Elizaveta Gonchar PhD Dissertation Defense Announcement

PhD Candidate: Elizaveta Gonchar

Dissertation Title: “Spatial Dimensions of Economic Modeling: Interdisciplinary Approaches to Labor, Trade, and Networks”

Abstract:

This dissertation explores the spatial dimensions of economic modeling through three distinct yet interconnected papers, each employing interdisciplinary approaches to shed light on various aspects of labor, trade, and networks.

The first paper investigates the heterogeneous impacts of the COVID-19 pandemic on domestic trade flows in Colombia. A novel trade exposure measure is constructed using an iterated factor analysis approach that integrates the spatial characteristics of municipalities. By employing a spatial autoregressive estimation technique, I demonstrate the presence of spatial spillovers. The analysis reveals substantial heterogeneity in the impact of the pandemic across municipalities, highlighting the importance of considering spatial factors and utilizing appropriate econometric methods in understanding trade dynamics during global shocks.

The second paper develops a methodology to assess regional discrepancies and labor mobility in response to the CHIPS and Science Act. It quantifies occupational requirement sufficiency by comparing the skill profiles of different occupations. Using this measure in conjunction with the generalized two-part fractional regression model, which allows for examining factors associated with the occurrence and extent of occupational transitions while accounting for the bounded nature of transition probabilities, regional labor supply is estimated. The analysis demonstrates the presence of regional variations in skill composition, highlighting the importance of considering spatial factors in the design and implementation of industrial policies.

The third paper introduces a new economic distance measure based on population and nightlights data that can be operationalized at any level of aggregation and can compute internal distance measures. This measure is constructed using granular spatial data and geographic information systems techniques and provides a scale-agnostic approach for incorporating distances in trade models. The paper applies this novel distance measure to two distinct cases: a cross-sectional analysis of trade flows within the United States and a panel data analysis of global trade flows. The time-varying nature of the proposed distance measure allows it to account for shifts in population and economic activity distributions, introducing responsiveness in the measure of distance used in trade analyses. The performance of this measure across the analyses considered, along with its compatibility with various trade flow considerations, underscores its potential to contribute to a more comprehensive understanding of trade patterns.

Committee:

Usha Nair-Reichert (Advisor), Associate Professor, Georgia Institute of Technology

Tibor Besedes, Mary S. and Richard B. Inman, Jr. Professor of Economics, Georgia Institute of Technology

Karen Yan, Assistant Professor, Georgia Institute of Technology

Christophe Combemale (external member), Associate Professor, Carnegie Mellon University

Mostafa Beshkar (external member), Associate Professor, Indiana University

Date: July 10, 2024 

Time: 10:00 am – 12:15 pm EDT

Location: Virtual (Zoom link)