Leila Agha

Department of Economics
Dartmouth College
6106 Rockefeller Hall
Hanover, NH 03755

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
NBER Program Affiliations: HC
NBER Affiliation: Faculty Research Fellow
Institutional Affiliation: Dartmouth College

NBER Working Papers and Publications

February 2018Team Formation and Performance: Evidence from Healthcare Referral Networks
with Keith Marzilli Ericson, Kimberley H. Geissler, James B. Rebitzer: w24338
How does team‐specific capital affect productivity? We examine the teams that primary care physicians (PCPs) assemble when referring patients to specialists. Our theoretical model finds that team‐specific capital is greater when PCPs concentrate their referrals within a smaller set of specialists. Empirically, we find patients of PCPs with concentrated referrals have lower healthcare costs, with no discernable reduction in quality. This effect exists for commercially insured and Medicare populations; is statistically and economically significant; and holds under identification strategies that account for unobserved patient and physician characteristics.
January 2017Fragmented Division of Labor and Healthcare Costs: Evidence from Moves Across Regions
with Brigham Frandsen, James B. Rebitzer: w23078
Policies aiming to improve healthcare productivity often focus on reducing care fragmentation. Care fragmentation occurs when services are spread across many providers, potentially making coordination difficult. Using Medicare claims data, we analyze the effect of moving to a region with more fragmented care delivery. We find that 60% of regional variation in care fragmentation is independent of patients' individual demand for care and moving to a region with 1 SD higher fragmentation increases care utilization by 10%. When patients move to more fragmented regions, they increase their use of specialists and have fewer encounters with primary care physicians. More fragmented regions have more intensive care provision on many margins, including services sometimes associated with overutilizat...

Published: Leila Agha & Brigham Frandsen & James B. Rebitzer, 2019. "Fragmented division of labor and healthcare costs: Evidence from moves across regions," Journal of Public Economics, vol 169, pages 144-159. citation courtesy of

January 2015The Local Influence of Pioneer Investigators on Technology Adoption: Evidence from New Cancer Drugs
with David Molitor: w20878
Local opinion leaders may play a key role in easing information frictions associated with technology adoption. This paper analyzes the influence of physician investigators who lead clinical trials for new cancer drugs. By comparing diffusion patterns across 21 new cancer drugs, we separate correlated regional demand for new technology from information spillovers. Patients in the lead investigator's region are initially 36% more likely to receive the new drug, but utilization converges within four years. We also find that “superstar ” physician authors, measured by trial role or citation history, have broader influence than less prominent authors.

Published: Leila Agha & David Molitor, 2018. "The Local Influence of Pioneer Investigators on Technology Adoption: Evidence from New Cancer Drugs," The Review of Economics and Statistics, vol 100(1), pages 29-44.

March 2014Negative Tests and the Efficiency of Medical Care: What Determines Heterogeneity in Imaging Behavior?
with Jason Abaluck, Christopher Kabrhel, Ali Raja, Arjun Venkatesh: w19956
We develop a model of the efficiency of medical testing based on rates of negative CT scans for pulmonary embolism. The model is estimated using a 20% sample of Medicare claims from 2000- 2009. We document enormous across-doctor heterogeneity in testing decisions conditional on patient risk and show it explains the negative relationship between physicians' testing frequencies and test yields. Physicians in high spending regions test more low-risk patients. Under calibration assumptions, 84% of doctors test even when costs exceed expected benefits. Furthermore, doctors do not apply observables to target testing to the highest risk patients, substantially reducing simulated test yields.

Published: Abaluck, Jason, Leila Agha, Chris Kabrhel, Ali Raja, and Arjun Venkatesh. 2016. "The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care." American Economic Review, 106 (12): 3730-64.

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