Workforce Dynamics Analysis

If we treated this like any other economic shock, we’d call it what it is: a structural failure. In my latest Fortune byline, I break down why the U.S. labor market is diverging along lines of race, gender, and pay: 🔹 Black women down 297,000 jobs since February 🔹 Men up +621,000 jobs 🔹 673,000 women still missing from the workforce since the pandemic 🔹 Job growth concentrated in the lowest-paying sectors for women 🔹 Pay gaps widening (again) This is not happening by accident. It’s happening by design. When the most educated female cohort in the country is pushed out of stable, high-wage sectors, and concentrated in the lowest-paying ones, that is a policy choice. When we continue to count only who is in the labor market, and ignore who has been pushed out, that is a modeling failure. And when we treat women’s economic participation as optional rather than foundational, that is a national risk. The Exit Economy is what emerges when exclusion becomes the operating system. It doesn’t just cost women. It costs the entire country. #GenderEconomist #LaborMarket #EconomicEquity #WomenAndTheEconomy #BlackWomenAtWork #IntersectionalEconomics #JobsReport #EconomicData #FutureOfWork #EquityAsEconomicStrategy Nick Lichtenberg Emma Hinchliffe Jessica Sibley AJ Hess Ray Vanessa Mobley Rachel Wolfe

Jobless Claims Fall as Reemployment Slows and Hiring Demand Softens The U.S. Department of Labor reported that initial jobless claims came in at 202,000, below expectations of 212K and down from a revised 211K the prior week. The four week moving average declined to 207,750. Continuing claims increased to 1.841 million, up from 1.816 million. At the same time, the March Challenger report showed that U.S. companies announced over 60,000 job cuts, an increase from the prior month, while JOLTS data has continued to show a gradual decline in job openings. On the surface, this is a stable labor market. Layoffs remain low and initial claims are still sitting in a range that does not suggest broad stress. But the more important signal is in continuing claims. That increase tells us that when people do lose jobs, it is taking longer to find the next one. The labor market is not weakening through layoffs. It is becoming less fluid. That shift is showing up across multiple data points. The Challenger report tracks announced job cuts before they appear in official labor data, and the recent increase suggests companies are becoming more selective in how they manage headcount. At the same time, JOLTS data continues to show fewer job openings, which points to softer hiring demand. Put simply, the labor market is not breaking. It is tightening. And that distinction matters. A spike in layoffs hits quickly and visibly. A slowdown in hiring is quieter, but it changes outcomes over time. It can mean longer job searches, more downward pressure on wages for those switching roles, and less overall mobility. This is what a no hire no fire environment looks like. Companies are holding onto workers, but they are not in a rush to add more. When uncertainty rises, hiring is usually the first place you see it. It is also worth noting that this data reflects conditions before the most recent geopolitical tensions involving Iran. If that uncertainty carries forward, the more likely response is continued hesitation in hiring rather than an immediate increase in layoffs. For the broader economy, this creates a more uneven dynamic. Employment is still supporting spending, but the experience of the labor market is getting more restrictive. Some households will not feel much change. Others will feel it in slower job transitions and fewer options. At Havas Edge, we spend a lot of time on this relationship between layoffs, hiring demand, and reemployment because it tends to show up in consumer behavior before it shows up in the headline economic data.

Before the Great Recession, the only time the long-term unemployed made up more than 25% of jobseekers in the postwar era was in 1983, during the S&L crisis. Since then, it's happened three times – most recently in December. What's going on? A dynamic labor market used to be the hallmark of the United States. Economists looked down their noses at countries like France, where a significant minority stayed unemployed for years while the majority had jobs for life. The constant churn in the United States was a sign that the economy always generated new opportunities for all kinds of workers. This clearly isn't the case anymore. Since data on hiring and firing rates became available in 2000, their sum – a rough measure of churn – has never been lower than in the past couple of years. The labor market is starting to look more, how do you say, French? I see two main causes for this transformation. One is the pace of technological change. When there's a rapid shift in the kinds of jobs available, it's harder for workers to adapt. It's especially hard for unemployed workers, since the best training usually happens on the job. So when technological revolutions start to arrive more frequently, the gap in skills between the employed and unemployed can widen. The other cause is inequality. We've seen enormous increases in wealth inequality over the past few decades, and we know that wealth affects access to opportunity. People with less wealth have fewer connections who can help them to get new jobs, and it's also harder for them to start businesses of their own. So they're more likely to stay unemployed. In most states, unemployment benefits end after 26 weeks. This means the long-term unemployed aren't receiving benefits, but they haven't stopped looking for jobs, either. They want to work; they just can't find the right fit. They need the labor market to return to its more dynamic self. To get there, we'll need to level the playing field for economic opportunity and help workers keep up with technological progress. More change is coming to the economy, so these are urgent matters. I'll have more on the problems and solutions in this Friday's High Yield Economics newsletter.

A new paper from David Autor, in collaboration with Neil Thompson, makes an important contribution to explaining how AI is likely to impact labor markets. Based on a rigorous model, confirmed with an analysis of 40 years of data, they provide a nuanced perspective on how automation impacts job employment and wages. Essentially, this depends on the extent to which easy tasks are removed from a role and expert ones are added, and how specialized a role becomes as a result. When jobs gain inexpert tasks but lose expertise, wages decline, but employment may increase. Think of how taxi driving became less specialized, and well-paid, but more common, due to Uber. In contrast, when technology automates the easy tasks inside a job, the remaining work becomes more specialized. Employment falls because fewer people now qualify, but the scarcity of expertise drives wages up. This is what seems to be happening with proofreading, which is now less about spell-checking and more about helping people to write, leading to lower job numbers but higher average wages. Their model helps us to understand the impacts of AI on labor markets. For instance, why AI tools can raise wages for senior software engineers, but decrease employment, while simultaneously reducing earnings, and increasing employment, for more entry level software engineering roles.

'Lower labour force participation and part-time work for mothers seem to be strong cultural norms in Australia, compared with many other countries. Worryingly, the Productivity Commission’s draft report appears to imply that this reflects the deliberate “choice” of mothers, and that using three days a week of child care is due to maternal preference rather than the unaffordability of paying for five days of early childhood education and care. If early childhood education and care were universally affordable and accessible in Australia, the evidence seems clear that norms and “choices” would change. Women in Australia have waited a long time for this structural reform, central to the wellbeing and development of children and redressing the financial burden of gender inequity, to be considered in earnest. That the PC has seemingly dismissed, at worst, or misconstrued, at best, the premise that much more affordable early childhood education and care would afford mothers in Australia genuine “choice” about how and when they work, is hard to swallow.' I co-wrote this op-ed in today's The Australian Financial Review with Prof Gordon Cleveland, emeritus associate professor of economics at the University of Toronto Scarborough, and a member of the Expert Panel on Early Learning and Child Care Data and Research. https://lnkd.in/gq2f8z7Q The Parenthood

I found a 1968 book on #women at work that could have been written this year. 📖 Every year I go back to Brazil to spend the festive season with my family, and this time – while helping my parents reorganise their bookshelves – I came across a Portuguese translation of "Histoire et Sociologie du Travail Féminin" ("History and Sociology of Women's Work"), by French feminist Évelyne Sullerot. The title alone could sit comfortably in a 2026 LinkedIn carousel. So I asked myself: what will surprise me here, as someone working in #diversity and #inclusion today? I started flipping through the pages with curiosity and certain realism: I suspected that many of the issues wouldn't feel entirely resolved. I was right. 🤷🏽♂️ Here are 3 things that felt uncomfortably persistent: 1️⃣ The "newness" myth. The author dismantles the idea that women working is a modern deviation. Women have always worked. What changes is whether that is recognised, paid, or valued. And 58 years later we are still debating unpaid care, emotional labour, and economic invisibility. 2️⃣ The male breadwinner model as default. The book describes how industrialisation separated "productive" (paid, male-coded) from "reproductive" (unpaid, female-coded) work. Many workplace norms today – availability, linear careers, constant upward mobility – are still built on that architecture. 3️⃣ Segregation disguised as protection. Policies framed as protective – like restrictions on night work, or maternity leave without equivalent paternity leave – often limited women's economic participation. That tension still shows up when well-intended support reinforces difference instead of redistributing power. But wait: it wasn't all stagnation. Here are 3 areas where we have moved – even if imperfectly: 1️⃣ Our understanding of gender has expanded. The book largely treats "women" as a stable, singular category. Today, we know that gender intersects with race, class, disability, sexuality, and migration status, while acknowledging that gender itself is not strictly binary or fixed. That matters because the logic that once justified rigidity starts to loosen. 2️⃣ The conversation has shifted from participation to power. Back then, much of the debate centred on whether women should work, and under what conditions. Today, the focus is increasingly on influence: we're no longer only asking if they can get in, but how they get to shape outcomes. 3️⃣ Organisational accountability exists. Pay gap reporting, parental leave reform, flexible work – these weren't mainstream conversations then. They are now. Progress is incomplete, but visible. Holding that book in my hands reminded me that inequity is rarely about lack of awareness: we've been diagnosing patterns for decades. The harder question is: are we willing to redesign the systems that keep alienating women? 💬 I'd love to read: what do you hope someone in 2084 will say about (y)our work today? 🤔

The U.S. economy is $31 trillion. Labor costs account for ~60% of that value. Everyone agree AI will transform the $18.6 trillion labor market... but they disagree on how. The disagreement in how AI will affect the future of work exists because people are looking at and arguing from different layers of the same labor market. Some focus on what AI can technically do. Others focus on how quickly companies deploy it. Others look downstream at what happens to workers once firms make those decisions. Each perspective has its own merits, but none is sufficient on its own. To make sense of these conflicting views, we can analyze the market through three distinct layers: Layer 1: Exposure This layer defines which tasks AI can plausibly perform given current and foreseeable capabilities. Within AI exposure, there’s a “New Moore’s Law”: the length of autonomous tasks AI can perform doubles every ~7 months. While earlier tech waves hit manual labor, generative AI is reaching cognitive non-routine work, once assumed safe. Layer 2: Adoption Exposure tells you what AI could do. Adoption tells you what it is actually doing. While 54.6% of U.S. adults use AI individually, fewer than 10% of businesses have integrated it into their production processes. This gap exists because firms are currently trapped in the “dip” of the Productivity J-Curve: productivity initially dips due to learning costs and “workslop”, low-quality outputs that knowledge workers spend nearly 2 hours per week fixing. Layer 3: Labor Market Response When firms finally adopt, the market responds in three ways: augmentation, displacement, or reinstatement. More on this in the deep dive. Taken together, this framework explains why the debate around AI and work feels unsettled. Different observers are reacting to different layers of the problem and level of analysis. This is why my research team spent the past three months building this deep dive. To navigate AI effects on the $16 trillion labor market, you need to map the mechanics of these three layers and how they constantly reinforce one another. Check the comments for the Deep Dive link.

You don’t get promotions, bonuses, or recognition for this job. But without it, nothing works. That’s me with my girls, many years ago on a trip back to India. They’re young adults now and about to enter the workforce. For nearly a decade, I raised them as a single dad—while leading in senior leadership and C-suite roles. Grocery shopping, cooking, cleaning, school matters, medical appointments, extra-curricular activities, friends, pick-up/drop-off runs… the list was endless. It wasn’t easy. I was juggling all day—work, kids, home—trying not to drop anything. And I was very fortunate to have had incredibly supportive leaders and team members who understood the challenge. But let me be clear—I’m not sharing this for your sympathy or support. I’m sharing this because the experience of raising my girls gave me a unique and often overlooked perspective on the hidden cost women pay when balancing professional careers and caregiving. For a moment, replace me with any other woman in your family—your partner, daughter, maybe even your mom—and you start seeing the bigger picture. This isn’t about saying men don’t contribute—many do. But the numbers tell a different story. 👇 🔹 Workforce gap – Women’s participation: 62.5% (men: 71.3%). 🔹 55% pay cut – Women’s earnings drop post-childbirth. Men’s? Unaffected. 🔹 Childcare penalty – High costs make full-time work unaffordable for many women. 🔹 Retirement gap – Women retire with 23% less Super, increasing financial insecurity. 🔹 Unpaid labour = another job – Women do 30+ hours/week of unpaid care (men: 22 hours). (Source: Women’s Economic Equality Taskforce, 2023 Report to the Australian Government). These issues are major contributors to the Gender Pay Gap. As a C-Suite leader, you have the power to break these barriers—starting now. Here are two steps you can take immediately: ✔️ Provide flexibility – Support caregiving without compromising career growth. ✔️ Encourage equal parental leave – Normalise men taking an equal caregiving role. 📩 If this resonates, let’s talk. I’d love to hear your thoughts—message me for a copy of my guide. "Closing the Gender Pay Gap & Accelerating Women into Leadership Positions." #Leadership #DiversityAndInclusion #GenderEquity #FutureOfWork --- For senior leaders navigating complex challenges, the journey to impactful leadership can feel daunting at times—but it doesn’t have to be walked alone. Anoop, with 30+ years of experience across three continents, a former Board member and CPO of a Fortune 10 company in Australia, and winner of the 2022 HR Leader of the Year award, advises senior leaders on making profound changes.

Work in 2026 won’t break because leaders failed to adopt AI. It will break because organizations misunderstood what actually changed when work moved into systems. As execution accelerates through agents and automation, the old buffers disappear. Unclear goals don’t just cause confusion; they create errors at scale. Entry-level work no longer builds judgment the way it used to, governance quietly becomes culture, and human value stops being evenly distributed. This is where many future-of-work conversations fall short.  They focus on tools and policies while missing the deeper redesign happening underneath: work is becoming a managed labor system, not a collection of roles. The article lays out the five shifts that will define 2026 and why getting them wrong creates damage you won’t see until years later. Read it before those consequences become permanent:

𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 I've been asked this at least 3 times in the last two months. "How do I know that my leaders are improving?" This is where we distinguish knowing from application. 10% of capability comes from learning from formal sources. 20% comes from networks and interactions. 70% comes from application to portfolios and projects. One thing that sets this all apart are data points. Even if I apply skills to my projects, how do I know I did it well? Most large companies have a 360-degree or leadership assessment process in place. So, I'll share my thought process for this in case you are attempting to develop this for your own organization. Step 1: Determine organizational strategy and business outcomes. This is necessary to align expectations of desired behaviors. This is where a Balanced Scorecard can come in handy. Step 2: Assess expectations of leaders. You'll then assess them across leadership behaviors for new, mid and even senior managers. Granularity of differences supports focus and clarity. Often, a list of pre-existing behaviors/competencies are used to make the exercise easier. Validated psychometric tools such as the 16PF help to anchor it to scientific rigor. Organizational psychologists like me conduct surveys to gather insights. Then, focus groups are used to drill down to details information. After that, we'll create categories basedon the information and produce working behavior-based definitions. Step 3: Prioritize the list Now, the leadership team decides which behaviors are more important by way of ratings. Step 4: Build the 360 We then build a 360-degree feedback survey questions. These questions are reviewed for validity. Step 5: Allocate the survey A system specializing in the 360 (there are many) can be used. Feedback Recipient selects 6 to 12 people to rate them. In organizations, to avoid selection bias, leaders of the feedback recipient can review and veto the people doing the rating. Then, the participant does the survey too (self-rating) Step 6: Debrief of survey Usually, participants need guidance from a trained coach who understands feedback requirements. This is to provide grounding and objective input. Often, 360 surveys tend to be met with resistance unless the coach is skilled in facilitating the reflection conversation. Step 7: Action Planning The participant then produces a set of actions for improvement. This plan and the priority of focus should be made known to the feedback givers. Step 8: Pulse Surveys After a designated time (within 6 to 12 month period) a validated pulse survey is set up for the observers to rate improvement in specific behaviors. Step 9: Continued Leadership Coaching, Mentoring and Peer Support A combination of these can be used to enhance development. Step 10: Final Comparison Survey Toward the end of the year, a comparison survey is done to see how the key areas have improved or not. ---