- Quantitative Marketing
- Machine Learning
- Empirical Industrial Organization
- Labor Economics
- How Efficient are Decentralized Auction Platforms? (with A. L. Bodoh-Creed and B. R. Hickman) Abstract
Review of Economic Studies, 2021
- We model a decentralized, dynamic auction market platform in which a continuum of buyers and sellers participate in simultaneous, single-unit auctions each period. Our model accounts for the endogenous entry of agents and the impact of intertemporal optimization on bids. We estimate the structural primitives of our model using Kindle sales on eBay. We find that just over one third of Kindle auctions on eBay result in an inefficient allocation with deadweight loss amounting to 14% of total possible market surplus. We also find that partial centralization–for example, running half as many 2-unit, uniform-price auctions each day–would eliminate a large fraction of the inefficiency, but yield lower seller revenues. Our results also highlight the importance of understanding platform composition effects–selection of agents into the market–in assessing the implications of market redesign. We also prove that the equilibrium of our model with a continuum of buyers and sellers is an approximate equilibrium of the analogous model with a finite number of agents.
- The Missing Men: World War I and Female Labor Participation (with V. Gay) Abstract
Journal of Human Resources, 2020
- Using spatial variation in World War I military fatalities in France, we show that the scarcity of men due to the war generated an upward shift in female labor force participation that persisted throughout the interwar period. Available data suggest that increased female labor supply accounts for this result. In particular, deteriorated marriage market conditions for single women and negative income shocks to war widows induced many of these women to enter the labor force after the war. In contrast, demand factors such as substitution toward female labor to compensate for the scarcity of male labor were of second-order importance.
- Platform Data Strategy (with H. K. Bhargava et al. O. Rubel, E. J. Altman, R. Arora, K. Daniels, T. Derdenger, B. Kirschner, D. LaFramboise, P. Loupos, G. Parker, and A. Pattabhiramaiah) Abstract
Marketing Letters, 2020
- Platforms create value by enabling interactions between consumers and external producers through infrastructures and rules. We define platform data strategy to encompass all data-related rules undertaken by platforms to foster competitive advantage over the long-term. Platform firms face growing pressure to increase accountability for how they use data; yet, an explicit treatment of platforms’ data strategies and a systematic discussion of forces influencing such data-related choices is absent in the academic literature. We articulate how a platform’s data strategy varies based on platform type and business circumstances. Given the interdependencies within a platform’s ecosystem, its data strategy must balance incentives of all stakeholders. Besides discussing these topics, the paper identifies promising research opportunities in platform data strategy to better inform future academic research, strategic decision-making, and regulatory analysis.
- Preparing for a Future COVID-19 Wave: Insights and limitations from a data-driven evaluation of non-pharmaceutical interventions in Germany (with Ashwin Aravindakshan, Ehsan Gholami, and Ashutosh Nayak) Abstract
Scientific Reports, 2020
- To contain the COVID-19 pandemic, governments introduced strict Non-Pharmaceutical Interventions (NPI) that restricted movement, public gatherings, national and international travel, and shut down large parts of the economy. Yet, the impact of the enforcement and subsequent loosening of these policies on the spread of COVID-19 is not well understood. Accordingly, we measure the impact of NPIs on mitigating disease spread by exploiting the spatio-temporal variations in policy measures across the 16 states of Germany. While this quasi-experiment does not allow for causal identification, each policy’s effect on reducing disease spread provides meaningful insights. We adapt the Susceptible-Exposed-Infected-Recovered (SEIR) model for disease propagation to include data on daily confirmed cases, interstate movement, and social distancing. By combining the model with measures of policy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs. Our model finds that in Germany policies that mandated contact restrictions (e.g., movement in public space limited to two persons or people co-living), closure of educational institutions (e.g., schools), and retail outlet closures are associated with the sharpest drops in movement within and across states. Contact restrictions appear to be most effective at lowering COVID-19 cases, while border closures appear to have only minimal effects at mitigating the spread of the disease, even though cross-border travel might have played a role in seeding the disease in the population. We believe that a deeper understanding of the policy effects on mitigating the spread of COVID-19 allows a more accurate forecast of disease spread when NPIs are partially loosened and gives policymakers better data for making informed decisions.
- Design and Implementation of a Privacy Preserving Electronic Health Record Linkage Tool in Chicago (with A. N. Kho et al. J. P. Cashy, K. L. Jackson, A. R. Pah, S. Goel, J. E. Humphries, S. D. Kominers, B. N. Hota, S. A. Sims, B. A. Malin, D. D. French, T. L. Walunas, D. Meltzer, E. Kaleba, R. Jones, and W. L. Galanter) Abstract
Journal of the American Medical Informatics Association, 2015
- Objective To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.
Methods The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches.
Results The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%.
Conclusions Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.
- Machine Learning (MSBA), UC Davis Graduate School of Management
- Data Design & Representation (MSBA), UC Davis Graduate School of Management
- Business of the Future, Wine Executive Program, University of California, Davis
- Introduction to Web Scraping (MSBA), UC Davis Graduate School of Management
- Economic Research Experience for Undergraduates (BA), Becker Friedman Institute, University of Chicago
- New Tools for Acquisition / Analysis of Internet Data (PhD), Harvard University, NBER
- Practical Computing for Economists (PhD), University of Chicago
- Introduction to Data Acquisition and Data Management for Economic Research (PhD), UCLA Anderson
- Introduction to Computational Methods in Economics (PhD), University of Chicago
- Principles of Microeconomics, Economic Analysis 1 (BA), University of Chicago