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The macroeconomic impacts of the COVID-19 pandemic: A SIR-DSGE model approach.

ABSTRACT: This paper proposes a susceptible-infected-removed dynamic stochastic general equilibrium (SIR-DSGE) model to assess the macroeconomic impact of the recent COVID-19 outbreak. The parameters of the SIR setting are calibrated to COVID-19 data from China. Using the model, we illustrate how the pandemic could result in consumption and output loss. We show that a combination of quarantine policy and random testing of the uninfected is effective in reducing the number of infected individuals and outperforms the alternative scenarios in which only one of the policies is implemented. Moreover, the economic impacts of both policies are evaluated. Compared with the decentralized equilibrium, we find that the Ramsey social planner allows output to decrease more substantially during the pandemic, in exchange for a faster economic recovery.

SUBMITTER: Chan YT

PROVIDER: S-EPMC8613005 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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The macroeconomic impacts of the COVID-19 pandemic: A SIR-DSGE model approach.

Chan Ying Tung YT

China economic review 20211125

This paper proposes a susceptible-infected-removed dynamic stochastic general equilibrium (SIR-DSGE) model to assess the macroeconomic impact of the recent COVID-19 outbreak. The parameters of the SIR setting are calibrated to COVID-19 data from China. Using the model, we illustrate how the pandemic could result in consumption and output loss. We show that a combination of quarantine policy and random testing of the uninfected is effective in reducing the number of infected individuals and outpe ...[more]

PMID: 35058682

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Project description:Public sector employment in China has exhibited pronounced non-cyclical characteristics, with a recruiting scale and wage level showing limited responsiveness to economic fluctuations. The allure of civil service jobs in China has seen a significant resurgence post-COVID-19, with an observable increase in demand among educated job seekers for stable government positions amid growing economic uncertainties. This study investigates the implications of public sector employment rigidity on macroeconomic stability using a dynamic stochastic general equilibrium (DSGE) model integrated with search and matching (S&M) theory. Simulations incorporating alternative government job policies reveal that non-cyclical public employment exacerbates macroeconomic cyclical fluctuations. The low elasticity of public sector wages with respect to corporate wages fosters stable expectations among workers regarding the future value of government jobs, increasing the perceived value of the current state of unemployment. This leads job seekers to voluntarily remain unemployed, reducing labor supply to firms. Meantime, it preserves workers' bargaining power with firms, reinforcing wage stickiness and undermining the stabilizing role of price adjustments in employment. Hypothetical scenario analyses indicate that adopting a pro-cyclical wage policy for the public sector can mitigate the obstacles of wage cuts for firms, stimulate the creation of new jobs during economic downturns, and consequently reduce the magnitude and duration of rising unemployment rates. In contrast, maintaining a non-cyclical public sector wage may not prevent a continuous rise in unemployment or a worsening economic situation, even with expanded sector recruitment. This finding holds significant relevance in the context of the post-COVID era characterized by an economic slump and employment tension, providing theoretical support for establishing a transparent and flexible wage adjustment mechanism in the public sector that is linked to market conditions.

Project description:BackgroundClassical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms.ObjectiveWe aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined, and recovered cases) for situations where just one data type is available to fit the model. Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.MethodsWe first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed (SIR) modelling framework. We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy: a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County, Montana, USA.ResultsWe demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data. We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification. Using a classical example of influenza epidemics in an England boarding school, we argue that the Susceptible-Infected-Quarantined-Recovered (SIQR) model is more appropriate than the commonly employed SIR model given the data collected (number of active cases). Similarly, we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.ConclusionsWe demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data. For both a classical influenza data set and a COVID-19 case data set, we demonstrate the implications of using the "right" data in the "wrong" model. The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements.

Project description:The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.

Dataset Information

The macroeconomic impacts of the COVID-19 pandemic: A SIR-DSGE model approach.

Publications

The macroeconomic impacts of the COVID-19 pandemic: A SIR-DSGE model approach.

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