Search results for: Job openings to unemployment ratio

Measuring employer and employee confidence in the economy: the quits-to-layoffs-and-discharges ratio

In 2016, there were, on average, 5.2 million hires, and 5.0 million separations, per month. The Bureau of Labor Statistics (BLS) Job Openings and Labor Turnover Survey (JOLTS) data show the number of workers hired into, and the number of workers separated from, jobs every month. JOLTS classifies separations into three categories. The first category, “quits,”1 consists of employees who voluntarily quit their job.The second category, “layoffs and discharges,2comprises employees who are involuntarily laid off by the organization they work for or who are discharged with cause. The third category, “other separations,” which consists of transfers, separations due to retirement, and other rare types of separations, is relatively small and remains consistent over time. The “other separations” data play a minor role in the analysis of separations.

What do these statistics tell us about employer and employee confidence in the U.S. economy? Quits tend to rise during an economic expansion and fall during an economic contraction. Therefore, quits can serve as a measure of workers’ willingness or ability to leave their jobs. Conversely, layoffs and discharges tend to fall during an economic expansion and rise during an economic contraction.

This Beyond the Numbers article highlights the ratio of the number of quits to the number of layoffs and discharges, or the Q/LD ratio. The ratio, which contrasts voluntary separations (Q) with involuntary separations (LD), provides a measure to gauge employers’ and employees’ confidence in the economy. The measure also can be used to analyze trends in employment levels in different industries. The Q/LD ratio is greater than 1.0 when the number of quits exceeds the number of layoffs and discharges, and is less than 1.0 when layoffs and discharges exceed quits. A value greater than 1.0 indicates that employee confidence is strong, while a value less than 1.0 indicates that employees are not so confident.

At the national level, the Q/LD ratio has been greater than 1.0 most of the time since the JOLTS survey began in December 2000. (See table 1.) The ratio reached its lowest point, 0.7, in March and April 2009, during the most recent recession, and its highest point, 2.0, in September 2016.3 From 2016 through June 2017, the ratio was frequently 1.9.

Observations at the national level

After the 2001 recession, both the Q/LD ratio and total nonfarm employment continued to decline until 2003, when they reversed and began to increase.4 As the 2007–09 recession approached, the ratio leveled off and then began to decline because of a decrease in quits. Employment did not decline until February 2008. (See chart 1.)

During the 2007–09 recession, the Q/LD ratio decreased rapidly as a result of declining quits and increasing layoffs and discharges.5 (See chart 1.) The series fell to a low of 0.7 in March 2009. Close to 1 year later, employment reached its most recent trough, 129,733,000. Following these series lows, the Q/LD ratio and employment trended upward. Still, the ratio remained below its prerecession level until December 2013, and employment remained below its prerecession level until April 2014. The ratio reached a high of 1.9 once before the last recession and has duplicated that figure fairly regularly since 2016, peaking at 2.0 in September of that year. Employment has remained well above its prerecession level since May 2014.

Ratio by selected industries

All industries experience a change in the ratio of quits to layoffs and discharges over the business cycle. However, regardless of the economic climate, there are also ongoing differences among the various industries—differences that are due to individual industry characteristics.6 Health care and social assistance is a high-turnover industry with very high quits and very low layoffs and discharges. In fact, quits have outnumbered layoffs and discharges by enough that the Q/LD ratio was greater than 1.0 even during the depths of the most recent recession. (See chart 2.) The only other industry for which the ratio is greater than 1.0 for the entire series is accommodation and food services. The ratio for health care and social assistance has fluctuated between 1.2 and 3.9 since the JOLTS series began in December 2000. Despite the fluctuation in the ratio, employment continued to trend upward, even during the 2001 and 2007–09 recessions, because of the high demand for health care workers.

Since December 2000, the Q/LD ratio in the manufacturing industry has moved above and below 1.0 with the business cycle, with trends matching employment. (See chart 3.) The ratio fell below 1.0 during the 2001 and 2007–09 recessions as quits declined and layoffs and discharges rose. The ratio remained below 1.0 during the early recovery periods following the two recessions, but as quits grew and layoffs and discharges remained steady, the ratio climbed above 1.0. After the most recent recession, the ratio climbed steeply as quits increased, although employment grew much more slowly.

In the construction industry, layoffs and discharges are much more prevalent than in other industries because, typically, projects are completed and employees move to other jobs. By contrast, quits are much lower in construction than in other industries. The Q/LD ratio for construction has been below 1.0 most of the time since 2000. (See chart 4.) The ratio has moved with the business cycle, increasing during expansions and decreasing during contractions. The movement of the ratio has been similar to that of employment across the entire period.
As with construction, the Q/LD ratio for professional and business services has moved with the business cycle and trended in synchrony with employment since 2000. However, in professional and business services the ratio fluctuated not far above and below the 1.0 mark for most of the time until about 2015, after which quits accelerated, moving the ratio to a high of 2.0. (See chart 5.)

Ratios by industry

Chart 6 shows the minimum and maximum values of the Q/LD ratio by industry since the start of the JOLTS series in December 2000. As mentioned earlier, the accommodation and food services industry and the health care and social assistance industry have had more people quitting than being laid off, creating a ratio that is always greater than 1.0. The industry with the lowest minimum ratio, 0.1, is the federal government, and the industry with the highest maximum ratio, 13.0, is real estate and rental and leasing. With the exception of three industries—construction; durable goods manufacturing; and arts, entertainment, and recreation—the maximum ratio exceeds 1.0 by much more than the minimum ratio falls below 1.0. These ranges shown indicate that quits increase during strong economic times by more than layoffs and discharges increase during weak economic times.

Summary: What the Q/LD ratio tells us

The ratio of quits to layoffs and discharges, a measure that can be derived from the JOLTS data, is an important tool for analyzing the business cycle and the differences among industries.

Quits move in a direction opposite that of layoffs and discharges, and the Q/LD ratio clearly reflects business cycle trends and turning points. The ratio rose during expansions and fell during contractions. The ratio stayed above 1.0 for most industries captured by the JOLTS series, illustrating that, during all but the most severe economic times, American workers are more likely to leave their job than lose their job.

The Q/LD ratio also shows that industries have different patterns of quits and layoffs and discharges. The health care and social assistance industry and the accommodation and food services industry are the only two industries for which quits always outnumber layoffs and discharges, producing a Q/LD ratio greater than 1.0, even during the most severe economic times. In other industries, such as construction, layoffs and discharges are more prevalent than quits, producing a ratio less than 1.0 in most months over the entire JOLTS series (2000–17). In still other industries, quits and layoffs and discharges rise and fall, creating a ratio that moves above and below 1.0 with the business cycle.

This Beyond the Numbers article was prepared by Kimberly Riley, economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. Email: riley.kimberly@bls.gov. Telephone: (202) 691-6497. BLS economists Robert Lazaneo and Jonathan Krause also contributed to the article.

Source: Bureau of Labor Statistics

Why Is the Job-Finding Rate Still Low?

Why Is the Job-Finding Rate Still Low?
Liberty Street Economics
Victoria Gregory, Christina Patterson, Ayşegül Şahin, and Giorgio Topa

 

 

Fluctuations in unemployment are mostly driven by fluctuations in the job-finding prospects of unemployed workers—except at the onset of recessions, according to various research papers (see, for example, Shimer [2005, 2012] and Elsby, Hobijn, and Sahin [2010]). With job losses back to their pre-recession levels, the job-finding rate is arguably one of the most important indicators to watch. This rate—defined as the fraction of unemployed workers in a given month who find jobs in the consecutive month—provides a good measure of how easy it is to find jobs in the economy. The chart below presents the job-finding rate starting from 1990. Clearly, the job-finding rate is still substantially below its pre-recession levels, suggesting that it is still difficult for the unemployed to find work. In this post, we explore the underlying reasons behind the low job-finding rate.

Ch1_job-finding-rate

According to the search and matching theory developed by Diamond, Mortensen, and Pissarides (see, for example, Pissarides [2000], Mortensen and Pissarides [1994], and Diamond [1982]), it is costly for workers and firms to form suitable matches because of the uncoordinated nature of the labor market. Workers must devote considerable time to sending out resumes, contacting job agencies, and interviewing for jobs, and firms must consume resources posting vacancies and recruiting candidates with suitable skills and talents. The process that matches workers to firms is typically summarized by a matching function, which determines the number of jobs formed given the number of vacancies and unemployed workers. According to the matching function, the main determinants of the job-finding rate are the ratio of job openings to the number of unemployed, (v/u), elasticity, α, and the matching efficiency, x:

Big_equation

Ch2_vacancy-ratio

Ch3_matching-efficiency

The vacancy-unemployment ratio summarizes demand and supply conditions in the labor market. When there are many job openings per unemployed individual, it is naturally easier to find jobs; conversely, it becomes harder to find jobs when there are many unemployed individuals competing for a small number of job openings. The parameter matching efficiency captures various factors that affect the efficiency of the matching process. An increase in skill or geographic mismatch, a decline in search effort of workers, or a decline in recruiting effort of employers would all lower the matching efficiency in the labor market. The elasticity, α, captures the responsiveness of the job-finding rate to the availability of jobs.

As seen in the charts above, both the vacancy-to-unemployment ratio and matching efficiency declined during the Great Recession and have not recovered since. The matching efficiency is constructed under the assumption that the elasticity, α, was unchanged over this period. It is important to understand the contribution of each factor to the recent behavior of the job-finding rate, since the vacancy-to-unemployment ratio reflects labor market conditions, while matching efficiency is a measure of how well the labor market forms new matches.

To isolate the contribution of these two factors, we regress the job-finding rate (unemployment-to-employment transition rate) on the vacancy-unemployment ratio using data until November 2007. The chart below shows that this regression captures the pre-recession behavior of the job-finding rate very well. We then use the relationship estimated using pre-recession data to generate predicted values for the job-finding rate starting in December 2007, as indicated by the bar. The predicted job-finding rate is an estimate of what the job-finding rate would be if matching efficiency had remained at its pre-recession level, but vacancies and unemployment had evolved as they did through the recession. As seen below, the actual job-finding rate currently lies below the predicted job-finding rate.

Ch4_Unemployment-rate

However, one can also see that even the predicted job-finding rate still sits at 23.4 percent, significantly below its 2007 average of 27.8 percent. This implies that even if matching efficiency had returned to its pre-recession level and the economy had moved to the predicted line, the job-finding rate would still be significantly below its pre-recession levels. Our calculations suggest that while the efficiency of the U.S. labor market has not yet recovered, the most important factor is still the low vacancy-to-unemployment ratio.

Finally, one can ask whether the observed decline in matching efficiency has been more or less pronounced in different sectors of the economy. The charts below examine this question for several representative industries, both in levels and using December 2007 normalized levels as the starting point. The plots show that matching efficiency has experienced declines across the board, with the possible exception of construction, where matching efficiency has returned to something close to December 2007 levels.

Ch5_matching-efficiency

We conclude that while matching efficiency has declined and remained low in virtually all industries, the most important factor in the low job-finding rate is the persistently low level of vacancies per unemployed.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.


Gregory_victoria
Victoria Gregory is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Patterson_christina
Christina Patterson is a former senior research analyst in the Research and Statistics Group.

Sahin_aysegul
Ayşegül Şahin is an assistant vice president in the Research and Statistics Group.

Topa_giorgio
Giorgio Topa is a vice president in the Research and Statistics Group.

Why Is the Job-Finding Rate Still Low?

Why Is the Job-Finding Rate Still Low?
Victoria Gregory, Christina Patterson, Ayşegül Şahin, and Giorgio Topa
Liberty Street Economics Feburary 19, 2014

 

 

 

Fluctuations in unemployment are mostly driven by fluctuations in the job-finding prospects of unemployed workers—except at the onset of recessions, according to various research papers (see, for example, Shimer [2005, 2012] and Elsby, Hobijn, and Sahin [2010]). With job losses back to their pre-recession levels, the job-finding rate is arguably one of the most important indicators to watch. This rate—defined as the fraction of unemployed workers in a given month who find jobs in the consecutive month—provides a good measure of how easy it is to find jobs in the economy. The chart below presents the job-finding rate starting from 1990. Clearly, the job-finding rate is still substantially below its pre-recession levels, suggesting that it is still difficult for the unemployed to find work. In this post, we explore the underlying reasons behind the low job-finding rate.

Ch1_job-finding-rate

According to the search and matching theory developed by Diamond, Mortensen, and Pissarides (see, for example, Pissarides [2000], Mortensen and Pissarides [1994], and Diamond [1982]), it is costly for workers and firms to form suitable matches because of the uncoordinated nature of the labor market. Workers must devote considerable time to sending out resumes, contacting job agencies, and interviewing for jobs, and firms must consume resources posting vacancies and recruiting candidates with suitable skills and talents. The process that matches workers to firms is typically summarized by a matching function, which determines the number of jobs formed given the number of vacancies and unemployed workers. According to the matching function, the main determinants of the job-finding rate are the ratio of job openings to the number of unemployed, (v/u), elasticity, α, and the matching efficiency, x:

Big_equation

Ch2_vacancy-ratio

Ch3_matching-efficiency

The vacancy-unemployment ratio summarizes demand and supply conditions in the labor market. When there are many job openings per unemployed individual, it is naturally easier to find jobs; conversely, it becomes harder to find jobs when there are many unemployed individuals competing for a small number of job openings. The parameter matching efficiency captures various factors that affect the efficiency of the matching process. An increase in skill or geographic mismatch, a decline in search effort of workers, or a decline in recruiting effort of employers would all lower the matching efficiency in the labor market. The elasticity, α, captures the responsiveness of the job-finding rate to the availability of jobs.

As seen in the charts above, both the vacancy-to-unemployment ratio and matching efficiency declined during the Great Recession and have not recovered since. The matching efficiency is constructed under the assumption that the elasticity, α, was unchanged over this period. It is important to understand the contribution of each factor to the recent behavior of the job-finding rate, since the vacancy-to-unemployment ratio reflects labor market conditions, while matching efficiency is a measure of how well the labor market forms new matches.

To isolate the contribution of these two factors, we regress the job-finding rate (unemployment-to-employment transition rate) on the vacancy-unemployment ratio using data until November 2007. The chart below shows that this regression captures the pre-recession behavior of the job-finding rate very well. We then use the relationship estimated using pre-recession data to generate predicted values for the job-finding rate starting in December 2007, as indicated by the bar. The predicted job-finding rate is an estimate of what the job-finding rate would be if matching efficiency had remained at its pre-recession level, but vacancies and unemployment had evolved as they did through the recession. As seen below, the actual job-finding rate currently lies below the predicted job-finding rate.

Ch4_Unemployment-rate

However, one can also see that even the predicted job-finding rate still sits at 23.4 percent, significantly below its 2007 average of 27.8 percent. This implies that even if matching efficiency had returned to its pre-recession level and the economy had moved to the predicted line, the job-finding rate would still be significantly below its pre-recession levels. Our calculations suggest that while the efficiency of the U.S. labor market has not yet recovered, the most important factor is still the low vacancy-to-unemployment ratio.

Finally, one can ask whether the observed decline in matching efficiency has been more or less pronounced in different sectors of the economy. The charts below examine this question for several representative industries, both in levels and using December 2007 normalized levels as the starting point. The plots show that matching efficiency has experienced declines across the board, with the possible exception of construction, where matching efficiency has returned to something close to December 2007 levels.

Ch5_matching-efficiency

We conclude that while matching efficiency has declined and remained low in virtually all industries, the most important factor in the low job-finding rate is the persistently low level of vacancies per unemployed.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.


Gregory_victoria
Victoria Gregory is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Patterson_christina
Christina Patterson is a former senior research analyst in the Research and Statistics Group.

Sahin_aysegul
Ayşegül Şahin is an assistant vice president in the Research and Statistics Group.

Topa_giorgio
Giorgio Topa is a vice president in the Research and Statistics Group.

Where Will the Jobs Come From?

Where Will the Jobs Come From?
By John Mauldin
Nov 19, 2012

 

 

The Next Bubble
A Hollow Powerhouse?
We’ve Seen This (Manufacturing) Movie Before
A Manufacturing Renaissance
Bismarck, Scandinavia, Greece, Geneva, and Writing Schedule

 

For the last year, as I travel around, it seems a main topic of conversation is “Where will my kids find jobs?” It is a topic I am all too familiar with. Where indeed? Youth unemployment in the US is 17.1%. If you are in Europe the problem is even more pronounced. The basket case that is Greece has youth unemployment of 58%, and Spain is close at 55%. Portugal is at 36% and in Italy it’s 35%. France is over 25%. Is this just a cyclical symptom of the credit crisis? Much of it clearly is, but I think there is something deeper at work here, an underlying tectonic shift in the foundation of employment. And that means that before we see a true recovery in the unemployment rate, there must be a shift in how we think about work and training for the future of employment. This week is the first of what will be occasional letters over the coming months with an emphasis on employment. (This letter will print a little longer, as there are a lot of charts.)

But first, the staff at Mauldin Economics is furiously putting the finishing touches on your free Post-Election Economic Summit webinar, which will air tomorrow at 2 pm Eastern. They are distilling multiple hours of discussion into a fast-paced, thoughtful (and often lively) conversation about what is in store in our economic future. Panelists and guests include Mohamed El-Erian, James Bianco, Barry Ritholtz, Gary Shilling, Barry Habib, and Rich Yamarone. We also have a truly unique interview with the chiefs of staff of Majority Leader Harry Reid and Senator Rob Portman. While we excerpted part of that interview for the webinar, the entire interview will be made available. If you want to get a true feel for what is going on in Washington, I suggest you listen in. You can sign up to listen here. Now, let’s think about employment.

 

The Next Bubble

Let’s look at a few facts put forth by the Young Entrepreneur Council from their list of 43 (available here):

·  1 out of 2 college grads  – about 1.5 million, or about 53.6 percent, of bachelor’s degree holders age 25 or younger  – were unemployed or underemployed in 2011.
·  For high school grads (age 17-20), the unemployment rate was 31.1 percent from April 2011-March 2012; underemployment was 54 percent.
·  For young college grads (age 21-24), unemployment was 9.4 percent last year, while underemployment was 19.1 percent.
·  According to some researchers, up to 95 percent of job positions lost occurred in low-tech, middle-income jobs like bank tellers. Gains in jobs are going to workers at the top or the bottom, not in the middle.
·  More college graduates are getting low-level jobs, period. U.S. bachelor’s degree holders are more likely to wait tables, tend bar or become food-service helpers than to be employed as engineers, physicists, chemists or mathematicians combined  – 100,000 versus 90,000.
·  According to new U.S. government projections, only three of the 30 occupations with the largest projected number of job openings in the next eight years will require a bachelor’s degree or higher. Most job openings by 2020 will be in low-wage professions like retail sales, fast food and truck driving.

While there may not be a bubble in education, there is definitely a growing debt bubble in student loans. More than 1/3 of young Americans of college age went back to school because of the economy, and in doing so have contributed to the $1 trillion in student loans. People are clearly going back to school and taking out loans as a way to make ends meet. The average college graduate has $25,000 in debt. Default rates are up 31% in the last two years. Student loans are relatively easy to get. They are like the old NINJA subprime mortgage loans available toward the end of the housing bubble: “No income, no job, no assets.” And they are just as likely to end up in default. But Congress recently passed new bankruptcy laws, and unlike housing loans, student loans cannot be discharged in a bankruptcy. The law of compound interest means that borrowers, mostly young, will be paying back this debt for many, many years.

(more…)

Worker Skills and Job Quality

Worker Skills and Job Quality
By David Neumark and Rob Valletta
FRBSF Economic Letter 2012-13
April 30, 2012

 

Some observers have argued that the nation’s high unemployment rate during the current recovery stems partly from widespread mismatches between the skills of jobseekers and the needs of employers. A recent San Francisco Federal Reserve Bank conference on workforce skills considered evidence that employers have had difficulties finding workers with appropriate skills in recent years. However, these mismatches do not appear to be much more severe than in the past. Overall, the conference proceedings suggested the U.S. economy can still produce good jobs for workers at a variety of skill levels.

~~~

This Economic Letter summarizes papers presented at the conference “Creating and Employing a Skilled Work Force: Challenges and Opportunities,” November 7, 2011, at the Federal Reserve Bank of San Francisco. Conference papers are available on our website.

The need for a skilled workforce is a perennial issue in any economy. Employers want workers with appropriate skills. Jobseekers want rewarding work that makes good use of their skills. Skill sets are dynamic, adapting to changes in technology and the structure of the economy. The need for such adaptations may have accelerated in the aftermath of the recent recession. During the slow labor market recovery that began early in 2010, job openings have risen more rapidly than unemployment has fallen, raising concerns that mismatches may be growing between the skills of jobseekers and the needs of employers.

This question has important implications for economic policy. Skill mismatches can affect how fast employment can grow and the rate of unemployment that the economy can sustain without igniting inflation. That makes it vital for Federal Reserve policymakers to understand this phenomenon. The San Francisco Federal Reserve Bank’s conference on worker skills brought together academics and workforce development professionals to explore the issue and bridge the gap between research and practice. This Economic Letter summarizes the presentations and roundtable discussion.

The conference proceedings suggested that current skill mismatches are limited and probably transitory. Existing mismatches appear to reflect the normal movement of workers between shrinking and growing sectors of the economy. Such shifts tend to accelerate during recessions but dissipate as recovery takes hold. Moreover, the likelihood that growing sectors will produce good jobs for skilled workers, and the ability of our educational system to provide skills that are in demand, appear to be stronger than is commonly believed.

Four papers on skills and job quality

Estevão and Tsounta (2011) seek to determine the magnitude of skill mismatches in the United States after the Great Recession. Most analysts agree that the high post-recession unemployment rate has a large cyclical component that reflects low demand for labor. The objective of the authors is to estimate the extent of structural unemployment, that is, the component of unemployment that will not dissipate when normal economic growth returns.

Among the leading potential sources of structural unemployment are, one, skill mismatches and, two, geographical mismatches between where jobs are available and where jobseekers are concentrated. To estimate how much each of these factors contributes to the unemployment rate, Estevão and Tsounta develop a statistical model to explain unemployment variation among states over time. They consider business cycle and housing market conditions in each state. The authors also measure skill mismatch by looking at worker educational attainment in each state, and the industrial structure and educational content of jobs.

Estevão and Tsounta’s results imply that structural unemployment accounts for about 1 to 1.75 percentage points of the roughly 5.5 percentage-point increase in the unemployment rate between 2006–07 and the recent peak in late 2009. They attribute about two-thirds of the structural unemployment increase to the effects of a depressed housing market, which makes it hard for many homeowners who are under water on their mortgages to relocate. Skill mismatch accounts for only about 0.5 percentage point of the structural unemployment increase and is likely to dissipate over time, as it typically does after a recession. Although the effects of skill mismatch in the current labor market may be limited, a 0.5 percentage point increase represents nearly one million workers. Thus, even at this relatively low level, reducing skill mismatch may be a valid public policy concern.

Holzer et al. (2011) highlights a long-run increase in the importance of selected skills in giving U.S. workers access to what are identified as good jobs. The authors use a massive data set that covers most workers and businesses in 12 states from 1992 to 2003. They identify employers that pay unusually high wages, which represents good jobs. Workers who earn unusually high wages relative to the wages paid for similar workers in their current firms are identified as good workers. This method of classification allows analysis of changes over time in the sorting of workers among employers.

Holzer et al. shows that, for less-skilled workers, sources of good jobs shifted away from the manufacturing sector toward the retail, administrative, construction, and health-care sectors from 1992 to 2003. Nonmanufacturing sectors account for a rising share of low-skill workers who earn above-average wages. Most importantly, this work suggests that it has become increasingly difficult for low-skill workers to land high-paying jobs. In other words, high pay is increasingly a function of one’s skills rather than one’s job, implying that high-paying jobs that do not require advanced skills have grown increasingly scarce. Moreover, other evidence suggests that soft job skills, such as the ability to communicate effectively, have become increasingly important, contrary to the common perception that technical skills are paramount. The study indicates that encouraging workers to acquire skills remains important to help them find better jobs, even if skill mismatch is limited in the current labor market.

Cardiff, LaFontaine, and Shaw (2011) also ask where the good jobs are, but focus more narrowly on the retail sector. The authors examine the well-known decline in manufacturing employment and rise in retail employment. Based on popular perception, this trend epitomizes the replacement of good jobs with bad jobs. However, by analyzing a detailed data set covering every Texas retail business from 1990 through 2006, the authors question this perception. Specifically, they identify a subset of the retail industry they characterize as “modern retail,” which consists of businesses that deliver products in innovative or cost-effective ways. For example, modern retailers may use effective supply-chain management or be unusually successful in marketing. Modern retailers represent a relatively limited portion of the sector, but the authors show that their share is growing rapidly.

What does this imply for good versus bad jobs? Despite the typically low wages paid in retail, Cardiff, LaFontaine, and Shaw find that high-wage jobs exist in this sector, particularly for first-line supervisors and managers. Moreover, relative educational levels of retail workers have risen over the past two decades. In the early 1990s, retail workers were much less educated than manufacturing workers. By 2010, educational attainment in the two sectors had largely converged. At all educational levels, retail pay is still substantially lower than in manufacturing. But, in the retail sector, education has an economic return that more or less corresponds to its return in manufacturing. Overall, the authors present evidence that the growth of the retail sector and the decline of manufacturing do not necessarily imply the disappearance of good jobs or elimination of payoffs to workers for acquiring traditional skills.

Furchtgott-Roth, Jacobson, and Mokher (2009) stress the ways community colleges can enhance skills and highlight how these institutions can do better. The authors use a large data set tracking 135,000 Florida students from the high school class of 2000. They match high school and post-secondary education with detailed earnings data from the unemployment insurance system. These data illuminate the associations between grades in school, degrees, courses of study, and labor market experiences. Community colleges are particularly interesting because they account for nearly half of U.S. undergraduate enrollments and provide disadvantaged groups a key route for upward mobility.

The study underscores the well-known relationship between educational attainment and earnings, including at the community college level. The authors add a new wrinkle by examining which community college subject areas have the largest earnings payoffs. They find that health care is a high-return field of study. Business, protective services, and trade and industry specializations offer moderate returns. Several other fields, including arts and sciences, are low return. The authors argue that, based on existing curriculum requirements, some students could switch from low-return to higher-return fields without substantially increasing their course loads or difficulty. Thus, there may be viable opportunities for community college students to acquire skills that provide better earning potential.

These opportunities appear to be untapped in part because students may not have information about earnings prospects in different fields. Thus, community colleges could place greater emphasis on career counseling that provides information about the relative returns in those fields. In addition, community colleges face financial obstacles to expanding course offerings in high-earnings fields. In some of these fields, instruction is more expensive. But community college funding is based on enrollment rather than the relative costs of different fields of study. That points to a need to create more rational incentives for community colleges to expand programs in high-return fields.

Discussion and common themes

The conference featured a roundtable discussion of the event’s themes by four individuals with extensive professional backgrounds in workforce development: Rob Black, chair of Workforce Investment San Francisco and director of the Golden Gate Restaurant Association; Mike Hannigan, chair of the Oakland Workforce Investment Board and president of an office supply company; Phyllis McGuire, a vice chancellor at City College of San Francisco, who oversees the school’s Office of Workforce and Economic Development; and Jack Mills of the Insight Center for Community Economic Development, a national research, consulting, and legal organization. These experts said they didn’t see new sources of skill mismatch. Instead, they emphasized the long-standing importance of providing low-skill workers in urban areas access to well-designed training programs and good jobs.

They also said that it is important to establish close working relationships between community colleges and local business communities. This point reinforced the finding of Furchtgott-Roth, Jacobson, and Mokher (2009) about how expansion of community college guidance programs can help students acquire skills that qualify them for existing job opportunities. The discussants also noted the benefits of targeted training programs that focus on putting low-skill workers in stable jobs in specific industries rather than seeking placements more generally throughout the local labor market.

Conclusions

The recent San Francisco Federal Reserve Bank conference on workforce skills examined labor market changes that may have accelerated during the Great Recession. These changes may have increased mismatches between employer needs and worker skills. In general, we find that this doesn’t appear to be the case. Estimates of the extent of skill mismatches in recent years indicate that it has been limited and is likely to dissipate. Moreover, the conference’s research presentations and a panel of workforce development specialists did not identify a noticeable increase in mismatches in recent years. Thus, concerns about growing skill mismatches may be overblown. On the other hand, successful integration of low-skilled workers into the workforce represents a continuing problem. Conference participants offered useful ideas on how to meet this challenge, stressing the roles of community colleges and well-designed training programs.

~~~

David Neumark is Chancellor’s Professor of Economics and Director of the Center for Economics & Public Policy at UC Irvine and a visiting scholar at the Federal Reserve Bank of San Francisco.

Rob Valletta is a research advisor in the Economic Research Department of the Federal Reserve Bank of San Francisco.

Conclusion: How Low Will the Unemployment Rate Go?

How Low Will the Unemployment Rate Go?
Jonathan McCarthy, Simon Potter, and Ayşegül Şahin
April 02, 2012

~~~

A major theme of the posts in our labor market series has been that the outflows from unemployment, either into employment or out of the labor force, have been the primary determinant of unemployment rate dynamics in long expansions. The key to the importance of outflows is that within long expansions there have not been adverse shocks that lead to a burst of job losses. To illustrate the power of this mechanism, we presented simulations in a previous post that were based on the movements in the outflow and inflow rates in the previous three expansions. These simulated paths show the unemployment rate declining to a level well below current consensus predictions over the medium term.

In this post, we run these simulations to their natural conclusion to see what happens to the unemployment rate if the current expansion lasts as long as any of the three most recent expansions. Recall that in these simulations, we assume that the inflow and outflow rates change at the same pace as they did in the expansions following the 1981-82, 1990-91, and 2001 recessions starting at thirty months into the expansion (roughly the point where we are now in the current expansion) through the start of the next recession (see the Okun’s Law post in this series for the length of each expansion). The simulated unemployment paths based on the three different scenarios are shown in the chart below.

The simulations demonstrate the importance of expansion length in reducing unemployment. Under the flow dynamics of the record-long 1990s expansion, the simulated unemployment rate falls to 4.7 percent, 5.3 percentage points below the peak unemployment rate observed in the recession period. For comparison, the actual unemployment rate fell to 3.9 percent in the 1990s expansion, 3.9 percentage points below its peak. The simulation using the flow rates in the 1980s expansion gives very similar dynamics: the simulated decline is 4.9 percentage points, whereas the unemployment rate actually fell 5.8 percentage points in the 1980s expansion to 5 percent. Consistent with the shorter duration of the 2000s expansion, the simulation based on the flow rates from that expansion has a minimum unemployment rate of 6 percent and the unemployment rate begins to rise toward the end of the simulation.

Three-Alt-Paths

Prospects for Flow Rates: What Do They Imply?
The importance of the outflow rate in unemployment rate dynamics during long expansions prior to the 2000s was virtually assured. In those periods, the rate of inflow to unemployment from nonparticipation was mainly driven by long-term trends, particularly the entrance of married women into the labor force (see the labor force participation post in this series). In most expansions, there have been temporary surges in the flow from nonparticipation to unemployment as job market prospects brighten. This was the case in February of this year, when the unemployment rate didn’t decline despite strong growth in employment because many prospective workers moved from being out of the labor force into unemployment.

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Where Will the Jobs Come From?

Where Will the Jobs Come From?
John Mauldin
March 17, 2012

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Getting Back to Full Employment
Who’s Participating in Employment?
4 Million New Jobs a Month!
Where Will the Jobs Come From?
Stockholm, Paris, San Francisco, and New York

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“Six years into our global data collection effort, we may have already found the single most searing, clarifying, helpful, world-altering fact.

“What the whole world wants is a good job.

“This is one of the most important dsicoveries Gallup has ever made. At the very least, it needs to be considered in every policy, every law, every social initiative. All leaders – policy makers and lawmakers, presidents and prime ministers, parents, judges, priests, pastors, imams, teachers, managers and CEOs – need to consider it every day in everything they do.

“That is as simple and as straightforward an explanation of the data as I can give. Whether you and I were walking down the street in Khartoum, Cairo, Berlin, Lima, Los Angeles, Baghdad, or Istanbul, we would discover that the single most dominant thought on most people’s minds is about having a job.

“Humans used to desire love, money, food, shelter, safety, peace and freedom more than anything else. The last 30 years have changed us. Now people want to have a good job. This changes everything for world leaders. Everything they do – from waging war to building societies – will need to be in the context of the need for a good job.”

– From The Coming Jobs War, by Jim Clifton, Chairman and CEO of Gallup

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Each month investors and politicians in countries all over the world obsess over the release of the monthly employment numbers. Even though these numbers are likely to be revised significantly from the original release, the markets can’t help responding to the variations from the expected number. Why the focus on numbers that are likely to be proven wrong in the coming years? Because the single most important factor in the direction of an economy is employment. Consumer spending, personal income, tax revenue, corporate profits, and a host of other variables all swing on rising and falling employment.

This week we begin a series of letters on employment. I have been researching the topic more than usual for the book I am writing with Bill Dunkelberg (the Chief Economist of the National Federation of Independent Businesses) on the entire employment issue. We will look at why employment is so critical. How are jobs created and what policies can be adopted to help foster more jobs? Should the US try and keep jobs that are going overseas, or develop whole new industries? Who exactly is the competition globally for jobs?

We will find that billions of jobs will disappear in the coming decades and even more will be created. There are today some 1.2 billion good jobs, but 1.8 billion people want them. Over the next 30 years the world economy will double and then almost double again. Where will the new jobs be and who will get them? What should you and you children be doing today to be sure that you have jobs in the future?

In order to try to answer these questions, we will start with a general view of the employment situation in the US. What has it looked like in the past and where is it going? Today, we will look at the direction of employment in the US and then focus on both what employment is likely to be in the next few years as well as the dynamics of the labor market. There is a lot to cover. (This letter might print a little longer, as there will be lots of charts.)

Getting Back to Full Employment

The headline unemployment rate is 8.3%, down from 10% only a couple years ago. But ten years ago it was less than half that, and at the beginning of the last decade it was less than 4%. 60 years ago it was less than 3%! Employment is a very volatile number, and as we have seen, it can rise substantially before and during a recession. The first graph we will look at is the unemployment rate, from the FRED database created by the St. Louis Federal Reserve.

Notice how much unemployment fell after the recession that followed World War II, during the ’60s, and then in the Reagan and Clinton years. What kept it from rising in the last decade less than after almost any previous recession, and what caused it to rise more following the recent recession than in any since WWII? We’ll look at that later, but for now let’s just get the lay of the land.

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Durable Goods soft, but Initial Jobless Claims a huge positive

Oct Durable Goods were well below expectations as orders fell 3.3% headline, 2.7% ex transports and 4.5 non defense capital goods ex aircraft. The consensus for all three was for gains of .1% to 1%. However, Sept figures were revised all higher by a good amount, so taken together the data was still weaker than thought but not as much as the Oct data implies. The declines for Oct were broad based and Shipments, which follow orders and get directly plugged into GDP, fell by .9%. Because inventories rose while shipments fell, the inventory to shipments ratio rose to 1.61 from 1.59, the highest since Aug ’09. Bottom line, while the revisions in Sept helped to mitigate the Oct decline, inventory levels have mostly normalized after the nice lift the build gave to GDP over the past few yrs. Thus, a big pick up in new orders from here will have to happen in conjunction with further improvements in end demand, which hopefully will continue.

Initial Jobless Claims were a huge positive surprise, totaling just 407k, well below expectations of 435k and down from 441k last week. The last time we saw a level this low was July ’08 and it brings the 4 week average down to 436k from 444k. Continuing Claims fell by 142k and Extended Benefits fell a net 262k. Bottom line, there is a clear improvement in the level of firings, and thus those applying for benefits, and we’ll see with next Friday’s Nov Payroll # how much new net hiring has followed. The important factor coming up that will impact Extended Benefits is the Nov 30th expiration of the last extension of unemployment benefits and that may cause about 2mm people to lose this insurance program. This is a large number but there are currently 3mm job openings in the economy right now. This is a simplistic look I know but my point is, don’t assume no one will be able to find new jobs rather quickly.

The Coming Job Boom (NOT)

“Forget those grim unemployment numbers. Demographic forces are about to put a squeeze on the labor supply that will make it feel like 1999 all over again.”
The Coming Job Boom, Business 2.0, September 2003

I’ve been meaning to post a response to this article, which I linked to on Monday. I find myself disagreeing with both the thesis and methodology of this writer’s perspective:

“The cause of the labor squeeze is as simple as it is inexorable: During this decade and the next, the baby boom generation will retire. The largest generation in American history now constitutes about 60 percent of what both employers and economists call the prime-age workforce — that is, workers between the ages of 25 and 54. The cohorts that follow are just too small to take the boomers’ place. The shortage will be most acute among two key groups: managers, who tend to be older and closer to retirement, and skilled workers in high-demand, high-tech jobs.”

It’s a rather flawed argument, as it fails to acknowledge the other side of the balance sheet.

When the Boomers retire from work, they also will be retiring, in large part, from consuming. It’s no accident that so much advertising is aimed at the 18-35 demographic – that’s the fattest part of the consumer bell curve; Peak earning power may be in your 50s, but peak spending power – new homes, furnishing, cars, clothes – is much younger.

That’s the weakness of the coming job boom argument. Certainly, a few age-specific sectors will see an increase; think health care, pharmaceuticals, assisted living facilities, and hospitals. The overall impact on the economy will be to experience a slowdown in demand for a variety of good and services. These two are very unlikely to offset each other.

The article also makes mention of the areas which will be experiencing the biggest growth in hiring (see graphic).

jobboom.gif

Few of these positions are heavily filled with boomers; Their retirement will be mostly irrelevant to new job openings in these fields – they are much more likely to grow organically.

Source: The Coming Job Boom
By Paul Kaihla, September 2003 Issue

thanks to Business Pundit for the pointer

When Will Wage Growth Begin in Earnest ?

Where’s Your Raise? It Should Be Coming
Pressures for bigger pay increases are building, but have yet to show up in the data.
Bloomberg, July 11, 2018

 

 

 

During the past few years, I have written that I expected wages would begin to rise. They have, at least on a nominal basis, or before accounting for inflation.

With unemployment at or near four-decade lows, I have argued that the only solution for employers is to raise wagesto attract and retain employees. There is certainly anecdotal evidence of companies raising pay, but across the board the signs are mixed.

If my thesis on rising wages is going to be proven wrong then lack of pay raises during the next 12 months might do it.

So today, I want to look for both confirming and contrary data points to see if we can glean what might happen next.

Let’s start with the basic wage data: The nominal average hourly earnings of all private-sector employees rose 2.7 percent year over year, up from a low of 1.5 percent in October 2012. That has been a slow but steady improvement since the recovery really took hold in 2010, but it is still a good deal below what we saw before the financial crisis.

Given that starting point, is the data supportive of higher wages or not? Consider these positives:

No. 1. Job openings to job seekers: In 2016, there were 1.3 job seekers for each opening. Today, its 0.9 people for each opening, meaning there are more jobs than job seekers. For context, at the height of the financial crisis the ratio was 6.6 to 1. The basic laws of economics tell us that when a good or service is in limited supply (labor) then the cost of that product (wages) should rise.

No. 2. Quit rates: As the Bureau of Labor Statistics reported, a record number of people were quitting their jobs — 3.56 million workers in May, the most since this data series began in December 2000. Job-hopping is increasing, and when quit rates rise, it is typically because lots of workers are leaving to accept better paying jobs. The challenge with this data series is determining if this is a broad trend or limited to specific narrow sector gains.

No. 3. Unemployment rate: The unemployment rate ticked up 0.2 percentage points to 4 percent in June, from 3.8 percent, but not because people were losing their jobs. It rose for the best possible reason: people who had previously left the labor market were re-entering the work force and looking for jobs. This typically speaks to confidence among workers that jobs are plentiful and are paying a worthwhile salary versus either being in school, retired or otherwise not working.

Those points all suggest pressures for higher wages are building.

Now for the counterpoints.

No. 1. Tax cuts: As has been widely reported, the benefits of the tax cuts have been going mostly to shareholders, not workers. An analysis earlier this year put workers’ total gains at $6 billion versus $171 billion for shareholders in buybacks and dividend increases. Since then, total announced buybacks have risen to $437 billion. If management continues these priorities, some argue, workers are less likely to see substantial wage gains.

No. 2. Productivity gains: This is a long-standing conundrum that won’t be settled here, but rising productivity hasn’t benefited workers very much. Despite producing more goods and services, labor’s share of the total pie — which has been falling for at least two decades — hasn’t gotten back to where it was before the financial crisis.

No. 4. Demographics: As my Bloomberg Opinion colleague Michael Strain has noted, “the composition of the work force is changing in ways that affect pay.” Older, better-paid workers are retiring, and more, young workers are accepting lower-wage jobs in services industries, such as restaurants and bars as the chart below shows. This skews the average wage gain.

The Young and the Low-Paid

Index of employment in food services and drinking places*

 

No. 5. Real Wages: As the chart below shows, even when wages finally do tick up, they tend to not keep up with inflation. This means that workers are merely treading water despite rising wages. If you thought stagnant wages created populist discontent before, wait until people figure out that their raises do nothing to raise living standards.

Wonder Why It Feels So Hard to Get Ahead?

Here’s how I see things when all these factor are taken into account. The negatives are mostly older, longer-term trends, while the positives are all fairly recent developments that will get stronger as the economy chugs ahead. Based on that, my expectation is that positive wage pressures should become more evident during the next several quarters as employers have no choice but to pay more to attract and keep workers.