Essays on production chains and disruptions: new input-output perspectives on time, scale and space

Modern production chains have captured the gains from economies of scale and industrial specialization by creating local and global networks of intermediate and final goods. Nonetheless, enhanced industrial interdependency has also magnified regional exposure to external shocks transmitted through both demand and supply channels. Natural and man-made disasters have a major role in creating these local disruptions, which regional reverberations depend on the magnitude of physical damages, location, timing and resilience of up and downstream industries. Although stock damages are well understood and measured in the literature, higher-order flow effects taking place in the post-disaster period tend to be overlooked. As a result, current mitigation and preparedness strategies are myopically applied to the affected region as if they had no spatial and temporal linkages. In this dissertation, I advance the theoretical background and broaden the policy implications of the input-output (IO) framework to disruptive events by revising the topics of time, scale and space. In Chapter 1, I explore the issue of intra-year seasonality in production chains and its implications for the IO framework. Due to the limited amount of multi-sectoral data at sub-annual level, I propose a novel methodology to disaggregate IO tables in time that relies solely on quarterly GDP information to estimate intra-year tables. I estimate the quarterly IO tables for Brazil in 2004 and show that the multipliers for agriculture in Brazil deviate more than 6% within year from the annual model. Because of the fine geographical scale of disruptive events, it is essential to be able to consider such seasonal variations at a regional level. In Chapter 2, I provide a roadmap of publicly available data to estimate quarterly IO tables in the US for any state and county. Since data is even scarcer at these scales, I devise a maximum cross-entropy solution that allows the inclusion of specific temporal information for the region. As an example, I highlight the seasonal economic characteristics of the State of Illinois and two of its counties (Cook and Iroquois). Chapter 3 introduces a dynamic demo-economic model that synthetizes existing contributions in the disaster literature and includes production scheduling, demographics and seasonality in assessing unexpected events. In Chapter 4, I apply this new dynamic model in a real disaster event, the 2007 Chehalis Flood in Washington State, and compare its results with current models in the literature. I highlight the importance of accounting for labor markets’ dynamics and fluctuations in the sectoral structure intra-year when assessing the costs of disruptive events. I also show how significant the timing of the disruption is in assessing economic losses of disasters. The advancements accomplished in this dissertation should provide the basis for more detailed analysis of production chains vulnerabilities and resilience, further reflections on seasonality patterns and their effect on industrial linkages, and the role of industrial linkages in regional dynamics.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Agricultural & Applied Econ

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Avelino, Andre Fernandes Tomon

Contributors dc:contributor

• Hewings, Geoffrey
• Dall'Erba, Sandy
• Önal, Hayri
• Okuyama, Yasuhide

• Natural Disasters
• Input-Output Analysis

Statement dc:rights

• Copyright 2018 Andre Fernandes Tomon Avelino

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101679

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101679