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ABSTRACT
With a rallying cry of ‘leave no one behind’, the Sustainable Development Agenda has moved inequalities centre stage. A number of the Sustainable Development Goals (SDGs) include a cross-cutting focus on inequalities and the advancement of some communities that have historically experienced discrimination. However, the litmus test for whether the SDGs will truly ‘leave no one behind’ is not the inclusion of such (aspirational) language, but whether this language will translate into implementation. In that regard, monitoring through indicators will play an important role. As metrics pegged to specific targets, indicators have the power to concentrate effort and attention. Moving beyond aggregate outcomes will require that the data related to these indicators be sufficiently disaggregated to demonstrate the existence, magnitude and interplay of multiple forms of inequalities. However, despite a mandate to produce disaggregated data, there has been little attention to disaggregation based on some of the most important axes of discrimination – especially race or ethnicity. Human rights call for focusing on those who are often pushed to the margins of society – through political, social and economic processes as well as by data collection and analysis itself.
Acknowledgements
Sections of the article are informed by the authors’ work with the WHO/UNICEF Joint Monitoring Programme on Water Supply and Sanitation (JMP) on equality and non-discrimination, in particular through the ‘Equity and Non-Discrimination Work Group’, and they are thankful to the JMP team and all those who contributed to the discussions over the last years. Inga T. Winkler presented an earlier draft of the article at a Workshop of the Economic and Social Rights Group at the University of Connecticut and is grateful for the useful input received from participants. The authors would also like to thank the reviewers for the helpful comments as well as Ajani Husbands, Yolanda Borquaye and Pauline Brosch for the research assistance they provided.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Inga Winkler is a Lecturer at the Institute for the Study of Human Rights at Columbia University and the Director of Undergraduate Studies for the Human Rights Program.
Margaret Satterthwaite is a Professor of Clinical Law at NYU Law School, the Director of the Global Justice Clinic, the Faculty Director and Co-Chair of the Center for Human Rights and Global Justice, and the Faculty Director of the Robert L. Bernstein Institute for Human Rights.
Notes
1 United Nations General Assembly, Transforming Our World: The 2030 Agenda for Sustainable Development, A/RES/70/1 (21 October 2015), para. 4.
2 Kevin E. Davis, Benedict Kingsbury and Sally Merry, ‘Indicators as a Technology of Global Governance’, Law and Society Review 46, no. 1 (2012): 71–104.
3 Leslie McCall, ‘The Complexity of Intersectionality’, Signs: Journal of Women and Culture in Society 30, no. 3 (2005): 1771–800. See also Naila Kabeer, ‘The Challenges of Intersecting Inequality’, Maitreyee 24 (2012): 5–10; Amanda Lenhardt and Emma Samman, In Quest of Inclusive Progress, Exploring Intersecting Inequalities in Human Development (London: Overseas Development Institute, 2015), 18.
4 See e.g. Joni Seager, Sex-disaggregated Indicators for Water Assessment Monitoring and Reporting. Technical Paper (Paris: UNESCO, 2015), http://unesdoc.unesco.org/images/0023/002340/234082e.pdf. See also UN Statistics Division, ‘Minimum Set of Gender Indicators’, https://genderstats.un.org/#/home (accessed July 12, 2017); and Plan International, Counting the Invisible: Using Data to Transform the Lives of Girls and Women by 2030 (Woking: Plan International, 2016).
5 For DHS: 1984–2011 and 2012–2015; for MICS: MICS 3, 2005–2011 and MICS 4, 2011–2013. Because there was greater variability among DHS questionnaires, a longer time period (including more countries) was chosen.
6 Jonathan Gray, Danny Lämmerhirt and Liliana Bounegru, “Changing What Counts: How Can Citizen-Generated and Civil Society Data Be Used as an Advocacy Tool to Change Official Data Collection?” (CIVICUS, 2016), http://civicus.org/thedatashift/wp-content/uploads/2016/03/changing-what-counts-2.pdf (accessed July 12, 2017).
7 Independent Expert Advisory Group (IAEG) on a Data Revolution, A World That Counts: Mobilising the Data Revolution for Sustainable Development (New York: United Nations, 2014), 2.
8 Sakiko Fukuda-Parr, ‘Global Goals as a Policy Tool: Intended and Unintended Consequences’, Journal of Human Development and Capabilities (2014): 118–31, 119.
9 Fukuda-Parr, ‘Global Goals’, 120.
10 Fukuda-Parr, ‘Global Goals’, 122–3.
11 Fukuda-Parr, ‘Global Goals’, 120.
12 Sally Engle Merry, ‘Measuring the World: Indicators, Human Rights, and Global Governance’, Current Anthropology 52 (2011): S92.
13 Center for Economic and Social Rights (CESR), ‘From Disparity to Dignity: Tackling Economic Inequality through the Sustainable Development Goals’ (CESR Human Rights Policy Brief, 2016), 33.
14 United Nations, Committee on Economic, Social, and Cultural Rights (UN CESCR), General Comment No: 20, Non-discrimination in Economic, Social and Cultural Rights, UN Doc. E/C.12/GC/20 (2009), para. 27 et seq.
15 See e.g. UN Human Rights Committee, Guidelines for the Treaty-Specific Document to be Submitted by States Parties under Article 40 of the International Covenant on Civil and Political Rights, UN Doc. CCPR/C/29/1 (2009), para. 25; UN Committee on Economic, Social and Cultural Rights, Guidelines on Treaty-Specific Documents to be Submitted by States Parties under Articles 16 and 17 of the International Covenant on Economic, Social and Cultural Rights, UN Doc. E/C.12/2008/2 (2008).
16 UN CESCR, General Comment No. 20, para. 41.
17 Gay McDougall, The First United Nations Mandate on Minority Issues (Leiden: Brill, 2016).
18 United Nations Development Programme (UNDP), Nepal Human Development Report: State Transformation and Human Development (2009), 153–76. Other national HDRs, including Brazil, India and Guatemala, have included similar types of disaggregation.
19 UNDP, Nepal Human Development Report, 155. For further examples of disaggregation according to minority status, in particular in relation to poverty see Gay McDougall, Report of the Independent Expert on Minority Issues, UN Doc. A/HRC/4/9 (2007), paras 25–37.
20 World Inequality Database on Education, http://www.education-inequalities.org/ (accessed July 12, 2017).
21 UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation, Progress on Drinking Water and Sanitation: 2014 Update (Geneva: World Health Organization/New York: United Nations Children's Fund 2014), 25.
22 Alainna Lynch and Tom Berliner, Who is Being Left Behind in Sub-Saharan Africa? An Illustration in Benin and Nigeria (London: Overseas Development Institute, 2016), 6.
23 Lenhardt and Samman, In Quest of Inclusive Progress, 18.
24 CESR, ‘From Disparity to Dignity’.
25 McDougall, The First United Nations Mandate on Minority Issues, para. 83.
26 Tanvi Bhatkal, Emma Samman and Elizabeth Stuart, Leave No One Behind: The Real Bottom Billion (London: Overseas Development Institute, 2015), 1.
27 Elizabeth Stuart, ‘How to Leave No One Behind: A Workable Plan for Ambitious Aims’, posted February 3, 2016, https://www.odi.org/comment/10287-leave-no-one-behind-workable-plan-ambitious-aims (accessed January 31, 2017).
28 Lenhardt and Samman, In Quest of Inclusive Progress, 18.
29 This kind of analysis is not a replacement for, but a complement to the legal assessments required under human rights law. Such assessments demand contextual, qualitative, and legal information lacking in the development monitoring context.
30 JMP, Progress on Drinking Water and Sanitation, 26.
31 Bhatkal, Samman and Stuart, Leave No One Behind, 3.
32 Ibid.
33 While the framework includes a list of 241 indicators, there are only 230 unique indicators as some are used for multiple targets.
34 Danish Institute for Human Rights (DIHR), Human Rights and Data: Tools and Resources for Sustainable Development (2017): 16.
35 Fukuda-Parr, ‘Global Goals’, 120.
36 UN Statistical Commission, Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators, UN Doc. E/CN.3/2016/2/Rev.1, Annex IV (2016).
37 Ibid., para. 26.
38 Ms Wasmália Bivar, Chair of the UN Statistical Commission, ‘Remarks to the Economic and Social Council Coordination and Management Segment’ (June 1, 2016).
39 Compilation of Metadata for the Proposed Global Indicators for the Review of the 2030 Agenda for Sustainable Development, Goal 10, p. 3, https://unstats.un.org/sdgs/files/metadata-compilation/Metadata-Goal-10.pdf (accessed July 12, 2017).
40 Ibid.
41 United Nations, The Sustainable Development Goals Report 2016, 50, http://unstats.un.org/sdgs/report/2016/.
42 Ibid.
43 United Nations Secretary-General, Progress Toward the Sustainable Development Goals, UN Doc. No. E/2016/75, 3 June 2016: para. 134.
44 See SDG Indicators Metadata Repository, http://unstats.un.org/sdgs/metadata/ (accessed July 12, 2017).
45 Sabina Alkire and Emma Samman, ‘Mobilising the Household Data Required to Progress toward the SDGs’ (OPHI Working Papers 72, University of Oxford, 2014).
46 See Alkire and Samman, Mobilising the Household Data.
47 Sustainable Development Solutions Network, Data for Development: A Needs Assessment for SDG Monitoring and Statistical Capacity Development (New York: Sustainable Development Solutions Network, 2015), 17.
48 Ibid., 12–13.
49 United Nations Statistical Commission, Report of the Global Working Group on Big Data for Official Statistics, UN Doc. E/CN.3/2015/4 (2015).
50 United States Census Bureau, American Fact Finder, http://factfinder.census.gov/faces/nav/jsf/pages/community_facts.xhtml (accessed July 12, 2017).
51 See European Union Agency for Fundamental Rights, EU-MIDIS: European Union Minorities and Discrimination Survey, http://fra.europa.eu/en/survey/2012/eu-midis-european-union-minorities-and-discrimination-survey; European Union Agency for Fundamental Rights, Roma Pilot Survey, http://fra.europa.eu/en/survey/2012/roma-pilot-survey (accessed 12 July, 2017).
52 See World Inequality Database on Education, http://www.education-inequalities.org/indicators/comp_prim_v2#?sort=disparity&dimension=ethnicity&group=all&age_group=comp_prim_v2&countries=all (accessed July 12, 2017).
53 Meg Wirth, Enrique Delamonica, Emma Sacks, Deborah Balk, Adam Storeygard and Alberto Minujin, ‘Monitoring Health Equity in the MDGs: A Practical Guide’ (CIESIN & UNICEF, January 2006), 21.
54 John Wrench, ‘Data on Discrimination in EU Countries: Statistics, Research and the Drive for Comparability’, Ethnic and Racial Studies 34, no. 10 (2011): 1715–730, 1723.
55 Roy Carr-Hill, ‘Missing Millions and Measuring Development Progress’, World Development 46 (2013): 37.
56 Carr-Hill, ‘Missing Millions’, 32.
57 Ibid., 37.
58 See Margaret Satterthwaite, ‘JMP Working Group on Equity and Non-Discrimination Final Report (2012)’, unpublished (on file with authors).
59 McDougall, The First United Nations Mandate on Minority Issues, para. 71.
60 Véronique de Rudder and François Vourc’h, ‘Quelles statistiques pour quelle lutte contre les discriminations?’, Journal des Anthropologues (2007): 110–11.
61 Julie Ringelheim, ‘Ethnic Categories and European Human Rights Law’, Ethnic and Racial Studies 34, no. 10 (2011): 1682.
62 See Wrench, ‘Data on Discrimination’, 1716.
63 Patrick Simon, ‘“Ethnic” Statistics and Data Protection in Council of Europe Countries: Study Report’ (European Commission against Racism and Intolerance, Strasbourg, 2007), 7.
64 Romesh Silva and Jasmine Marwaha, ‘Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India’ (Human Rights Data Analysis Group, 2013), https://hrdag.org/wp-content/uploads/2013/02/silva-marwaha-JSM-2011.pdf.
65 For an excellent example of informed consent in the context of risky data collection for human rights purposes, see Silva and Marwaha, ‘Collecting Sensitive Human Rights Data’.
66 Office of the UN High Commissioner for Human Rights (OHCHR), A Human Rights-based Approach to Data, Leaving No One Behind in the 2030 Development Agenda (2016), 10, http://www.ohchr.org/Documents/Issues/HRIndicators/GuidanceNoteonApproachtoData.pdf.
67 Simon, ‘“Ethnic” Statistics’, 12.
68 Ibid., 9.
69 Mark Elliot, Elaine Mackey, Kieron O’Hara and Caroline Tudor, ‘The Anonymisation Decision-Making Framework’ (2016), http://ukanon.net/wp-content/uploads/2015/05/The-Anonymisation-Decision-making-Framework.pdf.
70 See Zahra Rahman, ‘Dangerous Data: The Role of Data Collection in Genocides’, The Engine Room, November 21, 2016, https://www.theengineroom.org/dangerous-data-the-role-of-data-collection-in-genocides/.
71 Ringelheim, ‘Ethnic Categories’, 1685; Peter J. Aspinall, ‘Answer Formats in British Census and Survey Ethnicity Questions: Does Open Response Better Capture “Superdiversity”?’, Sociology 46 (2012): 354–64, 365.
72 OHCHR, Human Rights-based Approach to Data, 8; United Nations Committee on the Elimination of Racial Discrimination, General Recommendation VIII concerning the interpretation and application of article 1, paragraphs 1 and 4 of the Convention (1990); Simon, ‘Ethnic’ Statistics, 40.
73 OHCHR, Human Rights-based Approach to Data, 8.
74 Ibid.
75 Ibid.
76 Margaret Satterthwaite, ‘Background Note on MDGs, Non-Discrimination and Indicators in Water and Sanitation’ (JMP, 2012), 28, unpublished (on file with authors).
77 Satterthwaite, Background Note, 30.
78 United Nations Statistical Commission, ‘Principles and Recommendations for Population and Housing Censuses: the 2020 Round. Revision 3 – Draft’ (2015), 175, unstats.un.org/unsd/statcom/doc15/BG-Censuses.pdf.
79 OHCHR, Human Rights-based Approach to Data, 7.
80 See ICF International, ‘Survey Organization Manual for Demograpic and Health Surveys’ (2012), 5–9.
81 Ibid., 8.
82 Simon, ‘“Ethnic” Statistics’, 14.
83 Ringelheim, ‘Ethnic Categories’, 1683.
84 Ibid., 1689.
85 Carr-Hill, ‘Missing Millions’, 31.
86 Same-sex sexual acts between consenting adults are criminalised in 74 countries across the world: see ILGA, ‘Sexual Orientation Laws in the World: Criminalisation’ (2016), http://ilga.org/downloads/04_ILGA_WorldMap_ENGLISH_Crime_May2016.pdf.
87 Good practice examples can be found in the HIV/AIDS field, where organisations serving key populations have conducted inclusive and participatory research into discriminatory barriers to service affecting sex workers, men who have sex with men, and drug users. See e.g. Bridging the Gaps, ‘Operational Research with and for Key Populations’, http://www.hivgaps.org/about/operational-research/.
88 Sara L. M. Davis, William C. Goedel and John Emerson, ‘Punitive Laws, Key Population Size Estimates, and Global AIDS Response Progress Reports: An Ecological Study of 154 Countries’, Journal of the International AIDS Society 20 (2017): 21386, http://www.jiasociety.org/index.php/jias/article/view/21386.
89 See e.g. Kate Crawford and Jason Schultz, ‘Big Data and Due Process: Towards a Framework to Redress Predictive Privacy Harms’, Boston College Law Review 55, no. 93 (2014); Michael Schrage, ‘Big Data's Dangerous New Era of Discrimination’, Harvard Business Review (January 29, 2014), https://hbr.org/2014/01/big-datas-dangerous-new-era-of-discrimination.
90 See United Nations Statistical Commission, ‘Report of the Global Working Group on Big Data for Official Statistics’, UN Doc. E/CN.3/2015/4 (2015).
91 Independent Expert Advisory Group on a Data Revolution, A World That Counts, 9.
92 OHCHR explains that human rights-based data principles require that ‘Data collected to produce statistical information must be strictly confidential, used exclusively for statistical purposes and regulated by law’: OHCHR, Human Rights-based Approach to Data, 10. This is often not the case with Big Data: see Jacob Mertcalf and Kate Crawford, ‘Where are Human Subjects in Big Data Research? The Emerging Ethics Divide’, Big Data and Society, January–June 2016, 1–14.
93 Crawford and Schultz, ‘Big Data and Due Process’.
94 See Kate Crawford and Megan Finn, ‘The Limits of Crisis Data: Analytical and Ethical Challenges of Using Social and Mobile Data to Understand Disasters’, Geojournal 80 (2015): 491.
95 DIHR, Human Rights and Data, 31.
96 United Nations Human Rights Office of the High Commissioner, Universal Human Rights Index, http://uhri.ohchr.org/search/annotations (accessed July 12, 2017).
97 Commonwealth of Australia, ‘Australia's National Human Rights Action Plan’ (2012), 5, http://www.ohchr.org/Documents/Issues/NHRA/NHRPAustralia2012.pdf.
98 See Leave No One Behind Partnership website: http://action4sd.org/leavenoonebehind/.
99 Corinne Lennox and Carlos Minott, ‘Inclusion of Afro-Descendants in Ethnic Data Collection: Towards Visibility', International Journal on Minority and Group Rights 18 (2011): 257–75, 262 et seq.
100 The Leadership Conference Education Fund, ‘Race and Ethnicity in the 2020 Census: Improving Data to Capture a Multiethnic America’ (2014), 15, http://civilrightsdocs.info/pdf/reports/Census-Report-2014-WEB.pdf.
101 See e.g. Irene Karanja, ‘An Enumeration and Mapping of Informal Settlements in Kisumu, Kenya, Implemented by Their Inhabitants’, Environment and Urbanization 22 (2010): 217–39.
102 Indigenous Navigator, http://www.indigenousnavigator.org/index.html#tools (accessed July 12, 2017).
103 Datashift, Building the Capacity and Confidence of Civil Society Organizations to Produce and Use Citizen-Generated Data, http://civicus.org/thedatashift. For further examples see DIHR, Human Rights and Data, 37.