« Back to Results

CSMGEP Dissertation Session

Paper Session

Friday, Jan. 5, 2024 8:00 AM - 10:00 AM (CST)

Grand Hyatt, Lone Star Ballroom Salon A
Hosted By: American Economic Association & Committee on the Status of Minority Groups in the Economics Profession
  • Chair: Neville Francis, University of North Carolina-Chapel Hill

COBOLing Together UI Benefits: How Delays in Fiscal Stabilizers Impact Aggregate Consumption

Michael A. Navarrete
University of Maryland


The United States experienced an unprecedented increase in unemployment insurance (UI) claims starting in March 2020, mainly due to layoffs caused by COVID-19. State unemployment insurance systems were inadequately prepared to process these claims. Those states using an antiquated programming language, COBOL, to process UI claims experienced an increase in administrative burdens for potential claimants, which led to longer delays in benefit disbursement and an increase in discouraged filers. Using daily card consumption data from Affinity Solutions, I employ a two-way fixed effects estimator to estimate the causal impact of having an antiquated UI benefit system on aggregate consumption. The antiquated UI systems caused a 3.6 percentage point relative decline in total card consumption in COBOL states relative to non-COBOL states. This effect can be decomposed into three types: (1) delays in a claimant filing a claim, (2) delays in a UI benefit system processing a claim, and (3) an increase in discouraged filers. I find a 1.5 percentage point increase in the share of claims that experience a processing delay over 70 days in COBOL states relative to non-COBOL states. I also find suggestive evidence that the increase in administrative burdens for claimants in COBOL states led to an additional 5.6 million discouraged filers relative to non-COBOL states. These discouraged filers would have claimed UI benefits in COBOL states if the administrative burdens in their COBOL states had been as low as in non-COBOL states. Performing a back-of-the-envelope calculation using 2019 data, I find that a 3.6 percentage point decline in consumption in COBOL states in 2020 after the emergency declaration corresponds to a real GDP decline of $220 billion (in 2012 dollars).

Do Students and Parents Prefer Same-Gender Teachers? Evidence from an Online Tutoring Platform

Ini Umosen
University of California-Berkeley


The tendency for individuals to associate with others of the same gender (known as gender homophily), has been found to be applicable in a variety of social networks. This paper studies whether this gender preference extends to the classroom. In other words, do students prefer to work with teachers of the same gender? To add to the thin literature on student preferences for having a teacher of the same gender, I use innovative data from an online tutoring marketplace. I find that female tutors are 14pp more likely than male tutors to match with a female student, even after controlling for virtually all observable tutor characteristics. The gap balloons to 24pp when parents are more involved in the match-making process. Out of all academic subjects, gender matching is greatest when math is the subject being taught. I also find evidence that the Covid-19 pandemic increased gender homophily in student-tutor matches by changing the composition of students who use the platform.

Examining State R&D Policies and Federal Grant Opportunities: Analyzing the Effect on Small High-Tech Businesses from Socially Disadvantaged Groups

April Burrage
University of Massachusetts-Amherst


This paper examines how state R&D policy affects federal grant opportunities for high-tech startups from socially disadvantaged groups. I examine how state research and development (R&D) tax credits for startups owned by women and ethnic minorities affect engagement in the federal Small Business Innovation Research (SBIR) program, which is explicitly charged with encouraging minority and disadvantaged participation in technological innovation. Employing a doubly robust, staggered difference-in-differences estimator, I leverage variation in the implementation of state R&D tax credit programs to examine the impact on all startups, as well as the differential effects by race and gender. The results suggest that the impact of R&D policies on SBIR success for both underrepresented and non-underrepresented businesses varies by state, with underrepresented entrepreneurs showing post-implementation trends similar to non-underrepresented entrepreneurs.

International Production Networks and Economic Growth: Evidence from the U.S. Semiconductor Industry

Eric Neuyou
University of Arkansas


This paper investigates the extent and the mechanisms through which a shock to the semiconductor industry impacts U.S. economic growth. I use a historical perspective and network approach to examine the evolution of semiconductor sector’s role in the structural changes of the U.S. economy during the period of 1967-2012. Exploiting detailed input output tables from the Bureau of Economic Analysis and data on 364 industries from NBER CES manufacturing database, I combine network tools and a dynamic panel estimation to quantify the importance of semiconductor on U.S. GDP. My findings suggest that a one standard deviation shock to the semiconductor productivity would induce a 6% change of U.S. aggregate output. The supply effect of semiconductor’s role in the intersectoral linkages of U.S. economy explain 98% of this impact while the demand effect is insignificant. I identify and quantify semiconductor’s scale, scope, and speed effects in the network as the mechanisms driving its growing influence on U.S. economy. My study contributes to the vision to increase U.S. domestic production of semiconductor.

Maxim Massenkoff
Naval Postgraduate School
Matthew Notowidigdo
University of Chicago
Scott Stern
Massachusetts Institute of Technology
Kim Ruhl
University of Wisconsin-Madison
JEL Classifications
  • E0 - General
  • J0 - General