Strengthening the economy through advanced labor market information systems


Today’s changing world places many complex challenges to labor market governance and management: the slowdown of the global economy, the structural shifts and evolving skill demands connected to widespread digitalization, as well as increasingly dynamic career paths with more frequent job switching, geographical mobility and flexibility, and multiple transitioning between education/training and employment.

Advanced labor market information systems are key to improving labor market efficiency

To address these challenges, many governments have established active labor market polices (ALMPs) and public employment services (PES) to help workers find jobs and firms fill vacancies. However, due to the complexity and individual set of challenges in any given labor market, there is no simple answer as to how public employment services should be set up and organized. But a well-thought-out information strategy and infrastructure is certainly critical to the success of any PES. If nothing else, the most recent disruptions have shown that effective ALMPs and PES require agile and flexible frameworks to successfully adapt to rapid and at times dramatic shifts in their labor markets. But even the most agile of frameworks is only useful if it includes a system to identify labor market issues as they arise.

Identifying such issues relies critically on the availability and quality of data, information and analysis. Therefore, establishing an advanced labor market information system (LMIS) is an integral step towards more efficient and targeted employment and labor policies by delivering accurate, relevant and timely information to inform design, implementation, monitoring and evaluation of policies. According to the World Bank, advanced LMIS encompass institutional arrangements between key stakeholders (e.g. policy makers and the education system), collaborative partnerships with private sector actors and advanced technology solutions to gather, validate, analyze, and distribute information related to the labor market that is relevant, reliable, useful, and as comprehensive and up to date as possible.

Combining traditional labor market information with real-time data

Traditionally, labor market information (LMI) was primarily gathered from censuses, surveys, case studies, and administrative data. However, this traditional LMI has a disadvantage that is increasingly cumbersome: lag time. In an ever-faster changing world this carries risks such as policies being outdated before they can be implemented, rendering them ineffective if not obsolete. Therefore, an effective LMIS should also incorporate real-time (big) data from additional sources such as online job portals and networking sites. This type of data is not only much more up to date, it also typically contains more detailed information including job activities and requirements regarding education and skills. However, real-time LMI based on online job advertising data also has significant shortcomings: Apart from the challenges of duplicates and inconsistent levels of detail, it tends to be incomplete. Not all jobs are posted online, in particular, this type of data rarely captures the informal sector and is also often biased toward certain industries or occupations. In addition, the data may be distorted by ghost vacancies posted by non-hiring companies that want to cast a broad net for talent. Accordingly, real-time LMI is a complement to, rather than a substitute for traditional LMI.

Empowerment through interoperability

In addition to supporting policy makers and researchers, a strong LMIS should also provide additional services such as job matching, career and skills guidance and government support services through a government-managed online platform with interconnecting subsystems tailored to the different users. In this way, the LMIS strengthens the functioning of the labor market by helping all stakeholders in the labor market including workers, students, firms, and practitioners to make informed choices on a variety of topics such as job search and hiring strategies, curriculum design, career planning and training investments, and more.

International examples of modern LMIS

Worldwide, several countries offer examples of advanced LMIS incorporating LMI from traditional and big data sources and where the information feeds both into and from multiple interconnecting public interfaces to provide comprehensive, verified LMI for research and policymaking as well as job-matching, career guidance and skills development services. These sophisticated services include state-of-the-art tools and technologies such as AI/ML and big data analysis.

For instance, in Korea, information in the LMIS is used by the Korea Employment Information Service (KEIS) to monitor and evaluate public policies and generate analyses and forecasting for stakeholders such as job seekers, employers, researchers, and policy makers. Data is drawn from national statistics, surveys related to employment and skills, and databases from various interconnected KEIS networks, including HRD-net, a job-training platform, and Work-net. Originally established in 1998 as a publicly managed job-search portal by Korea’s Ministry of Employment and Labor, Work-net now provides comprehensive employment information and support services, including job matching and information on occupational outlooks, working conditions, and skills demand, as well as feeding user-generated data back to KEIS. With the progress of technology, it has added mobile services (2010), big data services (2018), chatbot services (2019) and AI-based job matching services (2020). [1]

The Norwegian LMIS also comprises interconnected subsystems that combine services for labor market supply and demand with data for decision makers and policy makers. The Norwegian Labor and Welfare Administration’s (NAV) online platform for job search and matching services,, has been using AI technology since 2019. It contains job advertisements both posted directly on the platform by employers and imported from external, privately managed job portals, as well as a CV database of job seekers, providing a comprehensive overview of the labor market. The system also has access to extensive information on the Norwegian education landscape to enhance the accuracy of job matching and career planning services. This modern digital platform provides automated and highly user-friendly services, and continuously self improves thanks to sophisticated machine learning algorithms in the backend. During the first wave of the pandemic, the system proved scalable by a factor of 8–10 within just a few days to deal with the surge in registrations caused by the dramatic disruptions in the labor market.

The technology behind the semantic search and matching engine and the underlying ontology of is provided by JANZZ has been collaborating with several public employment services across the globe to assist their LMIS development. Our services range from state-of-the-art AI-based solutions to gather real-world labor market data and transform it into smart labor market intelligence – including job and resume parsing and automated classification and contextualization of job and skills data – over intuitive and powerful analysis and dashboarding tools that generate actionable insights including skill or workforce gap analyses, training and career guidance or semantic job matching, to designing entire system architectures from scratch. Visit our website and discover the advanced solutions we have created for public employment services or watch the explainer video for our integrated labor market solution JANZZilms!.



Is Vietnam the next Singapore? Viet Nam

Vietnam hopes to achieve high-income status by 2045. The country’s vibrancy is evident by investments in innovation and technology adoption that spur an innovation-driven private sector to build resilient businesses. Vietnam had a GDP per capita of $500 (today’s dollars) in 1985 which was one of the lowest in the world, and by 2021 it had already created a couple billionaire entrepreneurs.[1]

Vietnam’s performance is impressive as it was one of the poorest countries globally that achieved lower middle-income status in under a generation and became a dynamic East Asian economy. Its’ success can be credited to connecting to global value chains and offering favorable conditions to investors—much as it continues to do today according to the Prime Minister of Vietnam.[2] GDP per capita rose three-fold to about $2,800 and poverty drastically declined to less than 2 percent between 2002-2020. The Economist points out “it has been one of the five fastest-growing countries in the world over the past 30 years” ahead of Malaysia, Thailand, the Philippines.[3]

Achieving high-income status is an ambitious goal for a frontier market that already knows much about steady growth and global supply chains. Yet it will require 7% growth per year to achieve. Vietnam knows how to sell its goods abroad; trade exceeds 200% of GDP. Additionally, foreign direct investment (FDI) has been much higher than in China or South Korea for the past thirty years. Global companies were attracted by Vietnam’s cheap wages and stable exchange rate fueling a boom economy. But this export trade is mostly driven by foreign companies and not domestic ones.

With COVID-19, Vietnam had early success limiting the virus and GDP growth remained positive, albeit the lowest in three decades at 2.9%.[4] Yet the Delta variant upended the Vietnamese economy with factories shutting down disrupting supply chains for global companies like Nike, LG Electronics, and Samsung. In the end, the country’s growth outlook performed lower than the world average of 6% between 2% and 2.5%. Nevertheless, it was deep linkages to global manufacturing that sustained Vietnam’s economy in the pandemic.

How does Vietnam achieve high-income status? Answer: Better jobs.

As global uncertainty looms, Vietnam is thinking ahead about its’ future jobs landscape. The country knows it’s overly dependent on FDI and domestic firms underperform. Meanwhile, it’s difficult to remain competitive with increasing wages and ever-changing value chains. So, what can Vietnam do?

For a start, there are limits to what foreign firms can do to drive Vietnam’s development.

Vietnam’s economic success is attributed to its’ 50+ million jobs in recent decades. A big push in services and manufacturing reduced poverty in a country where 3 in 4 Vietnamese work in either family farming, household enterprises (unincorporated, non-farm businesses), or uncontracted labor. Economic growth happened because labor productivity increased alongside wages.[5] Yet Vietnam needs to further develop its services sector improving the quality of jobs if it is to achieve high-income status.

There is a strong government push to foster a Vietnamese chaebol system comparable to South Korea’s. Chaebols are the large conglomerates that helped develop South Korea’s new industries, markets, and export production making it one of the Four Asian Tigers. Vietnam already has the Vingroup with operations across education, health, real estate, and tourism. Developing a system of “national champions” may be the way to offset the widening gap between foreign owned firms and domestic ones, which have more barriers to access capital.

Vietnamese firms can also benefit from the growing Asian consumer class. There is a large consumer market waiting to be untapped in the region, especially if Vietnam expands its knowledge intensive services and modernizes its agro-business sector. Perhaps by creating jobs away from more traditional sectors, it can play a role in developing small and medium enterprises that better integrate into the larger economy and enhances supply chain connectivity.

Of course, this is not to say that Vietnam should forget traditional sectors completely. They represent most jobs in the country, about 30 million. Jobs in farming should diversify agricultural output into higher value-added crops and local value chains. And household enterprises must increase the quality of goods and services to remain competitive regionally and globally.

Human capital investments will be key in fostering an agile workforce ready to embrace tomorrow’s jobs. The Vietnamese labor force should build 21st century skills with adequate education and training. Future industries in Vietnam will require new skills sets, ways of working, and business models to export and expand. Automation may also displace jobs and enable others to become more efficient and productive.

It is evident that trade and consumption is already changing and impacting Vietnam. Much like Singapore, it can remain business friendly and competitive by focusing on public-private collaboration, innovation and digital transformation, and connecting qualified workers to the right jobs.

Here at we are ready to assist Vietnam towards its 2045 development goals.


[1] The Economist. November 27th, 2021 Edition. Vietnam has produced a new class of billionaire entrepreneurs.
[2] World Economic Forum. October 29th, 2021. Prime Minister of Viet Nam Speaks with Global CEOs on Strategic Priorities in Post-Pandemic Era.
[3] The Economist. September 4th, 2021 Edition. The economy that COVID-19 could not stop.
[4] International Monetary Fund. March 2021. IMF Country Focus: Vietnam: Successfully Navigating the Pandemic. Washington, DC.
[5] The World Bank. Vietnam’s Future Jobs: Leveraging Mega-Trends for Greater Prosperity (Vol. 2): Overview (English). Washington, D.C.: World Bank Group.

When it comes to the use of AI in HR, it is past high time


There are numerous constitutional articles, laws, ordinances and regulations according to which companies must conduct their daily activities. And the number of these legal foundations is constantly increasing. A relatively new piece of legislation in Europe is the EU’s General Data Protection Regulation, or GDPR for short, which was adopted in 2016. The aim of this transnational regulation is to standardize the collection, processing, storage and deletion of personal data by private and public actors. During a transition period of two years that expired in May 2018, companies had the opportunity to take the necessary steps to comply with the new regulations in their daily working practices. However, a large majority of them are lagging behind in taking such measures, even though the GDPR should be on the minds of all compliance departments by now in 2022. In addition to all customer-data-processing areas, it is also the HR sector that is strongly affected. HR sections now often process data on (potential) employees with the help of artificial intelligence (AI)-based tools. As we have already explained, the potential of AI for labor-market-related processes is anything but unlimited, and further regulations are already being planned for its use, for instance in the EU.

As if the GDPR were not complex enough in itself, it thus poses additional pitfalls and risks for companies when it comes to the use of AI. In the event of non-compliance, businesses are threatened with heavy penalties and immense fines [1]. For all companies that do not yet comply with the regulation in the HR area (and, as already mentioned, there are many of them), it is time to immediately address this issue and, if necessary, to take appropriate measures, in particular by procuring correctly functioning IT.

Not only a lack of knowledge, but also of technology

Two years after the introduction of the regulation, there are still few cases of legal experience and therefore virtually no reliable points of reference for the interpretation of the GDPR in legal practice [2]. This confusing state of information about the collection and further processing of personal data leads to a lot of uncertainty among companies. In a large number of cases, especially such with no proper section with information security officers and IT specialists, ignorance is rife. Uncertainty is further exacerbated by the fact that the GDPR is likely to be tightened up in the future due to poor cooperation between data protection authorities, national disparities in the interpretation and enforcement of the rules, and in some cases questionable dealings with large digital corporations to date [3].  Furthermore, the GDPR is only the European example of such a legal remedy. Similar regulations that would have to be followed in an international (labor) market will soon exist in most parts of the world, another popular example already in place being the CCPA (California Consumer Privacy Act) in the US state of California.

There is another key reason why many companies would not currently withstand a GDPR compliance test. In addition to ignorance and misinformation, around 80% of HR and labor market management software solutions on the European market simply do not meet the standards set out in the regulation. Moreover, many companies use tools that do not originate from the GDPR area and therefore rarely comply with the regulations here anyway. Such non-conformities often affect the entire setup of a company’s HR department, from the recruiting process to the people analytics processes that are currently being hyped everywhere. Performance-based decisions about which applicants advance to the next round in the hiring process, as well as which existing employees are kept in a position – or promoted or fired from it – can all too quickly become legally (and ethically) critical when made with the help of data-driven, AI-based systems.

Potential for discrimination only part of the problem

Experiences from labor law practice on the consequences for non-compliance with the GDPR can, as already mentioned, be counted on a single hand. Nevertheless, one thing is already very clear: Anyone who cannot explain how their company’s software solution produces its results and bases their HR decisions on said outcomes has a huge problem – especially if AI is involved. Because in such a situation, in addition to the obvious lack of transparency, there is also no technical or practical option to intervene in the process. In Germany, for instance, this is already running counter to the guidelines of the national HR Tech Ethics Advisory Board [4]. A well-known example of this was provided in 2018 by a multinational tech company. Their AI-based recruitment tool for the pre-selection of applicants showed a bias in favor of white, middle-aged male applicants in the algorithm that could not even be corrected by its own IT department. Besides this ‘classic’ example of discrimination in IT processes linked to the labor market, it is however also conceivable that a bias could arise along other lines, such as towards specific types of study, languages or similar, if the data quality on which the automated program is based is not heterogeneous enough. Strictly speaking, a variant of such discriminatory filters is already in use, namely when it comes to so-called ‘equal opportunity employment’. Any company that gives preferential consideration to diversity and inclusion aspects in its HR workflow (e.g. in the form of quotas) is, in effect, ‘discriminating’ against anyone who cannot check any of these boxes. Moreover, the collection and recording of such sensitive information on applicants and employees is not without controversy in itself. It may make people identifiable and thus, in turn, invade their personal rights. [2]

In general, the potential for discrimination and the risk of profound personality screening are, however, only part of the problem when it comes to automated tools for decision-making in human resources. According to legal experts, in the event of a potential breach of the GDPR, it is also relevant whether the tool is legally permissible (as defined in Art. 6 (1b) in conjunction with Art. 88 GDPR). The so-called “prognostic validity”, i.e. whether an algorithm depicts comprehensible and scientifically verifiable relationships, plays a decisive role here. [2] Unsuccessful (and highly problematic) examples of violations of this rule in human resources include speech analytics software solutions whose performance, after initial enthusiastic anticipation, is now receiving negative awards for their “scientifically dubious, probably illegal and dangerous” technology. Promises such as that automatic facial analysis during the job interview can provide important insights into a candidate’s intelligence or the like may sound very tempting and exciting. However, they do not stand up to the condition of suitability of the GDPR and are more like snake oil in their usefulness. [5]

It is past high time

The bottom line for companies is the following: Be aware that there only has to be one big lawsuit – spoiler: it will come – until all businesses (large, medium or small) have to catch up immediately in order to still escape the high punishments for a breach of the European regulations on the handling of personal data. Waiting until that point, however, will not only leave you ignorant and unprepared. It will simply be an impossibility to suddenly implement compliant means for the data-driven processes in human resources in time. Because, again, it is well past high time now that the transition period has expired in 2018. As mentioned, the number of standards regulating this area are not decreasing either, and further and more far-reaching regulations are already being planned in connection with AI, currently for example in the EU.

Today, anyone who processes personal data in any form in their HR department with the help of machine-trained algorithms and who is not absolutely certain of the legality of these automated processes should really act immediately. Otherwise, serious and costly consequences can soon be expected. In Europe, the GDPR is the first port of call regarding such strongly advised revision and updating of in-house compliance knowledge and the HR practices affected by it. After reviewing all of these aspects, it is also likely that new IT solutions will have to be acquired due to the high proportion of non-GDPR-compliant HR software on the European market.

At, we offer solutions for all HR and labor market management processes that are not only GDPR- and CCPA-compliant, but also unbiased, multilingual and modular. This is all possible due to our globally unique approach, which bases our extremely powerful solutions and systems on ontology-based semantic matching. At its heart is the JANZZon! knowledge graph, which is curated daily by our experts of different ages and diverse backgrounds in terms of language, education, culture and experience, using ISCO-08, ESCO, O*Net and more than 160 other international taxonomies and classifications. Special attention is also paid to the careful selection of data and sources, so that JANZZ can ensure that the information used to maintain JANZZon! is always optimally balanced, representative and diverse. This is why, for example, in the area of annotations and machine learning, not only English-language documents are used, which is another unique selling point of JANZZ compared to most HR applications in use today. The multilingual nature of our ontology also allows, for instance, gender- or culture-specific differences between languages and their associated geographic areas to be normalized and thus bias-free matching results to be achieved. The results of our parser product JANZZparser! as well as all our other tools are therefore evidence-based, explainable and – in contrast to many other solutions on the market – not only fully DSGVO-compliant, but also scalable thanks to continuous training, foundation on high data quality and their backing in JANZZon! Their use makes it possible to make the factually best decision for every HR issue in a company and to exclude so-called ‘false-negative’ or ‘false-positive’ decisions – in short, to act in the best interest of the business using them.

If you would like to learn more about our services, please contact us at or via contact form, or visit our product page for an overview of all our solutions.


[1] 2021. EU-Datenschutzverordnung (DSGVO): Verbindliches Datenschutzrecht für alle! URL:  
[2] Diercks, Nina. 2021. Der Einsatz von KI in Recruiting-Prozessen – Diskriminierungspotential automatisierter Entscheidungsfindung im HR-Bereich. URL:
[3] IT-Daily. 2020. Drei Herausforderungen verschärfen die DSGVO-Problematik. URL:
[4] Ethikbeirat HR Tech. 2020. Richtlinien für den verantwortungsvollen Einsatz von Künstlicher Intelligenz und weiteren digitalen Technologien in der Personalarbeit. URL:
[5] White, Sarah. 2021. AI in hiring might do more harm than good. URL: