From left to right: Fatima Djalalova, Qiushuang Huo, Joanne Liang, and Juliana Quattrocchi
The Blueprint Labs team includes Predoctoral Researchers who contribute to education, health care, and workforce research. In this spotlight, Amanda Schmidt speaks with four of Blueprint’s second-year predocs: Fatima Djalalova and Qiushuang Huo from the Education team and Joanne Liang and Juliana Quattrocchi from the Workforce team. Interested in working at Blueprint? View our open positions.
Tell me about your background. How did you end up working at Blueprint Labs?
Fatima Djalalova: Prior to Blueprint, I studied economics and mathematics at Wellesley College. My interest in economics research began in my first-year economics writing class, where I discovered how economics can be applied to study policy-relevant questions, particularly in education. This inspired me to build my knowledge and skills, leading me to work as a research assistant during my junior and senior summers and complete my senior thesis. These experiences confirmed my interest in pursuing an academic research path in economics.
Qiushuang Huo: I joined Blueprint after receiving my M.A. in international and development economics from Yale University and my BSc. in economics from University College London. My interest in education and development economics began during my second year of college. I am eager to apply econometric frameworks to address education-related challenges in developing countries. Blueprint offers the ideal environment for me to receive rigorous econometric training and prepare to pursue a PhD in economics.
Joanne Liang: Before joining the workforce team, I graduated from the Chinese University of Hong Kong, Shenzhen in economics and spent a year on a master’s in social sciences. I was interested in questions on people’s choice and collective outcomes but never had the opportunity to be part of a large research project. I decided to work full-time to learn how economists approach a question and ended up in this perfect place.
Juliana Quattrocchi: As an undergraduate student, I worked as a research assistant in economics. I really enjoyed my experience with experimental field work and connecting with social service practitioners who were seeking answers to questions about how to improve the work they do on a daily basis for their communities. I saw a PhD in economics as an opportunity to continue rigorously seeking answers to important problems and Blueprint Labs as a great place to develop the skills I needed to apply to PhD programs and be successful as a researcher.
Describe one of the projects you’re currently working on.
Djalalova: One of my projects studies relationships between measures of school quality. School districts have traditionally relied on standardized test scores to measure school quality, but there has been a significant shift toward incorporating non-academic measures. This raises important questions about how well different measures predict student success.
We conducted this research in New York City Public Schools and found that both survey responses and test scores provide meaningful information about school effectiveness, as measured by high school graduation, college enrollment, and college persistence. Surveys are better predictors of high school graduation, while tests better predict college outcomes. Now we’re excited to extend this work to other settings and see whether and how our findings vary across different contexts.
Huo: One of my projects examines how scholarships impact students on their paths through college and post-college, focusing on outcomes including college enrollment, graduation, and earnings. We are currently setting up a regression discontinuity framework for analysis, leveraging the recipient selection processes, in which programs rank applicants using composite scores.
Liang: One of my projects studies the impact of demographic transition on economic growth. We use various econometric techniques to identify the causal effect of declining birth rate on wage changes in the U.S. We are working on disentangling the positive effects into channels such as education, female labor force participation, automation, and industrial shifts.
Quattrocchi: One of the projects I’m currently working on examines the effect of the China Shock on the educational, earnings, mobility, and social trajectories of the children living in areas impacted by the China Shock—that is, areas with a higher exposure to the flood of low-cost Chinese imports in the early 2000s due to their industrial composition.
What motivates you to be a part of this research?
Djalalova: I’m really excited to apply and develop my quantitative skills to study policy-relevant questions. My research team regularly meets with our school district partners to share findings and discuss ideas for further analysis, and it’s inspiring to see the direct exchange between academic and policy worlds. When school districts share their pressing questions with us, we collaborate to develop econometric models for those where our methods can provide meaningful insights. For example, we apply our econometric methods to compute school ratings that are now published on the NYC School Performance Dashboard and the NYC School Quality Snapshot—it’s rewarding to see academic research translate into real-world impact.
Huo: Recent studies highlight that scholarship awards may have especially large impacts for traditionally underrepresented students. However, most scholarship aid in the U.S. flows to students from more advantaged backgrounds. Our data provides a unique opportunity to analyze the impacts of private scholarships on a large scale, and I’m excited to study whether, how, and for whom scholarship programs can increase access to college and promote equity in post-secondary achievement.
Liang: This project addresses the longstanding concern that rapid population aging will harm the economy. We test our findings across different contexts using multiple approaches and still find them robust. I am deeply intrigued by studying an important question where no consensus has yet formed. The process of questioning, thinking, and solving is incredibly rewarding.
Quattrocchi: The opportunity to further practical knowledge production motivates me to be a part of research. All of the data that we work with is derived from the economic and social lives of real people and firms, so it’s a privilege to have the opportunity to use this information to try to better understand the world around us. I hope that this research can provide information to make more equitable, evidence-based economic decisions.
What does your typical day’s work look like?
Djalalova: My role puts me closest to the data and codebase, so my main contribution to our projects depends on developing a deep understanding of the data and implementing econometric analysis. Day-to-day, this involves coding econometric models in Stata and Matlab, analyzing results to test research hypotheses, and cleaning datasets when needed. I also present findings at weekly research meetings.
Huo: A typical day involves developing components of the analytical framework for the scholarship project – identifying aspects of the data worth investigating, programming and validating analytical approaches, and connecting these decisions to broader research questions. Tasks range from reviewing literature and cleaning data to implementing econometric frameworks and running simulations in Stata. For my charter school research projects, I also reach out to charter school leaders to collect lottery data.
Liang: My day-to-day work involves considering the remaining concerns and suggestions raised by Principal Investigators (PIs) in the latest meeting and finding the right data and methods to tackle them. To be more specific, I usually fetch and clean the data using Stata and Python. Then I immerse myself in the data to find appropriate measures, correlations, anomalies, and other interesting patterns. I also compile the findings in memos and presentations.
Quattrocchi: My day-to-day work can vary quite a bit. Some days consist mostly of constructing or analyzing data and communicating with faculty about project updates. Other days, I might conduct background research and fact-checking for one of the books we’re working on or assist with drafting information about our work for a broader audience.
What activities do you take on at Blueprint Labs beyond research?
Djalalova: I take classes during the year and attend department seminars, which have helped me develop personal research interests and a deeper understanding of the methodology used in our projects. I had the opportunity to attend the NBER Education meeting in December when our project was presented. I also presented our project at two policy conferences this year: the National Forum on the Future of Assessment & Accountability, and the annual meeting for the Association for Education Finance and Policy, which has been great for understanding how academic research connects to broader policy conversations.
Huo: I co-hosted the Blueprint Alumni Panel, where we invited former predoctoral researchers to share their career paths and experiences post-Blueprint. Additionally, I have had the opportunity to take a graduate-level applied econometrics course taught by Blueprint Labs Director Joshua Angrist, which has greatly enhanced my understanding of core empirical strategies used to address causal questions in applied microeconometric research. I’ve also attended weekly labor and development lunches, teas, and seminars, where PhD students and invited lecturers present their work.
Liang: Being a predoc here grants me access to resources I never would have imagined. I’ve had the opportunity to take classes with the PhD students and to attend weekly field seminars. These activities prepare and inspire me to do research. I have also served on the hiring committee, conducting data analysis to provide insights on hiring equity and efficiency. Over the past year, I have helped refine and streamline the hiring process.
Quattrocchi: At Blueprint, I currently co-lead the predoctoral researcher meetings and social committee. On the Stone Center on Inequality and Shaping the Future of Work team, I had the opportunity to work with a senator’s office last fall to share our views on how a social media advertising tax might operate. I have also co-organized our annual Junior Researcher Talk Series, where job market candidates can present their research to research assistants.
What skills have you learned at Blueprint that will be useful for your future?
Djalalova: I’ve learned advanced econometric methods, developed my understanding of market design in the centralized school assignment context, and significantly improved my coding skills in Stata and Matlab. I’ve also learned to present findings to different audiences and collaborate effectively in large research teams. The mentorship from my PIs has been incredibly valuable in shaping my understanding of academic research.
Huo: Working on projects at different stages, I’ve learned a lot from my PIs on all aspects of research, ranging from how to develop and approach research questions to how to test the validity of models and mechanisms. At the same time, I’ve significantly developed my soft skills through interactions with our data providers and external partners.
Liang: Blueprint has cultivated my taste for good research. I was taught what a good question to ask is, and I’ve learned to carry out serious research step by step. After experiencing the learning curve here, I feel confident in learning anything new. The collaboration and friendship with fellow predocs are also memories I will cherish.
Quattrocchi: At Blueprint, I’ve had the opportunity to develop my coding skills, build knowledge of empirical methods, and learn new ways to approach research.