Economic Research Methodologies

Did you know Australian women just set a record?🏅 Women's labour force participation rate reached a new high of 63.5% in January 2025. That's a half-percentage-point jump from the previous month. Which is big in labour statistics land. It's this rise in women's labour force participation that drove the overall increase in the national rate (as men's participation rate fell slightly). Proof why it's important to always add a gender lens. January is the month where many new jobseekers and new hires are joining the labour market. As well as mothers who have been juggling summer holiday demands, and whose children will now be starting childcare or school, changing their working availability and preferences. Some will still be in the process of looking for opportunities and yet to be matched to a suitable job. This start-of-year job searching can help explain why the unemployment rate ticked up very slightly to 4.1% (again it was women's unemployment rate that drove the overall change, as men's rate was unchanged). Also today we found out Australia's latest gender pay gap, reported for November 2024. Men are earning on average $2073 in full-time weekly wages, compared to $1826 for women. That's a gap of $247 a week, tallying to around $12,800 a year. It equates to women earning 11.9% less than men on average, a gap which has widened since the last calculation (11.5% in May 2024). Men's earnings surged more rapidly than women's during this six-month period, particularly in sectors such as the Real Estate where jobs growth is strong. And partly the gender earnings gap reflects compositional changes. For example, between May and Nov 2024, we saw a notable expansion in women's employment in the Preschool and School Education sector. But because that's not a high-paying sector, it can dampen the calculation of women's overall average earnings. The Australian Government's legislated pay rise for Early Childhood Education and Care Workers came into effect in Dec 2024, so will be reflected in the next gender pay gap calculation. These numbers come fresh from the Australian Bureau of Statistics' Labour Force and Average Weekly Earnings datasets released yesterday. The takeaway from these numbers is that women's opportunities to join and stay in the paid workforce – and gain economic independence – continue to grow. Government policies, company initiatives, working-from-home and hybrid work, as well as the financial necessity of cost of living pressures, are all likely factors contributing to this record-breaking rise in women's workforce participation. But, we still need sustained and strengthened efforts to undo patterns of gender concentration, rectify the undervaluation of female-concentrated sectors, and unravel the biases and barriers that still underpin the gender pay gap. There are still more records to be broken. #genderpaygap #gendergap #genderlens #womenintheworkforce #genderequality #economics #labourmarket #ausecon

A new analysis of millions of salary data points shows a troubling pattern, the gender pay gap doesn’t arrive fully formed. It compounds quietly over the course of a career: ∙ Entry level (0 years): 12% gap ∙ 10 years in: 19% gap ∙ 30 years in: 25% gap In the early stages, the disparity has little to do with unequal pay for the same work. But by the 10-year mark, a within-role gap begins to emerge. Women earn roughly 4% less than male peers in comparable roles with comparable experience. Beyond that point, something more structural takes hold. The within-role gap levels off at around 4–5%, yet the overall gap continues to widen because men are significantly more likely to move into higher-paying roles as their careers progress. The study also found that women’s earnings tend to plateau in their mid-30s, while men’s continue climbing into their 40s, a pattern that holds whether women re-enter the workforce after a break or remain continuously employed. The disparity extends beyond paychecks. Women report measurably lower workplace satisfaction (3.49 vs. 3.60 overall), with the largest gap in compensation and benefits. This holds true across industries, across companies, and even within the same organizations. The gender pay gap is no longer just a matter of equal pay for equal work. It is, at its core, a story of unequal access to career advancement too, and until that structural barrier is addressed, the possibilities are that the gap will continue to compound.

📢 The Gender Pay Gap Is Widening. And You’re Paying for It. The conversation about pay equity is missing two critical truths: 1️⃣ Moms are breadwinners in 40% of U.S. households with children under 18. In 71% of families, their income covers essentials like food, housing, and healthcare. 2️⃣ American taxpayers subsidize the pay gap every time companies underpay women. Let’s connect the dots. When moms don’t receive equitable pay, they’re more likely to turn to public assistance to cover the shortfall. And that cost doesn’t disappear—it gets passed along to the rest of us through our tax dollars. 📌 Half of all one-parent households receiving public assistance are led by women 📌 92% of single-adult SNAP households are headed by women 📌 73% of Medicaid households are led by women 📌 90% of wage-earning SNAP and Medicaid recipients work in the private sector That’s not just unjust—it’s inefficient. We’re using public money to patch private-sector inequity. At the same time, the economic penalty of pay inequity is steepest for Black breadwinner moms. They’re the primary earners in 51% of Black households with children under the age of 18—yet they earn just 44¢ on the dollar. That kind of wage gap doesn’t just hurt families—it undercuts community stability and stalls economic growth for everyone. This isn’t about personal choice or nostalgia. It’s about structural barriers—like rollbacks in flexible work, cuts to public-sector jobs, and the dismantling of DEI—that are boxing women out of the labor force and slowing down our economy. 🧮 Every 1-point drop in women’s labor force participation costs $146B in lost GDP 🧮 The U.S. could unlock $512B by closing the gender pay gap 🧮 That’s not just equity—it’s economic strategy 📣 We can’t afford to lose breadwinner moms. And we can’t afford to keep footing the bill for unfair pay. Let’s stop treating pay equity as a perk. It’s a pillar of prosperity. My bylines on these topics are linked in the comments below. #GenderPayGap #PayEquity #BreadwinnerMoms #TaxpayerImpact #EconomicGrowth #FutureOfWork #StructuralBarriers #DEI #WorkplaceEquity #EquityIsAnEconomicImperative Axios Emily Peck Kristen Parisi Nancy Jeff Green Emma Hinchliffe

Q. Is there still a pay gap in the US? A. Yes, a significant pay gap still exists in the U.S., with women earning considerably less than men, especially women of color, though the gap varies by age and occupation; however, recent data suggests the gap actually widened in 2024 after years of slow narrowing, driven by stagnant wages for women while men's increased, showing pay inequality remains a persistent challenge. Key Figures & Trends: Overall Gap: In 2024, women earned about 81-85 cents for every dollar men earned, a slight decline from prior years, with some sources citing figures as low as 75 cents on the dollar when looking at all earners. Widening Gap: The gap grew in 2024, the first increase in over two decades, as men's salaries rose while women's remained flat, worsening lifetime earnings. Younger Workers: The gap is smaller for younger women (ages 25-34) who earn closer to men (around 95 cents), but it expands significantly as they age. Women of Color: The disparity is starkest for Black and Hispanic women, who earn significantly less than white men and women. Why the Gap Persists: Discrimination: Gender and racial bias in hiring and promotion. Job Segregation: Women concentrated in lower-paying fields, and undervalued roles. Caregiving Burden: Women still bear more unpaid caregiving, impacting their career progression. Lack of Policy: Insufficient workplace policies supporting family care. Impact: Lower lifetime earnings, leading to less Social Security and retirement savings for women. A projection that pay equality won't be achieved until 2088 at the current rate of change. In essence, while some areas show progress, the U.S. still struggles with substantial pay inequality, with recent data indicating a concerning reversal in closing the gap.

$1.2 million. That's what the system quietly takes from a Hispanic woman over a 40-year career. For Black women, it's $884,800. And the gap widened two consecutive years, for the first time in decades. I think about these numbers a lot. Not abstractly. In the way you think about something when you've sat across from hundreds of women who are brilliant, strategic, and exhausted. Getting passed over anyway. 96% of women of color say their career matters to them. 88% want to be promoted. And still, representation more than halves by VP level. The problem has never been ambition. $2.5 million in economic impact. 14,000+ careers. That's what a decade of this work adds up to at Embrace Change. I'm not sharing that to take a bow. I'm sharing it because it's an argument. When women of color get real investment in their trajectory. Not a lunch and learn, not a panel. Things move. The wage and pipeline data show what happens without that support. The $2.5M is what happens with it. We built this to close the gap. The data says it's working. The gap is still there. That's why we keep going.

Every economics student I know is using ChatGPT. And almost every one of them has been burned by it. Confidently wrong answers. Hallucinated sources. Essays that sound smart but say nothing. As an IB Economics teacher, I see this in the work I mark. And I get it — the tool is there, it's fast, and economics is hard. But here's the thing: it's not that AI is bad for economics students. It's that most students are using the wrong AI for the wrong job. So in my latest EconDisrupted video, I ran the experiment myself — and here's the honest cost-benefit analysis: ✅ PERPLEXITY.AI → Best for: Research with real sources Actual citations. Actual links. Actual accountability. Use it to start your IA research, not end it. ✅ JULIUS.AI → Best for: Making sense of economic data Upload a dataset. Get professional graphs in minutes. Your IA will look like it was made by a junior economist. Still need your brain for the analysis though. ✅ CLAUDE.AI → Best for: Actually understanding the concepts The only AI that gets smarter the more you argue with it. Ideal for oral exam prep, untangling tricky theory, and the kind of 'but WHY?' questions your teacher is tired of. (Disclaimer: it's also writing this post.) Free versions of all three exist. Start there. But more importantly — use them to think MORE, not less. On exam day, it's just you and your brain. These tools should be training that brain, not replacing it. 🎥 Full video with live demos in the comments. Tag an economics student who needs to see this ↓ #EconDisrupted #IBEconomics #IGCSEEconomics #EconomicsStudents #AITools #StudyTips #GenAI #EdTech #LearnEconomics Thanks to upGrad and Edgewood University, Aditi Pote and the Worldwide Economic Teachers Collective for their help in putting this information together and suggestions as to how to go about the trials.

PhD Students / Researchers - What are your go-to tools for each stage of your research journey? 𝟏.𝐋𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐑𝐞𝐯𝐢𝐞𝐰: Comprehensive databases like Google Scholar, PubMed, or JSTOR to explore existing literature relevant to your research topic. Citation management tools such as Zotero or Mendeley can help organize and annotate your references. 𝟐.𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: For quantitative research, statistical software like SPSS tutorials, R, or Python's pandas library is indispensable for data analysis. Qualitative researchers may rely on tools like NVivo or ATLAS.ti for coding and thematic analysis. 𝟑.𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐥 𝐃𝐞𝐬𝐢𝐠𝐧: Design experiments and surveys efficiently using platforms like Qualtrics or SurveyMonkey. For experimental research in psychology and neuroscience, software packages such as E-Prime or PsychoPy are invaluable. 𝟒.𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧: Leverage programming languages like Python, MATLAB Coding, or R for modeling, simulation, and algorithm development. IDEs (Integrated Development Environments) such as PyCharm or RStudio streamline coding workflows. 𝟓.𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐏𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Communicate your findings effectively using data visualization tools like Tableau, Matplotlib, or ggplot2 for creating charts, graphs, and interactive dashboards. Presentation software like Microsoft PowerPoint or LaTeX Beamer is essential for crafting engaging presentations. 𝟔.𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Foster collaboration with peers and advisors using project management tools like Trello, Asana, or Basecamp to coordinate tasks and deadlines. Communication platforms such as Slack or Microsoft Teams facilitate real-time communication and file sharing. 𝟕.𝐖𝐫𝐢𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐏𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Streamline the writing process with word processing software like Microsoft Word or LaTeX for formatting manuscripts according to journal guidelines. Grammarly or Hemingway Editor can help polish your writing and ensure clarity and coherence. 𝟖.𝐄𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Stay compliant with ethical guidelines and regulations using tools like CITI Program for research ethics training and protocol management platforms for Institutional Review Board (IRB) submissions and approvals. Share your recommendations and insights in the comments below. #phdresearch #aitools2024

Excited to share that our new NBER working paper — co-authored with Ann Harrison and Noor Sethi — is getting some attention! Axios covered our research on how the gender wage gap among MBA graduates has evolved over the past three decades. The good news: the raw gap has shrunk by 33–50% for recent graduates, driven largely by women staying in the workforce and working more hours than prior generations. The harder question our paper raises: why does a significant wage gap now appear immediately at the start of women's careers — a pattern that didn't exist before 2006? In earlier decades, the gap only emerged over time as career and family demands diverged. Today, something different is happening from day one, and we can't fully explain it with observable factors like industry, hours, or credentials. We also examine the roles of "greedy work," background sexism, and childcare availability — and find that while these factors shape labor supply decisions, the early-career gap in recent cohorts remains stubbornly unexplained. Progress, yes. But also new questions worth taking seriously.

This National Bureau of Economic Research Working Paper provides evidence that the development and adoption of Generative AI is driving a significant technological shift for firms and for financial research. The authors review the literature on the impact of ChatGPT on firm value and provide directions for future research investigating the impact of this major technology shock. Finally, they review and describe innovations in research methods linked to improvements in AI tools, along with their applications, and offer a practical introduction to available tools and advice for researchers interested in using these tools.