The Shift in Unemployment Rates in the Information technology Manufacturing Industry in China

The Shift in Unemployment Rates in the Information technology Manufacturing Industry in China

Executive Summary

China is experiencing a shift in the unemployment rate in the Information Technology manufacturing industry. Primarily, the region is currently undergoing a government campaign to improve automation technology within the manufacturing industries. This is to reduce operational costs and counter the increased wage policies in IT  economic hub provinces, including Guangdong and Dongguan, in addition to a reduction in redundancies. These are among the many factors that have affected China’s employment status, resulting in increased unemployment rates. Emerging economies such as China may experience a different type of unemployment compared to developed nations such as America’s unemployment shifts. It is primarily linked to technological innovation, invention, investment and widespread adoption. Given that the Chinese government has provided incentives for the IT companies to use machinery and replace human capital resources, there are concerns about how this will affect the unemployment curve in the region. Currently, according to the International Federation of Robotics, China is ranked as the leading robotics nation in the world. Concurrently, China’s unemployment rate as of 2015 is at 14% and rising. The need to address the human issue is paramount on behalf of China’s ambitious plan to understand how policymakers, Foreign Direct Investors and business individuals can learn to incorporate automation and human capital together to eliminate the looming epidemic in mass unemployment that is bound to happen in the future.

1. Introduction
2. Background

It is a fact that China is one of the leading emerging economies of the world. The success is attributed to the policy-changes and institutionalization of economic reforms that date back to the 1970s under Deng Hsiao Ping (Fang and Yang 55). Due to the Chinese government’s ambitious project, the country has an incremental growth rate of 9% per annum from the 1970s to the 1990s and a 10.4% growth rate in the 21^st century to date (Fang and Yang 55). From a global perspective, China’s growth from the institutionalization of the economic reforms and opening-up of the country has been unprecedented (Fang and Yang 55). The consequence of this extraordinary performance has resulted in dramatic changes within the economy.

The economic policy and the ambitious project undertaken by the Chinese government alongside the Information Technology manufacturing industry have a great impact on the employment-unemployment structures in the region (Li et al. 570). Comprehending how the background factors on structural or frictional unemployment rates in the country. They are factors on finding jobs, background on unemployment and realities of unemployment structures are crucial to explaining how it is important for policymakers and business individuals in determining how they can resolve the foreseen problem (Schmitt and Warner 50). Studies about the country’s unemployment rates and structures have not yet grasped the consequential effect on its citizens both from a regional and national level and not necessarily from a global perspective (Leung and Xu 5). Only a limited number of studies have verified that increased automation in the Information Technology Industry has a significant impact on unemployment rates increase shortly.

Structural unemployment is a principle that explains that unemployment is gained from the fundamental shifts in an economy and exacerbated by extraneous factors including technology, competition and government policy (Leung and Xu; Wenyuan and Zhaoqi 45). Therefore, the forecasted outcome is that structural unemployment occurs when workers lack the requisite competence and job skills in acquiring employment positions (Meng 82). The issue is that jobs are available, but there is a serious mismatch between the companies offering them and the ability of the workers to match them (Herz and Van Rens 5). Concerning structural unemployment status in the Information Technology manufacturing industry in China, jobs are expected to be present. Still, they lack the ideal workers for them, given the increase in competence skills required.

A brief understanding of the economic reform will help create a bigger and vivid picture of the problem that concerns the present research. In 1979, an economic reform summit was initiated in China. The objective was to embark on an ambitious project in making China one of the leading contenders in an economic power-house partner on a global scale. At the time, China commanded at least 1.8% of global gross domestic product (compared to the U.S $) (Leung and Xu 5). Due to the economic reforms, China enjoys one of the largest and defined economic growths not only on the local and regional Asian level but globally as well.

1. Literature Review

Due to the ruthless economic reform, China enjoyed a 16% growth in exports between 1979 and 2009 (Easterlin et al. 9776). Significantly, the country today shares at least 9.6% of the global share in exports and 8.4% in the share of goods and non-factor services (Easterlin et al. 9776). Another significant change the country has undergone is the shift in economic structure. In the 1980s, China was known to be a low-income country with income per capital registered at only 30% above that of the average sub-Saharan African country (Easterlin et al. 9776). Currently, the nation enjoys an income per capita of $ 7 500, which is three-time higher than that of sub-Saharan African countries. As a result, the World Bank registers the nation as a middle-income country (Leung and Xu 5). But, behind this growth has been a substantial reform on urbanization and industrialization.

In the 1980s, China was primarily an agrarian economy, with at least 73.6% of the population living in rural areas (Fu and Lin 56). Consequently, the growth spout in agriculture was the main contributor to economic growth represented at 73.6%, which accounted for 27.1% of the GDP, according to Fu and Lin (60). The economic reform is regarded as a latecomer advantage given that China instinctively took up industrialization, technological innovation and research and development upgrades on tenets that already existed in the global frontier (Liu 108). Nevertheless, the country has enjoyed unregulated growth spout ranging from new technology and industrial growth. Consequently, Liu points out that the region has observed an increase in Foreign Domestic Investment from countries in Europe and America, which has arguably provided the nation with a competitive advantage (110).

The impetus growth spout of the country has been accompanied by increased undertakings to improve the industrialization concept. Primarily, the nation is undertaking serious projects in investment in cutting-edge technologies, including advanced robotics and artificial intelligence. It spans from the manufacturing industries, including Information Technology, to the customer service platforms (Chen 145). Automation is perceived under President Xi Jinping as a fundamental undertaking to boost the region’s economic productivity (Chen 145). The emergence of such investment has caused seismic impacts on the labour structures in the region (Chen 145). Despite the evidence that increased automation can benefit the country based on economic competitiveness, there are underlying issues that have gained policymakers, industrialists, social scientists, and labour unions concerning the present and future of the country’s labor force structures.

Literature reports on unemployment rates in the IT industry have assessed the impact of the industrialized campaign, increasing intelligent based technologies on employment structures in China (Sjöholm and Lundin 10). From a wider view, the employment structures have shifted from the 20^th century to the 21^st century concerning the labour force. The four factors that have been affected include manual jobs, lack of employment, routine cognitive jobs and non-routine jobs (Sjöholm and Lundin 10). The statistics indicate that unemployment overall in the manufacturing industry has decreased. With interest in the IT industry, non-employment has increased from 15.85% to 30.76%(Chen 145). Consequential effects on employability skills have been affected by routine cognitive jobs, increasing to 19.20% from 8.17% (Chen 145). The shift in cognitive skill-based jobs means that the poor agrarian-based economy citizens do not possess the right tools, skills and competence to acquire cognitive-based jobs, which are ideally synchronized with automation and investment in intelligent AI machinery.

The consensus is that the interactive unemployment curve and the increased machinery investment in the IT industry will favour the latter more than the former. The effect of increased machinery in the IT manufacturing industry is that the rise in routine cognitive-based jobs will increase (Chen 145). The pattern of change in unemployment in the industry and consequentially other industries is looming for the country. Understanding the impact of investment techniques such as Artificial Intelligence and automation companies in the Information Technology manufacturing industry will enlighten interested parties on the decomposition of the employment structures in China and how it will eventually disrupt the labour force in the region (X. Li et al. 101382). Factors including how the poor will manage to cope with increasing demand for cognitive-based employment, how the poor will also manage with routine jobs and how non-employment is linked to routine employment is necessary to analyze.

1. Justification

In the last few decades, the world has witnessed a noticeable polarization of the labour market in the developed nations of America and Europe. It means that in those countries, wage gains and employment equality shares gains have been noticeable at the top and lower-bottom occupations. For the middle occupations, there has been a dramatic decline (Ernst 154). The reason for the shift is due to the dramatic decline in routine occupations in the region. Jobs that were routine in the countries were more in the past century than they are in the current century (Ernst 154). The attributed reason, as most now know, is the dramatic shift of manufacturing companies to China. As a result, the decline in routine jobs, including cognitive-based were insufficient, and those who worked in the routine occupations are now working in non-routine and non-employment jobs.

China, an emerging economy, is not far from this reality. It is reported that developing nations such as Korea and China may not have the same type of structural unemployment from the developed ones. Ideally, the issue of technological innovation, research and development present a different scenario for the emerging economies compared to the developed economies (Huang 4). The basis of this reasoning is grounded on the fact that the higher costs of automation in manufacturing industries, for instance, require the employment of productivity growth, which trickles down to the development of opportunities for more challenging skills to upgrade jobs (Huang 4). In other words, with the trend of increased investment in automation and intelligence technology, China is en-route to the discovery of a decline in employment opportunities.

The study on how automation technologies would alter the employment structure in the Information Technology manufacturing industry in China deserves special attention. China, firstly, has witnessed rapid computerization in the recent past (Li 101382). To be precise, between 2000 and 2015, the average number of computers in each China household is estimated to rise sharply to 78.5% from 9.8% (Li 101382). Internet users have risen sharply in China, reaching an unpreceded mark of 772 million individuals (Giuntella and Wang 3). Conclusively, working with computers and smartphones is becoming an essential skill in the IT platform.

Also, China’s wage costs are rising. On average, in 2016, it is reported that the rise in wage costs rose from 12 671 yuan to 59 470 yuan in the manufacturing IT industry while, for the transport and storage workers, the wage has risen from 15 753 yuan to 73 659 yuan (Li 101382). Hiring workers’ high costs have prompted the government-sanctioned increase in investment in automation and intelligence technology in the Information Technology manufacturing industry alongside other production sectors (Li 101382). As a result, China is among the leading nations in the world with industrialized robots with reports on sales matching sales volume in America.

Despite the growth in industrialization and the need to change to automation, China\s demographic has considerably changed in the past decades. On one end, China is lauded for increased and improved education but, on the other, issues regarding rapid urbanization and an increase in the ageing population loom. The increased incentives from the Chinese government to promote Artificial Intelligence (AI) quickly undermine the importance of stability in employment structures. The potential, therefore, in replacement of AI to labor is quickly becoming an important policy consideration.

The following research focuses on previous literature regarding the impact of automation technology in the IT manufacturing industry on employment structures. The scope is to find whether there is a direct correlation between unemployment rates and automation technology investment in China.

1. Objectives

Main Objective

To investigate the shift in unemployment rates in the information technology manufacturing industry in China

Specific Objectives

1. To evaluate the shift in automation technologies in the information technology manufacturing industry in China.
2. To evaluate the major factors causing labor supply change in the information technology manufacturing industry in China.
3. To evaluate the major factor causing labor demand change in the information technology manufacturing industry in China.
4. Research Questions
5. What is the shift in unemployment rates in the information technology manufacturing industry in China?
6. What are the shifts in automation technology in the information technology manufacturing industry in China?
7. What are the major factors causing labor supply change in the information technology manufacturing industry in China?
8. What are the major factors causing labor demand to changes in the information technology manufacturing industry in China?
9. Hypothesis
10. The shift in the unemployment rates in the information technology manufacturing industry is not linked to increased automation technology investment and industry adoption.

1. Methodology

The research design for the present study was qualitative exploratory research. Exploratory research is conducted in cases where the problem or the research topic is not well articulated or defined (Goeman and Solari 589). In this case, the issue regarding the shift in unemployment rates in the information technology industry in China is a concept that has not been defined under any scholarly research report. Undertaking exploratory research is crucial to understanding the issue of the exploration of the problem (Goeman and Solari 589). The type of research will enable the researcher to form a strong foundation on further exploration of the idea from a descriptive statistical perspective with an assessment of the right variables (Goeman and Solari 589).

With this concept in mind, the present research conducted an inclusion-exclusion research format to determine the best articles and peer-reviewed journals to use for the report (Ferrari 231). An inclusion-exclusion criterion enables the researcher to understand which content is reliable and valid for the present research (Ferrari 231). An inclusion-exclusion criterion is an ideal format for any qualitative exploratory research. The concern on inclusion-exclusion criteria is the understanding and use of variables for the present study (Ferrari 231). It narrows down the search through the specifically selected variables used in the objectives and research questions addressed in the previous section. Therefore, the validity and reliability of the present study are based on the type of inclusion-exclusion criterion used as evidence below.

A set of keywords were used for the search (Green et al. 110). They were: China, unemployment, statistics, the shift in unemployment rates, automation technology, and intelligence technology and government policy.

Search engines used were Google Search, www.google.scholar, and NCBI for qualitative papers. The search included the inclusion criteria on the accepted papers were published work from 2013 and above with few exceptions on History on China’s government economic policy. The keywords used presented a search result of 1 432 articles. Given the scope of the study, qualitative and quantitative studies were also included. Elimination of exclusion criteria was further used to reduce the number of articles presented. Exclusion criteria disregarded reviews articles, comparison articles with other nations, including America and Europe and quantitative research on employment rates. With these exclusion criteria, a total of 400 articles were presented. Further exclusion was done using only peer-reviewed sources, which further presented 29 articles. These 29 articles were used for the current research.

• Limitations of the Study

The study is based on the perception that there is a shift in China’s unemployment rates within the Information Technology manufacturing industry. Preferably, the shift in non-employment is based on the rise in investment and adoption of artificial intelligence, robots and machinery in the industry. Due to this, the limitation of the study is that it is based on qualitative and not quantitative data. The report would have been more reliable and valid if there was more data evidence regarding the correlation between the unemployment rate shift in the manufacturing Information Technology industry.

Further, the limitation of the study is that it does not delve into understanding the link between demographics such as education, skill-set and availability of the workforce regarding the change in labor demand. It would have been more prudent to have such information about the industry on this basis.

1. Findings/Discussion

The report rejects the hypothesis. There is a link between the structural unemployment observed through a shift in unemployment rates within the Information Technology manufacturing industry in China. The following is a report on the literature review gathered from the sources approved for this research.

1. Automation in the Information technology Manufacturing Industry

It is estimated that the continued innovation investment in artificial intelligence and robotics in China will replace more than 100 million workers in the manufacturing industries, including the Information Technology manufacturing industry (either directly or indirectly) (Zhou 1). Companies such as the iPhone are estimated to use more robotics in their manufacturing process than before. The concern regards the increased incentive by the Chinese government on the 2014 kick-start campaign on increased automation of up to 30% by 2020 (Zhou 1). The idea is to replace manual labor with robots in regions of Jiangsu, Zhejiang and Guangdong. In an interview with an affected employee in the IT industry, Xia, a 34-year old worker in the Japanese owned electronics factor, cited that there is a growing concern over the extensive augmentation on automation in almost all industries. In his comment, he ‘fears that the industrialization agenda will affect job prospects in the future’ (Zhou 1).

The effect of the automation process affects both the cognitive skill-based demand and the vocational job skill requirements. More and more employees are transferring to small-scale based manufacturing, including the information technology industry as such sectors have not yet confirmed government incentivized shift to full automation (Min et al. 5). In Dongguan, it is estimated that nearly 280 000 workers have been replaced with 91 000 robots (Zhou 1). The pressures for workers who are used to non-routine and routine tasks are now feeling it. They are seeking additional income to foster and hone skills in automation or soon, as they fear, they will no longer be relevant. According to Xia, “I used to work for 10-hour shifts overseeing 104 machines on 13 production lines. However, today there are only two members of staff who oversee an entire production line” (Zhou 1). Companies such as Foxconn, that manufacture iPhone product, seek to fully automate its production line by 30% by the year 2020. As a result, by 2018, the company, Foxconn, had replaced more than 400 000 workers with tens of thousands of robots between 2012 and 2016 (Zhou 1). The region of Shenzhen is also reporting of upgrading workers with robotics technologies. Manufacturing companies such as Huawei, Google and other companies are seeking mechanical arms on their production lines (Qiu et al. 60). Due to the automation, Xia, alongside other workers in the Information Technology industry are reporting increased overtime to meet their end-meet and earn a living to sustain their livelihoods. It is reported that China has superseded Japan as a global industrial revolutionist in robotics since 2017. According to the International Federation of Robotics, nearly 800 000 robots will be installed in China by 2020 (Zhou 1).

The consensus is that structural unemployment is on the rise in the manufacturing industry. Within the Information technology industry, nearly 30-40% of job cuts are reported in China between 2015 and 2017 (Zhou 1). The unemployment structure has not only affected the factory workers. It has also affected top-level management and low-ranking workers in the storage, transportation and packaging sectors. Issues of demotions occur daily, and with it comes a reduction in pay (Kromann et al. 276). Transfer to other industries, including the service center, has seen an increase in job applications as well. In summary, the automation process is among the leading causes of the shift in unemployment in China.

1. Major Factors Causing Labor Supply Change

One of the growing issues surrounding the demographics in China is the growing ageing population in the region. A look at the history of China, the one-child policy rule in the region, has greatly affected the supply change in labor (Mann and Püttmann 14). It is estimated that within the past 25 years, the growing populace of the elderly has doubled compared to those of the working generation. In other words, the share of people who are above the age of 60 years is 8.6% rising from 5.6% in the 1990s and 10.5% in 2015 based on the last census (L. Li 70). The issue about the aging population is about the burden it has on young people as well as society.

Additionally, there is the issue of the average working population, which is a fraction of the younger generation compared to the older generation. It is reported that the younger generation is declining, with the share of the older population rising (Zhou 1). Therefore, the population is aggravating, given the rising demand for workers who need to work alongside the machines.

An additional factor that is determined to affect the supply of labor is the educational attainment in the robotics and automation arena. On one end, the government is lauded for the increased campaign in higher education attainment among the younger generation. Before the economic reform, there were nearly 0.61 million college students in 1990 (Chang et al. 13). As a result of the increased college enrollment, more than 7 million graduates are reported since 2015. The increase in the number of graduates also presents a problem. There is an influx of unemployed workers who need to seek employment. Alternatively, there is the issue of a college mindset (Acemoglu and Restrepo 4). The need to seek a cognitive job is most preferred compared to manual work. Interestingly, the need to find employment in China is exasperated by the ideology of increased competition for the ideal job position in elite companies (Bahrin et al. 140). Given the rise in automation, the lack of high-paid non-routine cognitive jobs means that highly-educated individuals will have to resort to manual work, which is impossible.

1. Major Factors Causing Labor Demand Change

The automation campaign across China has made it more difficult for labor demand to shift in favor of the human capital workforce. The issue may persist in the future, given the increased support from the Chinese government (LIU et al. 140). As noted, industrial robots and automated arms are replacing people at an alarming rate even though it is gradual. According to the International Federation of Robotics, China is becoming the world’s leading robotics manufacturer and user, especially in the Information Technology sector (Zhang et al. 5). In the IT industry, it is estimated that in 2004, the number of operational stock industrial robots were 7 000 units since then, the statistics have risen to more than 300 000 in 2016 (Bock 114). The excessive adoption of robots in the manufacturing industry, including the Information Technology industry, guarantees that routine employment will no longer be viable and that manual labor will be the most preferred or alternate opportunity for workers. The prediction is the increase in non-employment labour-intensive industries.

Another issue regarding the shift in unemployment is attributed to the state-owned shift in ownership. Since the late 1900s, the Chinese government has embarked on reducing small-size and medium-sized company ownership and taking possession of large-owned private companies (Li et al. .4). The mandate has greatly affected the manufacturing sector and, more so, the profitable Information Technology industry. Between 1995 and 2001, more than 34 million workers have been laid off from the state sector to reduce labour force redundancies (SU 578). The worse thing is that laid-off workers did not pursue new private-owned business opportunities in the IT industry. The survey showed that 63.5% of the workers opted to take on unemployment in manual labor compared to routine tasks. As a result, the unemployment rate has been on the rise since 1993 from 4.4% to 9.5% in 1997 and 14.9% in 2015 (Zeng 90).

The computerization and automation in the IT industry have considerably led to the job decomposition involving routine cognitive-based occupations. In December 2001, China joined the World Trade Organization. As an incentive, the government encourages the outsourcing of skills from off-shore companies and countries (Liu 20). The policy change encourages firms to undertake more outsourcing services, including information transmission, software and Information Technology industries abroad (Duanmu 65). Consequentially, the growth in computerization and automation has resulted in the creation of more cognitive occupations that prior experienced in China, which has made it more difficult, especially for the elderly generation, to seek such competing jobs (Huang et al. 4). It may explain why computers and robots are increasingly replacing the human capital workforce.

The major factor that leads to the replacement of human capital is the efficiency and efficacy of robots and artificial intelligence in the manufacturing Information Technology industry (Li et al. 570). Significant changes have been observed within the four categories of employment-unemployment structures. They include manual jobs, non-employment, routine cognitive occupations and non-routine occupations (Feng et al. 314). Due to the increase in China’s educated citizens, the prevalence of manual workers in the IT industry has decreased considerably. Manual jobs are the most affected, followed by unemployment rates (Feng et al. 10). The non-employed in the region is estimated to have begun increasing since the institutionalization of the new economic reform. The interaction effect on increased robotics and automation has seen most highly-skilled laborers losing their positions within the electronics and phone-based companies under the IT manufacturing industry (Kongshøj 10). Also, the pattern of change within the cognitive-based employment structure is affected drastically. Since the 1990s to 2015, the IT industry has seen an increase in the replacement of workers’ cognitive-based occupation by robots and machines (Yuang et al. 900; Liu et al. .10). The idea based on the companies in the efficiencies in operating the machines in the provision of quality and handling of more man work per hour rate compared to human workers.

Another major factor that has led to the labor demand change is the increase in wages, especially in technology-based cities such as Shenzhen. The policy in regions such as Guangdong Province is the mandatory increase in wages for IT-based manufacturing companies. The wage, for instance, in Guangdong, is 2 130 in 2013 from 900 in 2008 and Dongguan is 1 510 in 2013 from 770 in 2008 (Schlæger 10). The difficulties in manufacturing for IT-based companies are increased trade-war with America. Companies are experiencing reduced profits due to reduced exports in prime countries, including Europe. Chinese outsourced companies are feeling the pressure of having to deliver to pay their workers (Wübbeke et al. 10).

For this reason, there is a need to replace humans with machines. The idea is to reduce counterfeiting effects of the toughening business conditions. One of the growing business conditions of exportation is the originality issue on patented content in IT-based products to the U.S, Europe and Australia (Yu et al. 1325). The deployment of intelligent manufacturing is regarded as one of the best innovation strategies that companies have undertaken in value-chain addition.

Intelligent manufacturing is defined as the incorporation of communication technology alongside advanced manufacturing with improved product design, management and services (Tiang et al. 79). The merits of using technology-based production against labour-intensive production are the reduced operation costs, increased efficiencies and delivery optimization. The need to replace humans with machines is a government based initiative as well given the losses that provinces or regions in China have experienced as a result of the trade-war and underpinning economic crisis of 2008 (Miller 10).

The region of Dongguan is a perfect example to explain why there is a shift in unemployment rates in the IT manufacturing industry. Dongguan, for a period, enjoyed scrupulous profits, including the manufacture of more than 80 000 IT-based products employing more than 4 million immigrants (Qiao et al. 15). However, companies in the IT-based industry began declaring bankruptcy. For this reason, the Dongguan government opted to implement the ‘Replacing Humans with Machines’ policy outlining the need to recover the economy of Dongguan with intelligent manufacturing through automation (Bogue 10). In 2014, the resolution was passed, and immigrant workers in China, as well as the rest of the world, were fired due to redundancy issues and enforcement of the policy laws (Schlæger 10). In addition to the policy, the government ensured an annual reimbursement of 200 million yuan for 10-15 percent of equipment purchase procurement expenses (Price Waterhouse Coopers 10). The policy is currently widely adopted in China, including regions of Taiwan and Taipei, which are known as economic hubs for IT-based manufactured products. In regions of Taiwan, issues of reduced labor and increasing operational costs were among the main reasons for increased optimization of automation through the Manufacturing Automation Promotion Plan.

Therefore, the rise in non-routine manual jobs in China is guaranteed. The rate of unemployment is increasing among the Chinese people, given the increased attractiveness of immigrants who are willing to work for less. The demographic factor in employment and poverty is seen as a major factor in the shift in demand for labor in the IT manufacturing industry (Kongshøj 10). The reason is that the younger generation has more demand for higher-paying non-routine cognitive jobs, whereas there more supply of routine non-cognitive jobs.  Male workers are reported to prefer non-routine based work, whereas; females are reported to prefer cognitive non-routine jobs (Zhou 1). The difference in demand and supply highlights why there is a shift in unemployment rates given the rise in automation in the IT-based industry.

As a result, the fraction of people unemployed is growing since the 1990s to date. The propensity for this is the growing change for the human labor force in the manufacturing industry  (Kromann et al. 276). Given the higher population of the elderly in the community, their working competence with regard to automation gradually decreases over time. On the other hand, the smaller fraction of the working youth indicates the need to have a non-routine based working occupation (Qiu et al. 60). Therefore, unemployment rates among females, for instance, between 30 and 49 years, have risen considerably over the past two decades (Qiu et al. 60).

Similarly, for the male gender, the rise in non-working fraction has increased over the last two decades. With the given rise in automation and specifically, the intelligence factor in machinery, it is expected that the rise in unemployment rates within the IT manufacturing sector will increase (Kromann et al. 276). Eventually, companies in the associated industries will also shift their policy to automation, which is preferred for operational and financial reasons.

Reasons including the need for the electronics and Information Technology sector to replace workers are due to improved productivity.  Increased competition in the personal computer manufacturers, including HP and Lenovo, have seen increased automation strategies to reduce workforce employment (Tiang et al. 79). The cutting in production lines is also attributed to the decrease in workforce employment in the sector. The estimation in increased productivity using machines is regarded as offering better deadlines in sales volumes compared to human capital resources (Tiang et al. 79). Further, investment in automation equipment does not necessarily mean that the equipment has to be changed; the only shift is improved equipment performance through upgrading.

The human issue concerning increased automation in the Information Technology sector is that most workers are finding it difficult to shift from their routine based tasks (Qiu et al. 60). According to Xia, the need to educate further on automation is one of the pressures that workers are facing regardless of the economic status one is in (Zhou 1). The eventual future is that humans will now have to compete with machines in seeking job opportunities, which, based on the factors assessed in the current report, robots, machinery and artificial intelligence, seem to be winning the competition.

1. Conclusion

The above report has provided literary evidence suggesting an eventual and current shift in unemployment rates in the Information Technology manufacturing industry in China. Ideally, the evidence suggests that the change is attributed to the enormous and persistent government policy on automation replacing human workers. The shifts are occurring in major IT-based manufacturing hubs, including Taiwan and Shenzhen. More than 1 million workers have been laid off in total within the Information Technology manufacturing sector. In summary, the major factors that contribute to the shift in unemployment are demand for labor, the shift in labor supply and government policy on automation.

1. Recommendations

With this reality in mind, it is crucial to have a further understanding from a policy point of view as well as a business perspective regarding the impact of automation technology and link to shift in unemployment rates.

1. Recommendation 1: the first recommendation is to the policy observers in China as well as the rest of the world. It is crucial to understand the impact of automation and how it will affect the Chinese people along with the immigrants. The region is already experiencing a high unemployment rate, which may increase, especially due to the increased rural-urban migration in search of work. Also, there is the issue of selected unemployment where highly skilled workers are choosing occupations based on the type of pay and quality of work done. Redundancy is the option for most workers, which in the IT industry is quickly being replaced by robots for improved productivity and quality of work. For this, the policy observers and makers should revisit the government policy in line with this report and understand how they can address the growing concern over unemployment rates in IT hubs, including Taiwan.
2. Recommendation 2: The second recommendation is from an academic standpoint, which will help business investors, including Foreign Direct Investors. Further quantitative research should be conducted to understand the impact of a shift in unemployment in the present and future of China from a statistical point of view. Preferably, there should be an evaluation of how demographics based on age, gender and occupational factors on routine and non-routine work affect the need for manufacturers to shift to automation as well as how this affects non-employment trends.

Work Cited

Acemoglu, Daron, and Pascual Restrepo. Demographics and Automation. National Bureau of Economic Research, 2018.

Bahrin, Mohd Aiman Kamarul, et al. “Industry 4.0: A Review on Industrial Automation and Robotic.” Jurnal Teknologi, vol. 78, no. 6–13, 2016, pp. 137–143.

Bock, Thomas. “The Future of Construction Automation: Technological Disruption and the Upcoming Ubiquity of Robotics.” Automation in Construction, vol. 59, Elsevier, 2015, pp. 113–121.

Bogue, Robert. “Robotic vision boosts automotive industry quality and productivity.” Industrial Robot: An International Journal (2013).

Chang, Tien-Hsiang, et al. “The Effect of the Government’s Manufacturing Automation Promotion Policy in Taiwan.” Journal of Manufacturing Technology Management, Emerald Group Publishing Limited, 2006.

Chen, Aimin. “Urban Unemployment and Segmented Labor Markets.” Urbanization and Social Welfare in China, Routledge, 2018, pp. 141–166.

Duanmu, Jing-Lin. “Firm heterogeneity and location choice of Chinese multinational enterprises (MNEs).” Journal of World Business 47.1 (2012): 64-72.

Easterlin, Richard A., et al. “China’s Life Satisfaction, 1990–2010.” Proceedings of the National Academy of Sciences, vol. 109, no. 25, National Acad Sciences, 2012, pp. 9775–9780.

Ernst, Dieter. Advanced Manufacturing and China’s Future for Jobs. 2016.

Fang, Cai, and Lu Yang. “4 The End of China’s Demographic Dividend.” China: A New Model for Growth and Development, ANU E Press, 2013, p. 55.

Feng, Shuaizhang, Yingyao Hu, and Robert Moffitt. “Long-run trends in unemployment and labor force participation in urban China.” Journal of Comparative Economics 45.2 (2017): 304-324.

Ferrari, Rossella. “Writing Narrative Style Literature Reviews.” Medical Writing, vol. 24, no. 4, Taylor & Francis, 2015, pp. 230–235.

Fu, Tze-Wei, and Monli Lin. “Interest Rate, Unemployment Rate and China’s Exchange Rate Regime.” International Journal of Emerging Markets, Emerald Group Publishing Limited, 2012.

Giuntella, Osea, and Tianyi Wang. Is an Army of Robots Marching on Chinese Jobs? IZA Discussion Paper, 2019.

Goeman, Jelle J., and Aldo Solari. “Multiple Testing for Exploratory Research.” Statistical Science, vol. 26, no. 4, Institute of Mathematical Statistics, 2011, pp. 584–597.

Green, Bart N., et al. “Writing Narrative Literature Reviews for Peer-Reviewed Journals: Secrets of the Trade.” Journal of Chiropractic Medicine, vol. 5, no. 3, Elsevier, 2006, pp. 101–117.

Herz, Benedikt, and Thijs Van Rens. Structural Unemployment. 2011.

Huang, Yi, et al. “The Chinese labour market: High unemployment coexisting with a labour shortage.” (2019).

Huang, Yijiang. Chinese Green Job Guarantee: A Roadmap for Sustainable Prosperity. Working Paper, 2020.

Kongshøj, Kristian. “‘Moderate universalism’ in China and the Nordic countries: reviewing the major challenges in unemployment protection.” Nordic Journal of Social Research 6 (2015).

Kromann, Lene, et al. “Automation and Productivity—a Cross-Country, Cross-Industry Comparison.” Industrial and Corporate Change vol. 29, no. 2, Oxford University Press, 2020, pp. 265–287.

Leung, Joe CB, and Yuebin Xu. China’s Social Welfare: The Third Turning Point. John Wiley & Sons, 2015.

Li, Chang-shuai, and Zi-juan Liu. “Study on the relationship among Chinese unemployment rate, economic growth and inflation.” matrix 1.1 (2012): 4.

Li, Ling. “China’s Manufacturing Locus in 2025: With a Comparison of ‘Made-in-China 2025’ and ‘Industry 4.0.’” Technological Forecasting and Social Change, vol. 135, Elsevier, 2018, pp. 66–74.

Li, Qilong. “Empirical Analysis of Factors Influencing Employment in China-Based on the Analysis of Johansen Co-Integration Model and Error Correction Model.” Management & Engineering, no. 18, St. Plum-Blossom Press Pty Ltd., 2015, p. 33.

Li, Shi, et al. “China’s Higher Education Expansion and Unemployment of College Graduates.” China Economic Review, vol. 30, Elsevier, 2014, pp. 567–582.

Li, Xun, et al. “Transition from Factor-Driven to Innovation-Driven Urbanization in China: A Study of Manufacturing Industry Automation in Dongguan City.” China Economic Review, vol. 59, Elsevier, 2020, p. 101382.

Liu, Qian. “Unemployment and labor force participation in urban China.” China Economic Review 23.1 (2012): 18-33.

LIU, Shu-hua, et al. “Multi-Robot Task Allocation Based on Swarm Intelligence.” Journal of Jilin University (Engineering and Technology Edition), vol. 1, 2010, pp. 123–129.

Liu, Yang. “Labor Market Matching and Unemployment in Urban China.” China Economic Review, vol. 24, Elsevier, 2013, pp. 108–128.

Liu, Ying, Jikun Huang, and Precious Zikhali. “The bittersweet fruits of industrialization in rural China: the cost of the environment and the benefit from off-farm employment.” China Economic Review 38 (2016): 1-10.

Mann, Katja, and Lukas Püttmann. “Benign Effects of Automation: New Evidence from Patent Texts.” Available at SSRN 2959584, 2018.

Meng, Xin. “Labor Market Outcomes and Reforms in China.” Journal of Economic Perspectives, vol. 26, no. 4, 2012, pp. 75–102.

Miller, Claire Cain. “The long-term jobs killer is not China. It’s automation.” The New York Times 21 (2016).

Min, Yong-Ki, et al. “A Comparative Study on Industrial Spillover Effects among Korea, China, the USA, Germany and Japan.” Industrial Management & Data Systems, Emerald Publishing Limited, 2019.

PricewaterhouseCoopers, L. L. P. “The new hire: How a new generation of robots is transforming manufacturing.” Delaware limited liability partnership (2014).

Qiao, Hong, Rui Li, and Peijie Yin. “Robotics and automation activities in China [Industrial Activities].” IEEE Robotics & Automation Magazine 22.3 (2015): 14-17.

Qiu, Bin, et al. “An Empirical Study on the Impact of GPNs on China’s Manufacturing Industries’ Upgrading in Global Value Chains–An Analysis from the Perspective of ESI.” China Industrial Economics, vol. 1, 2012, pp. 57–67.

Schmitt, John, and Kris Warner. Deconstructing Structural Unemployment. Center for Economic and Policy Research, 2011.

Schlæger, Jesper. E-government in China: Technology, power and local government reform. Routledge, 2013.

Sjöholm, Fredrik, and Nannan Lundin. “Will Science and Technology Solve China’s Unemployment Problem?” Asian Economic Papers, vol. 9, no. 2, MIT Press, 2010, pp. 1–28.

Su, Z. (2014). Chinese online unemployment-related searches and macroeconomic indicators. Frontiers of Economics in China, 9(4), 573-605.

Tian, Xi, et al. “Management of used lead-acid battery in China: Secondary lead industry progress, policies and problems.” Resources, conservation and recycling 93 (2014): 75-84.

Wübbeke, Jost, et al. “Made in china 2025.” Mercator Institute for China Studies. Papers on China 2 (2016): 6-74.

Wenyuan, Du, and Peng Zhaoqi. “Research on Structural Unemployment and Its Countermeasures in Hebei Province.” 2019 4th International Conference on Social Sciences and Economic Development (ICSSED 2019), Atlantis Press, 2019.

Yuan, Jiahai, et al. “Smart grids in China.” Renewable and Sustainable Energy Reviews 37 (2014): 896-906.

Yu, Yanshan, Jin Yang, and Bin Chen. “The smart grids in China—A review.” Energies 5.5 (2012): 1321-1338.

Zhang, Tao, et al. “Real-Time Motion Planning for Mobile Robots by Means of Artificial Potential Field Method in Unknown Environment.” Industrial Robot: An International Journal, Emerald Group Publishing Limited, 2010.

Zeng, Qun. “Youth unemployment and the risk of social relationship exclusion: A qualitative study in a Chinese context.” International Journal of Adolescence and Youth 17.2-3 (2012): 85-94.

Zhou, C. “Man vs Machine: Automation Starting to Take a Toll on China’s Workforce.” South China Morning Post, 14 Feb. 2019. www.scmp.com, https://www.scmp.com/economy/china-economy/article/2185993/man-vs-machine-chinas-workforce-starting-feel-strain-threat.