Tobin Center / Economics Pre-doctoral Fellows Program
Professors Ayres and Liscow are each recruiting one Fellow to work on the following projects:
Ian Ayres, the William K. Townsend Professor at Yale Law School, and Zachary Liscow, Associate Professor at Yale Law School, are seeking two empirical research assistants to assist with research projects. Day-to-day work includes brainstorming, conceptualization of suitable empirical methodologies, collecting and structuring datasets, performing econometric analysis in Stata and interpreting results, undertaking literature reviews, and drafting manuscripts. Work also may include implementing field experiments and conducting surveys.
In addition to working closely with Yale faculty, the program provides formal training and mentoring, as well as membership in a group of peer RAs working on different projects through the Tobin Center for Economic Policy at Yale.
Previous Fellows have gone on to attend top law schools, enroll in PhD programs, and win prestigious fellowships.
Professor Ayres’s recent research has included work on innovation policy, behavioral economics, corporate finance and law, civil rights, and contract law. For more information, visit his website, http://www.ianayres.com.
Professor Liscow’s research focuses on tax policy, inequality, infrastructure costs, and local public finance. For more information, visit his website, https://www.law.yale.edu/zachary-liscow.
In particular, Professor Liscow is working on two sets of projects:
Why Does Infrastructure Cost So Much to Build?: There is widespread consensus that US infrastructure investment—and infrastructure quality—has been on the decline. In response, politicians across the ideological spectrum have called for increased infrastructure spending. How much infrastructure we would get depends on how much output is produced per dollar of spending. Yet we know surprisingly little about infrastructure costs across time and place. We help to fill this gap by using data we digitized on the Interstate highway system—one of the nation’s most valuable infrastructure assets—to document spending per mile over the history of its construction.
We make two main contributions. First, we find that spending per mile on Interstate construction increased more than three-fold (in real terms) from the 1960s to the 1980s. We date the inflection point of increase to the early 1970s. We further show that changes in observed geography over time do not explain these changes. Second, we provide suggestive evidence of the determinants of the increase in spending per mile. In particular, the increased spending per mile coincides with the rise of “citizen voice” in government decision-making in the early 1970s. And rising incomes and housing prices nearly completely statistically explain the increase in costs. We also largely rule out several common explanations for rising costs, such as increases in per-unit labor or materials prices. In the coming year, we will continue exploring what drives infrastructure spending, which holds out the hope of helping to transform how policymakers can build high-quality infrastructure at low costs.
The Political Economy of Redistribution and How to Redistribute Efficiently: Economics typically analyzes ideal policies, ignoring real-world institutions and constraints. It is helpful for real-world political actors, though, to have guidance for the real world, which this Article provides for policymakers setting policy with distributional impacts. Current guidance not considering real-world constraints may significantly hamper policymakers’ effectiveness at addressing today’s crisis of inequality.
This research agenda explores a particular set of political constraints: how ordinary people think about policy issues. In a range of scenarios from tax policy to regulatory cost-benefit analysis to expenditures on transportation, ordinary people may have intuitions that make the most efficient policies for redistribution unavailable. This agenda seeks to understand the contours of those beliefs so that efficient policies can be designed that accord with ordinary people’s intuitions about justice. To pursue this work, we will work on “survey experiments,” asking ordinary people online in carefully structured settings about economic policy choices to understand their psychology about such choices. Only with such an enhanced understanding of what actually drives redistributive views can economists be most effective at designing policies to grow the economy while helping everyone achieve their full potential.
• B.A. or M.A. in Economics/Statistics (or equivalent).
• Strong econometrics background and experience programming in Stata. Python programming experience a plus.
• Eagerness to take initiative and solve intricate problems.
Positions begin in June 2020. If you are interested in committing to work for two years (for which we have some preference), please state that in your cover letter.
Unfortunately, we cannot provide visa sponsorship.
Faculty sponsor(s): Barbara Biasi and Song Ma
Professors Biasi and Ma are jointly recruiting one Fellow to work on the following projects:
Higher education plays a key role in the production of human capital by equipping individuals with up-to-date knowledge and skills. What is the distance between the content of higher education and the frontier of knowledge? How does it vary across schools serving students from different backgrounds? How does it relate to students’ economic outcomes, economic growth, and inequality and social mobility?
This project uses a big data approach to characterize the content of higher education courses and to investigate its consequences. To track the content of higher education, we collect and process the text of nearly four million syllabi of courses taught in US institutions over the last three decades. We also collect more than 20 million academic publications (including journal publications, dissertation, etc.), five million US patent filings, and the text of 200 million job postings. We use the documents as benchmarks to compare the content of higher education courses to frontier research, innovation, and the demanded skills on labor market.
We plan to work on two projects:
In the first project (in progress), we develop a new measure: the “education-innovation gap,” defined as the textual similarity between the materials taught in college and university courses and the content of frontier research, patents, and employment postings. Armed with this new measure, we plan to investigate the following facts. First, does the gap vary considerably within field, and how did it change over time within fields? Second, are there significant gap differences across schools serving different populations of students? Third, how does the “education-innovation gap” affect student outcomes such as graduation rates, income, student loan situation, and rates of inter-generational mobility, especially for students in lower-tiered universities? Our preliminary analysis shows that the gap is smallest for Ivy-League and elite schools and for schools enrolling children of affluent parents, and largest for schools with the largest proportion of minority students.
In the second project, we plan to study how the content of higher education affects two key drivers of growth of the economy: innovation and entrepreneurship. We plan to do so along two dimensions. First, we plan to explore the relationship between the content of education in college and the likelihood that students create innovation and startups down the road. For example, we would like to study whether specific concepts or skills or certain types of teaching and evaluation methods matter more than others for innovation and entrepreneurship. Second, we plan to deepen our knowledge on the specific set of curricula which focus on innovation and entrepreneurship. We will characterize the content of these courses and evaluate their actual impact.
Candidates should have quantitative and coding skills, especially experience in general purpose languages like Python and statistical languages like Matlab, R, or Stata.
- Candidates are expected to perform large scale textual analysis, so exposure to related methodologies is a big plus.
- Exposure/knowledge of Machine Learning is a plus, but not required.
- The work will primarily use LINUX, so exposure of Linux and Cluster server computing is strongly recommended, but training can be provided if needed.
- Preference will be given to detail-oriented applicants.
Candidates need not be economics majors, though they should have experience with economics. We welcome applicants from other fields such as, but not limited to, computer science, engineering, mathematics, political science, psychology, and statistics. A love of working with data—cleaning it, understanding it, and presenting it in enlightening ways—is essential for this position.
Professor Cooper is recruiting three or more Fellows to work with him and his co-authors on the following projects:
The Pricing of Prescription Drugs: This project will study trends in prices for pharmaceutical products in the US and analyze the impact of competition on pharmaceutical price growth. The project will focus, in particular, on the pricing of biologic drugs and the impact of new product entry and the entry of biosimilars
Politics, Lobbying, and Health Spending: The health care industry spends more than virtually any other industry on lobbying each year. This project will explore how lobbying impacts health care legislation and health care spending in the US. We will explore how lobbying influences the incentives facing members of Congress and on decisions about whether or not to adopt new technologies.
Technology Adoption in Health Care: This project will explore what firms adopt new health care technologies and seek to understand how ownership structure and market structure influence adoption decisions.
Pre-doctoral fellows will also work on the Health Care Pricing Project, which uses claims data from three of the five largest insurers in the US to examine the variation and growth in private health spending on the privately insured. Research from the team has been cited in the New York Times, the Wall Street Journal, and the Economist and presented at the White House, the Department of Justice, the Federal Trade Commission, the Department of Health and Human Services, and the Centers from Medicare and Medicaid Services. Our aim is to produce rigorous scholarship that can directly inform public policy.
Candidates should have a long-term interest in pursuing economics-related research. The projects will involve building large datasets, analyzing data, creating presentations, and helping to prepare manuscripts. Previous pre-doctoral fellows have co-authored papers with the research team and regularly interact with faculty inside and outside of Yale. We have an excellent placement record, and previous fellows have gone on to top PhD programs in Economics, Public Policy, and Health Economics.
For more information about Professor Zack Cooper, visit https://zackcooper.com/
1. A cover letter describing your interest, dates available, your familiarity with programing languages, your prior research experience, and the names, email addresses, and phone numbers for 2-3 references;
2. A Current CV;
3. A transcript;
4. A writing sample;
5. A sample of your coding (preferably Stata).
Professors Davila and Huo are jointly recruiting one Fellow to work on the following projects:
The successful applicant will work with Professors Davila and Huo on projects of theirs and their coauthors. Most of the projects will involve quantitative work similar to the work described in http://www.quantecon.org/. Professor Davila’s recent research has included mostly normative work on financial frictions, household finance, financial trading, and financial intermediation. Professor Huo’s recent research has included work on information frictions, expectation formation, and quantitative heterogeneous-agents models. For more information, visit http://www.eduardodavila.com and http://zhenhuo.weebly.com. While a lot of the existing predoctoral research opportunities are geared towards the empirical analysis of datasets, we are looking for more theoretically oriented applicants who want to combine exposure to theoretical modeling with learning of computational methods in Economics.
The ideal candidate will
- have a strong analytic background;
- have strong computer and data skills, including programming in Matlab, Stata, Python, Julia, R, or similar;
- be able to work independently to solve problems, and
- have a long-term interest in pursuing research in Economics.
Background in Economics is a plus, but not necessary. We welcome candidates with strong technical backgrounds who are looking for more exposure to Economics.
This position is ideal for someone who has a long-term interest in Economics research and is planning to purse graduate studies in Economics. We will be hiring on a renewable one-year contract. Our preference is for candidates who can work for two years.
1. A short cover letter (no more than one page) describing:
(a) Your interest in the position and career goals
(b) The date you are able to start work
(c) Your familiarity with Julia, Python, Matlab, R, Fortran, or comparable languages
(d) Your prior experience as a research assistant and with independent research (e.g., a senior thesis)
2. A current CV
3. A transcript
Professor Humphries is recruiting one or more Fellows to work on the following projects:
Eviction, Rental Markets, and Housing Instability of the Working Poor
This project consists of a set of projects on the boundary between labor economics and the public economics aimed at studying evictions and housing instability among the urban poor. The predoctoral fellow would be working on a set of projects building on Humphries, Mader, Tannenbaum, and van Dijk (2019), which studies the effects of eviction in Cook County Illinois.
First, the research would involve working on a new project studying the impacts of eviction across several major US cities, and studying how recent policy changes around the eviction process affect the number and impact of evictions. This research is highly policy relevant given the on-going debate around eviction policy and housing instability.
Second, the researcher would contribute to a new project studying the increased concentration of ownership in rental markets in many US cities. Since the great recession, the ownership of rental properties in many cities has become more concentrated. Such concentration may lead to reduced operation costs for the owners, but may also increase market power. The recent concentration may be particularly salient for low-income renters who may be riskier tenants, but who may also have few other rental options.
The predoc hired for this job would work closely with my coauthors and me to answer a broad set of questions related to urban rental markets. As part of this project, the predoctoral fellow would have the opportunity to develop a broad array of research-related skills through working with a number of large administrative and proprietary data sets, documenting institutional details, and conducting empirical analyses.
The Role of Noncognitive Skills in Education and Labor Market Trajectories
This project consists of a set of studies in labor economics and the economics of education focused on the measurement and importance of non-cognitive skills in education and labor market outcomes. This research agenda broadly aims to understand how education career experience fosters non-cognitive skills such as conscientiousness and grit, and how those skills go on to affect labor market success. The predoctoral fellow would work closely with me and my coauthors to contribute to three new and on-going projects.
First, the predoc would work on a new project aimed at understanding how relative importance of cognitive and non-cognitive skills varies over workers’ careers. Evidence suggests that some non-cognitive skills may be less important for earnings early in a worker’s career, but that their relative importance increases with experience. This project aims at understanding this phenomenon, and to evaluate the importance of non-cognitive skills in promotion and career switching.
Second, the predoc would be integrated into a new project working with a company that prescreens highly skilled workers looking for jobs. The project aims to evaluate the effectiveness of modern psychometric methods to measure non-cognitive skills in a high-stakes environment, and to then evaluate the importance of these skills in the placement, retention, and promotion of workers. Based on the initial evidence, the project then plans to study which non-cognitive skills companies appear to screen on, and if those skills are indeed related to retention and promotion of hired workers. Third, the predoc would be working on a number of ongoing projects aimed estimating the returns to education among highly educated workers.
If hired, the predoctoral fellow would have the opportunity to develop a broad array of research-related skills through working with large data sets, applying econometric and machine learning tools, working with collaborators from other disciplines, and writing up and presenting results.
A love of working with data—cleaning it, understanding it, and presenting it in enlightening ways—is essential for this position. Methodological interests in labor economics, public economics, econometrics, machine learning, and statistics are also a big plus.
Candidates should have quantitative and coding skills, especially experience in general purpose languages like Python and statistical languages like Matlab, R, or Stata. Candidates experienced working with R, Stata, and Latex are preferred. Candidates need not be economics majors, though they should have experience with economics. We welcome applicants from other fields such as, but not limited to, computer science, engineering, mathematics, physics, political science, psychology, and statistics.
Faculty sponsor(s): Yusuke Narita
Professor Narita is recruiting one Fellow to work on the following projects:
Today’s society increasingly resorts to data and algorithms for decision-making and resource allocation. For example, judges and police officers make legal judgments using predictions from past data and machine learning algorithms. Similar algorithms are also used by financial institutions (such as banks and insurance companies), retailers, tech companies, and governments. School districts, college admissions systems, and labor markets use computational algorithms for position allocations, producing useful data. Data-driven, algorithmic decision-making thus impacts the lives of many people.
This observation motivates me to work on “data mechanism design,” with applications to public policy and business. By “data mechanisms,” I mean data-generating decision/allocation systems that impact individuals’ welfare and incentives.
I am seeking a full-time research assistant to help in related on-going projects. Below are a few examples:
Project 1: Machine Learning is Natural Experiment
Machine learning algorithms produce a growing portion of decisions and recommendations. Such algorithmic decisions are natural experiments (conditionally quasi-randomly assigned instruments) since the algorithms make decisions based only on observable variables. We formalize this observation and characterize the sources of identification of causal effects for a class of supervised learning, reinforcement learning, and bandit algorithms. Data from almost every machine learning algorithm is shown to identify some causal effect. This identification result translates into easily-implemented instrumental variable estimators of causal effects that are free from the curse of dimensionality. We are applying this method to a variety of policy and business problems.
Project 2: Hearing the Voice of the Future: Trump vs Clinton
Many countries face growing concerns that population aging may make voting and policy-making myopic. This concern begs for electoral reform to better reflect voices of the youth, such as weighting votes by voters’ life expectancy. This paper predicts the effect of the counterfactual electoral reform on the 2016 U.S. presidential election. Using the American National Election Studies (ANES) data, I find that Hillary Clinton would have won the election if votes were weighted by life expectancy. I plan to extend this analysis to use better data and measure the effects on the welfare of different generations
This project involves econometric/statistical and empirical aspects. An ideal candidate is someone who has done coursework in econometrics, statistics, machine learning, and empirical microeconomics.
Professor Zilibotti is recruiting one Fellow to work on the following projects:
Professor Fabrizio Zilibotti in Economics Department at Yale University is seeking a FULL-TIME research assistant for one year, beginning August 2020 (start date and duration of work periods are both negotiable).
The influences of parents and peers are key factors in children’s development that shape the future of the American society. In the recent book “Love. Money and Parenting: How Economics Explains the Way We Raise Our Kids,” published by Princeton University Press in February 2019. The book discusses how the trend of growing inequality has changed the way parents interact with their children in the United States and around the world as well as the consequences on the future society. See
The objective of the research is to examine the connections between parenting on the one hand and neighborhoods and peers on the other hand, using data and economic models of parenting decisions. We use data from the Add Health, PSID-CDS, and ACS data sets to empirically test the theoretical predictions and to estimate structural models that can be used for policy analysis. Over a longer time horizon, we also plan to collect new data and run surveys focusing on the effect of social media. Model simulations will also be used to assess the implications of alternative policy scenarios for the long-run evolution of economic inequality, social mobility, and residential segregation.
The project comprises two subprojects on these issues, one focusing on the role of peers, and the other and on the role of neighborhoods.
The first subproject focuses on peer selection among adolescents. Once children pass into adolescence, the direct influence of parents on their children tends to wane, whereas influences from peers become more important. However, parents can still influence their children at this stage runs through their impact on the peer selection of their children. In principle, this influence may take many forms, from choosing neighborhoods and schools to encouraging children at an earlier stage to adopt activities and hobbies that later on, in the parents’ mind, are associated with a favorable peer group.
We first collect evidence on how parents’ incentive to intervene in children’s peer selection hinges on the quality of the pool from which children choose friends. The next stage will be the estimation of a structural model. We will then to run counterfactual simulations about policy intervention such as desegregation busing that move children from schools in poorer and more problematic neighborhood to schools in wealthier neighborhood. Our approach allows us to evaluate the response of families to such interventions taking general equilibrium effects into consideration.
In the second subproject, we model the choice of the environment. The specific choices that are particularly relevant here are that of a neighborhood to live in and that of a school for the child to attend. These two choices in large part determine the composition of the group from which peers are chosen, which was the starting point of the first subproject. The choice of neighborhood is an important parenting decision that interacts with other aspects of parenting. The way residential segregation interacts with parenting practices is the novel dimension of this part of the project.
Applications are invited from students with a strong background in economics and statistics. Skills and interest in data collection and econometric analysis are important. Knowledge of Stata and the ability to merge datasets are essential skills (please dwell on this in the application). Other quantitative skills (e.g., programming skills) are appreciated but are not essential. We expect the RA to work in team with younger students and to help coordinate their work.
Frequently Asked Questions
Yes; we sponsor J-1 and TN visas. If applicable, candidates eligible for F-1 Optional Practical Training (OPT) are encouraged to use this visa if granted a fellowship.
We encourage our Fellows to either audit or take for credit one course per semester. You may audit any course at Yale for free, with permission of the instructor. Courses taken for credit cost approximately $2,700 each, and this potential expense is accounted for in setting salary for our Fellows.
2019-20 is the inaugural year for the program, so we are still gathering data. However, previous RAs for faculty members who are participating in the program have matriculated to top Ph.D. programs in economics and closely related fields or taken prestigious positions in government or industry.
We are biased, but we think New Haven is awesome. Yale is located in the geographic center of New Haven, and contributes much to the city’s character and culture. Beyond Yale, there are many places to eat, drink, shop, and experience arts and culture. Should you want more, it is also very easy to get to nearby cities such as New York (2 hours) or Boston (3 hours) by car or train. The cost of living is affordable, and the city is generally safe (violent crime rates, for example, are currently lower than the national average).
A set of R, Stata, or Python code you have written for an empirical project or a class. You may submit more than one if applying to multiple positions with different language preferences.
A paper that displays your writing and analytic skills and capacity to execute independent research. A senior thesis, Master’s thesis, or a term paper are all fine choices. Any length is acceptable.
Describe your interest in the program and specific faculty projects; the date you are available to start work; whether you prefer to work for one or two years; your familiarity with programing languages (Stata, R, Python, Matlab, etc); your prior experience as a research assistant and with independent research (e.g., a senior thesis); the names, email addresses, and phone numbers for 2-3 references; any other relevant information.