Topic Archives: Why This Counts

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.

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:

 

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

Why This Counts: What Does BLS Know about Certifications and Licenses?

Whether you are an aspiring doctor or lawyer, teacher or barber, chances are you need a license to legally work. Or you may already have a license and are now rushing to get your continuing professional education courses done before the end of the year! Whatever your status, what does BLS know about certifications and licenses and how can the information help?

While BLS and other federal statistical agencies have long produced data on educational attainment, there used to be few public sources of information on nondegree credentials like certifications and licenses. To meet this need, BLS added new questions to the Current Population Survey, the national household survey best known as the source of the official unemployment rate, back in 2015. These data help researchers, policymakers, business owners, workers, and jobseekers better understand how holding a certification or license relates to employment, unemployment, and earnings.

At BLS, we define certifications and licenses as nondegree credentials that show the holder has the skill or knowledge needed to perform a specific job. Certifications come from a nongovernmental body, such as a professional or industry organization. Licenses come from a government agency and show a legal permission to work in an occupation.

In 2016, about 44.5 million people (almost the number of people who live in Spain) held a currently active professional certification or license. People with a certification or license had an unemployment rate of 2.5 percent, compared with 5.6 percent for people without one of these credentials. One-fourth of the employed held a certification or license in 2016.

The prevalence of certifications and licenses varies by a worker’s occupation. In 2016, there were four occupation groups where more than half of workers held a certification or license: healthcare practitioners and technical occupations (77.0 percent); legal occupations (66.8 percent); education, training, and library occupations (55.5 percent); and healthcare support occupations (50.9 percent).

Chart showing percent of workers in each occupational group who had a certification or license in 2016.

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

We also have information on how much workers with or without a certification or license earn. In 2016, the median weekly earnings of full-time wage and salary workers with a certification or license was $1,032—35 percent higher than the median for workers without a certification or license ($765). These broad comparisons do not account for other important reasons that may explain differences in earnings, such as educational attainment and a worker’s specific job roles and responsibilities.

Whether you are a jobseeker, business owner, policy maker, or researcher, BLS data on professional certifications and licenses help you understand the important role that these credentials play in the U.S. labor market.

Percent of employed people with a certification or license by occupation, 2016 annual averages
Occupation With a license With a certification
but no license
Healthcare practitioners and technical 72.6% 4.4%
Legal 63.4 3.4
Education, training, and library 53.6 1.9
Healthcare support 47.2 3.6
Community and social services 33.5 5.0
Protective service 36.1 1.6
Personal care and service 27.9 3.1
Architecture and engineering 22.4 4.0
Life, physical, and social science 22.3 3.1
Total, 16 years and over 22.3 2.7
Business and financial operations 20.0 4.0
Installation, maintenance, and repair 18.3 5.3
Management 19.3 3.2
Transportation and material moving 20.7 1.5
Construction and extraction 17.5 2.2
Sales and related 14.3 1.8
Computer and mathematical 6.8 7.4
Arts, design, entertainment, sports, and media 8.5 3.1
Production 8.0 2.2
Office and administrative support 8.2 1.4
Farming, fishing, and forestry 8.3 0.8
Food preparation and serving related 6.7 1.0
Building and grounds cleaning and maintenance 6.5 1.1

Looking Under the Hood of Jobs Data: Job Openings and Hires by Firm Size

Let’s not bury the lead. Newly released experimental information from the U.S. Bureau of Labor Statistics shows that firms employing 500 workers or more consistently have more job openings and more hires than smaller firms. During the most recent recession, these larger firms cut job openings at a faster pace than did smaller firms. Following the recession, job openings grew more rapidly in larger firms.

Chart showing the number of job openings by firm size from 2000 to 2016

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

OK, those are the highlights. But maybe you want to know more. Or maybe you have a comment or question. Read on.

Monthly headlines from BLS show the change in the number of jobs and information about the labor force, such as the unemployment rate. For example, employers added 228,000 jobs in November 2017, and the national unemployment rate stood at 4.1 percent. But behind those top-side numbers, there’s a lot going on in the job world. BLS provides much of that detail, including unemployment rates by demographic groups, jobs created by new versus expanding firms, and employment by occupation.

Today we take a look at some experimental information recently released from the Job Openings and Labor Turnover Survey. The survey provides monthly information on the number and rate of job openings at the end of the month, as well as job turnover (hires and separations) during the month. For example, at the end of October 2017, employers had 6.0 million job openings. There were 5.6 million workers hired and 5.2 million workers separated during October. And of those separations, quits outnumbered layoffs and discharges by a ratio of 2 to 1 (3.2 million quits, 1.6 million layoffs and discharges).

The Job Openings and Labor Turnover Survey began in 2000. It provides nearly two decades of data that span business cycles, including the moderate recession in the early 2000s and the deep recession in the late 2000s. These monthly reports highlight differences by industry. For example, over the past several years, job openings have outpaced hires in the health care and social assistance, suggesting a continual need for skilled labor. In contrast, hires outpaced job openings in the construction industry, indicating a steady availability of labor.

What effect does firm size have on job openings and labor turnover? To unpack this question, BLS staff developed newly available experimental measures by firm size. A firm is defined as a related set of job sites. A firm may be a single location, such as Joe’s Plumbing Supply. Or a firm may have many different sites across industries and geography, including manufacturing, transportation and warehousing, and multiple retail locations. To develop these firm-level estimates, BLS staff identified entities with multiple locations and used the combined employment to slot firms into size categories. The new information is available for 3 groups: small (1–49 workers), medium (50–499 workers), and large (500 workers or more).

Some highlights from the data:

  • Large firms have twice as many job openings as do small and medium-sized firms.
  • Large firms also have the highest job opening rate, which is the ratio of job openings to the sum of employment plus job openings.
  • The number of hires by firm size is similar to the pattern of job openings; hires in large firms are nearly twice that of small and medium-sized firms.
  • The rate of hires, which compares the number of hires to employment, is about the same across firm size classes, especially in the past few years.

Chart showing the number of hires by firm size from 2000 to 2016.
Editor’s note: Data for this chart are available in the table below.

We want to hear from you. BLS develops experimental measures like these to provide greater understanding of the job market. As we continue to work on these and other measures, we seek your input. Send your questions and comments about the usefulness of these data to the Job Openings and Labor Turnover Survey staff.

Job openings levels by firm size, seasonally adjusted
Month Firm Size 1 (1-49) Firm Size 2 (50-499) Firm Size 3 (500+)
Dec 2000 1,063,970 1,212,812 2,166,647
Jan 2001 1,254,187 1,040,641 2,268,253
Feb 2001 1,150,018 1,001,138 2,330,520
Mar 2001 1,010,503 1,009,600 2,186,925
Apr 2001 998,581 1,025,288 2,321,541
May 2001 1,026,328 957,490 1,969,731
Jun 2001 935,187 944,611 1,972,341
Jul 2001 999,543 1,029,769 1,854,611
Aug 2001 889,501 900,890 1,881,136
Sep 2001 1,024,910 836,926 1,764,339
Oct 2001 888,204 725,598 1,564,310
Nov 2001 758,424 742,852 1,613,718
Dec 2001 802,515 748,965 1,658,384
Jan 2002 819,942 764,312 1,732,144
Feb 2002 656,857 762,218 1,545,264
Mar 2002 824,546 775,262 1,577,612
Apr 2002 692,005 764,940 1,506,129
May 2002 649,226 803,459 1,574,740
Jun 2002 675,642 769,944 1,454,594
Jul 2002 685,787 752,854 1,547,899
Aug 2002 692,592 803,598 1,530,478
Sep 2002 675,723 779,605 1,475,356
Oct 2002 689,451 798,260 1,660,696
Nov 2002 747,380 823,511 1,545,712
Dec 2002 591,276 749,792 1,382,984
Jan 2003 659,814 846,905 1,695,355
Feb 2003 777,288 711,685 1,530,725
Mar 2003 630,879 703,489 1,376,433
Apr 2003 726,806 723,117 1,365,285
May 2003 691,890 690,646 1,440,691
Jun 2003 682,601 765,484 1,493,878
Jul 2003 588,080 756,229 1,457,439
Aug 2003 635,308 754,020 1,513,807
Sep 2003 577,984 743,507 1,512,187
Oct 2003 587,303 829,886 1,413,296
Nov 2003 665,540 763,665 1,531,106
Dec 2003 749,094 773,746 1,524,435
Jan 2004 694,810 797,141 1,522,552
Feb 2004 769,596 786,344 1,543,821
Mar 2004 738,410 791,238 1,543,012
Apr 2004 685,387 823,964 1,690,254
May 2004 761,604 791,158 1,646,144
Jun 2004 665,849 751,611 1,621,204
Jul 2004 851,274 894,772 1,771,733
Aug 2004 748,355 827,281 1,591,622
Sep 2004 837,001 860,695 1,661,575
Oct 2004 731,538 862,201 1,733,695
Nov 2004 705,789 847,738 1,517,289
Dec 2004 847,389 876,217 1,741,215
Jan 2005 709,852 847,541 1,696,328
Feb 2005 808,783 899,841 1,813,490
Mar 2005 788,508 885,484 1,848,385
Apr 2005 903,479 885,600 1,894,028
May 2005 824,299 875,760 1,761,937
Jun 2005 870,511 925,176 1,870,100
Jul 2005 931,449 914,756 1,961,559
Aug 2005 898,673 941,476 1,844,530
Sep 2005 871,357 985,556 1,985,304
Oct 2005 895,342 914,615 2,075,976
Nov 2005 871,046 965,398 2,282,418
Dec 2005 861,935 930,504 2,127,341
Jan 2006 926,197 961,337 1,911,830
Feb 2006 870,674 935,421 2,182,583
Mar 2006 900,388 1,026,440 2,202,735
Apr 2006 880,717 1,040,964 2,159,372
May 2006 814,733 1,039,701 2,156,708
Jun 2006 866,922 1,045,813 2,002,970
Jul 2006 737,882 966,338 1,907,429
Aug 2006 876,173 1,016,973 2,192,267
Sep 2006 916,373 1,011,528 2,120,209
Oct 2006 839,329 1,058,198 2,144,379
Nov 2006 935,772 1,037,996 2,217,716
Dec 2006 856,726 1,041,513 2,177,949
Jan 2007 1,006,703 1,008,318 2,165,823
Feb 2007 981,978 1,095,137 2,069,850
Mar 2007 982,120 1,083,153 2,182,194
Apr 2007 869,195 1,175,773 2,075,431
May 2007 824,392 1,111,851 2,178,150
Jun 2007 1,026,474 1,033,211 2,150,486
Jul 2007 931,291 995,952 2,052,904
Aug 2007 944,960 1,014,174 2,094,487
Sep 2007 1,066,472 1,032,697 1,980,685
Oct 2007 972,154 970,969 1,913,129
Nov 2007 801,655 1,093,272 2,006,124
Dec 2007 827,479 1,097,070 2,015,637
Jan 2008 838,863 1,078,494 1,894,825
Feb 2008 761,539 1,009,438 1,970,060
Mar 2008 774,405 928,884 1,920,134
Apr 2008 710,503 890,409 1,929,298
May 2008 710,516 961,534 1,793,445
Jun 2008 638,053 883,051 1,861,865
Jul 2008 701,938 886,335 1,804,058
Aug 2008 633,271 783,453 1,761,845
Sep 2008 623,029 723,106 1,530,778
Oct 2008 581,304 773,531 1,461,046
Nov 2008 606,081 660,174 1,378,183
Dec 2008 622,312 673,974 1,360,074
Jan 2009 495,874 569,429 1,320,120
Feb 2009 587,551 606,813 1,264,330
Mar 2009 497,280 576,812 1,029,149
Apr 2009 530,082 443,185 865,437
May 2009 526,902 526,912 1,012,171
Jun 2009 542,708 536,379 1,013,176
Jul 2009 444,382 493,845 954,306
Aug 2009 428,605 513,075 1,010,607
Sep 2009 542,212 574,082 1,108,933
Oct 2009 501,879 510,090 959,637
Nov 2009 536,770 473,118 1,086,248
Dec 2009 457,470 575,790 1,145,838
Jan 2010 643,666 596,114 1,032,360
Feb 2010 580,156 536,242 1,149,156
Mar 2010 502,226 558,306 1,239,238
Apr 2010 592,456 631,601 1,203,511
May 2010 639,745 537,033 1,282,322
Jun 2010 533,152 599,522 1,202,898
Jul 2010 597,570 607,774 1,359,417
Aug 2010 599,959 646,695 1,319,523
Sep 2010 531,391 595,451 1,331,049
Oct 2010 593,875 616,584 1,348,310
Nov 2010 643,919 659,356 1,496,531
Dec 2010 558,604 582,033 1,418,940
Jan 2011 506,982 659,392 1,499,176
Feb 2011 544,751 706,101 1,519,162
Mar 2011 584,823 727,292 1,547,740
Apr 2011 549,428 711,284 1,595,959
May 2011 567,386 682,449 1,611,429
Jun 2011 544,848 690,205 1,657,846
Jul 2011 605,337 767,058 1,619,459
Aug 2011 556,717 677,988 1,648,114
Sep 2011 614,540 778,966 1,731,454
Oct 2011 574,275 746,781 1,725,148
Nov 2011 577,001 791,911 1,558,570
Dec 2011 638,596 810,484 1,611,179
Jan 2012 856,476 852,412 1,641,457
Feb 2012 647,857 790,085 1,731,296
Mar 2012 664,828 844,686 1,906,753
Apr 2012 803,914 809,055 1,514,008
May 2012 683,030 808,866 1,790,451
Jun 2012 752,012 831,447 1,765,436
Jul 2012 584,262 817,409 1,796,499
Aug 2012 626,419 886,155 1,694,821
Sep 2012 694,209 800,200 1,654,448
Oct 2012 645,981 826,044 1,854,123
Nov 2012 689,248 821,095 1,838,717
Dec 2012 609,350 797,497 1,909,677
Jan 2013 605,325 732,763 2,107,198
Feb 2013 775,285 897,160 1,964,961
Mar 2013 740,705 845,484 1,879,283
Apr 2013 655,993 797,119 2,017,197
May 2013 712,099 886,951 1,791,954
Jun 2013 751,267 824,265 1,921,027
Jul 2013 759,741 823,744 1,875,751
Aug 2013 695,439 848,376 1,976,546
Sep 2013 759,238 777,782 2,131,494
Oct 2013 775,829 890,226 2,031,372
Nov 2013 665,386 906,564 2,065,046
Dec 2013 863,397 850,283 1,902,300
Jan 2014 738,287 868,753 1,955,377
Feb 2014 719,973 894,977 2,131,462
Mar 2014 724,779 908,744 2,140,206
Apr 2014 672,254 968,735 2,226,002
May 2014 897,433 1,021,611 2,100,846
Jun 2014 809,532 1,065,672 2,279,388
Jul 2014 843,835 1,045,672 2,201,345
Aug 2014 1,023,698 1,076,735 2,405,701
Sep 2014 848,249 1,082,785 2,271,364
Oct 2014 925,023 1,112,348 2,407,717
Nov 2014 913,184 1,071,576 2,471,944
Dec 2014 1,020,488 1,045,791 2,519,759
Jan 2015 978,576 1,133,080 2,406,159
Feb 2015 1,034,832 1,137,611 2,474,784
Mar 2015 1,069,993 1,123,116 2,497,265
Apr 2015 1,127,145 1,124,431 2,752,632
May 2015 912,763 1,193,958 2,716,834
Jun 2015 922,279 1,148,527 2,670,402
Jul 2015 1,098,547 1,283,295 2,934,229
Aug 2015 1,081,112 1,179,710 2,696,909
Sep 2015 1,009,519 1,227,686 2,769,545
Oct 2015 1,126,124 1,179,108 2,681,410
Nov 2015 1,107,200 1,149,924 2,799,185
Dec 2015 1,089,582 1,265,657 2,777,688
Jan 2016 1,072,685 1,183,639 2,832,936
Feb 2016 1,200,143 1,183,363 2,836,629
Mar 2016 1,259,657 1,245,163 2,897,275
Apr 2016 1,031,362 1,166,838 2,759,454
May 2016 1,104,170 1,160,643 2,838,929
Jun 2016 1,085,279 1,157,550 2,818,792
Jul 2016 1,146,899 1,197,112 2,871,515
Aug 2016 1,010,185 1,167,324 2,885,284
Sep 2016 1,096,455 1,213,755 2,939,373
Oct 2016 1,074,446 1,186,426 2,853,713
Nov 2016 1,170,857 1,278,675 2,920,800
Dec 2016 1,099,875 1,251,094 2,843,566
Hires levels by firm size, seasonally adjusted
Month Firm Size 1 (1-49) Firm Size 2 (50-499) Firm Size 3 (500+)
Dec 2000 1,318,760 1,465,514 2,226,675
Jan 2001 1,446,901 1,367,157 2,489,504
Feb 2001 1,305,908 1,567,814 2,318,121
Mar 2001 1,353,462 1,552,926 2,422,797
Apr 2001 1,426,138 1,359,579 2,351,722
May 2001 1,370,351 1,395,206 2,350,992
Jun 2001 1,336,154 1,399,976 2,094,197
Jul 2001 1,293,642 1,454,056 2,076,131
Aug 2001 1,302,158 1,337,434 2,047,302
Sep 2001 1,281,957 1,384,610 1,967,274
Oct 2001 1,353,912 1,338,900 2,011,421
Nov 2001 1,334,527 1,257,781 1,941,739
Dec 2001 1,307,061 1,295,405 1,881,384
Jan 2002 1,279,438 1,287,183 2,021,261
Feb 2002 1,325,149 1,303,163 1,997,492
Mar 2002 1,146,362 1,277,928 1,965,695
Apr 2002 1,235,074 1,346,882 2,001,240
May 2002 1,271,869 1,358,353 1,991,768
Jun 2002 1,337,649 1,346,893 1,854,062
Jul 2002 1,427,260 1,308,277 1,916,786
Aug 2002 1,337,754 1,280,995 1,888,414
Sep 2002 1,388,080 1,249,723 1,895,845
Oct 2002 1,317,940 1,245,359 1,904,631
Nov 2002 1,328,551 1,249,760 1,942,073
Dec 2002 1,403,834 1,263,550 1,973,256
Jan 2003 1,425,007 1,301,127 1,889,634
Feb 2003 1,355,952 1,218,959 1,896,054
Mar 2003 1,257,855 1,151,789 1,798,934
Apr 2003 1,378,746 1,221,686 1,690,254
May 2003 1,298,053 1,219,640 1,793,446
Jun 2003 1,344,844 1,226,149 1,876,850
Jul 2003 1,369,431 1,195,969 1,812,395
Aug 2003 1,390,340 1,217,625 1,823,133
Sep 2003 1,391,621 1,285,592 1,895,176
Oct 2003 1,372,786 1,277,518 1,891,770
Nov 2003 1,312,049 1,284,619 1,871,773
Dec 2003 1,460,039 1,282,030 1,899,105
Jan 2004 1,404,237 1,273,957 1,866,649
Feb 2004 1,377,906 1,268,343 1,792,621
Mar 2004 1,533,492 1,365,907 1,971,024
Apr 2004 1,417,666 1,352,287 2,050,848
May 2004 1,380,450 1,277,778 1,959,227
Jun 2004 1,435,713 1,295,345 1,928,735
Jul 2004 1,382,952 1,340,491 1,856,892
Aug 2004 1,408,878 1,372,834 1,968,591
Sep 2004 1,403,414 1,355,400 1,895,594
Oct 2004 1,540,296 1,328,544 1,892,138
Nov 2004 1,486,331 1,346,403 1,905,103
Dec 2004 1,439,186 1,371,908 2,059,142
Jan 2005 1,440,918 1,427,256 2,071,931
Feb 2005 1,547,511 1,355,205 2,103,535
Mar 2005 1,512,477 1,369,490 2,029,732
Apr 2005 1,515,338 1,369,141 2,071,545
May 2005 1,527,484 1,406,320 2,022,931
Jun 2005 1,497,350 1,435,937 2,128,139
Jul 2005 1,391,798 1,318,066 2,193,308
Aug 2005 1,564,504 1,398,008 2,126,327
Sep 2005 1,526,094 1,491,917 2,045,598
Oct 2005 1,428,827 1,302,892 1,965,450
Nov 2005 1,502,986 1,374,253 2,058,195
Dec 2005 1,356,557 1,376,234 1,990,594
Jan 2006 1,410,289 1,432,683 2,054,309
Feb 2006 1,460,785 1,447,390 2,096,165
Mar 2006 1,407,262 1,447,276 2,095,818
Apr 2006 1,450,448 1,413,888 2,065,425
May 2006 1,484,521 1,439,687 2,224,986
Jun 2006 1,419,368 1,353,346 2,132,509
Jul 2006 1,452,650 1,423,682 2,040,980
Aug 2006 1,405,847 1,380,246 2,052,351
Sep 2006 1,358,985 1,316,145 2,073,467
Oct 2006 1,331,918 1,398,602 2,144,326
Nov 2006 1,446,960 1,387,794 2,229,577
Dec 2006 1,420,679 1,370,741 2,154,608
Jan 2007 1,409,639 1,287,272 2,115,281
Feb 2007 1,393,078 1,294,136 2,202,493
Mar 2007 1,411,430 1,370,502 2,178,838
Apr 2007 1,309,820 1,373,503 2,111,258
May 2007 1,410,149 1,352,687 2,181,448
Jun 2007 1,333,598 1,389,660 2,037,214
Jul 2007 1,324,344 1,343,331 2,045,869
Aug 2007 1,337,920 1,370,599 1,992,727
Sep 2007 1,355,047 1,309,281 2,104,084
Oct 2007 1,383,515 1,395,818 2,027,436
Nov 2007 1,281,548 1,363,251 2,099,231
Dec 2007 1,309,850 1,288,603 2,019,383
Jan 2008 1,217,771 1,269,877 2,023,082
Feb 2008 1,262,912 1,291,378 2,015,008
Mar 2008 1,263,120 1,239,302 1,857,792
Apr 2008 1,231,862 1,215,344 2,160,571
May 2008 1,236,957 1,230,516 1,823,008
Jun 2008 1,273,816 1,219,170 1,896,089
Jul 2008 1,187,865 1,150,430 1,816,436
Aug 2008 1,269,645 1,155,033 1,813,791
Sep 2008 1,089,156 1,111,869 1,772,296
Oct 2008 1,201,954 1,106,524 1,818,684
Nov 2008 1,110,536 992,021 1,525,636
Dec 2008 1,209,249 1,057,698 1,600,882
Jan 2009 1,225,559 940,159 1,634,854
Feb 2009 1,243,700 986,210 1,429,135
Mar 2009 1,150,065 887,705 1,332,012
Apr 2009 1,220,912 869,669 1,361,420
May 2009 1,136,971 889,487 1,493,545
Jun 2009 1,097,273 865,400 1,328,077
Jul 2009 1,320,304 893,442 1,308,501
Aug 2009 1,114,072 880,755 1,419,193
Sep 2009 1,185,031 936,367 1,418,408
Oct 2009 1,238,379 933,386 1,239,511
Nov 2009 1,115,148 971,214 1,501,134
Dec 2009 1,226,484 919,104 1,434,415
Jan 2010 1,170,752 973,298 1,373,246
Feb 2010 1,087,871 962,895 1,420,048
Mar 2010 1,158,651 986,494 1,529,647
Apr 2010 1,255,357 1,025,533 1,419,978
May 2010 1,116,114 963,500 1,530,405
Jun 2010 1,121,494 1,002,751 1,571,730
Jul 2010 1,162,768 1,046,762 1,595,877
Aug 2010 1,138,075 930,594 1,594,389
Sep 2010 1,111,015 997,738 1,576,427
Oct 2010 1,137,516 999,908 1,609,164
Nov 2010 1,120,077 1,055,164 1,605,815
Dec 2010 1,160,044 1,075,095 1,607,322
Jan 2011 1,048,173 1,017,702 1,574,526
Feb 2011 1,192,245 996,951 1,688,950
Mar 2011 1,169,580 1,072,233 1,685,502
Apr 2011 1,131,876 1,119,162 1,671,499
May 2011 1,106,814 1,020,239 1,750,821
Jun 2011 1,216,172 1,035,178 1,756,111
Jul 2011 1,109,922 998,628 1,740,574
Aug 2011 1,145,240 1,079,415 1,644,789
Sep 2011 1,195,530 1,101,563 1,682,709
Oct 2011 1,086,715 1,019,294 1,811,912
Nov 2011 1,201,244 1,063,548 1,698,367
Dec 2011 1,136,007 1,064,969 1,698,529
Jan 2012 1,193,211 1,109,032 1,675,329
Feb 2012 1,178,924 1,045,587 1,920,240
Mar 2012 1,162,416 1,067,826 1,850,816
Apr 2012 1,190,873 1,084,135 1,719,193
May 2012 1,139,691 1,138,575 1,872,741
Jun 2012 1,154,599 1,165,322 1,793,172
Jul 2012 1,089,151 1,032,845 1,765,991
Aug 2012 1,112,769 1,152,600 1,816,371
Sep 2012 1,118,053 1,103,702 1,742,666
Oct 2012 1,065,148 989,872 1,906,174
Nov 2012 1,212,687 1,115,565 1,819,064
Dec 2012 1,136,490 1,080,390 1,871,245
Jan 2013 1,121,119 997,951 1,958,366
Feb 2013 1,238,795 1,125,022 1,892,729
Mar 2013 1,107,566 1,101,311 1,752,245
Apr 2013 1,052,228 1,093,062 2,080,310
May 2013 1,250,398 1,154,137 1,829,976
Jun 2013 1,148,502 1,072,681 1,988,865
Jul 2013 1,147,374 1,104,954 1,918,433
Aug 2013 1,208,593 1,155,864 2,036,422
Sep 2013 1,177,088 1,079,386 2,113,766
Oct 2013 1,110,623 1,143,208 1,839,957
Nov 2013 1,073,131 1,065,698 2,167,660
Dec 2013 1,106,693 1,074,856 2,039,483
Jan 2014 1,104,023 1,154,049 2,028,844
Feb 2014 1,055,989 1,148,844 2,122,444
Mar 2014 1,122,872 1,148,640 2,169,515
Apr 2014 1,042,346 1,119,847 2,143,104
May 2014 1,108,411 1,173,336 2,109,039
Jun 2014 1,083,067 1,211,803 2,170,004
Jul 2014 1,240,653 1,251,579 2,172,983
Aug 2014 1,189,251 1,175,148 2,206,985
Sep 2014 1,226,232 1,190,158 2,254,999
Oct 2014 1,207,446 1,312,524 2,231,064
Nov 2014 1,218,237 1,225,569 2,312,003
Dec 2014 1,267,948 1,232,018 2,284,648
Jan 2015 1,213,062 1,228,166 2,272,373
Feb 2015 1,250,159 1,263,418 2,126,148
Mar 2015 1,231,678 1,241,767 2,279,434
Apr 2015 1,245,923 1,264,923 2,264,581
May 2015 1,291,302 1,207,006 2,302,261
Jun 2015 1,289,104 1,271,444 2,259,159
Jul 2015 1,210,158 1,243,010 2,271,680
Aug 2015 1,275,162 1,237,645 2,271,298
Sep 2015 1,268,954 1,248,053 2,245,990
Oct 2015 1,315,735 1,255,112 2,338,626
Nov 2015 1,246,228 1,243,741 2,414,955
Dec 2015 1,270,772 1,331,290 2,524,798
Jan 2016 1,253,571 1,222,686 2,334,028
Feb 2016 1,310,998 1,271,022 2,471,003
Mar 2016 1,270,692 1,260,761 2,389,419
Apr 2016 1,163,963 1,233,095 2,307,158
May 2016 1,187,302 1,261,620 2,345,003
Jun 2016 1,237,558 1,301,508 2,281,249
Jul 2016 1,254,032 1,229,814 2,398,035
Aug 2016 1,212,786 1,316,852 2,313,075
Sep 2016 1,104,791 1,251,080 2,324,681
Oct 2016 1,243,323 1,291,155 2,331,589
Nov 2016 1,283,522 1,349,233 2,237,211
Dec 2016 1,296,891 1,277,402 2,311,098