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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.

How United Parcel Service Uses BLS Data

I recently attended a BLS Data Users Conference in Atlanta, which included a lively panel discussion of how companies use BLS data in their everyday work. I was especially struck by the examples shared by Cathy Sparks, the Director of Corporate Workforce Strategy & Analytics for United Parcel Service. As a result, I asked Cathy to write a short blog post that I could share with all of you. My hope is to have more posts in the future highlighting how our data users put our data to work for them!

Cathy shares:

From Reporting to Problem Solving

I am certain that, in the 109-year history of United Parcel Service (UPS), this is the most exciting time to be in Human Resources and working with data.

In 2015, UPS processed nearly 70 million online tracking requests every day and operated more than 1,990 facilities employing roughly 444,000 people. Data is part of everything we do at the world’s largest transportation and logistics company. We tap into data to deliver lasting results. From an HR perspective, we are in the foundational stages of building a true analytics team. We want to use business intelligence to better understand our workforce and align those findings with broader strategic goals.

The recent BLS Data Users Conference in Atlanta was a great opportunity to highlight how we’re using analytics to create value and enhance our problem-solving skills.

Cathy Sparks and her team at UPS discussing data.

Our challenge is to transition from simple reporting to diagnosis. We are finding new opportunities to integrate our internal UPS data with BLS external data to analyze human capital trends, including predictive staffing models, safety correlations, and engagement risks. For example, using our data, we have created a model to evaluate state-by-state seasonal staffing needs. We incorporate BLS data to control for economic conditions, thus enriching the model. We hope to predict employee attrition risks and forecast a two-year, five-year, and seven-year staffing blueprint for our largest metropolitan areas.

The greatest data-driven opportunities are yet to come. UPS data, combined with BLS economic indicators, provide new insights and value throughout our global organization, improving service for our customers around the world.

BLS releases data from the new Occupational Requirements Survey

Pop the corks! We published the first-ever Occupational Requirements Survey estimates and news release this morning. The survey provides unique information about the physical demands, environmental conditions, education and training, and mental requirements of jobs in the United States. We’re running the survey under an agreement with the Social Security Administration so they can make decisions about their disability programs. Employers, jobseekers, and state and local workforce agencies can also use the data to match people with jobs that are right for them. Researchers will find the survey useful for expanding our understanding of the labor market.

Here are a few highlights from the survey for 2016.

  • 31 percent of jobs in 2016 had no minimum education requirement; 17.5 percent of jobs required at least a bachelor’s degree.
  • 75 percent of jobs required some on-the-job training, and 48 percent required prior work experience.
  • 47 percent of jobs involved working outdoors at some point during the workday.
  • 66 percent of jobs involved some reaching overhead.
  • 39 percent of jobs involved regular contact with others several times per hour.

Chart showing percentage of jobs with selected physicial requirements in 2016

Creating new gold-standard information like this takes years of testing and development. Staff from BLS and the Social Security Administration worked closely together to get it right. After today’s news release, we will highlight the survey data in several publications in the coming year. We will feature selected job requirements and occupations. For more information on the new survey, including Frequently Asked Questions about it, please see www.bls.gov/ors.

Measuring Uncertainty in the Producer Price Index

Our mission at the U.S. Bureau of Labor Statistics is to publish information about the labor market and economy. We always seek to improve our methods and provide the most accurate data in a cost-effective manner. All statistics, however, come with some uncertainty. Last year I wrote about how we deal with uncertainty in our measures. Today let’s talk about how we recently have improved our uncertainty measures in the Producer Price Index.

You may think it’s odd that an agency that tells the public what we know also works hard to explain what we don’t know. It may seem like we’re airing our dirty laundry, but that’s not how we see it. At BLS, one of our core values is to be transparent about our methods. Not only don’t we consider the laundry dirty, but we believe that airing it—that is, giving you more information about the strengths and the limitations of our data—is central to our mission. It’s part of our responsibility to give you information you can use to make better decisions.

The Producer Price Index (PPI) program measures the average change over time in the prices U.S. businesses receive for the goods they produce and the services they provide. BLS started publishing the PPI 126 years ago, making it one of our oldest measures. In 2014, the PPI expanded its coverage to provide a broader view of price change for goods, services, and construction. The PPI for final demand measures price change for goods and services sold for personal consumption, capital investment, government, and export. The PPI for intermediate demand tracks price change for goods, services, and construction products sold to businesses.

The PPI for final demand was unchanged in October 2016 and was up 0.8 percent over the last 12 months. But these figures are subject to sampling error. What’s that? It’s the uncertainty that results when we collect data from a sample of prices, rather than gathering prices from each of the millions of transactions that occur every day. For the PPI, we collect about 93,500 prices every month. A different sample of prices might give us different estimates of price change. Fortunately, we have tools to measure this sampling error. Most BLS programs collect data from sample surveys because it is far too expensive and would overburden businesses and workers to send all our surveys to everyone. Instead, we select samples carefully using scientific methods. These sampling methods work well, but they can’t avoid the possibility that the characteristics of a sample may differ from those of the population. We provide estimates of this sampling error by publishing variance estimates with the data. We recently released the first-ever variance estimates for the PPI.

If you aren’t into math, skip the next paragraph.

The measure of variance we use for the PPI is called a standard error. We use the standard error to calculate what statisticians call a confidence interval around the estimate. For example, the 1-month median absolute percent change in the PPI for final demand in 2015 was 0.30 percent. The standard error of that median was 0.11 percent. We can use these two numbers to calculate a confidence interval. In this example, we will use what we call a 95-percent confidence interval. To calculate that confidence interval, we take the estimated median price change of 0.30 percent, plus and minus two times the standard error of 0.11 percent. This gives us a confidence interval between 0.08 percent and 0.52 percent. We call this a 95-percent confidence interval because, if we were to choose 100 different samples of producer prices, the median price change would be between 0.08 percent and 0.52 percent in 95 of those samples.

Chart showing median 1-month changes in Producer Price Indexes in 2015 and the 95-percent confidence intervals around those changes.

OK, if you don’t like math, you can come back now. The chart above shows estimates of 1-month PPI changes (the red dots) each surrounded by its sampling uncertainty (the blue bars). If the blue bar crosses the 0.0 percent line, it means the change is not significantly different from zero.

Variance estimates are just one way BLS evaluates and explains the quality of our data and our methods. We have published information about our methods almost since our beginnings in 1884. Carroll Wright, the first BLS Commissioner, insisted on the “fearless publication of the facts.” We believe the fearless publication of the facts means not just explaining our measures and methods in highly technical terms. We want our measures and the uncertainty around them to be understood by a wide range of people, not just those who have advanced degrees in economics or statistics. We continue to seek clearer ways to explain uncertainty. One way is a new chart we are publishing on the monthly changes in nonfarm employment. In the future, we hope to publish more charts like this and simpler explanations of our methods. If you have ideas on how we can explain our data and methods more clearly, please share them with us below.

BLS data are the gold standard of economic statistics. But even gold bars have marks to indicate their impurities. Similarly, we at BLS don’t hide our impurities. We want you to understand the strengths and limitations of our data so you can use that knowledge to make good decisions.

Seeing Significance: New Chart Showing Confidence Intervals for Nonfarm Employment Changes

Last year I wrote about how we’ve been using more charts and maps to explain our data. In another post I wrote about how BLS deals with uncertainty in our measures and how we explain the strengths and limitations of our data. Today I want to tell you about a new chart that will help you see measures of uncertainty in one of our most closely watched series—nonfarm employment.

We recently fielded a survey to ask data users about our ideas for creating charts that show confidence intervals. A confidence interval is a measure of the uncertainty in our estimates. We asked data users to tell us if confidence intervals would give them useful information. If so, how can we present confidence intervals in a clear, visually appealing way? Based on your responses, we will add a chart showing confidence intervals to the package of interactive graphics we update each month with The Employment Situation report. We will post the new chart when we release the November 2016 national employment numbers on Friday, December 2.

The responses to our survey confirm you want to know more about the limitations of the employment data. The red dots in the chart show the over-the-month employment changes for total nonfarm and the major industries. The blue bars show the 90-percent confidence intervals of the employment changes for each of these groups.

Chart showing nonfarm employment changes for major industries in October 2016 and the confidence intervals for those changes.

What does this chart tell us? The red dot for total nonfarm employment shows a gain of 161,000 jobs in October, as we reported on November 4. That number is an estimate based on our monthly sample survey, rather than a complete count of jobs each month. Different samples of employers might give us different estimates of employment change. We can measure the sampling error, the variation that occurs by chance because we collected the number from a sample of employers instead of all employers. With our measure of sampling error, we can calculate a confidence interval. The blue bar for total nonfarm shows the 90-percent confidence interval ranged from 46,800 to 275,000. We call this a 90-percent confidence interval because, if we were to choose 100 different samples of employers, the October nonfarm employment change would be between 46,800 and 275,000 in 90 of those samples.

We also learn from this chart whether an employment change is statistically significant. A change is statistically significant if the blue bar in the chart does not cross the zero line. For example, the confidence interval for construction includes zero, so we can’t say with confidence that construction employment increased in October. For education and health services, however, the confidence interval does not include zero, so we can say more confidently that employment in the industry rose over the month.

The chart here shows the 1-month employment changes. When we begin publishing it on December 2, we will also let you choose charts that show 3-, 6-, and 12-month changes.

Check out the new charts and let us know what you think in the comments below. We’re always looking for better, clearer ways to explain our data, and I welcome you to share your ideas.