Hannah Acheson-Field: Applying Causal Inference to Workforce and Higher Ed Research

p201810_11, class, prgs class, discussion, nabeel qureshi, hannah acheson-field, pau alonso garcia-bode, ashley woo

Hannah Acheson-Field (center) participates in a class discussion

Diane Baldwin/RAND Corporation

Hannah Acheson-Field (cohort '18) may have taken a circuitous route to her current research focus, as is true of many Pardee RAND students, but each step has provided experiences that honed her skills and made her appreciate the role of different research methods.

And although she wasn't exposed to causal inference methods until she arrived at Pardee RAND, she says she is grateful for her coursework and her work with the Center for Causal Inference, as they have opened her eyes to research and career possibilities.

Acheson-Field began her academic career at Brown with a focus on software engineering and computational biology—that is, computer programming applied to big data in the biological sciences. "But I realized I was really interested in questions relating to human resources and the workforce like diversity, equity, and inclusion within companies," she said. "Over time, I learned that was an actual area of study."

Her degree and interests led her to the Institute for Defense Analyses, where she worked in their Science and Technology Policy Institute examining the STEM workforce. "I worked on projects similar to ones I'm working on now, but they had a narrower focus, like an evaluation of a scholarship-for-service program and National Science Foundation evaluations," she said.

She applied to Pardee RAND thinking she'd continue her focus on the STEM workforce, "but I've realized there's a lot more to workforce research than just STEM. It's all related, but it has informed what I want to do after I graduate," she said.

"My introduction to causal inference came in the last quarter of my first year at Pardee RAND, in the last class of the econometrics sequence," she said. "I also took advanced econometrics courses as electives, which involves the application of statistics into economic and social data."

“Causal inference methods help researchers try to find the causal impact of a policy. I think having a better handle on causal inference methods will be invaluable after I graduate.”

Despite having a strong background in math and statistics, "I hadn't even taken economics before Pardee RAND," she said. "I really like it, though, and I wish I had, but it wasn't on my radar."

Although she hasn't pursued computational biology professionally or academically since college, she said her programming background has been quite useful. "Computer programming and software engineering are different from statistical programming, but the concepts I learned in computer programming are extremely helpful in learning new languages quickly and doing some of the basic data analysis."

Acheson-Field applied for the CCI Fellowship after directors Matt Baird and Claude Setodji wrote to students asking for applicants. Because of her newfound interest in causal methods, she was eager to work with them and with the center.

As CCI Fellow, her responsibilities include coordinating the center's internal Brown Bag Lunch series and inviting researchers to present their causal inference work. She also reviews publications submitted by RAND researchers who work with the center and classifies them by method, "to get a sense of how and when RAND does causal inference work."

She also applied because she knew the fellowship would allow her to work on her dissertation research, which she already knew would require causal methods. "I wanted time to dedicate early on to figure out how best to evaluate Maine’s educational opportunity tax credit," she said.

"I'd taken the Cost-Benefit Analysis at Pardee RAND in my first year," she said, "and I wrote about their tax credit program. Of course it was a much simpler look, using secondary data. One of the professors said it was interesting and suggested getting actual data. I didn't think it would be possible, but it turns out it might be."

During her fellowship, she said, "I've been thinking about how to assess the program’s effectiveness using public data sets, and what methods to use. This tax credit in Maine has received some national publicity lately, and this is an interesting program. Other programs help students repay debt, but they're structured differently."

Acheson-Field said the program's goals are to increase educational attainment, which lags neighboring states, and to address the challenges of an aging workforce as part of a 10-year workforce strategy to increase the number of workers in Maine.

A Mainer herself, she said, "My question is, what was the impact of the program on outmigration. I'm still refining my methods, likely using matching, an event study, and or a difference-in-differences approach," as well as several noncausal questions.

As she looks to the future, Acheson-Field said she hopes to continue her workforce, labor, and higher education research. "I could see myself at a quasi-governmental organization like RAND, or as a researcher at the federal, state, or local government level. But I also toy with the idea of entering the private sector to perhaps work in people analytics."

Wherever her career takes her, she knows her methodological background will help.

"Causal inference methods help researchers try to find the causal impact of a policy. For example, we know that workers with a bachelor's degree tend to earn more than those with no degree. That fact alone doesn't mean that the bachelor's degree causes those workers to earn more; there may be other factors," she said.

"Policy researchers try to answer these types of questions frequently," she added, "and I think having a better handle on causal inference methods will be invaluable after I graduate."

— Monica Hertzman