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. 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 ; 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 . 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.
 Christensen et al., (2005), Cahuc, Postel-Vinay, and Robin (2006), and Bagger and Lentz (2019), among others,
 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.
 In the US, long-term unemployment is defined as (active) unemployment for longer than 6 months; in the EU for longer than 12 months.