Introduction
The World Health Organisation declared COVID-19 a global pandemic in March 2020 (Yamin, 2020). Originating in Wuhan, China, the virus has spread quickly across the globe sending a shockwave of uncertainty, fear and confusion (ibid.). Unlike previous global pandemics such as the Spanish Flu which had taken around 40 million lives between 1918-1920, COVID-19 has seen roughly 1.5 million deaths as of December 2020 yielding a mortality rate of around 2% (Altig et al., 2020). Although this is not the deadliest virus we have encountered, it has had unprecedented implications on the global economy, leaving economists fearing that an impending economic crisis is on our door-step (Nicola et al., 2020).
This study hopes to develop an understanding of how government initiatives towards economic stability will affect the rate of economic recovery post-COVID-19.
As such, I will try to answer these research questions:

Will economic fragility caused by COVID-19 outlive the virus and lead to greater health and well-being issues?
To what extent has economic policy enacted in the UK, US, China and Singapore effected the rate of their recovery?

The forecasting and analysis of data collected in 2020 although limited gives a basic understanding of how a pandemic influences a modern economy (Yamin, 2020). It is imperative that this data is used to formulate mitigation efforts in the event that the pandemic is prolonged, both economic and social. Like an earthquake, after the initial pandemic shock, aftershocks that proceed will likely have greater impacts, directly focused on domestic and regional economies (Kimera et al., 2020).
Literature Review
My research is grounded in an economic framework, focusing on the actions of nations in response to the COVID-19 pandemic. I will address the implications of the virus and government stimulus on unemployment rates and GDP as well as their effects on the expected time of recovery.
COVID-19’s economic impacts are an anomaly, in comparison to previous pandemics (Spanish Flu and annual influenza outbreaks) with the sudden economic shock having unprecedented consequences, visible in the high unemployment rates of many developed nations (Altig et al., 2020). The World Bank (WB) has projected that the effects of the COVID-19 pandemic will cause global GDP to fall by 2% (WB, 2020). The International Monetary Fund (IMF) argues that this indicates a high risk for economic crises following the initial financial shock (2020). In order to curb this financial crisis, the WB suggests that a quick policy response is necessary, exemplified in East Asia, to not only flatten he curve of infection but also reduce economic impact facilitated by global lockdowns (Kimeria et al., 2020). East Asia has implemented a mixed approach of both social and economic measures to help alleviate the stress on both the healthcare system and the economic costs of fatalities (ibid.).

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China managed to reduce impacts and infection rates of COVID-19 after a 41-day lockdown between the 22nd January and 29th February allowing room to recover quickly from the initial shock of the pandemic (John Hopkins School of Public Heath, 2020). As of October, 22nd 2020, the IMF announced that China implemented a RMB 4.6 trillion (4.5% of GDP) fiscal stimulus package (IMF, 2020). This was in hopes of increased economic spending towards pandemic mitigation and control as well as an accelerated endorsement of unemployment benefits (ibid.). Both their policy regulation and mitigation has made the effects of the lockdown temporary for China’s economy which as of now, is argued to be the only economy that has managed to succeed so far (Kimera et al., 2020). However, in 2020 China’s GDP is estimated to rise at its lowest rate with forecasts ranging between 1-3% (Malden & Stephan, 2020)
Similarly, Singapore has managed to keep the virus at bay. Much of its experience in containing COVID-19 sparks from its extensive contract tracing system formed through their response to the SARS epidemic in 2004-05 (Woo, 2020). Singapore also carries the title of having one of the best healthcare systems in the world being described as both high quality and low cost (Haseltine, 2013). Although COVID-19 has had an impact on their unemployment, with rates reaching 2.9% in June 2020, the virus’s impact has been less than that of many Western nations (Iwamoto, 2020). With its total reserves estimated at around SG$500 billion, Singapore has a very prominent national reserve, that has and will continue to be drawn from in times of crisis (Woo, 2020). The Singaporean government began distributing relief packages on the 18th of February with a SG$6.4 billion supplementary Resilience Budget, a SG$5.1 billion Solidarity budget in April and a SG$33 billion fortitude budget in May (Lim, 2020; Tee, 2020 & Woo, 2020). The IMF has also indicated that fiscal support measures (SG$100 billion) have gone towards families with children under 20, low income families and the unemployed (IMF, 2020). These reserves are imperative to help mitigate the economic fall-out of the COVID-19 pandemic in Singapore.
Unlike China and Singapore, the United states (US) response to the COVID-19 pandemic has been suggested to lack the regional cooperation that has been seen throughout many East-Asian countries (Berquist et al., 2020). Despite the US spending the largest amount per capita on their healthcare system, its outcomes and coverage are poor (ibid.). The IMF indicated that the US government has spent $2.3 trillion towards its Coronavirus Aid, Relief and Economic security Act (CARES) which includes a variety of packages pertaining to unemployment benefits, Bankruptcy relief packages for large and small business as well as medical funding (IMF, 2020). This however, is only one of the stimulus packages that has been provided by the Trump Administration throughout this year. Notwithstanding, the US GDP dropped by a staggering 9%, the greatest decline in 70 years, with 66.7 million US citizens applying for unemployment benefits (CRS, 2020).
Likewise, the UK has also encountered the devastating effects of COVID-19, with the virus having profound implications on the labour market highlighted by unemployment due to redundancies and furlough schemes (Bank of England, 2020). The UK’s GDP fell by 22% over the first and second quarter of the year during the nationwide lockdown (Saunders, 2020). Saunders suggests that unemployment is likely to rise until the end of 2020, yet a rise in money growth has suggested that the UK’s fiscal and monetary stimulation has cushioned the initial blow (2020). This window for fiscal support is argued by the Bank of England to closing and that final recovery from economic fall-out will occur in up to two years (Bank of England,2020). In July, the IMF indicated that the UK adopted fiscal packages in order to develop and protect jobs and support financial recovery (IMF, 2020). This included supplying £1000 to firms per each furloughed employee and paying minimum wage for employees thar worked over 25 hours a week to protect young individuals in danger of long-term unemployment (ibid.). This was furthered with Chancellor Riski Sunak announcing the government will provide a £330bn pound fiscal support packaged in order to provide emergency loans for those who are in financial difficulty (Nicola et al., 2020).
Pandemics such as this end when a high proportion of the population acquires immunity (either through contraction or vaccine), however in cases where mortality rate are high, adopting containment measures can alleviate the stress. On the other hand, Eichenbaum et al. state that problems can ensue with this approach, as although lockdowns may reduce the rate of infection, this can cause a cycle of depression, and if populations never obtain herd immunity infections will rise when measures are relaxed (2020). As a result, nations diverge into a continuum of lockdown patterns which will eventually lead to further economic damage. In order to limit this, the International Monetary Fund (IMF) has suggested that more advanced economies must sustain fiscal support for both business and consumers as a means of stimulating economic growth (Congressional Research Service, 2020).
Overall, the effects of the pandemic on unemployment and GDP have become worse in nations were lockdown measures have been relaxed and reinstated as more financial aid is required to make this possible. I have used this literature to create a basic understanding of the importance of government initiatives in the wake of a modern pandemic, and the example East Asia has made in terms of economic recovery and the importance of regional and social cooperation. It is clear that Western containment measures have generally lacked regional and social cooperation, and notwithstanding their financial initiatives, seems to be leading to a prolonged medical and potentially financial recovery.
Research and Design Methodology
Research Design
This study will be looking at the impacts that COVID-19 has on specific countries GDP and a comparison of unemployment rates arising in other economic crises (pre-crisis, peak-crisis and recovery post-crisis). In addition, I will be looking at government debt levels pre-crisis and post-crisis as a proxy for government policy initiatives to mitigate the financial downturns both in terms of depth and time of recovery. Under Okun’s law, GDP and unemployment have a statistical relationship and can be used to explain how economic growth needs to occur for unemployment rates to decrease (Cuaresma, 2003). In the times of a pandemic and/or economic crisis, GDP and unemployment rates are important factors in developing policies to mitigate economic fragility. Using the principle of Okun’s law, I will attempt to estimate how long it will take for unemployment to return to pre-crisis levels after COVID-19 has been controlled.
Data Collection
This study will be conducted through the use of secondary data provided by governmental organisations (e.g. IPUMS) and global organisations (e.g. WB and IMF) through online archives. This will be a quantitative study, so data will be numerical and presented through graphs and table sets in order to be analyzed efficiently.
Methods
Unemployment is very much dependent on a country’s economic activity. Thus, in times of economic insecurity, unemployment can highlight how severe a country may be affected. By using previous viral and economic crisis, I am hoping to estimate the rate of recovery for Western and Eastern nations to return to pre-COVID-19 unemployment levels in relation to the difference in their mitigation processes.
This study will be achieved through two comparisons, the US and China and the UK and Singapore. These comparisons will occur on the basis of three previous viral and economic events, (1) the Spanish Flu which occurred between 1916-1920, (2) the Great Depression which occurred between 1929-1933 and (3) the 2007-2008 global financial crisis. By comparing these events and the related government mitigation initiatives, I am hoping to be able to estimate the speed at which these nations are likely to recover.
Two main groups of variables will be used (See Appendix B for STATA variables):

Economic variables – e.g. income, CPI, real GDP, unemployment rate etc.
Demographic variables – e.g. Population size, age etc.

The data sets will be collected through government and global organisations as previously stated and will be focused on a controlled group between the ages of 25-65. This age range is used as the vast majority of this demographic are in full time employment.
I will analyze this using multivariate regression (STATA). Multivariate regression models are used to measure to what degree an independent variable and several dependent variables are linearly related in order to produce an outcome of interest (Grant et al., 2019). In this case, this method of regression will be used to understand if the economic policies and government stimulation programs are efficient enough to mitigate the economic impact in a timely fashion.
A linear multivariate regression is denoted by this equation:
Y= β0+ β1X1+ β2X2+ ϵ
Where:
Y = outcome
β0 = Model intercept
βx = Coefficients
Xx = Covariates
ε = Random error
I wish to see if there is a linear correlation between economic policy and GDP/unemployment rates and the effectiveness of the government stimulus (using debt levels as an indicator of spend). I will anecdotally consider the methods used within that spend and their effectiveness to regain economic stability.
By comparing previous economic crises, I will be able to develop an understanding of how the quantum of governmental spend and the measures being developed to help combat COVID-19 will potentially enhance the speed to recovery. The benefits of using a multivariate regression model to estimate is that it takes into account a multitude of variables allowing the results to be similar or more realistic to that of the situation at hand. However, the results are not always easy to interpret and thus additional qualitative analysis may be necessary in explaining the outcomes of my results.
Limitations
Limitations may occur throughout this study due to research on COVID-19 being in its infancy. Data is still being collected therefore, as my research progresses, more information about the economic impacts of the virus may come to light. Values may continue to vary and be updated. As such, my research will mainly be a forecast of data that has been collected thus far, and how it affects future economic recovery. I would also like to note that data collected in the US may not be completely accurate, as the Trump Administration relocated their COVID-19 stats from the Centre of Disease Control (CDC) to their own Department of Health and Human Services which as of August 9th has been riddled with missing information, inconsistencies and delays of upload (Berquist et al., 2020)
Ethics
No ethics approval will be required as all data will be collected through public domains. No consent will be needed from individuals or organisations involved (i.e. WHO, IMF, WB, CDC etc.). See ethics form provided.
Conclusion and Timetable of Research
The aim of this study is to develop an understanding of how COVID-19 has compared to other previous pandemics/economic crises to predict the potential time to recovery. GDP and unemployment are useful mechanisms to determine the successfulness of economic policy and what now needs to be done to encourage a fast recovery. If the economic crisis does outlive the virus it has potential to cause more health and wellbeing issues that may be more damaging than the initial pandemic shock itself.
Leaders from the G20 predict that economic recovery will take the shape of a ‘V’ curve (Nicola et al., 2020). This has been countered by the Secretary General of the OEDC, Angel Gurria who believes that economic recovery may resemble a ‘U’ shaped curve, with years of economic inactivity to follow the containment of COVID-19 (Chan, 2020). My study hopes to understand how economic recovery will present itself post-COVID-19 with focus on GDP/unemployment rates and if government initiatives will have a beneficial outcome on stabilizing current economic fragility.
The timetable of my study will cover a 5-month period as showcased in appendix A. This outline presents how I wish to accomplish my study throughout this time period. I structured the outline in the format of a table to indicate how parts of the study may overlap and how I will manage my time at these points.
Appendix A

Time Table

January

February

March

April

May

Data collection

Start Data collection, focusing on GDP and Unemployment Rates

Data collection should be finished by mid- to end of this month

No data collection should occur past this point unless new information has arisen

N/A

N/A

Data Analysis

Data analysis should not occur yet whilst data is being collected

Timeseries data analysis and unemployment regression should be started

Analysis for both Timeseries and unemployment regression should be finished by the end of this month with new information added if it arises

N/A

N/A

Literature Review

Literature should be collected to either develop or argue against my information in order to create a discourse surrounding the economic impacts of COVID-19

Literature should be collected in agreement with my argument and providing additional substance to my research

Additional information that may occur about the economic impacts of COVID-19 in 2020 should be collected

Any additional research about the impacts of COVID-19 on the economy should be included to make my research as up to date as possible

N/A

Write-up

N/A

N/A

Methodology and analysis of data collected and placed into the regression and Timeseries should be analyzed during this period 
Plan of write-up for introduction and literature review should be completed

Plan of study should be completed in this month
The write up should be completed by the end of this month including methodology and data analysis in order for review of the work to be completed in May

Final touch ups and changes should be made in this month before submission to make sure work is completed to a high standard and exemplifies my argument and research accordingly

Appendix B
Economic Variables

Variables

Definition

Incwage

An individual’s annual salary

CPI (year)

To adjust all income to real dollars to take into account inflation (for constant variable)

RealGDP

Inflation adjusted measure

Unemloy

Proportion of those unemployed annually

Wagelab

Those who are not self-employed

Debtlev

Governmental debt levels pre-crisis

Demographic variables

Variables

Definition

Pop

Population of country

Age

Age of individual

labforce

Labour force

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