The Emergence of the Covid-19 Epidemic Led to Major Structural Changes

The Emergence of the Covid-19 Epidemic Led to Major Structural Changes
Abstract:
Unemployment is a social problem that affects social stability and economic development. The emergence of the Covid-19 epidemic led to major structural changes in the labor market worldwide. In the UK, Covid-19 resulted in a widespread unemployment rate nationwide. However, China's political and social conditions are different from those in the UK, such as China's much larger land area. Covid-19 caused the economy and geography to deteriorate in many provinces in China in 2020. Because the crisis led to different policies in these provinces, the unemployment rates in these regions varied. The study findings are consistent with literature findings in the UK, although different methods were used to measure and analyze the Covid-19 impact on China's unemployment rate. This paper will run a regression model and time series model to analyze how Covid-19 affected the unemployment rate in China at the province level in 2020. The control and dependent variables data were scraped from the Chinese government database. The data of Covid-19 was downloaded from Harvard University. After train-testing the model and visualizing the result, the study found that Covid-19 increased unemployment rates in the nationwide model. It caused unemployment in 22 provinces, while nine provinces had a decreasing unemployment rate in 2020. Covid-19 impacts were also divided geographically into two parts: West-Southern and East-Northern.
The study increases the current understanding of Covid-19 impacts on China because of the dearth of research at the provincial level in China. One limitation of the data sourced from the Chinese government is that the Chinese government sometimes posts fake data to keep social stability and avoid citizens' anxiety. The covid dataset was recorded at the provincial level and not at the community level. Consequently, the regression model was not entirely accurate owing to the relatively small selection of control variables. In sum, Covid-19 increased China's unemployment rate at the provincial level, but the impacts varied in different provinces.
Introduction
Covid-19, later renamed Coronavirus, gripped China since the beginning of 2020 (Qiu et al.). Wuhan in Hubei province was the first city to record covid19 cases in China. Covid-19 spread rapidly worldwide and brought an unignorable crisis in both economics and medical systems. By the end of November 2021, there were more than 63 million reported cases and 1.4 million deaths worldwide (Brodeur et al.). Because Covid-19 is a respiratory infectious disease, countries published several policies regarding social distance and quarantine to reduce Covid-19 infections. This contagious disease affected global economics and politics (Ceylan et al.). In China, the government enforced measures that locked down the cities with Covid-19 cases, thereby disrupting economic activities.
The cost of the Covid-19 was a burden to China's economy. The control measures to prevent the pandemic led to a 2.7% loss in China's annual gross domestic product. According to BBC News, from January 23, Wuhan was locked down for 72 days, which means that no one could exit or enter the city. Citizens in Wuhan could not get out of their homes. They could only stay and wait for the government to deliver necessities. China also took action at the national level. For instance, the risk classification for each country was based on the principle of formulating guidelines for classification and implementing the strategy of "internal prevention of spread and external prevention of import" to reduce the possible impact of imported cases on China. Other actions included establishing a joint prevention and control mechanism at ports of entry, organizing the Civil Aviation Administration, and coordinating customs, public security, health, foreign affairs, border control, airports, and other departments in identity registration, health monitoring, emergency response, and other related work. Due to Covid-19, exports and imports were limited, which led many businesses to collapse. They had to cut their number of employees to reduce operations expenses, thereby causing higher unemployment rates.
On the other hand, unemployment levels did decrease as more remote jobs were provided during the Covid-19 period. Many women who were housewives and had given up their jobs to take care of their families had a chance to balance their work and families. In addition, because of the remote option, people had more options for applying for jobs, thereby decreasing the unemployment rate.
China is a socialist country whose labor markets reformed and transformed into a market-driven market system. After the Reformation, people were not allowed to work outside of their birthplace, so there were a lot of migrant workers affected by Covid19, the quarantine, and lockdown policies. Unemployment is a historical problem and has become more serious with time in China because it is a populous country. According to the United Nations, the world population was 7.6 billion, and the population of the Mainland was 1.39 billion, which was 18.3% of the world. China only has a 7.059% land area share of the world. From January 1996 to September 2002, the unemployment of urban residents increased from 6.1% to 11.1% (Giles et al.). In economics, unemployment is a significant factor behind poverty and income disparity in China. In 1999, urban unemployment was a significant cause of urban poverty, and the growing urban poverty became a significant factor that worsened urban inequality. This inequality in China has had an increasing influence on migrant households (Xue & Zhong). In addition to the economic perspective, unemployment also causes psychological and health problems. The unemployed group could have more significant symptoms like depression and anxiety than the employed group.
Moreover, unemployment is central to policy debate and aggregate resource utilization (Gali et al.). Furthermore, unemployed people are more likely to visit a physician than employed people (Linn et al.). There are 31 provinces in China, and they implemented various policies regarding Covid-19 depending on their situation. The policy could also be affected by geographic differences. In the previous study, scholars analyzed the Covid-19's effects on unemployment in different races and gender groups in the United States. Furthermore, they found that the effects of Covid-19 on unemployment were significantly different on races and genders (Gezici and Ozay). In China, scholars studied the covid impacts at the national level in 2020 because it was evident that the unemployment rate increased in 2020 compared with previous years. In Brazil, researchers found that the burden of covid 19 is more significant in areas with high social deprivation. Until August 6, 2020, Bahia has 179,139 confirmed cases and 3,767 deaths. There was a spatial association between the epidemiological indicators and SDI observed. Twenty-two municipalities had a higher incidence, which was 1.6 times higher than the state rate. Furthermore, 40 cities had a 1.2 times higher death rate than the state one, while they also had a 4.1 times higher incidence than the state rate (Souza et al.). These statistics motivated the researcher to study the Covid-19 impacts on unemployment at the provincial level in China rather than at the national level. The unemployment rate of provinces had different trends, and some fluctuations were caused by seasonal, structural factors. Accordingly, to face, solve, and predict the social problems of Covid-19, the study sought to analyze the reason for the variance in employment rates caused by Covid-19.
To study the research question of how Covid-19 affected the unemployment rate in China at the provincial level in 2020, the study utilized both regression and time series models to make predictions and analyze the results from dataset scrapping of the National Bureau of Statistics of China. Before starting the work, the hypothesis was that Covid-19 would promote the unemployment rate in China at the provincial level in 2020. The regression model can show the causal factors between covid-19 and unemployment. From the regression model, one unit of confirmed increase led to a 0.066 unit increase in unemployment, proving the hypothesis that Covid-19 will promote the unemployment rate of provinces in China. The time series can predict unemployment by avoiding using control variables' data which could be affected by Covid-19. The study found the Covid-19 impacts varied based on geographical reasons and led to different impacts on provincial unemployment rates.
The limitation of the dataset could not be ignored. In the regression model, the selection of control variables was not comprehensive. The dataset of control variables was scraped from the Chinese government, which may not have been the actual value. Besides, the time series predicted trends over time, so it may not have considered exceptional cases in the model like policy impact. The strength of the research is using two different models for studying the impacts of covid and clearly showing the initial finding by data visualization on Tableau.
Because Covid-19 is likely to continue affecting China, this research can serve as a guideline to the Chinese government and policymakers on which regions have high unemployment rates. In addition, the model can help the government predict future unemployment conditions and better prepare. Although every country has different policies and conditions, this study can still provide useful insights on Covid-19 effects regionally.
Literature Review
The world, particularly the Asian region, has witnessed the worst epidemics in the last two decades, including the Swine flu, MERS, SARS, Ebola, and the Coronavirus. The Covid-19 pandemic affected nearly every economic sector and disrupted global supply chains. Developing countries, including China, were most affected by the pandemic: government-imposed lockdowns caused serious economic interruptions, resulting in higher unemployment rates. The theoretical framework underpinning the relationship between the Covid-19 pandemic and employment rates is interdisciplinary in that the public health crisis was directly tied to economic activity and, by extension, the labor market (Xiang et al.). This research paper will use a theoretical framework that combines economics and public health. From an economic perspective, the supply and demand relationship was affected by the epidemic, which caused widespread uncertainty and reduced supply and demand. Labor, a critical macroeconomic variable, is introduced into the public health-economic theoretical model since it is a product of the supply and demand relationship dynamic.
Macroeconomic changes caused by public health disruptions change supply and demand forces and affect employment opportunities directly tied to economic activity rates; when demand and supply are high, economic activity increases, and so do the employment rates. On the other hand, when demand and supply decrease due to financial uncertainties brought by an epidemic, economic activity decreases, and employment rates. According to Su et al., the uncertainty shock created by the Coronavirus resulted in a reduction in economic activity, thereby causing reduced hiring decisions by affected firms. The effect of the Covid-19 epidemic on global employment rates arose from uncertainty among firms about consumption and investment decisions. Uncertainty in times of the public health crisis complicated the decision-making capacities of firms and consumers in all sectors of the economy, from retail to manufacturing to hospitality. This uncertainty caused a reduction in production rates across the sectors and a fall in employment opportunities. In addition to the severity of financial uncertainty on employment rates, domestic government policies such as the implementation of lockdowns, not only in China but the entire world, created spillover effects of uncertainty and a further reduction in both production and employment rates.
Various governments imposed widespread lockdown measures and curfews across all sectors to curb the rapid spread of the coronavirus and mortality rates. The control measures were meant to reduce respiratory pathogen transmission caused by direct contact between infected individuals and others. Some of the lockdown and curfew measures included restrictions on traveling, public gatherings, learning, and workplace activities. Unfortunately, these precautionary measures by various nations were applied indiscriminately: all sectors of the economy were affected by the control measures (Haldar and Sethi). While these measures were effective in containing the virus, they caused significant disruptions to economic activity. During the pandemic, lockdown measures by various countries also resulted in massive disruptions in global supply networks: the increasingly interconnected world of today has created a global supply network where the activities of one country inevitably affect the economic activities of other countries. Domestic and global-wide lockdown measures were not only disruptive to the economies of host countries but those of others.
For instance, the restriction of movement in China at the height of the epidemic resulted in a major slowing down of economic activity. However, production operations in China were also affected by lockdown measures in other countries: the restriction of the free flow of goods and services across the globe caused massive disruptions in China's sectors, particularly the hospitality, retail, and export industries. Therefore, reduced economic activity in China and other countries during the Covid-19 pandemic can be tied to domestic and external government actions (Xiang et al.). This double effect significantly affected employment rates throughout the world, particularly in those sectors that were especially vulnerable to lockdown measures and disruptions in global supply chains (Haldar and Sethi). However, the restriction of movement and imposition of curfews was not wholly negative in that they allowed governments to curb the spread of the virus and so begin re-revitalizing the economy.
The government and global Covid-9-mitigation policies affected the economy in the near term, but they also helped reduce the public health crisis and, in turn, the financial uncertainty it caused. Restricting the movement of goods and services helped bring COVID-19 infections to manageable levels, raising firm confidence in the economy. Once the public health crisis was averted, most government immediately opened up their economies and in doing so ensured investors resumed their operations and increased employment opportunities (Xiang et al.). The elimination of financial uncertainty and resumption of global supply chains resulted in higher employment rates. On the whole, an interdisciplinary theoretical framework that explains the relationship between public health crisis and economic activity is best placed to underpin studies into the impact of the Covid-19 pandemic on employment rates. The Covid-19 pandemic resulted in: (1) financial uncertainty among companies, which sought to limit downsides related to reduced demand and supply by cutting back on production activities and in turn causing higher unemployment rates, and (2) government precautionary measures such as restriction of movement, which disrupted the free flow of goods and services and by extension overall economic activity thereby causing higher unemployment rates.
China has witnessed fascinating economic growth over the past thirty years despite regional disruptions such as the Asian and 2008 financial crises. The country's high economic growth rates have been accompanied by massive shifts in employment patterns in all economic segments, from primary to secondary to even tertiary sectors. Some of the significant changes in the Chinese labor market were the growing flow of rural workers to urban cities, enforcing stricter labor regulations, and restructuring of the urban employment system (Kemp and Spearritt). For instance, reforms in the 1980s and 1990s eased the Hukou system for registering households where the labor population was separated into a rural and urban workforce. According to Ma et al. (2021), these new labor policies scrapped away the "iron rice bowl" arrangement where urban workers were given a permanent job in a state-owned enterprise (SOE) by allowing rural workers to move beyond their particular regions and seek employment in urban areas. China's decision to open up its market to foreign investors resulted in the marketization of labor, lower labor market frictions, and a more efficient labor distribution system.
Unfortunately, the coronavirus disease disrupted these economic and labor market developments by exacerbating unemployment rates and causing a global economic recession. Before the Covid-19 crisis, China was already experiencing a slowing economy and growing unemployment rates. This slowdown was essentially the result of government efforts at restructuring the economy towards higher-value production output. The Chinese government sought to gradually shift from three decades of uncontrolled economic expansion to more focused quality growth (Chen and Li). However, this transition did not come without costs, including a growing number of unemployed college graduates, which put further pressure on jobs, especially those in lower-value manufacturing output. According to UNDP, the first wave of the Covid-19 pandemic in late 2019 marked the start of an employment crisis in China: the government implemented widespread lockdowns that effectively brought the entire domestic economy to a standstill. Not only were the majority of businesses closed, but all labor movements were halted.
Despite efforts by the government to support enterprises and protect millions of vulnerable workers, successive waves of the Covid-19 pandemic resulted in more workers losing their jobs, accepting reduced working hours, or taking unpaid extended leave. However, the adverse effects of the pandemic on China's employment rates were diverse: some sectors, worker populations, and regions were more affected than others. For instance, migrant workers, predominantly female and senior workers (those older than 40), suffered the worst effects of the economic slowdown (Wang et al.). Although there are many similarities between China's labor market and that of other developing economies, its socialist history resulted in several institutional legacies that uniquely distinguish its labor market. One example is the existence of a large population of domestic migrants. These laborers are primarily from China's rural regions. According to Zhang,a significant number of migrant workers are from other urban cities: China's migrant workforce consists of people who hold rural Hukou (rural-urban migrants) and urban Hukou (urban-urban migrants).
Nearly a third of China's entire labor force and more than half of the urban labor force constitute migrant workers. Shortly after the first Covid-19 wave in December 2019, the government shut down nearly all parts of the economy to curb the spread of the virus. This move resulted in 70 million migrant workers (around 16 percent of the urban workforce) losing their jobs by February 2020 (OECD). Those still employed had to contend with sharp declines in the average hours worked: estimates indicate that the total urban hours worked fell by a third between December 2019 and February 2020. However, economic pundits speculate that the adverse effects of the Covid-19 pandemic on China's employment rates were more severe than implied, given the inaccurate official labor market estimates (Zou et al.). The country's labor market statistics fail to include rural residents, who constitute a significant proportion of the agricultural workforce and are also affected by the widespread lockdowns and sector closures. Leaving out rural residents in official labor market statistics means that this population's unemployment rates were largely unmeasured.
However, despite the massive layoffs of urban workers, the unemployment rates rose moderately in successive Covid-19 waves. This phenomenon was mainly because the vast majority of urban workers rendered jobless in the early months of the pandemic left the urban labor force entirely. According to Wang et al. (2021), many urban workers were unable to secure placement and changed their residence or were unable to start working even if they were offered a job in a neighboring city. Many migrants who traveled to their hometowns for the Chinese New Year festival could not return to their urban residences after the imposition of movement restrictions. Those who were laid off could not begin searching for work. Many of these displaced urban workers did not re-enter the urban labor force but stayed in their hometowns. This was partly because new urban employment declined by more than half by February 2020, so very few gross new jobs emerged in urban areas (ILO). This circumstance, along with the government's movement restrictions, adversely impacted the ability or confidence of urban workers to capitalize on the limited work opportunities.
Migrant workers were most affected by the pandemic because they accounted for the more significant percentage of the urban labor force. They were also concentrated in sectors that were hit the hardest by the disruptive lockdowns. According to Che et al., cities with a larger share of their gross domestic product (GDP) in the retail, export, and hospitality industries experienced the worst unemployment rates compared to cities with many finance and healthcare industries. The retail, export, and hospitality industries were brought to a halt by the government's movement restrictions as well as global wide lockdowns: the three sectors rely on global value chain coordination, and lockdowns in other countries created a hostile business environment, and in doing so, pushing companies to close shop. For instance, during the first quarter of 2020, the hospitality sector suffered a 35 percent decline in output, while that for the wholesale and retail sector was 18 percent. Other employment-intensive sectors that hire many migrant workers have also been affected: the construction industry experienced an 18 percent decline in output; 14 percent in warehousing, transportation, and postal services; and 10 percent in manufacturing. In February 2020, China's top 500 manufacturing companies revealed that more than half had reduced their workforce by more than 60 percent and were not recruiting new staff (OECD). A significant reason for the massive layoffs was the disruptions in global supply chains which saw many of these sectors, mainly export and manufacturing organizations, suffering order cancellations and incomplete orders.
On the other hand, other less employment-intensive sectors such as finance and information technology were, for the more significant part, insulated from global supply chain shocks and the disruptive effects of lockdowns. The tendency of these industries to be male-dominated resulted in more women than men bearing the negative impact of the pandemic on employment rates. For instance, women's employment share in the hospitality sector is 55 percent, 49 percent in the retail sector, 41 percent in the manufacturing sector, and 96 percent in the domestic service sector (Chen and Li). These industries were the most brutal hit, and therefore unemployment rates for women migrant workers were higher than for men. Another group that suffered worse unemployment rates was micro, small, and medium-sized enterprises. According to Chen and Li (2021), there were over 18 million such businesses in China in 2018, employing more than 233 million people, which is around 80 percent of the migrant workforce in enterprises. Most of these micro, small, and medium-sized companies were in export, hospitality, wholesale and retail or constituted part of manufacturing chains.
Given their limited resilience to economic downturns, these low-capital enterprises were the first to be affected by the pandemic. Many were unable to operate normally shortly after the government imposed countrywide lockdowns. The large number of workers working in these businesses were laid off after their owners could not inject the required liquidity to weather the economic crisis. On the other hand, the failure of China's labor market statistics to include rural residents created data limitations that made it challenging to determine the pandemic's impact on rural workforce employment rates (Wang et al.). However, several studies indicate that the Spring Festival, which is usually celebrated around the time most businesses started firing their workers, saw the return of 125 million migrant workers to the rural regions. However, by the end of the two-week festival, only about 100 million of the 125 million migrant workers had returned to their urban hometowns (Chen and Li). Approximately 25 million workers chose to stay behind mostly because their companies were not looking to hire them after closing production. They had no desire to look for jobs in a saturated labor marketplace.
Rural neighborhoods were also locked down when the government decided to implement lockdowns in cities and towns. With minimal movement permitted, most non-farm activities in rural areas were brought to a halt, either because of transport challenges or reduced activity. According to Chen and Li (2021), even the rural workforce suffered similar unemployment rates as the urban workforce. Besides, rural workers tend to earn less than urban workers, and therefore the decline in non-farm economic activity caused a lop-sided wage effect. Rural households experienced more significant declines in per capita household income than urban households. It is also likely that the relocation of urban workers to their hometowns increased competition for the few jobs in rural areas, thereby putting downward pressure on wages (Zhang). On the whole, the rapid and widespread impact of the pandemic on employment rates was primarily caused by the highly non-contractual nature of Chinese employment, especially among migrant workers.
While the informal nature of most work arrangements in the country allows for greater market flexibility, workers are greatly affected by the relatively simple way employers can lay off workers. Even those still employed had to contend with lower average weekly working hours: urban households witnessed a 3.9 percent decline in per capita disposable income while rural households decreased by 4.7 percent (Ma et al.). Unlike the 2003 SARS epidemic or the 2008-9 global financial crisis, the shock to employment caused by the Covid-19 pandemic was more severe and lasted longer. According to OECD (2021), the pandemic had a more significant adverse impact on employment rates in China because the country was already experiencing growing unemployment rates. Most workers were employed in sectors that relied on a working global supply chain. These factors, combined with the informal nature of work arrangements in China, resulted in high unemployment rates.
Fortunately, the Chinese government's quick response to the pandemic and commendable management of new infections greatly factored in the country's quick recovery in employment rates. The coronavirus epidemic was brought under control in urban and rural areas within a short period so that most employment-intensive sectors began resuming operations by April. In developing its economic recovery strategy, the Chinese government focused on containing the Covid-19 pandemic while protecting employment using phased resumption of economic operations (Kemp and Spearritt). In the first stage, during the widespread lockdown, the government attempted to address the issue of unemployment through schemes for vocational training, job retention, and social assistance. However, marked improvements in employment rates were witnessed in the second stage, after new cases had dropped to single digits. The government directed all economic sectors to resume activities over and above work to protect workers' health (ILO). The first sectors to open were public services and transport, then manufacturing and construction, then retail and hospitality. This phased resumption of production saw a marked increase in employment rates post-pandemic. As markets started to recover, most migrant workers who were previously unemployed or underemployed had returned to pre-pandemic employment levels.
Data and Method
The study's objective was to figure out the impact of Covid-19 on the employment rate in China's provinces. The hypothesis is that Covid-19 will hurt the employment rates of provinces in 2020, which means it will increase their unemployment rate. Many factors impacted employment besides Covid-19, so the study chose ...