Tag Archives: Survey redesign

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.

Why This Counts: What is the Producer Price Index and How Does It Impact Me?

The Producer Price Index (PPI) – sounds familiar, but what is it exactly? Didn’t it used to be called the Wholesale Price Index? It is related to the Consumer Price Index, but how? How does the PPI impact me?

Lots of questions! In this short primer we will provide brief answers and links for more information. Note, if you are an economist, this blog is NOT for you. It’s an introduction for everyone else!

Video: Introduction to the Producer Price Index

Before we go any further – what is an index? (You said this was a primer!)

An index is like a ruler. It is a way of measuring the change of just about anything. Producer price indexes measure the average change in prices for goods, services, or construction products sold as they leave the producer.

Here is an example of how an index works:

  • Suppose we created an index to track the price of a gallon of gasoline.
  • When we start tracking, gasoline costs $2.00 a gallon.
  • The starting index value is 100.0.
  • When gasoline rises to $2.50, our index goes to 125.0, which reflects a 25-percent increase in the price of gasoline.
  • If gasoline then drops to $2.25, the index goes to 112.5. The $0.25 decline in price reflects a 10-percent decrease in the price of gasoline from when the price was $2.50.

If you are a gasoline dealer, you might find a gasoline index useful. Instead of driving around every day to write down the prices of each competitor’s gasoline and averaging them together, the index can provide the data for you. (Question #5 in the PPI Frequently Asked Questions explains how to interpret an index.)

PPI is called a “family” of indexes. There are more than 10,000 indexes for individual products we release each month in over 500 industries. That is one big family!

OK, so PPI has lots of data – but what kind of data?

PPI produces three main types of price indexes: industry indexes, commodity indexes, and final demand-intermediate demand (FD-ID) indexes.

An industry refers to groups of companies that are related based on their primary business activities, such as the auto industry. The PPI measures the changes in prices received for the industry’s output sold outside the industry.

  • PPI publishes about 535 industry price indexes and another 500 indexes for groupings of industries.
  • By using the North American Industry Classification System (NAICS) index codes, data users can compare PPI industry-based information with other economic programs, including productivity, production, employment, wages, and earnings.

The commodity classification of the PPI organizes products by type of product, regardless of the industry of production. For example, the commodity index for steel wire uses pricing information from the industries for iron and steel mills and for steel wire drawing.

  • PPI publishes more than 3,700 commodity price indexes for goods and about 800 for services.
  • This classification system is unique to the PPI and does not match any other standard coding structure.

We also have more information on the differences between the industry and commodity classification systems.

The FD-ID classification of the PPI organizes groupings of commodities by the type of buyer. For example, the PPI for final demand measures price change in all goods, services, and construction products sold as personal consumption, capital investment, export, or to government. As a second example, the PPI for services for intermediate demand measures price change for services sold to business as inputs to production.

  • PPI publishes more than 300 FD-ID indexes.
  • This FD-ID classification system is unique to the PPI and does not match any other standard coding structure.

Now let’s go back to the beginning

  • 1902: Wholesale Price Index program begins, which makes it one of the oldest continuous set of federal statistics. The Wholesale Price Index captures the prices producers receive for their output. In contrast, the Consumer Price Index captures the prices consumers pay for their purchases.
  • 1978: BLS renames the program as the Producer Price Index to more accurately reflect that prices are collected from producers, rather than wholesalers.
  • PPI also shifts emphasis from a commodity index framework to a stage of processing index framework. This minimized the multiple counting that can occur when the price for a specific commodity and the inputs to produce that commodity are included in the same total index. For example, think of gasoline and crude petroleum both included in an all-commodities index.
  • 1985: PPI starts expanding its coverage of the economy to include services and nonresidential construction. As of January 2018, about 71 percent of services and 31 percent of construction are covered.
  • 2014: PPI introduces the Final Demand-Intermediate Demand system.
  • The “headline” number for PPI is called the PPI for Final Demand. It measures price changes for goods, services, and construction sold for personal consumption, capital investment, government purchases, and exports. We also produce a series of PPIs for Intermediate Demand, which measure price change for business purchases, excluding capital investment.
  • Let me give you an example: Within the PPI category for loan services, we have separate indexes for consumer loans and business loans. The commodity index for consumer loans is included in the final demand index and the commodity index for business loans is mostly in an intermediate demand index.
  • The Frequently Asked Question on the PPI for Final Demand provides even more information on this new way of measuring the PPI. The blog, Understanding What the PPI Measures, may also be helpful.
  • We also have an article that explains how the PPI for final demand compares with other government prices indexes, such as the CPI.

Why is the PPI important?

To me?

  • Inflation is the higher costs of goods and services. Low inflation may be good for the economy as it increases consumer spending while boosting corporate profits and stocks.
  • A change in producer prices may be a leading indicator of consumers paying more or less. Higher producer prices may mean consumers will pay more when they buy, whereas lower producer prices may mean consumers will pay less to retailers. For example, if the PPI gasoline index increases, you may see an increase soon at the pump!

To others (which may impact me!)?

  • Policymakers, such as the Federal Reserve, Congress, and federal agencies regularly watch the PPI when making fiscal and monetary policies, such as setting interest rates for consumers and businesses.
  • Business people use the PPI in deciding price strategies, as they measure price changes in inputs for their goods and services. For example, a company considering a price increase can use PPI data to compare the growth rate of their own prices with those in their industry.
  • Business people adjust purchase and sales contracts worth trillions of dollars by using the PPI family of indexes. These contracts typically specify dollar amounts to be paid at some point in the future. For example, a long-term contract for bread may be escalated for changes in wheat prices by applying the percent change in the PPI for wheat to the contracted price for bread.

Video: How the Producer Price Index is Used for Contract Adjustment

PPI is a voluntary survey completed by thousands of businesses nationwide every month. BLS carefully constructs survey samples to keep the number of contacts to a minimum, making every business, large and small, critical to the accuracy of the data. We thank you, our faithful respondents! Without you, BLS could not produce gold-standard PPI data.

Finally, check out the most recent monthly PPI release to get all the latest numbers. Head to the PPI Frequently Asked Questions to learn more. Or contact the PPI information folks at (202) 691-7705 or ppi-info@bls.gov.

Want to learn more about BLS price programs? See these blogs:

 

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

Innovating for the Future

Erica L. Groshen was the 14th Commissioner of Labor Statistics. She served from January 2013 to January 2017. This is her final post for Commissioner’s Corner.

Image of former BLS Commissioner Erica L. Groshen

It didn’t take long after I became Commissioner of Labor Statistics in January 2013 for me to appreciate the skill, dedication, and innovation of the staff that works here. Whether they’re doing sampling, data collection, estimation, or dissemination; whether they’re the IT professionals or the statisticians or the HR staff; whether they’re the newest employees who are so tech-savvy or the more senior employees who hold a wealth of institutional knowledge. To a person they are phenomenal. I am honored to have had the pleasure of leading them — and letting them lead me — during the past 4 years.

 

I have had many opportunities to observe and encourage innovation during my tenure at the U.S. Bureau of Labor Statistics, from listening tours to senior staff conferences to regional office visits to discussions with a wide variety of stakeholders. From these efforts, we have identified several activities that will help us develop and implement the next generation of labor statistics. These days, we call these efforts a variety of names, such as “modernization” and “reengineering.” But, in truth, they just continue the impressive progress that has been the hallmark of BLS for the past 133 years.

In my final Commissioner’s Corner post, I want to tell you a little about some of our current reengineering efforts.

One of the things we do best at BLS is data collection, largely because we are always looking for ways to improve. Recent efforts include identifying alternative data sources, expanding electronic collection, and “scraping” information directly from the Internet. These efforts can expand the information we provide, lessen the burden we place on employers and households that provide data, and maybe even save some money to provide taxpayers the best value for their data dollar.

These efforts are not new. One source of alternative data we’ve used for many years comes from state unemployment insurance filings, which identify nearly every employer in the country. We tabulate these data but also use them as the source of our sample of employers for certain surveys and as a benchmark of detailed employment by industry. We also use information from private sources and from administrative sources, like vital statistics. Our latest efforts involve examining techniques to combine data across multiple sources, including mixing survey and nonsurvey data.

We want to give employers the opportunity to leverage the electronic data they already keep so it’s easier to respond to our surveys. These efforts include allowing employers to provide electronic information in multiple formats; identifying a single source of electronic data from employers, reducing the number of locations and number of requests made to multiple sites of the same organization; and working with employers to allow BLS to access their data directly from the Internet. We rely on good corporate citizens to supply the information that we use to produce important economic data. Making data collection easier is a win-win.

The innovation doesn’t stop at collection. We are using electronic text analysis systems extensively to streamline some of our data-processing activities. Much of the information we collect is in the form of text, such as a description of an industry or occupation, details about a workplace injury, or summaries of employee benefit plans. Transforming text into a classification system for tabulation and publication used to be a manual task. BLS has begun to transform this task through the use of machine-learning techniques, where computers learn by reviewing greater and greater amounts of information, resulting in accurate classification. As we expand our skills in this area and find more uses for these techniques, the benefits include accurate and consistent data and greater opportunities for our staff to use their brainpower to focus on new, unique, and unusual situations.

We are also modernizing our outputs, producing more with the information we have. For example, we have begun several matching projects, combining data from two or more sources to produce new information. One example is new information on nonprofit organizations. By linking our employment data with nonprofit status obtained from the Internal Revenue Service, we now have employment data separately for the for-profit and nonprofit sectors. And we took that effort one step further and produced compensation information for these sectors as well. Look for more output from these matching efforts in the future.

Finally, we’ve made great strides in how we present our information, including expanded graphics and video. And we are not stopping there. Each year we are expanding the number of data releases that include a companion graphics package. We are developing prototypes of a new generation of data releases, with more graphics and links to data series. And we have more videos to come.

My 4 years as Commissioner of Labor Statistics have flown by. I’m excited to see so many innovations begin, thrive, and foster additional innovations. I have no doubt that the culture of innovation at BLS will continue. As my term comes to an end, I know now more than ever that the skill, dedication, and creativity of the BLS staff will lead this agency to even greater advances in the years to come.