“No unemployed candidates will be considered at all” – the crux of unemployment.

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Back in 2008, when we first started developing our solutions, the work of Diamond, Mortenson and Pissarides provided the scientific basis for our job and skills matching technology. With their Nobel prize winning labor market theory and the DMP model, they provided a first coherent, complete framework to think about labor market dynamics in a structured way. In their theory, labor markets are viewed as markets with search frictions: workers look for suitable jobs and employers look for suitable workers, both investing considerable time and effort; search frictions are the process, or time factor, of matching the two.

The DMP model itself describes the search activity of the unemployed, the recruiting behavior of businesses and wage formation. When jobseekers and employers find each other, they negotiate wages based on the labor market situation: the number of unemployed workers and the number of vacancies, as well as other factors such as how long it will take to find that job, the workers’ unemployment benefits and what value the worker attributes to not having to work while searching. The model can thus be used to estimate the effects of different labor-market factors on unemployment, the average duration of unemployment, the number of vacancies and real wage. Such factors may include the level of unemployment benefits, the real interest rate, the efficiency of employment agencies, hiring and firing costs, etc.

On-the-job search and its effects on labor market dynamics

This framework significantly furthered understanding of how mismatch problems and a lack of symmetry between different search mechanisms and the resulting imbalance between supply and demand affect the functioning of the labor market. However, one key aspect of the labor market is completely ignored here, namely that not all jobseekers are unemployed. The majority of the literature since then typically also focused on the unemployed, not only because the standard DMP framework does not include on-the-job search, but also due to limited availability of on-the-job search data. More recently, however, research has begun to include on-the-job search and job ladders. The idea of a job ladder is that all workers agree on which jobs are more desirable in the sense of job and wage satisfaction and slowly climb the job ladder from “bad” or unsatisfactory jobs to “good” jobs through job-to-job transitions. Occasionally, negative shocks throw them off the ladder and back into unemployment. A growing number of studies have documented the importance of on-the-job search and its related job ladder dynamics for macroeconomic outcomes.[1] Some argue that the labor market is segmented in that employed and unemployed jobseekers are unlikely to directly compete with each other for jobs because they have different job-relevant characteristics and apply for different jobs. For example, Longhi and Taylor (2013) state that the unemployed only apply for “bad” jobs and the employed for “good” jobs and so they do not compete. However, they do not investigate the reasons for this behavior and it may well be that the cause is somehow tied to the search behavior of employed workers or related dynamics. For instance, they find that a larger proportion of the unemployed “prefer” a part-time job compared to the employed and state that this supports their claim of a segmented labor market, ignoring the fact that this may not be an inherent “preference”, but instead a higher flexibility on part of the unemployed based on their more pressing need to find any employment at all. Even though they note themselves that part-time workers are more likely to search on the job, probably because they are “unsatisfactory in terms of labor supply preferences”. Similarly, they find that the two groups tend to use different search methods, with the employed focusing more on using their networks and the unemployed relying more on job centers and employment agencies. They use this as another argument for their conclusion that they are not applying for the same jobs, apparently because the jobs available through these different channels differ. But this could instead have more to do with the fact that with increasing length of unemployment, jobseekers’ personal and professional networks decline and the unemployed become more reliant on institutional support. It does not necessarily imply that the unemployed actually want to apply for different jobs.

Indeed, the bulk of recent literature finds that on-the-job search has a clear effect on macroeconomic outcomes and the chances of unemployed jobseekers on the labor market. Moscarini and Postel-Vinay (2019) and Faccini and Melosi (2019) link on-the-job search to inflation, arguing that when employment is concentrated at the bottom of the job ladder, typically following a recession, employed workers search to find a better job. As workers climb the job ladder, the labor market tightens and generates inflation pressures through wage negotiations. Eeckhout and Lindenlaub (2019) provide an elegant theory where the search behavior of employed workers generates large labor market fluctuations even in the absence of other shocks through a strategic complementary between on-the-job search and vacancy posting. According to this theory, the labor market itself can generate cycles, contrary to the longstanding assumption (based on the DMP model) that such cycles can only be generated by exogenous shocks. The authors state that active on-the-job search improves the quality of the jobseeker pool, which encourages vacancy posting through firms, which makes on-the-job search more attractive. This corresponds to an economic boom with little mismatch, abundant job creation and low unemployment. On the other hand, during a recession, the jobseeker pool has a much lower proportion of on-the-job searchers. As a result, firms have less incentive to post vacancies, which generates a low matching rate for workers which cannot compensate the cost of on-the-job search, leading to high mismatch and high unemployment. The authors show that their theory, in particular the search behavior of the employed, can explain many important labor market phenomena, including large fluctuations in unemployment and the fact that unemployment rates take much longer to recover than vacancies and productivity, say, following a recession.

It may seem counterintuitive that the behavior of the employed could explain unemployment. But the employed typically have a share of over 90 percent of the labor force and apply for job openings in the same labor market as the unemployed. Therefore, any minor change in their behavior has deep aggregate implications for unemployment. Even if they search much less intensively than the unemployed, on average, almost half of the new jobs are filled by employed workers. Particularly at the end of a recession, the employed searchers crowd out the unemployed ones. As job creation picks up, jobs go disproportionately to the on-the-job searchers and not to the unemployed. All the renewed activity thus initially translates in better jobs for the employed, but not in improved prospects for the unemployed.

Based on a survey that focuses on job search behavior regardless of labor force status, Faberman et al. (2020) find evidence supporting Eeckhout and Lindenlaub’s theory in the following three facts: (1) on-the-job search is pervasive, and is more intense at the lower rungs of the job ladder; (2) the employed are about four times more efficient than the unemployed in job search [2]; and (3) the employed receive higher-quality job offers than the unemployed.

The stigma of unemployment

What these theoretical models and studies do not mention, is why the employed are more successful in job search and receive higher-quality job offers than the unemployed. Much of this may have to do with the stigma of unemployment – especially long-term unemployment [3]. The quote in the title of this article is from a job posting by Sony Ericsson, and they are not alone. Various studies (for example, the ones described here and here or here) have shown consistently over the years that hirers are biased against unemployed applicants, often assuming that the unemployed are lazy, less productive and less competent workers than employed applicants with otherwise equal characteristics. A 2019 study found that, based on stereotypical perception of unemployed applicants, hirers even condemn their character: unemployed job candidates are seen as less warm, less trustworthy, less well-intentioned, less friendly, and less sincere compared to employed job candidates. No wonder the unemployed are forced to settle for “bad jobs” – if they find employment at all.

And this biased perspective is not only found in hirers, it also seems to be widespread among researchers. For instance, at the core of Eeckhout and Lindenlaub’s theory is the implicit assumption that employed jobseekers are more attractive and valuable than unemployed ones (active on-the-job search improves the quality of the jobseeker pool). Even the DMP model takes a stigmatized view of unemployment: the result that higher unemployment benefits raise unemployment rates is rooted in the assumption that higher income through benefits decrease the unemployed worker’s motivation to search for a job and thus to successfully reenter the labor market. To put it bluntly, the model assumes that unemployed workers prefer leisure to work (are lazy) and puts the blame on them (a motivated unemployed person could find a job at any time).

This, together with the fact that research demonstrates that long-term unemployment also leads long-lasting damage such as to lifetime lower wages, increased health issues, lower quality of life and diminished lifespan as well as an increased risk of suicide, clearly shows that unemployed jobseekers should be protected and that efforts should be increased to prevent further unemployment and to mitigate long-term unemployment. One small but simple step is already apparent: promote solutions that prevent this bias, at least in the first steps of the candidate selection process, by using labor intermediation systems that mask labor force status. However, many current systems and platforms offered by PES only provide access to unemployed jobseekers. These systems are rarely successful, often barely frequented by companies and potential employers. And the stigma of unemployment is a key reason for this issue. To be sustainable in the long term and offer unemployed jobseekers a real chance to return to work, a good PES platform must include the whole universe of workers and specialists from all fields and industries and competences.

Of course – contrary to what some software providers claim – simply introducing the right software will neither fully solve the problem of discrimination against the unemployed, nor can it reduce unemployment on its own. This is a complex issue depending on many factors which needs to be tackled from multiple angles. Nevertheless, such solutions can serve as an effective component of well-designed labor market and anti-discrimination policies.

Here at JANZZ, we don’t just go with quick marketing headlines, we develop evidence-based solutions and  have already been deploying them successfully since 2010. Our job and skills matching solutions are fair and non-discriminatory, producing completely unbiased results according to the OECD principles on AI. This guarantees that the best candidate with the best aptitude in all individual criteria achieves the best match – regardless of labor force status or other non-relevant characteristics such as origin, age or gender. Which is one of the many reasons why we are a trusted partner of an ever-growing number of public employment services across the globe.

If you want to take this first step in breaking the cycle and contributing to a fairer labor market for the unemployed, contact us at info@janzz.technology or visit our product site for PES.

 

[1] Christensen et al., (2005), Cahuc, Postel-Vinay, and Robin (2006), and Bagger and Lentz (2019), among others,
[2] If they had relied only on transition rates – a common approach in the literature due to lack of data on job search effort – they would have found the opposite result of Fact (2), namely that the unemployed are about seven times more efficient.
[3] In the US, long-term unemployment is defined as (active) unemployment for longer than 6 months; in the EU for longer than 12 months.

AI, automation and the future of work – beyond the usual bubbles

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In recent years there have been many posts, articles and reports on how AI and automation will shape the future of work. Depending on the author’s perspective or agenda, these pieces go one of two ways: either the new technology will destroy jobs and have devastating effects on the labor market, or it will create a better, brighter future for everyone by destroying only the boring jobs and generating better, much more interesting ones. As always, the truth probably lies somewhere between these two extremes. In this post, we want to take a more nuanced view by discussing the most common arguments and claims and comparing them with the facts. But before we get into this, let us first clarify what the AI-driven digital transformation is. In a nutshell, it is all about automation, using AI technology to complete tasks that we do not want humans to perform, or that humans cannot perform. Just as we did in the past, in the first, second and third industrial revolutions.

From stocking looms to AI art

With each of these revolutions came the fear that human workers would be rendered obsolete. So why do we want to automate? Even though in some cases, inventors were, and still are, simply interested in the feat of the invention itself, more often than not an invention or development was driven by business interests. As is widespread adoption. And no matter which era, businesses rarely have other goals than staying competitive and raising profits. 16th century stocking looms were invented to increase productivity and lower costs by substituting human labor. Steam-powered machines in 19th century mills and factories and farm machinery were used for the same reason. Robots in vehicle manufacture in the second half of the 20th century – ditto. Whether the technology is tractors, assembly lines, or spreadsheets, the first-order goal was to substitute human musculature by mechanical power, human handiwork by machine-consistency, and slow and error-prone “humanware” by digital calculation. But so far, even though many jobs were lost to automation, others have been created. Massively increased production called for jobs related to increased distribution. With passenger cars displacing horse-powered travel and equestrian occupations, and increasing private mobility, jobs were instead created in the expanding industry of roadside food and accommodation. Increasing computational power used to replace human tasks in offices also led to entirely new products and the gaming industry. And the rising wealth and population growth accompanying such developments led to increased recreational and consumption demands, boosting these sectors and creating jobs – albeit not as many as one may think, as we will see below. However, we cannot simply assume that the current revolution will follow the same pattern and create more jobs and wealth than it will destroy just because this is what happened in the past. Unlike mechanical technology and basic computing, AI technologies not only have the potential to replace cheap laborers, say, with cleaning or agricultural robots. They have also begun outperforming expensive workers such as pathologists diagnosing cancer and other medical professionals diagnosing and treating patients, and are also touching on creative tasks such as choosing scenes for movie trailers or producing digital art. Of course, we should also not simply assume a dystopian future of less jobs and sinking wealth. But we must keep in mind that currently, in many cases it is more cost effective to replace expensive workers with AI solutions than cheap laborers such as textile workers in Bangladesh.

So, working towards a more differentiated view, let us take a look at the currently most common claims and how they stand up to closer scrutiny.

Claim 1. AI will create more/less jobs than it destroys

This is the main argument put forth in utopian/dystopian scenarios, including reports by WEF (97m new jobs vs. 85m displaced jobs across 26 countries by 2025), PwC (“any job losses from automation are likely to be broadly offset in the long run by new jobs created”), Forrester (job losses of 29% by 2030 with only 13% job creation to compensate) and many others. Either way, any net change can pose significant challenges. As BCG states in a recent report on the topic, “the net number of jobs lost or gained is an artificially simple metric” to estimate the impact of digitalization. A net change of zero or even an increase of jobs could cause major asymmetries in the labor market with dramatic talent shortages in some industries or occupations and massive worker surplus and unemployment in others. On the other hand, instead of causing unemployment – or at least underemployment, less jobs could also lead to more job sharing and thus shorter work weeks. Then again, although this may sound good in theory, it also raises additional questions: How will pay and benefits be affected? And who reaps the bulk of monetary rewards? Companies? Workers? The government? It is admittedly too soon to see the effects of AI adoption so far regarding overall employment or wages. But past outcomes, i.e., of previous industrial revolutions, do not guarantee similar outcomes in the future. And even those outcomes show that job and wealth growth were not necessarily as glorious as they are often portrayed. The ratio of employment to working age population has remained fairly constant in OECD countries since 1970, rising only from just over 64% to just under 69%.[1] Much of this increase can be attributed to higher labor participation rates, especially of women. And the increased wealth is clearly not evenly distributed, e.g., in the US:

 

AI, automation and the future of work – beyond the usual bubbles

Source: Economic Policy Institute, https://www.epi.org/publication/charting-wage-stagnation/

 

AI, automation and the future of work – beyond the usual bubbles

Source: Economic Policy Institute, https://www.epi.org/publication/charting-wage-stagnation/

 

There are simply no grounds to assume that AI and automation will automatically make us wealthier as a society or that the increased wealth will be distributed evenly. We should thus be equally prepared for more negative scenarios and discuss how to mitigate the consequences. For instance, would it be acceptable to treat AI processes like human labor? If so, we could consider taxing them to support redistribution of wealth or to finance training or benefits and pensions for displaced workers.

In addition, these estimates should be questioned on a basic level. Who can confidently say that this job will decline? How can we know what kind of jobs there will be in future? None of these projections are truly reliable or objective – they are primarily based on some group of people’s opinions. For instance, the WEF’s Future of Jobs Report, one of the most influential reports on this topic, is based on employer surveys. But it is simply naive to think anyone, let alone a cadre of arbitrary business leaders, can have a confident understanding of which jobs and skills will be required in the future. One should not expect more from this than from fortune telling at a fair. Just take a look at the predictions about cars in the early 19th century, remote shopping in the 1960s, cell phones in the 1980s, or computers since the 1940s. So many tech predictions have been so utterly wrong, why should this change now? And yet, such predictions are a key element in the estimates for the “future of work”.

Fact is, scientifically sound research on this topic is extremely scarce. One of the few papers in this area studied the impact of AI on labor markets in the US from 2007 to 2018. The authors (from MIT, Princeton and Boston University) found that greater AI exposure within businesses is associated with lower hiring rates, i.e., that AI adoption has so far been concentrated on substitution as opposed to augmentation of jobs. The same paper also finds no evidence that the large productivity effects of AI will increase hiring. Some people may be tempted to say that this supports the dystopian view. However, we must also note that this study is based on online vacancy data, and thus the results should be treated with caution, as we explained in detail in one of our other posts. In addition, due to the dynamics of technological innovation and adoption, it is almost impossible to extrapolate and project such findings to make robust predictions for future developments.

And on a more philosophical note, what would it mean for human existence if we worked substantially less? Work is ingrained in our very nature; it is a defining trait.

Claim 2: Computers are good at what we find hard and bad at what we find easy

Hard and easy for who? Luckily, we do not all have the same strengths and weaknesses, so clearly, we do not all find the same tasks “easy” and “hard”. This is just yet another extremely generalizing statement based on completely subjective judgement. And if it were true, then most people would probably consider repetitive tasks as typically easy, or at least easier. This directly contradicts the next claim:

Claim 3: AI will (only) destroy repetitive jobs and will generate more interesting, higher-value ones.

The WEF states that AI will automate repetitive tasks like data entry and assembly line manufacturing, “allowing workers to focus on higher-value and higher-touch tasks” with “benefits for both businesses and individuals who will have more time to be creative, strategic, and entrepreneurial.” BCG talks of the “shift from jobs with repetitive tasks in production lines to those in the programming and maintenance of production technology” and how “the removal of mundane, repetitive tasks in legal, accounting, administrative, and similar professions opens the possibility for employees to take on more strategic roles”. The question is, who exactly benefits from this? Not every worker who is able to perform repetitive tasks has the potential to take on strategic, creative and entrepreneurial roles, or program and maintain production technology. It is simply a fact that not everyone can be trained for every role. More satisfying, interesting tasks for intellectuals (such as the advocates of a brighter future of work thanks to AI) may simply be too challenging for the average blue-collar worker whose job – which may well have been perfectly satisfying to them – has just been automated. And not every white-collar worker can or wants to be an entrepreneur or strategist. Also, what exactly does “higher-value” mean? Who benefits from this? The new jobs created so far, like Amazon warehouse workers, or Uber and Postmates drivers, are not exactly paying decent, secured living wages. And since the early 1970s, businesses have demonstrated a clear disinterest in sharing the added value from productivity gains with workers:

 

AI, automation and the future of work – beyond the usual bubbles

Source: Economic Policy Institute, https://www.epi.org/publication/charting-wage-stagnation/

 

On the other hand, a vast number of the AI applications that are already available perform higher- to highly-skilled tasks based on data mining, pattern recognition and data analysis: diagnosis and treatment of medical conditions, customer service chatbots, crop optimization and farming strategies, financial or insurance advising, fraud detection, scheduling and routing in logistics and public transport, market research and behavioral analysis, workforce planning, product design, and much more. The full effect of these applications on the job market is not yet clear, but they are certainly not only removing mundane, repetitive tasks from job profiles.

Claim 4: We (just) need to up-/reskill workers.

While we certainly do not disagree with this statement in general, it is often brought up as a more or less simple remedy to prepare for the future AI-driven shifts in the labor market and “embrace the positive societal benefits of AI” (WEF). Fact is, this comes with several caveats which make it a far from simple solution.

First off, we cannot repeat enough that it is not possible to predict the “future of work” reliably, especially which jobs really will be in demand in future and which will not. Also, based on the effects of the previous industrial revolutions and current research, it is highly likely that widespread adoption of AI will introduce new jobs with profiles that we cannot anticipate at the moment. This means we need to equip current and future professionals with the skills necessary for jobs that we currently know nothing about. One often suggested way to work around this issue is to encourage lifelong learning and promote more adaptable and short-term forms of training and education. This is certainly a valid option and clearly on the rise. However, there are several aspects to keep in mind. For instance, 15–20% of the US and EU adult population[2] have low literacy (PIAAC level 1 or below). This means that they have trouble with tasks such as filling out forms or understanding texts about unfamiliar topics. How can these people be trained to succeed at “more complex and rewarding projects” if they cannot read a textbook, navigate a manual or write a simple report? In addition, around 10% of full-time workers in the US and EU are working poor.[3] These people typically have neither the time, resources, nor support from employers for lifelong learning and thus no well-informed access to efficient, targeted and affordable (re)training.

By the time such issues have been addressed, many of these workers may have already missed the boat. In 2018, US employers estimated that more than a quarter of their workforce would need at least three months of training just to keep pace with the necessary skill requirements of their current roles by 2022.[4] Two years later, that share has more than doubled to over 60%, and the numbers are similar across the globe.[5] In addition, even before the post-Great Recession period, only roughly 6 in 10 displaced US workers were re-employed within 12 months in the 2000 to 2006 period.[6] In 2019, this rate was the same in the EU.[7] With increasingly rapid changes in skills demands, combined with lack of time and/or resources for vulnerable groups such as the working poor and workers with low literacy, not to mention lacking safety nets and targeted measures in underfunded workforce development systems, the prospects for these workers are highly unlikely to improve.

Moreover, the pandemic has massively accelerated the adoption of automation and AI in the workplace in many sectors. Robots, machines and AI systems have been deployed to clean floors, take temperatures or food orders, replace employees in dining halls, toll booths or call centers, patrol empty real estate, increase industrial production of hospital supplies and much more within an extremely short period of time. In the past, new technology was deployed gradually, giving employees time to transition into new roles. This time, employers scrambled to replace workers with machines or software due to sudden lockdown or social distancing orders. This is a crucial difference to the preceding industrial revolutions. Many workers have been cut loose with simply not enough time to retrain. Similarly disruptive events may well occur in the future – be it another pandemic or a technological breakthrough – and as a society, we need to be prepared for these events and provide affected workers with swift, efficient and above all realistic support.

Claim 5: Employers should view up- and reskilling as an investment, not as an expense.

If a company replaces all of its cashiers by robots, why would they want to reskill the newly redundant workers? Even governments have a hard time taking this stance on training and education. Many countries focus primarily on college or other education for young workers rather than retraining job seekers or employees. For instance, the US government spends 0.1% of GDP to help workers navigate job transitions, less than half what it spent 30 years ago – despite the fact that skills demand is changing much faster than it did three decades ago. And the vast majority of businesses is primarily interested in maximizing profits – that is just how our economy works. Remember: we live in a world where even sandwich makers and dogwalkers are forced to sign noncompete agreements to prevent them from getting a raise by threatening to move to a competitor for higher pay.

Well-performing conversational software could enable a company to take a 1,000-person call center and run it with 100 people plus chatbots. A bot can respond to 10,000 queries in an hour, far higher than any realistic volume even the most efficient call-center rep could handle. In addition, a chatbot does not fall ill, need time off work, or ask for perks and benefits. They make consistent, evidence-based decisions and do not steal from or defraud their employers. So, if the quality of this software is sufficient and the price is right, there would most probably be an uproar amongst shareholders if a company did not go for this offer. After all, a solution that increases efficiency and productivity while lowering expenses is the ideal of businesses of our time. So, if this company doesn’t opt for it, its competition will. And despite the “tech for social good” propaganda we constantly hear from Silicon Valley, most companies are simply not interested in the future of soon-to-be-ex workers.

Beyond the bubble

The bottom line is that we cannot afford to overdramatize or simply reassure ourselves that there will be enough jobs to go round, or we will constantly be playing catch up. Most of the commonly cited problems or solutions tend to be discussed within the academic or high-income bubble of researchers, tech entrepreneurs and policy makers, mixed with a substantial amount of idealism. But to get ahead of these developments that – for good or for bad – have vast potential to completely transform our labor markets and society, we need to look beyond our bubble and design realistic strategies for the future based on facts and objective data.

 

[1]    https://stats.oecd.org/Index.aspx?DatasetCode=LFS_SEXAGE_I_R#
[2]   US: https://www.libraryjournal.com/?detailStory=How-Serious-Is-Americas-Literacy-Problem
        EU: http://www.eli-net.eu/fileadmin/ELINET/Redaktion/Factsheet-Literacy_in_Europe-A4.pdf
[3]   US: https://www.policylink.org/data-in-action/overview-america-working-poor
        EU: http://www.europeanrights.eu/public/commenti/BRONZINI13-ef1725en.pdf
[4]   The Future of Jobs Report 2018, World Economic Forum, 2018.
[5]   The Future of Jobs Report 2020, World Economic Forum, 2020.
[6]   Back to Work: United States: Improving the Re-employment Prospects of Displaced Workers, OECD, 2016.
[7]   https://skillspanorama.cedefop.europa.eu/en/dashboard/long-term-unemployment-rate?year=2019&country=EU#1

 

Building the AI-ready workforce: China’s Artificial Intelligence Plan pushed by both central and local governments

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This is part of a series of articles we conduct to analyze government policies and practices on the strategies to build AI workforce. Previously, we have analyzed how Singapore is helping mid-career PMETs to switch to the tech sector and a collaborative effort between government, tech companies and education providers in Saudi Arabia. Our third stop is China.

As the world’s major economies have announced the development of artificial intelligence as a national strategy, China as well released its New Generation Artificial Intelligence Development Plan in 2017.  It is the first time AI was specifically mentioned in the country’s national work report and China aims to build its core AI industry worth over 400 billion RMB (around 57 billion USD) by 2025 and to become the world leading AI power by 2030.

Chinese Ministry of Industry and Information Technology has recently published a report on AI talent development. The report identified three major issues in its current AI talent pool, which are a significant mismatch between skills and jobs, a short supply of highly skilled AI talents, and a regional unbalance of AI talents. It has forecasted a shortage of 300,000 AI workforces in the coming years. To tackle these problems, joint efforts from central and local governments have been made to actively promote the building of AI talents.

In April 2018, an action plan has been laid out for higher education institutions by the country’s Ministry of Education. The plan urged to incorporate AI programs into higher education curricula, to build 100 “AI+X” models to nurture AI talents in specific fields by 2020, to build 50 AI colleges, AI research centers or cross-collaborative research centers by 2020 and to introduce AI in primary and secondary schools. According to the announcement on their website, a total number of 215 higher education institutions currently offer AI major for undergraduate students.

In January 2020, the Ministry of Education, along with the National Development and Reform Commission and the Ministry of Finance announced a joint circular to further develop the “AI+X” model and promote postgraduate students in the AI fields. During the same year, the Ministry of Education expanded the number of master’s students by 189,000 and AI is among the popular majors for expansion [1]. Proposals in the joint circular include encouraging corporation of leading AI enterprises and universities, offering flexible employment models to attract AI experts from enterprises and research institutes to work in universities and funding projects involving collaborative training of AI students between enterprises and universities.

The local governments have as well launched their efforts. The Beijing-Tianjin-Hebei region, the Yangtze River Delta region, the Guangdong-Hong Kong-Macau-Great Bay Area and the Sichuan-Chongqing region are the main development heights of the AI industry in China. Chongqing, China’s industrial heartland city has become one of the 13 “AI pilot zones” in China in early 2020. As a means of China’s AI strategy, such AI pilot zones are going to be granted with financial support and favorable local regulations to encourage the expansion of AI industry. According to Chongqing government website, the first 73 major projects with a total investment of about 29.6 billion RMB (around 4.2 billion USD) have been approved.

In the meantime, the government of Chongqing has released several policy measures to expand the city’s AI talent pool, including releasing an in-demand talent barometer to perform statistically analyses and forecast on workforce, financially supporting AI enterprises to carry out one-to-two-year apprenticeship in AI related positions, and financially supporting AI enterprises to offer AI related internship positions with no more than 12 months.

JANZZ.technology was invited twice to Chongqing in 2019 thanks to the Consulate General of Switzerland in Chengdu and Sino-Swiss TechnoPark, and we were also horned to participate in the Smart China Expo a high standard tech event hosted annually in Chongqing. We have witnessed the huge potential within this traditional manufacturing city. As worldwide governments embark on the journey of digital transformation, their public employment services (PES) seek suitable solutions to support them in the increasingly important role they play in job matching, enhancing employability, addressing skill gaps and aligning education offerings with market needs.

To learn about how JANZZ.technology assists PES in tackling these challenges, please visit our product site for PES or contact us at info@janzz.technology

 

[1] http://www.xinhuanet.com/2020-04/28/c_1125917657.htm