Tag Archives: Interviews

Why This Counts: Tracking Workers over Time

In many ways, BLS is very much about the now. For example, two of our major statistical programs are the Current Employment Statistics and the Current Population Survey. But to understand the U.S. labor market, we also need a longer-term focus.

The National Longitudinal Surveys (NLS) program provides information about the long-term workings of the economy.

What is a “longitudinal survey”?

A longitudinal survey interviews the same sample of people over time. At each interview, the surveys ask people about their lives and changes since their prior interview. With this information we create histories that allow researchers to answer questions about long-term labor market outcomes. For example, how many jobs do people hold over their lifetimes? How do earnings grow at different stages of workers’ careers? How do events that happened when a person was in high school affect labor market success as an adult?

How does the NLS work?

The NLS program is more than 50 years old, and today we have two active cohorts, or nationally representative samples of people, whom we interview every year or two:

  • The National Longitudinal Survey of Youth 1979 (NLSY79) consists of people born from 1957 to 1964, who were ages 14 to 22 when first interviewed in 1979.
    • The NLSY79 cohort has been interviewed 27 times since the late 1970s.
    • The children of the women in this sample (captured in the NLSY79 Children and Young Adults survey) have been assessed and interviewed 16 times since 1986.
  • The National Longitudinal Survey of Youth 1997 (NLSY97) consists of people born in the years 1980 to 1984, who were ages 12 to 17 when first interviewed in 1997.
    • The NLSY97 cohort has been interviewed 17 times.

These surveys are voluntary, and what a commitment our participants have shown! A big “thank you” to our respondents for their help!

What information is available from NLS?

By gathering detailed labor market information over time, researchers can create measures that are not available in other surveys.

One measure is the number of jobs held across various ages. The chart that follows is from the most recent NLSY79 news release.

  • The chart shows the cumulative number of jobs held from ages 18 to 50.
  • People born from 1957 to 1964 held an average of 11.9 jobs from ages 18 to 50. From ages 18 to 24 these baby boomers held an average of 5.5 jobs. The number steadily fell over time until these baby boomers held an average of just 0.8 job from ages 45 to 50.
  • The decline in the slope of the curves shows that workers change jobs more often when they are younger.

Cumulative number of jobs held from ages 18 to 50, by sex and age

Editor’s note: Data for this chart are available in the table below.

The decline in the number of jobs held over time is also true for the NLSY97 cohort.

A second measure available from the surveys is the percentage of weeks worked over various ages. Let’s look at data from the most recent NLSY97 news release.

  • The chart below shows the percent of weeks worked from ages 18 to 30, by educational attainment and sex.
  • Women with less than a high school diploma were employed an average of 40 percent of weeks from ages 18 to 30. Men with less than a high school diploma were employed 64 percent of weeks.
  • Among people with a bachelor’s degree and higher, women were employed an average of 80 percent of weeks, while men were employed 78 percent of weeks.

Percent of weeks employed from ages 18 to 30, by educational attainment and sex

Editor’s note: Data for this chart are available in the table below.

Who uses the NLS?

The main users of these data are researchers in academia, think tanks, and government. They use the surveys to examine how life experiences are connected. For example, how do early life events (schooling, employment during one’s teens, parental divorce) affect adult outcomes (employment, income, family stability)?

“Studies using the NLS cover a staggeringly broad array of topics. Looking through them, I was startled to realize how much of what we know about the labor market is only knowable because of the NLS.” — Janet Currie, Henry Putnam Professor of Economics and Public Affairs, Princeton University

Researchers value the surveys’ combination of large samples, long histories, and range of topics. These features allow researchers to study our economy and society from a rare and complex perspective.

Researchers have used the data in thousands of journal articles, working papers, Ph.D. dissertations, and books that shape theory and knowledge in economics, sociology, education, psychology, health sciences, and other fields.

You can find information about more than 8,000 studies in the NLS Bibliography. Looking at journal articles published in 2018, I found these studies using NLS data:

  • Racial and Ethnic Variation in the Relationship between Student Loan Debt and the Transition to First Birth
  • The Impact of Childhood Neighborhood Disadvantage on Adult Joblessness and Income
  • The Effect of an Early Career Recession on Schooling and Lifetime Welfare
  • The Early Origins of Birth Order Differences in Children’s Outcomes and Parental Behavior
  • Earnings Dynamics: The Role of Education Throughout a Worker’s Career

“[From the NLS] I learned that we cannot understand why adults have such diverse employment and earnings trajectories without going back to their youth to understand the skill and background differences that shaped how they transitioned into adulthood.” — Derek Neal, Professor of Economics, University of Chicago

How can I get more information?

The data are free to the public and provided online with search and extraction tools and detailed documentation.

If you have a specific question, you might find it answered in our Frequently Asked Questions. Or you can always contact the staff by email or phone at 202-691-7410.

If you care about the long view—how peoples’ careers evolve over time, how people fare after job loss, how childbirth affects women’s careers, and so on—the National Longitudinal Surveys may be just what you need! Check out these gold-standard data!

Cumulative number of jobs held from ages 18 to 50, by sex and age
Age Men Women
18 1.6 1.5
19 2.4 2.3
20 3.1 2.9
21 3.8 3.5
22 4.5 4.2
23 5.1 4.7
24 5.7 5.3
25 6.2 5.7
26 6.7 6.2
27 7.2 6.6
28 7.6 7.0
29 8.0 7.3
30 8.3 7.6
31 8.6 7.9
32 8.9 8.2
33 9.2 8.5
34 9.5 8.8
35 9.7 9.0
36 10.0 9.3
37 10.2 9.5
38 10.4 9.8
39 10.5 10.0
40 10.7 10.1
41 10.9 10.3
42 11.0 10.5
43 11.2 10.6
44 11.4 10.8
45 11.5 11.0
46 11.6 11.1
47 11.7 11.3
48 11.9 11.4
49 12.0 11.5
50 12.1 11.6
Percent of weeks employed from ages 18 to 30, by educational attainment and sex
Education Men Women
Less than a high school diploma 63.5% 40.3%
High school graduates, no college 75.5 64.4
Some college or associate degree 79.4 72.0
Bachelor’s degree and higher 78.4 80.1

Ensuring Gold-Standard Data in the Eye of a Storm

“Hurricanes Harvey, Irma and Maria were the most notable storms of 2017, leaving paths of death and destruction in their wake.”
Colorado State University’s Tropical Meteorology Project 2017 summary report

Colorado State University’s Tropical Meteorology Project is forecasting the 2018 hurricane season activity (as of May 31) to be average, with 13 named storms, 6 hurricanes, and 2 major hurricanes expected. Is BLS ready?

How does BLS deal with hurricanes?

Since June starts hurricane season, we want to share with you one example of how last year’s storms affected our data. We present a case study using our national employment survey, the Current Employment Statistics program. This program provides monthly estimates we publish in The Employment Situation—sometimes called the “jobs report.”

We have procedures to address natural disasters. We highlight some of our challenges and how we address them. We do everything possible to provide you with gold-standard data to help you make smart decisions!

2017 Hurricane Destruction

Two major hurricanes—Harvey and Irma—blasted the U.S. mainland in August and September 2017. Hurricane Maria devastated Puerto Rico and the U.S. Virgin Islands later in September.

  • Harvey first made landfall in Texas on August 25. The Federal Emergency Management Agency (FEMA) declared 39 Texas counties eligible for federal disaster assistance after Harvey. Harvey also caused heavy damage in Louisiana.
  • Irma hit the Florida Keys on September 10 and then later hit Florida’s southern coast. FEMA declared 48 Florida counties eligible for federal disaster assistance. Before Irma hit the lower Florida Keys, the hurricane already had caused severe damage in St. Thomas and St. John in the U.S. Virgin Islands and in Puerto Rico.
  • Hurricane Maria made landfall in St. Croix in the U.S. Virgin Islands and in Puerto Rico on Wednesday, September 20, causing catastrophic damage. These areas already had suffered damage from Hurricane Irma earlier in the month.

Some things to know about the monthly employment survey

The monthly employment survey is a sample of nonfarm businesses and government agencies. The reference period is the pay period that includes the 12th of the month. The sample has just over 23,000 active reporting units in the disaster areas, representing about 6 percent of the entire active sample.

What does it mean to be employed? If the employer pays someone for any part of the reference pay period, that person is counted as employed.

How did BLS collect data in these disaster areas?

Our biggest challenge is to collect representative sample data so we publish high-quality estimates. In the “old days,” the survey was a mail survey (yes, I mean snail mail), but no more! Now we collect data electronically by several different methods. These are the most common:

  • About half the total sample uses electronic data interchange. That’s a centralized electronic data reporting system for multi-establishment firms. The firm provides an electronic file directly from their payroll system to BLS for all establishments included in the report. Most of the firms reporting are outside of the hurricane-affected areas, although they may report on establishments within the affected areas.
  • About 23 percent of establishments use computer-assisted telephone interviews.
  • Another 16 percent report using our Internet Data Collection Facility.

Using these methods, we were able to collect data from most sampled businesses in these areas using normal procedures.

What about the emergency workers working in the disaster areas? How are they counted?

  • We count emergency workers where their employer is located, not where they are working.
  • We don’t count volunteers as employed because they are not paid.
  • Activated National Guard troops are considered active duty military and are outside the scope of the survey.

Did the estimation procedures change?

Once we collect the data from businesses in the affected areas, we consider whether we need to change our estimation procedures to adjust for missing data. The survey staff determined that we didn’t need to change our methods because the collection rates in the affected areas were within normal ranges.

How did the hurricanes affect national employment data for September 2017?

Hurricanes Harvey and Irma reduced the estimate of national payroll employment for September 2017. We can’t measure the effects precisely because the survey is not designed to isolate the effects of catastrophic events. National nonfarm employment changed little (+14,000) in September 2017, after increasing by an average of 189,000 per month over the prior 12 months. A steep employment decline in food services and drinking places and below-trend growth in some industries likely reflected the impact of Hurricanes Harvey and Irma.

What about Puerto Rico and the U.S. Virgin Islands?

National nonfarm employment estimates do not include Puerto Rico or the U.S. Virgin Islands.

Because of the devastation caused by Hurricanes Irma and Maria, Puerto Rico and the U.S. Virgin Islands could not conduct normal data collection for their establishment surveys. The September estimates for Puerto Rico and the Virgin Islands were delayed. The October and November estimates for the Virgin Islands also were delayed. Puerto Rico and the Virgin Islands eventually were able to produce estimates for September, October, and November 2017.

Want more information?

For more information on the impact of Harvey, Irma, and Maria, check out these pages:

What else does BLS know about hurricanes?

The Quarterly Census of Employment and Wages produces maps of businesses and employment in flood zones for states on the Atlantic and Gulf Coasts that are vulnerable to hurricanes and tropical storm. You can read more about those maps in another recent blog.

We hope the 2018 hurricane season won’t bring the loss of life and destruction of property that we saw in 2017. Regardless of what the season brings, BLS will be ready to continue providing gold-standard data about the labor market and economy.

Reaching out to Stakeholders—and Steakholders—in Philadelphia

The U.S. Bureau of Labor Statistics has staff around the country who serve several critical roles:

  • Contacting employers and households to collect the vital economic information published by BLS
  • Working with partners in the states who also collect and review economic data
  • Analyzing and publishing regional, state, and local data and providing information to a wide variety of stakeholders

To expand the network of local stakeholders who are familiar with and use BLS data to help make good decisions, the BLS regional offices sponsor periodic Data User Conferences. The BLS office in Philadelphia recently held such an event, hosted by the Federal Reserve Bank of Philadelphia.

These Data User Conferences typically bring together experts from several broad topic areas. In Philadelphia, participants heard about trends in productivity measures; a mash-up of information on a single occupation—truck drivers—that shows the range of data available (pay and benefits, occupational requirements, and workplace safety); and an analysis of declines in labor force participation.

Typically, these events provide a mix of national and local data and try to include some timely local information. The Philadelphia conference included references to the recent Super Bowl victory by the Philadelphia Eagles and showed how to use the Consumer Price Index inflation calculator to compare buying power between 1960 (the last time the Eagles won the NFL Championship) and today.

We also tried to develop a cheesesteak index, a Philadelphia staple. Using data from the February 2018 Consumer Price Index, we can find the change in the price of cheesesteak ingredients over the past year.

Ingredient Change in Consumer Price Index, February 2017 to February 2018
White bread 2.5 percent decrease
Beef and veal 2.1 percent increase
Fresh vegetables 2.1 percent increase
Cheese and related products 0.8 percent decrease

Image of a Philadelphia cheesesteak

These data are for the nation as a whole and are available monthly. Consumer price data are also available for many metropolitan areas, including Philadelphia. These local data are typically available every other month and do not provide as much detail as the national data.

While the Data User Conferences focus on providing information, we also remind attendees the information is only available thanks to the voluntary cooperation of employers and households. The people who attend the conferences can help us produce gold standard data by cooperating with our data-collection efforts. In return we remind them we always have “live” economists available in their local BLS information office to answer questions by phone or email or help them find data quickly.

Although yet another Nor’easter storm was approaching, the recent Philadelphia Data User Conference included an enthusiastic audience who asked good questions and left with a greater understanding of BLS statistics. The next stop on the Data User Conference tour is Atlanta, later this year. Keep an eye on the BLS Southeast Regional Office webpage for more information.

Up and Down the Turnpike: The Power of State Estimates of Consumer Spending

You may know New Jersey for its Turnpike, its Parkway, and ribbons of highways crisscrossing the state, but new information shows that New Jersey households have fewer vehicles than the U.S. average. New Jersey households have an average of 1.4 vehicles, compared with an average of 1.8 vehicles nationwide.

This is just one of the tidbits we can glean from experimental state weights in the Consumer Expenditure Survey just released for New Jersey. Producing state estimates is part of our continuing plan to expand the use of data on consumer spending. The first available state weights are for New Jersey. We hope to release weights for more states in the coming months.

The survey is a nationwide household survey designed to find out how U.S. consumers spend their money. It is the only federal government survey that provides information on the full range of consumer spending, incomes, and demographic characteristics. One way BLS uses the consumer spending data is to create the market basket of goods and services tracked in the Consumer Price Index. Besides the spending information, the survey also collects the demographic characteristics of survey respondents. The new state weights allow us to examine what the typical New Jersey household looks like.

New Jersey looks similar to the United States as a whole, and even more similar to the New York metro area, which encompasses much of the northern part of New Jersey. One notable difference between New Jersey and other areas is the number of vehicles. Transportation in the Consumer Expenditure Survey includes vehicle purchases and gasoline and other car-related expenses. We would expect to see lower transportation spending in New Jersey compared with the nation because of fewer vehicles present in the state and other reasons.

A chart showing income and consumer spending levels in 2016 in New Jersey, the New York metro area, and the United States.

Editor’s note: Data for this chart are available in the table below.

Now that we can produce statistically valid state estimates from the survey, we can answer all kinds of interesting questions. Many researchers look at different spending categories to examine public policies and to evaluate how certain decisions affect consumer behavior. Because we can now use the survey data to make estimates for certain states, researchers can explore these kinds of questions with more geographic detail. The chart below shows how New Jersey compares with the New York metro area and the nation in five of the broadest spending categories.

Average annual consumer spending in 2016 for selected categories in New Jersey, the New York metro area, and the United States.

Editor’s note: Data for this chart are available in the table below.

Policymakers, researchers, and other data users have often asked for data about spending habits and income for states. Many times, household surveys just do not have enough sample to provide reliable estimates for all possible user needs. With our continuing improvements to the Consumer Expenditure Survey, we are learning which states provide enough responses for us to produce statistically valid state estimates. Once we create these weights for the states that can support them, data users will be able to explore a wider range of questions about consumer spending.

You can learn more from BLS economist Taylor Wilson’s article, “Consumer spending by state: BLS puts New Jersey to the test.”

Average annual income and selected expenditures, 2016
Measure New Jersey New York
Metropolitan
Statistical Area
United States
Income $89,927 $87,212 $74,069
Total expenditures 63,100 65,764 54,157
Transportation expenditures 7,295 6,828 8,755
Average consumer spending in selected categories, 2016
Geography Housing Food Transportation Healthcare Entertainment
New Jersey $23,617 $8,641 $7,295 $5,239 $2,097
New York Metropolitan Statistical Area 24,308 9,190 6,828 4,260 2,277
United States 17,774 7,203 8,755 4,373 2,497

Why This Counts: Maximizing Our Data Using the Consumer Expenditure Survey

Almost all BLS statistical programs are based on information respondents voluntarily give us. We want to squeeze as much information as we can out of the data respondents generously provide. Limiting respondent burden while producing gold-standard data is central to our mission.

Let’s take a look at how one program, the Consumer Expenditure (CE) Survey, squeezes every last drop of information from the data to provide you, our customers, with more relevant information.

What is the Consumer Expenditure Survey?

The CE survey is a nationwide household survey that shows how U.S. consumers spend their money. It collects information from America’s families on their buying habits (expenditures), income, and household characteristics (age, sex, race, education, and so forth). For example, we publish what percentage of consumers bought bacon or ice cream and how much they spent on average.

A little back story: The first nationwide expenditure survey began in 1888. BLS was founded in 1884, so the CE Survey is one of our first surveys! It wasn’t until 1980 that we began publishing CE data each year, however. A 2010 article, The Consumer Expenditure Survey—30 Years as a Continuous Survey, provides more historical information.

How is the CE program doing more with what we have?

We’ll briefly look at four different areas, starting with the most recent improvements:

  • Limited state data
  • Higher-income data
  • Generational data
  • Estimating taxes

Limited State Data – Starting with New Jersey

  • Regarding geographical information, the CE survey is designed to produce national statistics. Enough sample data are available to produce estimates for census regions and for a few metropolitan areas.
  • Up to now, however, we did not produce state data. The CE program recently published state weights for New Jersey, which will allow for valid survey estimates at the state level for the first time.
  • State-level weights are available for states with a sample size that is large enough and meet other sampling conditions.
  • Right now, the state-level weighting is experimental. We provide state-level weights to data users to gauge interest and usefulness.

 Higher-Income Table

  • We evaluated the income ranges of the published tables and found that over time more and more households were earning more, and the top income range had not increased to keep pace. To provide greater detail, we divided the existing top income range of “$150,000 and over” into two new ranges: “$150,000 to $199,999” and “$200,000 and over.” We integrated these changes into the 2014 annual “Income before taxes” research table, allowing more robust analysis for our data users.
  • In addition, we added four new experimental cross-tabulated tables on income without the need for additional information from our respondents.

Generational Table

Grouping respondent information by age cohort can be helpful, since a person’s age can help to predict differences in buying attitudes and behaviors. The CE program has collected age data for years, but never grouped the data into generational cohorts before. A Pew Research Center report defines five generations for people born between these dates:

  • Millennial Generation: 1981 or later
  • Generation X: 1965 to 1980
  • Baby Boomers: 1946 to 1964
  • Silent Generation: 1928 to 1945
  • Greatest Generation: 1927 or earlier

The 2016 annual generational table shows our most recent age information for the “reference person” or the person identified as owning or renting the home included in the CE Survey. In 2016 we wrote a short article on Spending Habits by Generation, including a video, which used 2015 data. We’ve updated the chart using 2016 data:

A chart showing consumer spending patterns by generation in 2016.

Editor’s note: Data for this chart are available in the table below.

Estimating Taxes

CE respondents used to provide federal and state income tax information as part of the survey. These questions were difficult for respondents to answer.

Starting in 2013, the CE program estimated federal and state tax information using the TaxSim model from the National Bureau of Economic Research and removed the tax questions from the survey. As a result, the quality and consistency of the data increased, and we have reduced respondent burden!

If you have any questions or want more information, our staff of experts is always around to help! Please feel free to contact us.

This is just one example of how we at BLS are always looking for ways to maximize our value while being ever mindful of the costs—and one of those important costs is the burden our data collection efforts place on our respondents. Maximizing our data means providing gold-standard data to the public while reducing the burden on our respondents—a true win-win!

Annual consumer spending by generation of reference person, 2016
Item Millennials, 1981 to now Generation X, 1965 to 1980 Baby Boomers, 1946 to 1964 Silent Generation, 1928 to 1945 Greatest Generation, 1927 or earlier
Food at home $3,370 $4,830 $4,224 $3,450 $2,023
Food away from home 2,946 4,040 3,100 2,042 1,095
Housing 16,959 22,669 18,917 14,417 17,858
Apparel and services 1,753 2,577 1,602 920 615
Transportation 8,426 10,545 9,762 5,952 3,142
Healthcare 2,473 4,492 5,492 6,197 5,263
Entertainment 2,311 3,613 3,144 2,114 1,223
All other spending 10,338 15,766 14,963 6,671 4,125