Industrial Persistence in the U.S. and the 1918 Influenza Pandemic Público

Harty, Conor (Spring 2023)

Permanent URL: https://etd.library.emory.edu/concern/etds/8p58pf568?locale=pt-BR
Published

Abstract

The 1918 flu was one of the deadliest pandemics in history, killing over 50 million people worldwide, and over 600,000 in the U.S. alone (Taubenberger and Morens 2006). In contrast to other illnesses, which typically kill very young and elderly individuals, this flu disproportionately killed working age people between the ages of 20 to 40. Therefore, this begs the question: How did high mortality amongst prime working age people impact industries across the U.S over time and did these effects persist? Using census data from the Integrated Public Use Microdata Series (IPUMS) database as well as U.S. city-wide excess influenza data from Beach et al. (2022b), this paper evaluates whether the Spanish flu was associated with different industrial trajectories in the decades that followed. Specifically, I examine whether industries that employed large fractions of the affected age group, in cities with high levels of excess mortality, experienced greater industrial decline in the post-influenza period. The goal is to understand which industries flourished and declined across U.S. cities and see whether 1918 flu deaths impacted the share of people in industries over time. My results shows that higher excess influenza did not appear to substantially impact the industrial composition of prime age working individuals in the decades after the flu. At the end of the paper, there is also a discussion relating my work to the economic effects of the COVID-19 pandemic and highlighting future research opportunities. My findings may provide insights surrounding its effects as more data becomes available, considering that both the 1918 flu and COVID-19 had remarkable epidemiological similarities.

Table of Contents

1

Introduction

1

2

Literature Review

3dw

3

Data and Method

6

3.1

Data Sources

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

3.2

Data Cleaning and Processing

. . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

3.3

Descriptive Statistics

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

3.3.1

Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

3.3.2

Graphs

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

3.4

Methodology

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

4

Results

16

4.1

Correlates of Excess Mortality

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16

4.2

Main Results

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20

5

Conclusion

29

6

Bibliography

33

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