Meet our Economist: Understanding the future of work

I started my career in economic research at the New York Fed, then took a job as an economist in Glassdoor’s Economic Research Function after graduate school in the UK. It was there that I first dived into the intersection of tech data and labor economics. After a brief detour into fintech data science at a startup and Stripe, I wanted to return to the world of labor economics, and the opportunity to shape my own economics function was too good to pass up!

In this role, I get to do a whole host of pursuits that I enjoy: collaborate with academics, go to conferences, analyze our own data, speak to journalists, read academic papers, appear on panels, and collaborate closely with our comms and policy colleagues. It blends the intellectual stimulation that I liked about academia with the pace and incredible data access of tech. As the Chief Economist at Glassdoor used to say, jobs like these allow us to “write the first draft of history,” which is then refined by more careful academic work!

How does your background in data science and research inform how you approach labor economics?

I’m always eager to try new data science techniques. In one of my master’s theses, I looked at how analyzing tweets could predict whether a NYC neighbourhood gentrified over a ten-year period. Ever since then, I’ve been really into analyzing unstructured human language data (a technique that’s called ‘natural language processing’) to see what insights we can derive from what people are saying and writing. Even though I wrote that thesis only four years ago, the NLP tools that we have access to have changed so much with the rise of LLM chatbots.

My background in economic research means that I tend to be very thorough in inspecting the data (which means I can be slower than I’d like at times) and that I’m really into reading new economics papers, which often inform the questions that I’m interested in.

What labor data are you most looking forward to diving into?

There’s a lot of good government data on things like salaries and pay, but very little on questions like benefits and time off, especially how gender, geographic location, and other demographics shape these outcomes. Much of the conversation around, for example, the difference between countries revolves mostly around vibes rather than actual data.

I’m also very excited about diving into how AI might be impacting the labor market or what the state of immigration looks like. The data in these areas is thin or nascent, and I know Deel data can help shed some light on these questions.

What stories do you think aren’t being told about hiring and work trends?

There are very few data-based stories about immigration and relocation trends, largely because of the lack of good data outside official government statistics and a few other rare examples (Meta’s recent paper on this question comes to mind).

Remote work has been a very popular story over the past few years, but data on remote work and global hiring (working for a foreign country) outside the US is pretty thin. Our data on this question is genuinely unique, and I think we have insights worth sharing.

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As AI, immigration, and remote work reshape labour markets, the questions become more urgent, and the data becomes more critical. Lauren is here to dig into those questions with rigor and nuance.

Look out for more of her insights in the months ahead as we explore what's actually happening in the economics of work.