Victoria Angelova

I am a Ph.D. candidate in Economics at Harvard University working on topics in Labor Economics and Public Finance.

Prior to starting my Ph.D., I received my B.A. in Economics from Wellesley College and worked as a Research Assistant at the Industrial Relations Section at Princeton University. I will be on the 2025-2026 academic job market.

You can email me at vangelova AT g DOT harvard DOT edu and access my CV here.

P.S. I was born and raised in Sofia, Bulgaria. I share my name with one of the first female architects in Bulgaria.

Job Market Paper

The Effect of the Supply of Physicians on Health in Underserved Areas with Shreya Tandon [draft]

Millions of Americans live in federally designated areas with an undersupply of primary care physicians. We estimate the causal effects of new physician arrivals to underserved areas on net physician supply, healthcare utilization, and health outcomes of local residents using quasi-random variation in the timing of physician placements through the National Health Service Corps program, linked to Medicare claims data. We find that physician arrivals increase overall primary care supply. Half of the healthcare provided by new entrants would have been satisfied by other doctors, while the other half constitutes an increase in overall utilization. The additional utilization is concentrated in preventive care services and elective inpatient procedures and leads to significant reductions in emergency hospitalizations and mortality. Health improvements persist for at least four years following physician arrival and are particularly pronounced for beneficiaries with chronic conditions and those living in more disadvantaged and rural locations.

Publications

Algorithmic Recommendations and Human Discretion with Will Dobbie and Crystal S. Yang, Accepted at Review of Economic Studies [draft]

Human decision-makers frequently override the recommendations generated by predictive algorithms, but it is unclear whether these discretionary overrides add valuable private information or reintroduce human biases and mistakes. We develop new quasi-experimental tools to measure the impact of human discretion over an algorithm on the accuracy of decisions, even when the outcome of interest is only selectively observed, in the context of bail decisions. We find that 90% of the judges in our setting underperform the algorithm when they make a discretionary override, with most making override decisions that are no better than random. Yet the remaining 10% of judges outperform the algorithm in terms of both accuracy and fairness when they make a discretionary override. We provide suggestive evidence on the behavior underlying these differences in judge performance, showing that the high-performing judges are more likely to use relevant private information and are less likely to overreact to highly salient events compared to the low-performing judges.

Working Papers

The Making of a National Mortgage Market and Its Effects on American Cities with Leonardo D'Amico [draft]

How does mortgage affordability shape city growth and fertility choices? We study the revolutions in mortgage financing that took place in the U.S. between 1933 and 1940, which created a national mortgage market facilitating mortgage capital to move from the financial centers to the rest of the country. By digitizing city-level census data and a new sample of loan-level data, we show that differences in mortgage rates across cities went from nearly 300 basis points to just over 100 in just six years. This national mortgage market allowed initially capital-scarce places to grow more than capital-abundant ones. In the decades following the housing policies, cities that had higher rates before the shock saw higher growth in rates of homeownership, population, housing construction, and house prices. Young households in these cities witnessed higher birthrates even before the post-World War II baby boom.

From Kanpur to Kentucky: Foreign Doctors and Health Outcomes in Appalachia with Shreya Tandon

There are vast disparities in access to healthcare across the United States, contributing to poor health outcomes. Up to eighty million individuals live in federally designated health professional shortage areas. Visa waiver programs were introduced to address physician shortages by granting work authorization to international medical graduates in exchange for a three-year commitment to practice in shortage areas. We are the first to study the effects of these programs on local physician supply and health outcomes. Focusing on Appalachia, we use proprietary data on all primary care physicians (PCPs) awarded visa waivers between 1999 and 2022, combined with Medicare claims data, to show that the arrival of a foreign PCP increases net physician supply. While initial gains are driven by visa waiver recipients, many of whom leave after three years, replacement by other PCPs keeps supply stable. We also find increases in healthcare utilization, especially ER visits, preventive testing and imaging, and cardiovascular procedures. Higher utilization increases diabetes and hypertension diagnoses among the previously undiagnosed, mirroring predicted rates of undetected cases among older adults. Foreign physicians thus play a critical role in providing care in medical deserts.

Other Publications

Algorithmic Recommendations When the Stakes Are High: Evidence from Judicial Elections with Will Dobbie and Crystal S. Yang

American Economic Review: Papers & Proceedings, 114: 633-637, 2024.

We ask whether increased public scrutiny leads to the more effective use of predictive algorithms. We focus on the context of bail, where judges face heightened public scrutiny during competitive partisan elections. We find that judges up for reelection are much more likely to follow the algorithmic recommendation to detain high-risk defendants just before an election. However, release decisions return to normal shortly after the election, and there is little change in pretrial misconduct rates, indicating that heightened public scrutiny, at least through competitive partisan elections, will not lead to the more effective use of predictive algorithms in bail.

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