Tag Archives: Statistics awareness

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.

Topics: Consumer Spending | Geographic Information | Inflation and Prices | Survey Respondents | U.S. Statistical System | Why This Counts
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State Productivity: A BLS Production

We have a guest blogger for this edition of Commissioner’s Corner. Jennifer Price is an economist in the Office of Productivity and Technology at the U.S. Bureau of Labor Statistics. She enjoys watching theatrical performances when she’s not working.

I recently had the pleasure of attending a high school play. The cast was composed of a male and female lead and at least a dozen supporting actors. The program listed the performers and acknowledged many other students, parents, teachers, and administrators. They all played some important role to bring the play to life—lighting, sound, painting props, sewing costumes, creating promotional materials, selling tickets, working concessions. All of these pieces came together harmoniously to make the performance a success.

Setting the Stage: New Measures of State Productivity

We can view the health of the nation’s economy through the same lens. Our diversified economy is made up of lead performers and supporting roles in the form of industries. Some industries contribute more heavily to growth in output or productivity, playing the star role. Other industries are supporting characters, contributing to a smaller, but necessary, share of growth. Our productivity program recently published a webpage that examines how industries contribute to the nation’s private business output and productivity growth.

We also can examine these roles geographically. Until recently, BLS productivity measures were only produced at the national level. Last June, BLS published experimental measures of state labor productivity for the private nonfarm business sector. These measures, which cover the period from 2007 to 2017, will help us learn more about productivity growth in each state and how each state contributes to national productivity trends.

Measuring productivity for all states allows us to credit the role played by each state, not just the total performance of the national economy or region. Just as each person, no matter how small their role, was necessary for the success of the school play, each state contributes to how we evaluate national or regional productivity. When we examine the contribution of each state to total productivity trends, we find that, like actors, no two states perform identically. Similar individual growth rates may have different impacts on the productivity of the nation or region. By analyzing state productivity trends over the long term, we learn more about regional business cycles, regional income inequality, and the role of local regulations and taxes on growth.

From 2007 to 2017, labor productivity changes ranged from a gain of 3.1 percent per year in North Dakota to a loss of 0.7 percent per year in Louisiana.

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

We estimate each state’s annual contribution to national or regional productivity growth by multiplying the state’s productivity growth rate by its average share of total current dollar national or regional output. The economic size of each state influences its contribution to national and regional estimates. From 2007 to 2017, California was our lead performer, with the largest contribution to national productivity growth. The state’s productivity grew 1.7 percent per year on average, and its large economy means it contributed more than one-fifth of the 1.0-percent growth in national labor productivity.

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

Supporting actors included Texas and New York. Making a cameo appearance was North Dakota; despite having the largest productivity growth rate, it ranked 28th in terms of its contribution to national productivity growth. Stars in each region included Illinois (Midwest), New York (Northeast), Texas (South), and California (West). Understudies—those states with the largest growth rates—were North Dakota (Midwest), Pennsylvania (Northeast), and Oklahoma (South). Oregon and Washington shared this role out West.

Second Act

For now, our new measures cover the private nonfarm sector for all 50 states and the District of Columbia from 2007 to 2017. These measures include output per hour, output, hours, unit labor costs, hourly compensation, and real hourly compensation. Our measures of labor productivity for states are experimental, meaning we’re still assessing them and considering ways to improve them. In the second act, we will be looking into producing state-level measures for more detailed sectors and industries.

For an encore performance, check out our state labor productivity page. We’d love to hear your feedback! Email comments to productivity@bls.gov.

Annual percent change in labor productivity in the private nonfarm sector, 2007–17
StateAnnual percent change

North Dakota

3.1

California

1.7

Oregon

1.7

Washington

1.7

Colorado

1.6

Oklahoma

1.6

Maryland

1.5

Montana

1.5

Pennsylvania

1.5

Massachusetts

1.4

New Mexico

1.4

Vermont

1.4

Idaho

1.3

Kansas

1.3

Nebraska

1.1

New Hampshire

1.1

South Carolina

1.1

Tennessee

1.1

Texas

1.1

West Virginia

1.1

Alabama

1.0

Hawaii

1.0

Kentucky

1.0

Minnesota

1.0

New York

1.0

Rhode Island

1.0

South Dakota

1.0

Virginia

1.0

Georgia

0.9

Arkansas

0.8

Missouri

0.8

Ohio

0.8

Utah

0.8

Illinois

0.7

North Carolina

0.7

Delaware

0.6

Florida

0.6

Iowa

0.6

Indiana

0.5

Mississippi

0.5

New Jersey

0.5

Wisconsin

0.5

Alaska

0.4

Arizona

0.4

District of Columbia

0.4

Michigan

0.4

Maine

0.3

Nevada

0.3

Wyoming

0.1

Connecticut

-0.5

Louisiana

-0.7
States with the largest contributions to national labor productivity, average annual percent change, 2007–17
StateState contribution to U.S. labor productivity

California

0.22

Texas

0.10

New York

0.08

Pennsylvania

0.06

Washington

0.04

Massachusetts

0.04

Illinois

0.03
Topics: Geographic Information | Productivity | U.S. Statistical System | Why This Counts
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Projected Occupational Openings: Where Do They Come From?

Toward the beginning of each school year, BLS issues a new set of Employment Projections, looking at projected growth and decline in occupations over the next decade. These estimates are important for understanding structural changes in the workforce over time. But to identify opportunities for new workers, we need to look beyond occupational growth and decline, to a concept we call “occupational openings.”

Occupational openings are the sum of the following:

  • Projected job growth (or decline)
  • Occupational separations — workers leaving an occupation, which includes:
    • Labor force exits — workers who leave the labor force entirely, perhaps to retire
    • Occupational transfers — workers who leave one occupation and transfer to a different occupation.

This video explains the concept of occupational openings further.

BLS publishes the projected number of occupational openings for over 800 occupations. Not surprisingly, some of the largest occupations in the country have some of the largest number of openings. For example, certain food service jobs, which include fast food workers, are projected to have nearly 800,000 openings per year over the next decade. I guess this isn’t a surprise in an occupation with over 3.7 million workers.

But when we delve into the information on occupational openings a little further, more stories emerge. Some related occupations have very different patterns of openings. And some occupations have similar levels of openings for different reasons. Let’s take a look at a few examples.

In 2018, there were over 800,000 lawyers in the U.S., and a projected 45,000 annual openings for lawyers, about 5.5 percent of employment. At the same time, there were fewer than half the number of paralegals and legal assistants (325,000), with projected annual openings around 40,000 per year – 12.4 percent of employment. These two related occupations had similar numbers of projected openings, but those openings represented different proportions of current employment. Such differences reflect required education, demographics, compensation, and other variables. Lawyers tend to have professional degrees that are specialized for that occupation and are therefore more closely tied to their occupation than paralegals, who have more diverse educational backgrounds. You can find out more about how worker characteristics affect these numbers in the Monthly Labor Review.

Now let’s look at the sources of occupational openings. In this first example, we compare two occupational groups: installation, maintenance, and repair occupations and healthcare support occupations. These are broad categories that include a number of different individual occupations.

Average annual occupational openings for installation, maintenance, and repair occupations and healthcare support occupations, 2018–28

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

In this example, both occupational groups have projected annual openings of a little over 600,000 per year, yet they come from different sources. Two-thirds of the openings among installation occupations result from workers leaving to go to other occupations; in contrast, just under half the openings among healthcare support occupations are from people moving to other occupations. Looking at projected job growth, BLS projects that healthcare support occupations, the fastest growing occupational group, will add more than three times as many new jobs as installation occupations, annually over the next decade (78,520 versus 23,320).

Now let’s look at two individual occupations — web developers and court, municipal, and license clerks. These are very different jobs, but both are projected to have about 15,000 annual openings over the next decade. Here, too, occupational openings come from very different places, as this chart shows:

Average annual occupational openings for web developers and court, municipal, and license clerks, 2018–28

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

In this case, around 67 percent of openings for web developer jobs come from workers transferring to other jobs, compared with only 49 percent transfers for clerks. But a greater share of clerks are exiting the labor force. Once again, differences are due to a variety of factors, although the age of workers is a significant factor in this case — web developers have a median age of 38.3, while clerks tend to be older, with a median age of 49.1. Younger workers are more likely to transfer occupations, while older workers are more likely to exit the labor force, as for retirement.

So what does all this really mean? If nothing else, you can see that the thousands of individual data elements available through the BLS Employment Projections program tell a thousand different stories, and more. Whether large or small, growing or declining, there’s information about hundreds of occupations that can be helpful to students looking for careers, counselors helping those students and others, workers wanting to change jobs, employers thinking about their future, policymakers considering where to put job training resources, and on and on. These examples just scratch the surface of what BLS Employment Projections information can tell us. Take a look for yourself.

Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Installation, maintenance, and repair occupations

23,320195,700413,900

Healthcare support occupations

78,520235,500299,600
Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Web developers

2,0902,90010,100

Court, municipal, and license clerks

6707,0007,300
Topics: Career Outlook | Employment Projections | Job Openings, Hires, and Separations | Occupations | Why This Counts
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Ensuring Security and Fairness in the Release of Economic Statistics

The U.S. Bureau of Labor Statistics is the gold standard of accurate, objective, relevant, timely, and accessible statistical data, and I am committed to keeping it that way. As Commissioner, it is my obligation to do everything possible to protect the integrity of our data and to make sure everyone has equitable access to these data.

One step toward equitable access and data security is coming soon; on March 1, 2020, the U.S. Department of Labor (DOL) will eliminate all electronics from the lock-up facility where we allow members of the media to review economic releases and prepare news stories before the official release of the data. We are changing the procedures to better protect our statistical information from premature disclosure and to ensure fairness in providing our information to the public.

For many years the news media have helped BLS and the Employment and Training Administration (ETA) inform the public about our data. Since the mid-1980s, BLS and ETA have provided prerelease data access to news organizations under strict embargoes, known as “lock-ups.” We have provided this early access consistent with federal Statistical Policy Directives of the Office of Management and Budget. BLS uses the lock-up for several major releases each month, including the Employment Situation and Consumer Price Index. ETA uses the lock-up for the Unemployment Insurance Weekly Claims data. These economic data have significant commercial value and may affect the movement of commodity and financial markets upon release.

Because of technological advancements, the current lock-up procedure creates an unfair competitive advantage for lock-up participants who provide BLS data to trading companies. Today, the internet permits anyone in the world to obtain economic releases for themselves directly from the BLS or DOL websites. However, unlike media organizations with computer access in the current lock-up, others who use the data do not have up to 30 minutes before the official release to process the data. Their postings about the data may lag behind those released directly from the lock-up at official publication time, 8:30 a.m. Eastern. High-speed algorithmic trading technology now gives a notable competitive advantage to market participants who have even a few microseconds head start. To eliminate this advantage and further protect our data from inadvertent or purposeful prerelease, no computers or any other electronic devices will be allowed in the lock-up.

In recent years, BLS and ETA have devoted significant resources to introducing improved technologies that strengthen our infrastructure and ensure data are posted to the BLS or DOL websites immediately following the official release time.

We at BLS and ETA are committed to the principle of a level playing field—our data must be made available to all users at the same time. We are equally committed to protecting our data. We are now positioned to continue helping the media produce accurate stories about the data, while also ensuring that all parties, including the media, businesses, and the general public, will have equitable and timely access to our most sensitive data.

You can find more details about these changes in our notice to lock-up participants. We also have a set of questions and answers about the changes to the lock-up procedures.

Topics: Benefits and Compensation | Earnings and Wages | Employment | Industries | Inflation and Prices | Labor Force Characteristics | Productivity | U.S. Statistical System | Unemployment
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Planning BLS Strategy for 2025 and Beyond

The start of the New Year seems like an appropriate time to share the new BLS Strategic Plan, which is designed to provide a roadmap for BLS over the next 5 years and beyond. Today, I want to tell you a little bit about how we developed this plan and then highlight some of its content.

We have a lot of resources to guide us in crafting the strategic plan. Consider:

  • As an agency of the U.S. Department of Labor, BLS provides statistical guidance and support to the department and its agencies. As noted in the Department of Labor’s Strategic Plan, BLS provides sound and impartial information about the economy for decision making.
  • As part of the decentralized U.S. statistical system, BLS works with its sister statistical agencies to share ideas, coordinate common activities, and improve operations.
  • We adhere to various laws, regulations, and policies to ensure that we provide accurate, objective, relevant, timely, and accessible information. Of particular note is the Foundations for Evidence-Based Policymaking Act of 2018, which reaffirms the confidentiality of statistical information and encourages cooperation and efficiencies across the statistical system.

Using all these inputs, BLS senior staff spent the last year looking both inward and outward to refine our mission and vision, and to identify broad strategies and individual goals and objectives for the coming years. We considered our strengths and weaknesses, looked for opportunities and identified threats, and refined a laundry list of ideas into a concise yet comprehensive plan.

It starts with our mission statement:

The Bureau of Labor Statistics measures labor market activity, working conditions, price changes, and productivity in the U.S. economy to support public and private decision making.

We then present the values and principles that guide us in fulfilling that mission, including:

  • Independence from partisan interests
  • Consideration of the needs of a diverse set of customers
  • Confidentiality of our data providers
  • Innovation
  • Stewardship of our staff and our resources

The plan includes five strategies, the first of which is to produce objective statistics and analysis, the core work of our agency. While always striving to improve, we must never lose focus on the hundreds of new data releases we produce each year.

The remaining strategies focus on how we do our work, and how we improve upon that work. Strategy 2 is about making improvements in the information we provide and what techniques we use to produce that information. Strategy 3 is about our source data, with special focus not only on traditional survey respondents but also on alternative data sources. Strategy 4 focuses on managing the resources that allow us to do our work, including our people, our funding, and our infrastructure. Finally, Strategy 5 is about you—our customers who come to us for information. We strive to let you have a seamless customer experience today, and we look for ways to make that experience even better tomorrow.

One of our many challenges in developing this strategic plan was to ensure all BLS staff see themselves in the strategies, goals, and objectives. We also want all BLS stakeholders—data providers, data users, researchers, policymakers, and more—to see their unique perspectives addressed. We hope you will take a few minutes to review the BLS Strategic Plan and let us know if we’ve met this challenge. Feel free to leave a comment below.

Topics: BLS Management | U.S. Statistical System
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