Design Example: Job Search 2.0
Job Search 2.0
Amy J. Ko
As the world has globalized, and the internet has enabled remote work, job searches have never been more complex, competitive, or global. A recent Time magazine article Links to an external site., for example, pulled together several market research surveys, finding that in the U.S., there are around 12 million job seekers per year in recent years, but they increasingly face numerous barriers to not only getting a job, but even applying. Employers are posting "ghost" positions to gather recruiting data and inflate perceptions of their growth, even though they have no intent of hiring. Many applicants are being asked to complete skills assessments prior to being eligible, and research — including some from my own research lab [1] — and finding that they are not only completely opaque, but are full of hidden rules and biases that govern whether applicants are ever considered. It also highlighted record high hiring timelines of 44 days on average. And these trends are apparently worst for college graduates, who are far more likely to not receive job offers, to receive inconsistent information, and to have to conduct multiple rounds of interviews. Gone are the days were one applies to a dozen local positions and gets hired. Now, getting a job means applying to hundreds of places across the country, hearing nothing from most of them, and living in limbo for months, if not longer. Or getting an instant auto-reject from an algorithm.
In my view, the root cause of all this added complexity is not globalization, or remote work, but the inattention to high paying, low status careers. For every one single high paying software development or data science job, there are dozens of other jobs that are not being filled. Medical assistants, nurses, plumbers, electricians, carpenters, science, math, and special education teachers, long haul truckers, dental hygienists, restaurant managers, and more — there are many high demand jobs that people simply don't want.
The question is why. One reason with clear evidence is that students often select career goals based on limited information about career opportunities and too late to pursue them in their education [3]. For example, imagine a student who does not know what they want to do on the first day of college; they're likely to choose courses that help them discover what they might want to do, but since courses tend not to be designed to help with that learning, they do not develop career interests. And once they do, perhaps through peer learning, career fairs, or other informal learning, it is often too late to align their education with their career goals. Contrast this with a student who knows what career they're pursuing from day one of college: they can organize their learning around that career, leverage the resources in college to support their career, and perhaps even leave college for careers that don't require it. None of this is possible because students don't have the information they need to form career interests prior to college.
One might argue that the U.S. system of Career and Technical Education (CTE) was designed to do exactly this. But it does not: CTE instructors often do not have broad career knowledge, and specialize in particular areas such as computer science, business, or mechanics. And CTE courses are generally not required, and so even if they did offer broad career knowledge, most would not take them. Schools also struggle to hire CTE instructors, because they are often paid far less than if they took an industry job in their career. This means that those who are hired are often people at the end of their careers, or who never worked in industry, limiting the relevance of their career knowledge.
I think we can do better. I propose a new information system to help students develop career interests earlier, and do it in a way that meets students where the are. The general concept is to have a U.S. federal organization charged with making high demand careers visible to youth. Their job would be to track Bureau of Labor Statistics job demand data, and each year, produce highly engaging short video content for social media about those positions, interviewing people who have recently pursued those careers. The videos would have metadata about ways to pursue each of the careers, curated by the federal team, and provided by academic institutions and trade associations annually. The videos would be posted and shared broadly, aligned with when educational pathways solicit applications, creating a direct line from youth to learning opportunities. Institutions and trade groups would respond to this by awaiting the annual announcement of priority careers, and filling out a federal survey to be in the official listing of learning opportunities.
Crucially, the videos produced by the team and the metadata gathered by the team would be geotagged. Each career would have multiple videos that highlight someone across different geographical areas of the country, allowing youth to find video content that not only resonates with their interest and curiosities, but is also geographically close to where they are. This is important, because most youth in the U.S. do not travel far for education or jobs after high school — the opportunities shown therefore need to be specific to where they are, as they are likely to stay there.
A third key element is that all of these videos would be explicitly tagged by year in their titles, and removed from the internet if BLS data shows that they are no longer in demand. This is crucial, as labor markets change rapidly — otherwise, old videos might misrepresent demand, leading youth into careers where demand has been met.
There are many other kinds of metadata that might be gathered as part of this annual effort. Moderated community conversations about careers could augment the videos with other pros and cons about careers. Videos could include salary range metadata. Historical data about how a career has changed over time could also help youth understand that occupations are not fixed, stable things, but always changing.
While this new system could better align youth knowledge of careers with opportunities, it does have some risks. For example, using BLS data might prioritize larger labor markets at the expense of important but smaller markets. For example, the world does not need many professors, politicians, and not-for-profit directors, but it does need some, and this effort could move attention away from those careers, towards the high demand careers. Who is highlighted in videos could also inadvertently reinforce stereotypes about who pursues those careers, and so the effort would need particular attention to the identity of the people shown. Finally, because this would be a government entity, it could easily be politicized or weaponized by the executive or legislative branches, giving the government undue influence on labor markets and education.
If these risks can be managed, however, we might just be able to reduce the complexity of current job searches, but better aligning youth career interests with labor market needs.
References Cited
1. Alana Semuels (2023). You’re Not Imagining It—Job Hunting Is Getting Worse. Time Magazine, June 14th. https://time.com/6287012/why-finding-job-is-difficult/, Links to an external site. retrieved November 20th, 2023.
2. Lena Armstrong, Jayne Everson, Amy J. Ko (2023). Navigating a blackbox: Students' experiences and perceptions of automated hiring . ACM International Computing Education Research Conference (ICER) https://doi.org/10.1145/3568813.3600123 Links to an external site.
3. Kim, S., Klager, C., & Schneider, B. (2019). The effects of alignment of educational expectations and occupational aspirations on labor market outcomes: Evidence from NLSY79. The Journal of Higher Education, 90(6), 992-1015.