Topic Archives: Survey Respondents

BLS Measures Electronically Mediated Work

Are you a ride-share driver using a mobile app (like Uber or Lyft) to find customers? Maybe you do household chores or yardwork for others by finding short-term jobs through a website (such as TaskRabbit or Handy) that arranges the payment for your work. Or perhaps you perform online tasks, like taking surveys or adding descriptive keywords to photos or documents through a platform (like Amazon Mechanical Turk or Clickworker). If so, you are an electronically mediated worker. That’s a term BLS uses to identify people who do short jobs or tasks they find through websites or mobile apps that connect them with customers and arrange payment for the tasks. Have you ever wondered how many people do this kind of work?

BLS decided to find out. In the May 2017 Contingent Worker Supplement to the Current Population Survey, we asked people four new questions designed to measure electronically mediated employment.

Measuring electronically mediated work is difficult

After studying respondents’ answers to the new questions and other information we collected about them, we realized the new questions didn’t work as intended. Most people who responded “yes” to the questions clearly had not found their work through a website or app. For example, a vice president of a major bank, a local police officer, and a surgeon at a large hospital all said they had done electronically mediated work on their main job. Many people seemed to think we were asking whether they used a computer or mobile app on their job. That could apply to many jobs that aren’t electronically mediated.

But it wasn’t all for naught. After extensive evaluation, we concluded we could use the other information in the survey about respondents’ jobs to identify and recode erroneous answers. That allowed us to produce meaningful estimates of electronically mediated employment.

So, who does electronically mediated work?

Based on our recoded data, we found that 1.6 million people did electronically mediated work in May 2017. These workers accounted for 1.0 percent of total employment. Compared with workers overall, electronically mediated workers were more likely to be ages 25 to 54 and less likely to be age 55 or older. Electronically mediated workers also were slightly more likely to be Black, and slightly less likely to be White, than workers in general. In addition, electronically mediated workers were more likely than workers overall to work part time (28 percent versus 18 percent).

Workers in the transportation and utilities industry were the most likely to have done electronically mediated work, with 5 percent of workers in this industry having done such work. Self-employed workers were more likely than wage and salary workers to do electronically mediated work (4 percent versus 1 percent).

What’s next?

We currently don’t have plans to collect information on electronically mediated work again. And even if we did, we wouldn’t want to use the same four questions. At the least, we would need to substantially revise the questions so they are easier for people to understand and answer correctly.

Taking a broader look, we are working with the Committee on National Statistics to learn more about what we should measure if we field the survey again. The committee is a federally supported independent organization whose mission is to improve the statistical methods and information on which public policies are based.

How can I get more information?

The data are available on our website, along with an article that details how we developed the questions, evaluated the responses, recoded erroneous answers, and analyzed the final estimates.

If you have a specific question, you might find it in our Frequently Asked Questions. Or you can contact our staff.

Digging Deeper into the Details about the Unemployed

National employment indicators, such as the unemployment rate, get attention as we release them each month. In August 2018, the unemployment rate stood at 3.9 percent, the same as in July. The rate in May, 3.8 percent, was the lowest since 2000. In addition to reporting this headline number, the Bureau of Labor Statistics provides considerable detail about those who are employed – and those who are unemployed. Let’s explore.

But first, a reminder. The unemployment rate and details about the unemployed come from the monthly Current Population Survey, a survey of roughly 60,000 households. We collect information about household members age 16 and over. These individuals are counted as “employed” if they say they performed at least one hour of work “for pay or profit” during the reference week, the week including the 12th of the month. People are “unemployed” if they say that during the reference week they (1) had not worked; (2) were available for work; and (3) had actively looked for work (such as submitting a job application or attending a job interview) sometime during the 4-week period ending with the reference week.

Together, the employed and unemployed make up the “labor force.” The unemployment rate is the share of the labor force who are unemployed. Those who are neither employed nor unemployed are “not in the labor force.” This category includes students, retirees, stay-at-home parents, people with a disability, and others who are not working or actively looking for work.

We have more measures that help to provide a fuller picture of America’s labor force. These include people who work part time but would prefer to work full time. We also count people who have searched for work in the past 12 months but not in the past 4 weeks (and are therefore not counted as unemployed). Further, we count a subset of this group who are not looking because they do not believe work is available for them. People who fall into these categories are included in the alternative measures of labor underutilization, which we publish each month.

Let’s look at the unemployed in more detail. We can sort the unemployed into 4 groups: (1) new entrants to the labor force (such as recent graduates now looking for work); (2) reentrants to the labor force (those who had a job, then left the labor force, and are now looking for work again); (3) job leavers (those who recently left a job voluntarily); and (4) job losers (those who left a job involuntarily, such as getting laid off or fired or completing temporary jobs).

Typically, the largest share of the unemployed are job losers, and this share jumps during economic downturns. While the other categories are less volatile, they make up a larger share of the total as job losers decline. For example, in August 2018, 44 percent of the unemployed were either reentrants or those who recently left a job. The share of the unemployed in both of these categories is higher than in 2009, when job losers accounted for nearly two-thirds of the unemployed. A potential reason for people to reenter the labor market, or leave an existing job to look for another, is that they perceive jobs are readily available. In periods of high unemployment, reentrants make up a smaller proportion of the unemployed. For example, when the unemployment rate reached 10.0 percent in October 2009, reentrants made up only 22 percent of the unemployed. Similarly, in 2009 and 2010, the share of the unemployed who were job leavers (those who quit their jobs voluntarily) was less than 6 percent, about half of the current share.

A chart showing the number of unemployed by the reason for unemployment from 1998 to 2018

Editor’s note: Data for this chart are available from our data-retrieval tool.

Another measure to assess the strength of the labor market is the number of people quitting their job. These data are from our Job Openings and Labor Turnover Survey. That survey asks employers about the number of “separations” over the past month. It classifies separations as either quits (voluntary), layoffs or discharges (not voluntary), or other (including retirements, deaths, and disability). The most recent data, for July 2018, identified 3.6 million quits over the month, an all-time high. (The survey began in 2000.) The quit rate, which divides quits by total employment, was 2.4 percent, also close to a record high.

Most of the time, quits exceed layoffs and discharges, except in periods of high unemployment.

A chart showing the number of people each month who quit their jobs, were laid off or discharged from their job, or separated for other reasons from 2000 to 2018

Editor’s note: Data for this chart are available from our data-retrieval tool.

At any given time, there is a lot of movement in and out of jobs, and in and out of the labor market. And individuals have a variety of reasons for making such moves. But the overall trend in recent years toward individuals coming back into the labor market and voluntarily quitting their jobs suggests that individuals may feel that job opportunities are available.

A Clearer Look at Response Rates in BLS Surveys

Hands holding a tablet computer and completing a surveyPeople know BLS for our high-quality data on employment, unemployment, price trends, pay and benefits, workplace safety, productivity, and other topics. We strive to be transparent in how we produce those data. We provide detailed information on our methods for collecting and publishing the data. This allows businesses, policymakers, workers, jobseekers, students, investors, and others to make informed decisions about how to use and interpret the data.

We couldn’t produce any of these statistics without the generous cooperation of the people and businesses who voluntarily respond to our surveys. We are so grateful for the public service they provide.

To improve transparency about the quality of our data, we recently added a new webpage on response rates to our surveys and programs. We previously published response rates for many of our surveys in different places on our website. Until now there hasn’t been a way to view those response rates together in one location.

What is a response rate, and why should I care?

A response rate is the percent of potential respondents who completed the survey. We account for the total number of people, households, or businesses we tried to survey (the sample) and the number that weren’t eligible (for example, houses that were vacant or businesses that had closed). Response rates are an important measure for survey data. High response rates mean most of the sample completed the survey, and we can be confident the statistics represent the target population. Low response rates mean the opposite, and data users may want to consider other sources of information.

Do response rates tell the whole story?

A low response rate may mean the data don’t represent the target population well, but not necessarily. How much a low response rate affects how well the estimates represent the population is called nonresponse bias. Some important research by Robert M. Groves and Emilia Peytcheva published in the January 2008 issue of Public Opinion Quarterly looked at the connection between response rates and nonresponse bias in 59 studies. The authors found that high response rates can reduce the risk of bias, but there is not a strong correlation between response rate and nonresponse bias. Some surveys had a very low response rate but did not have evidence of high nonresponse bias. Other surveys had high nonresponse bias despite high response rates.

This means we should look at response rates with other measures of data quality and bias. BLS has studied nonresponse bias for many years. We have links to many of those studies in our library of statistical working papers.

What should I be looking for on the new page?

With response rates from multiple surveys in a single place, you can look for patterns across surveys and across time. For example, across every graph we see that response rates are declining over time. This is happening for nearly all surveys, government and private, on economic and other topics. It is simply getting harder to persuade respondents to answer our surveys.

Individual survey response rates are also interesting compared with other BLS surveys. We see that some surveys have higher response rates than others. To understand why this might be, we’ll want to look at the differences between the surveys. Each survey has specific collection procedures that affect response rates. For example, the high response rate for the Annual Refiling Survey (shown as ARS in the second chart) may catch your eye. When you see that it has a 12-month collection period and is mandatory in 26 states, the rate makes more sense.

We also can see how survey-specific changes have affected a survey’s response rate. For example, we see a drop in the response rate for the Telephone Point of Purchase Survey around 2012. This drop likely resulted from a change in sample design, as the survey moved from a sample of landline telephones to a dual-frame sample with both landlines and cell phones. Because the response rate for this survey continues to decline, we are developing a different approach for collecting the needed data.

What should I know before jumping into the new page?

There’s a lot of information! We’ve tried to make it as user friendly as possible, including a glossary page with definitions of terms and a page to show how each survey calculates their response rates. On the graphs, you can isolate a single survey by hovering over each of the lines. You can also download the data shown in each graph to examine it more closely.

We hope you will find this page helpful for understanding the quality of BLS data. Please let us know how you like it!

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.