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

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.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Difference-in-Difference results (X and T random and true level = 6x6 resolution).

<p>(A) ρ = 0. (B) ρ = 0.5. (C) ρ = 0.9.</p

Goldilocks and the Raster Grid: Selecting Scale when Evaluating Conservation Programs

<div><p>Access to high quality spatial data raises fundamental questions about how to select the appropriate scale and unit of analysis. Studies that evaluate the impact of conservation programs have used multiple scales and areal units: from 5x5 km grids; to 30m pixels; to irregular units based on land uses or political boundaries. These choices affect the estimate of program impact. The bias associated with scale and unit selection is a part of a well-known dilemma called the <i>modifiable areal unit problem</i> (MAUP). We introduce this dilemma to the literature on impact evaluation and then explore the tradeoffs made when choosing different areal units. To illustrate the consequences of the MAUP, we begin by examining the effect of scale selection when evaluating a protected area in Mexico using real data. We then develop a Monte Carlo experiment that simulates a conservation intervention. We find that estimates of treatment effects and variable coefficients are only accurate under restrictive circumstances. Under more realistic conditions, we find biased estimates associated with scale choices that are both too large or too small relative to the data generating process or decision unit. In our context, the MAUP may reflect an <i>errors in variables problem</i>, where imprecise measures of the independent variables will bias the coefficient estimates toward zero. This problem may be pronounced at small scales of analysis. Aggregation may reduce this bias for continuous variables, but aggregation exacerbates bias when using a discrete measure of treatment. While we do not find a solution to these issues, even though treatment effects are generally underestimated. We conclude with suggestions on how researchers might navigate their choice of scale and aerial unit when evaluating conservation policies.</p></div

Sample chart and conversion table for the horizontal axis.

<p>Sample chart and conversion table for the horizontal axis.</p

OLS Results (X correlated, T dependent on X, ρ = 0).

<p>(A) True level = 1x1 resolution (pixel level). (B) True level = 6x6 resolution.</p

Percent difference in coefficient estimates between discrete and continuous and over aggregation.

<p>Percent difference in coefficient estimates between discrete and continuous and over aggregation.</p

OLS versus STSLS results (true level = 6x6 resolution).

<p>(A) OLS result (X correlated, T = f(X), ρ = 0.9). (B) STSLS result (X correlated, T = f(X), ρ = 0.9). (C) OLS result (X correlated, T = f(X), ρ = 0).</p

Comparison of methods and scale used in studies that evaluate conservation impact.

<p>(T: # treatment units; C: # control units).</p

Aggregation and Disaggregation Procedure.

<p>Example of a spatial process with true level = 4x4 resolution.</p