Not entirely a “she-cession” but globally women are the key to economic recovery

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Advanced economies in Europe and North America are finally emerging after a year of COVID-19 lockdowns largely due to mass vaccinations, while populations in Africa and hard-hit South Asia and Latin America grapple with both vaccine access and labor informality. In a year mired by uncertainty, the economic and societal shocks of the pandemic impacted women and men differently—across the world, women were more likely to lose jobs, cut back paid hours worked, and became the default childcare providers in households.

In much of the world, women were left with no choice. In the United States, the pandemic erased the strides women had made in labor force participation rates since the 1960s.  By 2019, American women made up more of the workforce than men (approximately 50.04 percent of payrolls). [1]  Today, American women’s labor force participation stands at 57.4 percent which is lower than the pre-pandemic 59.2 percent in February 2020 and the lowest level since December 1988. [2] It is true that women’s employment suffered because many works in the services sector which witnessed hard hits in retail, healthcare and hospitality, while in developing countries women face high levels of informality which lack social safety nets to buttress the financial impact of a pandemic.

Last summer, an analysis by the McKinsey Global Institute showed that women comprise 39 percent of the global labor force but represented 54 percent of total job losses due to Covid-19. Many women’s choices disappeared as the pandemic created a childcare and education crisis with disruptions to everyday life.

Reactivating the economy with women in mind in a post-COVID world

Unemployment impacts men and women differently because society expects men to work and be the breadwinners, while women even with similar education levels as partners or husbands spend more time caring for the household. Perhaps it can be said that unemployment is shaped by gender, class, and social norms. While unemployment increased for those without university degrees or the ability to work from home, women and minority populations were disproportionately impacted. Even prior to the pandemic, gender inequality put women under greater financial pressure with unstable work contracts and less access to education and technology than their male peers.

The World Economic Forum’s Global Gender Gap 2021 report points out that the pandemic has increased the timeframe it would take to close the gender parity gap from 99.5 years to 135.6 years in terms of salary, education, and political empowerment. Overall, jobs provide people with earnings but more importantly they offer an identity that contributes to happiness and self-esteem. Covid-19 has left a void for women used to navigating a world of work instead of childcare.

The story of this pandemic is that women left the workforce independent of their education level or jobs held –at least in the United States, it appears this decision was based on whether children returned to in-person school. In Peru, children have yet to return to in-person schooling since March 2020, while other countries in the region have turned to hybrid models.  According to the International Labour Organization, approximately 13 million women in Latin America and the Caribbean witnessed their jobs vanish in 2020 due to the pandemic—with a regional total of 25 million women unemployed or out of the labor force. In developing countries, many women work informal jobs in vulnerable sectors usually earning daily wages with no sick leave or the safety net of unemployment insurance.

COVID-19 exacerbates digital biases and the gender pay gap

Additionally, women have dealt with digital biases (i.e., approximately 300 million fewer women have access to smartphones in low- and middle-income countries or 20 percent less than men according to the World Bank) in a world that overnight became more reliant on connectivity for survival coupled with the ever-present gender pay gap. Even today, in the United States women with school-aged children are slowly narrowing the gender divide and returning to work.

The pandemic accelerated the uptake of digital technologies by everyone from pre-school children to grandparents, from the “mom and pop” shop to women entrepreneurs turning to e-commerce platforms to sell goods and services, as well as the many other occupations that quickly adapted to a new normal for economic survival. With this faster than expected digital transformation, the workforce and especially women will have to adjust and gain new skills to remain competitive and stay employed.

A few years ago, McKinsey estimated that anywhere from 40 million to 160 million women would have to make occupational transitions due to automation. This holds truer today because the pandemic accelerated digitization of industries and services. Globally, policymakers are having discussions on the interplay between skills demand and the role of human capital to foster digital transformations across industries and firms.

In short, countries are reemerging at a time when technology and artificial intelligence (AI) are shaping how we live, work, and play at a faster rate than pre-pandemic levels.  Societies cannot afford to have women lag behind in terms of employment opportunities in both advanced and developing countries. In OECD countries, job growth pre-pandemic had been led by a demand surge for high skills benefitting women instead of men—partly because more women graduate with tertiary degrees than men.

In low- and middle-income countries, women entrepreneurs comprise a large percentage of the labor force. In Africa, women comprise about 50 percent of the continent’s self-employed workforce in the non-agricultural sector. [3] But the impacts of the pandemic stripped away sources of support from both high-skilled and low-skilled women essentially pushing many out of jobs and now preventing them from job seeking—limiting lifetime earnings and stunting a country’s economic growth.  Economic recovery depends on governments ensuring that women have equal access to jobs, digital connectivity, and digital skills.

JANZZ.technology is here to guide public employment services to navigate the post-COVID economic recovery by providing AI-driven digital solutions that empower women and the most vulnerable job seekers in the labor market to match their skills and talents with quality jobs.  Let’s rethink how we approach women’s labor force participation using actionable insights with non-discriminatory and unbiased jobs matching results by contacting info@janzz.technology and visiting our product site for PES.

 

 

[1] Time Magazine. Women are Now the Majority of the U.S. Workforce—But Working Women Still Face Serious Challenges. January 2020. https://time.com/5766787/women-workforce/

[2] National Women’s Law Center Factsheet. Ewing-Nelson and Tucker. April 2021. https://nwlc.org/wp-content/uploads/2021/04/March-Jobs-Day-2021-v1.pdf

[3] World Bank. Profiting from Parity: Unlocking the Potential of Women’s Business in Africa. 2019. https://openknowledge.worldbank.org/handle/10986/31421

“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

Building the AI-ready workforce: A collaborative effort between government, tech companies and education providers

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To follow the trend of future work, upskilling and reskilling, and digital transformation, we are posting a series of articles to analyze government policies and practices to learn how countries are taking strategies to build their workforce for these challenges. In the previous article, we examined how Singapore is helping mid-career PMETs to switch to the tech sector. Our second stop is Saudi Arabia.

The Kingdom of Saudi Arabia has shown strong commitment to the implementation and development of AI as it seeks to diversify the economy, reduce the dependence on oil and shift away from public sector-driven welfare within a strategic framework called Saudi Vision 2030. According to estimates by PwC, the contribution of AI to the national economy in Saudi Arabia will reach US$135.2 bn by 2030, around 12.4% of its total GDP.

Saudi Vision 2030 comprises a host of ambitious targets for the labor market, including lowering the unemployment rate from 11.6% to 7% [1], increasing female participation from 22% to 30% [2], and bolstering the private sector with a special emphasis on small and medium-sized enterprises (SMEs) to increase their contribution to GDP from 20% to 35% by 2030 [3].

Under Saudi Vision 2030, Saudi Arabia is spending heavily on the communication and information technology (ICT) sector. The International Data Corporation (IDC) predicts that spending on IT in Saudi Arabia will exceed $11 billion in 2021. In addition to Saudi Vision 2030, the Kingdom has launched its National Strategy for Data and Artificial Intelligence (NSDAI) and last year signed a series of partnership agreements with international tech companies last year to advance AI in Saudi Arabia. One of the key pillars of the NSDAI is to build an AI-ready workforce in the Kingdom.

The growing industry is putting high pressure on a domestic labor market that lacks a sufficiently large and experienced national ICT workforce. Saudi Arabia is highly reliant on expat workers, and many of its professionals, including most in ICT, are sourced internationally. This has hampered many private companies and especially SMEs due to high recruiting costs. Furthermore, as skilled talents are increasingly in short supply globally, Saudi Arabia needs to focus on its young domestic population.

With 60% of the population under 30 years of age in 2020, Saudi Arabia has one of the world’s youngest populations. However, youth employment remains low in the Kingdom, and 16% of Saudi youth between 20 and 24 are classified as NEET (not in education, employment or training) [4]. Several reports indicate that there is a notable gap between the future-proof skills requested in the labor market and those possessed by the young people, and that the Kingdom is lacking an effective skill matching mechanism. Current skills gaps include STEM skills (science, technology, engineering and math), soft skills, and industry-specific international skills due to a lack of vocational training [5]. Therefore, part of the NSDAI agenda is to provide professional AI training to Saudi university students, researchers and developers, as well as up- and reskilling opportunities that enable Saudis to utilize AI and data in both the public and private sectors. Programs including the National AI Capability Development Program and the National AI Talent Cultivation and Onboarding Program have already been set up by the government as a collaboration between tech partners and education providers to attract, develop and retain AI talent in Saudi Arabia with a target of creating 20,000 AI and data specialists and experts by 2030.

As countries embark on the journey of digital transformation, their governments and 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] Readiness for the Future of Work, ATKearney and MISK.

[2] KSA Vision 2030: Strategic Objectives and Vision Realization Programs 61.

[3] KSA National Transformation Program Delivery Plan – 2018-2020

[4] International Labour Organization, ILOSTAT database.

[5] Building the talent pipeline in Saudi Arabia, City&Guilds Group

The importance of localizing ontologies, illustrated on the education systems in Peru and Colombia

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In one of our recent posts, we explained the difference between an ontology and a taxonomy. Although choosing an ontology over taxonomies is an important step towards smart and accurate matching, it is not the only aspect to consider. Even if you are only interested in a monolingual ontology, but even more so with multilingual ones, localization is another key feature. For high performance and satisfactory matching results, it is simply not enough for the ontology to cover your language of choice, especially not if that language is spoken in several countries. The system needs to truly understand context, including regional or national variations in occupational, legal, educational and linguistic matters. For instance, certain occupations may require official certifications or authorizations in one country, but not in another. And most often these certifications will have different names depending on the country they are issued in. A certain job title may be widely used in one country, and completely uncommon in another, e.g., joiner in the UK, Australia and New Zealand – a type of carpenter. This term is practically not used in the US (even though the largest US trade union for carpenters is called the United Brotherhood of Carpenters and Joiners of America).

Of course, this issue is not just limited to job titles and authorizations. It is also essential to understand implicit skills, i.e., those not mentioned in a job description or candidate profile but that can be derived from other information such as education and training. And required education must be factored in as well. Suppose, for instance, you are located in a Spanish-speaking country and looking to hire someone with a bachelor’s degree, i.e., who has completed undergraduate university studies. In Peru, you may ask for a Bachiller. However, in Colombia, this term will give you matches with candidates who have the equivalent of a high school degree: a Bachiller Académico or a Bachiller Técnico. If you ask for a Licenciado, another common term in Spanish-speaking countries that often corresponds to a bachelor’s degree, your Colombian candidates will have a degree more or less equivalent to a Bachelor of Education in the US.

 

Localizing Ontologies

 

To avoid unsatisfactory to outright useless matching results, an ontology must be carefully enriched with country-specific information such as linguistic variations, localized job titles, mapping of national classification systems and details from the country’s education system including names of degrees and diplomas, and – ideally – curricula and taught skills. This may require extensive work by subject matter experts familiar with the country in question. But it is an investment well worthwhile that will dramatically improve matching results and all associated services such as career counseling, education/job matching platforms, labor market analytics and more, as well as enhance usability of interactive services and features, for instance, with smart suggestions and typeaheads that actually make sense to the users.

You may have already found out the hard way that using standard taxonomies like ESCO does not really work in your country. If not, don’t do it. Go straight for a well-localized ontology. At JANZZ.technology, this is one of our key services for new country clients and we have successfully implemented localizations of our ontology JANZZon! for countries across the globe. If you want to enhance your system with the extensive knowledge from the world’s largest multilingual job and skills ontology, or learn more about our highly performant ontology-driven products and technologies, feel free to contact us at info@janzz.technology.

A resume of CV parsing. Great candidate experience vs. ATS optimization – is a trade-off truly inevitable?

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In recent years, online job and candidate searches have become increasingly important and a growing number of CVs and resumes is now available in digital form. Despite recurring announcements of their demise, and even if the delivery format and selection of information has changed, the need for details on a candidate’s professional background persists. Thus, more and more businesses are turning to automation tools to handle the increasing volume of CVs and resumes. However, because these documents are not standardized, the available recruitment automation and resume parsing tools are faced with significant challenges and are known to screen out good candidates. So, an essential and far from trivial question is how to process this information to produce smart, meaningful data that can be utilized by recruiting tools to pinpoint the best candidates for the vacancy at hand.

Even with the rapid advance of AI-based systems, most ATS resume parsing algorithms are outdated and unintelligent, often causing essential information to get distorted or lost. Candidates are thus advised to submit a standardized resume that is neither visually appealing, nor shows any personality, to avoid being sorted out by the system. This is in stark contrast to the wealth of online advice on how to stand out from the crowd with an appealing modern resume. It also undermines efforts to improve the candidate experience. But there seems to be a general consensus that a trade-off is inevitable: either hirers choose to obtain quality resume information in the first step of the recruiting process – and run the risk of disgruntling best-in-class candidates. Or they improve the candidate experience by not asking too many questions and then simply make do with potentially jumbled parsed resume information – running the risk of great candidates falling through the ATS.

Here at JANZZ.technology, we refuse to ignore these challenges and simply hand the problem off to applicants. Our mission is to help improve both the recruiting and the candidate experience by providing efficient application processes whilst ensuring the candidates’ freedom of individual expression. With our cutting-edge CV/resume parser, we already have the text processing down, using strategies from deep learning models trained specifically for resume content to semantic technologies that translate the myriad variations in occupational jargon to a common vocabulary. As for visual aspects such as formatting, layout and graphical representations, we are currently very actively engaged in the R&D phase, developing pioneering technology to tackle this.

To find out more about the challenges and latest advances in CV and resume parsing – and to take an intriguing walk through the history of the CV – read our white paper:

A resume of CV parsing. Great candidate experience vs. ATS optimization– is a trade-off truly inevitable?

And if you want to use our state-of-the-art semantic parsing tool and benefit from the top quality in multiple languages, all services are also available via API and can be easily integrated into existing ATS or platforms. For a demo or quote, feel free to contact us at info@janzz.technology

Building the AI-ready workforce: A switch for mid-career PMETs to the tech sector

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According to the Future of Jobs Report 2020, the World Economic Forum estimates that 85 million jobs will be displaced while 97 million new jobs will be created across 26 countries by 2025 through an AI-driven shift in the division of labor driven. AI technology will have a profound effect on the nature of work for many jobs and our workers will require reskilling or constant upskilling to prepare for changing and new jobs. To follow this trend, we will post a series of articles focusing on government policies and practices regarding these challenges to learn how countries are developing strategies to build their AI-ready workforce. Our first stop is Singapore.

As many other countries, Singapore has a tech talent shortage. According to Minister Vivian Balakrishnan, in charge of the national Smart Nation Initiative, Singapore will require an additional 60,000 tech talents in the next three years. Its education system is producing 2,800 ICT graduates per year, which leaves a gap of 51,600 in three years’ time. As one of the most important business hubs in the Asia Pacific region, Singapore has attracted tech giants to setup headquarters in this city-state. However, many international businesses are concerned that the tight local tech talent pool is slowing down their speed of development. [1]

In its National Artificial Intelligence Strategy, Singapore plans to establish more local talent pipelines in order to raise both the quantity and quality of its AI workforce in the long run. Among the various programs aiming to meet the demand for ICT talent is TeSA Mid-Career Advance, a program under the TechSkills Accelerator (TeSA) initiative for mid-level ICT and non-ICT professionals aged 40+ to make a switch to tech-related careers through company-led reskilling and upskilling.

This newly designed program is supported by government, industry and the National Trades Union Congress. They believe mature workers should also benefit from opportunities created in the fast-growing tech sectors and that the digital momentum must reach all segments of the economy and society. Currently, ten companies have partnered with the program, offering around 500 tech jobs. Eligible mature workers can be hired and trained in a variety of tech jobs by one of these ten partner companies for up to 24 months.

According to the Minister for Communications and Information S Iswaran, TeSA Mid-Career Advance is targeting 2,500 place-and-train opportunities over the next three years. As a start, the government will invest 70 million SGD for the initial job placements under the program. Participating companies will receive government subsidies as a contribution to the additional training costs and salaries. [2]

Besides meeting the tech talent demand, the program is also an attempt to fight against the issue of displacement of mature workers, another long-term challenge faced by Singapore. Based on the 2019 Labor Market Report from Singapore’s Ministry of Manpower, 6,790 locals were laid off that year, over half of which were professionals, managers, executives and technicians (PMETs) aged 40 and above. The 6-month re-entry rate of all laid-off local workers by age group was 65.8% for workers aged 40-49, and 52.2% for the over-50s. These rates are considerably lower than the 82.5% and 76.3% for workers aged below 30 and 30-39, respectively.

The perceived difficulty adjusting to the tech industry at later stages of a career is a concern, as most in-demand tech-related roles are becoming more technical in nature and it can be challenging to pick up skills such as programming languages, software proficiency and data analysis when transitioning from unrelated fields. However, the tech talent shortage is across the entire spectrum of roles so there are also many opportunities in “tech-lite” roles such as technology project managers, digital sales advisors and “for-tech” roles that contribute non-tech knowledge to the development of solutions (e.g., an HR professional working on an HR tech solution). In these positions, the domain knowledge and expertise brought in by experienced PMETs will contribute in creating technology applications that meet business needs. [3]

Despite various successful approaches in Singapore, Wong Wai Weng, chairman of tech trade association SGTech, believes that more proactive efforts should be made, before displacement occurs and before jobs are lost. At JANZZ.technology, we also believe public employment services (PES) must act now to prepare for further shifts and turbulences in the labor market. We provide comprehensive AI-driven solutions and services tailored to the needs of PES across the globe, helping them to actively match jobseekers to suitable jobs, strengthen their labor market resilience and design and implement effective active labor market policies (ALMPs). To learn more about our solutions for PES please visit our product site for PES or contact us at info@janzz.technology

 

 

 

 

[1] https://techwireasia.com/2020/09/is-singapore-facing-a-tech-talent-crunch/

[2] https://www.channelnewsasia.com/news/singapore/70-million-programme-help-mid-career-get-tech-jobs-training-12494274

[3] https://www.businesstimes.com.sg/brunch/skillsfuture-forum-2020/saving-the-senior-execs-career

If not now, then when? Digitalizing PES in times of COVID – and what it costs.

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The current worldwide pandemic has catapulted the labor market into a state of unprecedented turbulence. According to the OECD, the impact on jobs just within the first three months has been 10 times that of the 2008 financial crisis. Entire sectors such as hospitality, civil aviation and cultural sectors have been hit hard, resulting in mass job losses and collapsing self-employed incomes. On the other hand, e-commerce and supermarkets, courier and logistics services, manufacturers of food or hygiene products, pharmaceuticals and others have thrived, creating new opportunities by dramatically increasing their workforce. Even if some of these jobs are only temporary, they can be lifesavers for those in need of income.

Amidst these turbulences, Public Employment Services (PES) have faced a historical stress test, being inundated with a number of new jobseekers far beyond what their often outdated systems are typically designed for – if such systems are in place at all. With youth struggling due to the closing down of entry-level jobs and apprenticeships, and low-paid workers, women, ethnic minorities as well as self-employed and informal workers among the hardest hit by the crisis, existing vulnerabilities have been exposed and inequalities amplified. Now more than ever, PES must find new ways to best serve their people in this crisis and in future. These vulnerable groups need to be reconnected with good jobs as quickly as possible to avoid potentially long-lasting scarring effects. And PES need to be prepared for further shifts and turbulences in the labor market with innovative digital solutions that help strengthen labor market resilience by ensuring efficiency and scalability as well as providing creative placement solutions and valuable, timely insights – into the labor market and for jobseekers and employers.

Even if a country’s PES is only just being built up, now is the right time to initiate a digital transformation. In fact, there may never be a better time – especially for countries that are just getting started. A well-designed solution does not require a perfect starting point. It does not need a lot of in-house data or even a well-organized PES. It performs well in markets with only few highly skilled professionals and supports transitions from the informal to the formal economy. In addition, choosing a mobile-first approach geared towards self-service features over a cumbersome expert system plays to the strengths of one of the most affected groups of the pandemic: young people, who are used to taking matters into their own hands, finding information and discovering their options with their devices. The digital transformation has begun and those who want to be part of it must act now.

To ensure effective digitalization in these challenging times, PES should look for solutions with:

Full profile matching based on ontologies for a wider variety of fitting placement solutions

Instead of simply comparing job titles, jobseekers can be matched based on their full profile of skills, education, previous experience and many more relevant criteria. This will particularly help those who need to seek new lines of work due to collapsing economic sectors, where matching on job title alone is ineffective. In addition, using labor market-specialized ontologies enriched with country-specific content helps identify hidden skills based on education and experience. This can substantially improve jobseeker profiles and thus broaden the search for suitable jobs and candidates, whilst significantly increasing the accuracy of matches.

Searchable jobseeker profiles and simple recruitment processes for enhanced visibility and virtual mobility

Providing jobseekers with a platform to present themselves with a searchable, well-structured profile enhances their visibility and gives them the opportunity to be found by potential employers. To avoid bias, the open profile should only contain professional information. Integrating the first steps of selection and recruitment process into the system reduces the need to travel in person until a real opportunity presents itself. These features improve both visibility and virtual mobility for jobseekers, which is especially important for vulnerable groups such as low-paid or informal workers and minorities and in times of increased remote working and hiring. Easy-to-use recruitment processes also encourage smaller businesses to transition from informal candidate search, e.g., by word of mouth, to online vacancy posting, making their business and open positions visible to a wider pool of jobseekers.

Non-discriminatory, explainable matching for unbiased and transparent results

Matching processes must be explainable and auditable to ensure transparency and accountability. Moreover, solutions should be designed in a way to guarantee that by default the best candidate with the best aptitude in all individual criteria achieves the best match – regardless of gender, ethnicity, disability or other personal characteristics. This ensures that all jobseekers have equal opportunities, including youth, women and minorities.

Gap analysis for targeted job and career pathing

In times of dramatic shifts in the labor market, many jobseekers are required to identify new lines of work and change professions entirely. By determining the closest match to currently available positions and identifying missing skills, training or other relevant criteria, gap analyses can help find a path out of the employment crisis. Based on real-time labor market data and comprehensive ontologies, they can be used to counsel individual jobseekers, or even to redirect entire workforces from disappearing professions or sectors.

Smart labor market intelligence to identify real-time shifts and labor market shocks

In a turbulent labor market environment with fast and unpredictable changes, smart labor market intelligence delivered in real time as well as well-designed intelligence management tools can make the crucial difference. Processed with a powerful labor market ontology, these data can provide better, more accurate and timely insights – key to effective management and rapid response.

JANZZ systems provide all of the above features and more. We cannot create jobs, but we can help countries smooth out the effects of COVID-19 in labor markets and guide PES in a digital transformation to become more efficient and more sustainable. We can support the transition from guessing to knowing what it will take to give employment back to as many people as possible. For instance, PES can leverage our integrated labor market solution JANZZilms! to take action in key areas and rapidly respond with timely strategies that have real impact.

 

JANZZ Integrated Labor Market Solution (ILMS)

 

 

Since the beginning of March 2020, we have been able to prove how powerful and scalable our systems are. In a large European country, almost 10% of the working population has been forced to register with the unemployment office due to the impact of the pandemic. At the beginning of the crisis, the system, which was designed for about 30,000 registrations per year, was saturated with almost 400,000 registrations within just a few weeks. Although nobody had anticipated a scenario like this during the planning phase, our systems processed almost ten times the volume of transactions without any problems. Performance and stability of the systems were always fully maintained in these important times. In addition, thanks to intuitive and intelligent design, the national PES was able to both increase job counseling capacities and reduce average time to market reintegration. This way, we were able to make a valuable contribution to the government’s efforts to quickly and efficiently register, counsel and reintegrate almost 400,000 job seekers in the country.

Quick and efficient implementation – with no surprises

Our agile methods have led time and again to outstanding products – developed on time and on budget. The standard solutions can be realized in 120 – 180 days, or 90 days in a language we have already deployed before. This provides great value in terms of implementation, operation and maintenance. Moreover, prices are fixed over several years, ensuring financial predictability. For instance, the full bundle solution JANZZilms! including all components such as matching and gap analysis, profiler, ontology, several languages, parsing, dashboards and much more, with up to one million active/concurrent users costs around USD 1 per user and year after implementation. As a complete SaaS solution, no further investments for the systems and hardware, regular maintenance, SLA’s or updating processes etc. are necessary. For larger systems with up to 5 million users in all roles (e.g., PES counsellors, job seekers, companies and job providers, third party providers like education etc.) the price drops to around 60-70 cents per user and year. With even larger systems, the price quickly falls below 50 cents per year.

Moreover, our solutions run in secure, GDPR-compliant cloud environments that are perfectly suited for any IT infrastructure including simpler setups in emerging markets. They also offer great accessibility for all users – from digital natives to tech newbies, on mobile and small screen devices and with slow internet connections.

For more information on how our services and solutions can help strengthen your labor market resilience, visit our product site for PES or contact us at info@janzz.technology. If not now, then when?

Analyzing skills data. Can you see the gorilla?

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This is the fourth and last in a series of posts about skills. If you haven’t already, we recommend you read the other posts first: Cutting through the BS and Sorry folks, but “Microsoft Office” is NOT a skill and The poison apple of “easy” skills data – are you ready to give up that sweet taste?

In the third post of this series, we discussed the challenges and opportunities of online job advertisement (OJA) data. Suppose we have the perfect definition of a skill and have extracted all relevant information, including occupation, required experience and training, explicit and implicit skills, etc., from clean, duplicate-free OJA data. What now?

This is the point in many related projects where the data is made accessible in form of, say, an interactive website for interested users. Sometimes there will be a disclaimer stating that the data is not representative or that it is biased in some way. The idea of these websites is to provide information for policy makers, employers, education providers, career starters, etc., so that they can make informed decisions. This is certainly a noble idea. But a disclaimer does not change the fact that these users are still being given a distorted picture. They are enticed to make fact-driven decisions – without being given all the facts. How is a student supposed to make an informed career choice, e.g., trade or profession, if there is only information available for the professional route? How is a policy maker supposed to decide which training projects to allocate funds to if the data is biased towards certain industries or occupations? How is an education provider going to align curricula with market demand if there is no information on how critical a given skill is for a job?

Let us take a look at some of the challenges in analyzing this data.

Increased number of advertisements is not equal to actual expansion of demand. OJA data can only approximate gross changes in demand because the data includes both new positions resulting from growth as well as existing positions left vacant as a result of staff turnover. This means that the future of a given occupation cannot be predicted purely based on the growth of the number of OJA for that job. The same is true for skills.

Frequently mentioned requirements are not necessarily crucial requirements. Even assuming that we have been able to extract all implicit skills (for instance, occupational skills that hirers assume are implicitly obvious to prospective candidates, or skills presumably acquired in training/education), there are still challenges here: First off, there may be a tendency to demand more than necessary for vacancies that are easy to fill and less than necessary for hard-to-fill positions. Also, certain positions tend to ask for education that, per se, is not necessary. An example of this is a STEM degree for quantitative professions. There is rarely need for a graduate’s expert knowledge in physics or biology in consulting, but STEM students typically also acquire skills such as critical thinking, complex problem solving, quantitative reasoning, communication and presentation skills, etc. It is these transferrable skills that such employers are interested in. So simply counting mentions of skills will not reveal the most in-demand skills that are truly relevant to employers.

Implicit skills extraction comes with its own challenges. Profiles for jobs and educations vary greatly on various levels. There is neither a standard skill set for a given occupation, nor is there one for a given education. [1] For instance, the skill set acquired in vocational training for carpenters will depend on the duration of the training (e.g., 1.5 years in Nicaragua vs. four years in Switzerland), or the choice of specialty, and many more factors. Some of the skills required for a nurse in an urban private clinic will differ from those needed by a nurse in a rural state-run hospital, even if they have the same specialty. Thus, skills demand cannot be directly extrapolated from jobs/education demand either.

Ignoring these and other challenges leads precisely to the common misinterpretations we have discussed in this series. One could also call this inattentional blindness, a phenomenon that was famously demonstrated in the “gorilla experiment”. In the experiment, the study participants were told to focus on a specific detail in a video of two teams passing a ball. Mid-way through the video, a gorilla walks through the game, stands in the middle, pounds its chest and exits. More than half the subjects missed the gorilla entirely – and were sure they could not have. Similarly, by focusing on “easy data” and the current publications with their quick and dirty interpretations, we run a big risk of losing sight of what is right in front of us. We think we have a shortage of skills in certain areas and miss the utterly obvious. To illustrate this, let us take a closer look at the most in-demand skill of 2020 according to CEDEFOP data: adapt to change. This also happens to be one of the current global buzz skills.

Note: We will be using several examples from CEDEFOP’s online OJA data tool Skills-OVATE over the course of this post. This does not mean that these data are in any way worse than that of other OJA data providers, we are not here to mock anyone. We simply want to provide real facts from real data in the spirit of a fact-based discussion and we decided to focus on one source for consistency.

Among the top 10 occupations that require the skill adapt to change are Athletes and Sports Players, Aircraft Mechanics and Repairers and Firefighters. Here are all skills listed in the CEDEFOP data for these occupations, ordered by count:

Analyzing skills data. Can you see the gorilla?

These lists are right in the comfort zone, containing many of the current buzz skills. But what about the crucial skills and knowledge [2] that workers in these occupations actually need? Shouldn’t aircraft mechanics have knowledge of aircrafts? Or firefighters be able to tolerate stress? And using office systems cannot possibly be a key skill for athletes and sports players.

So now what?

First off, we should move away from generalizing lists and easy statements. As we have seen in this series of posts, these are clearly not conclusions we can sensibly draw from the available data. Instead, we should move towards more differentiated interpretation and communication – even if it is less sexy. In addition, we should steer clear of normalizing and summarizing skills into generic groups when communicating results. Broad terms such as sales & marketing, computer skills or teamwork abilities may be useful for statistics, but they simply do not convey any useful information in other contexts. Sales skills differ dramatically depending on whether they are sought for a position in retail, selling advertisements for magazines, machinery or an entire power plant. Given in their context, there are millions of skills and these cannot sensibly be squeezed into, say, the just over 13,000 ESCO skills without losing critical information – even if the majority had been parsed and extracted properly, which is clearly not the case in general.

We must find ways to determine crucial skills and distinguish them from buzz skills. Expert knowledge as used to create job-specific skill profiles in taxonomies such as O*NET and ESCO tends to be inaccurate or too generalized because of the wide variety of skill profiles for a given occupation. Thus, determining crucial skills will presumably remain a huge challenge until hirers start to consistently highlight which skills are truly necessary and which are not in job advertisements. In the meantime, skills should at least be analyzed as they arise in the context of an occupation, depending on regions, industries, etc., recording must-have skills in OJA data when available and supplementing this with survey data from both employers and employees.

If the aim is to make the data available to users such as students or policy makers, we should gather and provide additional information where OJA data are insufficient and explain how to look at the data instead of (at best) adding simplified disclaimer blurb. The information must be supplemented to provide a balanced picture of in-demand occupations and skills. By solely relying on OJA data, we are actively pointing even more young people away from occupations and industries in dire need of new talent: skilled trades and construction, nurses, care workers and more – many of which are still clearly “futureproof”. And we are encouraging continued potential misallocation of billions in funding for upskilling and reskilling in the wrong areas. Then again, it may be less expensive than gathering additional data through time-consuming surveys and other costly means and paying for extensive vocational, technical or higher (re-)training… And of course, there is also the challenge of measuring skills supply, which is another key aspect for informed policy making for the labor market. That, however, goes far beyond the scope of this post.

Oh, and we should perform basic sanity checks.

According to CEDEFOP data from 2020, 2% of advertised bartender jobs in the EU require skills in conducting land surveys, aquaculture reproduction, collecting weather-related data or analyzing road traffic patterns.

Analyzing skills data. Can you see the gorilla?

Did you know that 1 in 25 musicians, singers and composers is required to know Java?

Analyzing skills data. Can you see the gorilla?

Does anyone really require freight handlers to know yoga or Bihari? Or should someone maybe go back and check the data collection processes?

Analyzing skills data. Can you see the gorilla?

Sadly, it is very easy to find examples like this, you can find many more in data from any one of your favorite OJA data providers. (Again, we are only using examples from CEDEFOP for consistency, not because their data is any worse than that from other sources.) The only conclusion we can draw from this is that these dashboards and statistics are simply not checked. Otherwise, this could not possibly go unnoticed. It does, however, also shed light on what the authors of the ESSnet Big Data report (mentioned in the previous post) meant by “the quality issues are such that it is not clear if these data could be integrated in a way that would enable them to meet the standards expected of official statistics.”

And because more and more institutions and organizations work with the same few data providers – along the lines of “if everyone works with them, their data can’t be that bad.” – the same mistakes are made over and over, multiplying faster and faster. Quoted, posted and shared everywhere by more and more people. The thing is, repeating them often enough does not make these mistakes better or truer. And now, as of January 20, 2021, the time has come to move past alternative facts. So, let’s start looking for fact-based alternatives.

 

[1] For more on this, take a look at this study or our whitepapers on standard skill profiles and education zones.
[2] CEDEFOP data is based on the ESCO taxonomy, which includes knowledge in its definition of skills.