In the wake of COVID-19 and associated lockdowns, businesses in both the oil and gas industry and the recreation industry saw a 20 percent reduction in revenues when comparing the revenues generated between April 2020 to March 2021 with revenues generated between April 2019 to March 2020. The top performing industries during the same time period can be accessed here.
The only industries that registered a positive change in the GDP in the 2nd quarter of 2020 compared to the 1st quarter, were health services, and public administration and defense. In contrast, the most affected industry by the coronavirus (COVID-19) pandemic in Romania was tourism and hospitality, followed by culture and arts. However, by the 3rd quarter of 2020, all the industries apart from education, agriculture, and public administration and defense, registered a positive change in GDP.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.
The outbreak of coronavirus in Poland will significantly reduce labor demand. According the source, bankruptcies of companies, dismissals of employees, the need to take care of children due to closed educational institutions, and limited possibilities of remote work in some sectors have a direct impact on the labor market during the pandemic. In total, nearly 4.2 million people work in industries strongly exposed to the economic consequences of the lockdown. Of this figure, three million are employed, and just over one million are business owners and co-owners. More than half of the jobs at risk are in the trade.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.
According to recent estimates, the most affected sectors by the coronavirus pandemic in Latin America would be wholesale and retail trade as well as services in general, such as tourism, foodservice, transport, and communications. In 2020, this group of most affected sectors was forecasted to represent more than 16 percent of Brazil’s gross domestic product (GDP). Among the countries shown in this graph, Brazil is the nation where sectors moderately affected by the pandemic could represent the highest contribution to GDP (75.8 percent).
Which Latin American economies were most vulnerable to the pandemic? In 2020, the economic sectors most affected by the coronavirus pandemic - wholesale and retail, hotels and restaurants, transport and services in general - were forecasted to account for 35.5 percent of Panama’s GDP. In addition, the moderately and most affected economic segments were estimated to contribute the most to Panama’s GDP (a combined 97.6 percent) than any other country in this region. A similar scenario was projected in Mexico, where the sectors that would least suffer the pandemic's negative effects would account for only 3.4 percent of GDP.
Did the pandemic put a stop to economic growth in Latin America? Economic growth changed dramatically after the COVID-19 outbreak. Most of the largest economies in Latin America fell under recession in 2020. Estimates predict a more optimistic scenario for 2021, with countries such as Mexico, Colombia, and Argentina growing their GDP at least five percent.
According to a study in mid-March 2020, around **** percent of jobs in the leisure and hospitality industry in the United States are at risk from the global coronavirus pandemic (COVID-19). This amounts to around **** million jobs nationwide.
With the Coronavirus Job Retention Scheme having drawn to a close on 30 September 2021, we've looked at which regions and sectors were the biggest users of the scheme.
In a survey conducted in Chile at the end of March 2020, nine out of ten company executives thought that the tourism and hospitality sector would be one of the most affected by the coronavirus (COVID-19) pandemic. In turn, respondents' answers showed that the public sector, along with telecommunications and technology, would be likely spared from this crisis' negative effects. According to the same survey, over two thirds of Chilean respondents expected the country's GDP to fall in 2020.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment
May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.
To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.
Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.
The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.
Arataki - potential impacts of COVID-19 Final Report
Employment modelling - interactive dashboard
The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.
The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).
The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.
Find out more about Arataki, our 10-year plan for the land transport system
May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.
Data reuse caveats: as per license.
Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.
COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]
Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:
a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.
While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.
Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.
As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.
As per recent data from the March 2020 survey, 40 percent of the global electronics manufacturers and suppliers surveyed reported they believed that consumer electronics were likely to be the most impacted industry due to the coronavirus (COVID-19) outbreak. A further 24 percent of respondents claimed they expected industrial electronics to be most impacted, with 19 percent suggesting that the automotive electronics segment would be hardest hit.
What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages
Access the Data HereWhat is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Scottish economy, such as the United Kingdom (UK) economy, has been exposed to several adverse shocks over the past 5 years. Examples of these are the effect of the United Kingdom exiting the European Union (Brexit), the effects of the COVID-19 pandemic, and more recently Russia–Ukraine war, which can result in adverse direct and indirect economic losses across various sectors of the economy. These shocks disrupted the food and drink supply chains. The purpose of this article is 3-fold: (1) to explore the degree of resilience of the Scottish food and drink sector, (2) to estimate the effects on interconnected sectors of the economy, and (3) to estimate the economic losses, which is the financial value associated with the reduction in output. This article focuses on the impact that the sudden contraction that the “accommodation and food service activities”, resulting from the pandemic, had on the food and drink sectors. For this analysis, the study relied on the dynamic inoperability input–output model (DIIM), which takes into account the relationships across the different sectors of the Scottish economy over time. The results indicate that the accommodation and food service sector was the most affected by the COVID-19 pandemic lockdown contracting by approximately 60%. The DIIM shows that the disruption to this sector had a cascading effect on the remaining 17 sectors of the economy. The processed and preserved fish, fruits, and vegetable sector is the least resilient, while preserved meat and meat product sector is the most resilient to the final demand disruption in the accommodation and food service sector. The least economically affected sector was the other food product sector, while the other service sector had the highest economic loss. Although the soft drink sector had a slow recovery rate, economic losses were lower compared to the agricultural, fishery, and forestry sectors. From the policy perspective, stakeholders in the accommodation and food service sector should re-examine the sector and develop capacity against future pandemics. In addition, it is important for economic sectors to collaborate either vertically or horizontally by sharing information and risk to reduce the burden of future disruptions. Finally, the most vulnerable sectors of the economy, i.e., other service sectors should form a major part of government policy decision-making when planning against future pandemics.
IBISWorld has looked at which UK regions have received the most financial support since the outbreak of COVID-19, assessing the reasons why.
https://www.globaldata.com/privacy-policy/https://www.globaldata.com/privacy-policy/
COVID-19, commonly referred to as the Coronavirus, is dominating headlines the world over. No industry has seen a greater impact than airlines. Read More
https://fatposglobal.com/privacy-policyhttps://fatposglobal.com/privacy-policy
Measuring COVID-19 and stay home orders given in most of the world, In the US market, the advertising expenses have been cancelled, postponed and in some limited cases improved. Yet the same results do not affect all sectors and businesses. For certain sectors, businesses in the advertisement sector are now showing sparkling bright lights. Nonetheless, several businesses are just switching off their advertisement budgets, at least for now. Many advertising supervi.....
The outbreak of COVID-19, also known as novel coronavirus, is impacting almost all industries and sectors worldwide. Two of the most impacted sectors are manufacturing and travel & transportation. Both sectors are set to be severely impacted by coronavirus pandemic.
The impact is ranked on a 5-point scale from minor impact to severe impact:
1 - minor impact
2 - moderate impact
3 - significant impact
4- major impact
5 - severe impact
The impact of the novel coronavirus (COVID-19) can be seen on every sector of the most affected countries as well as globally. In the week starting January 4, 2021, the number of scheduled flights worldwide was down by 43.5 percent compared to the week of January 6, 2020. The impact of COVID-19 on the Chinese aviation reached a peak in the week starting February 17, 2020, with flight numbers down by 70.8 percent. Aviation market prior to COVID-19 outbreak Before the coronavirus outbreak hit the globe, the aviation industry was improving at a steady pace across countries. For instance, the projected annual growth of revenue ton-miles (RTM) for international flights by U.S. commercial air carriers was at roughly four percent for the period between 2020 and 2040. Prior to the coronavirus outbreak, the forecasted aircraft maintenance, repair and overhaul (MRO) market size in North America was over 22 billion U.S. dollars in 2020. After the adjustments with respect to radical changes driven by coronavirus shock, the North American MRO market is now estimated to generate roughly 12 billion U.S. dollars during the same period. Besides, it was estimated that between 2019 and 2038 over 260,000 technicians in the aviation industry will be demanded in the Asia Pacific region only. Aviation market after COVID-19 shock Coronavirus pandemic hit the passenger aviation much worse than cargo aviation because of lockdowns and bans restricting international travel across the globe. As a result of persisting COVID-19 shocks, passenger aviation is expected to lose roughly 370 billion U.S. dollars in 2020. Even though some countries started to recover as the coronavirus spread is being contained, the desired level of recovery may take at least several quarters or years. The change of airlines’ capacity will most likely remain at least ten percent below the 2019 levels. The longer recovery periods are attributed to several factors including the COVID-19 economic recession, confidence of people to travel, and stringent travel restrictions. Therefore, some institutions forecast the aviation industry to recover at a much slower pace than what was expected.
This dataset contains numbers of COVID-19 outbreaks and associated cases, categorized by setting, reported to CDPH since January 1, 2021.
AB 685 (Chapter 84, Statutes of 2020) and the Cal/OSHA COVID-19 Emergency Temporary Standards (Title 8, Subchapter 7, Sections 3205-3205.4) required non-healthcare employers in California to report workplace COVID-19 outbreaks to their local health department (LHD) between January 1, 2021 – December 31, 2022. Beginning January 1, 2023, non-healthcare employer reporting of COVID-19 outbreaks to local health departments is voluntary, unless a local order is in place. More recent data collected without mandated reporting may therefore be less representative of all outbreaks that have occurred, compared to earlier data collected during mandated reporting. Licensed health facilities continue to be mandated to report outbreaks to LHDs.
LHDs report confirmed outbreaks to the California Department of Public Health (CDPH) via the California Reportable Disease Information Exchange (CalREDIE), the California Connected (CalCONNECT) system, or other established processes. Data are compiled and categorized by setting by CDPH. Settings are categorized by U.S. Census industry codes. Total outbreaks and cases are included for individual industries as well as for broader industrial sectors.
The first dataset includes numbers of outbreaks in each setting by month of onset, for outbreaks reported to CDPH since January 1, 2021. This dataset includes some outbreaks with onset prior to January 1 that were reported to CDPH after January 1; these outbreaks are denoted with month of onset “Before Jan 2021.” The second dataset includes cumulative numbers of COVID-19 outbreaks with onset after January 1, 2021, categorized by setting. Due to reporting delays, the reported numbers may not reflect all outbreaks that have occurred as of the reporting date; additional outbreaks may have occurred that have not yet been reported to CDPH.
While many of these settings are workplaces, cases may have occurred among workers, other community members who visited the setting, or both. Accordingly, these data do not distinguish between outbreaks involving only workers, outbreaks involving only residents or patrons, or outbreaks involving both.
Several additional data limitations should be kept in mind:
Outbreaks are classified as “Insufficient information” for outbreaks where not enough information was available for CDPH to assign an industry code.
Some sectors, particularly congregate residential settings, may have increased testing and therefore increased likelihood of outbreak recognition and reporting. As a result, in congregate residential settings, the number of outbreak-associated cases may be more accurate.
However, in most settings, outbreak and case counts are likely underestimates. For most cases, it is not possible to identify the source of exposure, as many cases have multiple possible exposures.
Because some settings have been at times been closed or open with capacity restrictions, numbers of outbreak reports in those settings do not reflect COVID-19 transmission risk.
The number of outbreaks in different settings will depend on the number of different workplaces in each setting. More outbreaks would be expected in settings with many workplaces compared to settings with few workplaces.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Social insurance is an essential component of a contemporary social security system since it protects people’s fundamental well-being, but it also incurs a heavy cost for businesses. If social security costs are excessively high, business profitability will suffer, and innovation will be discouraged. The most affected companies would be those in labor-intensive industries and medium-sized enterprises. Chinese businesses have suffered severe losses as a result of the COVID-19 outbreak. Given the circumstance, China enacted additional tax cuts and preferential social insurance premium plans. This article suggests a lower ratio of contribution as a strategy to cut the cost of social insurance premiums for businesses, given the growth of the social security fund in recent years and the proportion of participants to recipients in pension funds. It would be possible to increase firm profitability and lessen the impact of COVID-19 on industries by minimizing this operation burden. In order to compare the financial performance of state-owned manufacturers (SOMs) to that of their non-state-owned peers, who have a lower ratio of contribution, this study uses a multiple regression model. The ratio of contributions was inversely correlated with an enterprise’s financial performance. In other words, financial performance will improve as the ratio of contribution lowers; nevertheless, this effect is more pronounced in SOMs. The final section of this study proposed optimized approaches for social insurance premiums reform.
In the wake of COVID-19 and associated lockdowns, businesses in both the oil and gas industry and the recreation industry saw a 20 percent reduction in revenues when comparing the revenues generated between April 2020 to March 2021 with revenues generated between April 2019 to March 2020. The top performing industries during the same time period can be accessed here.