1. Introduction and Overview Table 1....

1. Introduction and Overview
Table 1.	Cumulative Cases and Deaths (January 5, 2020–February 11, 2024).	2
2. The Heterodox Economics of Passenger Airlines, Plagues, Pandemics, and Other Unhealthy Occurrences
Table 1.	Timeline of Major Epidemics and Pandemics Since 1980.	24
Table 2.	Comparison of 2017–2018 Seasonal Influenza Season to the COVID-19 Pandemic in the United States.	27
Table 3.	State Support Rendered to Airlines Between January and August 2020 by Order of Aggregate Amounts.	37
3. Nonmarket Strategies of Airlines in a COVID-19 World and Beyond
Table 1.	Selected European Airlines Bankruptcies.	54
Table 2.	Financial Support Offered to Select European Airlines.	56
4. COVID-19 Uncertainty and the Cross-Sectional Stock Returns of Airlines
Table 1.	Descriptive Statistics on Daily Return.	75
Table 2.	Empirical Results of Eq. (1).	79
Table 3.	Descriptive Statistics.	85
Table 4.	Bivariate Regression of Pandemic Beta on Airline Attributes.	86
Table A1.	Empirical Results for Eq. (2).	89
Table A2.	Empirical Results for Eq. (3).	90
5. COVID-19 and Airlines: A Final Analysis Through the Lens of Complex Networks
Table 1.	The Calculation Process for Betweenness Centrality of Node 1 (j < k) in Fig. 2.	99
Table 2.	Top 50 Airlines by Fleet Sizes, Sorted by Regions (Airlines Analyzed in This Study Are Highlighted in Bold).	103
6. Rebuilding Airline Networks in the Post-COVID-19 Era: New Network Configurations in Europe?
Table 1.	Variations in Total Seats Offered by Business Model and Type of Operation Between Pre- and Postpandemic Periods (09/2019 vs. 09/2022).	124
Table 2.	Variation Number of Competitors per Route (09/2019–09/2022).	128
Table 3.	Share of Routes by Period of Operation (09/2019–09/2022).	130
7. Competition Between Full-Service Carrier and Low-Cost Carrier and the Impact of COVID-19 Pandemic: Evidence From China
Table 1.	Profile of Current Chinese LCC as of 2023.	150
Table 2.	Comparison of Unit Operating Costs Between Spring Airlines and the Big Three Airlines.	151
Table 3.	Comparison of Traffic Between Spring Airlines and the Big Three Airlines.	152
Table 4.	The Rank of City Air Passenger Traffic.	164
Table 5.	The Number of Different Routes Before and After the Pandemic Outbreak.	166
Table 6.	The Ratios of Different Types of Routes When Comparing the Level of December 2022 With December 2019.	167
Table 7.	Comparison of FSC–LCC Market Contact Before and After the Pandemic.	168
8. Measuring the Risk of COVID-19 Spread via the US Air Transportation Network
Table 1.	Descriptive Statistics, 2020.	182
Table 2.	Descriptive Statistics, 2021.	183
Table 3.	Top 20 at-Risk Destination Counties, 2020 Versus 2021.	188
Table 4.	Correlation Table.	190
Table 5.	Travel Risk Regression Results Accounting for County- and State-Route Fixed Effects. The Dependent Variable is the Relative Risk at Destination (R^j).	193
Table 6.	Travel Risk Log-Log Regression Results Accounting for State-Route Fixed Effects. The Dependent Variable Is Natural Log of Relative Risk at Destination (ln(R^j)).	195
Table 7.	Travel Risk Regression Results. The Dependent Variable Is the Risk Spread From Origin to Destination Counties (r_ij).	198
Table 8.	Travel Risk Regression Results Accounting for County- and State-Route Fixed Effects. The Dependent Variable Is the Risk of COVID-19 Spread From Origin to Destination Counties (r_ij).	200
Table 9.	Travel Risk Log-Log Regression Results. The Dependent Variable Is Log of Risk of COVID-19 Spread From Origin to Destination Counties (ln(r_ij)).	201
Table 10.	Travel Risk Log-Log Regression Results Accounting for County- and State-Route Fixed Effects. The Dependent Variable Is the Natural Log of Risk of COVID-19 Spread From Origin to Destination Counties (ln(r_ij)).	203
Table 11.	2SLS Regression Results. The Dependent Variable Is the Relative Risk at Destination (R^j).	206
9. Effects of the COVID-19 Pandemic and Related Policies on Airport Short-Term Costs
Table 1.	Descriptive Statistics, 50 US Airports, 2012–2021.	224
Table 2.	ITSUR Estimation Results of Translog Cost Function With Negative Attributes, COVID-19 Cases, and Related Policies, 50 US Airports, 2012–2021.	226
Table 3.	Characteristics of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Full Sample, 2012–2021.	229
Table 4.	Decomposition for Percentage Change in Average Variable Costs of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Full Sample, 2012–2021.	231
Table 5.	Decomposition for Percentage Change in Average Variable Costs of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Large Hubs, 2012–2021, Subtotals.	232
Table 6.	Decomposition for Percentage Change in Average Variable Costs of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Medium Hubs, 2012–2021, Subtotals.	233
Table A1.	Airport List.	243
Table A2.	Data Sources.	244
Table A3.	Characteristics of Representative Airports for Pre-COVID-19 and COVID-19 Periods, by hub Size, 2012–2021.	245
10. COVID-19's Effect on the Technical Efficiency and Productivity of US Airlines: An Industry Sectoral Analysis
Table 1.	Descriptive Statistics by Service Type From 2006 to 2021, Mean.	260
Table 2.	Descriptive Statistics by Service Type During COVID-19 (2020, 2021) and During Pre-COVID-19 (2018, 2019), Mean.	262
Table 3.	Descriptive Statistics by Service Type in 2020 and 2021, Mean.	264
Table 4.	Technical Efficiency Scores by Carrier.	271
Table 5.	Tobit and Fractional Logit Regressions of Technical Efficiency (TE) Scores.	272
Table 6.	Total Factor Productivity and Its Components by Service Type.	274
11. Analysis of the Impacts of COVID-19 on US Airline Schedule Planning and Service Delivery, 2018 to 2022
Table 1.	Business Resilience Measures for China, ECAA, and the United States.	295
Table 2.	Business Resilience Measures for US Airport Categories.	296
Table 3.	Business Resilience Measures for Top 10 US Passenger Airlines.	297
Table 4.	Monthly Schedule Differences by Airport Category: Summary Metrics.	300
Table 5.	Summary and Comparative Statistics for Top Four Airlines, 2018–2022.	304
12. Starting From the Backhaul: Evaluating the Effects of COVID-19 on Airline Traffic Flows
Table 1.	Output Measures (RPM and RFTM, in Millions) for Air Carriers by Country.	326
Table 2.	Carrier Continent/Country and COVID-19 Travel Restrictions.	330
Table 3.	Fronthaul RFTM (in Millions), Air Carriers by Country, and COVID-19 Travel Restrictions.	331
Table 4.	Backhaul RFTM (in Millions), Air Carriers by Country, and COVID-19 Travel Restrictions.	331
Table 5.	Nonparametric Breakpoints in Fronthaul and Backhaul Load Factors by Carrier Continent.	336
13. Global Airline Employment and the COVID-19 Pandemic: Impacts, Comparisons, and Implications for the Future
Table 1.	Change in Total Employment for US airlines From February 2020.	356

1. Introduction and Overview
Table 1.	Cumulative Cases and Deaths (January 5, 2020–February 11, 2024).	2
2. The Heterodox Economics of Passenger Airlines, Plagues, Pandemics, and Other Unhealthy Occurrences
Table 1.	Timeline of Major Epidemics and Pandemics Since 1980.	24
Table 2.	Comparison of 2017–2018 Seasonal Influenza Season to the COVID-19 Pandemic in the United States.	27
Table 3.	State Support Rendered to Airlines Between January and August 2020 by Order of Aggregate Amounts.	37
3. Nonmarket Strategies of Airlines in a COVID-19 World and Beyond
Table 1.	Selected European Airlines Bankruptcies.	54
Table 2.	Financial Support Offered to Select European Airlines.	56
4. COVID-19 Uncertainty and the Cross-Sectional Stock Returns of Airlines
Table 1.	Descriptive Statistics on Daily Return.	75
Table 2.	Empirical Results of Eq. (1).	79
Table 3.	Descriptive Statistics.	85
Table 4.	Bivariate Regression of Pandemic Beta on Airline Attributes.	86
Table A1.	Empirical Results for Eq. (2).	89
Table A2.	Empirical Results for Eq. (3).	90
5. COVID-19 and Airlines: A Final Analysis Through the Lens of Complex Networks
Table 1.	The Calculation Process for Betweenness Centrality of Node 1 (j < k) in Fig. 2.	99
Table 2.	Top 50 Airlines by Fleet Sizes, Sorted by Regions (Airlines Analyzed in This Study Are Highlighted in Bold).	103
6. Rebuilding Airline Networks in the Post-COVID-19 Era: New Network Configurations in Europe?
Table 1.	Variations in Total Seats Offered by Business Model and Type of Operation Between Pre- and Postpandemic Periods (09/2019 vs. 09/2022).	124
Table 2.	Variation Number of Competitors per Route (09/2019–09/2022).	128
Table 3.	Share of Routes by Period of Operation (09/2019–09/2022).	130
7. Competition Between Full-Service Carrier and Low-Cost Carrier and the Impact of COVID-19 Pandemic: Evidence From China
Table 1.	Profile of Current Chinese LCC as of 2023.	150
Table 2.	Comparison of Unit Operating Costs Between Spring Airlines and the Big Three Airlines.	151
Table 3.	Comparison of Traffic Between Spring Airlines and the Big Three Airlines.	152
Table 4.	The Rank of City Air Passenger Traffic.	164
Table 5.	The Number of Different Routes Before and After the Pandemic Outbreak.	166
Table 6.	The Ratios of Different Types of Routes When Comparing the Level of December 2022 With December 2019.	167
Table 7.	Comparison of FSC–LCC Market Contact Before and After the Pandemic.	168
8. Measuring the Risk of COVID-19 Spread via the US Air Transportation Network
Table 1.	Descriptive Statistics, 2020.	182
Table 2.	Descriptive Statistics, 2021.	183
Table 3.	Top 20 at-Risk Destination Counties, 2020 Versus 2021.	188
Table 4.	Correlation Table.	190
Table 5.	Travel Risk Regression Results Accounting for County- and State-Route Fixed Effects. The Dependent Variable is the Relative Risk at Destination (R^j).	193
Table 6.	Travel Risk Log-Log Regression Results Accounting for State-Route Fixed Effects. The Dependent Variable Is Natural Log of Relative Risk at Destination (ln(R^j)).	195
Table 7.	Travel Risk Regression Results. The Dependent Variable Is the Risk Spread From Origin to Destination Counties (r_ij).	198
Table 8.	Travel Risk Regression Results Accounting for County- and State-Route Fixed Effects. The Dependent Variable Is the Risk of COVID-19 Spread From Origin to Destination Counties (r_ij).	200
Table 9.	Travel Risk Log-Log Regression Results. The Dependent Variable Is Log of Risk of COVID-19 Spread From Origin to Destination Counties (ln(r_ij)).	201
Table 10.	Travel Risk Log-Log Regression Results Accounting for County- and State-Route Fixed Effects. The Dependent Variable Is the Natural Log of Risk of COVID-19 Spread From Origin to Destination Counties (ln(r_ij)).	203
Table 11.	2SLS Regression Results. The Dependent Variable Is the Relative Risk at Destination (R^j).	206
9. Effects of the COVID-19 Pandemic and Related Policies on Airport Short-Term Costs
Table 1.	Descriptive Statistics, 50 US Airports, 2012–2021.	224
Table 2.	ITSUR Estimation Results of Translog Cost Function With Negative Attributes, COVID-19 Cases, and Related Policies, 50 US Airports, 2012–2021.	226
Table 3.	Characteristics of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Full Sample, 2012–2021.	229
Table 4.	Decomposition for Percentage Change in Average Variable Costs of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Full Sample, 2012–2021.	231
Table 5.	Decomposition for Percentage Change in Average Variable Costs of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Large Hubs, 2012–2021, Subtotals.	232
Table 6.	Decomposition for Percentage Change in Average Variable Costs of Representative Airports for Pre-COVID-19 and COVID-19 Periods, Medium Hubs, 2012–2021, Subtotals.	233
Table A1.	Airport List.	243
Table A2.	Data Sources.	244
Table A3.	Characteristics of Representative Airports for Pre-COVID-19 and COVID-19 Periods, by hub Size, 2012–2021.	245
10. COVID-19's Effect on the Technical Efficiency and Productivity of US Airlines: An Industry Sectoral Analysis
Table 1.	Descriptive Statistics by Service Type From 2006 to 2021, Mean.	260
Table 2.	Descriptive Statistics by Service Type During COVID-19 (2020, 2021) and During Pre-COVID-19 (2018, 2019), Mean.	262
Table 3.	Descriptive Statistics by Service Type in 2020 and 2021, Mean.	264
Table 4.	Technical Efficiency Scores by Carrier.	271
Table 5.	Tobit and Fractional Logit Regressions of Technical Efficiency (TE) Scores.	272
Table 6.	Total Factor Productivity and Its Components by Service Type.	274
11. Analysis of the Impacts of COVID-19 on US Airline Schedule Planning and Service Delivery, 2018 to 2022
Table 1.	Business Resilience Measures for China, ECAA, and the United States.	295
Table 2.	Business Resilience Measures for US Airport Categories.	296
Table 3.	Business Resilience Measures for Top 10 US Passenger Airlines.	297
Table 4.	Monthly Schedule Differences by Airport Category: Summary Metrics.	300
Table 5.	Summary and Comparative Statistics for Top Four Airlines, 2018–2022.	304
12. Starting From the Backhaul: Evaluating the Effects of COVID-19 on Airline Traffic Flows
Table 1.	Output Measures (RPM and RFTM, in Millions) for Air Carriers by Country.	326
Table 2.	Carrier Continent/Country and COVID-19 Travel Restrictions.	330
Table 3.	Fronthaul RFTM (in Millions), Air Carriers by Country, and COVID-19 Travel Restrictions.	331
Table 4.	Backhaul RFTM (in Millions), Air Carriers by Country, and COVID-19 Travel Restrictions.	331
Table 5.	Nonparametric Breakpoints in Fronthaul and Backhaul Load Factors by Carrier Continent.	336
13. Global Airline Employment and the COVID-19 Pandemic: Impacts, Comparisons, and Implications for the Future
Table 1.	Change in Total Employment for US airlines From February 2020.	356

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