The Great Resignation or just a Great Misperception?

There have been many “great” waves in economy, especially in the US: the Great Depression (1929–1933), the Great Inflation (1965–1982), the Great Moderation (mid 1980s–2007), the Great Recession (late 2007–2009), and now we have a new one: the Great Resignation. But while the previous “great” events were undoubtedly real and had far-reaching impact on the economy and the labor market, this time round, there is reasonable doubt as to whether this wave of quits really is so great. Despite the huge buzz this catchy term has generated in the media – which is often based on shaky data promoted by data providers whose main interest is self-marketing.

The Great Resignation, a term allegedly coined by Anthony Klotz of Texas A&M University, was originally a prediction. Back in May 2021, Klotz anticipated a rise in quits based on pent-up resignations that hadn’t happened since early 2020 due to the significant job uncertainty brought on by the pandemic. He claimed that these numbers would be multiplied by “pandemic-related epiphanies” about family time, remote work, life and death and so on. Now that the quits numbers really have gone up, his prediction seems to have been turned into a prophecy, with the widespread consensus that it is happening for all the reasons Klotz stated. But there are good reasons to take a more critical view on this thesis, the most pertinent being the glaring lack of reliable evidence to support it.

The lesser resignation

In October 2020, 4.2m workers in the US quit their jobs, which is almost 3% of total employed workers. Viewed as an isolated number, this quits rate has indeed risen significantly over the last 12 months. However, the job openings rate has also increased dramatically to 7% of total employment plus new openings. This 60% increase from pre-pandemic levels far exceeds the numbers of the past two decades. Accordingly, the hires rate increased to a level last seen 20 years ago. If we put the quits into this context, say, by considering the ratios of quits to job openings, the situation looks far less dramatic.

 

The great misperception

 

It certainly makes sense to view these values together, since quits are strongly correlated with job openings: For the available monthly data from 2000 through 2021, the correlation coefficient is 0.82 – higher than for quits with unemployment (-0.77) or hires (0.75). And in relation to job openings, the current quit rate is by no means an outlier. Both the current rates of job openings and of quits are higher than usual, straying away from the typical cluster as shown in the figure below. And yet, their relationship still follows the same pattern as before the pandemic, when these changes could not have been attributed to, say, a desire for life changes newly discovered in lockdown. It thus seems more likely that quits have risen primarily for the more mundane reason that an unusually high number of job opportunities are opening up to workers, i.e., quits are simply at the level we would expect them to be, given the number of job openings. As Josh Bersin put it: Right now, there are just too many jobs and not enough people. And so we are likely seeing a great job hop as opposed to a great resignation. But it is not necessarily about doing something new.

 

The great misperception

 

The lesser wages

Looking at the figures in more detail, we see that of the four (sub)industries most affected by quits, three belong to the by far lowest paying industries: Retail Trade and Leisure and Hospitality. The latter was also the group that experienced exorbitantly high layoff rates at the beginning of the pandemic, and many front-line workers in retail were forced to work under precarious conditions with little to no monetary reward.

 

The great misperception

The great misperception

 

These low-wage workers are hardly quitting to indulge “pandemic-related epiphanies” or “craft careers”. Instead, they have been struggling to make a living since long before the pandemic. And now, with their industries among those with the highest net new job creation in 2021, these workers have a window of opportunity. If you have a job with no security, no appreciation and a salary that is barely enough to survive on, why not quit it for a job that pays $1 an hour more? Especially in a wage band that has experienced close to no growth in the past two decades.

 

The great misperception

 

The lesser reasons

Despite these numbers, much of the news coverage and reports have focused on burnout and remote work as the main drivers of the Great Resignation, claiming that white-collar professionals are shifting their career paths and leaving their jobs for companies that offer work arrangements that better suit their newly found values and preferences. If at all based on data, the most often cited source is LinkedIn. But LinkedIn data is extremely biased towards white-collar professionals. If we take a look at the much more representative data from the Job Openings and Labor Turnover Surveys, we see that in the largest white-collar industry, professional and business services, quits have risen at less than half the rate for the leisure and hospitality industry – despite an above-average job openings rate. In finance, real estate or information, which includes software, internet and publishing companies, quits are not rising much at all. In other words, employees more likely to be working remotely and thus with an increased risk of (self-reported) burnout are in fact less likely to quit.

Another supposed factor is health concerns related to COVID-19. However, many of the workers with these concerns had already left the workforce in 2020. And although labor force participation rates are still below pre-pandemic levels, they have been increasing steadily since April 2020 for most groups – except for older Americans (unsurprisingly, as the pandemic poses a much higher health risk to older people). But instead of calling them ‘resignations’, these quits could also simply be called retirements.

 

The great misperception

The great misperception

 

The lesser regions

It is also worth noting that, while there is a lot of talk about the Great Resignation being a global phenomenon, it is in fact just talk. At first glance, the situation looks similar in the UK: a high quits rate coupled with a high level of vacancies and fast wage growth. And again, taking a closer look at the figures, resignations as a proportion of job moves are simply back to pre-pandemic levels, with low-skill/low-wage workers driving the surge in quits – presumably for similar reasons as in the US: painfully low wages, bad working conditions and disloyal employers. Rather than quitting for something new (as in rethinking careers), a higher proportion of workers are moving to new jobs in the same industry. Moreover, while there was a surge in wage growth in Q2 of 2021, this figure rapidly decreased back to the level of Q1 over the second half of 2021.

 

The great misperception

The great misperception

The great misperception

The great misperception

The great misperception

 

And the UK is the only other country with any kind of similarity. In Canada, the number of quits is still far below pre-pandemic levels – especially the number of people who left their job because they are dissatisfied. In Japan, the percentage of people who quit their job for this reason has remained comparable to the level in 2015.

In the EU, there is no comparable quits measure. But there is also no other measure that suggests a big rise in resignations. Wage growth across the EU is the lowest ever recorded and the labor force participation rate has returned to pre-pandemic levels, with rates in individual countries like Spain and France even exceeding those levels. According to the latest labor force survey conducted in Spain, the number of voluntary resignations in 2021 is still lower than before the pandemic.

 

The great misperception

The great misperception

 

In Latin America and the Caribbean, the national economies are still reeling from the effects of the pandemic in terms of GDPs bottoming out and unemployment rates going through the roof. In most of these countries, the labor market is still far from recovery. The quality of available jobs has decreased, and the number of weekly hours of paid work is still significantly less than before the pandemic. According to the latest ILO report on Covid and the world of work, this is in fact true generally for low-income and lower-middle-income countries.

 

The great misperception

 

Unsurprisingly, the number of quits has not risen in these countries either. Overall, there is simply no evidence of a big rise in resignations anywhere in the world apart from the US and the UK. Let alone for the reasons promoted by the media.

The great hype

Basically, if you take the time to look at the hard data, there is neither reason to panic, nor to celebrate this not-so-great resignation. For a truly great resignation in the sense conveyed by the media and self-marketing data providers, the majority of workers would need to be in the comfortable position of actually having a choice regarding their lifestyle and consumption needs, and the job that fits those needs. Currently, we are still very far from any such scenario.

What has happened in the US and UK is that low-wage workers who previously had to take whatever work they could get, now have some agency. After a long period of employers exploiting the fact that they had workers on tap and thus hiring on whatever terms they deemed fit, the tables have turned. Jobs paying minimum wage may have to change their pay and benefits to become more attractive. The question is how long it will last. And if it does last, will pay rises and perks be enough? Or will it require more profound changes and rethinking our attitudes to low-wage jobs as a society? More on this in our upcoming whitepaper.

‘So clever I don’t understand a word of what I am saying’ – AI’s potential for handling text-based data is far from unlimited

janzztechnology

The often-expressed fear that AI robots are on the verge of infiltrating and taking control of every aspect of our lives is admittedly understandable, given the capabilities of AI already proclaimed today: writing guest articles for newspapers, answering basic customer service queries, diagnosing medical conditions, solving long-standing scientific problems in biology and much more – if you believe the sources [1],[2]. But are these purported successes really evidence of unlimited potential? Will AI systems really be able to solve any task given enough time and data? You may not want to hear this, but the answer is a resounding NOPE. At least not if researchers and developers stick to the knowledge-lean approaches they are currently so fixated on.

To start with, artificial intelligence is not even remotely comparable to human intelligence. Any AI system is a sophisticated ‘bag of tricks’ designed to lull humans into believing it has some kind of understanding of the task at hand. So, to develop AI-based technologies that are intelligent in any sense of the word there is no way around feeding these technologies with extensive human knowledge – which involves a substantial amount of human labor and will do for the foreseeable future. As a consequence – and again, you may not want to hear this – relying on solutions for HR and labor market management based solely on deep learning (DL) or other statistical/machine-learning (ML) approaches is a bad investment. And it is not simply a waste of money, it is also an ethical issue: Especially in the field of HR, the reliability of data and evaluations is crucial as it can deeply affect human lives. For instance, all too often perfectly suitable candidates are screened out by AI-based systems like ATS just because their resume does not contain the exact keywords specified in the filter or is associated with false contexts. Which a human recruiter would have realized, had they seen the resume themselves. This is just one of many examples of how real people can be affected by underperforming AI technology.

Artificial – as in fake

While researchers define AI systems as ones that perceive their environment and take actions that maximize their chance of achieving their goals, the popular perception of AI is that it aims to approach human cognition. Intelligence is typically defined as the ability to learn, understand, and form judgments or opinions based on reason or to deal with new or trying situations. However, this requires a key cognitive ability: storing and using commonsense knowledge, which we humans develop through a combination of learning and experience – and that so far, AI systems simply do not have and won’t achieve in the foreseeable future. These limitations are most obvious in natural language processing (NLP) and natural language understanding (NLU) techniques based on ML because commonsense knowledge is absolutely essential when it comes to understanding natural language. As an example, consider the following statement:

Charlie drove the bus into a tree.

Nowhere in this sentence does it explicitly state that Charlie is a human being, was in the bus, or that this is uncommon behavior. And yet our commonsense knowledge allows us to draw these and many other conclusions from this simple sentence without much effort. This ability, coined ‘linguistic competence’ by linguist Noam Chomsky, distinguishes computer systems trained in NLP and NLU fundamentally from human cognition. While we humans acquire this linguistic competence at an early age and can use it to discern the meaning of arbitrary linguistic expressions, knowledge-lean AI models will never be able to do so to the same extent because they work on a purely quantitative basis: their ‘intelligence’ is based on statistical approximations and (occasionally pointless) memorization of text-based data. ML systems can, at times, sidestep the problem of understanding and give the impression that they are behaving intelligently – provided they are fed enough data and the task is sufficiently narrowed down. But they will never actually understand the meaning of words; they simply lack the connection between form (language) and content (relation to the real world) [1].

This is precisely why even the most advanced AI models still struggle with these types of statements: because they contain so much implicit, often important information and causalities. For example, GPT-3, a state-of-the-art AI-based language model (which wrote the newspaper article cited at the beginning), was unable to correctly answer the simple question of whether a toaster or a pencil was heavier [1]. This is somewhat reminiscent of a quote from Oscar Wilde’s The Remarkable Rocket: “I am so clever that sometimes I don’t understand a single word of what I am saying”…

A major reason for this problem is that commonsense knowledge comprises an unconceivable number of facts about how the world works. We humans have internalized these facts through lived experience and can use them in expressing and understanding language without ever having to encode this staggering amount of knowledge into a written form. And precisely because this tacit knowledge is not captured systematically, AI systems have no access to it – or at least knowledge-lean AI systems don’t, i.e., systems based purely on statistical/ML approaches. So these systems are faced with unsurmountable challenges when tasked with understanding language. Because it is ‘unexpected’.

Another simple example: In a statistical analysis of words related to the English word pen, an ML system may spit out the words Chirac and Jospin, because these names are often mentioned together with the French politician Marie Le Pen, who of course has nothing to do with writing tools. It gets even more complicated when the same expression takes on different meanings depending on the context – think writing pen versus sheep pen. Systems based purely on ML often have great difficulty in discerning the nuances of such everyday language because they do not store the meanings of a word; connections are just based on cooccurrence. So, in the knowledge-lean world, there is still a long way ahead to reliable NLU.

No AI without HI

Having been around since the 1950s, AI has cycled through phases of hype and disillusionment many times. And right now, at least in the subfield of NLU, we are cycling back into the ‘trough of disillusionment’, as Gartner has so aptly coined it. Nevertheless, many are still clinging on to the great promises, blithely publishing, touting and investing in knowledge-lean AI technologies. But relying completely on ML-based algorithms for any application that requires language understanding is nothing but an expensive mistake. As we already explained, it is a huge leap from automated processing of textual data (NLP) to meaningful human-like understanding (NLU) of this information by machines. Thus, many automation plans will remain an illusion. It is high time to switch to a strategy that can succeed in these challenging tasks by effectively creating artificial intelligence through human intelligence.

In our area of expertise here at JANZZ, where we (re)structure and match job-related data, we understand that many automated tasks in big data require a significant amount of human labor and intelligence. Our job and resume parsing tool JANZZparser! has relied on NLP and NLU since the beginning – but always combined with human input: Our data analysts and ontology curators carefully and continuously train and adapt the language-specific deep learning models. NLP tasks are trained using our in-house, hand-selected corpus of gold standard training data. Parsed information is standardized and contextualized using our hand-curated ontology JANZZon!, which is the most comprehensive multilingual knowledge representation for job-related data worldwide. This machine-readable knowledge base contains millions of concepts such as occupations, skills, specializations, educations and experiences that are manually linked by our domain-specialized experts according to their relations with each other. JANZZon! integrates both data-driven knowledge from real job postings and resumes as well as expert information from international taxonomies such as ESCO or O*Net. This is the only way to ensure that our technologies can develop the kind of language understanding that actually deserves the name artificial intelligence. Generic phrases such as flexibility are given the relevant context, be it in terms of time management, thinking, or other aspects. As a result, false matches such as Research and Ontology Management Specialist with occupations like those in the figure below, due to overlap in wording but not in content, are excluded from matching results in our knowledge-based systems. The unique combination of technology and human intelligence in machine-readable form can achieve highly accurate, reliable and cross-linguistic/cross-cultural results when processing job-related data. Errors like the one in the pen example simply do not occur because each word is conceptually linked to the correct and relevant meanings and points of association.

 

Throwing good money after bad

The fact that we are on the right track with our hybrid, knowledge-based method of combining human intelligence with state-of-the-art ML/DL methods is not only confirmed by our own experiences and successful cooperation with businesses and public employment services (PES) across the globe, but also widely recognized by – non-commercial – NLU researchers. The outlined problems around the missing cognitive component in knowledge-lean AI systems will not be resolved in the next 50 years. As soon as human language is involved, there will always be countless cases where a 3-year-old child can make the correct semantic connection while a machine-learned tool either fails or does so only with absurdly high effort. Although knowledge-based systems like ours provide reliable and explainable analysis of language, they fell from grace because researchers and developers perceived the manual effort of knowledge engineering as a bottleneck. And the search for other ways to deal with language processing led to the knowledge-lean paradigm. Nowadays, supported by the immense speed and storage capacity of computers, most have come to rely on applying generic machine learning algorithms to ever-growing datasets for very narrow tasks. Since this paradigm shift, many developers and consumers have invested a lot of time and money in these systems. Being so heavily invested financially, they are simply not prepared to admit that this approach cannot produce the results they are looking for, despite the growing evidence against them.

However, the hybrid, knowledge-based approach of combining ML-based features with human-generated semantic representations can significantly improve the performance of systems that depend on language understanding. In our case, by adopting this approach, our technology can avoid the pitfalls of knowledge-lean systems based on uncontrolled AI processes, simple keyword matching and meaningless extractions of intrinsically context-poor and quickly outdated taxonomies. Instead, our matching and analytics solutions can draw on the smart data generated by our ontology. This context-based, constantly updated knowledge representation can be used in a variety of ways for intelligent classification, search, matching and parsing operations, as well as countless other processes in the area of job-related data. Especially in HR analytics, our solutions achieve above-average results that far exceed the performance of comparable offerings on the market. Thanks to these insights, employers are able to make well-informed decisions in talent management and strategic workforce planning based on smart, reliable data.

Do the right thing and do it right

Finally, there are the ethical concerns of applying AI to textual data. There are numerous examples that illustrate what happens when the use of machine learning systems goes awry. In 2016, for example, a software manufacturing giant’s chatbot caused a public controversy because, after an unsolicited, brief training session by Internet trolls, it almost immediately started spouting sexist and racist insults instead of demonstrating the company’s NLP technology in an entertaining and interactive way as planned. The challenge of developing AI that shares and reliably acts in accordance with humanity’s moral values is an extremely complex (and possibly unsolvable) task. However, given the trend toward entrusting machine learning systems with real-world responsibilities, this is an urgent and serious matter. In industries such as law enforcement, credit or HR, the inadequate use of AI and ML is all the more delicate. Talent and labor market management, for instance, directly affects the lives of real people. Therefore, every decision must be justifiable in detail; faulty, biased or any kind of black-box automation with a direct impact on essential decisions in these matters must be weeded out. This stance is also taken by the European Commission in their whitepaper on AI and the associated future regulations, especially in the HR sector. As a matter of fact, almost all of the highly praised AI systems for recruiting and talent management on the market, mainly originating from the USA, would be banned under these planned regulations. JANZZ.technology’s approach is currently the only one that will be compatible with these planned regulatory adjustments. And this has a great deal to do with our knowledge representation and how it allows us to produce not just AI technology that comes very close to understanding language, but in fact explainable AI. So ultimately, the way forward is to appreciate that – in the words of NLU researcher McShanethere is no bottleneck, there is simply work that needs to be done.

At JANZZ.technology, we have done this work for you, with experts from diverse backgrounds in terms of language, experience, education and culture. Their pooled knowledge is incorporated into our ontology JANZZon! and made readable and processable for both machines and humans. Together, our experts have created and continuously curate the best possible and most comprehensive representation of the ever-growing heterogeneity of job-related knowledge in the field of human resources and labor market administration. Enabling multilingual, modular and bias-free solutions for all HR processes – and bringing you a step closer to truly intelligent HR and labor market management solutions. If you would like to learn more about our expertise and our products, or benefit from advice tailored to your organization’s individual situation, please contact us at info@janzz.technology or via contact form, visit our product page for PES and listen in on our new podcast series.

 

[1] Toews, Rob. 2021. What Artificial Intelligence Still Can’t Do. URL: https://www.forbes.com/sites/robtoews/2021/06/01/what-artificial-intelligence-still-cant-do/amp/
[2] GPT-3 (Guardian). 2020. A robot wrote this entire article. Are you scared yet, human? URL: https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3

Teleworking, teletravail, teletrabajo… Who is working remotely?

In Washington D.C., metro ridership is only 30 percent of the 2019 ridership. The hustle and bustle of the city has not returned as employers are uncertain on when and how to reopen offices due to the Delta variant and at present Omicron. A Capital Covid survey conducted by the Greater Washington Partnership revealed that less than half of employees were expected to be back in the office on an average workday this fall. The slow return to work in the Washington region highlights new trends in work-from-home and hybrid arrangements becoming the business norm.

Across the world, employers and workers alike are coming to terms with more flexible working arrangements. In 2020, employers were not ready for their entire workforce to work remotely. Prior to the pandemic, about 17 percent of American’s worked remotely 5 days a week. Today, 45 percent of full-time employees in the United States were partly or fully remote in September 2021 per Gallup’s monthly employment trends update. About two-thirds of white-collar workers remain working from home (41 percent) and/or with a hybrid option (26 percent).[1]

In Europe, highly skilled professionals were more likely to be working from home (WFH) pre-pandemic than other workers. Approximately, 5 percent of EU nationals worked from home before Covid-19 while now that figure stands at 12.3 percent who do “home office” as it is called in Europe per Statista data. These figures vary depending on where in the European Union workers find themselves.

Home-based work is nearly non-existent in Eastern European countries such as Bulgaria and Romania with less than 3 percent working remotely. In comparison, one in four Finnish workers do home office (25 percent) followed by Luxembourg and Ireland with about 20 percent teleworking. In countries such as France, Germany, Spain, and Portugal between 10 to 15 percent partake in WFH.

Unsurprisingly, the prevalence of remote-based work also varied by industry and profession pre-pandemic. Knowledge workers or those in ICT-intensive sectors in the Netherlands and Sweden (about 60 percent) did some form of telework, while less than 30 percent did so in Italy, Austria, and Germany.[2]

Yet, this is not an option for workers in professions that require face-to-face interactions such as healthcare, hospitality, retail, and education. The gap between those who are WFH and in-person appears to create societal cleavages making society more unequal – as is currently seen in public debates in Switzerland.

On the employer-side, remote work brings new challenges to companies that rely on knowledge and creativity to spark new ideas and drive innovation. Workers miss out on face-to-face contact or “water cooler” chats that foster collaboration and help employees share information in ways that are limited or siloed by Slack channels, chat rooms, and email. Many executives also believe WFH cannot replace personal interactions that foster company culture. Productivity gains may also suffer in the long-term as collaboration declines amongst workers.

But so far, the Economist Intelligence Unit finds divergent views on workplace productivity. Nearly 39 percent of executives believe WFH has increased productivity while 33 percent find it has declined.

Globally, the study finds that company size and nature of the business impact productivity more than geographic location. Larger firms have more resources and digital tools to allow business continuity remotely – so, perhaps smaller firms without ICT uptake witnessed a productivity decline during the pandemic.

The uptake of remote working was accelerated by the pandemic, yet it remains more pronounced in the United States than Europe – even as EU countries encourage home-based work a few days per week due to the nascent Omicron variant. Overall, American workers report being happier with the more flexible WFH lifestyle and improved well-being, coupled with lessened commute times. Gallup’s State of the Global Workplace reveals that 91 percent of U.S. workers who work at least partially remote hope to continue splitting time between the office and work. Hybrid work is favored by 70 percent of workers partially on WFH and almost half of those fully on-site with jobs that can be feasibly performed from home. Only 6 percent of fully remote workers stated wanting to return to the office full time.[3]

Reevaluating work and the hybrid paradox

It has been nearly two years since the world heard about Covid-19. In that time, organizations and employees have been nimble to embrace all the surrounding complexities and disruptions to work life. The pandemic upended individual’s relationship with work and made many rethink not only how they work but also when and where.

While the United States witnessed the “Great Resignation”, worldwide about 40 percent of workers considered leaving their current job in 2021. Microsoft’s Work Trend Index points towards a new social contract between organizations and employees. Successful organizations are those likely to appease to individual’s different work styles. Globally a “hybrid paradox” appears to be gaining momentum with workers – people want to work from anywhere yet crave in-person connection.

At JANZZ.technology we strive to connect job seekers with jobs and businesses with talent powered by cognitive computing to find the best fit in labor market solutions.

 

[1] Saad and Wigert. October 2021. Remote work resisting and trending permanent. Gallup News. https://news.gallup.com/poll/355907/remote-work-persisting-trending-permanent.aspx
[2] European Commission.  2020. Telework in the EU before and after the COVID-19: Where we were, where we head to. Science for Policy Briefs. Brussels.  https://ec.europa.eu/jrc/sites/default/files/jrc120945_policy_brief_-_covid_and_telework_final.pdf
[3] State of the Global Workplace: 2021 Report. Gallup. https://www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx?thank-you-report-form=1

 

“Dr. Cab Driver”: High rates of over- and under-qualification, despite ‘progress’ in education.

Does this situation seem familiar to you? On the Uber ride to the airport, you get caught up in a conversation with the driver and before you know it, you’re in the middle of a discussion about the potential of genetically modified bacteria to create cancer drugs. It quickly turns out that there is an extremely educated person in the driver’s seat who is currently putting his education to rather limited use. A new report from the International Labor Organization (ILO) on this matter now shows that only half of all workers worldwide have an occupation that matches their level of education. At the same time, many employers report difficulties in finding qualified personnel. This situation not only points to a significant gap between educational institutions and the working world but, depending on the facts, it also undermines the relevance of omnipresent keywords such as ‘upskilling’, the hype of which we have already questioned in the past.

 

janzztechnology dr

The Report

The main findings of the report published by the ILO in September reflect realities that we, at JANZZ, have been anticipating for some time. Despite improved access to education and an increased level of education, the overall match between workers’ level of education and the actual skill level required for their occupation is a sobering 50% worldwide. Breaking this mismatch down into its individual components results in over- and under-qualification situations. [1] In concrete terms, this means that oftentimes there is a gap between the work that people can or want to do, and the job profiles and qualifications for which there is an actual demand. Indeed, from our own data analysis, we can cite as an example the fact that, in Europe, there is currently a shortage of about 400,000 truck drivers, as well as hundreds of thousands of nursing and care workers and employees in the catering and hotel industry. The main problem here, however, is not a lack of skills but rather a lack of willingness to learn certain professions. The reality is that the previously mentioned occupational fields are often underpaid, thus, resulting in a general lack of interest to then remain in those positions for a longer period of time.

Accordingly, the ILO study found that while there is over- and under-skilling in all countries, the patterns differ depending on the income level of each country. The matching rate increases with progressively higher median income in a country, which means that in countries with lower wages, just one in four workers have a job that correlates with his or her level of education, like our opening example of the biotechnologist driving a cab. There is a tendency for undereducation to be more common in lower income countries, while overeducation is more common in high income countries. [1]

There are different reasons for both circumstances: Workers are sometimes overqualified for their jobs because they accepted them for motivations other than aspiration, for example because the position offered specific benefits such as shorter commute times, better work-life balance, or an experience opportunity for later, more demanding jobs. However, these people are only part of the workforce. Another part can be attributed to distortions in the labor market, according to which the supply of workers with higher levels of education exceeds their demand. In the case of undereducation, the main causes are in turn the relatively low completion rates and the lack of formal qualifications amongst a large workforce, which is more common in low-wage countries where educational opportunities tend to be poorer. [1] In addition, given the low chances of finding a job in their own level of education, many also take available jobs for which they are overqualified, but which provide them with an immediate source of income for their livelihood. From our own sources, we outline the example of Paraguay, where around six times more lawyers are trained than the labor market can accommodate after their graduation. Many of them end up in jobs in call centers, retail or administration, where there is hardly any connection to their legal skills and knowledge.

Among the inadequately trained employees, some can at best make up for the lack of qualifications by acquiring the necessary skills through on-the-job training or self-study. Overall, however, there are still numerous negative consequences of these mismatches due to over- or under-qualification that affect the workforce, the employer community, and society as a whole. For example, a high degree of overqualification can lead to a loss of motivation and suboptimal returns on investment in education and training. In addition, every overqualified person always occupies a position that could or should be held by someone more suitably qualified. On the other hand, many underqualified employees face difficulties in the transition from the informal to the formal economy, which can have negative consequences on productivity and innovation, as well as economic growth. Since the examples that we mentioned are rather longer-term phenomena, according to the ILO, systematic, i.e., political measures are usually required to solve them. [1]

The problem of upskilling and reskilling

Let us make a few more comments on one specific finding of the ILO study. Among other things, the report emphasizes that in countries with medium-high to high average incomes, about one-fifth of all workers today are overqualified [1]. In countries like Finland, where just under 75% of the population has a university degree but where there are considerably fewer jobs at this level of education, this rate is even higher [2]. At the same time, keywords like ‘upskilling’ and ‘reskilling’ are on the lips of everyone who talks about the development and future of labor markets. Such training processes can make sense within a company, but they are not automatic, especially not when it comes to the entire labor market of a state or region. As soon as experienced employees who are well established in their positions are moved or promoted to another function or activity because of upskilling, their old position must first be filled with someone suitable. This task alone can cause red flags for HR, as the assumption that all such ‘pre-upskilling’ positions will immediately cease to exist is misleading and incorrect. The situation may become even more convoluted when a country has a harsh migration policy and there is a lack of workers for low-wage jobs (often disdained by citizens), for instance, as can be observed nowadays in the nursing field in the UK [3].

Furthermore, in his or her new position, it is not certain that the promoted person will perform equally well or be as satisfied as before, or whether the promotion and the accompanying increased responsibility is a right fit for that person. Management literature knows the so-called Peter Principle, according to which a large part of the inefficiency on the labor market is due to the fact that in hierarchical structures, such as a labor market, every employee tends to rise to the point of incapacity. Although this theory has a satirical undertone, it nevertheless seems to be somewhat true, not least when considering the rather low downshifting rates [4]. When it comes to reskilling, the situation looks somewhat better in that it is conceivable in this process to be able to transfer and adapt at least so-called “core skills” from the old job to the new one.

Back to the findings of the ILO report: Even if none of the above problems occur, the fact remains that in our geographical location many people are overqualified for the work that they do on a daily basis. A prime example of this is the trend among medical students who seek to become a specialist, even though, depending on the area, the occupational demand for it can be rather limited and there is an existing deficit of family doctors. Moreover, one can also wonder, why at Swiss universities, efforts are being made to attract not only geography students but also lateral entrants from geology studies to train as geography teachers for secondary level II. What is problematic about this is that there is already no (increasing) demand for this profession, while at the same time, there is also a shortage of primary and lower secondary level teachers. Furthermore, data from an OECD survey indicates that, compared to EU/OECD countries, Swiss nationals are more often overqualified than immigrants, with the latter accounting for an above-average share of the workforce in the low-wage sector (60%) [5]. This also indirectly reflects where there is a real need for personnel…

Quo vadis?

Over- and under-skilling remain a current problem in most labor markets, despite considerable progress in global access to education. Both conditions reflect underutilization of human capital and can carry high economic and social costs. What needs to change?

To reduce such mismatches overall, it is first necessary to capture and assess the extent to which the level of education of workers in a labor market matches the level of education required for their jobs. This is done by means of analyses such as the ILO’s cited here. (See also our English-language contribution to a Nobel Prize-winning paper that systematically explains the asymmetry between the large number of unfilled jobs and simultaneous unemployment within a market). As a next step, this data would also need to be incorporated into a country’s education planning and human resource development, which may be complemented by policy measures. In countries with high rates of undereducation, the undereducation of those who already occupy highly skilled jobs or will do so in the future must be raised by qualitatively adequate means. It should be noted, however, that simply upskilling all job seekers in a labor market that offers many low-skilled positions will neither automatically eliminate the mismatch, nor lead to a reduction in the unemployment rate.

Furthermore, it would be important to actively create more transparency for the public about the actual demand of a labor market, especially in countries with a high rate of overqualification. Ideally, such information would motivate future workers to train in a ‘meaningful’ direction or at least to be aware of the suboptimal employment opportunities after graduation and to inquire about real (and often equal in terms of wages in German-speaking countries) alternatives, such as apprenticeships. What is therefore certainly not conducive is to propagate upskilling and reskilling towards trendy degrees and competencies as ‘the thing to do’, which unfortunately also happens regularly today due to published (and quoted) misinformation on this topic. The latter can have rather dangerous and far-reaching consequences for society, especially if done through governmental and political voices, because such false promises and forecasts will widen the skills gap more and more and possibly have global implications in areas such as migration.

Finally, more emphasis should be put on adequacy in the actual matching process between candidates and jobs. This requires, first and foremost, reliable and information-dense data collection, analysis and classification. Here at JANZZ, we gather just such information through a variety of projects, including collaborations with the Public Employment Services (PES) of countries around the world. This has allowed us to develop market-leading evidence-based solutions since 2010. Our systems are not only efficient, scalable and extremely powerful, they also rely on ontology-based semantic matching. Furthermore, our tools all deliver unbiased results according to the OECD principles on AI. We are keen to stimulate fact-based discussion on all issues related to labor markets and processes, and to raise social awareness about them. After reading this article you might think about how many people in your environment are working in a profession that truly corresponds to their degree (level)…

If you would like to learn more about our offerings, please contact us at info@janzz.technology or via contact form, or visit our product page for PES.

 

[1] ILO. 2021. Only half of workers worldwide hold jobs corresponding to their level of education. URL: https://ilostat.ilo.org/only-half-of-workers-worldwide-hold-jobs-corresponding-to-their-level-of-education/

[2] Clausnitzer, J. 2021. Population with educational qualification in Finland 2019, by level of education. https://www.statista.com/statistics/528083/finland-population-with-educational-qualification-by-education-level/

[3] Inman, Phillip. 2021. Does the UK have a wage problem? URL: https://www.theguardian.com/money/2021/oct/06/uk-wage-boris-johnson-skilled-skilled-economy

[4] Donzé, René. 2021. Mehr Leben, weniger Hamsterrad: Wieso die wenigsten einen beruflichen Neustart wagen. URL: https://nzzas.nzz.ch/spezial/downshifting-wieso-nur-wenige-einen-beruflichen-neustart-wagen-ld.1612299

[5] Loos, Melanie. 2018. Schweizer öfter überqualifiziert als Zuwanderer. URL: https://www.handelszeitung.ch/konjunktur/schweizer-ofter-uberqualifiziert-als-zuwanderer

 

Wanted: healthcare workers – but why aren’t these jobs being filled?

Smiling female nurse holding senior woman’s hand. 

Despite improvement, there will still be a significant gap between supply and demand of healthcare staff by 2029 in Switzerland, according to the national 2021 report on future healthcare staff needs, published by the Swiss Health Observatory in September.

The report estimates that by 2029, the personnel demand in the healthcare sector may rise to 222,100. Compared to a base number of 185,600 recorded staff in 2019, an additional number of 36,500 staff will be required. To fill these additional positions, as well as to compensate for those who retire and leave the industry early, presents an enormous challenge to the next generation of health workers. The report further stated these jobs remain difficult to fill mainly due to demographic and epidemiological developments.

The growing need for qualified healthcare professionals is not specific to Switzerland, a trend can be seen around the globe, and it is not a recent trend. Back in 2016, World Bank published the research on Global Health Workforce Labor Market Projections for 2030. It predicts that global health workers’ demand will increase to 80 million, with a supply of 65 million health workers over the same period, resulting in a global shortage of 15 million health workers.

In a previous article, we have also discussed the shortage of healthcare professionals in the Swiss labor market (see Switzerland 2030: The risks and opportunities of digitization). Today, due to the effects of the global pandemic, it might have impacted the situation negatively. The pandemic has brought renewed attention to the frightening shortage of health workers, but remember, this extraordinary situation existed long before the Covid-19.

The same pattern is clearly evident in some other industries and occupations (see Free movement of skilled workers in the EU and beyond are more important than ever), with fewer and fewer young people willing to learn such skills. It is a basic problem and therefore, needs some fundamental changes to effect sustainable action. Share with us your thoughts and let us know your opinion on this topic.

Follow-Up on Equal Pay, or; The monster in our closet that we all ignore

This is a follow-up post to our last article on the Gender Pay Gap (GPG), in which we suggested that focusing on a pay gap based on gender is not enough and that shifting the focus to the concept of performance would be useful. As a kind of continuation, we turn here to the topic of fast fashion and discuss this ubiquitous ‘monster’ in all our closets, including from the perspective of equal pay.

Lack of initiative despite well-known problems
We all know it,  » Read more about: Follow-Up on Equal Pay, or; The monster in our closet that we all ignore  »

To build public services especially public employment services for a more resilient future

When COVID-19 came last year, many countries scrambled to cope with the disruption to vital public services and the closure of schools and universities. However, a few countries such as Norway and Estonia, have managed to keep everything in place, thanks to their decade-long development in digital infrastructure. Today, Estonia has built one of the world’s most advanced digital societies.

Every two years, the United Nations Department of Economic and Social Affairs publishes the E-government rankings. The aspects considered in the rankings cover the range and quality of online services, the current state of the telecommunications infrastructure and available human resources. In the latest rankings, Estonia led the way in e-government among the 193 UN member states. How did this small Baltic country surpass powerful nations like the US and China and realize such an achievement?

Being ahead of the curve since the 1990s

After independence in 1991, Estonia quickly moved to take advantage of its technological prowess. In 1997, Estonia launched electronic-governance, followed by e-tax in 2000 and digital ID in 2001. In 2005, Estonia started i-voting, and three years later blockchain technology was introduced along with e-health. In 2014, Estonia aimed to create a country without borders and became the first country to offer electronic residency to people from outside of the country. The digital reforms that took place from the 1990s to the present known as the “e-Estonia model”, are well-known worldwide and its success inspires and intrigues many across the globe.

 

Source: Estonia: the most digitally advanced society in the world? (raconteur.net)

 

Estonia’s success is much more than technological innovation. In an interview with Tommas Hendrik IIves, the former president of Estonia with the International Monetary Fund (IMF), Mr Ilves stated that it’s not only the technology, but also political will, policies, laws and regulations that have made things happen in Estonia. The former president is recognized worldwide and has made Estonia today one of the most advanced countries in digital governance.

The decision to embrace digital life has certainly paid off during the lockdown. The e-Governance has ensured sustainability and continuity of the public sector services for citizens and enterprises in Estonia. Online options have already existed for a large number of daily procedures including civil and business registry, unemployment insurance registry, digital content management system to deliver learning materials, testing, assessment/evaluation and analytics to all students.

 Covid-19 pushes more government activities online

After the pandemic, more countries are pursuing the digital government strategies. According to the United Nations E Government Survey, a global trend in e-government development can be observed, for example, as a key indicator, the global average E-Government Development Index (EGDI) raised from 0.55 in 2018 to 0.60 in 2020.

 

Source: 2020 United Nations E-Government Survey

 

As a very important part for modern e-government, public employment services (PES) are embracing digital technology as well to better match people to jobs and to perform other new tasks such as providing career guidance and addressing skill gaps. According to the International Labor Organization (ILO), PES in 69 countries across all regions have the capacity to provide basic online services, such as publish open vacancies and register citizens for job matching. In these countries, one third of them has provided solutions with artificial intelligence (AI) for both job seekers and employers.

The Norwegian Labor and Welfare Administration (NAV) has implemented JANZZ’s AI-driven semantic search and match engine, multilingual and most comprehensive ontology as well as other solutions as SaaS for its new job matching platform in 2019. Before the numbers of unemployment/registered people at NAV exploded during the pandemic, JANZZ’s robust system has extraordinarily prepared NAV with a volume of more than 10 times the planned volume and successfully managed any sudden increased load without incident.

Be prepared for the next unexpected moment

Worldwide, 50% of the population use the internet for various purposes including job searching. However, there are still wide disparities in digital government transformation including in PES across regions. Not all countries are sufficiently prepared to promote innovation and leverage digital technologies to provide accessible and reliable services.

It is as vital to tackle PES as it is to have other digital public services. As shown in the Estonia example, digital government transformation is not just about technology, it is also about political will. Today, in the context of pandemic, all countries should seize the opportunity and start their projects on digital technologies soon, before there is a new and again unexpected need for such services like 2020/2021.

To learn more about our AI-driven semantic search and match solutions, multilingual and most comprehensive ontology and other solutions for Public Employment Services (PES), please visit our product site for PES or contact us at info@janzz.technology.

Equal Pay – Let’s not fall into the gender pay trap…

For some, Equal Pay Day marks the annual ‘free labor of women’, for others it is an ‘ideologically motivated lie’ – the truth is even more complicated. This article highlights the uselessness of many of the statistics surrounding ‘equal pay’ and explains why a focus on gender falls short when it comes to pay equity. Instead, we would do well to place more emphasis on actual, individual performance.

Is a focus on gender even justified?

First of all, let’s take a look at the terminology. Equal Pay Day (EPD) is generally understood as an international day of action for equal pay between women and men. The day is intended to draw attention to the so-called ‘gender pay gap’ (GPG), i.e. the gender-specific wage gap between the average gross hourly wage of women and men. The EPD is celebrated individually in each country, symbolically on the day up to which women would work for free if they were paid the same amount as men across society as a whole. In Switzerland, the EPD falls on February 20. So much for the definition.

Let us now turn to the methodology used to measure and prove the GPG. Two statistical approaches dominate here. Either the gross wages of women and men are compared by means of a regression analysis, without taking into account objective factors such as level of education, qualification, age, professional experience, overtime hours or the exercise of a management function. In the case of Switzerland, this led to the following result in 2018: Women earn on average a striking 19% less per hour than men [1].

Due to the fact that objective factors were neglected in the analysis this result is, however, questionable. Such a calculation literally compares apples with oranges. The other approach used concerns the calculation of the so-called ‘adjusted wage differential’, in which a distinction is made between ‘explained’ and ‘unexplained’ differences in pay. ‘Explained’ are those pay inequalities that exist due to objective reasons such as full-time/part-time employment or work experience. The ‘unexplained’ share refers to the pay gap that is potentially attributable to subjective inequality based on gender and amounted to 8.1% in Switzerland in 2018 – still a considerable, but nevertheless notably smaller figure than the result of the first calculation [1].

Although the first approach is now considered outdated, its results are regularly cited in the media to create politicized headlines (for example here) or to serve the often shouted slogan “equal pay for equal work.” But even the adjusted GPG omits important explanatory factors. For instance, comparisons are not made task-specifically but rather on an industry-wide basis, or projections are made nationwide despite very large regional differences in the cost of living. If all these relevant aspects were also included in the statistics, the explained difference would probably be even lower. A slightly different objection is that if such huge differences really existed despite equal qualifications, every profit-oriented company would actually have to hire only women, which definitely does not correspond to reality.

One real consequence of all this is a (media-driven) battle between the sexes, which unfortunately seems to lead nowhere except to diminished solidarity between men and women. A prime example of this is Switzerland’s Equality Act, which was amended in 2020. Since July of last year, the largest Swiss companies have had to monitor their wages for discrimination and fulfill their obligation to inform their employees about the findings. Apart from the fact that the law only obliges companies with more than 100 employees, which excludes a large part of Swiss SMEs, it does not provide for any sanctions [2]. Recently, the first evaluations of this federally mandated wage analysis were published – with surprising results for some. Only 5 percent of the analyzed companies do not meet the stipulated requirement, which on the one hand is below the granted tolerance threshold and on the other hand clearly contradicts the previous statistics of the federal government. Interestingly, the reactions of GPG critics to this result are either non-existent or skeptical [3]. One almost wants to say, because they do not fit their expectations or their world view…

The bureaucratic effort that is made today for the purpose of equal pay leads in the end not only to contradictory statistics and little real change. Moreover, the analyses also distort the representation of the actual economic situation of a non-negligible share of men who are below the average value of their gender, since male top earners statistically make all men appear to be better earners.

Focus should be on performance

The slogan “equal pay for equal work” quoted in the last section is therefore misleading, since the standard analytical methods used to examine gender pay inequality do not compare like with like. Another, even more important point in the discussion of equal pay concerns the concept of performance. Achievement can be divided into two components, the effort made and the result achieved within a given period of time. However, the first component is often equated with the second, or considered an equivalent substitute for the lack of the latter. An insistence on actually achieving results is sometimes even frowned upon as inappropriately exercised pressure to perform. And yet, in the end, it is the performance that matters. Let’s use an analogy to the world of sports: Recently, at the Summer Olympics in Tokyo, the on average smaller beach volleyball players from Japan played against the on average taller athletes from Germany. Although body size can definitely play a decisive role in this sport, neither the relative performance of the Japanese nor which team had trained x hours more, nor who had already participated five times more in the tournament counted for the final result, but only their performances in the match.

From the perspective of both fair and profit-oriented employers, the situation is similar. When it comes to salary, only the actual performance – measured by the contribution to a company’s income statement – of an employee counts or should count. Of course, this means that unchangeable attributes such as height, gender, ethnicity or social background should have absolutely no influence on a person’s salary. In radical economic terms, however, such a position would mean that even objective factors such as the type and duration of education would no longer have to be taken into account. Even if a completed degree means high education costs and temporary wage sacrifice, this qualification is sometimes not sufficient to outperform a naturally skilled or otherwise trained worker. If person A assembles twice as many parts in an hour on the assembly line as person B, then person A should also be paid more in the long run or be allowed better employment and advancement opportunities. Accordingly, fair hiring and compensation are ideally based on individually measurable and comparable performance – i.e., on the reason why a person is originally hired or should be hired. Thus, the well-known guiding principle can be rephrased as follows: Employers should not adhere to the slogan ‘equal pay for equal work’, but to ‘equal pay for equal performance‘. A deviation from this principle is therefore a genuine act of discrimination – including against women who perform measurably better and more consistently.

To return to the topic of equal pay in relation to gender, a marginal comment should be made here: If remuneration is to be based solely on a person’s contribution to economic success, the question arises at least as to why domestic work and child rearing in one’s own household/family, which is still mainly carried out by women, is not also considered to be work worthy of compensation. After all, it represents a significant pillar of our current economic and social system.  Or, if this work is ‘outsourced’ to a room attendant and a day care center, why are such important services are paid so low? We all tolerate this circumstance without a shred of bad conscience. This indicates that there would likely be no broad willingness to make a fair wage possible for people working in these sectors, for example by means of tax-financed subsidy payments. But more about this in the next section…

What counts as (equal) performance; or, The real problems

As alluded to, the principle of ‘equal pay for equal performance’ raises the question of what should actually count as equal – or rather equivalent – performance. Here, too, the comparison with the world of sports can be made: While the winner of the FedEx Cup golf tournament receives around 10 million US dollars in prize money, a ski jumper only gets one thousandth of this amount at the most for winning the competition. Both athletes perform exceptionally well (and entertain spectators), and yet the pay is set completely differently. Such gaps do indeed exist across the economy and cement specific sectors as high or low wage industries that often also correlate with gender. Of course, one can always appeal to women’s self-responsibility when it comes to career choice, à la ‘It’s your own fault if you choose a low-paying job that can be done part-time – no one is forced to start a family’. But we could also think about why so-called ‘female professions’ such as sales assistants, hairdressers or geriatric nurses are systematically paid less and whether this is fair. If the pandemic has shown us anything, it is that many of these professions are ‘essential’ for the functioning of our society. The prime case is nursing staff, who despite unpleasant working hours, stress and indispensable service provision earn much less than, for example, construction foremen. The real problem is therefore the wage inequality across sectors, which widens the gap between the rich and the poor and does not pay ‘equal’ performance equally.

For the individual, the consequences of choosing an occupation in the low-wage segment mean not only lower pay but also fewer opportunities for further training and promotion, as well as a lower pension in old age. But, to return to the GPG aspect: Discrimination can occur not only on the basis of gender, but along many lines, such as skin color, appearance, age, disability or religion. So why this focus on gender when solidarity and alliances can be formed much more broadly? On this point, one critic of the EPD suggests, “What would be really interesting would be a ‘social’ ‘Equal Pay Day’. […] The gender issue hides wealth and hides social inequality. The gender pay gap is […] a gender trap – a ‘gender pay trap’, so to speak” [4].

In summary, we as a society need to ask ourselves whether we are willing to accept the structural reasons for wage inequality as an unchangeable fact or whether we are willing to do our part to change the status quo. In terms of gender, one commentator summarizes that having children is not part of the plan in today’s economic model, as it often means being locked into a traditional role distribution and lower employment opportunities for women [5]. Here, a rethinking and a restructuring on different levels – individuals, large corporations, politicians – seems to be called for.

The examples of gastronomy and care can also be used to illustrate that social change requires everyone to take responsibility. Are we willing, provided we can afford it, to pay more for consumption in a restaurant or even higher health insurance contributions so that the waitress and the nurse are paid ‘more equally’ for their services, or do we prefer to be stingy so that we can keep more for ourselves? Do we elect policymakers who will advocate for better minimum wages in care, or do we think we’ve contributed enough by clapping from our home balcony? Or how about the next time you pick up your kid from daycare, you tip the caregiver a few hundreds to show that you appreciate their efforts and are aware of the wage disparities in the care sector?

At JANZZ, we are not satisfied with a couple of heart emojis posted on social media as extra pay for caregivers, but develop evidence-based solutions and have been using them successfully since 2010. Our job and skill matching solutions are fair and non-discriminatory and deliver completely unbiased results according to the OECD principles on AI. This ensures that the best performing candidates in all individual criteria receive the best match – regardless of employment status or other irrelevant characteristics such as origin, age, BMI or gender. This is one of the many reasons why we are a trusted partner for an ever-growing number of Public Employment Services (PES) around the world.

Want to take that first step to change the status quo and contribute to a more equitable labor market for all? Then contact us at info@janzz.technology or visit our product page for PES.

 

 

[1] Eidgenössisches Büro für die Gleichstellung von Mann und Frau. Plattform Lohngerechtigkeit. URL: https://www.ebg.admin.ch/ebg/de/home/themen/arbeit/lohngleichheit.html

[2] Tagesanzeiger. Ab Juli müssen Unternehmen die Lohngleichheit kontrollieren. URL: https://www.tagesanzeiger.ch/ab-juli-muessen-unternehmen-die-lohngleichheit-kontrollieren-711426685692

[3] Steck, Albert. Frauen werden kaum diskriminiert, wie die neue Überprüfung der Löhne zeigt. URL: https://nzzas.nzz.ch/wirtschaft/loehne-in-der-schweiz-frauen-werden-kaum-diskriminiert-ld.1640462

[4] Moser, Thomas. 2017. Ten Years Gender Pay Gap-Mistake – Ein Irrtum wird zehn Jahre alt. URL: https://www.heise.de/tp/features/Ten-Years-Gender-Pay-Gap-Mistake-Ein-Irrtum-wird-zehn-Jahre-alt-3652060.html?seite=all

[5] Zufferey, Marcel. Die Mär von den unfairen Frauenlöhnen. URL: https://blog.tagesanzeiger.ch/mamablog/index.php/10791/die-mar-von-den-unfairen-frauenlohnen/

Global Labor Market News: Despite strong vacancy recovery, long-term unemployment remains

A new analysis of online job postings from the UK confirms that there has been a strong recovery of job vacancy rates this summer, well above the same period two years ago. The data, coming from one of the largest online job search engines in the UK shows:

  • New vacancies are mainly in IT, construction, trades or warehousing and logistics with 330,000 in total, nearly one-third of all vacancies.
  • The numbers of vacancies in healthcare, nursing and social work are reaching 130,000.
  • The numbers of vacancies in both sales and hospitality are nearly 75,000.

Despite the increasing job advertisements, what raised more concerns, however, is that the unemployment rate remains high. The long-term unemployment rate keeps rising at the fastest rate in a decade and the unemployment claims are still double the size compared to the time before the pandemic.

Not only in the UK, the same trend can also be seen in many other rich economies. A report released by the OECD in July warned that rich countries may face sustained growth in long-term unemployment. Their data shows, by the end of 2020, the number of people in OECD countries who have been unemployed for more than six months is 60% higher than the pre-pandemic level. This is because the low-skilled workers who are most likely to lose their jobs in the early stages of the crisis do not have the skills needed to enter the most extensively recruited industries.

To deal with the situation, governments are suggested to develop more targeted employment retention programs to ensure that they do not support businesses that are unlikely to survive in the open market. Meanwhile, they should better cooperate with employers to support reskilling and job matching.

Global Labor Market News: Will China get old before it gets rich?

In May 2021, China announced that couples are allowed to have up to 3 children, due to a consecutive four-year decline in the population. In 2016, China abolished its one-child policy, replacing it with a two-child limit, which had little impact on its fertility rate. The Chinese government hopes to salvage the situation by further relaxation of birth control. However, the coming of the so-called “three-child policy” is responded to by most with just a shrug. The lack of social services to help families in both parenting and education was blamed for the reluctance to have more children.

A survey, conducted by the National Health Commission on the demand for childcare services for infants and toddlers under the age of 3 in urban families shows that more than 33% of such urban families expressed needs for childcare, and the demand among families without the help of grandparents is much higher reaching 43.1%. However, the actual enrollment rate of children aged 0 to 3 in 2020 was only 4.1% which indicates a huge shortage of supply. In OECD countries, about 25% of infants and toddlers aged 0 to 3 receive childcare services and the number even goes up to more than 50% in countries such as Denmark and Iceland.

Increasing spending on education is another reason for the lack of interest in having more children. According to research from a leading research and strategy consulting organization in China, the average annual expenditure on children’s education by urban families in China accounts for 30.1% of total family income and 35.1% of their total expenditure. In addition to the financial investment, the time and energy invested by Chinese parents in rushing their children to various training institutions tend to increase the burden on families.

It is widely believed that China will eventually drop the birth policy completely allowing couples to have as many children as they want, but is it already too late? Worldwide, we are experiencing a rapid aging population, with declining birth rates and increasing life spans. What is stopping us from having more children? Please share your thoughts with us.