In the United States, the number of unemployed and number of open jobs has remained above 6 million since June 2017. Most recently in September 2017, the United States had 6.8 million unemployed people while there were 6.2 million open jobs. Thus, it is evident that the current problem is not unemployment but a “broken employment infrastructure” which fails to match workers with jobs. Given all the other reports and complaints about a skilled labor shortage in our modern workforce, all of these evidence seems to point toward the U.S. skills gap. However, the existence of a skills gap is yet to be agreed on (please refer to “Is Skills Gap Real?”). The debate on this topic is somewhat unclear as different reports use different methods to prove existence or non-existence of it and there is lack of studies that directly measure skills.
Our first step would be to settle the dispute if we are to come up with a solution to fix it, if there is one, and ultimately address the “broken employment infrastructure.” We need some sort of consistent and quantifiable measurement of the skills gap to this otherwise confusing discussion. There have been several attempts to do so.
- Skills Gap Misery Index (SGMI)
The SGMI, developed by CIO Magazine Publisher Gary Beach, employs a method similar to the “Misery Index”, a measure of the effects of the economy on average citizens since the 1960s. The SGMI takes the Seasonally Adjusted Nonfarm Employment Rate (an indicator of current economic trends in the U.S. each month) multiplied by the number of Americans included in the monthly U-6 Total Unemployment Rate (millions of Americans who are presently unemployed and underemployed, including those who have stopped looking for work). This number is added to the job openings number from the monthly Job Opening Labor Turnover (JOLT). Then this number is benchmarked to the original date when JOLT reporting began, December 2000.
((Seasonally Adjusted Nonfarm Employment * U-6 Total Unemployed) + JOLT)/(JOLT Baseline from December 2000)*100
This number reveals the nation’s ability (or inability) to place unemployed workers into unfilled positions over time. It has been calculated monthly since December 2000 and it can be used to plot the effects and relative change of the skills gap since January 2001. Rising SGMI means that the skills gap is widening while decreasing numbers indicate that it is improving (details). Some criticize that the metric is too simple and too broad; job vacancies don’t serve as a good baseline as they vary depending on economic conditions; U6 rate is affected by many other factors other than skills gap such as fiscal policy or politics.
- Beveridge Curve
Beveridge curve represents a relationship between unemployment (horizontal) and the job vacancy rate (vertical axis). The number of unfilled jobs is expressed as a proportion of the labor force. It is hyperbolic shaped and slopes downwards as a higher rate of unemployment normally occurs with a lower rate of vacancies. The position of the curve indicates the state of the economy in the business cycle. High unemployment and low vacancies indicate recessions, and low unemployment and high vacancies indicate expansions. The curve shifted upward in mid-2009: given an unemployment rate, the number of job openings increased (purple line). From the supply-side, this indicates that currently unemployed are unable or unwilling to fill the newly created positions.
A number of factors move the Beveridge curve. Greater skill mismatches would shift the Beveridge Curve outward) and improvements in the efficiency of the matching system would shift the curve towards the origin. But other effects also come into play. Increase in labor force participation, long-term unemployment (causing deterioration of human capital), increase in frictional unemployment and more economic uncertainty would shift the curve outward (details). Therefore, we cannot perfectly equate job vacancies with the skills gap.
- Education Attainment & Work Experience
Level of education may serve as a proxy for skill level and skill gap can be defined as the gap between the education levels (i.e., high school, bachelor’s, associates, masters) needed for a job versus education level attained by workers. Almost all high-skill jobs require bachelor’s degree as a minimum. Middle-skill jobs that historically required more than a high school diploma but less than a four-year college degree, prefer bachelor’s credentials as a rough, rule-of-thumb screening system to recruit better workers. According to research conducted by Peters, the skill supply was determined by using education data from the U.S. Census. Demand was defined as the average proportion of high, semi, and low-skilled workers within an industry (using breakouts of occupations within the industry from the U.S. BLS Occupational Employment Survey) with the level of educational attainment used a proxy for skill. Another way of measuring it would be to look at employment data released by the BLS. BLS provides information on typical education needed for entry by detailed occupation. If we know education attainment level among applicants sourced from multiple job boards, the proportion of applicants who do not satisfy the required education level would represent the skills gap. Similarly, given the information on needed work experience for entry in a related occupation by detailed occupation provided by the BLS, the proportion of applicants who do not fulfill the number of relevant work experience would represent the skills gap.
- Longer time fills & wage premium
Burning Glass Technologies, an analytics software company, largely defines wage premium (higher than average wage) and longer time fills (time it takes to fill a job since it first opened) as a measure of skills gap. This metric is particularly useful to identify high skills gap in STEM fields (science, technology, engineering, and math). For example, demand for Data Science and Analytics (DSA) jobs is growing and is projected to grow but the supply of these jobs do not meet the growing demand. DSA jobs remain open for 45 days, 5 days longer than the market average, and the difficulty employers have filling these jobs drives up salaries. Their advertised annual salary is at $80,265, a premium of $8,736 relative to all bachelor’s and graduate-level jobs. Some DSA jobs, such as Data Scientists and Data Engineers pay over $100,000.
They can also be useful to identify skills gap among middle-skill jobs that require digital skill. Digital middle-skill jobs include healthcare technology, health informatics, and manufacturing positions. Middle-skill jobs, defined as those that typically require less than a bachelor’s degree while paying a living wage, comprise about 48% of overall labor demand. More than 8 in 10 middle-skill jobs (82%) these days require digital skills. These jobs pay 17% premium over non-digital roles.
- Foregone Productivity
It is a gap between a firm’s current productivity and the skills it needs to achieve its desired productivity. It is the point at which a firm can no longer grow or remain competitive because it cannot fill critical jobs with employees who have the right knowledge, skills, and abilities. One way of measuring foregone productivity due to skills gap would be to calculate the amount of labor input lost due to delayed time fill. Let’s say wage of employees who have the desired skills is a rough measure of the hourly value of productivity a firm wishes to achieve. Multiplying wage by the number of extra days to hire those right workers and number of working hours would represent the amount of labor lost due to the delayed duration of time fills.
hourly wage*extra days to fill the worker*8 (working hour per day)
- Hybrid Approaches
Hybrid approaches use a mix of workforce survey research and aggregate labor market supply/demand indicators. One example of this approach is to determine skill supply from responses to telephone-based labor shed surveys and skill demand on employer job vacancy data. Another example is to compare aggregate labor market supply indicator from the BLS occupational projections data with employer job vacancy survey data to represent skill demand. The hybrid approach provides a balanced approach that incorporates both aggregate and customized data to determine gaps in skills.
In conclusion, each method has its own advantage and disadvantage. The SGMI and the Beveridge curve provides a snapshot of the aggregate condition of the labor market structure but they are also too broad to isolate the effect of the skills gap. Education attainment level would be the most common and standard proxy for skill level but critics say that path to skills acquisition, especially among the middle skills, does not require a college degree. Also, it does not capture a holistic view of an individual’s knowledge, skills, attitudes, and personality characteristics when determining occupational fit and identifying skills gaps. Hybrid approaches enable a more accurate capture of what types of skills employers and employees demand. However, acquiring customized survey data of a reasonably representable sample size would be costly. Longer time fills and wage premium, and the derived forgone productivity avoids these drawbacks. They are more specific than the SGMI and the Beveridge curve to capture the skills gap effect, and less costly than the hybrid approach to acquire and extract a reliable skill demand and supply information.