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

Why This Counts: What are the U.S. Import and Export Price Indexes?

Cargo ship in port at nightThe U.S. Bureau of Labor Statistics provides data of all kinds for workers, jobseekers, students, employers, investors, and policymakers. Most BLS measures provide information on U.S. labor markets and living conditions: the national labor force participation rate; the unemployment rate in Illinois; the Consumer Price Index for Anchorage, Alaska. But did you know we also provide international data? With a focus on global trade, we publish the U.S. import and export price indexes.

What are import and export prices indexes?

Import and export price indexes describe changes in the prices for goods and some services exchanged between people and businesses in the United States and trading partners around the world. BLS collects prices of imported and exported products from businesses and calculates price trends monthly.

A brief history of international prices:

  • BLS published the first import and export price indexes in 1973.
  • We published the first all-goods price indexes for imports in 1983 and for exports in 1984.
  • Monthly publication launched in 1989 and expanded in 1994.
  • Import price indexes by country of origin began publication in 1992.

What is an import? An export?

To measure import and export prices, we first need to define “import” and “export.” An import is any product entering the United States from a foreign country; an export goes the opposite direction. A good becomes an import or export when it crosses the border. An imported service is bought by a U.S. resident from a foreign resident, while an exported service is sold by a U.S. resident to a foreign resident.

What is a price index?

A price index measures the average change in prices for a basket of the same products over time. We measure price changes for thousands of imports and exports each month. We publish these price changes for specific products and for the specific industries and U.S trading partners that import or export the products. To learn more about price indexes, see our blog about the Producer Price Indexes.

How do we collect the data?

Like the Consumer Price Index and Producer Price Indexes, the import and export price indexes depend on the cooperation of businesses—in this case, U.S. establishments importing and exporting goods and services. Thousands of public-minded businesses voluntarily provide data through a monthly survey. With all the data we collect, we strive to minimize the burden on our respondents and protect their confidentiality and privacy.

What do import and export prices measure?

If you’ve ever taken an introductory economics course, you know markets determine price changes through supply and demand. On the most basic level, import and export price indexes measure how supply and demand affect prices for goods and services traded internationally. Let’s look at a quick example, the export price index for computers. A U.S. computer manufacturer may look at current trends to figure out short-term sales strategies. Then consider the flip side—the price index for import computers. A U.S. resident shopping for a new computer may want to research whether prices have risen or fallen over the past few months. Or that computer shopper might look at the data from the past few years to see if there is a certain time of year that prices fall. But the importance of import and export prices extends even further than individuals and companies.

  • The indexes are used to account for inflation in other official U.S. statistics like trade balances published by the Census Bureau and the international accounts for U.S. Gross Domestic Product published by the Bureau of Economic Analysis.
  • When economists calculate measures of U.S. industries’ competitiveness compared with our trading partners, they use import and export price indexes.
  • A change in the import price index can tilt domestic inflation in the same direction.
  • When exchange rates between currencies rise and fall, the indexes can show how much of that change is “passed-though” to an import or export price.

Why do import and export price indexes data matter?

The data matter because U.S. consumers depend on imports! Simply put, many of the products sold to consumers in the United States are imported from abroad. And there is a good chance what you buy for your home depends on import prices. But consumers aren’t the only ones who care about these prices. U.S. producers sell abroad and buy from overseas. Producers care about import prices because many imports to the United States go into the goods and services produced domestically. U.S. auto manufacturers care about the prices of auto parts they import from abroad. Producers who export goods to foreign countries benefit from having access to price information. Knowing trends in export agricultural prices, for example, could influence what crops a U.S. grower chooses to produce.

Want to find out more?

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.

BLS Big Data Delivers Hurricane Flood Zone Maps

Information is key to preparing for a natural disaster. That’s why we have updated our maps of businesses and employment in flood zones for states on the Atlantic and Gulf Coasts that are vulnerable to hurricanes and tropical storms.

These maps combine data from the Quarterly Census of Employment and Wages with the most up-to-date information from the U.S. Census Bureau and U.S. Geological Survey. The result is high-resolution graphics for every county with hurricane flood zones along or inland from the Atlantic and Gulf Coasts.

The Quarterly Census of Employment and Wages is our “Big Data” program. It gathers data from 9.9 million reports that almost every employer in the United States, Puerto Rico, and the U.S. Virgin Islands files each quarter. We have been producing maps of businesses and employment in disaster areas since 2001, when we created zip code maps and tables of Lower Manhattan. We began mapping hurricane zones in 2014, combining BLS data with flood zones created by the U.S. Army Corps of Engineers and state emergency management agencies.

These maps are one way we use Big Data to create new products without increasing the burden on our respondents. Within BLS, we use these maps for research into the data collection and economic effects of a storm. We also provide these maps to state labor market information offices to use for their statistical analysis and emergency response.

Hurricane maps highlight how we use emerging technologies. We create these maps with open source mapping software, part of our open data practices that make it easier for decision makers to get and use the data.

This isn’t our only example of matching Quarterly Census of Employment and Wages data with data from other federal agencies to deliver new insights. We have matched our data with publicly available Internal Revenue Service data to measure employment and wages in nonprofit organizations. We also are working with our colleagues at the Bureau of Economic Analysis to improve understanding of foreign direct investment in the United States. When these data become available, users can analyze employment and wages by industry and occupation in firms with and without foreign direct investment.

All of these efforts improve the quality and breadth of information available for decision makers. If you have ideas about other partnerships with our Big Data team, please send us a message or give us a call!

Using Seasonally Adjusted Data or Not: a Case Study

The Current Employment Statistics survey helps us track employment trends in the economy. The headline figures, such as the 164,000 increase in payroll employment in April, are seasonally adjusted. Seasonal adjustment smooths out increases or decreases that occur around the same time each year to make it easier to see the underlying movements in the data.

Consider the construction industry, where employment varies throughout the year, often because of the weather. The chart below shows employment each month in 2017, both seasonally adjusted and not seasonally adjusted. The not seasonally adjusted level ranged from about 6.4 million to 7.2 million jobs, but it is hard to see a trend. The seasonally adjusted level was consistently between 6.8 million and 7.1 million jobs. When we remove the seasonal variation, we can see a slight increase in construction employment over the year.

Construction employment in 2017, seasonally adjusted and not seasonally adjusted

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

While seasonally adjusted data help us see long-term trends, there are times when short-term trends can provide some insight. One example is holiday-season hiring. Certain industries, such as retail trade and parcel delivery services, ramp up hiring in the fall to prepare for increased business during the holiday season. We can see this holiday-related employment buildup with data that are not seasonally adjusted. For example, employment growth in selected retail trade industries increased by 609,000 from October to December 2017, less than the 650,000 jobs gained in the same months of 2016.

Note: Selected retail trade industries include furniture and home furnishings stores; electronics and appliance stores; health and personal care stores; clothing and clothing accessories stores; sporting goods, hobby, book and music stores; general merchandise stores; miscellaneous store retailers; and nonstore retailers.

Seasonal holiday employment buildup in selected retail trade industries, 2012–17 (not seasonally adjusted)

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

We have to be careful when we use data that are not seasonally adjusted. For example, sometimes there are 4 weeks between monthly surveys and sometimes there are 5 weeks. Seasonal adjustment accounts for these differences. When using not seasonally adjusted data, users must be aware that an extra week between surveys can exaggerate seasonal employment increases or decreases. For example, in 2017, there were 5 weeks between surveys in November, just as there were in 2012 and 2013.

Looking across the October-to-December period, the seasonal employment buildup in retail trade slowed each year following a large increase from 2012 to 2013. In each of the next four holiday seasons, job gains over the 3-month (13-week) period were less than the prior year. But 2017 included some anomalies – a strong November (72 percent of the seasonal total), followed by a weak December (7 percent of the seasonal total).

Share of seasonal holiday employment buildup in each month, selected retail trade industries, 2012–17 (not seasonally adjusted)

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

Examining the not seasonally adjusted data may provide some insights into changing hiring patterns, especially in seasonal industries. The 2017 retail trade data suggest declining holiday employment buildup but also earlier holiday employment buildup. Will this pattern continue? We’ll know more when Current Employment Statistics data come out later this year.

We can analyze other industries with seasonal patterns in a similar way. One industry is transportation, and specifically couriers and messengers, which includes parcel delivery services. As the trend in online shopping continues, employment in parcel delivery services has increased, especially during the holiday season. Other seasonal industries include ski resorts in the winter, gardening shops in the spring, and amusement parks in the summer. We can also use not seasonally adjusted data to look at layoff patterns in seasonal industries, such as certain retail industries after the holiday season.

All these data are available from the Current Employment Statistics program.

Construction employment in 2017
Month Seasonally adjusted Not seasonally adjusted
Jan 6,873,000 6,459,000
Feb 6,919,000 6,527,000
Mar 6,922,000 6,634,000
Apr 6,917,000 6,816,000
May 6,924,000 6,990,000
Jun 6,940,000 7,157,000
Jul 6,934,000 7,197,000
Aug 6,962,000 7,228,000
Sep 6,971,000 7,177,000
Oct 6,988,000 7,182,000
Nov 7,030,000 7,117,000
Dec 7,072,000 6,970,000
Seasonal holiday employment buildup in selected retail trade industries, 2012–17 (not seasonally adjusted)
Year October November December
2012 132,000 456,000 103,000
2013 142,000 435,000 184,000
2014 169,000 392,000 158,000
2015 175,000 389,000 127,000
2016 148,000 358,000 144,000
2017 128,000 438,000 43,000
Share of seasonal holiday employment buildup in each month, selected retail trade industries, 2012–17 (not seasonally adjusted)
Year October November December
2012 19.1% 66.0% 14.9%
2013 18.7 57.2 24.2
2014 23.5 54.5 22.0
2015 25.3 56.3 18.4
2016 22.8 55.1 22.2
2017 21.0 71.9 7.1