Tag Archives: Employment Situation

Ensuring Gold-Standard Data in the Eye of a Storm

“Hurricanes Harvey, Irma and Maria were the most notable storms of 2017, leaving paths of death and destruction in their wake.”
Colorado State University’s Tropical Meteorology Project 2017 summary report

Colorado State University’s Tropical Meteorology Project is forecasting the 2018 hurricane season activity (as of May 31) to be average, with 13 named storms, 6 hurricanes, and 2 major hurricanes expected. Is BLS ready?

How does BLS deal with hurricanes?

Since June starts hurricane season, we want to share with you one example of how last year’s storms affected our data. We present a case study using our national employment survey, the Current Employment Statistics program. This program provides monthly estimates we publish in The Employment Situation—sometimes called the “jobs report.”

We have procedures to address natural disasters. We highlight some of our challenges and how we address them. We do everything possible to provide you with gold-standard data to help you make smart decisions!

2017 Hurricane Destruction

Two major hurricanes—Harvey and Irma—blasted the U.S. mainland in August and September 2017. Hurricane Maria devastated Puerto Rico and the U.S. Virgin Islands later in September.

  • Harvey first made landfall in Texas on August 25. The Federal Emergency Management Agency (FEMA) declared 39 Texas counties eligible for federal disaster assistance after Harvey. Harvey also caused heavy damage in Louisiana.
  • Irma hit the Florida Keys on September 10 and then later hit Florida’s southern coast. FEMA declared 48 Florida counties eligible for federal disaster assistance. Before Irma hit the lower Florida Keys, the hurricane already had caused severe damage in St. Thomas and St. John in the U.S. Virgin Islands and in Puerto Rico.
  • Hurricane Maria made landfall in St. Croix in the U.S. Virgin Islands and in Puerto Rico on Wednesday, September 20, causing catastrophic damage. These areas already had suffered damage from Hurricane Irma earlier in the month.

Some things to know about the monthly employment survey

The monthly employment survey is a sample of nonfarm businesses and government agencies. The reference period is the pay period that includes the 12th of the month. The sample has just over 23,000 active reporting units in the disaster areas, representing about 6 percent of the entire active sample.

What does it mean to be employed? If the employer pays someone for any part of the reference pay period, that person is counted as employed.

How did BLS collect data in these disaster areas?

Our biggest challenge is to collect representative sample data so we publish high-quality estimates. In the “old days,” the survey was a mail survey (yes, I mean snail mail), but no more! Now we collect data electronically by several different methods. These are the most common:

  • About half the total sample uses electronic data interchange. That’s a centralized electronic data reporting system for multi-establishment firms. The firm provides an electronic file directly from their payroll system to BLS for all establishments included in the report. Most of the firms reporting are outside of the hurricane-affected areas, although they may report on establishments within the affected areas.
  • About 23 percent of establishments use computer-assisted telephone interviews.
  • Another 16 percent report using our Internet Data Collection Facility.

Using these methods, we were able to collect data from most sampled businesses in these areas using normal procedures.

What about the emergency workers working in the disaster areas? How are they counted?

  • We count emergency workers where their employer is located, not where they are working.
  • We don’t count volunteers as employed because they are not paid.
  • Activated National Guard troops are considered active duty military and are outside the scope of the survey.

Did the estimation procedures change?

Once we collect the data from businesses in the affected areas, we consider whether we need to change our estimation procedures to adjust for missing data. The survey staff determined that we didn’t need to change our methods because the collection rates in the affected areas were within normal ranges.

How did the hurricanes affect national employment data for September 2017?

Hurricanes Harvey and Irma reduced the estimate of national payroll employment for September 2017. We can’t measure the effects precisely because the survey is not designed to isolate the effects of catastrophic events. National nonfarm employment changed little (+14,000) in September 2017, after increasing by an average of 189,000 per month over the prior 12 months. A steep employment decline in food services and drinking places and below-trend growth in some industries likely reflected the impact of Hurricanes Harvey and Irma.

What about Puerto Rico and the U.S. Virgin Islands?

National nonfarm employment estimates do not include Puerto Rico or the U.S. Virgin Islands.

Because of the devastation caused by Hurricanes Irma and Maria, Puerto Rico and the U.S. Virgin Islands could not conduct normal data collection for their establishment surveys. The September estimates for Puerto Rico and the Virgin Islands were delayed. The October and November estimates for the Virgin Islands also were delayed. Puerto Rico and the Virgin Islands eventually were able to produce estimates for September, October, and November 2017.

Want more information?

For more information on the impact of Harvey, Irma, and Maria, check out these pages:

What else does BLS know about hurricanes?

The Quarterly Census of Employment and Wages produces maps of businesses and employment in flood zones for states on the Atlantic and Gulf Coasts that are vulnerable to hurricanes and tropical storm. You can read more about those maps in another recent blog.

We hope the 2018 hurricane season won’t bring the loss of life and destruction of property that we saw in 2017. Regardless of what the season brings, BLS will be ready to continue providing gold-standard data about the labor market and economy.

Using Seasonally Adjusted Data or Not: a Case Study

The Current Employment Statistics survey helps us track employment trends in the economy. The headline figures, such as the 164,000 increase in payroll employment in April, are seasonally adjusted. Seasonal adjustment smooths out increases or decreases that occur around the same time each year to make it easier to see the underlying movements in the data.

Consider the construction industry, where employment varies throughout the year, often because of the weather. The chart below shows employment each month in 2017, both seasonally adjusted and not seasonally adjusted. The not seasonally adjusted level ranged from about 6.4 million to 7.2 million jobs, but it is hard to see a trend. The seasonally adjusted level was consistently between 6.8 million and 7.1 million jobs. When we remove the seasonal variation, we can see a slight increase in construction employment over the year.

Construction employment in 2017, seasonally adjusted and not seasonally adjusted

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

While seasonally adjusted data help us see long-term trends, there are times when short-term trends can provide some insight. One example is holiday-season hiring. Certain industries, such as retail trade and parcel delivery services, ramp up hiring in the fall to prepare for increased business during the holiday season. We can see this holiday-related employment buildup with data that are not seasonally adjusted. For example, employment growth in selected retail trade industries increased by 609,000 from October to December 2017, less than the 650,000 jobs gained in the same months of 2016.

Note: Selected retail trade industries include furniture and home furnishings stores; electronics and appliance stores; health and personal care stores; clothing and clothing accessories stores; sporting goods, hobby, book and music stores; general merchandise stores; miscellaneous store retailers; and nonstore retailers.

Seasonal holiday employment buildup in selected retail trade industries, 2012–17 (not seasonally adjusted)

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

We have to be careful when we use data that are not seasonally adjusted. For example, sometimes there are 4 weeks between monthly surveys and sometimes there are 5 weeks. Seasonal adjustment accounts for these differences. When using not seasonally adjusted data, users must be aware that an extra week between surveys can exaggerate seasonal employment increases or decreases. For example, in 2017, there were 5 weeks between surveys in November, just as there were in 2012 and 2013.

Looking across the October-to-December period, the seasonal employment buildup in retail trade slowed each year following a large increase from 2012 to 2013. In each of the next four holiday seasons, job gains over the 3-month (13-week) period were less than the prior year. But 2017 included some anomalies – a strong November (72 percent of the seasonal total), followed by a weak December (7 percent of the seasonal total).

Share of seasonal holiday employment buildup in each month, selected retail trade industries, 2012–17 (not seasonally adjusted)

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

Examining the not seasonally adjusted data may provide some insights into changing hiring patterns, especially in seasonal industries. The 2017 retail trade data suggest declining holiday employment buildup but also earlier holiday employment buildup. Will this pattern continue? We’ll know more when Current Employment Statistics data come out later this year.

We can analyze other industries with seasonal patterns in a similar way. One industry is transportation, and specifically couriers and messengers, which includes parcel delivery services. As the trend in online shopping continues, employment in parcel delivery services has increased, especially during the holiday season. Other seasonal industries include ski resorts in the winter, gardening shops in the spring, and amusement parks in the summer. We can also use not seasonally adjusted data to look at layoff patterns in seasonal industries, such as certain retail industries after the holiday season.

All these data are available from the Current Employment Statistics program.

Construction employment in 2017
Month Seasonally adjusted Not seasonally adjusted
Jan 6,873,000 6,459,000
Feb 6,919,000 6,527,000
Mar 6,922,000 6,634,000
Apr 6,917,000 6,816,000
May 6,924,000 6,990,000
Jun 6,940,000 7,157,000
Jul 6,934,000 7,197,000
Aug 6,962,000 7,228,000
Sep 6,971,000 7,177,000
Oct 6,988,000 7,182,000
Nov 7,030,000 7,117,000
Dec 7,072,000 6,970,000
Seasonal holiday employment buildup in selected retail trade industries, 2012–17 (not seasonally adjusted)
Year October November December
2012 132,000 456,000 103,000
2013 142,000 435,000 184,000
2014 169,000 392,000 158,000
2015 175,000 389,000 127,000
2016 148,000 358,000 144,000
2017 128,000 438,000 43,000
Share of seasonal holiday employment buildup in each month, selected retail trade industries, 2012–17 (not seasonally adjusted)
Year October November December
2012 19.1% 66.0% 14.9%
2013 18.7 57.2 24.2
2014 23.5 54.5 22.0
2015 25.3 56.3 18.4
2016 22.8 55.1 22.2
2017 21.0 71.9 7.1

Local Unemployment: How’s My State Doing?

The Local Area Unemployment Statistics program publishes monthly and yearly estimates of unemployment and the labor force for about 7,000 areas:

  • States
  • Counties and county equivalents
  • Metropolitan areas
  • Cities with 25,000+ population

If you’re looking for local unemployment data, we’ve got you covered!

State Unemployment Rates

This map shows the 2017 unemployment rates for all states (The lighter the color, the better!)

State unemployment rates in 2017

How do states compare with the 2017 national unemployment rate of 4.4 percent?

  • Hawaii and North Dakota had the lowest unemployment rates, 2.4 percent and 2.6 percent, respectively.
  • Alaska had the highest unemployment rate, 7.2 percent.
  • Seven states recorded the lowest annual unemployment rates since the series began in 1976: Arkansas (3.7 percent), California (4.8 percent), Hawaii (2.4 percent), Maine (3.3 percent), North Dakota (2.6 percent), Oregon (4.1 percent), and Tennessee (3.7 percent).

To learn more, table 1 in the Regional and State Unemployment — 2017 Annual Averages news release provides the unemployment rate for each state.

State Employment–Population Ratios

Another interesting piece of information is the employment–population ratio, which answers the question: “What percent of the population age 16 and older is employed?”

State employment–population ratios in 2017

The national employment–population ratio in 2017 was 60.1 percent. The employment–population ratio continues to climb from its recessionary low of 58.4 percent in 2011. Here are some 2017 state highlights:

  • North Dakota had the highest proportion of employed people, 69.6 percent. The next highest ratios were in Minnesota, 67.8 percent, and Utah, 67.2 percent.
  • West Virginia had the lowest employment–population ratio among the states, 50.5 percent.

To learn more, table 2 in the Regional and State Unemployment — 2017 Annual Averages news release provides the employment–population ratios by state.

County Unemployment Rates

What if you want to do a deep dive to find out about unemployment in the counties in your state? That’s easy to do, too. You can create your state map showing the current unemployment rates for all your counties in less than a minute by following these instructions:

Mapping Unemployment Rates (States and Counties) -> Counties tab -> Select a state from the dropdown -> Select unemployment data and time period -> Hit draw map.

That’s it! Below the map is a chart with the county data listed in alphabetical order. For comparison purposes, you may want to pick up the state unemployment rate from the state tab. Be sure to choose the not seasonally adjusted data, since the county data are not seasonally adjusted.

We have a webpage where you can get annual data for all counties for 2017 and earlier years.

How does BLS get all of this great data?

Unlike many of our other statistical programs, the Local Area Unemployment Statistics program is not a survey. Instead, the program uses survey and administrative data from multiple sources to produce its estimates, including:

We have a short summary of the estimation methods. We also have a longer description (including formulas!) in our Handbook of Methods.

What is the relationship between local information and the national unemployment rate?

We use the same definitions for our local estimates as we do for the national unemployment rate, which we get from the Current Population Survey. Through a feature known as real-time benchmarking, the local data are controlled to the national totals each month to make the data comparable.

Video: Understanding BLS Unemployment Statistics

Why should I care about local data anyway?

We’re glad you asked! As you can see, individual states and areas can have very different economic conditions than the country as a whole. Local labor force measures provide critical information for states and areas that can help local leaders, communities, and businesses make better economic decisions. The local unemployment estimates also are used by 25 federal programs across 9 departments and independent agencies.

  • Most programs use the data to help determine how to spread funds to communities across the country.
  • Some programs use the data to determine funding eligibility.
  • See the Administrative Uses of Local Area Unemployment Statistics for the full list of federal uses of local unemployment data.

Finally, check out the most recent monthly state and metropolitan area news releases to get all the latest numbers. Like maps and graphics? See our series of graphics for the most recent unemployment data for states and metropolitan areas. Head to our Frequently Asked Questions to learn more.

Have more questions? Contact the information folks at (202) 691-6392 or by email. You also can contact the offices on our State Labor Market Information Contact List.

Do You Understand Your Local Economy?

The national unemployment rate may make the headline news every month, but many folks are most interested in understanding their own local economy.

BLS has a stat for that (really MANY statistics for that)! In fact, BLS data were highlighted in a webinar focusing on local data sponsored by the Association for Public Data Users, the American Statistical Association, and the Congressional Management Foundation.

Dr. Martin (Marty) Romitti, a Senior Fellow at the Center for Regional Economic Competitiveness, presented a webinar called “Understanding Your Congressional District’s Economy and Workforce Using Federal Statistical Data.” Though geared to Congressional staff, the information is applicable to anyone interested in knowing more about their local economy.

By using an extended example of the Napa, California, metropolitan area (where we immediately think, “Wine Country!”), Dr. Romitti finds some interesting information that may shatter some of your preconceived notions of that region.

He does this by answering 10 questions — 5 about “our people,” where he uses U.S. Census Bureau data and 5 about “our economy,” where he uses BLS data.

We are going to focus on the BLS portion (run time 31:12)* of the webinar. The five questions Dr. Romitti poses about our economy are:

  1. How healthy is my economy now?
  2. How many unemployed people live in my area?
  3. What are the largest employing industries?
  4. Which industries pay most to workers?
  5. What are our economic strengths?

Below are some steps and tips if you want to access the same information as Dr. Romitti on www.bls.gov. Note that he uses Internet Explorer; use a different browser and your screen will look different.

Dr. Romitti uses two BLS tools; we have included the path and links to pages as appropriate:

  • To answer Questions 1 and 2: Economy at a Glance -> California -> Napa (Dr. Romitti suggests clicking on the maps.)
    • Tips:
    • For context, suggest you compare your area data to your state numbers. Beware: Your state unemployment rate is seasonally adjusted, while your area data are not.
    • Also, for context, you may want to look at the data over time, such as the last 10 years. Just remember the “Great Recession” occurred starting in late 2007.
  • To answer Questions 3, 4, and 5: BLS Data Tools -> Employment -> Quarterly -> State and County Employment and Wages -> Tables

By following these instructions, you can uncover the same information as Dr. Romitti. We believe Dr. Romitti does a good job of explaining how to answer questions related to local economic data in under an hour!

But wait, there’s more! Let me offer two more resources in your quest for local data:

  1. Are you familiar with our Economic Summaries? These summaries present a sampling of economic information for the area covered, such as unemployment, employment, wages, prices, spending, and benefits. For example, take a look at San Francisco. If you are looking for something quick and easy, you might find what you need in one of these summaries.
  2. The Economic Summaries are produced by the BLS regional information offices. The BLS regional office staff stand ready to assist you with questions about your local economy.

*The taped webinar starts with a musical interlude and some brief introductions. The real action starts at the following run-time intervals:

Run Time                    Presentation Topic   

6:46                             Introduction by Dr. Romitti

11:30                           About our people (Census Bureau data)

31:12                           About our economy (BLS data) begins

52:36                           Regional Economic Accounts (Bureau of Economic Analysis data)

58:53                           Conclusion

60:00                           End

Recalibrating the Jobs Thermometer

The U.S. Bureau of Labor Statistics is responsible for measuring labor market activity. Each month BLS releases some of the most up-to-date measures of economic health in The Employment Situation, often called “the jobs report.” We also release the Commissioner’s statement each month at the same time as the jobs report.

Most of the attention focuses on the headline numbers—how many jobs were added (or lost) that month and did the unemployment rate change? However, with the release of January data each February, we make some yearly updates to improve the accuracy of the numbers. In our survey of households, which is the source for the unemployment rate and other measures, we update the U.S. population totals to reflect the latest information about births, deaths, and international migration. In our survey of nonfarm establishments, which is the source of the jobs count, we make our annual benchmark revisions. Today I’m going to focus on the establishment data.

Each month, the establishment program surveys a sample of businesses and governments around the country. The survey asks how many people worked or received pay for the pay period that included the 12th of the month. While the establishment sample is large, covering about one-third of all nonfarm jobs, the employment changes reported each month are still subject to revisions. Monthly revisions result from more establishments reporting their numbers or correcting previous reports, and from updated information about seasonal employment patterns.

The establishment survey also benefits from another source of data, the Quarterly Census of Employment and Wages. That is a nearly complete count of all establishments, although it is available with a delay of about 6 months. A full count of employment helps us in several ways. We use the data to measure the error associated with the establishment survey. This way data users don’t have to guess how accurate the monthly employment data are. We have a stat for that! In case you are wondering, the data are very accurate. Annual benchmark revisions (which I will explain in a moment) have averaged only 0.3 percent in absolute terms over the past 10 years.

Besides measuring error, once a year we realign the sample-based estimates with the full count of employment. We call this “benchmarking.” This realignment makes sure the employment levels do not stray too far from the “truth” over time. (For several reasons that I won’t go into right now, the establishment survey employment totals will not exactly equal employment totals from the full counts. If you really want to know the details, you can read more about benchmarking, but remember I tried to spare you.)

During this annual benchmarking, we also introduce other changes to the survey. Sometimes we update the industry classification, like we will this year. We also use new information to update the statistical model that accounts for business births and deaths. We review the establishment sample for size, coverage, and response rates, and we may drop some series if the data quality doesn’t meet our standards. We also update the models and information used in seasonal adjustment.

As you can see, there’s a lot going on during this annual updating. All establishment data, including employment, hours, and earnings, are subject to adjustment. I hope this brief explanation and the material we have on our website help to make everything more transparent and easier to understand.

The same basic benchmarking that occurs nationally also happens for the state and local employment estimates. Want to know more? Visit their homepage. If you still have questions, call or email us. We are here to help.