Uncertainty Analysis of Business Interruption Losses in the Philippines Due to the COVID-19 Pandemic

In this study, we utilize an input–output (I–O) model to perform an ex-post analysis of the COVID-19 pandemic workforce disruptions in the Philippines. Unlike most disasters that debilitate physical infrastructure systems, the impact of disease pandemics like COVID-19 is mostly concentrated on the workforce. Workforce availability was adversely affected by lockdowns as well as by actual illness. The approach in this paper is to use Philippine I–O data for multiple years and generate Dirichlet probability distributions for the Leontief requirements matrix (i.e., the normalized sectoral transactions matrix) to address uncertainties in the parameters. Then, we estimated the workforce dependency ratio based on a literature survey and then computed the resilience index in each economic sector. For example, sectors that depend heavily on the physical presence of their workforce (e.g., construction, agriculture, manufacturing) incur more opportunity losses compared to sectors where workforce can telework (e.g., online retail, education, business process outsourcing). Our study estimated the 50th percentile economic losses in the range of PhP 3.3 trillion (with telework) to PhP 4.8 trillion (without telework), which is consistent with independently published reports. The study provides insights into the direct and indirect economic impacts of workforce disruptions in emerging economies and will contribute to the general domain of disaster risk management.

Pandemic wave trends in COVID-19 cases, mobility reduction, and climate parameters in major metropolitan areas in the United States

In this paper, we analyzed the association among trends in COVID-19 cases, climate, air quality, and mobility changes during the first and second waves of the pandemic in five major metropolitan counties in the United States: Maricopa in Arizona, Cook in Illinois, Los Angeles in California, Suffolk in Massachusetts, and New York County in New York. These areas represent a range of climate conditions, geographies, economies, and state-mandated social distancing restrictions. In the first wave of the pandemic, cases were correlated with humidity in Maricopa, and temperature in Maricopa and Los Angeles. In Suffolk and New York, cases were correlated with mobility changes in recreation, grocery, parks, and transit stations. Neither cases nor death counts were strongly correlated with air quality. Periodic fluctuations in mobility were observed for residential areas during weekends, resulting in stronger correlation coefficients when only weekday datasets were included in the analysis. We also analyzed case-mobility correlations when mobility days were lagged, and found that the strongest correlation in the first wave occurred between 12 and 14 lag days (optimal at 13 days). There was stronger but greater variability in correlation coefficients across metropolitan areas in the first pandemic wave than in the second wave, notably in recreation areas and parks. In the second wave, there was less variability in correlations over lagged time and geographic locations. Overall, we did not find conclusive evidence to support associations between lower cases and climate in all areas. Furthermore, the differences in cases-mobility correlation trends during the two pandemic waves are indicative of the effects of travel restrictions in the early phase of the pandemic and gradual return to travel routines in the later phase. This study highlights the utility of mobility data in understanding the dynamics of disease transmission. It also emphasizes the criticality of timeline and local context in interpreting transmission trends. Mobility data can capture community response to local travel restrictions at different phases of their implementation and provide insights on how these responses evolve over time alongside disease trends.

Epidemiological and economic impact of COVID-19 in the US.

This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of the lockdown. Sectors that are worst hit are not the labor-intensive sectors such as the Agriculture sector and the Construction sector, but the ones with high valued jobs such as the Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown.

The Economic Impact of Lockdowns: A Persistent Inoperability Input-Output Approach

The COVID-19 pandemic has forced governments around the world to implement unprecedented lockdowns, mandating businesses to shut down for extended periods of time. Previous studies have modeled the impact of disruptions to the economy at static and dynamic settings. This study develops a model to fulfil the need to account for the sustained disruption resulting from the extended shutdown of business operations. Using a persistent inoperability input-output model (PIIM), we are able to show that (1) sectors that suffer higher levels of inoperability during quarantine period may recover faster depending on their resilience;(2) initially unaffected sectors can suffer inoperability levels higher than directly affected sectors over time;(3) the economic impact on other regions not under lockdown is also significant.

Epidemiological and Economic Impact of COVID-19 in the US.

This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lock down, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of lockdown. Sectors that are worst hit are not the labor-intensive sectors such as Agriculture and Construction, but the ones with high valued jobs such as Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown.

Using input-output analysis to model the impact of pandemic mitigation and suppression measures on the workforce.

The "flatten the curve" graphic has recently become a common tool to visualize the extent to which pandemic suppression and mitigation measures could potentially reduce and delay the number of daily infections due to a pandemic. The COVID-19 pandemic has challenged the capacity of the many healthcare systems and created cascading economic impacts on interdependent sectors of the global society. This paper specifically explores the impact of pandemics on the workforce. The model proposed in this paper comprises of three major steps. First, sources for epidemic curves are identified to generate the attack rate, which is the daily number of infections normalized with respect to the population of the affected region. Second, the model assumes that the general attack rate can be specialized to reflect sector-specific workforce classifications, noting that each economic sector has varying dependence on the workforce. Third, using economic input-output (IO) data from the US Bureau of Economic Analysis, this paper analyzes the performance of several mitigation and suppression measures relative to a baseline pandemic scenario. Results from the IO simulations demonstrate the extent to which mitigation and suppression measures can flatten the curve. This paper concludes with reflections on other consequences of pandemics such as the mental health impacts associated with social isolation and the disproportionate effects on different socioeconomic groups.