How Machine Learning Can Help Ease the U.S. Labor Shortage

Experts have debated the causes of the labor shortage in the United States, but one thing is poignantly clear: the number of jobs available (more than 10 million) and the number of workers looking for work. (about 6 million people), there is a surprising disparity.

In this short article, we’ll take a step back and look at how we got here, the multiple factors that contributed to this disparity, and some of the solutions that have been implemented to combat this problem. In particular, we’ll look at machine learning (ML) and how it’s being used to mitigate both the causes and effects of the US labor shortage.

Current U.S. Labor Shortage

According to the US Chamber of Commerce, the labor force participation rate has fallen from 63.3% to 62.3% in recent years. A 1% drop in the number of qualified workers joining the labor force, he said, might not otherwise be a major issue nationwide, but that would be a significant increase for well over 30 million workers. It’s happening after a pandemic that left people out of work.

The hardest-hit industries include leisure and hospitality, food service, durable goods manufacturing, education and medical services. But there are practically no areas of activity that are not affected.

What are the causes of labor shortages?

The COVID-19 pandemic has certainly rocked the labor market. About 250,000 people of working age died from the disease, 500,000 left the workforce due to the lingering health effects of the virus, and a similar number retired directly from the disease, according to the study. is shown.

This labor force decline should have been compensated for by job seekers trying to enter the market, but it has not. Instead, monthly quit rates are rising across all sectors in the United States. Some industries, such as leisure and hospitality, have monthly turnover rates above his 6%. Traditionally more stable sectors, such as business and professional services, still record alarming turnover rates of over 3%.

Many workers have expressed a desire to continue working from home. This is a difficult expectation for some industries, such as medical services and manufacturing. But this shift in employee expectations is only superficial. Childcare services at work, shorter workweeks, better work-life balance, and ongoing training are high on the list of things employees want from employers, and companies are looking to improve employee-employer dynamics. It takes time to catch up and adapt to changes in This partly explains why there are still millions of positions left in companies across all sectors, even though national hiring rates are much higher than usual.

What is machine learning

Although often used interchangeably with AI (artificial intelligence), ML is more accurately a subset or application of AI. Simply put, ML is an application of big data in which machines (computers) use mathematical models to develop new understandings without explicit instructions.

For example, image recognition is a widely used application of ML. Image recognition allows computers to recognize and match faces (“tagging” posts on social media platforms), and to identify cancerous growths on X-rays.

ML is also widely used in the financial sector, known as statistical arbitrage. In other words, algorithms are used to analyze securities in relation to set economic variables.

ML also enables computers to examine large datasets, identify causality and correlation, and extrapolate from predictions and probabilities. Predictive insights help you get the most out of your data. Applications of this predictive capability can be found in real estate pricing, product development, and other areas. Predictive analytics also help job seekers and recruiters find better matches than they’ve ever found.

How is Machine Learning Helping the US Labor Shortage?

The current US labor shortage, combined with alarmingly high turnover, presents a problem. Workers struggle to find suitable jobs.

Recruiters and job seekers alike are increasingly turning to advanced algorithms and big data statistical analysis to mitigate this problem.

ML has the ability to analyze large data sets. In this case, we analyze workers who have retired or been released from work and those who are in endurance or have been promoted to identify common attributes, traits, and skills. This understanding allows recruiters to more quickly and accurately filter out candidates who are unlikely to succeed in the position they are applying for. As a result, job searches are faster, smoother, and far more likely to lead to positive results.

ML not only improves the matching process, but also positively impacts the speed and duration of the hiring process. Having job seekers spend so long applying and then interviewing for jobs they are unlikely to get or are not satisfied with will only make job seekers worse. In times of crisis, you need job seekers who are enthusiastic about the hiring process and not frustrated.

The evolution of online job portals

Online job portals have traditionally been places where job seekers can peruse the jobs available in their location and field of activity, read various descriptions and requirements, and then follow the steps to apply for a job. This is a staple of today’s online job portals, but the more successful portals go one step further.

By uploading their resumes to an online job portal that uses ML, job seekers are directed to jobs that best suit their skills and experience.

But ML can do more than that. Just because you have the necessary skills and experience doesn’t mean you’re the right fit for the open position. Consideration should be given to the personality and priorities of the job seeker. ML can do just that too. Online job portals that use ML can ask job seekers to fill out surveys, take personality tests, or complete problem-solving tests that incorporate gamification to find out what job seekers think, Gain valuable insight into what kind of company and position you hold. are more likely to succeed in

in a nutshell

There are millions more job openings in the US than there are people looking for work. And high employment rates can barely keep up with staggering rates of workers leaving their jobs. It helps recruiters and job seekers find matches that are likely to be successful in the short and long term.

Gergo Vari is the founder and CEO of Lensa, Inc.