世界银行-缺少可持续发展目标性别指标（英文原版）.pdf

Policy Research Working Paper 10544 Missing SDG Gender Indicators Kathleen Beegle Umar Serajuddin Brian Stacy Divyanshi W adhwa Development Data Group lack of reporting on existing data is detected to be a problem. For example, of the 32 gen- der-related indicators that are sex disaggregated, if countries that had a population estimate also had a sex-disaggregated estimate (which is almost always feasible), the Sustainable Development Goal gender coverage rate would be 43 per- cent instead of 31 percent. Second, better statistical systems are a major part of the solution, as statistical system strength is correlated with higher coverage. Third, poorer countries are doing no worse in reporting on gender-related Sus- tainable Development Goal indicators than high-income countries, despite weaker statistical systems. Lastly, sizable over (and under) performance in reporting, conditional on statistical strength, suggests that country-level advocacy and focus can yield wins in Sustainable Development Goal gender indicator coverage. This paper is a product of the Development Data Group and Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at kbeegle@worldbank.org. Missing SDG Gender Indicators Kathleen Beegle, Umar Serajuddin, Brian Stacy, Divyanshi Wadhwa* Key words: statistical indicators, gender, national statistical system JEL: C8, J16, I00, O1 All authors are with the World Bank. Corresponding author: Kathleen Beegle kbeegle@worldbank.org. The authors are grateful to comments from Hai-Anh Dang, Anna Fruttero, and Lauren Harrison. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. 2 1 Introduction The Sustainable Development Goals (SDGs) lay out an ambitious agenda including that of achieving gender equality by 2030. This agenda is paired with a set of goals and targets measured by concrete indicators and is adopted by nearly all countries. SDG 5 focuses on gender equality and sets 9 measurable targets (with 14 indicators) on issues that especially affect women and girls (United Nations, 2022). But gender cuts across a far wider range of the SDGs than just the indicators under Goal 5. For example, SDG 3 on ensuring good health and well-being includes a target on reducing maternal mortality (target 3.1). The SDG agenda also calls for sex disaggregated data across several goals where monitoring of gender disparities is essential for effective policy. For example, SDG 8 on promoting decent work and economic growth sets a target of achieving full employment and equal pay for all women and men (target 8.5). These gender data are promoted as key to understanding if and how patterns of progress differ between women and men or girls and boys (UN Women, 2022). Countries are, however, falling short on reporting on gender-related indicators of the SDGs. This paper analyzes the patterns underlying these data gaps. The objective of this paper is to look at this globally agreed-upon set of gender data indicators and identify key country patterns related to the existence, or lack of, such data. We focus on the availability of data reporting on the SDGs since they represent an internationally agreed- upon set of goals to meet and for countries to report on (UNSD, 2022). Missing gender data is not a new concern. There are different approaches to diagnosing the causes of the lack of gender data. One approach put forth by Bonfert et al (2022) emphasizes four obstacles to more gender data (Figure 1): (i) lack of data sources such as core and/or specialized surveys, censuses or relevant administrative data (that is, the data simply are not collected); (ii) methodological flaws in data collection (e.g., collecting land holdings of households but not 3 identifying which household member has the rights/ownership to this land); (iii) insufficient processing of existing data; and (iv) lack of dissemination even when data are available and processed. Figure 1: Sources of gender data gaps Source: Bonfert et al. 2022 Related, but not identical, Buvinic Furst-Nichols, and Koolwal (2014) discuss gender data gaps as driven by four gaps: (i) lack of regular production at the country level; (ii) lack of international standards; (iii) lack of information across domains; (iv) lack of granularity, i.e., lack of large, detailed datasets making possible disaggregation. In this paper we look at the production and reporting of SDG indicators on gender, clearly laying out the availability (alternatively the lack of) data along indicator types – uniquely gender focused versus cross-cutting. We then focus on the challenges posed by insufficient processing of available data or the lack of dissemination even when processed data and constructed indicators are available. While focusing on improving the statistical systems is an important part of the agenda to fulfill the goal of reporting on gender-related SDGs, some rapid improvements can be made from existing data. 4 2 Gender indicators for the SDGs Although nearly all countries have agreed to report on the SDG indicators, major gaps exist in indicator availability since the SDG agenda’s inception in 2015 (Dang and Serajuddin 2020). Gender-related SDG indicators are no exception. There are 231 unique SDG indicators. Many of the indicators, even if not obviously related to gender, nonetheless have sub-indicators, such as, indicators by sex, age, or disability status. The UN global SDG indicators database provides access to the data compiled for tracking progress toward fulfilling the SDGs. We use this data source, rather than individual NSO websites, because data submitted to the UN Global SDG monitoring database goes through a standardized process including a certain level of quality control and documentation review. We explore the coverage of the 50 gender-related SDG indicators out of the 231 unique indicators. 1 As noted earlier, gender-related SDG indicators are not limited to SDG 5 on gender equality, but rather span indicators across 10 out of 17 of the SDG goals. All 50 SDG-gender indicators are Tier 1 or 2 SDG indicators. 2 While most are related to sex disaggregation of data 1 These 50 indicators closely match the UN Women minimum set of 52 quantitative gender indicators from the SDGs (United Nations Economic and Social Council 2012), subsequently revised to be 51 quantitative indicators, with a few exceptions. These exceptions are: (i) indicators 4.7.1, 4.a.1, and 13.3.1 are in the UN Women minimum set but not here as in our view they are not gender-related or sex-disaggregated measures. (ii) indicator 1.1.1 includes the “working poor“ (employed population below international poverty line) by sex component which is in Table 1 but not in the UN Women list. Relatedly, Open Data Watch (2019) refers to 32 SDG gender indicators and another 36 “additional” SDG gender indicators. The difference between their 68 and our 50 SDG gender indicators is that some of theirs are, in our assessment, gender neutral in terms of the present drafting of the indicator (such as 1.5.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population). 2 Tier 1 indicators, according to the UN, are indicators that are conceptually clear, have an internationally established methodology and standards available, and data are regularly produced by countries for at least 50% of countries and of the population in every region where relevant. Tier 2 indicators are conceptually clear and have an internationally established methodology and set of standards, but are not regularly produced by countries. There are no gender equality indicators in the third category, Tier 3, which is defined as an indicator with no internationally established methodology or standards established, and, thus, these indicators are likely to have the lowest rate of coverage. (UNSD, 2022). 5 (32 of the 50), the 18 others are related to goals specific to females– highlighting that gender- related SDG indicators are not only about sex disaggregation. Table 1 shows the share of countries for which there is at least one annual data point in the five-year period from 2016 to 2020 for each of the 50 gender-related indicators. 3,4 The average coverage rate of indicators is around 34% for 181 countries; that is, an average country will have data reported in the SDG website for about 17 out of 50 indicators. Over 90% of the world’s population lives in a country where less than half of the 50 SDG gender indicators are available for any year in this 5-year period. Indeed, the gender-related SDG indicators are more likely to be unreported than other indicators. For the overall 181 SDG indicators, the average reporting rate is 65% for this same period. 5 Next, we unpack several notable aspects of the availability (or lack of) SDG gender indicators. For Tier 1 SDG indicators (18 out of 50), arguably those that will or should have the greatest availability, availability is much higher; countries have a recent value for only about half (51%) of the indicators. For Tier 2 indicators (32 out of 50), the average country has a recent value 3 Countries with populations of less than 200,000 (34 out of 215 countries in the UN SDG Database) were excluded from this analysis. These are: American Samoa, Andorra, Antigua and Barbuda, Aruba, Bermuda, British Virgin Islands, Cayman Islands, Channel Islands, Curacao, Dominica, Faroe Islands, Gibraltar, Greenland, Grenada, Guam, Isle of Man, Kiribati, Liechtenstein, Marshall Islands, Micronesia, Fed. Sts., Monaco, Nauru, Northern Mariana Islands, Palau, San Marino, Seychelles, Sint Maarten (Dutch part), St. Kitts and Nevis, St. Lucia, St. Martin (French part), St. Vincent and the Grenadines, Tonga, Turks and Caicos Islands, Tuvalu, Virgin Islands (U.S.). Encarnacion et al (2022) note that the poorest performers in terms of lowest SDG-gender indicators are small islands and nations. These countries have, on average, very low reporting rates for SDGs, including, but not only, those related to gender. In general, small islands and nations are under-performers in terms of statistical performance conditional on their income and human captial index level (Dang et al 2021). 4 The SDG database includes actual (survey/census or other primary data source estimates) as well as additional modeled estimates for indicators when primary sources are not available for the country. We do not use modeled estimates. 5 This is our own calculation. Dang and Serajuddin (2020) report lower rates of SDG indicator reporting in part because they focus on an earlier period (2012-2016) and because they include small islands and nations. 6 for only a quarter (24%) of the indicators. Annex 1 presents the availability of Tier 1 and Tier 2 SDG gender-related indicators by region and by country income grouping. For the 14 indicators under Goal 5, the country average availability is 37%, only marginally higher availability compared with the average availability for all gender-related indicators. Figure 2 shows this distribution. No country has more than 10 of these 14 indicators in the 5-year period. Forty-one countries report three or fewer indicators. Annex 1 presents the availability of SDG5 gender-related indicators by region and country income grouping. Figure 2. Availability of SDG5 gender-related indicators (N=181 countries) Note: The figure shows the coverage of the 14 SDG 5 indicators where coverage is defined as having at least one annual data point in the five-year period from 2016 to 2020 for an indicator as compiled by the UN. Among indicators that require sex disaggregation (32 out of the 50), both the population data and the sex-disaggregated data are not reported for any country for five indicators (such as for indicator 10.2.1). For four of these 32 indicators, the sex-disaggregated and population coverage rates match, as we would expect if the underlying data identified individual sex, was 0 5 10 15 20 25 30 35 40 0 1 2 3 4 5 6 7 8 9 10 Percent of countries Number of SGD 5 gender indicators 7 collected for both males and females, and was processed accordingly. We would not expect the sex-disaggregated coverage rate to exceed the population coverage, and it never does. Moreover, if the country has a sex-disaggregated data point they also have a population estimate. But the reverse is not the case. For six of these 32 indicators (19%), while there is some reported data for the population indicator, there is no sex-disaggregated data reported. As an example, 56% of countries report a population rate for SDG indicator 10.2.1 (the proportion of people living below 50 percent of median income), yet no country reports this statistic by sex. These missing data are not the result of missing sex in underlying data source (in this case, household surveys). The measure itself (living below an income threshold) is defined at the household level and so one can produce a sex-disaggregated estimate based on the households in which individuals reside. For example, Munoz Boudet et al. (2021) report poverty rates by sex. 6 In the remaining 17 cases (out of 32), where there is some reported data for both the population and by sex-disaggregation, in a handful of cases there are large gaps between the percentage of countries with a recent value by sex and those reporting a population estimate (i.e. comparing the last two columns in Table 1 when both columns are non-zero). For SDG indicator 1.3.1, on the proportion of population covered by social protection floors/systems, 79% of countries have a recent value for the population, but only 8% of countries have a sex disaggregated data point. A less drastic example is SDG indicator 4.1.1, related to early childhood education: 64 percent of countries have a population estimate for this indicator but only 53 percent have an estimate by sex. 6 This is not related to the issue of measuring income or poverty at the individual level versus a household measure. It is simply the point that if a population estimate is produced based on a household-level measure, then there is no methodological argument against producing additional estimates for population sub-categories (be it urban/rural, male/female, for children, etc).