participation is the smallest domain, with only seven gender indicators, all included in the SDGs. Three of these indicators concern the proportion of women holding high positions in government, business, and other public institutions. Two record contact with public services. Perhaps the most important for improving the overall quality of gender statistics is the proportion of children who have been registered with a civil authority.
Figure 9: Public participation indicators available in national databases, 2010-2020
Figure 9 shows that Bangladesh has the most complete set of public participation indicators in its national databases, where out of seven indicators, six are available: five have sex disaggregation and one indicator lacks disaggregation. Mongolia also has six available indicators but two lack sex disaggregation. No country has data for indicators 16.7.2 (Proportion of the population who believe decision making is inclusive and responsive) in national or international databases. The least reported indicator with some available data is the SDG indicator 16.5.1 on bribery, for which only one country has data with sex disaggregation and two countries have data but lack sex disaggregation. Data on bribery may be difficult to capture, as it relies on crime victimization surveys or household surveys with more-specific modules on bribery (UNSD 2016).
On both national and international databases, the following indicators are available in all five countries with sex disaggregation:
- Proportion of seats held by women in (a) national parliaments and (b) local governments (5.5.1)
- Proportion of women in managerial positions (5.5.2)
- Proportion of children under five years of age whose births have been registered with a civil authority (16.9.1)
The goal of this section is to create a record of the availability of the microdata used to produce each SDG indicator in national databases, to identify systematic reasons for gaps in the statistical record, to determine the means of filling those gaps, and to provide recommendations for producing sex-disaggregated data. One of the outcomes includes the creation of an indicator summary sheet for all 91 SDG indicators, which are available upon request. Further information on the indicator summary sheets is available in the corresponding Bridging the Gap Methodology Report.
Typically, surveys and administrative sources are the primary source of data across all five countries with available data. Censuses are important for establishing population size, location, and age distribution needed for constructing sampling frames. Censuses may also yield additional information needed to compute other demographic indicators, such as birth rates and mortality rates. The microdata sources most frequently used to construct gender indicators were household health surveys and other specialized health surveys. Household health surveys Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). These survey programs are sponsored by USAID and UNICEF and have played a major role in establishing a core set of data for women and children, especially in the health and education domains. These surveys also cover elements of dwelling and living conditions, such as household access to water and sanitation. Aside from the DHS and MICS surveys, there were other specialized health surveys available, such as surveys on tobacco or on nutrition. Of the available indicators where data are available, DHS and MICS surveys comprise 31.4 percent of the microdata sources. Four of the five countries in Asia and the Pacific have multiple household health surveys since 2010; however, Samoa only has one household health survey.
Our findings also show that labor force surveys and income/expenditure and multi-topic household surveys comprise 15.4 percent of the microdata sources. As evident, labor force surveys cover various indicators pertaining to the labor force, such as data on unemployment, employment by sector, active versus inactive populations. Income/expenditure and multi-topic surveys include surveys such as the Living Standard Measurement Survey (LSMS) and other household income surveys. These surveys cover various indicators, such as data on poverty, social protection, income, and poverty. These surveys may also have modules on employment and on dwelling and living conditions.
Additionally, 11.5 percent of the indicators use other surveys, such as agricultural surveys or censuses, population and housing censuses, time use surveys, gender-based violence surveys, police or community perception surveys, and specialized surveys on education.
Of the available data, administrative data comprise 26.3 percent of the available sources. Administrative data sources include civil registration and vital statistics (CRVS), health administrative systems or health management information systems (HMIS), and education administrative systems or education management information systems (EMIS). CRVS and HMIS provide data on mortality and cause death data along with disease incidence and prevalence, but these systems are often incomplete and may not conform to international standards in reporting. EMIS sources cover data on student enrollment and completion, along with data on teachers.
Access to documentation of administrative data remains the largest gap in the microdata sources for gender statistics. The study found no instances of public access to administrative records or their metadata and, in many cases, documentation of indicators referred only to the ministry or agency that produced the indicators with no information about the specific dataset deployed.
Across national databases with available data, we found that 15.4 percent of available data come from unknown sources. That is, due to the lack of metadata available, our team was unable to find sufficient information on the source of the data; as a result, we were unable to determine whether the available data comes from surveys or from administrative systems. Lack of metadata is more prevalent in national SDG reporting platforms; furthermore, to overcome this issue, it is strongly recommended to provide adequate metadata such as the definition or calculation of the indicator, the name of the data source, and the date of upload/date of last update.
Table 6: Microdata sources of gender data
Proportion of indicators from source (%)
|-Household health and other health surveys||
|-LFS and LSMS/HIES||
It is universally acknowledged that reliable, timely, and suitably disaggregated data are critical for formulating policies and measuring and monitoring progress toward achieving national and global goals for gender equality and women’s autonomy. In this study we have documented the gender indicators included in the SDGs or recommended by UN Women that can be found in national and international databases and the gaps in those collections. But data are not butterflies that can be pinned to a board and stored in a drawer. The cost of collecting data and maintaining statistical systems can only be justified by their use.
Throughout this report, we have focused on the early stages of the value chain (Figure 10): data collection and publication. But to realize their full value, there must be uptake: data must be incorporated in decision-making processes and policies that have an impact on people’s lives. In this section we examine policies adopted by the study countries to promote gender equality and improve the status of women and ask whether there are data available to guide their policies and monitor outcomes. The availability of data does not always mean data have been used to set policies or will be used to monitor progress, even when quantified goals have been set. Nor does the lack of data mean that policies were ill-informed. But aligning a country’s data resources with its policies and goals will increase transparency and encourage evidence-based decision-making that can increase their effectiveness.
The countries in this study have made commitments to improved gender policies supported by data. All five have ratified the Convention on the Elimination on All forms of Discrimination against Women (CEDAW) (United Nations 1979). Regionally, ESCAP is aware of the importance of data for monitoring international conventions and for policies. In support of CEDAW, ESCAP held an expert group meeting on gender statistics and indicators of violence against women in 2008. In 2012, in the years leading up to the SDGs, UN Women and ESCAP initiated a Working Group on Gender Statistics (UN Women n.d.). Furthermore in 2015, ESCAP developed a Regional Core Set of Gender indicators based on the Global Minimum Set of Gender Indicators, which was endorsed by the Committee on Statistics at its fourth session (ESCAP 2015). During the SDG era, ESCAP went a step farther by creating a data-policy initiative. One of the outcomes of the data-policy initiative was the creation of the Every Policy is Connected (EPIC) tool. EPIC is a sophisticated tool that allows policymakers to monitor progress towards policy outcomes using data (ESCAP 2018).
Four of the study countries have published gender policies or strategies, although the policies in two countries have since expired: Armenia’s in 2015 and Samoa’s in 2020. Lacking current policy statements, we reviewed the old policies to see what data were available to support them. The remaining two countries, Bangladesh and the Philippines, have strategies and plans covering the period from 2019 to 2025. Mongolia does not have a specific gender policy or strategy in place, but it has an ambitious plan for producing gender statistics. As with the other countries, we compare Mongolia’s plan with the gender data that are currently available in its national databases. Not all the objectives of these plans are well represented in the SDGs. They may require additional, more specialized indicators. While pointing out the gaps in national databases, therefore, the analysis also points to limitations of the SDGs and the need to think more broadly of the scope of gender data.
We also looked at the countries’ scores in the 2020/21 Open Data Inventory (Open Data Watch 2020). The Open Data Inventory (ODIN) provides a broad assessment of the coverage and openness of 22 categories of statistics in national databases. Ten of these categories include gender-relevant indicators. There were 187 countries included in the 2020/21 assessment. Its ratings provide a comparative index of the ability of a national statistical system to provide the open access to the data and statistics needed to implement policies for social, economic, and environmental development.
The following are brief summaries of the policy review. A more detailed analysis of each country’s plan and a listing of indicators that could be used to support the plan are contained in a separate set of five country policy reports.
Armenia’s Gender Policy Strategic Action Plan for 2011–2015 did not include a set of measurable indicators; nonetheless, it provides a useful organizing framework for evaluating Armenia’s current set of gender indicators. Its implementation strategy addresses six sectors: power and decision-making, socioeconomic, education, health, culture and information, and gender-based violence and human trafficking (Republic of Armenia 2011). Figure 11 summarizes the availability of indicators for each sector to monitor this plan.
The 2020/21 ODIN assessment gave Armenia an overall score of 56.6, eighth among the 18 Western Asian countries. Weaknesses in Armenia’s statistical offerings were found in measures of education facilities and outcomes, health facilities and outcomes, food security and nutrition, gender statistics, and crime and justice categories.
In 2020, the Bangladesh Planning Commission released its Gender Diagnostics, Policy, Strategy and Action Plan for the National Social Security Strategy of Bangladesh. According to this plan:
Bangladesh targets women and girls in many social security programs and in the absence of concrete guidance and planning, the gain towards gender equality were less than their potential. Therefore, a Gender Policy was developed and approved by the Central Management Committee (CMC) of National Social Security Programmes under the Chair of the Cabinet Secretary in 2018. This Strategy and Action Plan is a step forward in realizing the objectives of the NSSS to reduce the gender gap (Bangladesh Planning Commission 2020, p. 11).
The Plan has eight detailed policy commitment and actions. A full list is available below. Among these, we consider seven commitments that can be mapped to the Bridging the Gap indicators. Not included among these is Commitment 4: Old age and elderly care, for which there are no relevant indicators in our list, although a few SDG indicators specify disaggregation by age group. Commitment 7: Support for women with disability, minority, ethnic and other marginalized groups is not well represented in the available SDG indicators unless the indicators are fully disaggregated by the relevant characteristics. We include here only one indicator on the proportion of people reporting that they have felt discriminated against (SDG 10.3.1). Likewise, Commitment 8: Resilience from climatic and other shocks and vulnerabilities is represented here only by the SDG indicator on deaths and missing persons attributed to natural disasters (SDG 1.5.1).
List of the Gender Diagnostics, Policy, Strategy and Action Plan detailed policy commitment and actions:
- Policy Commitment 1: Childhood support
- Policy Commitment 2: Support for working age women
- Policy Commitment 3: Childbearing and maternity
- Policy Commitment 4: Old age and elderly care
- Policy Commitment 5: Affordable healthcare
- Policy Commitment 6: :Protection from violence, changing gender roles, and social norms
- Policy Commitment 7: Support for women with disability, minority, ethnic and other marginalized groups
- Policy Commitment 8: Resilience from climatic and other shocks and vulnerabilities
ODIN 2020/21 gave Bangladesh has an overall score of 36.4, eighth among the nine middle-income Southern Asian countries. Weaknesses in Bangladesh’s statistical offerings were found in measures of population and vital statistics, education facilities, health facilities and outcomes, food security and nutrition, gender statistics, crime and justice, labor, price indexes, and the built environment.
In 2014, the UN Economic Commission for Europe (UNECE) published a Global Assessment Report on the National Statistical System of Mongolia. It noted that Mongolia’s NSO had developed a framework of gender statistics consisting of 216 indicators, of which, 81 key indicators were approved by the National Committee for Gender Equality (UNECE 2014). In 2019, the NSO updated the set of indicators, which now consists of 241 indicators in 14 domains (National Statistics Office of Mongolia 2020). Eleven domains specify gender indicators derived from population-based data. The remaining three, “Mechanisms,” “Gender budget,” and “Gender Index,” include 23 policy indicators that describe characteristics of programs or institutions, such as whether there exist laws, policies, or programs concerning women. The eleven domains are shown in Figure 13.
- I: Population
- II: Poverty – Ownership of Property
- III: Education
- IV: Health
- V: Violence Against Women
- VI: Employment, Labor and Economy
- VII: Power and Decision Making
- IX: Human Rights
- X: The Media
- XI: The Environment
- XII: Girls and Boys
Mongolia’s overall ODIN 2020/21 score of 78.3 placed it first among seven Eastern Asia countries. Its strongest categories included population and vital statistics, reproductive health, crime and justice, and labor statistics. Relatively weak categories were gender statistics and education facilities.
The Gender Equality and Women’s Empowerment Plan for the years 2019–2025 focuses on increasing opportunities for women and girls —especially from more marginalized and vulnerable communities—in the domains of social, economic, and human development (Philippine Commission on Women n.d.). The policy has seven “axes” with measurable targets. Many of these measurable targets are based on SDG indicators. The seven axes include:
- Axis 1: Expanded Economic Opportunities for Women
- Axis 2: Accelerated Human Capital Development through Investing in Gender Equality and Women’s Empowerment
- Axis 3: Significant Reduction in Gender Based Violence and Enhanced Gender Perspective in Justice, Security and Peace
- Axis 4: Expanded Opportunities for Women’s Participation, Leadership and Benefit in Disaster Resilience and Humanitarian Action
- Axis 5: Expanded Opportunities for Women’s Participation, Leadership, and Benefit from Science, Technology, Innovation, ICT, Infrastructure, and Energy
- Axis 6: Enhanced Women’s Participation, Leadership and Benefit in Politics and Government Service
- Axis 7: Transformed Social Norms and Culture Promote Gender Equality and Women’s Empowerment
Figure 14: Indicators available to support the Philippines’ Gender Equality and Women’s Empowerment Plan
The ODIN 2020/21 assessment gave the Philippines an overall ODIN score of 72.7, second among the 11 South-Eastern Asian countries. Weaknesses in the Philippines’ statistical offerings were found in measures of education facilities and outcomes, health outcomes, reproductive health, crime and justice, and the built environment.
Samoa’s Ministry of Women, Community and Social Development (MWCSD) has recently completed the implementation of its National Policy for Gender Equality from 2016 to 2020 with the goal of “All women and girls [having] equal access to opportunities that guarantee their full participation in, and [benefiting] from, the sustainable development of Samoa.” (MWCSD 2016). The policy is organized around six priority outcomes corresponding broadly to the domains of human security, health, economic opportunity, public participation, education, and the environment. The National Policy for Gender Equality does not specify measurable indicators to monitor the success of the plan, however many of the gender indicators included in the SDGs and assessed in the Bridging the Gap study are relevant to the first six policy outcomes. The seventh policy outcome includes a strategic action of completing a gender statistical framework with Samoa’s Bureau of Statistics.
Indicators the first six Policy outcomes are shown below and in Figure 15:
- Policy outcome 1: Safe families and communities
- Policy outcome 2: Healthy women and girls
- Policy outcome 3: Equal economic opportunities for women
- Policy outcome 4: Increased participation of women in public leadership and decision-making
- Policy outcome 5: Increased access to education and gender sensitive education curricula
- Policy outcome 6: Community resilience and climate change and disaster preparedness informed by gender sensitive information and approaches
The ODIN 2020/21 assessment gave Samoa an overall ODIN score of 46.6 ranked first among the eight middle-income Pacific Island countries. While Samoa generally scores well on the availability of indicators at the national level, it lacks historical data needed to make comparisons over time for most indicators.
|1.1.1||UNW||Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)||ECON|
|1.2.1||UNW||Proportion of population living below the national poverty line, by sex and age||ECON|
|1.2.2||UNW||Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions||ECON|
|1.3.1||UNW||Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work- injury victims and the poor and the vulnerable||ECON|
|1.4.2||AGI||Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenure||ECON|
|1.5.1||AGI||Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
Duplicate indicators: 11.5.1, 13.1.1
|2.1.1||AGI||Prevalence of undernourishment||HEAL|
|2.1.2||AGI||Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)||HEAL|
|2.2.1||AGI||Prevalence of stunting (height for age < -2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age||HEAL|
|2.2.2||AGI||Prevalence of malnutrition (weight for height > +2 or < -2 standard deviation from the median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight)||HEAL|
|2.2.3||AGI||Prevalence of anemia in women aged 15 to 49 years, by pregnancy status (percentage)||HEAL|
|2.2.y||SUP||Share of women aged 15-49 whose BMI is less than 18.5 (underweight)||HEAL|
|2.3.2||AGI||Average income of small-scale food producers, by sex and indigenous status||ECON|
|3.1.1||UNW||Maternal mortality ratio||HEAL|
|3.1.2||UNW||Proportion of births attended by skilled health personnel||HEAL|
|3.2.1||AGI||Under-five mortality rate||HEAL|
|3.2.2||AGI||Neonatal mortality rate||HEAL|
|3.3.1||UNW||Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations||HEAL|
|3.3.2||AGI||Tuberculosis incidence per 100,000 population||HEAL|
|3.3.3||AGI||Malaria incidence per 1,000 population||HEAL|
|3.3.4||AGI||Hepatitis B incidence per 100,000 population||HEAL|
|3.3.5||AGI||Number of people requiring interventions against neglected tropical diseases||HEAL|
|3.4.1||AGI||Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease||HEAL|
|3.4.2||AGI||Suicide mortality rate||HEAL|
|3.5.2||AGI||Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol||HEAL|
|3.6.1||AGI||Death rate due to road traffic injuries||HEAL|
|3.7.1||UNW||Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods||HEAL|
|3.7.2||UNW||Adolescent birth rate (aged 10-14 years; aged 15-19 years) per 1,000 women in that age group*
•For the purpose of this research, aged 10-14 will be omitted.
|3.9.1||AGI||Mortality rate attributed to household and ambient air pollution||ENVT|
|3.9.2||AGI||Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)||ENVT|
|3.9.3||AGI||Mortality rate attributed to unintentional poisoning||HEAL|
|3.a.1||AGI||Age-standardized prevalence of current tobacco use among persons aged 15 years and older||HEAL|
|3.b.1||AGI||Proportion of the target population covered by all vaccines included in their national programme||HEAL|
|4.1.1||AGI||Proportion of children and young people: (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex||EDUC|
|4.1.2||AGI||Completion rate (primary education, lower secondary education, upper secondary education)||EDUC|
|4.1.X4||SUP||Illiteracy rates, by sex||EDUC|
|4.1.X6||SUP||Education inequality indicators: (a) Proportion of women with less than 4 or 6 years of education; or (b) proportion of women with less than secondary education||EDUC|
|4.2.1||AGI||Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sex||HEAL|
|4.2.2||UNW||Participation rate in organized learning (one year before the official primary entry age), by sex||EDUC|
|4.3.1||UNW||Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sex||EDUC|
|4.3.X||SUP||Primary and secondary out of school rates, by sex||EDUC|
|4.4.1||AGI||Proportion of youth and adults with information and communications technology (ICT) skills, by type of skill||EDUC|
|4.5.1||AGI||Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregated
• To score AA: sex disaggregation plus one other disaggregation must be available.
|4.6.1||UNW||Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex||EDUC|
|4.a.1||UNW||Proportion of schools with access to (a) electricity; (b) the Internet for pedagogical purposes; (c) computers for pedagogical purposes; (d) adapted infrastructure and materials for students with disabilities; (e) basic drinking water; (f) single-sex basic sanitation facilities; and (g) basic handwashing facilities (as per the WASH indicator definitions)
Note: Only component F is assessed.
|4.c.1||AGI||Proportion of teachers with the minimum required qualifications, by education level||EDUC|
|5.2.1||UNW||Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual, or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age||HUMN|
|5.2.2||UNW||Proportion of women (aged 15–49) subjected to sexual violence by persons other than an intimate partner, since age 15*||HUMN|
|5.3.1||UNW||Proportion of women aged 20-24 years who were married or in a union before age 15 and before age 18||HUMN|
|5.3.2||UNW||Proportion of girls and women aged 15–49 years who have undergone female genital mutilation/cutting, by age||HUMN|
|5.4.1||UNW||Proportion of time spent on unpaid domestic and care work, by sex, age and location||ECON|
|5.5.1||UNW||Proportion of seats held by women in (a) national parliaments and (b) local governments†||PART|
|5.5.2||UNW||Proportion of women in managerial positions||PART|
|5.6.1||UNW||Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care||HEAL|
|5.6.X||SUP||Proportion of women who have an independent/joint say in own health care||HEAL|
|5.a.1||UNW||(a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure||ECON|
|5.b.1||UNW||Proportion of individuals who own a mobile telephone, by sex||ECON|
|6.1.1||AGI||Proportion of population using safely managed drinking water services||ENVT|
|6.2.1||AGI||Proportion of population using safely managed sanitation services, including a hand-washing facility with soap and water||ENVT|
|7.1.X||SUP||Proportion of women with access to clean cooking fuel||ENVT|
|7.1.2||AGI||Proportion of population with primary reliance on clean fuels and technology||ENVT|
|8.10.2||AGI||Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider||ECON|
|8.3.1||UNW||Proportion of informal employment in total employment, by sector and sex||ECON|
|8.5.1||UNW||Average hourly earnings of female and male employees, by occupation, age and persons with disabilities||ECON|
|8.5.2||UNW||Unemployment rate, by sex, age and persons with disabilities||ECON|
|8.5.X||SUP||Labor force participation rate, by sex||ECON|
|8.6.1||AGI||Proportion of youth (aged 15–24 years) not in education, employment or training||ECON|
|8.7.1||UNW||Proportion and number of children aged 5–17 years engaged in child labor, by sex and age||ECON|
|8.8.1||UNW||Frequency rates of fatal and non-fatal occupational injuries, by sex and migrant status||HEAL|
|9.1.1||AGI||Proportion of the rural population who live within 2 km of an all-season road||ENVT|
|9.2.2||AGI||Manufacturing employment as a proportion of total employment||ECON|
|9.5.2||AGI||Researchers (in full-time equivalent) per million inhabitants||ECON|
|10.1.1||AGI||Growth rates of household expenditure or income per capita among the bottom 40 percent of the population and the total population||ECON|
|10.2.1||AGI||Proportion of people living below 50 percent of median income, by sex, age, and persons with disabilities||ECON|
|10.3.1||AGI||Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law
Duplicate indicator: 16.b.1
|10.7.3||AGI||Number of people who died or disappeared in the process of migration towards an international destination||HUMN|
|10.7.4||AGI||Proportion of the population who are refugees, by country of origin||HUMN|
|11.1.1||AGI||Proportion of urban population living in slums, informal settlements or inadequate housing||ENVT|
|11.2.1||UNW||Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities||ENVT|
|11.7.1||AGI||Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities||ENVT|
|11.7.2||AGI||Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months||HUMN|
|16.1.1||UNW||Number of victims of intentional homicide per 100,000 population, by sex and age||HUMN|
|16.1.2||AGI||Conflict-related deaths per 100,000 population, by sex, age and cause||HUMN|
|16.1.3||AGI||Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months||HUMN|
|16.1.4||AGI||Proportion of population that feel safe walking alone around the area they live||HUMN|
|16.2.1||AGI||Proportion of children aged 1-17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month||HUMN|
|16.2.2||UNW||Number of victims of human trafficking per 100,000 population, by sex, age and form of exploitation||HUMN|
|16.2.3||UNW||Proportion of young women and men aged 18-29 years who experienced sexual violence by age 18||HUMN|
|16.3.1||AGI||Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms||HUMN|
|16.3.2||AGI||Unsentenced detainees as a proportion of overall prison population||HUMN|
|16.3.3||AGI||Proportion of the population who have experienced a dispute in the past two years and who accessed a formal or informal dispute resolution mechanism, by type of mechanism||HUMN|
|16.5.1||AGI||Proportion of persons who had at least one contact with a public official and who paid a bribe to a public official, or were asked for a bribe by those public officials, during the previous 12 months||PART|
|16.6.2||AGI||Proportion of population satisfied with their last experience of public services, specifically a) healthcare services, b) education services and c) government services||PART|
|16.7.1||AGI||Proportions of positions in national and local public institutions, including (a) the legislatures; (b) the public service; and (c) the judiciary, compared to national distributions, by sex, age, persons with disabilities and population groups||PART|
|16.7.2||AGI||Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population group||PART|
|16.9.1||AGI||Proportion of children under 5 years of age whose births have been registered with a civil authority, by age||PART|
|16.10.1||AGI||Number of verified cases of killing, kidnapping, enforced disappearance, arbitrary detention and torture of journalists, associated media personnel, trade unionists and human rights advocates in the previous 12 months||HUMN|
|17.8.1||AGI||Proportion of individuals using the Internet||ECON|
Note: The table includes only websites or data portals where gender indicators were found. Other sites were examined but yielded no data.
|Armenia national databases|
|Statistical Committee of the Republic of Armenia (ArmStat)||https://www.armstat.am/|
|SDG Hub (ArmStat)||https://armstat.github.io/sdg-site-armenia/|
|Bangladesh national databases|
|Bangladesh Bureau of Statistics||http://www.bbs.gov.bd/|
|SDG Tracker (Government of Bangladesh)||https://www.sdg.gov.bd/|
|Mongolia national databases|
|National Statistics Office of Mongolia||http://www.en.nso.mn/|
|SDG Portal (Government of Mongolia)||http://sdg.gov.mn/|
|1212 Portal – General Database (NSO Mongolia)||http://www.1212.mn/|
|Health Development Center of the Ministry of Health||http://hdc.gov.mn/|
|Ministry of Labor and Social Protection||https://www.mlsp.gov.mn/|
|Philippines national databases|
|Philippines Statistics Authority (PSA)||https://psa.gov.ph/|
|Food and Nutrition Research Institute||https://www.fnri.dost.gov.ph/|
|Department of Health||https://www.doh.gov.ph/|
|Department of Education||https://www.deped.gov.ph|
|Samoa national databases||.|
|Samoa Bureau of Statistics||https://www.sbs.gov.ws|
|Police Service Commission||https://www.psc.gov.ws/|
|Ministry of Women, Community, and Social Development||https://www.mwcsd.gov.ws|
|Office of Ombudsman | National Human Rights Institution||https://ombudsman.gov.ws/|
|Ministry of Education, Sports, and Culture||http://mesc.gov.ws|
|Ministry of Health||https://www.health.gov.ws|
|International databases (all countries)|
|SDG Global Database||https://unstats.un.org/sdgs/indicators/database/|
|OPHI: Multidimensional Poverty Index||https://ophi.org.uk/multidimensional-poverty
|International Labour Organization||https://www.ilo.org/ilostat/|
|World Health Organization||https://apps.who.int/gho/data/node.main|
|Food and Agriculture Organization||http://www.fao.org/faostat/|
|International Telecommunication Union||https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx|
Bangladesh Planning Commission. 2020. Gender Diagnostics, Policy, Strategy and Action Plan. Bangladesh. http://socialprotection.gov.bd/wp-content/uploads/2020/03/Gender-Diagnostics-Policy-Strategy-and-Action-Plan-for-NSSS.pdf
Buvinic, Mayra, Rebecca Furst-Nichols, Gayatri Koolwal. 2014. Mapping Gender Data Gaps. Data2X. https://data2x.org/wp-content/uploads/2019/05/Data2X_MappingGenderDataGaps_FullReport.pdf
Data2X and Open Data Watch. 2019. Bridging the Gap: Mapping Gender Data Availability in Africa. https://data2x.org/resource-center/bridging-the-gap-mapping-gender-data-availability-in-africa/
———. 2020. Bridging the Gap: Mapping Gender Data Availability in Latin America and the Caribbean. https://data2x.org/resource-center/bridging-the-gap-mapping-gender-data-availability-in-latin-america-and-the-caribbean/
———. 2021. Bridging the Gap: Methodology Report. https://opendatawatch.com/publications/bridging-the-gap-methodology-report/
ESCAP (United Nations Economic and Social Commission for Asia and the Pacific). 2015. Developing a core set of gender indicators for Asia and the Pacific. http://undocs.org/en/E/ESCAP/CST(4)/9
———. 2017. Regional Roadmap for Implementing the 2030 Agenda for Sustainable Development in Asia and the Pacific. https://www.unescap.org/sites/default/files/publications/SDGs-Regional-Roadmap.pdf
———. 2018. Connecting policymakers and data users. https://www.unescap.org/blog/connecting-policymakers-and-data-producers
———. 2019. Mainstreaming gender in environment statistics for the SDGs and beyond: Identifying priorities in Asia and the Pacific. https://www.unescap.org/sites/default/d8files/knowledge-products/SD_Working_Paper_no.10_Oct2019_gender_in_environment.pdf
Grantham, Kathleen. 2020. Mapping Gender Data Gaps: An SDG Era Update. Data2X. https://data2x.org/resource-center/mappinggenderdatagaps/
IAEG-GS (Interagency and Expert Group on Gender Statistics). 2019. The United Nations Minimum Set of Gender Indicators. United Nations Statistics Division: 30 April 2019. .https://genderstats.un.org/files/Minimum%20Set%20indicators%202018.11.1%20web.pdf
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———. 2020. Tier Classification for Global SDG Indicators. (Updated 28 December 2020). https://unstats.un.org/sdgs/files/Tier%20Classification%20of%20SDG%20Indicators_17%20July%202020_web.v3.pdf
MWCSD (Ministry of Women, Community, and Social Development). 2016. Samoa National Policy for Gender Equality 2016-2020. Samoa. https://www.mwcsd.gov.ws/images/2017/Documents/DFSD/Samoa-gender-policy.pdf.
National Statistics Office of Mongolia. 2020. “Re: Frame of Indicators for Gender Statistics.” Received by Eric Swanson and Tawheeda Wahabzada. 01 December 2020. Email interview.
Open Data Watch. 2020. Open Data Inventory. https://odin.opendatawatch.com
Philippine Commission on Women. n.d. Gender Equality and Women’s Empowerment Plan 2019-2025. https://pcw.gov.ph/assets/files/2020/05/GEWE-Plan-2019-2025-Results-Matrices.pdf?x12374
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United Nations. 1979. Convention on the Elimination of All Forms of Discrimination against Women (CEDAW). New York. https://treaties.un.org/doc/Treaties/1981/09/19810903%2005-18%20AM/Ch_IV_8p.pdf
UN Economic Commission for Europe (UNECE). 2014. Global Assessment Report: National Statistical System of Mongolia. https://unece.org/fileadmin/DAM/stats/documents/technical_coop/GA_Mongolia_EN.pdf
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———. 2020. Global SDG Database. “Metadata for SDG Indicator 10.7.3.” https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-03.pdf
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 The list of SDG indicators includes some duplicates, and some indicators specify more than one measure. The count of 232 is the agreed enumeration of unique indicators in the 2016 listing. The 2020 listing now counts 231 indicators of which 16 are repeated. (IAEG-SDGs 2020).
 In late-2020, UN Women released Progress of the Sustainable Development Goals: The Gender Snapshot. This recent report includes 51 gender-relevant indicators, where these gender-relevant indicators consist of some indicators from the original “minimum set,” along with other gender-relevant SDG indicators that could be disaggregated by sex. The Bridging the Gap study will continue to assess relevant indicators in the “minimum set.” Some of the new indicators that are identified as gender-relevant in the 2020 report are already included in the Bridging the Gap study. These indicators are grouped in the 59 additional SDG indicators that could be disaggregated by sex.
 Grantham (2020) provides a comprehensive list of data needed to monitor women’s economic opportunities.