Eva Vivalt

I am looking to hire 1-2 pre-doctoral fellows at the University of Toronto in applied microeconomics. The deadline is March 31, 2025, but early application is requested and there will be rolling interviews.

Here are descriptions of the different types of work available:

1. Work on the Social Science Prediction Platform

Prof. Stefano DellaVigna (UC Berkeley) and I are seeking a pre-doctoral research fellow to assist with the Social Science Prediction Platform (SSPP) and be an integral part of the team. The Social Science Prediction Platform is an online platform that enables researchers to collect ex ante forecasts of what their studies will find. These forecasts can be useful in a number of applications, some of which are summarized in this Science Policy Forum piece.

Since the SSPP started, we have seen an enormous growth of projects posted on the platform, and we are beginning to analyze data from the first 100 projects.

2. Work on guaranteed income experiments

I have been evaluating several different guaranteed income experiments with Alex Bartik (UIUC), David Broockman (UC Berkeley), Patrick Krause (OpenResearch), Sarah Miller (Michigan), and Elizabeth Rhodes (OpenResearch). Some results have been written up, but there is still more to do, including analysis of children’s outcomes, crime, intrahousehold outcomes, and potential analysis of heterogeneity across sites. Some of this work may include building and estimating models.

This role would also include the chance to work closely with OpenResearch, a nonprofit research organization with a startup mentality, and would provide the opportunity to learn about cutting-edge best practices in research.

3. More open-ended work

There is the opportunity for more open-ended work in evidence-based decision-making and topics in AI. Past pre-docs have worked on existing projects while developing their own projects through active discussion and mentorship.

Pre-docs may work on one or more of the three topics above, depending on fit, availability, and interests.

Eligibility

Applicants must, at minimum, have:

• A bachelor’s degree (or be graduating this year);
• Experience in R or Stata;
• Work authorization in Canada, whether by citizenship or an open work permit. This is strictly required. In practice, citizens of several countries can often obtain an open work permit. In the pre-interview application screener, you will see some questions designed to help you figure out if you might be eligible (though you may need to do further investigation on your own).

The ideal candidate would have:

• A strong quantitative background and potentially a master’s degree;
• Proficiency with more than one programming language;
• Familiarity with ML;
• Previous research experience, such as through past research assistantships or an independent research project;
• An interest in pursuing a PhD in Economics or a related field.

To apply:

To apply, please fill out a pre-interview screener here, including uploading a transcript and CV. Only shortlisted applicants will be contacted for an interview.

At long last, the first working papers have been released for a project I’ve been working on since 2016: an evaluation of a program that provided $1000/month unconditionally for 3 years to 1000 low-income individuals, while another 2000 received $50/month as the control group.

The papers:

• The Employment Effects of a Guaranteed Income: Experimental Evidence from Two U.S. States
• Does Income Affect Health? Evidence from a Randomized Controlled Trial of a Guaranteed Income

In time, I’ll say a lot more about what we found, but for now I just want to recognize what an immense team effort it has been. It’s been an incredible experience working with Alex Bartik, David Broockman, Patrick Krause, Sarah Miller, and Elizabeth Rhodes. But also, so, so many people have contributed to this project in one way or another.

It also wouldn’t be an exaggeration to say that OpenResearch really enabled a different kind of project than is typical in economics. Massive amounts of thought went into this, from a lengthy piloting phase to Elizabeth Rhodes tracking down participants and designing a font that looked like her handwriting so she could add personalized notes to postcards sent to keep people engaged. All the details were thought of, all the bases covered, all the grants were applied to, and all the outcomes collected. Partners helped to pass a law in Illinois to ensure participants would not lose important benefits. A custom mobile app was developed. We were even able to collect biomarkers at endline to investigate potential health effects, a massive logistical effort.

It’s been a real pleasure working with this team. Some first papers are linked above, but expect more coming soon.

Recently, there’s been a surge of interest in and attention on work on the evidence-to-policy pipeline. Calling it a “pipeline” perhaps implies something too direct: the path by which evidence can affect policy is often circuitous and hard to pin down.

Nonetheless, I thought it might be a good time to share a round-up of related media links, based on work with Aidan Coville and Sampada KC.

First, at VoxDev, I summarize results from two sets of experiments run at the World Bank and the Inter-American Development Bank. This talk focuses more on what we learned about how policymakers interpret different kinds of evidence, i.e., what were the takeaways?

The next link, from a lecture given as part of the Social Science Research Council (SSRC) College and University Fund Lecture Series, also highlights what we learned, but with more emphasis on how we learned it. It goes more in-depth on the experiments and draws some broader conclusions.

There remains plenty of fertile ground for further research in this area, and I’d encourage interested grad students or faculty to pursue it. Given the importance of the decisions policymakers make, small improvements in decision-making quality could potentially have large impacts.