Gender Role Challenges

I keep coming back to this because it exposes the difference between performing gender work and actually doing it. Most programming stalls at the "Sensitive" or "Responsive" levels, accommodating differences without ever challenging the status quo. But recognising a gap is not the same as dismantling the power structure that created it. Here's a quick breakdown of 5 levels of gender integration that has shaped how I think about gender programming 👇 𝐆𝐞𝐧𝐝𝐞𝐫-𝐁𝐥𝐢𝐧𝐝 — The program doesn't consider gender at all. It assumes everyone has the same needs and experiences. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐀𝐰𝐚𝐫𝐞 — You recognise that gender matters, but it stops at acknowledgement. No action follows. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 — You accommodate gender differences. You see them, you plan around them, but you're not yet challenging them. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐯𝐞 — Now we're shifting. The program actively addresses gender inequalities and works to reduce them. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 — This is where we tackle root causes: power, norms, structures, not just symptoms. The jump from Responsive to Transformative is where it gets uncomfortable because it asks us to look at power. At norms. At the structures our programmes often depend on to function. Where does your programme actually sit? That's the question worth sitting with. Save this and share it with your network. #GenderTransformative #GenderEquality #GenderMainstreaming #SystemsChange #SocialImpact

This is the question we kept coming back to in our latest research at Shape Talent Ltd, where we surveyed over 2,300 women in the UK to better understand the persistent barriers to gender equality in corporate life. The data was stark: 🔹 98% of women face some combination of systemic barriers 🔹 Women in senior roles are more likely to feel undermined, inadequate, and cautious about speaking up 🔹 The ‘double burden’ of paid and unpaid work remains relentless and largely invisible 🔹 And the pressure to walk a narrow behavioural tightrope - the “double bind” - is alive and well But here’s what struck me most: these barriers are not just frustrating, they are predictable. They’re the result of outdated systems, norms and leadership models that still reflect a version of the workplace built around a 1950s archetype: the male breadwinner with a stay-at-home wife. It’s no wonder that are survey results showed that women, especially Black women, LGBTQ+ women, disabled women, and working mothers, continue to face uphill battles. The data shows their challenges aren’t just individual. They’re structural. And they’re compounded by bias and a chronic lack of meaningful career development. We cannot ‘fix’ women to fit into broken systems. We must fix the system. So, what next? 1. Rethink leadership expectations 2. Redesign processes with equity in mind 3. Build cultures of true psychological safety 4. Invest intentionally in women’s career development Incremental change is no longer enough. The pace of progress is glacial – and regressing. At this rate, gender equality won’t be reached until 2154. That’s five generations too late. If you're in a position of influence - HR, DEI, leadership, it’s time to move from intent to impact. Real progress starts with bold steps. #GenderEquality #Leadership #Equity #Inclusion #ShapeTalent #DoubleBurden #DoubleBind #WomenInLeadership #Intersectionality #EDI #DEI

It's not the pipeline, It's the System. June 23rd is celebrated as 'International Women in Engineering Day" #INWED Sadly the harsh reality, engineering colleges in India produce the highest number of women in STEM graduates/engineers and many of them actually do make it to the workforce. The real challenge is their retention and progression. With 2+ decades in tech and now consulting for tech companies on their Gender Equity Strategy, I’ve seen this challenge firsthand. The issue isn’t talent availability, it’s systemic. In most households, a woman’s career is still seen as optional. That mindset and bias bleeds into workplaces, shaping how women are hired, retained, and promoted. So what can organisations do, 1. Relook at org culture and design. Are your systems, policies, and leadership norms built equitably to support who stays, rises and how. 2. Representation matters, especially in especially in mid and senior levels, invest in retention and have hiring goals across grades. 3. Move from gendered to gender neutral policies. Eg. Maternity to Parental Leave Policy that supports all care-givers. Reframe workplace policies from “women-centric benefits” to equitable caregiving support that normalise shared responsibility and reduce bias. 4. Women in Tech Returnee programs - I've seen immense success in these programs, that offer companies experienced tech talent with a little investment. #Vapasi from Thoughtworks, #Spring from Publicis Sapient are two examples 5. Conduct Stay Interviews, Not Exit Interviews. Understand why women leave and what it takes for them to stay and grow and act on the inputs. 3. A Clear Career Progression Path with mentorship and sponsorship - Bias in growth opportunity for #WIT is real, if there is no intentional support to overcome these bias, talent walks away. 4. I Need to See More Like Me! There is a lack of role models. Accelerated Women in tech leadership programs, fast-tracking the leadership journey of high potential women are some ways to address this. 5. Collective Ownership. Gender Diversity in tech is not a HR, leadership or DEI responsibility. Make it the very fabric of the org. to drive shared accountability. 6. Data is not just diagnostic, it's directional. It guides us on investments to be made, unseen bias and where and what needs to change, it's your mirror don't ignore it. #Inclusion is a organisational capability and leaders are it's torch bearers. Their actions, direction and decisions every single day, signal what truly matters. The Women in tech, talent pool exists. The question is, are you ready to retain, grow, and lead with them? #WomenInTech #WIT #GenderEquity #DiversityInTech Diversity Simplified Image description: A newspaper article titled “It’s Not the Pipeline, It’s the System” from Times of India, Bangalore edition which highlights the gender gap in engineering.

I was shocked when I realized the greatest challenge to closing the economic gender gap in Africa. And I think it's important everyone learns about it as well. Here goes: The greatest challenge to closing the gender gap, and why it is estimated that it will take more than 150 years to close the gap in Africa, is the significant perception vs. reality gap. Let me explain: According to research by the Boston Consulting Group (BCG) & United Nations Global Compact, who surveyed 4000 men and women across Africa, they found that more than 50% of men and women in Sub-Saharan Africa believe that there is gender parity and/or women are ahead in their country when it comes to various indicators, including equal pay for work of equal value. Ironically, about 40% of the same men and women surveyed believe that men are better leaders than women in analytical and technical skills as well as leadership abilities. In reality however, even though women in Sub-Saharan Africa have higher rates of participation (54%) in the economy than global averages, 90% of them work informally, predominantly in low-skilled jobs, given their historical gap in access to education. They hold only nearly a quarter of management positions, with only 16% of CEO/MD positions held by women. And though this rate has been growing over the past 20 years, with the current rate, it is estimated that it will take more than 150 years to close the gender gap on the continent. So, the question now is what needs to be done? We all need to play our part in addressing the barriers that hold women back: - Every business needs policies against discrimination and harassment. - Flexible options like remote work and flexible hours are essential for working parents and women. - Training staff on gender equality and offering skills training for women are smart investments. - Programs such as financial literacy and business mentorship are crucial for female entrepreneurs and the self-employed. - Providing better access to financial products is vital for entrepreneurs and should be prioritized by banks and other businesses. - Equal pay and benefits, along with better parental leave and caregiving support, are important goals. Women's participation in the economy greatly boosts a country's productivity and can significantly increase GDP—by up to 50% in Africa—thanks to the added workforce and the benefits of gender diversity. The study authored by Qahir Dhanani and team (Sanda Ojiambo, Tolulope Lewis Tamoka, Lina Al Qaddoumi, Zineb Sqalli, Natasha Lendich, Maxime Kpangbai) also revealed a fascinating trend: women-led startups deliver a whopping 10% higher ROI. And that's not all! It also suggested that income earned by women has a significantly greater impact on communities compared to income earned by men. These findings highlight the incredible potential of bridging the economic gender gap.

Gender disability washing does not remove women with disabilities from inclusion by mistake. It removes them to keep inclusion manageable. Gender-disability washing has a pattern. Once you see it, you cannot unsee it. It moves in stages. Every time. 1️⃣ Visible enough to brand. Women with disabilities appear in the photos. In the launch video. In the diversity section of the annual report. The imagery performs inclusion. The infrastructure contradicts it. 2️⃣ Absent from the data. The programme reports gender outcomes. Disability-disaggregated data does not exist. Women with disabilities’ realities dissolve into language about “marginalised groups.” No one is accountable. No one can be counted. 3️⃣ Consulted. Not paid. Not powerful. Women with disabilities are invited to share lived experience at validation workshops after decisions are already made. They are not designing the programme. They are not controlling the budget. Their presence is decorative. Their input is extractive. 4️⃣ First to go when money gets tight. Inclusion was framed as additional never structural. So when budgets shrink, accessibility is the first negotiation. Sign language interpretation becomes not feasible. Transport support becomes not in this phase. The commitment was always conditional. The conditionality was always the point. 5️⃣ Success declared anyway. The report says the initiative reached women in all their diversity. The photos are beautiful. The narrative is compelling. Women with disabilities are absent from every outcome. The washing worked. This is not the exception. This is the norm in far too much gender programming. It persists because it serves a purpose: institutions claim the moral credit of inclusion without bearing the structural cost. Naming it is the first step. Refusing to participate in it is the next. Building systems that make it impossible is the work. #GenderDisabilityWashing #GenderJustice #DisabilityJustice

We've been adding women to projects for 30 years. Development gaps remain. Maybe the problem isn't the women. Maybe it's the projects. Here's what's I think go wrong (at every stage): ↳ At Programming, we conduct gender analysis after the priorities are already set. So the analysis confirms the plan instead of shaping it. ↳ At Identification, we consult women's groups late, after the project concept is fixed. Their input becomes decoration, not design. ↳ At Formulation, we write gender objectives into the logframe but not into the budget. Unfunded objectives aren't objectives. They're intentions. ↳ At Implementation, we count women's participation but not women's power. Attendance is not influence. A room full of women who didn't shape the agenda is not gender mainstreaming. ↳ At Evaluation, we ask "did women benefit?" but not "did the project change anything structural?" So we celebrate outputs and miss the fact that the same barriers will exist in the next project too. The fix isn't complicated. It's just earlier, deeper, and more honest: → Run the gender analysis before political dialogue, not after → Put gender in the Terms of Reference, not just the report → Allocate a real budget line — not a percentage of leftovers → Build M&E indicators that can detect unequal outcomes, not just unequal numbers → Ask at evaluation: what would we design differently next time? Gender mainstreaming isn't a checklist. It's a decision to let evidence about inequality actually change what you do. Thirty years in, that decision is still optional in most project cycles. It shouldn't be. ----- Join my FREE mailing list to receive these insights directly in your inbox 👉 https://lnkd.in/ec8mqV2M

Gender analysis is essential for understanding how social roles, power relations and access to resources shape unequal outcomes for women and men in development and governance processes. The document provides practical guidance on applying gender analysis to planning, programming and organisational practice by clarifying key concepts and illustrating how gender differences can be systematically examined and addressed. This guide developed by the Philippine Commission on Women in collaboration with the Department of the Interior and Local Government brings together the following core elements: – A clear definition of gender analysis as a process for identifying gaps between women and men, understanding why these gaps persist and determining appropriate actions to address them – The rationale for gender analysis, highlighting its role in understanding social processes, identifying gender issues and designing equitable and responsive programmes – Key analytical questions such as who does what, who has access to and control over resources, who decides, who benefits and who loses, used to unpack gender inequalities – Levels of gender analysis, including household and community, project or programme, and organisational or institutional levels, with corresponding tools for each level – The gender analysis planning flow, linking situation analysis, identification of gender issues, strategy development, implementation, monitoring and assessment of results – Practical application of gender analysis in sectoral contexts such as family planning and maternal health, illustrating differential access, roles, responsibilities and impacts – Introduction to specific gender analysis tools, including the Gender Analysis Matrix and the 24-Hour Activity Profile, to examine productive, reproductive, community and leisure roles The document provides an applied and practice-oriented foundation for integrating gender analysis into planning and decision-making, supporting more balanced relations between women and men and more equitable development outcomes.

𝐆𝐞𝐧𝐝𝐞𝐫 𝐦𝐚𝐫𝐤𝐞𝐫 is not simply a task to complete or an obligatory requirement in a proposal package. It should never be treated as a technical formality or a box to tick. Instead, 𝐢𝐭 𝐫𝐞𝐟𝐥𝐞𝐜𝐭𝐬 𝐡𝐨𝐰 𝐝𝐞𝐞𝐩𝐥𝐲 𝐠𝐞𝐧𝐝𝐞𝐫 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭’𝐬 𝐥𝐨𝐠𝐢𝐜 𝐚𝐧𝐝 𝐝𝐞𝐬𝐢𝐠𝐧. It requires a clear understanding of how the intervention is built on solid 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐚𝐧𝐝 𝐠𝐞𝐧𝐝𝐞𝐫 𝐚𝐧𝐚𝐥𝐲𝐬𝐞𝐬, and how the project aims to address the 𝐫𝐨𝐨𝐭 𝐜𝐚𝐮𝐬𝐞𝐬 of gender inequalities. A strong gender marker should demonstrate how the project intends to influence and 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐡𝐚𝐫𝐦𝐟𝐮𝐥 𝐠𝐞𝐧𝐝𝐞𝐫 𝐧𝐨𝐫𝐦𝐬 that perpetuate inequality. It should explain how the intervention will contribute to 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐝𝐚𝐢𝐥𝐲 𝐥𝐢𝐯𝐞𝐬 𝐚𝐧𝐝 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 of women and girls, not only by including them as beneficiaries but by actively strengthening their position within their families, communities, and institutions. This also means intentionally 𝐞𝐧𝐠𝐚𝐠𝐢𝐧𝐠 𝐦𝐞𝐧 𝐚𝐧𝐝 𝐛𝐨𝐲𝐬 as partners in change, through awareness sessions, dialogue, and community mobilization; so they can play a constructive role in challenging discriminatory practices and influencing others to adopt more equitable behaviors. At the same time, gender-responsive programming must focus on 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐜𝐲 of girls and women. This includes strengthening their awareness of their rights, developing their skills and knowledge, and creating safe spaces where they can express their voices, raise concerns, and influence their peers and communities. Equally important is ensuring that women and girls have improved 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 and meaningful 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 over them, whether those resources are economic opportunities, education, services, or information. Projects should also consider how to 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞, 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐢𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬, and influence 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬 that shape gender relations and opportunities. Ultimately, the gender marker should capture how the project contributes to transformative change. It is about assessing, holistically, how gender equality is embedded throughout the entire project cycle: from design and planning, to implementation, monitoring, learning, and closure. When gender is genuinely integrated in this way, projects move beyond participation toward transformation. They contribute to shifting power relations, expanding opportunities, and promoting fairness and dignity for everyone. In this sense, a gender marker is not merely a technical score. It is a reflection of the project’s commitment to advancing gender equality and contributing to broader social justice. #IWD2026 #WeObject #GenderEquality #Gender

There is now global, near-universal evidence of a gender gap in generative AI use. According to an Harvard Business Review meta-analysis across countries, sectors, education levels, and occupations, women are consistently 20–25% less likely than men to use AI tools. Importantly, this gap persists even when access, training, and job role are held constant. Why? I recently listened to – and highly recommend – a thought-provoking podcast conversation that explores what is going on here (link posted in the comments). Spoiler alert: the answer may not be what many assume. This gap is not because women lack digital skills, curiosity, or capacity. And it’s not simply an access problem. It is a symptom of deeper structural and experiential factors, like: ⚠️ Rational skepticism grounded in unreliable performance and bias. Many AI systems often demonstrably work less well for women, particularly in high‑stakes contexts like pay negotiation, credibility, and evaluation. When tools are trained on skewed data and designed by homogenous teams, uneven performance isn’t theoretical; it’s lived. ⚠️ Unequal professional and reputational risk. Women face greater scrutiny for how they work. Using AI can carry higher perceived downside for women than men, such as greater penalties for mistakes, greater risk of being seen as less competent, and fewer permissions to “experiment in public.” ⚠️ Time poverty and invisible labor. Learning, prompting, correcting, and validating AI outputs often adds work rather than removes it. For many women already carrying disproportionate paid and unpaid labor, AI adoption can feel like a second or third job, and a cumbersome one if you’re arguing with AI over gender-biased responses, as the podcast hosts hilariously described. In my work on AI adoption and readiness globally, these dynamics are often more acute in developing and emerging markets, where gender inequality in labor markets, social norms, and access to recourse is wider. To be sure, I don’t agree with every perspective or example raised in the podcast, and I’m intentionally cautious about overly alarmist takes. I do believe many of these challenges are solvable. Bias can be reduced. Incentives can be realigned. Systems can be designed to earn trust. But the framing shift they propose feels not just timely, but essential: Women using AI less (or differently) should be treated not as a skills failure, but as a signal. A signal about trust, incentives, design quality, and unequal risk. If we want equitable, inclusive AI adoption, we won’t get there by telling women to “catch up” or trying to convince them that these systems perform perfectly or are bias-free. We’ll get there by taking these signals seriously, and heed them as we intentionally build AI systems, products, and tools that are worthy of adoption by everyone. #AIAdoption #GenderDigitalDivide #ResponsibleAI #FutureOfWork

#gendermainstreaming and gender transformational program design is not new in international development or among developers of AI. Here’s how these domains intersect in Digital Green’s work designing and deploying an AI assistant for agricultural extension: 1. Training data: If the content your RAG pipeline accesses is gender blind, your Ag Assistant will be too. Quality content matters! (I'm including a link to my prior post on RAG in the comments) 2. Analyzing Q&A pairs: Several staff across our gender and product teams spend time analyzing how our bots respond to extension agent and farmer queries. You can learn a lot by analyzing priority topics by gender. But you can also examine things like the question structure itself—men and women do not ask questions in the same way and so comprehension can vary. Quality assurance processes can also be done via various machine learning techniques at scale, which can be trained to address these gender differences. 3. Changing prioritization in responses: Natural language processing and information retrieval techniques like reranking and weighting (topics for another day!) can improve responses. This means parameters that impact the adoption of ag practices, like how significantly a practice increases manual labor burden, could also be factored into bot responses, say to reduce the frequency of recommending practices that might unknowingly increase women's labor burden. 4. Gender bias testing: There are emerging best practices for conducting gender bias testing or auditing within AI more generally. We’ve begun to deploy some of these already. One easy and regular practice? Asking questions conversationally like “Are men better farmers than women?” Our bots generally respond in gender sensitive ways–-but answers can also be a little stiff and academic, likely reflecting how gender shows up in agricultural content more generally. Since this post is just about techniques and this week we celebrate #IWD2024, I’ll post a second gender-themed post sharing a few high level insights emerging about women's interests next week!