About Me
I'm Franco Calle, a Ph.D. and MBA student in Economics at The University of Chicago Booth School of Business, blending econometric tools, economic theory, and artificial intelligence with practical applications to solve business problems.
My work bridges academic research and industry applications in economics. In research, I've focused on optimizing spatial models for capital allocation, estimating demand and firm profits in the water utility sector, and applying large language models for expense classification. Additionally, I've used causal inference to study the effects of college education on political donations and applied machine learning to predict real-time individual income using big data.
In the private sector, I've applied my empirical toolset to practical challenges. This includes using causal inference and structural models and cloud services in AWS to assess the profitability of financial options such as buy-now-pay-later programs. I'm also involved in mobile app initiatives that leverage AI to enhance small business operations in the service sector.
Conference Presentations
I've had the opportunity to present my research at several notable conferences, including:
- Congreso Anual 2024 de la Asociacion Peruana de Economia at Universidad de Lima
- 2024 Development and Political Economics Conference at Stanford University
- Rising Scholars Conference at Chicago Booth School of Business
- Chicago Booth Student Research in Economics Seminar
Additionally, my co-authors have presented our joint research at prestigious events such as the NBER Education Program Meeting and The Econometric Society conference, further expanding the reach of our work.
Research Grants and Fellowships
My research has been supported by various grants, reflecting its potential impact:
- Grant from Becker Friedman Institute
- Grant from the Weiss Fund
- Research grant from the Booth School of Business
- Schmidt Futures Fellow - NYU for computational economics
- Several smaller grants for work in economics
For more details on my experience and publications, please refer to my CV and research portfolio below.
Featured Research
Term Limits and the Spatial Allocation of Capital by the Public Sector
A study on how term limits affect local mayors' investment decisions and their impact on local markets and consumer welfare in Peru.
Key Findings:
- Mayors preferred more proximate capital investments after term limit changes
- 20 pp reduction in households receiving untreated water
- 10 pp increase in families connected to water network
- 15% increase in water expenditure
- 6 pp annualized increase in internal rate of return on investments
Partisan Colleges and Political Donations
A study on how educational institutions shape political ideologies and activism in Chile.
Co-author: Estefano Rubio
Key Findings:
- Left-leaning universities significantly impact political donation behaviors
- 0.69 pp increase in likelihood of donating to any political party
- 0.48 pp surge in donations favoring left-wing campaigns
- 80% of leftist donations from left-leaning college alumni due to causal effect
- No significant effects for graduates of right-oriented colleges
Screening and Recruiting Talent At Teacher Colleges Using Pre-College Test Scores
An evaluation of policies using pre-college academic achievement to restrict or incentivize entry to teacher-colleges in Chile.
Co-author: Sebastian Gallegos, Christopher Neilson
Key Findings:
- Positive relationship between pre-college achievement and teacher productivity
- Targeted scholarships effective at shifting career choices of high-achieving students
- Screening policies can identify a significant portion of ex-post low performing teachers
- Screening low-performing students more effective than targeting only high-achieving students
- Better data and prediction methods can improve teacher recruitment and investment targeting
Household Income Prediction in Real Time: An analysis with Peruvian Data
An innovative approach to predicting household income one year in advance using a combination of traditional survey data and big data sources.
Co-author: Hernan Winkler
Key Findings:
- Integration of traditional survey data with big data from various sources
- Use of state-of-the-art machine learning methods for income prediction
- Incorporation of weather patterns and nightlight data as economic indicators
- Enhanced precision in income predictions
- Valuable insights for policy-making and economic planning
Other Projects
In addition to my main research projects, I've worked on various other initiatives and developed software tools to support economic analysis and modeling.
Discrete Choice Models - Matlab and Julia
A package for computing different versions of static discrete choice models that maximize the likelihood, or the joint likelihood, of a decision problem.
Learn MoreMunicipal Investment Optimization Web App
A web application that uses my research results to identify locations that would benefit from higher investment based on model estimation.
Learn MoreEconomics PhD Repository
A comprehensive collection of code for demand and supply estimation, dynamic discrete choice estimation, Kalman Filtering, Bayesian updating, and more.
Learn MoreMedia Coverage
Here are some highlights of my contribution to public discourse on important economic and political issues.
Featured Articles
Good Intentions, Bad Results
An analysis of the unintended consequences of the non-reelection policy for mayors and governors in Peru.
Read ArticleExperience Time of Mayoral Candidates Reduced Between 4 and 2.5 Years
An examination of the declining experience levels among candidates for mayoral positions.
Read Article