Topic Archives: Survey Respondents

Update on the Misclassification that Affected the Unemployment Rate

How hard can it be to figure out whether a person is employed or unemployed? Turns out, it can be hard. When BLS put out the employment and unemployment numbers for March, April, and May 2020, we also provided information about misclassification of some people. I want to spend some time to explain this issue, how it affected the data, and how we are addressing it.

In the monthly Current Population Survey of U.S. households, people age 16 and older are placed into one of three categories:

  • Employed — they worked at least one hour “for pay or profit” during the past week.
  • Unemployed — they did not work but actively looked for work during the past 4 weeks OR they were on temporary layoff and expect to return to work.
  • Not in the labor force — everyone else (including students, retirees, those who have given up their job search, and others).

Again, how hard can this be? It starts to get tricky when we talk to people who say they have a steady job but did not work any hours during the past week. In normal times, this might include people on vacation, home sick, or on jury duty. And we would continue to count them as employed. But during the COVID-19 pandemic, the collapse of labor markets created challenges the likes of which BLS has never encountered. People who reported zero hours of work offered such explanations as “I work at a sports arena and everything is postponed” or “the restaurant I work at is closed.” These people should be counted as unemployed on temporary layoff. As it turns out, a large number of people—we estimate about 4.9 million in May—were misclassified.

With the onset of the COVID-19 pandemic, the unemployment rate—at a 50-year low of 3.5 percent in February—rose sharply to 4.4 percent in March and to 14.7 percent in April, before easing to 13.3 percent in May. Despite the stark difference from February, we believe the unemployment rate likely was higher than reported in March, April, and May. As stated in our Employment Situation news releases for each of those months, some people in the Current Population Survey (also known as the CPS or household survey) were classified as employed but probably should have been classified as unemployed.

How did the misclassification happen?

We uncovered the misclassification because we saw a sharp rise in the number of people who were employed but were absent from their jobs for the entire reference week for “other reasons.” The misclassification hinges on how survey interviewers record answers to a question on why people who had a job were absent from work the previous week.

According to special pandemic-related interviewer instructions for this question, answers from people who said they were absent because of pandemic-related business closures should have been recorded as “on layoff (temporary or indefinite).” Instead, many of these answers were recorded as “other reasons.” Recording these answers as “on layoff (temporary or indefinite)” ensures that people are asked the follow-up questions needed to classify them as unemployed. It does not necessarily mean they would be classified as unemployed on temporary layoff, but I’ll get into that in a moment.

When interviewers record a response of “other reasons” to this question, they also add a few words describing that other reason. BLS reviewed these descriptions to better understand the large increase in the number of people absent from work for “other reasons.” Our analysis suggests this group of people included many who were on layoff because of the pandemic. They would have been classified as unemployed on temporary layoff had their answers been recorded correctly.

What are BLS and the Census Bureau doing to address the misclassification?

BLS and our partners at the U.S. Census Bureau take misclassification very seriously. We’re taking more steps to fix this problem. (The Census Bureau is responsible for collecting the household survey data, and BLS is responsible for analyzing and publishing the labor market data from the survey.) Both agencies are continuing to investigate why the misclassification occurred.

Before the March data collection, we anticipated some issues with certain questions in the survey because of the unprecedented nature of this national crisis. As a result, interviewers received special instructions on how to answer the temporarily absent question if a person said they had a job but did not work because of the pandemic. Nevertheless, we determined that not all of the responses to this question in March were coded according to the special instructions. Therefore, before the April data collection, all interviewers received an email that included instructions with more detailed examples, along with a reference table to help them code responses to this question. However, the misclassification was still evident in the April data. Before the May data collection, every field supervisor had a conference call with the interviewers they manage. In these conference calls, the supervisors reviewed the detailed instructions, provided examples to clarify the instructions, and answered interviewers’ questions.

Although we noticed some improvement for May, the misclassification persisted. Therefore, we have taken more steps to correct the problem. Before the June collection, the Census Bureau provided more training to review the guidance to the interviewers. The interviewers also received extra training aids. The electronic survey questionnaire also now has new special instructions that will be more accessible during survey interviews.

Why doesn’t BLS adjust the unemployment rate to account for the misclassification?

As I explained above, we know some workers classified as absent from work for “other reasons” are misclassified. People have asked why we just don’t reclassify these people from employed to unemployed. The answer is there is no easy correction we could have made. Changing a person’s labor force classification would involve more than changing the response to the question about why people were absent from their jobs.

Although we believe many responses to the question on why people were absent from their jobs appear to have been incorrectly recorded, we do not have enough information to reclassify each person’s labor force status. To begin with, we don’t know the exact information provided by the person responding to the survey. We know the brief descriptions included in the “other reasons” category often appear to go against the guidance provided to the survey interviewers. But we don’t have all of the information the respondent might have provided during the interview.

Also, we don’t know the answers to the questions respondents would have been asked if their answers to the question on the reason not at work had been coded differently. This is because people whose answers were recorded as absent from work for “other reasons” were not asked the follow-up questions needed to determine whether they should be classified as unemployed. Specifically, we don’t know whether they expected to be recalled to work and whether they could return to work if recalled. Therefore, shifting people’s answers from “other reasons” to “on layoff (temporary or indefinite)” would not have been enough to change their classification from employed to unemployed. We would have had to assume how they would have responded to the follow-up questions. Had we changed answers based on wrong assumptions, we would have introduced more error.

In addition, our usual practice is to accept data from the household survey as recorded. In the 80-year history of the household survey, we do not know of any actions taken on an ad hoc basis to change respondents’ answers to the labor force questions. Any ad hoc adjustment we could have made would have relied on assumptions instead of data. If BLS were to make ad hoc changes, it could also appear we were manipulating the data. That’s something we’ll never do.

How much did the misclassification affect the unemployment rate?

We don’t know the exact extent of this misclassification. To figure out what the unemployment rate might have been if there were no misclassification, we have to make some assumptions. These assumptions involve deciding (1) how many people in the “other reasons” category actually were misclassified, (2) how many people who were misclassified expected to be recalled, and (3) how many people who were misclassified were available to return to work.

In the material that accompanied our Employment Situation news releases for March, April, and May, we provided an estimate of the potential size of the misclassification and its impact on the unemployment rate. Here we assumed all of the increase in the number of employed people who were not at work for “other reasons,” when compared with the average for recent years, was due solely to misclassification. We also assumed all of these people expected to be recalled and were available to return to work.

For example, there were 5.4 million workers with a job but not at work who were included in the “other reasons” category in May 2020. That was about 4.9 million higher than the average for May 2016–19. If we assume this 4.9 million increase was entirely due to misclassification and all of these misclassified workers expected to be recalled and were available for work, the unemployment rate for May would have been 16.4 percent. (For more information about this, see items 12 and 13 in our note for May. We made similar calculations for March and April.)

These broad assumptions represent the upper bound of our estimate of misclassification. These assumptions result in the largest number of people being classified as unemployed and the largest increase in the unemployment rate. However, these assumptions probably overstate the size of the misclassification. It is unlikely that everyone who was misclassified expected to be recalled and was available to return to work. It is also unlikely that all of the increase in the number of employed people not at work for “other reasons” was due to misclassification. People may be correctly classified in the “other reasons” category. For example, someone who owns a business (and does not have another job) is classified as employed in the household survey. Business owners who are absent from work due to labor market downturns (or in this case, pandemic-related business closures) should be classified as employed but absent from work for “other reasons.”

Regardless of the assumptions we might make about misclassification, the trend in the unemployment rate over the period in question is the same; the rate increased in March and April and eased in May. BLS will continue to investigate the issue, attempting both to ensure that data are correctly recorded in future months and to provide more information about the effect of misclassification on the unemployment rate.

When Worlds Converge: Statistics Agencies Learning from Each Other during the Pandemic

We never know when our worlds are going to converge. I have used this blog to tell you about how BLS operations are continuing—and changing—due to the COVID-19 pandemic. I also plan to tell you about our international activities and will continue writing about the BLS Consumer Price Index (CPI) and other programs. Today, all three of these topics converge into one.

The COVID-19 pandemic has compelled BLS and statistical agencies worldwide to examine our processes and concepts to ensure the information we collect and publish reflects current conditions. For BLS, this means suspending all in-person data collection and relying on other methods, including telephone, internet, and email. Adding to our toolbox, BLS is now piloting video data collection. To be flexible, we have changed some collection procedures to accommodate current conditions. For example, we are now doing all of our work at home instead of in our offices. We are learning more every day about teleworking more effectively, and we are training our staff as we learn.

Once we collect the data, we are examining how we need to adapt our processing and publication. Will our typical procedures to account for missing data still apply? Will seasonal patterns in the data change due to COVID-19? Will we be able to publish the level of detail our data users have come to expect? These and more are open questions. We will make informed decisions as we learn more about the pandemic’s impact on our data and operations. What I do know is that BLS has a long practice of sharing its procedures and methods, including any changes. We already have extensive information about COVID-19 on the BLS website, and we continue to update that information. We also provide program-specific information with each data release to alert users to any unique circumstances in the data.

Since BLS has long been known for producing gold-standard data, information about our procedures and methods is also of great interest to our international colleagues. In fact, BLS has helped statistical organizations throughout the world with the collection, processing, analysis, publishing, and use of economic and labor statistics for more than 70 years. We provide this assistance primarily by our Division of International Technical Cooperation. They strengthen statistical development by organizing seminars, consultations, and meetings for international visitors with BLS staff. This division also serves as the main point of contact for the many international statistical organizations that compile information, publish comparable statistics worldwide, share concepts and definitions, and work to incorporate improvements and innovations.

A hallmark of our international activities has been onsite seminars at BLS, often attended by a multinational group of statistical experts and those working to become experts. At these seminars, BLS technical staff present details on every aspect of statistical programs, including concept development, sampling, data collection, estimation procedures, publishing, and more. In recent years, funding, travel restrictions, and other limitations have reduced the number of in-person events, replaced to some extent by virtual events. And of course, the current COVID-19 pandemic and related travel restrictions mean all such events are now being held virtually. But they still go on.

Recently, our international operations converged with our COVID-19 response when the International Technical Cooperation staff set up a virtual meeting between BLS staff primarily from our Consumer Price Index program and their counterparts at India’s Ministry of Statistics and Programme Implementation (MOSPI). They met to discuss challenges in producing consumer price data during the ongoing pandemic. The discussion was largely about methodology: what to do with missing prices and how to adjust weights to reflect real-time shifts in spending that consumers are making in response to the pandemic. It is helpful to hear from worldwide colleagues who are facing similar challenges. These issues are unprecedented, and we know the potential solutions for one country may not be ideal for the nuanced conditions in another country.

In India, for instance, commerce has been limited to essential commodities—food, fuel, and medicine. This will likely leave them unable to publish some indexes. While this is unfortunate in the present time, it is fairly straightforward; they can’t publish what they don’t have. It gets more complicated a year from now. What does it mean to have an annual price change when the denominator is missing? The CPI deals with this by having a fairly robust imputation system—basically “borrowing” price change from similar areas and items—but we will be monitoring the situation closely to make sure our assumptions about what is similar remain valid.

One advantage BLS has over MOSPI is that we are able to collect data by telephone, email, or on the web. MOSPI has traditionally only done in-person collection. Both agencies are transitioning to different modes of collection, but we have significantly greater experience.

Sharing information with our international colleagues, about the CPI and other programs, and about our COVID-19 experience, is a key part of the BLS mission. These worlds continue to converge, not just during organized meetings but also on websites and wikis maintained by statistical organizations and through participation in expert groups and conferences. For example, the United Nations Economic Commission for Europe hosts a ”statswiki” that currently has pages dedicated to COVID-19 and Official Statistics. It is a small world after all, and the worldwide social distancing we are all experiencing makes it clear that we are all in this together. And together, BLS and our international colleagues, reacting to COVID-19 and making adjustments to consumer price indexes and other statistics, will continue to provide vital information that tracks changes in the world economy.

Paid Leave Benefits When You Are Unable to Work

Many American workers have lost jobs or had their work hours reduced as a result of the COVID-19 pandemic and response efforts. Many other workers still have jobs, but their work environment probably has changed since March. It’s reasonable to assume more people are working from home now than the 29 percent we reported who could work at home in 2017–18. At BLS we are still working to provide you with the latest economic data and analysis, but nearly all of us are now working from home, instead of in our offices.

Still, there are many jobs that just can’t be done from home. In these challenging times, I know we all are grateful for the healthcare workers who are treating patients who have COVID-19 and other medical conditions. We’re grateful for our emergency responders and for the truck drivers, warehouse workers, delivery workers, and staff in grocery stores, pharmacies, and other retail establishments that provide us with the necessities of daily life. As much as I think of these men and women as superheroes, I know they are humans. Even extraordinary humans can get sick, or they may need to take care of family members who get sick. Let’s look at the leave benefits available to them if they need it.

According to our National Compensation Survey, 73 percent of private industry workers were covered by paid sick leave in 2019. Among state and local government workers, 91 percent were covered by paid sick leave. The availability of sick leave benefits varied by occupation, ranging from 94 percent of managers in private industry to 56 percent of workers in construction and extraction occupations.

The share with paid sick leave also varies by industry, pay level, size of establishment, and other characteristics of jobs and employers. The following chart shows sick leave availability for employers of different sizes.

Percent of workers in private industry with access to paid sick leave by establishment size, March 2019

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

Paid sick leave plans commonly provide a fixed number of days per year. The number of days may vary by the worker’s length of service with the employer. The average in private industry in 2019 was 7 paid sick leave days.

Average number of paid sick leave days per year for workers in private industry, by length of service and establishment size, March 2019

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

About half of workers with such a plan could carry over unused days from year to year.

We recently posted a new fact sheet on paid sick leave that provides even more detail.

In the past few years, some states and cities have mandated that certain employers provide their workers with paid sick leave. We include these mandated plans in our data on paid leave. A Federal law passed in March 2020 requires paid sick leave for certain workers affected by COVID-19.

In addition to paid sick leave, some employers offer a short-term disability insurance plan when employees can’t work because of illness. These plans are sometimes called sickness and accident insurance plans. This was traditionally a blue-collar or union benefit, and it often replaces only a portion of an employee’s pay. In 2019, 42 percent of private industry workers had access to such a benefit. Like sick leave, the availability of short-term disability benefits varies widely across worker groups. Some states provide Temporary Disability Insurance plans that provide similar benefits.

While the National Compensation Survey asks employers what benefits they offer to workers, the American Time Use Survey recently asked workers whether paid leave is available from their employer and whether they used it. In 2017–18, two-thirds of workers had access to paid leave at their jobs. These data include information on age, sex, and other characteristics. For example, younger workers (ages 15–24) and older workers (age 65 and older) were less likely to have access to paid leave than were other workers.

Percent of workers with access to paid leave by age, 2017–18 averages

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

While the survey did not ask workers to classify the type of leave, they were asked the reasons they could take leave. Of those with paid leave available, 94 percent could use it for their own illness or medical care, and 78 percent could use it for the illness or medical care of another family member.

I hope you and your loved ones remain healthy and are able to take care of each other in these challenging times. High-quality data will be vital in the public health response to the COVID-19 pandemic. High-quality data also will be vital for measuring the economic impact of the pandemic and recovery from it. My colleagues at BLS and our fellow U.S. statistical agencies remain on the job to provide you with gold standard data.

Percent of workers in private industry with access to paid sick leave by establishment size, March 2019
Establishment sizePercent

1–49 workers

64%

50–99 workers

68

100–499 workers

80

500 workers or more

89
Average number of paid sick leave days per year for workers in private industry, by length of service and establishment size, March 2019
Length of serviceAll establishments 1 to 49 workers50 to 99 workers100 to 499 workers500 workers or more

After 1 year

76678

After 5 years

77679

After 10 years

77779

After 20 years

77779
Percent of workers with access to paid leave by age, 2017–18 averages
AgePercent

Ages 15–24

35.4%

Ages 25–34

70.3

Ages 35–44

71.7

Ages 45–54

74.4

Ages 55–64

74.2

Age 65 and older

51.7

How Have We Improved the Consumer Price Index? Let Me Count the Ways

Soon after I became Commissioner, the top-notch BLS staff was briefing me on the many programs and details that make up BLS. I asked the staff working on the Consumer Price Index (CPI) if they could list for me some of the improvements that have occurred over the past few years. It has been nearly a quarter century since the Boskin Commission studied the CPI and recommended enhancements. I knew many of these enhancements had been implemented, along with other improvements. But I was shocked to see my staff come back with an 8-page, detailed listing of 77 substantive improvements that have been implemented since 1996.

You may have thought a price index that has been around since 1913 is happy to rest on its laurels. Well, you’d be wrong. There are improvements to the CPI going on all the time. As I reviewed the list, I noticed a number of improvements involve the introduction of new or changed goods and services, such as cell phones or streaming services. I also noticed improvements in how we handle product changes, as I wrote recently in a blog about quality adjustment. But these topics only scratch the surface.

I’m not going to describe every CPI enhancement that has taken place over the past 24 years; you can find much more detail on the CPI webpage. But I will whet your appetite by highlighting a few categories of improvements.

Keeping the CPI market basket up to date

The goal of the CPI is to track the change in the prices consumers pay for a representative market basket of goods and services. Let’s look at the what and where of that market basket.

  • What we collect — goods and services. We collect prices for a market basket of goods and services, designed to represent what consumers are buying. In January 2002, we switched from updating that market basket every 10 years to every 2 years, providing a better representation of current spending patterns.
  • What we collect — housing. We track the cost of housing by a separate sample of housing units. In 2010, we increased that sample to improve accuracy. In 2016 we began rotating that sample every 6 years. Previously, the housing sample was only rotated when new geographic areas were introduced after the U.S. census.
  • Where we collect — outlets. We collect prices from stores and businesses that are chosen based on where consumers shop and buy goods and services. In January 1998, we switched from updating this sample of “outlets” every 5 years to every 4 years. And in January 2020, we switched the source used to determine those outlets to the Consumer Expenditure Survey, which is also the source of our spending information for the market basket of goods and services people buy. Previously we used a separate survey of households to identify outlets.
  • Where we collect — geography. We collect prices for goods and services in selected geographic areas, designed to represent all urban areas of the United States. In January 2018, we updated the geographic areas, designed to represent current population trends. We last updated these areas in 1998.

Collecting CPI information

According to folklore, CPI data collection was accomplished by staff who dressed up in high fashion, the ladies in fancy hats and white gloves and the gentlemen in the finest haberdashery, who then went shopping to determine the latest prices. That’s not how it happens. CPI staff are not paid to “shop” to collect prices. We use trained experts who are skilled at gaining cooperation from many different types of businesses; ensuring they are obtaining price information for goods and services that are consistent from one month to the next or making appropriate adjustments; and gathering information from thousands of outlets about hundreds of thousands of goods and services over a short data-collection period. Let’s look at how the data-collection process has improved:

  • When we collect — In June 2005, the CPI switched from a collection period that spanned the first 15–18 days of the month to collection across the entire month. This provides more representative data, especially for items that frequently vary in price within the same month.
  • How we collect — In January 1998, the CPI began using computerized data-collection tools, which automate certain math functions and screen for errors or inconsistencies. We continue to upgrade our processes; CPI data-collection staff recently began using a new generation of tablet computers.
  • Alternative collection — Not all price information comes from traditional collection with stores. Some information comes from websites, corporate data files, third parties that combine data from different sources, and more. In fact, the CPI and other BLS programs are focused on identifying even more alternative collection methods in the coming years.

Calculating the CPI

Once we collect the prices on all these goods and services, we need to calculate an index. In simple terms, we find the difference between the price in month 1 and the price in month 2, and express that difference as a rate of change from month 1. We publish rates of change and also express current prices as an index, which is equal to 100 in a base period.

Many factors and decisions go into combining data for an item and then combining data for all items into the published CPI. We’ve improved those calculations in several ways over the past few years.

  • Geometric mean — In January 1999, the CPI switched the formula for calculating price changes at the component item level from an arithmetic mean to a geometric mean. This allows the overall index to capture substitutions consumers make across specific products within a component item category when the prices of those products change relative to one another. With the geometric mean formula, BLS does not assume consumers substitute hamburgers for steak, which are in different component categories. The formula only captures substitution within a component category, such as among types of steak.
  • More decimal places — In January 2007, the CPI began publishing index numbers to 3 decimal places, which improved consistency between published index numbers and rates of change.

New information available to the public

While the CPI has been around for over a century, we have added a number of new indexes over time, to provide a variety of inflation figures. Here are some of the newest products in the CPI family:

  • Research (Retroactive) series — The CPI Research Series incorporates many of the improvements that came out of the Boskin Commission. The series provides a pretty consistent way to measure price changes from 1978 up to the most recent full year.
  • Chained CPI series – The Chained CPI uses an alternative formula that applies spending data in consecutive months to reflect any substitution that consumers make across component item categories in response to changes in relative prices. For example, this index would capture consumer substitution of hamburger for steak. This measure is designed to come closer to a “cost-of-living” index than other BLS measures. The series was first produced in 2002.
  • Elderly research series – The CPI for the Elderly reweights the component CPI data based on the spending patterns of elderly households. This series, mandated by Congress, began in 1988. In 2008, we extended the series retrospectively back to 1982.

As I have mentioned in the past, we are always working to improve the CPI. We recently contracted with the Committee on National Statistics, part of the National Academy of Sciences, to provide guidance on a variety of issues. I’ll use this space to report on the Committee’s work, as well as other improvements underway in the CPI.

How We Collect Data When People Don’t Answer the Phone

I was asked recently how the U.S. Bureau of Labor Statistics can collect data these days when no one answers the telephone. A legitimate question and one we grapple with all the time. I had two answers – one related to data collection methods and one related to sources of data. I will elaborate here about both.

Beige wall phone with rotary dial

But first, do you remember the days before caller ID, when everyone answered the phone? If you were at home, the rotary phone, permanently attached to the kitchen wall, always rang during dinner.

If you were in the office, the phone probably had a row of clear plastic buttons at the bottom that would light up and flash. In either case, who was on the other end of the phone was a mystery until you answered. In those days, your friendly BLS caller could easily get through to you and ask for information.

Vintage office phone with rows of buttons

Fast forward to today’s world of smart phones and other mobile devices. Nobody talks on the phone anymore. Many phone calls are nuisances. A call from BLS might show up as Unknown Number, U.S. Government, or U.S. Department of Labor on your caller ID, or identified as potential spam. With the spread of “spoofing,” many people do not answer calls from numbers they don’t recognize. How do we get around these issues?

Data Collection

At BLS, we consider data collection as much an art as a science. Sure, our staff needs to be well-versed in the information they are collecting. But they also need to be salespersons, able to convince busy people to spend a few minutes answering key questions. Part of that art is making a connection. There are old-fashioned ways that still work, such as sending a letter or showing up at the door. And there are more modern techniques, such as email and text. We are nothing if not persistent.

Our data-collection techniques have been called “High Touch, High Tech.” We start by building a relationship—the High Touch step. BLS has a wide range of information that people and businesses can use to help make informed decisions. We can help you access that information, and we love to see survey respondents use BLS data they helped us produce. In return, we ask for some information from you. There’s where High Tech comes in. We continue to add flexibility to our data-collection toolkit. You can provide information in person, on paper, or on the phone. You also can email information or an encrypted file. Or you can access our online portal anytime and anywhere to provide information or upload a data file. We need your information, and we want to make providing that information as easy as possible.

For example, this chart shows the number of employer self-reports that we’ve received through our online portal over the past several years. Internet data collection has really taken off.

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

Another data-collection strategy we use is asking businesses to allow us to get the information we need from their website. This might involve web scraping data or using an Application Programming Interface (API). We have had success showing businesses that we can get what we need from their website, often eliminating the need for them to compile data.

Alternative Data

Beyond these data-collection strategies, we are expanding efforts to get information from alternative sources, lessening our need to contact businesses and households. Some BLS programs, such as Local Area Unemployment Statistics, the Quarterly Census of Employment and Wages, and Productivity Studies, rely heavily on administrative data and information from other surveys. In these cases, there is little need to contact businesses or people directly.

Other BLS programs, such as the Consumer Price Index (CPI) and the Employment Cost Index (ECI), need to capture timely information. But there are alternatives that can complement direct data collection. The CPI, for example, has produced an experimental price index for new vehicles based on a file of vehicle purchase transactions provided by J.D. Power. Using information from sources like that may eventually lessen the need to have BLS employees contact vehicle dealerships. The ECI found that it was easier to capture employer premiums for unemployment insurance from state tax records than to ask employers.

Alternative data come in many forms, from government records, data aggregators, scanners, crowdsourcing, corporate data files, and many more. BLS is investing heavily in alternative data-collection techniques and alternative data sources. The High Touch and High Tech approach we use every day in our data-collection operations helps us to maximize data quality and minimize respondent burden and cost.

The telephone may go the way of the dinosaur, but that’s not stopping us from using every tool at our disposal to continue to produce gold standard data to inform your decisions.

Number of transactions with BLS internet data collection
YearNumber of transactions

2004

105,145

2005

148,754

2006

219,923

2007

534,555

2008

972,605

2009

1,544,795

2010

1,909,410

2011

2,322,540

2012

2,769,694

2013

3,236,376

2014

3,288,665

2015

3,554,639

2016

4,013,415

2017

4,513,297

2018

4,685,414

2019

4,868,939