Skillscape: How skills affect your job trajectory, and their implications for automation by AI

This blog post summarizes the key findings of our new paper Unpacking the polarization of workplace skills published in Science Advances by our research team including: Ahmad Alabdulkareem, Lijun Sun, Bedoor Alshebli, Cesar A. Hidalgo , and Iyad Rahwan. We propose a new framework for skill complementarity that captures important labor trends, including a worker’s career mobility. Insights from our model have implications for viable worker retraining programs, explaining job polarization, and understanding the impact of automation from AI. Others can explore our analysis and insights by visiting skillscape.mit.edu
Current trends suggest career advancement is harder for some than for others.

How do workers move up the corporate ladder and how can they maximize their career mobility? Increased wealth disparity, increased job polarization, and decreases in absolute income mobility (i.e. the fraction of children who earn more than their parents) all suggest that upward mobility is difficult for today’s workers. It’s as if the rungs on the ladder to career success are there for some and absent for others. But who is stuck, and why?

By conventional wisdom, education determines a worker’s entry into the labor force. Workers who start higher up on the ladder have a better chance of reaching the top. However, returns on higher education have not kept pace with growing costs and mid-career workers are generally unwilling to return to school.

Instead, most workers utilize their existing knowledge, ability, and skills — along with social connections— to advance their careers. That is, a worker is more likely to fill a job opening if their capabilities meet the job’s requirements. These capabilities more aptly represent the rungs on the corporate ladder, which are present for some and absent for others.

This theory of skills is not new and skill matching has long been considered a key mechanism in the job matching process (just ask the winners of the 2010 Nobel Prize in Economics). Earlier studies of job polarization and limited career mobility have taken note and focused on different types of labor. For example, Daron Acemoglu and David Autor measure the annual wages of occupations and observe a “hollowing of the middle class,” which they describe as growing employment share for low- and high-skill employment at the expense of middle-skill employment. They argue that high-skill employment leverage cognitive skills, while low-skill employment rely more prominently on physical skills.

These cognitive and physical labor categories — in addition to traditional measures of education and wage — are very broad. For example, consider that Civil Engineers and Medical Doctors are both professions that fall into the same conventional labor categories; they both have high educational requirements, make high wages, and require cognitive non-routine labor. Yet, their skill sets are largely non-transferable. To explain why Civil Engineers are unlikely to become Medical Doctors — and to explain where skill sets might limit other workers’ career mobility — we need a higher resolution framework for specific workplace skills.

Our study answers this call for a better resolution into workplace skills. Rather than focusing on broad expertly-derived skill categories, we employ a completely data-driven approach using high-resolution occupational skill surveys carried out by the US Department of Labor. By examining how pairs of skills co-vary in importance across occupations and controlling for ubiquitous skills, we identify pairs of skills with high complementarity. Skill pairs exhibiting complementarity tend to support each other by boosting the productivity of workers who possess both skills, or by the ease of acquiring skills simultaneously. For example, Mathematics and Programming have high complementarity, but Programming and Explosive Strength do not.

Constructing the Skillscape.

Recall that earlier studies of job polarization measured wages but concluded that job polarization is a divide between “high-skill” and “low-skill” employment. So, what are “low” and “high” skills? To answer this question, we introduce a new approach for modeling the complementarity of workplace skills.

Although we are interested in job opportunities, skills are atomic in the labor system, and so , we consider each job title as a bundle of skill requirements. Using survey data from the U.S. Department of Labor, we identify skill pairs that tend to be bundled together. For example, the skill Spatial Orientation and the skill Peripheral Vision are important to many similar job titles:

The jobs that rely most strongly on the skill Spatial Orientation, and the skill Peripheral Vision.

On the other hand, the skill of Complex Problem Solving supports a very different set of occupations:

The jobs that rely most strongly on Complex Problem Solving.

Observations like these lead us to connect skills that support similar jobs while disconnecting skills that support different jobs:

Connecting complementary skill pairs.

By considering every pair of skills in this way, we connect complementary skills to produce a network that we call the Skillscape:

The Skillscape: a high-resolution network of skills connected by skill complementarity. Skill polarization separates socio-cognitive skills (on the left) from sensory-physical skills (on the right). Skills are colored according to O*NET Skill Category.

The most striking feature of the Skillscape is the polarization of workplace skills. That is, the aggregate structure of the network separates socio-cognitive skills (e.g. Negotiation and Mathematics) from sensory-physical skills (e.g. Low-Light Vision and Manual Dexterity). We find that occupations relying more strongly on socio-cognitive skills tend to have higher annual salaries, and, similarly for cities with higher median household incomes. This draws a direct connection between the work of earlier studies and suggests that the Skillscape’s sensory-physical skills and socio-cognitive skills are the low- and high-skills (respectively) from previous studies. This evidence suggests that skill polarization underlies job polarization.

Reliance on socio-cognitive skills leads to higher annual wages and wealthier cities. We project individual occupations onto the Skillscape using black circles to identify an occupation’s skill set. Occupation dollar amounts correspond to on-average annual wage for workers in 2015. Similarly, we combine occupational skill requirements with employment distributions to identify the key features of an entire urban workforce and find increasing median household income (dollar amount) relates to increased reliance on socio-cognitive skills.

How do workers navigate with skills?

“Can I get there from here? Am I prepared to take the leap?”

The Skillscape adds resolution to traditional models by incorporating specific workplace tasks and skills. This improved resolution sheds new light on where bottlenecks limit career mobility due to skill mismatch. So, how can a worker leverage their existing skills to grow their skill set and open-up new career opportunities?

According to skill matching theory, workers should be able to obtain new jobs if their existing skill set is similar enough to the skill requirements of a job opportunity. Our analysis demonstrates that skill complementarity, which define the links between skills in the Skillscape, accurately predicts the skills of a worker’s new job from the skill requirements of the worker’s previous job. However, this is not the only labor trend we can explain with our framework! The Skillscape also predicts temporal changes to occupational skill requirements and even how the skills of entire urban labor markets evolve over time. This level of insight demonstrates how workplace skills underly the US economy and suggests that our framework has the potential to inform worker retraining programs and urban policy aimed at maintaining employment opportunities in an increasingly competitive economy due to globalization and automation.

The consequences of skill polarization

While exciting, our results are also concerning. When we combine the skill polarization of the Skillscape with our ability to predict workers’ transitions between jobs, we see precisely how skill matching might create a bottleneck to workers’ career mobility and create job polarization as a result. In fact, when we combine our measure for an occupation’s reliance on socio-cognitive skills, we observe three types of workers: (1) cognitive workers, (2) physical workers, and (3) workers who are stuck straddling between the two sets of skills. Socio-cognitive workers have access to many other socio-cognitive job opportunities because their skills are nearby to other socio-cognitive skills. This is because the Skillscape’s socio-cognitive skills form a densely connected network community. Likewise, workers who rely on physical skills also enjoy lateral mobility among similarly physical employment opportunities. However, this lateral mobility is not upward mobility. Occupations with higher annual wages tend to rely more strongly on socio-cognitive skills (see above). This means that physical workers who are looking to climb-up the career ladder must bridge the gap in the Skillscape between the two skill sets. However, workers who attempt this transition actually get stuck according to national employment statistics! Effectively, we find that skill polarization acts as a bottleneck to career mobility.

Examples of real worker transitions projected onto the Skillscape. Black circles represent the occupation’s skill set and dollar amounts correspond to on-average annual wage for US workers in 2015.

Implications for AI and automation

It has long been thought that off-shoring and technological change contribute to growing wealth disparity and job polarization in the US. Although occupations and employment are often the units of interest, these mechanisms actually operate on skills directly. Consider that a specific technology is often narrow in scope (e.g. robotic arm has finite degrees of motion or a particular machine learning algorithm is designed to solve a specific class of problems). This means that each piece of technology actually alters the demand for very specific skills (e.g. the robotic arm diminishes demand for workers with Manual Dexterity). These microscopic perturbations to skill demand accumulate and diffuse throughout the national labor system as macroscopic labor trends including technological unemployment, worker migration, and occupational skill redefinition. We must understand the role of individual workplace tasks and skills in the broader labor system if we are to improve our understanding of off-shoring and automation. Our study is the first step in that direction.

A worker can climb the career ladder only if enough rungs are in place. Accordingly, our research demonstrates that a worker’s set of skills, knowledge, and abilities directly influences their opportunities for career mobility. And our focus on specific workplace skills could not have come at a better time! As rungs from the ladder are systematically removed by off-shoring and automation, we must continue to improve labor models that account for these microscopic perturbations to skills. The improvements we offer in this study reveal the important role of skill polarization as an underlying mechanism for job polarization. This important fact will help policy makers as they design policy to maintain or grow current employment opportunities in an increasingly polarized economy.

Actual footage of the research team constructing the Skillscape.