Topic Archives: Industries

Thinking about Summer Jobs

It’s the last few days of school for many high schoolers, and college students have already started their summer break. Time to look for a summer job? Or maybe not. According to information from our Current Population Survey, fewer than half (43.2 percent) of teenagers ages 16–19 participated in the labor force in July 2016, meaning they either worked or were actively looking for work.

This is a sharp contrast from my own summer experience 40 years earlier, when I was either looking for opportunities to get out of the house and make some money, or it was made clear by my mom and dad that I wouldn’t be sitting around the house all summer. Apparently my experience wasn’t unique, as the labor force participation rate among 16–19 year-olds in July 1978 was 71.8 percent.

A chart showing labor force participation rates of 16-19 year-olds and 20-24 year-olds in July from 1948 to 2016.

Editor’s note: A text-only version of the graphic is below.

Yes, kids worked in the summer. And what were we doing? You name it.

My buddy down the street delivered newspapers, winter and summer. You may have heard of a newspaper; it’s kind of like printing the entire Internet every day on grey paper. And it was typically delivered by kids on bicycles—twice a day where I grew up. My father enjoyed the afternoon newspaper and an adult beverage when he came home from work every day. Afternoon newspapers included partial box scores for day baseball games, as well as noon stock prices from Wall Street.

And the newspaper was the source of my first summer job. Every summer, the local newspaper would let kids place free want ads. You may have heard of want ads; it’s kind of like Craigslist on grey paper. Kids would advertise to babysit, do chores, mow the lawn, or any other kind of service. My jack-of-all-trades ad got me several jobs helping older folks clean out basements, attics, and assorted other overgrown spaces. It was hard work; I definitely earned my pay.

A graphic showing the top 10 industries employing 16-19 year-olds n July 2016.

I worked at the local cheese factory one summer, or should I say part of the summer. The smell wasn’t very pleasant. I spent several summers as a cafeteria worker, mostly working the cash register but occasionally serving food as well. A key skill needed to keep the cafeteria line moving was the ability to make change. In those days, the cash register didn’t tell you how much change to provide. In fact, at the end of each shift I had to reconcile my till against the day’s receipts. I quickly learned to provide the proper change lest I had to dig it out of my own pocket. And under the heading of “employee benefits,” I got free lunch every day, including ice cream.

And then there were the psych experiments. I lived near a university that was always looking for “subjects” for their experiments. They were mostly cognitive activities, like grouping items into categories. Only occasionally were there wires attached to my head. These activities might be considered an early version of a gig job, as they were typically scheduled at random times and always paid in cash. (There was no Venmo back then.) And yes, I reported every dime on my tax return.

I suspect summer jobs have changed over the years. I hear of kids getting internships to build skills and advance their future careers. And many students are spending their summers in school, or practicing sports, or in specialized programs to build skills, like computer programming.

Every year, the Bureau of Labor Statistics releases a special report on youth employment. We will release the 2017 report on August 16.

 

Labor force participation rates of young people in July 1948–2016, not seasonally adjusted
Year Ages 16 to 19 Ages 20 to 24
1948 65.5 66.7
1949 63.5 68.2
1950 63.2 66.1
1951 65.2 66.4
1952 63.5 63.9
1953 61.5 62.7
1954 59.5 63.8
1955 61.0 64.5
1956 65.5 66.5
1957 64.2 67.6
1958 60.3 67.5
1959 59.8 66.3
1960 61.3 68.0
1961 61.2 66.9
1962 60.5 68.0
1963 58.8 68.4
1964 57.7 68.8
1965 60.9 69.9
1966 64.4 69.2
1967 64.9 70.6
1968 64.8 70.8
1969 65.5 71.7
1970 64.5 72.8
1971 65.1 72.4
1972 65.7 74.3
1973 67.3 76.1
1974 68.5 77.3
1975 67.9 77.5
1976 68.8 78.8
1977 69.5 79.1
1978 71.8 80.5
1979 70.9 81.2
1980 70.7 80.8
1981 67.9 81.0
1982 66.9 80.7
1983 67.8 81.3
1984 68.9 81.6
1985 69.6 81.4
1986 68.5 82.7
1987 67.6 82.9
1988 69.8 82.4
1989 69.6 83.8
1990 66.5 81.7
1991 64.4 80.4
1992 65.0 81.7
1993 65.1 81.5
1994 65.4 80.9
1995 66.6 80.5
1996 64.8 80.6
1997 63.6 81.2
1998 63.9 80.7
1999 62.9 81.3
2000 61.8 80.2
2001 60.3 79.4
2002 57.5 79.3
2003 53.7 78.3
2004 53.6 78.1
2005 53.0 77.7
2006 53.5 77.5
2007 50.0 77.5
2008 49.6 78.1
2009 46.5 76.7
2010 42.6 74.7
2011 41.6 73.5
2012 43.4 73.8
2013 43.3 73.6
2014 42.3 74.2
2015 41.3 74.1
2016 43.2 73.1

Some Interesting Numbers about the Oscars

The annual Academy Awards ceremony was held Sunday, February 26, to recognize excellence in cinematic achievements in the U.S. film industry. Impress your friends with these facts we’ve gathered about the Oscars and the motion picture business.

This year’s Oscar for Best Picture went to La La Land Moonlight.

  • Not all actors reach the top, but lots are trying: Actors in the U.S. can be found coast to coast with a total employment of 50,570. Almost one-third, or about 14,560, work in the greater Los Angeles metro area alone. Employment of actors is projected to grow 10 percent from 2014 to 2024, faster than the average for all occupations.

Walt Disney is the most Oscar-nominated person ever with 59 nominations.

  • Walt may be gone, but his legacy lives on: Today there are 30,240 multimedia artists and animators employed in the U.S. California employs about a third (10,110) with half of those in the greater Los Angeles area (5,830). Employment of multimedia artists and animators is projected to grow 6 percent from 2014 to 2024, about as fast as the average for all occupations.

Since 1945, the accounting firm Price Waterhouse (now called PricewaterhouseCoopers) has tabulated the Oscar ballots to ensure the secrecy of the results.

  • There are a total of 1,226,910 accountants in the United States, and California again has the largest employment with 144,540. Employment of accountants and auditors is projected to grow 11 percent from 2014 to 2024.

Oscar weekend is a boon to the beauty industry: Before walking down the red carpet, many use the services of a hairstylist – and house calls reportedly start at $500.

  • Nationwide, 348,010 hairstylists are employed. The five states with the most are California (26,340), New York (25,420), Pennsylvania (24,210), Florida (23,840) and Texas (22,050). The metropolitan area with the most hairstylists is New York-Jersey City-White Plains, NY-NJ, with 20,790. Employment of barbers, hairdressers, and cosmetologists is projected to grow 10 percent from 2014 to 2024.

The Academy of Motion Picture Arts and Sciences identified 336 feature films eligible for the 2016 Academy Awards.

The first Academy Awards ceremony was on May 16, 1929, at the Roosevelt Hotel’s Blossom Room with 270 attendees. The price of admission was $5, which included a broiled chicken dinner.

The Oscar statuette is 13.5 inches tall and weighs 8.5 pounds. A New York foundry casts them in bronze before they receive a 24-karat gold finish.

  • Workers who make these kinds of items are part of a small industry, known as “other nonferrous foundries, excluding die-casting,” with only 12,372 employees nationwide. About half are employed in three states: Michigan, Oregon and Ohio. Employment in the foundries industry is projected to decrease by about 17 percent from 2014 to 2024.

After the Oscars ceremony, you may be inspired to go to a movie. But did you know how much these prices have changed over the last 10 years?

  • Admission to movies, theaters and concerts is up 21 percent, carbonated drinks are up 19 percent, and candy and chewing gum are up 28 percent. We don’t track popcorn — sorry!

Editor’s note: Oscar-specific facts are from the official Oscars website, unless another source is provided.

Seeing Significance: New Chart Showing Confidence Intervals for Nonfarm Employment Changes

Last year I wrote about how we’ve been using more charts and maps to explain our data. In another post I wrote about how BLS deals with uncertainty in our measures and how we explain the strengths and limitations of our data. Today I want to tell you about a new chart that will help you see measures of uncertainty in one of our most closely watched series—nonfarm employment.

We recently fielded a survey to ask data users about our ideas for creating charts that show confidence intervals. A confidence interval is a measure of the uncertainty in our estimates. We asked data users to tell us if confidence intervals would give them useful information. If so, how can we present confidence intervals in a clear, visually appealing way? Based on your responses, we will add a chart showing confidence intervals to the package of interactive graphics we update each month with The Employment Situation report. We will post the new chart when we release the November 2016 national employment numbers on Friday, December 2.

The responses to our survey confirm you want to know more about the limitations of the employment data. The red dots in the chart show the over-the-month employment changes for total nonfarm and the major industries. The blue bars show the 90-percent confidence intervals of the employment changes for each of these groups.

Chart showing nonfarm employment changes for major industries in October 2016 and the confidence intervals for those changes.

What does this chart tell us? The red dot for total nonfarm employment shows a gain of 161,000 jobs in October, as we reported on November 4. That number is an estimate based on our monthly sample survey, rather than a complete count of jobs each month. Different samples of employers might give us different estimates of employment change. We can measure the sampling error, the variation that occurs by chance because we collected the number from a sample of employers instead of all employers. With our measure of sampling error, we can calculate a confidence interval. The blue bar for total nonfarm shows the 90-percent confidence interval ranged from 46,800 to 275,000. We call this a 90-percent confidence interval because, if we were to choose 100 different samples of employers, the October nonfarm employment change would be between 46,800 and 275,000 in 90 of those samples.

We also learn from this chart whether an employment change is statistically significant. A change is statistically significant if the blue bar in the chart does not cross the zero line. For example, the confidence interval for construction includes zero, so we can’t say with confidence that construction employment increased in October. For education and health services, however, the confidence interval does not include zero, so we can say more confidently that employment in the industry rose over the month.

The chart here shows the 1-month employment changes. When we begin publishing it on December 2, we will also let you choose charts that show 3-, 6-, and 12-month changes.

Check out the new charts and let us know what you think in the comments below. We’re always looking for better, clearer ways to explain our data, and I welcome you to share your ideas.

Why This Counts: Productivity and Its Impact on Our Lives

How can we achieve a higher standard of living? One way might simply be to work more, trading some free time for more income. Although working more will increase how much we can produce and purchase, are we better off? Not necessarily. Only if we increase our efficiency—by producing more goods and services without increasing the number of hours we work—can we be sure to increase our standard of living.

That’s why BLS produces labor productivity statistics every quarter that tell us how well we are improving our economic efficiency. These measures compare the amount of goods and services we produce with the number of hours we work. How can we can improve labor productivity? There are many ways. We can use more and newer machinery and equipment. We can develop new technologies that streamline production. We can improve organization and communication in the workplace and manage people more effectively. Or, we can increase worker skills through education or job training.

So, how much has U.S. labor productivity improved over the years? Compared to 1947, we now produce 330 percent more goods and services per hour of work. On average, thanks to advances in technology, education, management, and so on, you can do in 15 minutes what your grandparents or great grandparents needed more than an hour to do in 1947. This is a substantial increase, and we can see it in the many improvements in living standards since World War II.

Productivity growth in recent years hasn’t been as strong, however. It may seem surprising, given all the new technologies and products in recent years, but we are now living through one of the lowest productivity-growth periods ever recorded. Since the Great Recession of 2007–09 began in the fourth quarter of 2007, labor productivity has grown just 1.0 percent per year. That is less than half the long-term average rate of 2.2 percent since 1947. Although the U.S. economy has been experiencing slow productivity growth since 2007, some industries have been doing well. For instance, the wireless telecommunication carrier industry has had annual labor productivity growth of over 15.0 percent since the beginning of the Great Recession.

Labor productivity growth in the nonfarm business sector is lower in the current business cycle than during any of the previous ten business cycles. Chart 1 shows average annual labor productivity growth during business cycles since World War II.

Chart 1. Average annual percent change in labor productivity in the nonfarm business sector during business cycles

Multifactor productivity—which accounts for the use of machinery, equipment, and other capital, in addition to labor—has also increased more slowly over the current business cycle; it has grown 0.4 percent per year during the 2007–15 period, compared to its long-term rate of 0.9 percent per year since 1987.

Historically, productivity growth has led to gains in compensation for workers, greater profits for firms, and more tax revenue for governments. Compensation, which includes pay and benefits, has not always risen as fast as productivity, however. (See chart 2.) The difference between labor productivity gains and real hourly compensation growth is often called the “wage gap.” Real hourly compensation growth tracked labor productivity growth more closely before the 1970s. Since then, growth in real hourly compensation has lagged behind gains in productivity, widening the gap considerably. Since the start of the Great Recession in the fourth quarter of 2007, real hourly compensation has grown by only 0.6 percent per year; that’s less than half the long-term average of 1.6 percent per year.

Chart 2. Labor productivity and real hourly compensation in the nonfarm business sector, 1947–2015

Measures of gross domestic product and employment tell us how the U.S. economy is doing in producing goods and services and creating jobs. Measures of productivity link what our economy produces and the labor and capital used to produce it. Labor productivity is an important statistic to track because gains in productivity are essential to improving our lives and the well-being of our nation. That’s what Nobel Prize-winning economist Paul Krugman meant when he noted, “Productivity isn’t everything, but in the long-run it’s almost everything.”

You can stay up to date on productivity trends and other economic news by signing up for our email alerts or following us on Twitter.

Entrepreneurship Facts: Announcing New Research Data on Job Creation and Destruction by Firm Age and Size

I’m delighted to announce that we now have new research data on job gains and losses by firm age and size across industries and states.

For many years, policymakers, economists, and others have debated whether small or large firms create more jobs. Our Business Employment Dynamics program, which measures gross job gains and losses to help us understand net employment changes, informs that debate with data on firm size. A related question is whether startups or older establishments create more jobs. Again, BLS has a stat for that. We have data on employment and business survival rates by the age of the establishment.

While it’s useful to know the age of an establishment—that is, a single location of a business—for some questions, we need to know the age of the firm. A firm may include several or even many establishments. To understand entrepreneurship in particular, we want to know how both the age and size of firms affect job gains, job losses, and employment growth.

With these new data we can answer many interesting questions, including:

  • How much do older firms contribute to job growth? Firms 10 years or older created 800,000 jobs, or 29 percent of the total 2.7 million net employment gain in the year ending March 2015. See the chart below.
  • How much do startup firms contribute to job growth? In the year ending March 2015, startup firms—firms less than 1 year old—created 1.7 million jobs or 60 percent of total employment growth. More than half these jobs were from firms with fewer than 10 employees.
  • How does the age or size of the firm affect the rate of business closures? In 2015, 788,000 establishments closed. Of these, 55 percent were from firms 10 years or older; 16 percent were from firms 5 to 9 years old; and 28 percent were from firms less than 4 years old. Of the establishments that closed from March 2014 to March 2015, 91,000 of them, or 12 percent of the total, had 500 or more employees.
  • Which firm-age group accounted for most job losses during the last two recessions? Firms 10 years or older lost the most jobs during both recessions. Again, see the chart below.

net-job-changes-by-firm-age

The new research data measure annual gross job gains and gross job losses by firm age and size from March of one year to March of the next. We get the data on firms from the Quarterly Census of Employment and Wages by linking individual establishments over time. Besides firm age and size, we also measure establishment age and size. We have two methods to examine size. One method compares the current size of firms or establishments with the size at the beginning of the year (the base-sizing method). The other method compares the current size with the average size over the year (the average-sizing method).

I really want to know how you like these new data and what we can do to make them more useful. I invite you to explore the data and share your comments. Your feedback will help us develop the dataset and possibly move it into our regular production. Please write your comments below, or you can email the Business Employment Dynamics staff.