LABORHETEROGENEITY

Searching for patterns in labour data Vast amounts of information about job vacancies are available online, yet the skills and abilities of workers are not always closely matched to the demands of their role, which has a significant influence on employment patterns. We spoke to Philipp Kircher about his work in investigating unemployment and building a deeper understanding of labour markets The employment market

can seem daunting to job-seekers, with many thousands of people at any one point competing for the positions that best suit their skills and abilities. The individual motivations behind choosing a job and the route taken to get there may vary widely of course, a topic that Philipp Kircher has explored in the Labourheterogeneity project. “I want to know more about how people choose their occupation, or arrive at the occupation they end up in,” he outlines. The workforce is heterogenous in nature, in that people have different skills, educational histories and training backgrounds, all factors which affect the employment opportunities available to them, while there’s also an element of serendipity involved in finding a job. “A person might be in a region where it’s easier to find certain jobs than others for example, there are many factors to consider,” continues Kircher. “I aim to understand more deeply how this works, to sift through various ideas and to identify which work better in terms of explaining patterns we see in the data.”

This rests to a large degree on the wider economic conditions. The European labour market experienced a major upheaval following the financial crisis, and the impact continues to be felt. “There are indications that since the great recession there has been a greater mis-match in the labour market in specific occupations. That suggests people may still be searching for the wrong kinds of jobs – the economy has shifted and people are not reacting,” explains Kircher. The ‘wrong kinds of jobs’ could be those for which an applicant is over-qualified for example, or that are in a less productive sector of the economy, raising a number of questions that Kircher and his colleagues in the project investigate.

“Are people searching for jobs in occupations with few available jobs and low productivity, even though there are other occupations out there that are more promising?” he asks. “Once we’ve established that, we can think about why this is the case. Is it that

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people just can’t do these other jobs? Or could they do them with training, but aren’t aware that these jobs are available?”

Job market The wider aim in this research is to develop a deeper understanding of the root causes of unemployment and to help people find suitable jobs. Alongside the more theoretical research, Kircher and his colleagues are working on two sub-projects within Labourheterogeneity, the first of which builds on the idea that people still have quite a lot of learning to do when they choose an occupation. “We know that many people look for work in occupations where there are not many jobs on offer. We wondered – could we advise job-seekers about alternatives?” he outlines. A job search website has been developed to help people identify alternatives suited to their skills. “We don’t want to push people into unsuitable jobs, we want to provide relevant information,” explains Kircher. “We did a focus group with jobseekers, and while we found that they had

skills to do certain jobs, those jobs weren’t always available. There was a lack of understanding about where else those skills could be applied in the job market.”

This is where analysis of large datasets like Understanding Society, a study following the lives and careers of UK residents, can hold relevance. The rate of occupational change today is quite high, and analysis of job changes can help researchers identify which employment opportunities would be well-suited to an individual’s skills. “We can ask – are there people who have worked as pipe-fitters; where else have they worked? What did they do after they stopped working as pipe-fitters? Some of them may have gone on to be plumbers for example,” says Kircher. Researchers have analysed both UK and Danish datasets, gaining insights which can then inform the development of a new job search interface. “We have programmed our own job search interface.

In the base version, you type in your own key-words, the engine does a key word

Table 1: Effect of intervention on interviews

Each column represents two separate regressions. All regressions include group fixed effects, period fixed effects, individual random effects and individual characteristics. Columns (1)-(3) are Poisson regression models where we report [exp(coe cient) - 1], which is the percentage effect. Standard errors clustered by individual in parentheses. * p < 0:10, ** p < 0:05, *** p < 0:01.

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search, and it returns items that are related to your search,” continues Kircher.

“If you want to search for a different job, you have to decide what key word will get you closer to the desired job.”

The new job-search interface has an additional feature however that is designed to help job-seekers identify alternative employment opportunities. Instead of a jobseeker simply putting in key words, they are prompted to describe what occupation they’d like to work in, from which Kircher says other options can be identified.

“Somebody might say; ‘I’m looking for pipe-fitting jobs’. Then we look at where pipe-fitters have previously found jobs, and we generate a list, saying for example that previously pipe-fitters found work as plumbers or technicians,” he outlines. This helps job-seekers broaden their horizons, away from their own specific area of expertise, to think about other areas of the employment market where their skills could be relevant. “What we are aiming for is to help people who know their own skill-set, then to move from there to identify what other occupations they might also be good at,” says Kircher. “It is fairly simple – jobseekers give us some information, and we tell them related information.”

The website also offers other information, such as on skill transferability and on how tight competition for jobs in different occupations are. However, identifying where other people with similar skills have previously found jobs seems an easy path that other websites could follow.

This system has been tested so far on 300 job-seekers in Edinburgh, using jobs gathered from Universal Jobmatch, a website run by the UK Government. One part of the group spent the entire 12-week trial period searching for jobs in the conventional way, while the other had the opportunity to use the new system during the second half of the trial; Kircher says the results so far are positive. “On average the latter group looked at a broader set of job openings and seemed to get more interviews.

Those people who had very narrowly defined search criteria at the beginning of the period clearly seemed to benefit, they got a lot more interviews,” he says. The group was not large enough to draw wider conclusions about job finding rates and general equilibrium effects however, so Kircher plans to set up a bigger trial in the future, investigating the major issues around unemployment in greater depth. “If you divide too finely between young and old, poorly educated v highly educated, etc.,

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Figure 1: Screenshot of the tool (for preferred occupation ‘cleaner’). then you have only a few people in each category, and you can’t really draw wider inferences,” he explains. He believes that the relevance of this work reaches beyond academia, and should interest both governments and the private sector. In particular, governments have been pushing job seekers to search hard for jobs, and if easy ways to provide information turn out to be useful, they might want to invest in them. The key for success is for this information to be correctly integrated with the jobs people are looking for, as most job seekers do not want to read booklets before they get on with their search.

continues Kircher. “Switching is very costly for people who have invested a lot of human capital in an occupation, as they’ve built up their expertise, so they will stay.”

By contrast, in this context the people who have invested less human capital are more likely to leave, as they’ve spent less time, energy and money in building up their skill set. In this type of theory, the low earners would again leave an occupation and the high earners would stay. “The low earners would be the ones with low occupation-specific human capital,” explains Kircher.

We see there is a high probability that low earners will change occupations. That probability drops towards the middle earners, and then picks up again towards the higher earners Occupational mobility A second sub-project centred on analysing data from Denmark on how often people change occupations, and investigating the underlying reasons why. A number of theories are based on the idea that an individual in an occupation with low wages is more likely to want to change jobs, whereas if wages are high, then that person will want to stay. “One such theory would be where a person isn’t sure about their skills, so they pick an occupation at random then try to find out whether they’re sufficiently capable or not. In this model, the high earners in each occupation would stay and the low earners would leave,” outlines Kircher. Another theory also takes the wider economic climate into consideration. “For example, there may be less demand for plumbers in a recession. Which people leave the plumbing occupation and which stay?”

Typically an individual’s wages rise the longer they stay in an occupation, Kircher is now investigating why this is the case.

“Is it because they become better at the job? Or is it because only the good people stay in an occupation and the bad ones leave?” he asks. “We’ve been looking at data from Denmark across many different occupations. We are trying to look at specific occupations and ask of individuals; ‘within that occupation, how well are they paid? Are they at the bottom, the middle or at the top? Are they well paid in comparison to others?’ Then we look at the next year’s data – are they still in that occupation?”

Many of the conventional theories would suggest that the high-earning people would be highly likely to remain in the same occupation, while the lower earners would leave. However, Kircher says his findings show a different picture.

“What we see is that there is a high

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At a glance Full Project Title Labor Heterogeneity in Search Markets (LABORHETEROGENEITY) Project Objectives The work laid out in this proposal aims to change our understanding of labor markets by viewing both the mobility as well as the frictions in the market as a consequence of long-term worker heterogeneity. Despite the advances in information technology which substantially reduce the costs of sending information (job advertisements, job applications) extracting the relevant information about worker quality remains hard. Long-term differences in ability coupled with screening frictions are proposed as the main reason for mismatch, for mobility, and for the presence of unemployment.

Figure 2: Non-parametric plot of probability of switching occupation by worker’s percentile in the relevant wage distribution.

Project Funding Funded under: FP7-IDEAS-ERC. ERC-SG ERC Starting Grant. EU contribution: EUR 317 034,67 Project Partners • European University Institute, Italy • The University of Edinburgh, UK Contact Details Project Coordinator, Professor Philipp Kircher European University Institute and University of Edinburgh Department of Economics European University Institute Villa la Fonte, Via della Fontanelle 18 50014 San Domenico - Fiesole T: +39 055 4685 429 E: philipp.kircher@eui.eu W: http://homepages.econ.ed.ac. uk/~pkircher/

Professor Philipp Kircher

Philipp Kircher is a professor at the European University Institute in Florence and at the University of Edinburgh. He graduated from the University of Bonn and held faculty positions at the University of Pennsylvania, Oxford, and LSE. He is Chairman of the Review of Economic Studies and sits on the Executive Committee of the European Economic Association.

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Figure 3: Non-parametric plot of direction of occupational mobility, conditional on switching occupation, by worker’s percentile in the relevant wage distribution before the switch. probability that low earners will change occupations. That probability drops towards the middle earners, and then picks up again towards the higher earners,” he outlines. The lower-earners have a clear incentive to change occupation, but it’s less clear why a significant proportion of high-earners choose to move; Kircher and his colleagues are exploring this topic further. “People have to learn about their capabilities, to discover whether they’re good at an occupation or not,” he says. “The question then is whether you should stay in a high earning position, or move on to something where the labour market returns are even

higher. If you find that you are good at a specific job, then you may still leave it to find something better.”

This research holds important implications for our understanding of employment and wages, and Kircher is keen to build strong links with policy-makers to help inform future policy. The introduction of new technologies is set to further disrupt the employment market, an issue that Kircher intends to explore in future. “I would like to think more deeply about structural change, the introduction of robots and artificial intelligence for example, which will lead to massive upheaval in the labour market,” he outlines.

Figure 4: Illustration of the proof of Propositions 1 and 2.

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