A Grey Area: Gender Equality Across the Asia-Pacific
Please cite article as: Brown, C (2024). A Grey Area: Gender Equality Across the Asia-Pacific, JUST Impact Blog Archive.
Just over a week ago, the latest 2024 report on SDG progress across Asia-Pacific was published by UNESCAP. The findings highlight yet another year of data gaps on Gender Equality (SDG 5). JUST Impact co-founder, Chandni Brown, grapples with why this might be the case, and what this could reveal about the nature of policy making against the SDG landscape, as we enter the second half of the 2030 Agenda.
Women have spent many years breaking free from boxes. From stereotypes in marriage, the workplace through to domestic life.
But now, a new box has emerged - one that is rectangular, grey and largely defined by indifference.
UNESCAP's latest regional overview highlights a startling uncertainty in the state of gender equality across Asia and the Pacific, as data gaps continue to obscure the current state of affairs.
And this isn't the first time.
Last year we witnessed the same result, prompting us to work on a qualitative research project to add a bit of colour on how and where structural barriers are preventing gender equality across Bangladesh.
Granted, this was only a small snapshot of a much larger Asia-Pacific region – one with huge variations across everything from geography to culture to politics. But when there is only one grey box to work with, there really isn’t much available to inform policy and advocacy efforts.
So, you can imagine my disappointment to see, once again, that progress against SDG 5 is still suffering from ‘insufficient’ data.
Of course, there are myriad international reports on gender by NGOs and UN departments with very interesting insights. From the role of women in agrifoods, climate change and water management, the interplay of SDG 5 with various other international priorities offers one way of keeping gender equality on the global ‘to do’ list.
But why is it that in the framework of the Sustainable Development Goals, it is still falling by the wayside?
As States across the Asia-Pacific fail to collect and/or report against SDG 5, I am reminded of a singular notion: ‘no data’ is data.
In other words, while we may be unable to monitor trends in gender equality, the grey boxes are in themselves, a data trend.
This trend cannot be used to measure progress against gender-related targets, but it certainly suggests a design flaw in the SDG framework; one of broken metrics and the politics of reporting against them.
By examining a sample of SDG 5 indicators, I highlight here the need for a more comprehensive and inclusive approach to measuring progress on gender equality, considering both data collection methods and their political context.
Designed as an open invitation for discussion, I welcome all stakeholders working in gender and/or the Asia-Pacific region to take a look at my three main observations below and let me know your thoughts.
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I remember hearing that most people need to eat at least 8 fruits or vegetables in a day. But a strategic decision was made to reduce this to 5: an easier number to digest, as you can count it all on one hand. The SDG indicators sometimes feel a bit like this, with only 14 indicators for 9 targets.
Let’s take SDG 5.1 as an example. This target looks to:
“end all forms of discrimination against all women and girls everywhere.”
To assess whether this is being achieved, SDG 5.1.1 looks at:
This is the only indicator across all 17 SDGs to have been framed as a reductive ‘yes or no’ question. It is also the only indicator for establishing whether all forms of discrimination against all women and girls everywhere persist.
Creating just a single indicator for such a wide scope of ambition may be easier to digest, but it can lead to the disastrous result of conflating metrics and goals.
In this case, there is an insinuation that formal legal frameworks are the only proper solution; notwithstanding that gender inequalities are often lived and experienced beyond the watching eyes of the State, entrenched in deep-rooted religious, cultural and societal beliefs that may require more dynamic interventions.
Suddenly we are less concerned with tackling policy problems through an open and iterative process. Rather than working in consultation with those affected to identify what the real challenges are, and what in turn the best course of action might be, we have the problem and solution already at hand: formal legal frameworks.
There is no talk of awareness programmes, capacity building initiatives, the role of community or faith-based leaders, private sector solutions, digital solutions, or how a web of interventions might help.
No, formal legal frameworks become the only policy solution worth investing in. We put all our eggs in one basket, and cash them in to demonstrate that ‘yes’ we have legal frameworks in place, and therefore ‘no’ there isn’t a problem. Box ticked.
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A particularly concerning thought in this regard, is the realisation that SDG 16 (Peace, Justice and Strong Institutions) is the only other indicator UNESCAP flagged as having insufficient data. As this goal focuses predominantly on how well formal institutions are functioning, including legal and judicial systems, it is cause for concern that we default to formal solutions when we cannot yet assess how effective these are.
It is perhaps naïve to suggest that these limitations are the reason that data reporting is failing under SDG 5.
However, is the SDG framework doing itself a disservice by limiting policy solutions in this way? Would it be better if States were provided greater flexibility to ideate their own solutions to gender-related challenges?
Perhaps one way to improve the current indicator model would be to vary the metrics used for each target. For example, what if a tiered metric system was used, or additional scope provided for States to report against several types of policy vehicle?
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There are almost an equal number of men as there are women. Gender inequality has never been a minority issue or a problem of numbers. It is a problem of marginalisation, power structures and perceptions.
Unfortunately, however, there is a tendency to place numbers and statistics as the pinnacle of data collection.
This is not without reason of course. It is far easier to map trend data or compare progress in and amongst regions if data is quantifiable. At the intergovernmental level, where we are dealing with macro decision-making, it is also easier to consider problems at such scale if they have digits and percentage signs, as opposed to names and faces.
However, if SDG 5’s ambitions are truly to ‘empower women’ – by which we mean to elevate female voices and agency – there is an intrinsic error in omitting any reference to qualitative data.
After all, ‘empowerment’ is a complex, contextual concept, underpinned by ideals and aspirations that differ from country to country.
For example, in the context of SDG 5.3 – “eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation”– an understanding of how these practices are actually lived and experienced is surely essential to understanding the harm caused and the level of policy intervention required for those impacted. At present however, the indicators used for this target measure only the proportion of women married or in a union before age 15 and before age 18, or who have undergone female genital mutilation/cutting.
My own research (Sahan Shakti) looked at arranged marriage practices among the South Asian diaspora, taking a deeply qualitative and ethnographic approach. Contrary to my findings, there is no mention under SDG 5.3 of forced marriages taking place at an older age, nor of the concepts of ‘honour’ that underpin many of the power structures against which these practices persist.
This oversimplification of gender equality reduces challenges into mere quotas, percentages and proportions. We need to qualify why child and forced marriages still continue to take place, not just quantify how many there are.
We also need to realise that when we are dealing with systemic discrimination, structural inequalities and approximately 50% of the population, the numbers don’t always work. Rather they can easily contribute to a policy rhetoric at odds with reality. This has been seen in gender reporting on educational participation in Myanmar as well as in gender mainstreaming in global health activities.
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The UNESCAP Report found that: “More robust data are needed to understand nuances in progress, particularly in areas such as violence against women, early marriage and the division of unpaid care and domestic work.”
In finding more nuanced data, I wonder if there is space for us to develop the role of qualitative evidence, and perhaps even theory, in public policy-making? Surely it is the role of public policy to not only set goals and objectives, but to ask questions and initiate discourse.
For example, would it be useful if the SDG indicators were augmented with policy questions for States to explore – questions designed to interrogate inherent assumptions within the SDG framework’s design? Here the voluntary national reviews could offer a dedicated space for discussion, inviting deeper engagement with the problems at hand.
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It might be tempting to think that better data means better policy making, but power has a role to play in both the presence and absence of data, as well as the eventual solutions formed from it.
Of course this is not a new observation. In fact it underpins many calls for greater female representation, including SDG 5.5:
However, in the context of public policy, I want to put aside decision-making for a moment and look at the evidence-gathering stage.
The SDG framework is built on the notion of ‘evidence-based policy’ - i.e.: gather the data and the solution will follow. It should come as no surprise then, that faced with the issue of data gaps under SDG 5, the underwhelming response provided is: more robust data is needed.
A difficulty with this line of reasoning however, is that it fails to address the wider reasons for why reporting hasn’t worked.
In the context of gender equality, it has been argued that a linear approach to policymaking was ineffective from the beginning. This is because gender is a complex construct that is constantly evolving. It is neither static nor uniform. It is also heavily politicised, polarising and culturally embedded.
Against this landscape, it is not just the evidence that matters, but the role of exogenous factors such as managing political risk and “maintaining the support base for leadership groups and institutions” that make the act of reporting as important as the data itself. For example, some scholars, such as Rakhyun Kim, have suggested that there is a link between better data reporting and those SDG indicators based on GDP, due to invested interests in economic development.
At present, there is very little within the SDG framework that allows us to focus on the external drivers influencing the discourse on gender equality, nor any mechanisms to create a vested interest in reporting.
Where there is a lack of data against an indicator, it is not enough to simply request more. We need to look beyond the indicators, and towards the perceptions, values and politics that could be contributing to a lack of buy-in.
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It is clear there is a lack of data against SDG 5 - but what are the incentives and disincentives for reporting against gender in the Asia-Pacific region? Is there something compromising State buy-in or ability to monitor and promote progress on gender-equality?
Perhaps it would be useful if the indicator framework imposed greater accountability on States - not just to report against targets, but to qualify instances where they are unable to do so.
At the same time, it is important not to conflate data with progress. Gender is constantly being renegotiated and reconceptualised. How is ‘gender’ as a construct developing, and is this well reflected in the SDG framework?
Is there progress taking place on the ground that is unreported higher up - left to non-State actors perhaps (intentionally or otherwise)? Or is progress against gender equality simply not high on the list of government priorities - preventing investment in capacity building and monitoring mechanisms?
Are there barriers to collecting data in the private and domestic spaces so often associated with gender equality? Or are there barriers in data collection because unlike education, healthcare and industry, gender equality is not a traditional public service?
To understand these obstacles a bit better, would it be useful if the SDG framework drew more on other fields? For example, the use of feedback loops in complex systems that allow for concepts to grow and evolve. Or the notion of reflexivity in agile management, that allows for reporting failures to be seen as new opportunities for learning. These wider fields may help us account for external factors that have left us with a perpetual grey box.
As stated earlier, this is an open invitation for discussion! If you have any insights on any of the above, or can see any blindspots in the analysis, I would highly encourage you to get in touch at info@justimpact.co.uk.
Additional Reading:
Eden, L. & Wagstaff, M.F. (2020). Evidence-based policymaking and the wicked problem of SDG 5 Gender Equality. Journal of International Business Policy, Vol 4 (28-57) (link).
Consortium on Gender, Security and Human Rights (2017). Feminist critiques of the Sustainable Development Goals: Analysis and Bibliography (link).
